PHYSICAL DEVELOPMENT CONTRIBUTIONS TO
BIOMECHANICAL INJURY RISK IN FEMALE GYMNASTS
HANNAH EVE WYATT
A thesis submitted for the degree of Doctor of Philosophy
Cardiff Metropolitan University
October 2015
Director of Studies:
Dr Marianne Gittoes
Supervisor:
Professor Gareth Irwin
COPYRIGHT
Attention is drawn to the fact that copyright of this thesis rests with its author. This copy of
the thesis has been supplied on condition that anyone who consults it is understood to
recognise that its copyright rests with the author and that no quotation from the thesis and
no information derived from it may be published without the prior written consent of the
author.
© Hannah Eve Wyatt, 2015
I
DECLARATION
This work has not previously been accepted in substance for any degree and is not being
concurrently submitted in candidature for any degree.
Signed……………....................................................... (candidate)
Date...............................................................................
STATEMENT ONE
This thesis is the result of my own investigations, except where otherwise stated. Where
correction services have been used, the extent and nature of the correction is clearly marked
in a footnote(s).
Other sources are acknowledged by footnotes giving explicit references. A bibliography is
appended.
Signed……………....................................................... (candidate)
Date...............................................................................
STATEMENT TWO
I hereby give consent for my thesis, if accepted, to be available for photocopying and for interlibrary loan, and for the title and summary to be made available to outside organisations.
Signed……………....................................................... (candidate)
Date...............................................................................
II
ABSTRACT
Physical Development Contributions to Biomechanical Injury Risk in Female
Gymnasts
H. E. Wyatt, Cardiff Metropolitan University, 2015
Ongoing chronic back pain and chronic spinal injury prevalence in the gymnastics
population is a major concern for the health and wellbeing of female gymnasts. To inform
biomechanical screening approaches, the aim of the research is to develop understanding of
the contribution of physical development to biomechanical indicators of chronic spinal
injury risk in female artistic gymnasts.
Chronological ageing, maturation and growth of competitive female artistic gymnasts
between the ages of nine and 15 years were evaluated at three time points across a 12 month
period. CODA motion analysis and Kistler force plate data informed the quantification of
biomechanical risk indicators. Posture, general stability, centre of pressure range and lumbopelvic stability were determined through the performance of handstand and forward
walkover skills and informed the respective risk indicators.
Calculated through an image-based approach, anthropometric growth was established to
have the greatest influence on biomechanical risk indicators of the physical development
mechanisms. Within the gymnastics cohort, two forms of proportional growth were
evidenced. Longitudinal empirical data revealed gymnasts with increased bicristal breadth
growth in relation to biacromial breadth to have significantly greater biomechanical risk for
posture and lumbo-pelvic stability in the handstand (p<0.05). Gymnasts who had increased
growth rates of biacromial breadth in relation to bicristal breadth had significantly greater
biomechanical risk for general stability in the handstand and forward walkover skills
(p<0.05). Novel empirical quantification for the large influences of physical development
mechanisms on biomechanical risk (maximum r2 = 0.82) underpinned the importance of
proportional growth consideration in injury screening practice.
Evaluation of the transverse torso moment of inertia at a discrete time point provided
preliminary support for cross-sectional use of the inertial measure to forecast longitudinal
growth trends. Identification of prominent biomechanical risk indicators for individual
gymnasts using discrete data may provide direction of injury prevention focus for
practitioners.
III
PUBLICATIONS
International Conference Communications:
Wyatt, H. E., Gittoes, M. J. R. and Irwin, G. (2015). Influence of Short-Term Growth on
Mechanical Risk Indicators in Female Gymnasts. In: Proceedings of the 33rd International
Symposium on Biomechanics in Sports, Potiers, France.
Wyatt, H. E., Gittoes, M. J. R. and Irwin, G. (2015). Short-Term Ageing Effects on
Biomechanical Indicators for Injury Screening in Young Female Gymnasts. In: Proceedings
of the 62nd Annual Meeting of the American College of Sports Medicine, 6th World Congress
on Exercise is Medicine, and World Congress on the Basic Science of Exercise Fatigue, San
Diego, USA, 767.
National Conference Communications:
Wyatt, H. E., Gittoes, M. J. R. and Irwin, G. (2015). Interactions of Short-Term Growth
Measures and Mechanical Risk Indicators of Spinal Injuries in Female Artistic Gymnasts.
In: Programme and Abstract Book of the 30th Annual British Association of Sport and
Exercise Sciences Biomechanics Interest Group (Ed. C. Diss), University of Roehampton,
UK, 17.
Wyatt, H. E., Gittoes, M. J. R. and Irwin, G. (2015). Short-Term Physical Development
Measurement in Young Female Gymnasts. In: Programme and Abstract Book of the 30th
Annual British Association of Sport and Exercise Sciences Biomechanics Interest Group
(Ed. C. Diss), University of Roehampton, UK, 32.
Wyatt, H. E., Gittoes, M. J. R. and Irwin, G. (2015). Short-Term Physical Development
Measurements in Young Female Gymnasts. In: Proceedings of the 9th Academic Associate
Symposium (Ed. H. Cox), Cardiff Metropolitan University, UK, 9.
Wyatt, H. E., Gittoes, M. J. R. and Irwin, G. (2012). Age-Related Spinal Biomechanics of
Female Gymnasts. In: Proceedings of the 6th Academic Associate Symposium (Ed. H. Cox),
Cardiff Metropolitan University, UK, 12.
IV
ACKNOWLEDGEMENTS
I would like to express my thanks to the following people:
To Dr Marianne Gittoes, it has been a true privilege to learn from such an inspirational
academic. I cannot thank you enough for your guidance and all you have taught me. The
time that you have so generously given me over the years (for work and for chats) has been
invaluable.
To Professor Gareth Irwin for your time and support with supervising my research, and for
assisting in my training as a researcher through opportunities outside of the Ph.D.
environment.
To Sport Wales and Cardiff Metropolitan University without whom this Ph.D. project would
not exist. I am sincerely grateful for the opportunity you have provided for me.
To all of the participants and their parents, who truly made data collections a joy. Your
generosity and dedication was more than I could have asked for.
To each of the technicians who have helped out along the way.
To all of the Cardiff Met. Ph.D. students and staff who I have been lucky enough to have
shared the journey with. I am very fortunate to have been surrounded by such wonderful
people who have made my Ph.D. experience so enjoyable.
To my Mum and Dad, my sisters Kate and Meg, and the rest of my family. Thank you for
your unfailing encouragement and support.
To my friends, in particular Bethan. Thank you for your wisdom and for always being there.
Finally, to Jack, for your patience, your belief in what I can achieve and for your devotion
in showing me life outside of my Ph.D.
V
TABLE OF CONTENTS
Page
DECLARATION
ABSTRACT
I
II
PUBLICATIONS
III
ACKNOWLEDGEMENTS
IV
TABLE OF CONTENTS
V
LIST OF FIGURES
X
LIST OF TABLES
XIII
NOMENCLATURE
XVI
CHAPTER 1 - INTRODUCTION
1.1
Overview of the Research Area
1
1.2
Thesis Aim
2
1.3
Thesis Purpose
2
1.4
Research Questions
2
1.5
Chapter Organisation
4
1.5.1
Chapter 2
4
1.5.2
Chapter 3
4
1.5.3
Chapter 4
4
1.5.4
Chapter 5
5
1.5.5
Chapter 6
5
1.5.6
Chapter 7
5
CHAPTER 2 - REVIEW OF LITERATURE
2.1
Introduction
6
2.2
Injuries in Gymnastics
6
2.2.1
2.3
Chronic Back Pain and Chronic Spinal Injury Epidemiology
Chronic Spinal Injury Etiology: Exposure Factors
7
11
2.3.1
Physical Development in the Gymnastics Population
18
2.3.2
Injury Potential and Physical Development in Female Gymnasts
22
2.4
Chronic Spinal Injury Etiology: Biomechanical Risk Indicators
23
VI
2.4.1
Biomechanical Risk Factors
23
2.4.2
Biomechanical Etiological Determinants
35
2.5
Injury Prevention
2.5.1
2.6
Gymnastics Skills for Injury Prevention Approaches
Methods of Approach
37
41
45
2.6.1
Research Design
45
2.6.2
Physical Development Measurement and Analyses
48
2.6.3
Biomechanical Risk Indicator Measurement
56
2.6.4
Biomechanical Risk Indicator Data Processing
59
2.6.5
Biomechanical Risk Indicator Data Analysis
60
2.7
Chapter Summary
64
CHAPTER 3 - MEASUREMENT AND ANALYSIS OF BIOMECHANICAL RISK
INDICATORS
3.1
Introduction
65
3.1.1
Chapter Aim
66
3.1.2
Chapter Questions
66
3.2
Methods
68
3.2.1
Participants and Study Design
68
3.2.2
Experimental Protocol
71
3.2.3
Data Collection
76
3.2.4
Data Processing
78
3.2.5
Data Analysis
85
3.2.6
Statistical Analyses
89
3.3
Results
90
3.3.1
Biomechanical Risk Indicators
90
3.3.2
Group and Individual Biomechanical Risk Indicator Correlations
94
3.3.3
Fundamental Skill Biomechanical Risk Indicator Correlations
95
3.3.4
Biomechanical Risk Indicator Correlations
96
3.4
Discussion
3.5
Chapter Summary
97
102
VII
CHAPTER 4 - PHYSICAL DEVELOPMENT AND BIOMECHANICAL RISK
INDICATORS: A CROSS-SECTIONAL INVESTIGATION
4.1
Introduction
103
4.1.1
Chapter Aim
104
4.1.2
Chapter Questions
104
4.2
Methods
105
4.2.1
Participants and Study Design
105
4.2.2
Data Collection
105
4.2.3
Data Processing and Analysis
106
4.2.4
Statistical Analysis
108
4.3
Results
110
4.3.1
Physical Development Mechanisms
110
4.3.2
Physical Development and Biomechanical Risk Indicators
113
4.4
Discussion
119
4.5
Chapter Summary
123
CHAPTER 5 - ANTHROPOMETRIC GROWTH AND BIOMECHANICAL RISK
INDICATORS: A LONGITUDINAL INVESTIGATION
5.1
Introduction
124
5.1.1
Chapter Aim
125
5.1.2
Chapter Questions
125
5.2
Methods
126
5.2.1
Participants and Study Design
126
5.2.2
Data Collection
127
5.2.3
Data Processing and Analysis
128
5.2.4
Statistical Analyses
130
5.3
Results
133
5.3.1
Growth Measures across Time
133
5.3.2
Growth Measure Relationships
135
5.3.3
Biomechanical Risk Indicators across Time
136
5.3.4
Growth Measure Influences on Biomechanical Risk Indicators
138
5.3.5
Time Influence on Growth Measures and Biomechanical Risk Indicators 140
5.4
Discussion
141
5.5
Chapter Summary
145
VIII
CHAPTER
6
-
PHYSICAL
DEVELOPMENT
CONTRIBUTIONS
TO
RISK
INDICATORS: A BIOMECHANICAL PERSPECTIVE
6.1
Introduction
146
6.1.1
Chapter Aim
147
6.1.2
Chapter Questions
147
6.2
Methods
148
6.2.1
Participants and Study Design
148
6.2.2
Data Collection
148
6.2.3
Data Processing and Analysis
149
6.2.4
Statistical Analyses
155
6.3
Results
157
6.3.1
Growth Rate and Morphological Growth Rate Groupings
157
6.3.2
Body Segment Inertial Parameters
161
6.3.3
Preliminary Evaluation of Indicators for Screening
162
6.4
Discussion
164
6.5
Chapter Summary
170
CHAPTER 7 - GENERAL DISCUSSION
7.1
Introduction
171
7.2
Addressing the Research Questions
171
7.3
Appraisal of Methodological Approaches
180
7.3.1
Research Type and Design
182
7.3.2
Gymnastics Skill Selection
185
7.3.3
Biomechanical Risk Indicator Measurements
186
7.3.4
Physical Development Measurements
187
7.4
Contributions to Research and Practice
189
7.4.1
Contributions to Applied Research
189
7.4.2
Contributions to Applied Practice
190
7.5
Limitations and Directions for Future Research
192
7.5.1
Limitations
192
7.5.2
Directions for Future Research
194
7.6
Final Note
195
IX
REFERENCES
196
APPENDICES
236
A.1
EPIDEMIOLOGY LITERATURE
236
A.2
EXPOSURE FACTOR METHODS (CLINICAL)
250
A.3
BIOMECHANICAL RISK FACTOR METHODS (CLINICAL)
255
A.4
BIOMECHANICAL RISK FACTOR METHODS (BIOMECHANICAL)
260
A.5
PARTICIPANT RECRUITMENT SHEET
270
A.6
SAMPLE SIZE CALCULATION
272
A.7
PARTICIPANT INFORMATION SHEET
274
A.8
PARENTAL/GUARDIAN INFORMATION SHEET
276
A.9
PARENTAL/GUARDIAN CONSENT FORM
278
A.10 PARTICIPANT INFORMED CONSENT FORM
289
A.11 PARTICIPANT ASSENT FORM
280
A.12 PARENTAL/GUARDIAN PRE-TEST HEALTH QUESTIONNAIRE
281
A.13 PARTICIPANT PRE-TEST HEALTH QUESTIONNAIRE
282
A.14 PARTICIPANT QUESTION SHEET
283
A.15 TRIAL SIZE DETERMINATION
284
A.16 MARKER PLACEMENT RELIABILITY
285
A.17 RESIDUAL ANALYSIS
287
A.18 SAMPLING FREQUENCY
288
A.19 INERTIAL MEASUREMENT ISSUES
289
A.20 ALTERED DENSITY VALUES
290
A.21 MATURATION STATUS QUESTIONNAIRE
291
A.22 ANTHROPOMETRIC GROWTH MEASUREMENT ISSUES
292
X
LIST OF FIGURES
CHAPTER 2
Figure 2.1. Average prevalence rates of CBP in the gymnastics population in relation to the
time at which each study (n = 9) was conducted. ..................................................................7
Figure 2.2. Mean prevalence rates of chronic spinal injuries in the gymnastics population in
relation to the time at which each study (n = 13) was conducted; error bars represent SD. ..8
Figure 2.3. A model of sports injury etiology developed by Meeuwisse et al. (2007). .......39
Figure 2.4. ACL-focused injury prevention framework taken from Donnelly et al. (2012).40
Figure 2.5. A schematic of a handstand, standing to double support...................................42
Figure 2.6. A schematic of a forward walkover. ..................................................................43
Figure 2.7. Representation of the bicristal to biacromial ratio for male and females in
accordance with chronological age (taken from Malina et al. (2004)). ...............................52
Figure 2.8. Yeadon (1990)’s mathematical inertia model....................................................54
Figure 2.9. An image of the measurement of the Cobb angle taken from Kim et al. (2006).61
CHAPTER 3
Figure 3.1. The anatomical positioning of each of the 48 markers for collection of bilateral
positional data. .....................................................................................................................73
Figure 3.2. An aerial view diagram of the finalised data collection equipment setup; x is
indicative of the hand placement position on the force plate. ..............................................77
Figure 3.3. CODA motion equipment setup and placement of the Kistler force plate for the
collection of kinematic and kinetic data during the completion of a handstand trial. .........77
Figure 3.4. Whole-body height measured from inertia images. ...........................................79
Figure 3.5. Mean body mass values for three standing trials for each participant with standard
deviation error bars. .............................................................................................................79
Figure 3.6 The CoM position in relation to the base of support for the handstand double
support phase; the dashed line represents CoM distance from the average wrist position and
the solid line shows the CoM distance from the average MCP position. ............................82
Figure 3.7. A schematic demonstrating the phase of interest for the handstand skill. .........82
Figure 3.8. A schematic of the forward walkover, illustrating the phase of interest. ..........83
Figure 3.9. Anterior and posterior views of static models created using Visual 3D software.
..............................................................................................................................................85
Figure 3.10. An image demonstrating the two-segmented lumbar spine for the analysis of
posture angle. .......................................................................................................................86
XI
Figure 3.11. Example of the continuous data and the discrete BRIs from a) handstand centre
of pressure trace; b) forward walkover DPSI profile. ..........................................................87
Figure 3.12. Whole group mean data with standard deviation error bars for each of the
handstand and forward walkover BRIs. ...............................................................................91
Figure 3.13. Spearman's rho correlation analysis output for posture and DLPSI in the forward
walkover skill for one gymnast, where each data point represents a single trial. ................97
CHAPTER 4
Figure 4.1. A frontal plane image captured using the procedure outlined by Gittoes et al.
(2009), with indication of the biacromial and bicristal breadth measures. ........................107
Figure 4.2. Interaction of chronological age (months), maturation status (columns) and
anthropometric growth status (line) for a cross-sectional cohort of female artistic gymnasts.
............................................................................................................................................111
Figure 4.3. Chronological age in association with maturation status data for the gymnastics
cohort (n = 14); p<0.05. ....................................................................................................111
Figure 4.4. Maturation status in association with anthropometric growth status ratio data for
the gymnastics cohort (n = 14). .........................................................................................112
Figure 4.5. Chronological age in association with anthropometric growth status data for the
gymnastics cohort (n = 14); p<0.05. .................................................................................113
Figure 4.6. Quadratic regression outputs for physical development and BRI large effects
(r>0.5) in the handstand skill. ............................................................................................116
Figure 4.7. Forward walkover skill quadratic regression large effect outputs (r>0.5) for
physical development measures and BRI. .........................................................................117
CHAPTER 5
Figure 5.1. Whole-body height of each gymnast at initial and final data collections. .......134
Figure 5.2. Whole-body mass of each gymnast at initial and final data collections. .........134
Figure 5.3. Quadratic regression analyses for b-ratio and DPSI in a) the handstand and b) the
forward walkover; each data point represents the DPSI mean of all trials for a single gymnast
at one data collection point (n = 35). .................................................................................139
CHAPTER 6
Figure 6.1. Frontal plan images of a positive MGR gymnast at initial (left) and final (right)
time points, with indication of biacromial and bicristal positions by way of white dots. ..150
XII
Figure 6.2. Frontal plan images of a negative MGR gymnast at initial (left) and final (right)
time points, with indication of biacromial and bicristal positions indicated by white dots.150
Figure 6.3. Growth rate division approach (solid line); a) high growth rate group n = 6 and
low growth rate group n = 6, the dotted line is indicative of the 0.1 cut-off; b) high growth
rate group n = 5 and low growth rate group n = 7, the dotted lines represent inflection. ..151
Figure 6.4. Morphological growth rate division approach (solid line), negative
morphological growth rate group n = 3 and positive morphological growth rate group n = 9.
............................................................................................................................................152
Figure 6.5. Graphical representation of the linear influence of the torso moment of inertia
rate of change about the transverse axis on b-ratio rate of change (morphological growth
rate) for each of the gymnasts (n = 12); p<0.05. ................................................................162
Figure 6.6. Torso moment of inertia about the transverse axis of each of the gymnasts at
initial time point (n = 12). ..................................................................................................163
Figure 6.7. B-ratio of each of the gymnasts at initial time point (n = 12). ........................164
CHAPTER 7
Figure 7.1. Exemplification of the focus of each chapter (indicated in the key) in relation to
Meeuwisse et al. (2007)’s etiological model. ....................................................................181
Figure 7.2. Illustration of the multi-faceted approach taken to address the thesis aim, with
inclusion of details of research types, study designs, key areas of focus and prominent
outcomes from each chapter...............................................................................................183
Figure 7.3. Application of research findings to chronic back pain and chronic spinal injury
screening in the female gymnastics population. ................................................................191
APPENDICES
Figure A.1. An example of the cross-validation approach for one gymnast performing 22
handstand skills……………………………………………………….….………..…….. 284
Figure A.2. A graph of the a lumbar marker residuals with a Butterworth filter applied at 4
Hz increments and the optimal cut-off frequency…………………………………..….…287
XIII
LIST OF TABLES
CHAPTER 2
Table 2.1. Average prevalence rates for each spinal condition identified in previous
literature, with details of the informing number of studies and the number of gymnasts......9
Table 2.2. Details of chronic spinal injury exposure factors from previous literature.......122
Table 2.3. CSI exposure factors identified in previous literature, along with data of the
number of studies which informed each respective factor .................................................177
Table 2.4. Details of biomechanical risk factors from previous literature .........................244
Table 2.5. Kinematic and kinetic risk factors identified within previous literature, along with
the number of informing studies ........................................................................................333
Table 2.6. Indication (by way of a coloured box) of the biomechanical risk factors which are
included in the mechanics of the handstand and forward walkover skills .........................444
CHAPTER 3
Table 3.1. Individual gymnast and group mean (SD) details of age, training information and
performance level at the time of data collection ................................................................700
Table 3.2. Established models supporting the placement of trunk and pelvic markers .....744
Table 3.3.3. Initiating and terminating phase boundary events for each of the five handstand
and forward walkover phases .............................................................................................800
Table 3.4. An outline of the BRIs analysed in Visual 3D software, exported from the software
and the calculations undertaken using Microsoft Excel software ......................................888
Table 3.5. Biomechanical risk indicator interpretations informed by previous literature8989
Table 3.6. Mean (SD) BRI values for each gymnast, along with the whole group for the
handstand and forward walkover skills ..............................................................................922
Table 3.7. Whole group BRF and BED correlation outputs (r) and the percentage of
individual gymnasts with large effect (r>0.5) for BRI correlations in the handstand and
forward walkover skills. .....................................................................................................955
Table 3.8. Percentage of large positive (r>0.5) and negative (r>-0.5) correlations between
posture and BED for the handstand and forward walkover skills ......................................966
CHAPTER 4
Table 4.1. Descriptive data of the physical development measures for the female artistic
gymnastics cohort (n = 14)...............................................................................................1100
XIV
Table 4.2. Effect size (r) and statistical significance (p) outputs for the influence of physical
development mechanisms on BRIs in the handstand and forward walkover skills .........1188
CHAPTER 5
Table 5.1. Mean (SD) chronological age and training information for the gymnastics cohort
at each collection point.....................................................................................................1266
Table 5.2. Details of the segments included in whole-body, upper-body and lower-body
length and mass calculations ..........................................................................................12929
Table 5.3. Interpretation of positive directional responses across time of the three ratio
measures of anthropometric growth status .......................................................................1300
Table 5.4. Mean (SD) growth measure data for the gymnastics cohort at initial (n = 12), mid
(n = 11) and final (n = 12) collection time points ............................................................1333
Table 5.5. Effect size and significance outputs for linear correlations between B-ratio and
anthropometric growth status measures ...........................................................................1366
Table 5.6. Descriptive mean (SD) BRI data for the gymnastics cohort at each collection
(initial, mid and final) for the handstand and forward walkover skills ............................1377
Table 5.7. Quadratic regression effect size (r) and significance (p) outputs for b-ratio, length
and mass measures on BRIs .............................................................................................1388
Table 5.8. Effect size (n2 and W) and statistical significance outputs (p) for the influence of
time on growth measures and BRIs for each skill............................................................1400
Table 5.9. Friedman's post-hoc test outputs for handstand DPSI ....................................1411
CHAPTER 6
Table 6.1. BSIP measure details including the point at which each measure is calculated
relative to and which segments are included in each measure .........................................1544
Table 6.2. Descriptive mean (SD) BRI data for positive and negative MGR sub-groups and
statistical significance test outputs (p) for the BRI differences between positive and negative
MGR sub-groups ..............................................................................................................1588
Table 6.3. Friedman's and Kendall's W test outputs for the influence of time on BRIs within
MGR sub-groups ............................................................................................................15959
Table 6.4. Friedman's and Kendall's W post-hoc effect size (W) and significance (p) outputs
for the influence of specific time phases on BRIs within MGR sub-groups ...................1600
Table 6.5. Effect size (r) and significance (p) linear regression outputs for the influence of
inertial rate of change on b-ratio rate of change (MGR) ..................................................1611
XV
APPENDICES
Table A.1. Details of chronic back pain and chronic spinal injury epidemiological
literature…………………………………………………………………………………..236
Table A.2. Details of previous literature which have used a clinical method of analysis to
identify chronic spinal injury exposure factors…………………………………………...250
Table A.3. Details of previous literature which have used a clinical method of analysis to
identify chronic spinal injury biomechanical risk factors…………………………………255
Table A.4. Details of literature which has used a biomechanical method of analysis to
identify chronic spinal injury biomechanical risk factors…………………………………260
Table A.5. Average minimum lumbar angle and standard deviation values for two gymnasts
across 10 handstand trials…………………………………………………….......………273
Table A.6. Maximum marker placement differences for two segments and three anatomical
locations, in addition to the effect of the maximum error on mean segment length….……286
Table A.7. Example of density alterations from Dempster (1955)’s density values……..290
Table A.8. Bicristal and biacromial breadth reliability testing data……………………….292
Table A.9. Digitised (image-based approach) and mean (SD) measured (caliper approach)
bicristal breadth and biacromial breadth and the differences between the approaches…..293
XVI
NOMENCLATURE
Terminology abbreviations used throughout the thesis:
BED
B-ratio
BRF
BRI
BSIP
CBP
CODA
CoM
CoPx
CoPy
CSI
DLPSI
DPSI
Fx
Fy
Fz
GR
I
IQR
Iy
LPRoMx
LPRoMy
LPx
LPy
LPz
MGR
n
n2
p
PDS
r
r2
RGD
SD
SMS
W
Biomechanical etiological determinant
Bicristal breadth to biacromial breadth ratio
Biomechanical risk factor
Biomechanical risk indicator
Body segment inertial parameters
Chronic back pain
Cartesian Optoelectronic Dynamic Anthropometer
Centre of mass
Medio-lateral centre of pressure
Anterior-posterior centre of pressure
Chronic spinal injury
Dynamic lumbo-pelvic stability index
Dynamic postural stability index
Medio-lateral ground reaction force
Anterior-posterior ground reaction force
Vertical ground reaction force
Growth rate
Moment of inertia
Interquartile range
Moment of inertia about the transverse axis
Medio-lateral lumbo-pelvic range of motion
Anterior-posterior lumbo-pelvic range of motion
Medio-lateral lumbo-pelvic angle
Anterior-posterior lumbo-pelvic angle
Vertical lumbo-pelvic angle
Morphological growth rate
Number
Partial eta square
Probability level
Pubertal Development Scale
Correlation coefficient
Coefficient of determination
Relative group difference
Standard deviation
Sexual Maturation Scale
Kendall’s coefficient of concordance
1
CHAPTER 1 - INTRODUCTION
1.1
Overview of the Research Area
The initiation of training as young as five or six years of age is customary for female artistic
gymnasts (Caine et al., 2003); a significant proportion of a gymnast’s competitive life span
subsequently comprises the phases of childhood and adolescence. Physical development is
prominent throughout childhood and adolescence (Bjorklund and Blasi, 2011). Along with
chronological age, maturation, defined as progression towards a biologically mature state
(Baxter-Jones et al., 2002) and growth, defined as an increase in size of a whole-body and
its parts (Baxter-Jones et al., 2002), have been identified as essential facets of physical
development (Meyer, 1998; Siatras et al., 2009).
As one of many vital skeletal structures to undergo anatomical modification during
childhood and adolescence (Widhe, 2001), high susceptibility of the spine to chronic pain
and injury have been evidenced within the gymnastics population. Contemporary research
by Donatelli and Thurner (2014) has reported little reduction in the prevalence of spinal
pathologies in comparison with research by Jackson et al. (1976). Longstanding chronic
back pain (CBP) and chronic spinal injury (CSI) prevalence in the gymnastics population is
a major concern for the health and wellbeing of the predisposed female artistic gymnasts,
warranting the need for the development of prevention approaches.
Understanding of the causes and risks of injury are prerequisites for prevention strategies
(McLeod et al., 2011). To explore the predisposing factors for sports injury development,
Meeuwisse et al. (2007) developed a model which recognised the interaction of intrinsic risk
factors, e.g. physical development, and extrinsic risk factors, e.g. biomechanical risk
indicators (BRI), to establish a susceptible athlete. The complex relationship which has been
recognised between the biology and mechanics of physical development (Nuckley, 2013)
grounded the need for biomechanical exploration of the contribution of physical
development to biomechanical risk in female artistic gymnasts. Knowledge of the influence
of intrinsic risk factors on extrinsic risk factors in the female artistic gymnastics population
may assist in informing the development of an injury prevention-focused screening approach
(Donnelly et al., 2012).
2
1.2
Thesis Aim
The aim of the research is to develop understanding of the contribution of physical
development to biomechanical indicators of chronic spinal injury risk in female artistic
gymnasts performing fundamental gymnastics skills.
1.3
Thesis Purpose
The overall purpose of the research is to inform the effective development of injury
frameworks for the screening of female artistic gymnasts.
1.4
Research Questions
To underpin the research aim, four research questions were developed. Each of the questions
are presented with a summary of the informing theory.
RQ 1 Which quantitative measurement techniques and analyses are appropriate for
the identification and interrogation of biomechanical risk indicators in fundamental
gymnastics skills?
To address the research question, previous literature will be reviewed to identify the
prominent BRI for CSI, following which, empirical research will be used to analyse the
respective BRIs within fundamental skills. The appraisal of two skills, the handstand and
forward walkover, will provide insight into the role of fundamental skills for biomechanical
screening approaches. Empirical insights into BRIs within a female artistic gymnastics
cohort will additionally assist in the identification of BRIs of greatest relevance for inclusion
within a gymnastics-specific screening approach.
RQ 2 How does physical development influence biomechanical risk indicators in female
artistic gymnasts?
In isolation, chronological ageing, maturation and growth have each been identified to
influence CSI (Tanchev et al., 2000; Adams and Roughley, 2006; Baranto et al., 2009b).
Exploration of the extent to which physical development mechanisms contribute to BRIs
3
may enable extended understanding of the predisposition of female gymnasts to CSI. In
addition, insight into the influence of intrinsic onto extrinsic risk factors may play a crucial
role in informing the inclusion of physical development in CSI screening approaches.
RQ 3 How does physical development contribute to biomechanical risk indicators in
female artistic gymnasts across time?
Maturation and growth have been acknowledge to be dynamic and non-linear processes
(Rogol et al., 2000; Lloyd and Oliver, 2012). The inconsistencies in physical development
mechanisms which an individual experiences across time have been identified to have a
prominent influence on increased CSI potential (Baranto et al., 2006; DePalma and
Bhargava, 2006; Baranto et al., 2009b). Understanding of the influence of the rate of
physical development was subsequently warranted to extent knowledge of the longitudinal
nature of growth in relation to CSI risk factors. Address of the respective research question
may provide insight into the role of time in screening approaches, i.e. whether screening
needs to be conducted over a longitudinal period.
RQ 4 How can biomechanical parameters be used to explain physical development and
predict longitudinal biomechanical risk indicators in female artistic gymnasts?
The exploration of inertial parameters which have been associated with physical
development mechanisms (Jensen and Nassas, 1988; Cappozzo and Berme, 1990) may
develop understanding of the way in which physical development influences BRI.
Preliminary evidence of the extent to which the developed knowledge may be used to inform
CSI screening may be valuable for transfer of the empirical research to applied practice. An
evaluation of the use of discrete data to reflect the respective longitudinal-based research
findings may further understanding of the ability for cross-sectional screening to be
undertaken in female gymnasts, as is typical for musculoskeletal screening approaches.
Address of the thesis aim will subsequently be satisfied through the empirical findings to
inform the four research questions.
4
1.5
Chapter Organisation
An overview of the content of each chapter (Chapter 2 to 7) is provided below.
1.5.1
Chapter 2 – Review of Literature
Chapter 2 comprises a review of previous literature relating to the research aim. Appraisal
of previous literature contained in Chapter 2 underpinned the development of the respective
research questions. Literature concerning CSI epidemiology and etiology, physical
development, BRIs, injury prevention strategies and methods and analysis approaches of
BRIs and physical development measures are subsequently reviewed.
1.5.2
Chapter 3 – Measurement and Analysis of Biomechanical Risk Indicators
A descriptive study of BRIs within a cross-sectional female artistic gymnastics cohort is
provided in Chapter 3. To address the first research question, the chapter explores the
interaction of handstand and forward walkover BRIs and the extent to which group BRIs
underpin individual mechanics. The interaction of BRIs are also investigated to inform the
refinement of the most relevant BRIs to the respective cohort. The outcomes of Chapter 3
are used to inform Chapter 4, 5 and 6 analyses.
1.5.3
Chapter 4 – Physical Development and Biomechanical Risk Indicators: A CrossSectional Perspective
An inferential approach conducted on a cohort of female artistic gymnasts to address the
second research question is outlined in Chapter 4. Within the respective chapter, initial
descriptive appraisal of the interactions of chronological age, maturation status and
anthropometric growth status is advanced through investigation of the influence of each of
the respective measures of physical development mechanisms on BRIs.
5
1.5.4
Chapter 5 – Physical Development and Biomechanical Risk Indicators: A
Longitudinal Perspective
Informed through the findings from Chapter 4, anthropometric growth status is further
explored through a longitudinal investigation of female artistic gymnasts in Chapter 5. The
influences of a number of measures of anthropometric growth status on BRI are appraised,
in addition to exploration of the influence of time on anthropometric growth status and BRI.
The inferential research outputs contribute to the second and third research questions.
1.5.5
Chapter 6 – Physical Development Contributions to Risk Indicators and Screening:
A Biomechanical Perspective
The prominent anthropometric growth status measure, determined through empirical
analyses in Chapter 5, will inform the respective chapter. Interrogation of the influence of
the size and shape of female gymnasts’ growth on BRIs will be succeeded by an investigation
of the inertial underpinning of the growth of the respective cohort. The respective findings
will be evaluated using cross-sectional data to explore the translation of research findings to
inform individual gymnast CSI screening. Chapter 6 will contribute to the address of the
third and fourth research questions.
1.5.6
Chapter 7 – General Discussion
A summary of the major findings from the preceding chapters are provided in Chapter 7,
along with a discussion of the contributions of the chapters to the respective research
questions. Appraisal of the methodological approaches used to inform the respective
research will be provided. Implications of the findings to advance biomechanical knowledge
and inform applied screening practice are included within Chapter 7, in addition to future
research directions.
6
CHAPTER 2 - REVIEW OF LITERATURE
2.1
Introduction
The gymnastics population have been evidenced to have a high risk of chronic back pain and
chronic spinal injury development, however, the extent to which the respective pathologies
are evident in current gymnastics populations is unknown. A review of CSI within the
gymnastics population is anticipated to develop comprehension of the injury trend and
subsequently provide insight into the contemporary nature of the respective pathologies.
Appraisal of the personal and biomechanical factors which have been evidenced to incite
CSI will be proceeded by understanding of how the respective knowledge can inform injury
prevention. Finally, to develop understanding of how knowledge of CSI etiology can be used
to inform injury prevention practice within a female gymnastics population, appraisal of
potential methods of approach, including research design, measurement, processing and
analyses, will conclude the respective chapter.
2.2
Injuries in Gymnastics
Injury has been described as the most pressing and serious problem in the sport of gymnastics
(Sands et al., 2011; Dimitrova and Petkova, 2014). The claimed high injury levels in the
gymnastics population has been supported through studies such as that conducted by Caine
et al. (2003b), which documented a 76% injury prevalence rate within 79 female gymnasts
over a three year period. While acute and chronic injuries are evident in gymnastics, the
prevalence of chronic injuries is prominent (Brukner and Khan, 2002). The exposure of
musculoskeletal structures to highly repetitive forces below the acute injury threshold have
been considered an underpinning mechanism for chronic injury development (Hreljac et al.,
2000). The unique demands of the sport dictate specific structures of gymnasts’
musculoskeletal systems to be of heightened susceptibility to the development of chronic
injuries (Rossi and Dragoni, 1990), including the ankle, wrist and lower back (Caine et al.,
2003b). With lower back epidemiological rates as high as 18.4% in contemporary
gymnastics-based research (O'Kane et al., 2011), the back has been identified as a prominent
site for chronic injury development (Cassas and Cassettari-Wayhs, 2006), particularly for
the female athlete (Stracciolini et al., 2013).
7
2.2.1
Chronic Back Pain and Chronic Spinal Injury Epidemiology
Epidemiology research aims to study the ‘distribution and determinants of disease and injury
frequency within a given human population’ (Whiting and Zernicke, 2008). Enabling the
identification of prominent pathologies within a population, epidemiology data has informed
numerous pieces of valuable research, including the development of injury prevention
strategies, as exemplified by Alderson and Donnelly (2012). Use of injury surveillance data
to inform prevention approaches aligns with injury prevention frameworks, such as that
presented by Donnelly et al. (2012). To inform the extent to which CSI are prevalent within
the gymnastics population, a review of epidemiology research was warranted. Recognised
as a precursor to CSI (Adams and Dolan, 1995), the consideration of CBP in relation to CSI
has been advocated by a number of researcher, including Kruse and Lemmen (2009); Kruse
and Mehta (2011). A review of CBP and CSI epidemiology in the gymnastics population
was subsequently undertaken (Appendix A.1).
Sixteen articles were identified to report a prevalence rate for CBP or a specified CSI within
the gymnastics population; the informing peer-reviewed literature spanned between 1976
and 2014. To provide insight into the trend of CBP and CSI prevalence in the gymnastics
population, appraisal of the review findings in relation to the time at which each study was
conducted are presented in Figure 2.1 and Figure 2.2. Appraisal of the respective data trends
enabled conclusions of the contemporary nature of CBP and CSI within the gymnastics
Average Chronic Back Pain
Prevalance Rate (%)
population.
100
90
80
70
60
50
40
30
20
10
0
1976
1985
1990
1991
1993
Year
1999
2001
2006
2014
Figure 2.1. Average prevalence rates of CBP in the gymnastics population in relation to the
time at which each study (n = 9) was conducted.
8
Data which informed Figure 2.1 identified fluctuation in CBP prevalence between 30%
(Jackson et al., 1976) and 86% (Hutchinson, 1999) across the nine studies between 1976 and
2014. A steady decrease in CBP was evidenced from 1999 to 2014, however, a greater
percentage of gymnasts were found to experience CBP in 2014 than in 1985. The findings
evidenced the ongoing issue of CBP within the gymnastics population and supported the
need for exploration of ways in which a further decrease in CBP may be brought about.
Informed through previous epidemiology literature, Figure 2.2 was developed to gain
Mean Chronic Spinal Injury
Prevalance Rate (%)
understanding of the trend of CSI prevalence of the gymnastics population across time.
100
90
80
70
60
50
40
30
20
10
0
1976
1978
1985
1990
1991 1993
Year
2000
2003
2006
2014
Figure 2.2. Mean prevalence rates of chronic spinal injuries in the gymnastics population in
relation to the time at which each study (n = 13) was conducted; error bars represent SD.
The fluctuation of average prevalence data across the 13 studies provide inconclusive
evidence of the increasing or decreasing trend of the number of CSI experienced within the
gymnastics population. The contemporary nature of both CBP and CSI were subsequently
evidenced, supporting the need for the development of prevention strategies in attempt to
reduce the respective prevalence levels.
Further interrogation of the CSI epidemiology literature revealed 18 separate spinal injuries
within the gymnastics population, displaying overall prevalence rates ranging from 0.9%
(Donatelli and Thurner, 2014) to 79% (Swärd et al., 1991). To enable inter-condition CSI
prevalence comparisons with CBP, Table 2.1 was developed. The prevalence rate data in the
respective table were informed through mean (SD) of the prevalence rates across the
informing studies. In addition, the number of informing studies and the summation of the
gymnasts included in the respective studies (total number of gymnasts) are reported.
9
Table 2.1. Average prevalence rates for each spinal condition identified in previous
literature, with details of the informing number of studies and the number of gymnasts
Condition
Chronic back pain
Degenerated disc
Disc height reduction
Abnormal configuration of a vertebral body
Apophyseal ring injury
Schmorl’s node
Disc bulging
Disc protrusion
Sacralisation
Herniated disc
Spina bifida occulta
Schistorrhachis
Limbus vertebra
Spondylolysis
Disc space narrowing and anterior wedging
Scoliosis
Scheuermann’s disease
Spondylolisthesis
Lumbarisation
Mean (SD)
prevalence rate
(%)
62 (17)
47 (26)
38 (0)
33 (0)
32 (21)
30 (36)
29 (30)
29 (0)
25 (26)
25 (6)
21 (25)
20 (0)
18 (0)
16 (8)
14 (0)
12 (0)
9 (0)
8 (6)
7 (0)
Number
of
studies
11
5
1
1
2
3
2
1
2
2
2
1
1
12
1
1
1
4
1
Total
number of
gymnasts
437
189
24
24
43
135
128
104
66
26
135
41
104
1061
7
100
35
360
41
The average CBP prevalence rate of 62% which was reported for the gymnastics population
(Table 2.1), was considerable in comparison with epidemiological findings of 21% for nonathletes (Sato et al., 2011). The overall average prevalence rates for CSI conditions are
additionally concerning when comparisons are made with general population statistics.
Micheli and Wood (1995) identified a spondylolysis prevalence rate of 5% in adult controls,
therefore evidencing gymnasts to be in excess of three times more likely to develop
spondylolysis than the general adult population. In isolation, the high CBP and CSI are
highly concerning, however, additional concern is brought about by the number of
conditions which gymnasts have been evidenced to be susceptible to. The commonality of
chronic injuries in the gymnastics population means that they are often not even recognised
(Ciullo and Jackson, 1985; Harringe et al., 2004) suggesting the prevalence of pain and
injury may be even greater than that reported by the gymnastics cohorts (Meeusen and
Borms, 1992).
10
The majority of athletes in aesthetic sports such as gymnastics are female (d'Hemecourt and
Luke, 2012). The dominance of females within gymnastics has been evidenced by Overlin
et al. (2011) who identified 75% of United States gymnasts above the age of six to be female.
For the prevention of CBP and CSI to be as effective as possible, focus on the female
gymnastics population is therefore suggested. The respective trend was reflected by the
epidemiological research which revealed an increased number of female gymnast-based
studies (n = 39), in comparison with male-base studies (n = 32). The wide range of ages
informing the epidemiological data (33 years; six to 39 years) is accepted as a positive
feature as the application of knowledge is effective across a broad spectrum of ages;
however, age-based conclusions from the data set are consequently problematic. Further
understanding of the extent to which chronological age influences CBP and CSI prevalence
is subsequently advocated.
In addition to age, the studies from which the epidemiology results were derived were
inclusive of gymnasts ranging from pre-elite to Olympic. Through the assessment of the
correlations between performance level and CSI prevalence, it was illustrated that, elite and
previously elite gymnasts experienced the highest prevalence of CSI (49%), followed by
those at Olympic level (33%), district, regional, national and International levels (27%), and
finally, pre-elite (22%). The respective trend aligns with Meeusen and Borms (1992) who
acknowledged the existence of a proportional relationship between chronic injury risk and
skill level i.e. the higher skill levels (elite, previously elite and Olympic) are associated with
a greater risk of chronic injury development. The findings are suggestive of the need for
injury prevention approaches to be implemented prior to the elite stage, at which CSI are
most prevalent; initiation of prevention strategies for competitive gymnasts may assist in
combatting the respective trend.
From the reported information within the respective literature, the lumbar region is the most
commonly cited area of pain and injury (Brüggemann, 2005), followed by the lumbo-sacral
junction (Bennett et al., 2006). The repeated reporting of high pain and injury prevalence to
the lumbar region (Szot et al., 1985; Donatelli and Thurner, 2014) indicates the lower back
region to be of primary concern. Extended examination of the underpinning mechanisms is
subsequently timely to reduce the respective prevalence, for example, through scientifically
informed prevention and screening strategies.
Epidemiological research is highly advantageous for developing understanding of pathology
trends within a specific population; although beneficial, awareness of the limitations of the
11
reviewed literature are additionally vital. One of the main problems with the compilation of
numerous pieces of research, is the mix of methods which inform the overall outcome.
Although the implications of the varied methods on the prevalence rates are unable to be
quantified, interpretation of the combined study outcomes should take into account the
potential for prevalence fluctuations between studies as a result of the methods used. The
self-reporting approach typically taken to conclude prevalence rates of back pain (Bennett
et al., 2006), is anticipated to possess amplified inaccuracies in comparison with magnetic
resonance imaging (Bugg et al., 2012). A further limiting aspect of the epidemiology review
is that the majority of informing studies were conducted between 1976 and 1999. As
gymnastics is a rapidly advancing sport (Sands, 2000), the need for contemporary research
to meet the constantly increasing demands of gymnastics is great. Appraisal of CBP and CSI
trends across time (Figure 2.1 and Figure 2.2) assisted in alleviating the respective problem,
however, an increased quantity of contemporary studies may be beneficial.
2.3
Chronic Spinal Injury Etiology: Exposure Factors
The considerable CSI prevalence rates within the gymnastics population (up to 79%, (Swärd
et al., 1991)) were supported by CBP prevalence of up to 86% (Hutchinson, 1999). The
contemporary nature of the respective pathologies (Figure 2.1 and Figure 2.2), highlighted
the need for further understanding to inform potential preventative strategies within the
respective population. The injury prevention framework presented by Donnelly et al. (2012)
identified the successive stage to injury surveillance to be the appraisal of etiology. Within
the context of biomechanics, etiology has been defined as the study of the causes of sports
injuries (Bartlett and Bussey, 2012). Understanding of the underlying factors which induce
elevated the risk of CSI is anticipated to provide insight into the high vulnerability of the
gymnastics population to the respective pathologies. To enable comprehension of current
understanding of CSI etiology, the appraisal of previous CSI etiological literature was
undertaken. The reported etiology variables were found to form two distinct categories,
exposure factors and biomechanical risk factors (BRFs). Classified as personal factors
associated with the cause of CSI, the exposure factors were firstly appraised. Data to inform
the review of CSI exposure factors were derived from previous literature sources for all
populations, which identified a specific personal factor to induce an increased risk, or
prevalence of CSI (Table 2.2).
12
Table 2.2. Details of chronic spinal injury exposure factors from previous literature
Exposure Factor
Age
Exposure Factor Details
Young people who engage in strenuous activities
Vulnerability of the vertebra between 14 and 30 years
Age (16.8 years) – neural arches of the lumbar vertebrae
may not have completely ossified
Older athletes
Young people
Ageing
Childhood and adolescence, not adulthood
Adolescent (skeletal immaturity)
From the age of 12, particularly over 16 year olds
Ageing
Adolescent athletes
Highest frequency of spinal disorders found at 12-13
years
Child athletes
Adolescent athlete
Study
Year
(Cyron and Hutton)
(Cyron and Hutton)
(Foster et al.)
1978
1978
1989
(Murphy et al.)
(Stanitski)
(Adams and Dolan)
(Haun and Kettner)
(DePalma and Bhargava)
(Baranto et al.)
(Adams and Roughley)
(Kerssemakers et al.)
(Brüeggemann)
2003
2006
2005
2005
2006
2006
2006
2009
2010
(Maxfield)
(Kim and Green)
2010
2011
(Micheli)
(Elliott et al.)
(Hollingworth)
(Kujala et al.)
(Kujala et al.)
(Lonstein)
(Feldman et al.)
1979
1993
1996
1996
1997
1999
2001
Growth
Second (or adolescent) growth spurt
The adolescent growth period
Growth spurt
Vulnerability of the spine during periods of growth
Growth spurt
Growth
High (rapid) growth spurt
13
Growth spurt
Vulnerability during the growth spurt
Asymmetric growth
Growth spurt
Vulnerability during rapid growth
Growth spurt
Vulnerability during the growth period
(DePalma and Bhargava)
(Baranto et al.)
(Akyuz et al.)
(Roussouly et al.)
(Kerssemakers et al.)
(Baranto et al.)
(Brüeggemann)
2006
2006
2006
2006
2009
2009
2010
(Tanchev et al.)
2000
(Stewart)
(Battié et al.)
(Sambrook et al.)
(Manchikanti)
(Battié et al.)
(Adams)
(Leboeuf-Yde)
(Adams and Dolan)
(Haun and Kettner)
(Adams and Roughley)
(Andersson et al.)
(Battié and Videman)
(Debnath et al.)
(Brüeggemann)
1953
1995
1999
2000
2004
2004
2004
2005
2005
2006
2006
2006
2009
2010
(Richardson et al.)
1997
Maturity
Delayed maturity
Genetics
Familial component
Genetic influence
Strong genetic effect on disc degeneration
Genetic predisposition
Heredity
Genetic inheritance can alter injury vulnerability
Genetics
Genetics
Genetic predisposition
Genetic inheritance
Heredity
Familial factors, including genetics
Genetic influence
Genetics
Family history
Family history
14
Physiology
Low arch of the foot
(Foster et al.)
1989
High body weight
Excessive body weight
(Kujala et al.)
(Alexandru and Diana)
1997
2010
Strenuous physical activity
Vigorous physical activity
Highly competitive sports
High level of physical activity in children and
adolescents
High physical activity level
High total sitting time
Physical activity performed incorrectly
Heavy physical activity
(Burgoyne and Edgar)
(Kujala et al.)
(Wojtys et al.)
(Kovacs et al.)
1998
1999
2000
2003
(Auvinen et al.)
(Auvinen et al.)
(Alexandru and Diana)
(Maffulli et al.)
2008
2008
2010
2010
(Brüeggemann)
2010
(Micheli)
1979
(Hall)
1986
(Bak et al.)
(Brüeggemann)
1994
2005
(Szot et al.)
1985
Body composition
Physical activity
Age at onset of physical
activity
Age of onset of physical activity
Skill level
Too rapid advances in technique without proper
attention to maintaining strength and balance
Increase in impact forces with increased skill
complexity
Increase skill level increases injury incidence
Increased difficulty of skill
Training duration
Increased years of training
15
Hours of practice
Training error, too much over too short a period of time
Excessive daily training – dose related
Increased hours of practice
Hours spent training
Long intense training
High number of hours participation per week
Dose related – hours of training per week
(Micheli)
(Goldstein et al.)
(Bak et al.)
(McMeeken et al.)
(Barile et al.)
(Standaert)
(Pajek and Pajek)
1979
1991
1994
2001
2007
2008
2009
(McGill)
(DePalma and Bhargava)
(Hoops et al.)
(Thoreson et al.)
2004
2006
2007
2010
(Troup et al.)
(Hainline)
(Mulhearn and George)
(Lee et al.)
(Feldman et al.)
(Feldman et al.)
(Hubley-Kozey)
(Kim et al.)
(Standaert)
(Standaert)
(Ranson et al.)
1985
1995
1999
1999
2001
2001
2005
2006
2008
2008
2008
(Alexandru and Diana)
2010
Rest
The time following a period of bed rest
Insufficient rest periods
Inadequate rest time
Little recovery time
Musculature
Hypertension of the vertebral musculature
Low back muscle inflexibility
Instability of lumbar spine musculature
Imbalance in trunk muscle strength
Decreased hamstring quadriceps flexibility
Decreased quadriceps flexibility
Decreased abdominal strength and endurance
Imbalance between trunk muscles
Decreased muscular endurance
Lower-extremity muscle imbalance
Muscular asymmetry, particularly of the quadratus
lumborum muscles
Decreased trunk muscle strength
16
Thinner and more asymmetric transverse abdominis
muscle
Abdominal weakness
Tight thoracolumbar fascia
Iliopsoas muscle contractures
(Ota and Kaneoka)
2011
(Kim and Green)
(Kim and Green)
(Kim and Green)
2011
2011
2011
(Punnett et al.)
(Hainline)
(Adams et al.)
1991
1995
2000
(Greene et al.)
(McGill)
(Bono)
(Bennett et al.)
(Standaert)
(Alexandru and Diana)
2001
2004
2004
2006
2008
2010
(Standaert)
2008
(Adams and Roughley)
2006
(Feldman et al.)
2001
Previous injury
A history of ruptured disc or acute back injury
Shoulder or hamstring injury
Previous structural disruption to the endplate and
annulus
A history of low back pain
History of back trouble
History of low-back pain
History of non-current low back pain
Prior low back or lower extremity injuries
Prior pain at the level of the spine
Previous injury rehabilitation
Incomplete rehabilitation of prior injuries
Nutrition
Nutritional compromise
Smoking
Smoking
17
Informed by previous etiology-based literature, Table 2.33 documents a summary of the 18
CSI exposure factors identified in Table 2.2, in addition to the informing number of studies.
Details of the methods of approach used to identify the respective exposure factors are
documented in Appendix A.2. The two most prominent methods of approach were
questionnaire techniques and medical diagnostic techniques (i.e. imaging). The respective
information provides additional insight into the process through which exposure factors for
CSI risk were identified.
Table 2.3. CSI exposure factors identified in previous literature, along with data of the
number of studies which informed each respective factor
Exposure factors
Musculature
Age
Growth
Genetics
Previous injury
Physical activity
Hours of practice
Skill level
Rest
Body composition
Maturation
Training duration
Age at onset of physical activity
Family history
Physiology
Previous injury rehabilitation
Nutrition
Smoking
Number of studies
16
14
14
14
9
8
7
4
4
2
1
1
1
1
1
1
1
1
The identification of 18 exposure factors provided support for the observation by Hamill et
al. (2012) which recognised difficulty in determining the etiology of pain and injury, due to
the interactions of multiple risk factors. Although numerous exposure factors have been
identified to influence CSI, four prominent exposure factors were revealed through the
compilation of previous research findings in Table 2.3; each supported by 14 to 16 studies,
age, growth, genetics and musculature were distinguished in the review of CSI exposure
factors. The identification of age and growth as two of the prominent exposure factors
18
(Baranto et al., 2009b; Brüggemann, 2010), was of great interest, due to the contribution of
each of the respective factors to the process of physical development (Meyer, 1998; Siatras
et al., 2009). The recognition of the process of physical development as influencing CSI
supported the inclusion of maturation to gain a complete understanding of the respective
process. Although maturation was not found to be one of the prominent etiological variables
for CSI, appraisal of the respective measure in relation to chronological ageing and growth
is anticipated to provide crucial insight into the process of physical development. With
gymnasts typically training and competing throughout the time phases of childhood and
adolescence, which are underpinned by physical development (Pacey et al., 2010), age,
maturation and growth have great individual and combined relevance to the gymnastics
population. Subsequent to the exposure factor review findings, further exploration of
physical development mechanisms (ageing, maturation and growth) in the gymnastics
population was induced.
2.3.1
Physical Development in the Gymnastics Population
Within the respective research, the term physical development is accepted to encompass
three mechanisms, ageing, maturation and growth (Meyer, 1998; Siatras et al., 2009). Each
of the respective mechanisms have documented associations with CSI within previous
literature (e.g. Tanchev et al. (2000); Brüggemann (2010); Kerssemakers et al. (2009)).
Physical development is speculated to influence CSI susceptibility in part due to the
biomechanical adaptations necessary as a result of physical development-induced biological
modifications (Kerssemakers et al., 2009). To develop further understanding of the influence
of physical development on CSI potential, the respective section will appraise the literature
which identified ageing, maturation and growth as exposure factors, in addition to
developing insight into the relation of the respective physical development mechanisms to
gymnastics.
Chronological Ageing
The identification of age as meaningful for CSI susceptibility (e.g. Maxfield (2010)) was not
homogenous in the specifications of which ages induce greatest risk. Two studies, Cyron
and Hutton (1978) and Murphy et al. (2003), suggested risk increase for older athletes,
however, 71% (n = 10) of the studies which supported age as an exposure factor, identified
childhood and adolescence to induce the greatest CSI risk. As the majority of competitive
19
gymnasts are children and adolescents (Daly et al., 2001; Wesley, 2001), consideration of
the respective finding in relation to the gymnastics population revealed alignment between
the sport of gymnastics and CSI etiology.
Within the bracket of childhood and adolescence, the majority of studies suggest an
increased susceptibility of CSI with increased age (e.g. Adams and Roughley (2006)). In
attempt to further explore the area, Stracciolini et al. (2013) reviewed 2133 cases in which
children and adolescents visited the Division of Sports Medicine between 2000 and 2009.
Using the age groupings five to 12 years of age and 13 to 17 years of age, Stracciolini et al.
(2013) reported 5.6% of cases to be in reference to the spine within the younger group, and
11.5% in the older group. The relatively substantial increase in spinal concerns in sporting
individuals over the age of 13 was further supported by O'Kane et al. (2011). The
gymnastics-specific study by O'Kane et al. (2011) found overuse injury prevalence rates in
29.7% of gymnasts of ages seven to nine years, 38.2% in the 10-12 years age category and
52% in gymnasts ranging from 13-17 years of age. A suggested explanation of prolonged
exposure to injurious factors is offered by Steele and White (1986) for the identified trend
of increased injury susceptibility with increased age. The respective findings suggest the
need for prevention strategies to initiate prior to the age of 13. Previous gymnastics research
which focused on the conduction of injury prevention approaches supported the suggested
need for initiatives to be inclusive of younger gymnasts, particularly those at lower and
middle competitive levels, in addition to the need for injury prevention to focus on
fundamental gymnastics (Kolt and Kirby, 1999).
Maturation
Maturation is concerned with progression toward a biologically mature state (Baxter-Jones,
2013). The three prominent forms of maturation are commonly identified to be skeletal,
sexual and somatic (Baxter-Jones et al., 2002). The findings of moderate to high correlations
between the respective maturational indexes (skeletal, sexual and somatic) throughout
adolescence (Malina et al., 2004) have provided support for the focus maturation-based
research on a single maturational index. Owing to the findings of similarity between the
three measures of maturation, the respective approach has been common practice in previous
maturation-based studies (e.g. Till et al. (2014)).
Maturation has been evidenced to be highly topical within contemporary gymnastics-based
research (e.g. Bradshaw et al. (2014)). Previous literature which has explored maturation in
20
the gymnastics population has commonly attributed the late maturation of female artistic
gymnasts to intensive training from a young age (Georgopoulos et al., 2004; Markou et al.,
2004; Theodoropoulou et al., 2005). Tanchev et al. (2000) reported an explicit association
between delayed maturation and the development of scoliosis within the gymnastics
population. In explaining the respective finding, Tanchev et al. (2000) suggested the delay
in maturation to lead to a prolongation of vulnerability, exposing the growth plates to
‘unfavourable mechanical factors’, including pressure, impacts and microtrauma. In line
with Tanchev et al. (2000)’s finding, the paediatric spine has been found to undergo crucial
development throughout the process of maturation, dictating the need for the stabilising
subsystems of the spine to continually adapt to counteract the instability brought about by
the process of maturation (Donatelli and Thurner, 2014). Although the evidence for the
influence of maturation on CSI is less compelling than that provided for chronological age
and growth, to develop understanding of the influence of physical development on CSI risk,
the consideration of maturation is imperative.
Growth
Defined as increases in body size as a whole and of its parts (Baxter-Jones, 2013), growth is
accepted as a dynamic process (Rogol et al., 2000) which is predominant within the phases
of childhood and adolescence (Dainty, 1987). Fourteen separate research studies (e.g.
Roussouly et al. (2006)) identified growth to influence CSI (Table 2.3). Further interrogation
of the types of growth which previous literature identified revealed one study which found
asymmetric growth to be associated with the respective pathologies (Akyuz et al., 2006) and
29% which suggested the period of growth influenced CSI risk (e.g. Lonstein (1999)). Sixtyfour per cent of studies identified the growth spurt to be the feature of growth which has
greatest association with increased risk of CSI development (e.g. Kujala et al. (1997);
Baranto et al. (2009b)).
The etiology findings therefore aligned with the theory that growth is a dynamic process
(Baxter-Jones, 2013) and supported the theory that pain and injury susceptibility may
increase during the phase of growth as a result of the significant body proportionality
changes which occur during the childhood and adolescent periods (Hamill et al., 2012).
Within the gymnastics population, the period of the growth spurt has been reported to lack
sensitivity, with training commonly continued at high intensities throughout it (Kirialanis et
al., 2002; Daly et al., 2005). In line with the exposure factor findings, it is suggested that
21
consideration of growth may be an integral aspect of the development of injury prevention
approaches.
The ideal morphology for a female artistic gymnast has been documented as low wholebody height, low whole-body mass, narrow hips and broad shoulders (Claessens and
Lefevre, 1998; Georgopoulos et al., 2012). Studies of physical features which characterise
female artistic gymnasts have supported the low whole-body height and low whole-body
mass, reported to be ideal for gymnasts (Bass et al., 2000; Georgopoulos et al., 2002;
Ackland et al., 2003; Caine et al., 2003a; Claessens et al., 2006; Georgopoulos et al., 2012).
In addition, female gymnasts have been recognised to possess broad shoulders and narrow
hips (Claessens and Lefevre, 1998; Siatras et al., 2009). The mechanical advantage gained
from having the respective traits, in addition to short limbs, is proposed as a potential reason
for the population characteristics (Georgopoulos et al., 2012).
Inertial Changes
The process of physical development which underpins the periods of childhood and
adolescence is characterised by changing inertial characteristics (Jensen, 1981; Richards et
al., 1999; DiFiori et al., 2014). The segmental mass increases which occur during physical
development induce disruption in segmental and whole-body centre of mass (CoM) (Dainty,
1987), leading to varying segmental moment of inertias (Dainty, 1987; Jensen and Nassas,
1988; Richards et al., 1999; Hawkins and Metheny, 2001). The moment of inertia (I)
intuitively combines the inertial effects of height and mass (Richards et al., 1999). Changes
to I have been reported throughout the process of physical development in the gymnastics
population (Richards et al., 1999; Ackland et al., 2003). Physical development-induced
biological modifications, such as CoM and I, commonly necessitate biomechanical
adaptations (Kerssemakers et al., 2009). As reported by DiFiori et al. (2014), biomechanical
adaptations have been evidenced to affect both coordination and movement patterns. A study
of the inertial properties of female gymnasts between the ages of 10 and 12 years was
conducted by Ackland et al. (2003). Data gained over a 3.3 year period informed the
conclusion that growth changes had a number of mechanical effects, which were attributed
to changes in segment inertial properties. The advantages of gymnasts having lower wholebody moment of inertia as well as lower-body mass has been evidenced by Ackland et al.
(2003) who identified negative influences on rotational skills as a result of growth.
22
2.3.2
Injury Potential and Physical Development in Female Gymnasts
Chronological ageing, maturation and anthropometric growth have been identified to
underpin the process of physical development (Meyer, 1998; Siatras et al., 2009).
Consideration of the respective mechanisms in isolation has revealed associations with CSI
development (Lonstein, 1999; Tanchev et al., 2000; Brüggemann, 2010). Suggested causes
of increased susceptibility throughout physical development have been put forward by a
number of researchers. d'Hemecourt and Luke (2012) suggested the abundance of growth
cartilage in the growing athlete to increase susceptibility to spinal injury. The relationships
between physical development and physical qualities are additional elements which must be
considered for the young gymnasts throughout the process of physical development
(Ackland et al., 2003). A reduction in flexibility around joints has been identified during
growth spurts (Kerssemakers et al., 2009), leading to a reduced range of motion (Elphinston,
2008); in addition, a temporary disruption in motor coordination has been evidenced in
previous literature (Verhagen and van Mechelen, 2008). The balance between strength and
flexibility (Kerssemakers et al., 2009) as well as the muscle strength to bone length ratio
(Caine and Linder, 1985) have also been highlighted to contribute to injury occurrence
during physical development. Nonlinearity of growth, leading to an imbalance in the
development of body segments, is a further explanation offered for the increased injury
susceptibility during physical development (Brukner, 2012; Konopinski et al., 2012).
The vulnerability of young female gymnasts to CSI during the process of physical
development is highly concerning, particularly as the distinguished features of physical
development which may underpin the increased risk to pain and injury during the respective
period it is yet to be fully understood. Research which has explored the physical features
which characterise female artistic gymnasts has identified low height, low body mass, broad
shoulders, narrow hips and delayed maturation (Bass et al., 2000; Georgopoulos et al., 2002;
Ackland et al., 2003; Caine et al., 2003a; Claessens et al., 2006; Georgopoulos et al., 2012).
The isolated appraisal of growth and maturation qualities of the respective population is
insightful for potential influential factors for CSI risk, however, understanding of the
interactions between each of the physical development mechanisms may of value to the
extension of knowledge within the respective area. The processes of growth and maturation
have been documented to be associated (Beunen and Malina, 2008); a strong linear
relationship has additionally been reported (Stang and Story, 2005). However, scientific
research into the associations between the physical development mechanisms is yet to be
23
undertaken within the female gymnastics population. Understanding of the interactions
between the mechanisms is anticipated to develop knowledge which may extend to inform
injury screening approaches within the female artistic gymnastics population.
2.4
Chronic Spinal Injury Etiology: Biomechanical Risk Indicators
The etiological variables for CSI were identified to divide into two categories, exposure
factors and BRIs. The appraisal of exposure factors provided insight into the personal factors
which have been reported to increase CSI susceptibility and prevalence, whereas exploration
of BRIs was undertaken to establish initial mechanical understanding of how the respective
pathologies occur. As was identified for the exposure factors, the injurious CSI mechanisms
have been widely hypothesised to be multifactorial in nature (Goldstein et al., 1991). A
categorical strategy was therefore devised to provide some order to the extensive number of
BRIs identified in previous literature; BRIs were subsequently divided into biomechanical
risk factors (BRFs) and biomechanical etiological determinants (BEDs). BRFs were
identified as biomechanical variables with empirical support for their association with CSI,
whereas BEDs were identified as biomechanical variables which have been distinguished as
being causal for injuries in general. The term BRI encompasses BRF and BED, and therefore
will be referred to as a general concept overarching CSI etiological variables.
2.4.1
Biomechanical Risk Factors
To appraise the biomechanically-relevant CSI risk factors, a compilation of etiological data
was formed from previous literature (Table 2.4). The respective literature was unconstrained
in terms of date and the informing population, to enable a full understanding of factors which
mechanically contribute to the development of CSI to be gained. In accordance with variable
characteristics, BRFs were divided into three categories: ‘kinematic-related variables’,
‘kinetic-related variables’, and ‘kinematic and kinetic-related variables’. Across the BRFs,
it is important to note that not all variables have the same weighting in their importance to
CSI risk. To initially examine which variables are of greatest prominence for CSI etiology,
the number of pieces of informing literature, in addition to the methodological approaches
of the informing literature will be appraised.
24
Table 2.4. Details of biomechanical risk factors from previous literature
Biomechanical risk factor
Kinematic-related factor
Flexion
Biomechanical risk factor details
Study
Year
Duration of trunk flexion exposure
Sagittal flexion angle
Rapid flexion movements are more likely to injure the discs and ligaments
Fully flexed postures
Extreme forward bending
Extreme forward bending
(Punnett et al.)
(Marras et al.)
(Adams and Dolan)
(McGill)
(Seidler et al.)
(Seidler et al.)
1991
1993
1995
1998
2001
2003
Backward bending and torsion are most likely to damage the apophyseal joint surfaces
Frequent maximal lumbar extension
(Adams and Dolan)
(Kujala et al.)
1995
1997
Full flexion produces high intradiscal pressure
Extreme hyperflexion
(Adams et al.)
(Dolan and Adams)
1994
2001
Repeated hyperextension
Excessive hyperextension forces
Hyperextension
Lumbar hyperextension
Hyperextension of the lumbar spine
(Hall)
(Peer and Fascione)
(Keller)
(Kerssemakers et al.)
(Gottschlich and Young)
1986
2007
2009
2009
2011
Lateral trunk velocity
Lateral bending plus axial rotation
(Marras et al.)
(Schmidt et al.)
1993
2007
Extension
Hyperflexion
Hyperextension
Lateral bending
25
Loading and contralateral flexion
(Johnson et al.)
2012
(Roaf)
(Farfan et al.)
(Cyron and Hutton)
(Foster et al.)
(Hardcastle et al.)
(Panjabi)
(Marras et al.)
(Elliott et al.)
(Marras and Granata)
(Marras and Granata)
(Burnett et al.)
(Drake et al.)
(Burnett et al.)
(Drake et al.)
(Alexandru and Diana)
(Daly et al.)
(Kim and Green)
1960
1970
1978
1989
1992
1992
1993
1993
1995
1995
1996
2005
2008
2008
2010
2001
2011
(Pearcy and Hindle)
(Schmidt et al.)
(Drake et al.)
1991
2007
2008
Rotation
Rotation forces
Slowly applied rotation
Upper body rotation
Trunk rotation between back and front foot impact
Rotation
Increased rotation increases strain
Twisting trunk velocity
Counter-rotation of the trunk between back and front foot impacts
Twisting velocity
Axial twisting
Increased degree of trunk rotation
Static axial torque
Spinal rotation
Axial twist moment/motion
Twisting
Twist technique
Truncal rotation
Flexion and rotation
A combination of flexion and twisting
Axial rotation coupled with flexion
Axial twist motion and moments when combined with flexed postures
26
Extension and rotation
Axial rotation plus extension
(Schmidt et al.)
2007
Increased degree of lumbar trunk axial rotation, extension/flexion and lateral bending
Lateral bending plus flexion
Lateral bending plus extension
Flexion or extension loading coupled with rotation and or side bending
(Burnett et al.)
(Schmidt et al.)
(Schmidt et al.)
(Burnett et al.)
1996
2007
2007
2008
(Micheli)
(Marras et al.)
(Hardcastle et al.)
(Hardcastle et al.)
(Hainline)
(Hainline)
(Hollingworth)
(Kujala et al.)
(Congeni et al.)
(d'Hemecourt and Micheli)
(Dolan and Adams)
(Hutchinson)
(Lonstein)
(Manchikanti)
(Dolan and Adams)
(McGill)
1979
1993
1992
1992
1995
1995
1996
1997
1997
1997
1998
1999
1999
2000
2001
2004
Flexion or extension, lateral
bending and rotation
Repetitive movements
Repetitive flexion and extension activity of the spine
Repeated lifting (lifting frequency)
Repetitive rotation
Repetitive extension
Repetitive trunk extension and rotation
Repetitive trunk flexion and rotation
Rapid movement between hyperflexion and hyperextension
Repetitive extension manoeuvres
Repetitive hyperextension
Repeated hyperextension
Repetitive lifting (fatigue of the erector spinae muscles)
Repetitive lumbar extension
Repetitive hyperextensions
Repetitive (cyclic) flexion
Repetitive bending (fatigues back muscles)
Repeated full flexion motion
27
Repetitive lifting
Repeated trunk flexion
Repeated hyperextension of the lumbar spine
Repetitive hyperextension
Repetitive movements
Repetitive flexion, hyperextension and rotation
Repetitive flexion-extension movements
Repetitive hyperextension and rotation
Repetitive movements
Repetitive forward bending
Repetitive flexion
Repetitive extension
Repetitive flexion
Repetitive hyperextension
(Parkinson et al.)
(Caine and Nassar)
(Haun and Kettner)
(Cassas and Cassettari-Wayhs)
(DePalma and Bhargava)
(Marshall et al.)
(Barile et al.)
(Kruse and Lemmen)
(Baranto et al.)
(Scannell and McGill)
(Maxfield)
(Maxfield)
(Kim and Green)
(Kim and Green)
2004
2005
2005
2006
2006
2007
2007
2009
2009
2009
2010
2010
2011
2011
(Punnett et al.)
(Punnett et al.)
(Adams et al.)
1991
1991
1994
(Gerbino and Micheli)
(Adams and Dolan)
(Christie et al.)
(McGill)
1995
1995
1995
1998
Posture
Exposure to nonneutral trunk postures
Exposure to two or three nonneutral postures
Lordotic posture increases loading of the apophyseal joints, neural arch is the weightbearer
‘Flatter’ spines (thoracic hypokyphosis and lumbar hypolordosis)
Excessive lordotic postures
Increased lordosis
Non-neutral spinal postures
28
Pelvic tilt causing the spine to flex
Postural defects
Poor posture
Lumbar hypolordosis
Thoracic hypokyphosis
Lordotic posture
Extreme hyperlordosis
Lordotic curve of the lumbar spine
Females with flat posture (rather than neutral posture)
Chronic incorrect posture in orthostatic position
Increased lumbar lordosis
Increased lumbar lordosis
Increased sacral tilt (‘steep angulation of the first sacral vertebral segment’)
(McGill)
(Foster et al.)
(Mulhearn and George)
(Tanchev et al.)
(Tanchev et al.)
(Dolan and Adams)
(Miller)
(Kim et al.)
(Smith et al.)
(Alexandru and Diana)
(Kim and Green)
(Bugg et al.)
(Bugg et al.)
1998
1989
1999
2000
2000
2001
2006
2006
2008
2010
2011
2011
2011
(Drake et al.)
2005
Coupled postures involving twist motions
Addition of rotation to flexion/extension postures
Axial rotation in coupled postures
Axial twisting in coupled postures
(Marras et al.)
(Fazey et al.)
(Schmidt et al.)
(Drake and Callaghan)
1993
2006
2007
2008
Repetitive flexion-extension motions followed by axial torque/twist
(Marshall and McGill)
2010
Coupled postures
Combined loading and highly repetitive flexion-extension motion
Coupled posture and twisting
Coupled flexion-extension and
axial rotation
29
Range of motion
Decreased lower lumbar segments range of motion
Decreased maximal extension
End range of spinal motion
Hyper-mobility
Generalised joint laxity
Reduced lumbar lateral flexion range of motion to the bowling arm side
(Kujala et al.)
(Kujala et al.)
(McGill)
(Harreby et al.)
(Tanchev et al.)
(Stuelcken et al.)
1997
1997
1998
1999
2000
2008
(Marras et al.)
(Adams and Dolan)
(Marras and Granata)
(Kujala et al.)
(Callaghan et al.)
(Caine et al.)
(Adams and Dolan)
(Adams and Dolan)
(Adams and Roughley)
(Adams and Roughley)
(Andersson et al.)
(Baranto et al.)
(Baranto et al.)
(Brüeggemann)
(Ferdinands et al.)
1993
1995
1995
1996
2001
2003
2005
2005
2006
2006
2006
2006
2009
2010
2010
Kinetic-related factor
Mechanical loading
Load moment
Mechanical loading
Exertion load
Excessive loading during the growth spurt
Mechanical loading of the spine
Excessive loading
Excessive mechanical loading
Loading history
Excessive mechanical loading causes a disc to degenerate
Loading history
Mechanical loading
High loading of the spine during the growth spurt
Increased spinal loading during adolescents
Degree of mechanical load on the skeleton
Lumbar loading
30
Mechanical loads acting on various tissues in the low back
(Yang et al.)
2011
(Goldstein et al.)
1991
(Cyron and Hutton)
(Troup et al.)
(Hall)
(Adams and Dolan)
(Ikata et al.)
(Zioupos et al.)
(McCormack and Athwal)
(Solomonow et al.)
(Dolan and Adams)
(Au et al.)
(Schache et al.)
(Adams)
(Barile et al.)
(Engstrom and Walker)
(Maxfield)
1978
1985
1986
1995
1996
1996
1999
2000
2001
2001
2002
2004
2007
2007
2010
(Norman et al.)
(Fischer et al.)
(Seidler et al.)
(Seidler et al.)
1998
2007
2001
2003
Axial loading
Exposure to repetitive axial loading
Repetitive loading
Repetitive loading
Fatigue from repetitive loading
Repeated impact forces
Failure occurs at lower loads during repetitive loading
Repetitive stress on the lumbosacral spine
Repetitive loading
Repetitive loading
Cyclic loading
Repetitive loading, failure occurs at much lower loads
Repetitive torsional exertions
High repetitive loads
Repetitive loading leading to compressive damage
Repetitive loading activities
Repetitive loading of the lumbar spine (L4 and L5 pars lesions)
Repetitive loading
Cumulative loading
Cumulative loading
Cumulative lumbar spine loading
Cumulative loading
Cumulative physical workload
31
Shear loading
Horizontal shear forces
Shear loading
Shear loading
(Roaf)
(Marras and Granata)
(Norman et al.)
1960
1997
1998
Combined shear and compressive forces
(Alderson et al.)
2009
Asymmetrical overloading of the spine
Repetitive asymmetric forces
(Tanchev et al.)
(d'Hemecourt et al.)
2000
2002
(Elliott)
(Adams et al.)
(McGill)
(Parkinson and Callaghan)
(Brüeggemann)
2000
2000
2004
2009
2010
(Hall)
(Hollingworth)
(Elliott)
(Debnath et al.)
1986
1996
2000
2009
(Drake et al.)
2005
Shear and compressive force
Asymmetrical loading
Kinematic and kinetic-related factor
Flexion and loading
Lateral flexion during the performance of a skill
Heavy loading in flexion or hyperflexion
Flexion and shear forces
Loading in a flexed posture
Forward bending when high external forces are applied
Hyperextension and loading
Large impact forces and simultaneous extreme hyperextension
Excess loading in hyperextension
Hyperextension during the performance of a skill
Excessive loading in repetitive hyperextension
Rotation and loading
Axial torque coupled with combined loading
32
Flexion and compression
Combined hyperflexion and axial compression
Repetition of moderate compressive force to a fully flexed motion segment cause some
non-degenerated discs to prolapse
Asymmetrical bending and compression are most likely to damage the disc
Combined compression, lateral bending and forward bending
Full flexion motion with compressive load
Bending and compression
Repeated flexion with relatively modest compressive loads
(Pearcy)
(Adams and Dolan)
1989
1995
(Adams and Dolan)
(Dolan and Adams)
(McGill)
(Adams)
(Marshall and McGill)
1995
2001
2004
2004
2010
Combined compression and extension
Bending and compression
(Chosa et al.)
(Adams)
2004
2004
A combination of rotation and compression
Combined compression and extension or rotation
(Roaf)
(Chosa et al.)
1960
2004
Extension and compression
Rotation and compression
33
Constituting 55% of the BRFs identified within the respective review of previous literature,
kinematic variables were evidenced to be prominent in inducing biomechanical risk for CSI
development. The most prevalent BRFs within the kinematics section were repetitive
movements (Baranto et al., 2009b) and posture (Mulhearn and George, 1999), with support
for increased CSI risk provided by 30 and 20 studies, respectively. To further explore the
prominence of each BRF in previous literature, an overview of the number of studies which
identified each BRF was provided in Table 2.5.
Table 2.5. Kinematic and kinetic risk factors identified within previous literature, along with
the number of informing studies
Biomechanical risk factors
Kinematic-related variables
Repetitive movements
Posture
Rotation
Flexion
Range of motion
Extension
Flexion or extension, lateral bending and
rotation
Coupled posture and twisting
Lateral bending
Flexion and rotation
Coupled postures
Extension and rotation
Coupled flexion-extension and axial rotation
Kinetic-related variables
Mechanical loading
Repetitive loading
Cumulative loading
Shear loading
Asymmetrical loading
Axial loading
Shear and compressive force
Kinematic and kinetic-related variables
Flexion and compression
Flexion and loading
Hyperextension and loading
Extension and compression
Rotation and compression
Rotation and loading
Number of studies
30
20
17
8
6
7
4
4
3
3
1
1
1
16
15
4
3
2
1
1
7
5
4
2
2
1
34
The wealth of support for repetitive movements as a BRF was considerable. Repetitive
movements are a key gymnastics trait which typically characterise each training session
(Kolt and Kirby, 1999); as a result, the identification of repetitive movements as risk
indicators further supports the vulnerability of the gymnastics population to CSI (Swärd et
al., 1991). Although repetitive movements have been accepted as etiological in the way of
CSI development, little empirical quantification of repetitive movements as CSI risk factors
has been gained through clinical or biomechanical methods (Appendix A.3 and A.4). The
identification of repetitive movements as risk for CSI development is generally considered
in the way of repeated movements over a prolonged period of time (e.g. years), as oppose to
the association of repetitive movements within a cross-sectional period with CSI risk.
As the second most prominent BRF in previous literature, posture can be effectively
measured within a single data collection, and has been explored through numerous methods
of approach. Both clinical methods, as detailed in Appendix A.3, and biomechanical
methods (Appendix A.4) have informed the association of posture with increased CSI risk
in previous literature. The wealth of methods, from cadaveric methods (Adams et al., 1994)
to physical examinations (Tanchev et al., 2000), furthers support for the role of posture in
the development of CSI. Within the sub-category of posture, lumbar lordosis, defined by
Benzel (2015) as concave spinal curvature which is dorsally oriented, was the most highly
reported postural etiolgical variable (50%, Table 2.4). A high level of lordotic posture has
been reported in the gymnastics population (Guimarães et al., 2007), in addition, lumbar
lordosis has been shown to increase with growth (O'Brien and d'Hemecourt, 2013). The
associations of BRFs with gymnastics and physical development mechanisms further
warrants the need for exploration of the influence of physical development on CSI etiology
in the gymnastics population.
The second category of BRFs is a formulation of kinetic-associated variables. Within the
respective section, the sub-categories with the highest proportion of literature support were
mechanical loading and repetitive loading, with 16 and 15 supporting studies, respectively.
Although there has been limited research conducted into the assessment of mechanical
loading on the spine within a gymnastics context, the findings from other anatomical sites,
for example, Burt et al. (2010), whose research identified peak ground reaction forces of up
to 8.46 times body weight at the ankles during the round off skill, are suggestive of
potentially significant loads at the spinal level. Of the research which has identified
mechanical loading as a risk factor for CSI development, the majority of studies utilised a
35
biomechanical model to explore the respective variable (Callaghan et al., 2001;
Brüggemann, 2010). The development or use of a biomechanical model to examine spinal
loading throughout the performance of a specific movement or skill involves a complex
process. Although valuable data is able to be gained, the use of a biomechanical model may
be too advanced for use within an applied setting.
In line with the repetitive kinematics undertaken by the gymnastics population, the repetition
of loading has been recognised as a commonality of the sport of gymnastics (Brüggemann,
2010; Maxfield, 2010). As was found for the kinematic-related BRFs, the most prominent
kinetic-related BRFs have considerable relation to the feature of the sport of gymnastics,
furthering potential understanding of why gymnasts commonly experience high levels of
CSI. In alignment with the identification of repetitive movements as a prominent BRF, it is
considered that the repetition of load over an extended period of time may be unable to be
adequately explored within cross-sectional biomechanical data collections.
The available literature within the kinematic and kinetic-related variables category identified
flexion and compression (e.g. Marshall and McGill (2010)), and flexion and loading (e.g.
McGill (2004)) to be the most recurrently reported BRFs. In comparison with the individual
sections of kinematics and kinetics, the kinematic and kinetic BRF category comprised of a
relatively low frequency of research, and therefore is suggestive of the reduced influence of
combined kinematic and kinetic BRFs on CSI development. The review of BRFs for CSI
supported the suggestion that more research is needed for sound conclusions to be made
about many mechanical indicators, particularly as the explicit cause of pain and injury is
notoriously difficult to determine (Hamill et al., 2012).
2.4.2
Biomechanical Etiological Determinants
The exploration of previous literature which informed BEDs was undertaken subsequent to
the appraisal of CSI BRFs to inform the etiological section of BRIs. Defined as ‘the ability
to utilise the body’s structures in the safest, most efficient positional relationships for the
functional demands imposed upon them’ (Elphinston, 2008), functional stability is achieved
by way of a complex process, requiring multifaceted coordination of interrelated factors
(Wesley, 2001; Wikstrom et al., 2005). Denoted essential for the prevention of pain and
injury (Kirby, 2011), the inclusion of stability within CSI prevention approaches is further
36
supported by clinical practice in which spinal stability is considered for the clinical diagnoses
of low back pain (Granata and England, 2006; Pajek and Pajek, 2009).
Although it is accepted that control and stability problems are not generally confined to one
area (Wesley, 2001), spinal instability has been identified to have high associations with
CBP (Cholewicki and McGill, 1996; Panjabi, 2003; Granata and England, 2006; McGill,
2007), particularly at the lumbar level (Izzo et al., 2013). The excessive strain on the
paraspinal tissues which the loss of spinal stability brings about has been presented as a cause
of low back pain (Panjabi, 2003; Granata et al., 2004). Owing to this, Izzo et al. (2013)
further highlighted the need for spinal stability for the prevention of early biomechanical
deterioration of the spinal components. The wealth of associations between lumbar spinal
stability with CSI rationalises the need for lumbar spine stability assessment to develop a
full understanding of the BEDs within the gymnastics population.
Used to maintain static positions in addition to the control of dynamic movements (Bobić
and Radaš, 2010), functional stability is defined as a dynamic process (Frontera, 2007),
owing to the continuous nature of the measure. One of the most common measures involving
dynamic stability in previous literature is postural stability (Slobounov and Newell, 1996;
Wikstrom et al., 2005). The biomechanical goal of postural stability maintenance is to make
fine adjustments to the centre of mass in relation to the base of support (Cotoros and Baritz,
2010); subsequently, both body orientation and the base of support have been documented
to significantly influence postural control (Nashner and McCollum, 1985; Horak, 2006). To
achieve a high level postural stability, the control system must be insensitive to changes in
the inputs which mediate its reactions (Blaszczyk and Michalski, 2006) and thus, a mutual
relationship must exist between the body segment positions, the global body orientation and
the gravitational field (Blaszczyk and Michalski, 2006). The recognition that functional
stability is often attained along with the presence of minor mechanical disturbances (Granata
and England, 2006) provided suggestion of the potential interaction between BRFs and
BEDs. Although separately identified to influence CSI, to the researcher’s knowledge, the
intra-BRI relationships are yet to be explored within the gymnastics population.
Understanding of the respective interactions may be highly informative for the development
of effective injury prevention approaches.
37
2.5
Injury Prevention
It has been established that young female gymnasts are vulnerable to CSI development
(Chapter 2, Section 2.2), subsequently, the question of what can be done to reduce pathology
rates and assist the long-term health of children and adolescents participating in the sport
must be address (Hawkins and Metheny, 2001). Early detection of both pain and injury has
been advocated (Jackson et al., 1981; Gerbino and Micheli, 1995; Sands et al., 2011), with
the aim of ensuring the young athletes have the best chance of avoiding injury development
and preventing disability (Ciullo and Jackson, 1985). Although the benefits of effective
diagnostic and rehabilitation techniques are apparent, the accompanying loss of sports
participation time, risk of long term damage and the cost of such techniques (Daly et al.,
2001) highlight the aspiration for injury avoidance altogether. The development of a CSI
such as spondylolysis (a fracture of the pars interatricularis (Kim and Green, 2011)), is
generally proceeded by the suspension of gymnastics training, advised for periods of up to
twelve months (Debnath et al., 2009). Withdrawal from gymnastics training for a year-long
period can have significant consequences for gymnasts’ future careers; as a result, it is
suggested that focus on the creation of prevention strategies is crucial.
More than half of chronic injuries in young athletes have been reported to be preventable
(Smith et al., 1993; Andrews and Yaeger, 2014). With the aim of reducing the likelihood
and severity of pain and injury (UNICEF and W.H.O., 2008), the employment of prevention
techniques has been proposed by a number of researchers in a variety of research areas
(Richardson et al., 1997; Gamboa et al., 2008; Baranto et al., 2009b; Bradshaw and Hume,
2012). Injury prevention takes a variety of forms (Gerbino and Micheli, 1995), with the most
basic approach being education. The effectiveness of the simple strategy is outlined by
Ettlinger et al. (1995) whose educational programs to increase awareness of ACL injuries
resulted in a 62% decrease in the number of ACL injuries sustained by alpine skiers. In every
form of injury prevention, a set of distinguished guidelines are sought after which, if
followed, the risk of chronic injury development can be minimised (Hawkins and Metheny,
2001). In certain prevention strategies, such guidelines are offered in the form of training
programmes; existing programs for the development of balance and proprioception, strength
etc. for anterior cruciate ligament injuries have been outlined by Dai et al. (2012). In
association with the findings of increased susceptibility to CSI during physical development,
a reduction in training volume during periods of rapid growth has been presented as a further
approach for the attempt to prevent injuries in young athletes (Baxter-Jones et al., 1993;
38
Verhagen and van Mechelen, 2008; Baranto et al., 2009b; McLeod et al., 2011; Johnson et
al., 2012). Suggestions of the type of sporting activities to reduce during growth have
included repetitive sport (McLeod et al., 2011) and those which place extreme loads on the
spine (Verhagen and van Mechelen, 2008; Baranto et al., 2009b). Although detachment from
training and competition during growth is a commonly suggested prevention approach,
continuation of physical activity, with modified elements to reduce injury development risk
is of greater desirability. Injury prevention approaches which are not inclusive of the
respective ‘rest’ period have therefore been recommended (Conley et al., 2014).
The development of an effective injury prevention strategy is a complex process.
Quantification of the basics of the developmental procedure was initially outlined by van
Mechelen et al. (1992), who presented a prevention model consisting of four stages, the
intended use of which was to inform the development of applied injury prevention strategies.
The model as a whole must be considered for the development of an injury preventions
strategy, however, the first two stages are of greatest importance for the advancement of
knowledge of CSI within the gymnastics population. The first stage of the respective model
directed the researcher to ‘establish the extent of the problem’; a typical approach to fulfilling
the requirements of this stage is to distinguish which pathologies require attention, along
with identifying high-risk groups which should be targeted through the appraisal of
epidemiological research (Sands, 2000; McLeod et al., 2011). Once agreed that a specific
pathology within a certain population merits the need for the development of a prevention
initiative, the second stage of van Mechelen et al. (1992)’s model was to ‘establish etiology
and mechanisms of injury’. Knowledge of predisposing factors for injury is essential to attain
to develop understanding of how the pathologies come about; thus the appraisal of
etiological research findings are invaluable for injury prevention development (Hawkins and
Metheny, 2001; Verhagen and van Mechelen, 2008; McLeod et al., 2011; Stracciolini et al.,
2013). The importance of establishing the injury etiology has been recognised in a by
(Meeuwisse et al., 2007), who developed a model of sports injury etiology to illustrate the
complexity of the process (Figure 2.3).
39
Figure 2.3. A model of sports injury etiology developed by Meeuwisse et al. (2007).
The etiology model presented by Meeuwisse et al. (2007) documented the process in which
predisposed athletes (as a result of intrinsic risk factors) become susceptible athletes
(through exposure to extrinsic risk factors), following which an inciting event may lead to
injury. The respective model highlighted two distinct phases, the risk factors for injury and
the mechanisms of injury; although the mechanisms of injury are of great importance, focus
with the respective Chapter has been placed on the risk factors, in order to appraise current
knowledge of the role of intrinsic risk factors (exposure factors) and extrinsic risk factors
(BRIs) within the female gymnastics population. The translation from predisposed athlete to
a susceptible athlete, in line with Meeuwisse et al. (2007)’s model, is therefore advocated
for the development of understanding of why the gymnastics population are at heightened
risk of CSI development.
Although van Mechelen et al. (1992)’s four stage model is considered somewhat limited for
current practice (Finch, 2006), the framework provides an essential basis, from which a
number of further models have been developed. Using the four stage approach, Finch (2006)
produced the Translate Research into Injury Prevention Practice model, consisting of an
additional two stages for the evaluation of the preventative approach. Developing Finch
(2006)’s six stage approach, Donnelly et al. (2012) established a contemporary prevention
framework, which takes on a different structure to the linear approach used by both van
Mechelen et al. (1992) and Finch (2006) (Figure 2.4). The key professionals which inform
40
the framework have been identified to include epidemiologists, bioengineers,
physiotherapists, health promoters, public health experts and biomechanists (Alderson and
Donnelly, 2012). Subsequently, the use of Donnelly et al. (2012)’s model is prominent
within biomechanical injury prevention research.
Figure 2.4. ACL-focused injury prevention framework taken from Donnelly et al. (2012).
Other than structure alteration, Donnelly et al. (2012) made an important modification to
Finch (2006)’s framework in the addition of athlete screening. Musculoskeletal screening is
undertaken with the intention of identifying potential weaknesses for which modification
can result in reduced injury risk (Whatman et al., 2011). The respective screening approach
has been typically used as a clinical tool, however, contemporary research has explored the
use of biomechanical screening strategies for non-invasive musculoskeletal screening
(Whatman et al., 2011; Crewe et al., 2012a). The incorporation of biomechanical
mechanisms of injury and stability into injury prevention strategies, such as screening, has
been suggested by Baranto et al. (2009b); Wesley (2001); McLeod et al. (2011). A
considerable benefit of biomechanical screening approaches is the ability to assess dynamic
movements, subsequently developing sporting-specific prevention approaches. Medical
screening (e.g. imaging techniques) and physical examination screening by physiotherapists
are examples of approaches which are limited by dictation for static positioning throughout
the screening process. The use of Donnelly et al. (2012)’s framework to inform the
development of dynamic alterations of biomechanical components of technique to reduce
41
injury predisposition (Krosshaug et al., 2005) may therefore be able to inform effective
injury screening strategies for CSI risk in the gymnastics population. Long-term evaluation
of the effectiveness of screening strategies require continuous, annual injury surveillance
data (Donnelly et al., 2012), however, short-term evaluations of prevention strategies are
additionally advocated (Finch, 2011).
Given the vulnerability of child and adolescent athletes to CSI (Maxfield, 2010), the
initiation of injury prevention approaches at early ages have been advised (Kolt and Kirby,
1999; Johnson et al., 2012; Harringe and Caine, 2013). Within the gymnastics population, it
has been further identified that prevention strategies should be aimed at the lower to middle
competitive levels, as a result of the high number of gymnasts with similar injury rates as
those of a higher competitive level (Kolt and Kirby, 1999). The movements which form the
basis of injury screening should mimic those performed by the gymnasts (Bradshaw and
Hume, 2012). Aligning with the young gymnastics cohort, it was therefore suggested for the
focus of prevention approaches, fundamental gymnastics skills should be used (Kolt and
Kirby, 1999). The inclusion of physical development mechanism assessments in addition to
biomechanical screening has been suggested by McLeod et al. (2011), however, until
knowledge of the influence of physical development mechanisms on BRIs is gained, the
respective screening strategy may be limited in the depth of understanding gained.
2.5.1
Gymnastics Skills for Injury Prevention Approaches
The need for the development of reliable testing which closely mimics gymnastics
movement has been outlined by Hume et al. (2013) for biomechanical screening.
Furthermore fundamental gymnastics skills have been advocated to inform injury prevention
strategies (Kolt and Kirby, 1999). As previously acknowledged, the study of physically
developing gymnasts dictates focus on the phases of childhood and adolescence. A group of
gymnasts undergoing physical development may have a wide array of skill levels, therefore,
restrictions are placed on the spectrum of skills which can inform the study of the physically
developing population. It is imperative that each gymnast can perform the selected skills to
a similar standard to reduce the influence of performance ability as an extraneous factor.
Gymnasts learn skills at different rates (Miller, 2011) and therefore some physically
developing gymnasts may be unable to perform complex skills. The development of
fundamental gymnastics skills (core gymnastics skills which are taught at an early stage of
gymnastics development to inform skills with greater complexity), typically occurs during
42
the elementary years of gymnastics practice (Kolt and Kirby, 1999). Performance of
fundamental skills to an adequate standard should therefore be comfortable practice by all
competitive gymnasts. Secondary to the performance of leaps and jumps, skills such as the
handstand and walkover, which require inversion of the body are taught to young gymnasts
(Nassar, 2013).
As one of the most fundamental skills in gymnastics (Readhead and Hiley, 2008), the
acquisition of the handstand technique commences early on in a gymnasts’ career. Due to its
widespread application to skills of increased complexity (Croix et al., 2010), for example,
the limber, walkover and handspring (Mitchell et al., 2002), performance of the handstand
is subsequently sustained for the duration of a gymnasts’ career. During the performance of
the handstand skill, the gymnast aims to remain motionless (Yeadon and Trewartha, 2003),
however, the achievement of such a state requires small, controlled perturbations (Slobounov
and Newell, 1996). Balance and control mechanisms have been examined within handstand
studies, however, little understanding of stability from an injury perspective has been gained
and lumbo-pelvic mechanics are yet to be quantified within the skill.
The forward walkover skill is an additional fundamental gymnastics skill (Foidart-Dessalle
et al., 2005; Readhead and Hiley, 2008). Performance of the forward walkover requires the
lower-body to translate from a posterior to an anterior position without a flight phase
(Foidart-Dessalle et al., 2005). The similarity in the mechanics of the skills during double
support (hand balance) is illustrated in Figure 2.5 and Figure 2.6 and demonstrates the use
of the handstand skill as a progression to learning the forward walkover skill (Mitchell et al.,
2002).
Figure 2.5. A schematic of a handstand, standing to double support.
43
Figure 2.6. A schematic of a forward walkover.
The spine plays a vital role in assisting the motion of the forward walkover skill. The crucial
role of the spine is anticipated to be a primary reason for previous identification of the
forward walkover as one of the most commonly associated gymnastics skills with CSI
(Jackson et al., 1976; Hall, 1986; Kruse and Lemmen, 2009). The close relationship between
maximum lumbar hyperextension and impact force, as was evidenced by Hall (1986), may
be an additionally injurious element of the forward walkover skill. The high levels of stress
which are deposited on the posterior spine through the performance of the walkover skill has
been identified by Purcell and Micheli (2009). Along with the association between the
forward walkover and CSI development, extreme hyperextension incorporated in the skill
has further been identified as a motion which exacerbates CBP (Kruse and Lemmen, 2009)
and spondylolysis symptoms (Pope and Smith, 2006).
To develop initial insight into the BRFs which may be prominent within the handstand and
the forward walkover skills, a theoretical appraisal of the fundamental skill mechanics was
undertaken (Table 2.6). Eleven out of 28 BRFs (39%) were found to be distinguishable
within general handstand mechanics, with 16 (57%) BRFs demonstrated within forward
walkover mechanics. In addition to the level of BRFs in the handstand and forward
walkover, each of the respective fundamental skills require a high level of dynamic spinal
stability through the maintenance of posture in balanced positions.
44
Table 2.6. Indication (by way of a coloured box) of the biomechanical risk factors which are
included in the mechanics of the handstand and forward walkover skills
Biomechanical risk factor
Handstand
Forward walkover
11
16
Flexion
Extension
Hyperflexion
Hyperextension
Lateral bending
Rotation
Flexion and rotation
Extension and rotation
Flexion or extension, side bending and rotation
Repetitive movements
Posture
Coupled postures
Coupled postures and twisting
Coupled flexion-extension and axial rotation
Range of motion
Mechanical loading
Axial loading
Repetitive loading
Cumulative loading
Shear loading
Shear and compressive force
Asymmetrical loading
Flexion and loading
Hyperextension and loading
Rotation and loading
Flexion and compression
Extension and compression
Rotation and compression
Total
45
2.6
Methods of Approach
The appraisal of previous CSI epidemiology literature has identified concern for
development of the respective pathologies in the gymnastics population. Through appraisal
of CSI etiology, physical development, along with a number of BRIs (e.g. posture and
lumbo-pelvic stability), were identified to be prominent to the development of the respective
pathologies. However, translation of the respective knowledge to Meeuwisse et al. (2007)’s
model revealed the need for understanding of the influence of physical development
mechanisms on BRIs to inform the development of effective CSI prevention approaches for
the female gymnastics population. The methods to inform the respective area of research
will be considered in the remaining section through the appraisal of previous research
surrounding the research design, methods of data processing and methods of data analysis.
2.6.1
Research Design
Cross-Sectional v Longitudinal
Previous research studies have used both cross-sectional (Camargo et al., 2014) and
longitudinal (Burt et al., 2013) approaches to examine physical development within
gymnastics cohorts. Although cross-sectional approaches are relatively common, the
dynamic nature of physical development mechanisms are suggestive of the need for
longitudinal-based research, defined by Goldstein (1968) as information gathered on the
same sample at selected time points. Longitudinal research based on child and adolescent
gymnasts has been recognised to be limited (Thomis et al., 2005), yet it has been identified
as being highly valuable, particularly to the development of knowledge in relation to the
physical growth and biological maturation of gymnasts (Malina et al., 2013). Practicalities
of longitudinal research make large sample sizes difficult to obtain and manage, a subsequent
drawback of such research designs are the small sample sizes which are typical within
physical growth-related research (Goldstein, 1968). Konopinski et al. (2012) recognised the
majority of gymnastics-based research to be cross-sectional. Research undertaken using the
respective research design is concerned with gathering information at specific points on a
time scale (Goldstein, 1968).
46
Sample Size Determination Techniques
As is commonly the case within research, the inability to practically measure the target
population in its entirety necessitates the determination of a representative sample. Adequate
representation of the identified target population, is accepted to allow for true population
inferences from the research results obtained (Mullineaux et al., 2001), thus permitting the
generalisation of the results to the distinguished population (Fox et al., 2009; Gogtay, 2010).
Statistical procedures for the estimation of a suitable sample size for a study are varied,
however, the underlying concepts of the majority of methods are similar (Gogtay, 2010).
Sufficient statistical power is needed to minimise the likelihood of a study producing Type
I and Type II errors (Fox et al., 2009); a high level of statistical power additionally results
in an increased ability of discerning the effects which the research aims to seek (Whitley and
Ball, 2002; Fox et al., 2009). Within biomechanical research, the target power is typically
accepted to be 80% (Field, 2009; Fox et al., 2009) along with a 95% accepted level of
significance (Field, 2009). Prior to the calculation of the necessary sample size, an effect
size must be determined through use of a standard formulae, such as that presented in
Equation 2.1 by Cohen (1992). As the sample size was necessary to determine prior to the
study initiation, the values to input can be obtained from previous literature (Fox et al.,
2009).
Cohen’s d = mean1 – mean2
[2.1]
(SD1 + SD2)/2
Mean1 = Mean of first sample
Mean2 = Mean of second sample
SD1 = Standard deviation of first sample
SD2 = Standard deviation of second sample
Cooper et al. (2009) identified that Cohen’s d has a tendency to overestimate the
standardized mean difference in small samples; subsequently, Hedge’s g equation (Equation
2.2) has been advocated to produce an unbiased estimate of the standardized mean
difference.
47
3
𝐻𝑒𝑑𝑔𝑒’𝑠 𝑔 = Cohen’s D x (1 – (4 (n1 + n2) −9))
[2.2]
n1 = Number of participants in first sample
n2 = Number of participants in second sample
Interpretation of the effect size in terms of the quantity of total variance for which the effect
size accounts can be made using the Cohen’s d effect size categories outlined by Cohen
(1988).
0
0.1
0.3
0.5
=
=
=
=
No effect
small effect (the effect explains 1% of the total variance)
medium effect (the effect explains 9% of the total variance)
large effect (the effect accounts for 25% of the total variance)
With the determined power level, statistical significance level and effect size, the sample
size can be calculations through statistics software such as G*Power 3.1 (Faul et al., 2007).
Trial Size Determination Techniques
In addition to sample size, the number of trials per subject condition has been shown to
influence statistical power (Dufek et al., 1995). A multiple trial protocol is commonly
deemed necessary to adopt within scientific research (Rodano, 2002), particularly as a result
of the errors associated with single-trial analyses (Bates et al., 1992). To assist in ensuring
high validity, the minimum number of trials necessary to produce stabilised mechanical
outputs is important to determine (James et al., 2007). There is not a sole approach for the
calculation of the number of trials which are suitable within a study, a number of approaches
including intra-class correlation coefficient analysis have been used to inform previous study
designs (James et al., 2007). Seemingly the most popular method of approach within
previous biomechanical research, the sequential estimation procedure has been recognised
as an effective method of determining trial size (James et al., 2007). The approach aims to
decipher the minimum number of trials at which a stable mean is reached through the
examination of systematic trial effects. To do so, the cumulative trial value means for the
variable of interest are plotted, allowing for visual inspection of the trend (Hamill and
McNiven, 1990); the criterion for which a stable mean is accepted has previously been
arbitrarily selected (Rodano, 2002).
48
Group v Individual
Group-based analyses are dominant within contemporary biomechanical research (e.g.
Bradshaw et al. (2014)) and the ability to deduce generalisations from the results has been
recognised as an appealing feature of the respective analyses approach (Dufek et al., 1995).
However, resistance to the grouping approach has additionally been highlighted in previous
research; the creation of a mythical average performer which is not reflective of any one
individual Dufek et al. (1995) was one reason for the negative views of some researchers.
The subsequent de-emphasis of the importance of individual mechanics are particularly
viewed negatively when individual differences, which may be of high importance, are
masked. Given the limitations of group-based approaches, individual (single-subject)
analyses were subsequently favoured by researchers such as Bates (1996). It is important to
note that individual designs are not free from limitations, particularly in the way of
variability within and between individuals (Scholes et al., 2012). Each with strengths and
limitations, the decision of whether an individual or group-based design is of most suitability
to a particular research study was considered to be dependent on whether or not individual
performances accurately reflected by the group responses (Dufek et al., 1995). It is
subsequently necessary to establish the homogeneity of the group-based responses prior to
the grouping of participants to ensure the ‘average performer’ which is produced is reflective
of the individual participants included in the analysis (Dufek et al., 1995; Bates, 1996).
2.6.2
Physical Development Measurement and Analyses
Measurement of Chronological Age
Chronological age is a mechanism of physical development which is informed by a standard
measure. Within previous research, chronological age is calculated as the time from the date
of birth (Verhagen and van Mechelen, 2008).
Measurement of Maturational Status
As previously reported (Chapter 2, Section 2.3.1), three indexes of maturation have been
identified in previous literature - skeletal, somatic and sexual (Baxter-Jones et al., 2002).
The most valuable of the respective measures has been widely regarded to be skeletal age
(Baxter-Jones et al., 2002; Verhagen and van Mechelen, 2008), which is typically
determined through the use of x-ray or radiograph equipment to assess the development of
49
skeletal tissue in the hand and wrist (Georgopoulos et al., 2002; Panjabi, 2003; McGill,
2007). Although favourable, the specialised equipment for which the determination of
skeletal maturation status is necessary entails high costs along with radiation safety issues
(Baxter-Jones et al., 2002), and therefore, alternative methods are often required. Somatic
maturation can be indicated through measure of the age at peak height velocity, determined
to reflect the maximum growth rate of height during adolescence. One of the main
difficulties in assessing the age at peak height velocity is the need for longitudinal data
spanning from the age of 9 or 10 to 17 years of age (Malina et al., 2004). The inherent
difficulties with the collection of sufficient longitudinal data to attain accurate measures of
the age at which peak height velocity occurs has led to the more common use of predicted
age at peak height velocity, as exemplified by Nurmi-Lawton et al. (2004) through use of
three-year data for a cohort of female artistic gymnasts. The need for longitudinal data to
provide both a prediction and a direct measure of age at peak height velocity is speculated
to account for the increased quantification of maturation through the use of sexual maturation
assessment, in comparison with somatic, in the gymnastics population.
The gold standard technique of measuring sexual maturation has been identified as the use
of a physical examination (Dorn and Biro, 2011), commonly known as the Sexual
Maturation Scale (SMS) (Marshall and Tanner, 1969). Based on secondary sexual
characteristic development, the respective methods utilises photographs or drawings of
progressive stages of maturation in addition to the physical examination of breast and pubic
hair development for females (Smith et al., 1993; Bond et al., 2006). The individual
outcomes of the examinations are then matched with one of five reference stages, known as
Tanner’s stages (Tanner, 1962; Verhagen and van Mechelen, 2008), subsequently deducing
a SMS rating. Contemporary research has acknowledged the widespread use of Tanner
staging techniques (Dorn and Biro, 2011; Georgopoulos et al., 2012), however, due to its
invasive nature (Verhagen and van Mechelen, 2008), emphasis must be placed on the
sensitivity of the physical examination. As a result, such examinations are typically only
conducted by doctors or trained clinicians (Georgopoulos et al., 2002; McGill, 2007) and
have been deemed to be impractical in non-clinical settings (Bond et al., 2006).
In response to the limitations of the SMS approach, the Pubertal Development Scale (PDS)
was developed by Petersen et al. (1988). The self-assessment techniques, for which sexual
characteristics, specifically body hair, breast change, skin change and growth spurt, are rated
by children or adolescents via interview or questionnaire, has demonstrated consistency with
50
the sequencing and timing of pubertal events determined using the SMS (Marshall and
Tanner, 1969; Petersen et al., 1988; Bond et al., 2006). With additional evidence of high
correlations (r = 0.80-0.92) between nurses’ scores and self-assessment scores (Daly et al.,
2005), the PDS has been advocated as a useful alternative to the SMS approach (Bond et al.,
2006). Appraisal of the maturation techniques highlighted the appropriateness of the use of
self-assessment techniques for sexual maturation quantification in predisposed female
artistic gymnasts. Determination of maturation statuses for injury preventions approaches
required consideration of available equipment and the subsequent cost, the degree of
invasiveness, the suitability of the setting (e.g. to conduct the physical examination), along
with the availability of a person of expertise for data analysis (specifically for skeletal
maturity assessments). In line with the conclusion drawn from consideration of the female
artistic gymnastics population, the most appropriate maturation assessment technique for
screening approaches was identified to be self-assessed sexual maturation.
Measurement of Anthropometric Growth Status
Quantification of anthropometric growth status through use of the measure of chronological
age has been prominent within previous literature (Beunen and Malina, 1996), however, the
respective approach has developed extensive opposition (Beunen and Malina, 2008;
Verhagen and van Mechelen, 2008). The use of chronological age to represent
musculoskeletal growth has been deemed unreliable (Wright and Crée, 1998; Beunen and
Malina, 2008; Verhagen and van Mechelen, 2008; McLeod et al., 2011; Brukner, 2012;
Konopinski et al., 2012) and considerable individual variation of musculoskeletal growth
has been evidenced within those of the same chronological age (Smith et al., 1993),
particularly around the adolescent growth spurt (Mirwald et al., 2002; Malina et al., 2004).
Recognised as one of the most traditional measures of growth (Rogol et al., 2000), the use
of whole-body height to represent the growth of children and adolescence has been extensive
within previous research, including Richards et al. (1999); Georgopoulos et al. (2002);
Ackland et al. (2003); Panjabi (2003); Claessens et al. (2006); McGill (2007); Kuo et al.
(2008); Georgopoulos et al. (2012); Bradshaw et al. (2014). The ability to obtain a direct
measure of physical growth using a simplistic approach such as whole-body height is highly
beneficial, particularly within applied settings when contact time with athletes may be
limited. A further direct measurement which has been used to represent growth and imitates
the benefits and limitations of height, is whole-body mass (Richards et al., 1999;
Georgopoulos et al., 2002; Ackland et al., 2003; Panjabi, 2003; Claessens et al., 2006;
51
McGill, 2007; Kuo et al., 2008; Bradshaw et al., 2014). With slightly more complexity in
their calculation than the measures of height and mass alone, index measures have
additionally been used within previous research. The most favourable of the index measures
are body mass index (BMI) [mass (kg)/height (m)2] (Georgopoulos et al., 2002; Malina et
al., 2004; Roche and Sun, 2005; McGill, 2007; Bobić and Radaš, 2010; Talwar et al., 2012)
and the Ponderal index [height (cm)/ mass (kg) 1/3] (Talwar et al., 2012). Even with the
added complexity of the incorporation of two measures, the index measures are confined to
whole-body growth assessments. The inability of each of the respective measures to
distinguish between body proportions and composition (Rogol et al., 2000) was considered
a limitation as proportional growth has been recognised as being an important aspect of
gymnasts’ growth (Tanner, 1962; Claessens et al., 2006).
Further support for the need to look beyond the sole quantification of whole-body measures
was provided by Smith et al. (1993) who documented the non-uniform growth of body
segments, with reports of up to a 12 month difference between the maximum growth velocity
for the lower limb and the trunk segments. Maximum growth of the lower limbs has
additionally been identified to precede the upper-body by Malina et al. (2004). Recognition
that adolescents typically have relatively longer legs than the upper-body (Baxter-Jones et
al., 2002) is suggested to have implications for the mechanical impact of physical
development on CSI BRIs. Knowledge of the respective process provided support for the
measurement of upper and lower-body lengths, such as leg length, as prominent growthrelated measures (Richards et al., 1999; Hawkins and Metheny, 2001; Malina et al., 2004;
Claessens et al., 2006; Hebestreit and Bar-Or, 2008).
The isolated measures of individual body segments, such as leg length, allow for
understanding of segmental growth, however, the measures are limited in terms of context
as they are not reported in relation to any other measure. The quantification of ratio measures
may overcome the respective limitation, enabling the development of knowledge of the
interaction of nonlinear segmental growth and therefore increase the context of the
individual segment measures previously reported. Sitting height to whole-body height has
been used by Claessens et al. (2006) and Malina et al. (2004), along with Bobić and Radaš
(2010), who additionally measured armspan to sitting height; however, the most commonly
ratio measure of growth is bicristal to biacromial breadths (b-ratio). Gymnastics-based
studies such as Malina et al. (2004); Claessens et al. (2006); Armstrong and van Mechelen
(2008); Kuo et al. (2008); Siatras et al. (2009); Thomas et al. (2013) have provided
52
quantification of the pelvic breadth (bicristal breadth) relative to the shoulder breadth
(biacromial breadth). Representation of the b-ratio measure in relation to chronological age
was provided by Malina et al. (2004) (Figure 2.7). The respective data indicates the males
to experience a broadening of the shoulders in relation to the pelvis, with the opposite trend
found for the females.
Figure 2.7. Representation of the bicristal to biacromial ratio for male and females in
accordance with chronological age (taken from Malina et al. (2004)).
General population trends of b-ratio have identified an increased rate of bicristal breadth in
relation to biacromial breadth over time (Malina et al., 2004). Previous assessment of b-ratio
measure within the female gymnastics population revealed lower ratio outcomes for the
cohort in comparison with non-athletes of the same gender , thus indicating characteristics
of broader shoulders and narrow pelvis in female gymnasts (Siatras et al., 2009). The unique
b-ratio trends which have been evidenced in gymnastics populations provide initial
suggestion that the b-ratio measure may provide a more valid metric to of growth for the
specific population.
Measurement of Body Segment Inertial Parameters
The direct measure of body segment mass, mass centre location and principal moments of
inertia of living subjects are problematic. Indirect methods are therefore required to attain
approximations of inertia parameters. Comprehensive techniques for the estimation of body
53
segment inertia parameters (BSIP) commonly fall within three categories: cadaver-based
methods, medical diagnostic technologies, and mathematical inertia modelling.
Cadaver-based methods utilise ratio and regression equations and are informed by data
gained through the segmental dissection of human cadavers. Use of the cadaver-based
approach to determine subject-specific properties offers a relatively simplistic set-up in
terms of processing requirements. In addition, the time in which necessary subject
measurements can be gained is minimal. Although beneficial in its ease of attaining subjectspecific inertia properties, a limitation of cadaver-based methods is its reliance on previously
obtained data. Few studies have completed cadaver dissection with reported data which can
be used to inform BSIP calculation (Dempster, 1955; Clauser et al., 1969; Chandler et al.,
1975). The informing cadaver samples have been limited to few individuals, generally of
declining health preceding death, of a single gender and age category; for example Dempster
(1995)’s cadaver study was based on eight elderly male cadavers. The cadaver population
characteristics subsequently induce difficulties in applying the data to sporting populations.
Estimation of segment inertia parameters through tissue distribution measurement have been
acquired from medical diagnostic imaging of living subjects. The technologies used have
included gamma mass scanning (Zatsiorsky and Seluyanov, 1983), computer tomography
(CT) (Pearsall et al., 1996) and magnetic resonance imaging (MRI) (Pearsall et al., 1994).
Good agreement (5% error) between cadaveric leg density values and CT technique
outcomes has been previously reported by Ackland et al. (1988). A further favourable feature
of medical diagnostic technology approaches is the lack of constraints in regards to the
population which can be examined. Zatsiorsky and Seluyanov (1983) used a gamma mass
scanning technique on one hundred young students and researchers, demonstrating the
ability to collect large amounts of BSIP data on a diverse population in comparison with the
cadaver studies. However, the advance equipment necessary to attain the respective data, in
addition to the expertise required, the cost and time, and the potential harmful radioactive
waves involved restricts the use of medical diagnostic technology within biomechanics.
The mathematical inertia modelling technique is centred about the use of geometric solids
which are representative of individual body segments. Simplification of the musculoskeletal
system in such a manner brings about a common biomechanical concern in the impact of
body segments representation in a rigid manner (Robertson et al., 2014). Nevertheless, use
of mathematical models for the exploration of BSIP have been favoured above ratio and
regression in contemporary research (e.g. Challis et al. (2012)).
54
A number of mathematical inertia models have been developed, each with varying
measurement requirements and accuracy. Jensen (1978) developed a complex model which
required 408 measurements which were obtained using a photogrammetric method. The
approach dictated approximately 10 minutes of the subject’s time, however, extensive
digitising was required. The number of measurements needed was reduced to 242 for the
model presented by Hatze (1980), however, the method required direct measures which took
80 minutes of the subject’s time. Using the direct measure approach, Yeadon (1990)’s model
was informed by 95 anthropometric measurements, which took approximately 30 minutes
of the subject’s time. The mathematical inertia models developed by Jensen (1978), Hatze
(1980) and Yeadon (1990) reported predicted body mass error as <2%, <0.05% and <2.1%,
respectively.
Yeadon (1990)’s model was inclusive of a reduced number of measurements, with relatively
little compromise in model accuracy in comparison with the models presented by Jensen
(1978) and Hatze (1980). The model, developed specifically for gymnastics movements,
consisted of 40 solids, formed by length, perimeter, width and depth measures. A visual
representation of Yeadon (1990)’s model is provided in Error! Reference source not
found..
Figure 2.8. Yeadon (1990)’s mathematical inertia model.
From an injury screening perspective, use of Yeadon (1990)’s model appears to be of
greatest suitability due to the minimised number of anthropometric measurements required.
However, the ability to attain the necessary measurements via photographic measures and
therefore reduce the subject contact time, as was presented by Jensen (1978), was a further
desirable feature for screening approaches. Gittoes et al. (2009) presented an approach which
55
was based on Yeadon (1990)’s model, but rather than obtaining the measurements directly,
an image-based approach was developed. The short subject contact time and reduced
processing time in comparison with Jensen (1978)’s approach were achieved by Gittoes et
al. (2009)’s approach, along with a high level of accuracy, with estimation of subject’s body
mass to within 2.9%.
The mathematical approach has been favoured by research which has explored BSIP in
young athletic populations, e.g. Ackland et al. (2003); Crewe et al. (2011). However, there
is an apparent lack of consensus as to the most appropriate mathematical model and density
data set to permit the estimation of subject-specific mass properties. A number of density
data have been obtained through cadaver-based approaches, the most common of which were
documented by Dempster (1955); Clauser et al. (1969); Chandler et al. (1975). Each of the
density data sets were obtained from small samples of male cadavers between the ages of 28
and 83, therefore, inherent limitations existed for use of each set of density values within a
female gymnastics population.
Longitudinal Measurement of Physical Development
Physical development is a longitudinal process (Malina et al., 2013), therefore, to satisfy the
exploration of ageing, maturation and growth, as physical development mechanisms,
longitudinal studies have been recognised to be necessary (Smith et al., 1993; Beunen and
Malina, 2008; Malina et al., 2013). The majority of studies which provide insight in the
gymnastics population have been cross-sectional in nature, subsequently, understanding of
the process of physical development within the gymnastics population is limited (Caine et
al., 2013). Although few longitudinal gymnastics-based studies have been conducted, the
research which has been undertaken in the respective area has quantified the process of
physical development through use of growth rate measures (Richards et al., 1999; Bass et
al., 2000; Ackland et al., 2003; Daly et al., 2005). Measures of the temporal nature of wholebody growth, such as height velocity (Daly et al., 2005) and mass velocity (Richards et al.,
1999), have been reported, in addition to assessment of segmental growth rates, specifically
leg length and sitting height (Daly et al., 2005) and upper limb, trunk and lower limb
(Richards et al., 1999). Combining one of the prominent biomechanical measures of growth,
Ackland et al. (2003) conducted a study over 3.3 years, throughout which the rate of moment
of inertia development was calculated and monitored in female gymnasts. Such in depth
understanding of the way in which individuals physically develop over a crucial time period
is evidenced to be highly beneficial from both an injury and a performance perspective
56
(Ackland et al., 2003). The value of longitudinal research to explore the influence of physical
development on CSI risk in the female gymnastics population is subsequently evident.
The insight gained by longitudinal studies of physical development is significant to the
development of understanding in the area, however, consequent to the complexity of the
research, it is common for such studies to be short-term as well as being limited to small
samples (Baxter-Jones et al., 2002). Longitudinal research is commonly up against an
additional problem of relatively high drop-out rates (Baxter-Jones et al., 2002). The most
frequent approach taken for the longitudinal assessment of physical development in
gymnastics is the biannual collection of data (Bass et al., 2000; Daly et al., 2005). The
quantity of data provided by such an approach allows for the determination of growth rate,
while being feasible to undertake. The use of biannual measurements of physical
development may translate for use within musculoskeletal screening for injury prevention,
however, to the researcher’s knowledge, appraisal of the need for longitudinal screening
approaches is yet to undergo rigorous scientific study.
2.6.3
Biomechanical Risk Indicator Measurement
A review of previous literature identified a wealth of CSI BRF and BED (Chapter 2, Section
2.4); the respective BRIs were established through various methods of approach (Appendix
A.3 and A.4). The methods through which relevant variables were identified were deemed
to be of high importance for translation of knowledge to informing the development of injury
prevention strategies. In addition to enabling the study of BRIs throughout the performance
of fundamental skills, the in-vivo approach was evidenced to be of greatest suitability for
use within a screening approach, in comparison with in-vitro and in-silico strategies.
Informed through the appraisal of previous literature, the BRIs which were identified to be
prominent to the increased risk of CSI, the gymnastics population and physical development
were posture, anterior-posterior lumbo-pelvic range of motion, medio-lateral lumbo-pelvic
range of motion, anterior-posterior whole-body stability, medio-lateral whole-body stability,
whole-body general stability and lumbo-pelvic stability. The most prominent BRF which
was appropriate for inclusion within injury screening was posture. In addition to being wellsupported as a BRF, posture was identified as an etiological variable through a broad range
of methods of approach, as reported in Section 2.4.1. Range of motion was supported as an
etiological variable for CSI development by six pieces of previous research, and was
57
therefore not one of the most prominent BRF. However, the notable role of lumbo-pelvic
range of motion within the sport of gymnastics warranted consideration of the variable in
the exploration of BRI in the female gymnastics population.
Each of the BED variables were considered to be highly important for inclusion due to the
wealth of support of each as injurious (Section 2.4.2). Although the variables themselves are
well established, the measurements of anterior-posterior whole-body stability, medio-lateral
whole-body stability, whole-body general stability and lumbo-pelvic stability have lacked
consistency. To analyse each BRI throughout the performance of fundamental gymnastics
skills, previous methods of approach will be appraised and explored in the following
sections.
Kinematic Data Collection Techniques
A number of approaches are available for the collection of kinematic data with each being
utilised widely within biomechanics research. The collection of kinematic data through use
of video capture is the most long-standing approach within biomechanical research. The
extensive use of video capture have been evidenced in previous literature (Boyer et al.,
2009), with one of the key advantages being the relatively basic equipment needed. The
approach is highly valuable as it allows kinematic data to be obtained in situations where it
is unsuitable for the participant to alter any environmental constraints, for example, a
competition. The 2.5 x 10-3 m reported resolution of this approach (Kerwin, 1995) is,
however a limitation of this method, particularly due to further developed systems which
possess greater levels of accuracy. Capture of lumbar kinematic data through the
performance of fundamental gymnastics skills using video capture is therefore foreseen to
be problematic. Use of video for the collection of biomechanical data commonly warrants
the use of direct linear transformation (DLT), a method involving manual processing of
collected data. The extensive time requirements which accompany video capture
subsequently limit the appropriateness of use of the approach within musculoskeletal
screening.
Overcoming the need for arduous manual processing, automatic data collection systems have
been developed, with high capabilities of acquiring both accurate and precise measurements
(Richards, 1999). This highly desirable quality is, however, coupled with a considerable
increased cost in comparison with the equipment needed for video-based kinematic data
collection. Two of the most prominent automation capture systems within contemporary
58
biomechanical research are the VICON motion analysis system (Vicon Motion Systems,
Oxford Metrics, Oxford, UK), as exemplified by Jackson et al. (2011), and the Cartesian
Optoelectronic Dynamic Anthropometer (CODA) motional analysis system (Charnwood
Dynamics, Leicester, UK). Subsequent to calibration and alignment of the system, both
VICON and CODA enable the researcher to collect copious data in a fixed space, thus
increasing the research reliability (Rodano, 2002). The requirement of each system, for the
participants to wear markers, may be unsuitable for certain situations such as the collection
of competition data, however, the respective feature of the system is not foreseen to be
problematic within a musculoskeletal screening approach. The passive nature of the VICON
system makes use of emitted light from the scanning units; the lack of power source needed
enables the markers to be fairly succinct, a feature which is not matched by the CODA
system. Active markers are used by the CODA motion system, thus requiring the production
of a light source, necessitating the addition of a battery. Although less compact, one of the
main benefits of active marker use is the ability to code the markers prior to data collection,
thus wavering the problem of marker confusion faced by systems such as VICON. Richards
(1999) highlighted the confusion of positional data to be a concern for passive motion
analysis systems when markers are in close proximity (within 2 mm) of each other in a 3 m
long volume. The collection of spinal kinematic data has been evidenced to require markers
placed in close proximity to one another, therefore, the respective difference between the
two systems requires careful consideration to enable reliable data capture. The reporting of
accuracy values of 0.1 mm within a 3 m field of view, along with position resolutions in the
order of 5 to 10 x 10-5 m for the CODA motion analysis system make the system highly
appealing for use within biomechanical research.
Kinetic Data Collection Techniques
The quantification of anterior-posterior (Fy), medial-lateral (Fx) and vertical (Fz) external
forces produced as a result of human motion, are commonly measured through the use of
force plates (Bartlett, 2007). Allowing the researcher to quantify the reactive contact force
components between the performer and the ground (Bartlett, 2007), the use of force plates
within biomechanical research has been extensive (Nigg and Herzog, 1999). Referred to as
the ‘gold standard’ for centre of pressure measurement (Haas and Burden, 2000), Kistler
force plates (Kistler Instruments Ltd., UK) are frequently used for kinetic measurements in
sport biomechanics research, predominantly for the measures of centre of pressure and
ground reaction force (Bartlett, 2007). When determining the centre of pressure location with
59
the feet positioned symmetrically about the force plate centre, less than 2 mm error has been
reported for piezoelectric force plates in previous literature (Middleton et al., 1999). The
sophisticated and highly accurate devices are useful in a broad spectrum of research contexts
and are particularly advantageous as a result of the ability to synchronise the system with a
motion analysis system, thus allowing the simultaneous collection of kinematic and kinetic
data.
2.6.4
Biomechanical Risk Indicator Data Processing
Noise Reduction
An important aspect of data processing is the removal of noise from the raw data gathered
from both kinematic and kinetic data collections (Armstrong and van Mechelen, 2008). The
filtering process, in which noise is removed from the raw data, must be completed with care
to avoid the development of large errors and should be undertaken prior to calculation of
other variables. The removal of noise from the raw data is typically undertaken through use
of one of three methods: digital low-pass filters, Fourier series truncation and quantic spline
curve fitting. Human movement typically possesses a low-frequency content, while noise
generally has a higher frequency (Bartlett, 2007), therefore low-pass filtering approaches are
desirable for the removal of high-frequency noise. To satisfy the respective requirements,
the use of a fourth-order low-pass Butterworth filter is often favoured in sports
biomechanics, as exemplified by Farana et al. (2013).
The frequency at which a filter is applied is recommended to be calculated to be specific to
the research study in hand as oppose to using previously published frequencies (Bartlett,
2007). The use of optimising algorithms based on residual analysis to fulfil the process is
typical (Armstrong and van Mechelen, 2008). A commonly recognised approach to
determine the optimal cut-off frequency of a particular data set, known as residual analysis,
was presented by Wells and Winter (1980). The method is centred on the calculations of the
residuals between the unfiltered and filtered signals at a range of cut-off frequencies (Winter,
2009). The decision of the optimal cut-off frequency involves a degree of subjective
judgement (Bartlett, 2007) when determining the cut-off frequency at which the residual is
suitable approaching a asymptotic value on a plotted residual and cut-off frequency graph.
The collection of both kinematic and kinetic data in a single study is widespread in sports
biomechanics (e.g. Exell et al. (2012a)). The sampling rate of the respective data is
60
predetermined prior to the collection of data (Payton and Bartlett, 2008). Consideration of
the most appropriate sampling rate must be taken as a rate which is too low may lead to
oversight of the true extremes of the data, alternatively, if the rate is set too high, errors may
occur as a result of the integration process (Armstrong and van Mechelen, 2008). The
resolution, determined by the speed of the movement and the duration of the phase of
interest, must additionally be taken into account (Payton and Bartlett, 2008), therefore tradeoff between the sampling rate and resolution must be established.
As exemplified by Farana et al. (2013), the rate of kinetic data is captured typically much
higher than kinematic data. To allow for the exportation rates of the sets of data to coincide
for analysis purposes, a process of data resampling can be undertaken. Sample rate
conversion involves two signal processing operations, interpolation (increasing the sample
rate) and decimation (decreasing the sample rate) (Andrews and Yaeger, 2014). In order to
determine the optimal sampling rate for the combined systems, data must be exported at
increments (e.g., every 100 Hz between 100 Hz and 1000 Hz). The signal outputs can then
be examined (often graphically) to allow the researcher to make an informed decision of the
most appropriate sampling rate at which to process data from each of the systems.
2.6.5
Biomechanical Risk Indicator Data Analysis
Biomechanical Risk Factor Analyses
High association between CBP, CSI and lordotic posture have been evidenced within
previous literature (Kim and Green, 2011; Bugg et al., 2012). The use of sophisticated 3D
systems enable the assessment of posture throughout a movement skill in its entirety,
offering highly valuable information for the understanding of postural dynamics. The
assessment of dynamic posture, through the examination of lordosis angles has been
previously undertaken in activities such as walking and running (Levine et al., 2007) and
fast bowling in cricket (Ferdinands et al., 2009; Crewe et al., 2012b). However, to the
researcher’s knowledge, there has been no such research collected during the performance
of gymnastics skills. The examination of the lumbar lordosis angle is typically undertaken
through use of the Cobb angle definition, accepted as the gold standard by Vrtovec et al.
(2009); a method which encompasses all five lumbar segments, as demonstrated in Figure
2.9.
61
Figure 2.9. An image of the measurement of the Cobb angle taken from Kim et al. (2006).
Use of the respective method within motion analysis commonly places analysis markers on
the spinous processes of the lumbar vertebrae (Giglio and Volpon, 2007), as oppose to the
superior endplate of L1 and the superior endplate of the sacrum, as is used for radiography
methods (Been and Kalichman, 2013). The monitoring of lumbar posture during the
performance of fundamental skills is anticipated to provide valuable insight into CSI risk
within the female artistic gymnastics population.
It is common for range of motion tests to be included in pre-participation musculoskeletal
training (Dennis et al., 2008), owing to the associations between range of motion and
injury/pain risk (Stuelcken et al., 2008). Contemporary research, including Levine et al.
(2007); Ranson et al. (2008); O'Sullivan et al. (2012), has seen motion analysis techniques
in place for range of motion assessments. Although sagittal plane analyses are prominent in
the literature (Levine et al., 2007), range of motion assessments in more than one plane have
become more common, as exemplified by Crewe et al. (2011).
The interaction of the spine and pelvis has been recognised to be crucial in aesthetic sports
such as gymnastics (d'Hemecourt and Luke, 2012). Lumbo-pelvic measures are a
commonality of lower-back research, allowing for understanding of the interaction between
the lumbar spine segment and the pelvis (Schache et al., 2001; Schache et al., 2002; Saunders
et al., 2005; Crewe et al., 2012a; O'Sullivan et al., 2012; Crewe et al., 2013b). Pelvic tilt and
lumbar lordosis, although two separate parameters, have been accepted to be inter-dependent
and have been documented to alter with the process of physical development (Mac-Thiong
et al., 2004; O'Sullivan et al., 2012). The quantification of lordotic posture in addition to
lumbo-pelvic range of motion is anticipated to develop insight into current knowledge of the
lumbar and pelvic motions throughout the performances of fundamental gymnastics skills.
62
Biomechanical Etiological Determinant Analyses
A multitude of measures have been utilised in biomechanical research to provide an estimate
of stability. Standard deviations of joint excursions have been measured by Kerwin and
Trewartha (2001), who also took into account the variability of torque-time histories.
Although it has been acknowledged that there is no simple linear relationship between
variability and stability (van Emmerik and van Wegen, 2000), measurement of variability
has additionally been used to give indication of stability in research such as that conducted
by Tanaka et al. (2009). Time to stabilisation (Wikstrom et al., 2005), the Lyapunov
exponent (Graham et al., 2014) and joint stiffness (Malina and Bouchard, 1991) are
additional measures which have been used to quantify stability. The wealth of stability
measures have been evidenced, however, the most favourable measure of stability is
recognised to be qualities of the centre of pressure (van Emmerik and van Wegen, 2000;
Blaszczyk and Michalski, 2006; Hubbard et al., 2007; Kong et al., 2011). Measures of the
centre of pressure path have included centre of pressure area (Slobounov and Newell, 1996;
van Emmerik and van Wegen, 2000; Hubbard et al., 2007; Kong et al., 2011), centre of
pressure velocity (Hubbard et al., 2007) and centre of pressure displacement (Slobounov and
Newell, 1996; Hubbard et al., 2007; Kong et al., 2011; Granacher and Gollhofer, 2012). It
is typical for the displacement of the centre of pressure to be reported in terms of range with
the anterior-posterior centre of pressure range (Slobounov and Newell, 1996; Hubbard et al.,
2007; Kong et al., 2011; Granacher and Gollhofer, 2012) and medio-lateral centre of
pressure range (Slobounov and Newell, 1996; Kong et al., 2011; Granacher and Gollhofer,
2012) both prominent within previous stability research. Centre of pressure findings are
generally interpreted to indicate decreased stability with an increased range of centre of
pressure motion (Slobounov and Newell, 1996). The popularity of the centre of pressure in
estimating the stability of a body is speculated to be attributed to its relative ease of
measurement (Blaszczyk and Klonowski, 2001; Maribo et al., 2011); the quantification of
centre of pressure data is subsequently considered to be of high suitability for incorporation
within a musculoskeletal screening approach.
The importance of dynamic stability has been recognised in previous literature (Cotoros and
Baritz, 2010), however, analysis of the etiological determinant is limited in previous research
(Granata and England, 2006). Wesley (2001), suggested that research should strive to gain
an understanding of whole-body stability, which has been further supported by Kirby (2011),
who highlighted the importance of whole-body dynamic stability quantification for
63
performance, injury prevention and rehabilitation. A measure of whole-body dynamic
stability was presented by Wikstrom et al. (2005) through use of ground reaction forces; the
measure was identified as the dynamic postural stability index (DPSI). The stability measure
has been used to inform previous insight into whole-body dynamic control mechanisms
(Kirby, 2011). With increased complexity in comparison with the centre of pressure
positional data analysis, the measure makes use of kinetic data in all three planes (anteriorposterior, medio-lateral and vertical). The formulae used by Wikstrom et al. (2005) were
modified, through the addition of normalisation, by Malina and Bouchard (1991). The
formulae for the calculation of the medio-lateral stability index (MLSI), the anteriorposterior stability index (APSI), the vertical stability index (VSI) and the dynamic postural
stability index (DPSI) are presented in Equation 2.3.
DPSI = √ [∑(0-Fx)2+∑(0-Fy)2+∑(body weight-Fz)2]/number of data points
[2.3]
Fx = medio-lateral ground reaction force
Fy = anterior-posterior ground reaction force
Fz = vertical ground reaction force
Precision of the DPSI measure has been reported as 0.03 standard error of measurement
(Wikstrom et al., 2005), in addition to high between test session reliability, evidenced
through 0.96 intraclass correlation coefficient figures (Wikstrom et al., 2005). Whole-body
measures of dynamic stability, such as the centre of pressure excursions and the DPSI allow
for the increased understanding of global stability however, few studies have provided
insight into the dynamic stability of the spine (Granata and England, 2006). Lumbo-pelvic
stability has been reported to be concerned with the orientation of the spine on the pelvis
segment (Mitchell et al., 2003). The stability of specific anatomical regions is necessary to
understand to allow for optimal function, as suggested by Kibler et al. (2006) in relation to
the lumbo-pelvic-hip complex. As a result of the apparent absence of in-vivo lumbo-pelvic
stability measures within previous literature, adaptation of the DPSI measurement technique
is advocated for the novel estimation of lumbo-pelvic dynamic stability. The replacement of
medio-lateral, anterior-posterior and vertical ground reaction force data with kinematic
lumbo-pelvic data, and the removal of the normalisation process (Equation 2.4) is speculated
to enable in-vivo quantification of dynamic lumbo-pelvic stability index (DLPSI).
64
DLPSI = √ [∑(0-LPx)2+∑(0-LPy)2+∑(0-LPz)2]/number of data points
[2.4]
LPx = medio-lateral lumbo-pelvic angle
LPy = anterior-posterior lumbo-pelvic angle
LPz = vertical lumbo-pelvic angle
The analyses of posture, lumbo-pelvic range of motion (anterior-posterior and mediolateral), centre of pressure range (anterior-posterior and medio-lateral), DPSI and DLPSI
throughout the performance of fundamental skills is anticipated to provided important
insight into CSI biomechanical risk within the female artistic gymnastics population.
2.7
Chapter Summary
Considerable prevalence of CBP and CSI were revealed through an appraisal of
epidemiology in the gymnastics population. The informing research, which spanned 38
years, provided evidence of the ongoing risk of gymnasts to CSI development and
subsequently highlighted the need for insight into potential prevention measures. Through
the review of CSI etiology, physical development was recognised as a prominent exposure
factor, in addition to numerous BRIs. The eminent BRIs with the greatest association with
gymnastics were found to be posture, lumbo-pelvic range of motion, centre of pressure
range, whole-body stability and lumbo-pelvic stability. Appraisal of injury prevention
models provided context to the role of intrinsic and extrinsic risk factors in CSI development.
Further appraisal of gymnastics skills, physical development mechanisms and BRI methods
of approach developed insight into the potential for the respective methods to inform injury
prevention research and injury screening approaches.
65
CHAPTER 3 - MEASUREMENT AND ANALYSIS OF
BIOMECHANICAL RISK INDICATORS
3.1
Introduction
Injury screening strategies, which focus on the early detection of susceptible athletes, have
been documented to be favourable for the avoidance of injury development (Sands et al.,
2011). Donnelly et al. (2012) proposed understanding of the causal mechanisms for specific
pathologies as an essential foundation for effective injury prevention approaches; such
mechanisms may be further divided into intrinsic and extrinsic risk factors, as identified by
Meeuwisse et al. (2007). Extrinsic risk factors are fundamental to screening approach
development and the inclusion of functional movement biomechanics within
musculoskeletal screening approaches for injury prevention have been increasingly used to
underpin extrinsic risk (Hume et al., 2013). Although current knowledge of CSI risk in the
female artistic gymnastics population remains indistinct, empirical biomechanical insights
may offer important contributions to understanding of the causal mechanisms. Prominent
CSI BRIs were identified to include posture, lumbo-pelvic range of motion, centre of
pressure range, general stability and lumbo-pelvic stability in Chapter 2 (Section 2.4.1).
Interrogation of the respective measures within fundamental gymnastics skills is anticipated
to provide initial insight to inform CSI prevention measures.
Screening approaches are typically informed through group responses (e.g. Crewe et al.
(2012a), but conducted on individuals due to the desirable customisation of prevention
strategies (Gittoes and Irwin, 2012; DiFiori et al., 2014). The ability to generalise the results
to an extended population through group-based screening (Dufek et al., 1995) may be
desirable in practice, however, to develop effective group screening approaches, the
biomechanical outputs of the cohort as a whole must be reflective of individual responses
(Dufek et al., 1995; James and Bates, 1997). To inform CSI risk screening approaches for
female artistic gymnasts, the extent to which individual gymnast biomechanical responses
reflect whole-group BRIs necessitated evaluation.
The need for prevention techniques to focus on fundamental skills has been highlighted by
Kolt and Kirby (1999). An appraisal of the use of fundamental gymnastics skills for injury
screening was included in Chapter 2 (Section 2.5.1), through which, two of the most
fundamental skills in gymnastics, the handstand and forward walkover, were selected to
66
underpin the respective research. Biomechanical exploration of the handstand and forward
walkover skills have been previously documented within performance-based research
including Yeadon and Trewartha (2003); Asseman and Gahery (2005); Gautier et al. (2007).
From a performance perspective, the skills have sufficient mechanical associations to
establish the handstand as a progression to the forward walkover (Mitchell et al., 2002).
Knowledge of the mechanical similarities of the two skills from a CSI risk perspective is
necessary, in order to ascertain whether the handstand skill is sufficient to screen the female
gymnasts, or if BRI disparities indicate the need for the inclusion of both skills in screening
protocol.
The extrinsic indicators of CSI risk (biomechanical risk indicators) were selected in
accordance with isolated associations with the respective pathologies and relation to
gymnastics practice (Chapter 2, Section 2.4). Although identified independently, researchers
such as Hamill et al. (2012) and Whiting and Zernicke (2008) have highlighted the inherent
inter-dependence which mechanical risk factors possess. The wealth in developing empirical
insight into biomechanical extrinsic risk may be of particular relevance to musculoskeletal
screening as the number of screening measures necessary may reduce in line with BRI
similarity. As such, interrogation of the relationships between BRIs may offer valuable
comprehension of the underlying nature of CSI etiology.
3.1.1
Chapter Aim
The aim of the chapter was to quantitatively investigate primary biomechanical risk indicator
measures in a female artistic gymnastics cohort performing fundamental gymnastics skills.
3.1.2
Chapter Questions
CQ 3.1 What group-based biomechanical risk indicators are reflected by individual
mechanics within fundamental gymnastics skills?
The grouping of participants has informed injury screening approaches (e.g. Crewe et al.
(2012a)) and is typical within gymnastics-based biomechanical research (Ackland et al.,
2003; Gautier et al., 2009). However, effective use of the approach in applied practice
commands individual differences to be minimal, thus ensuring pertinent individual
information is not concealed by the single group response (Bates, 1996). Within the
67
respective research, grouping of a gymnastics cohort is anticipated to inadequately reflect
the individual mechanical outputs; therefore individual-centred screening approaches are
speculated to be preferable. As the remainder of the chapter will additionally inform injury
screening BRI measurement and analyses, the findings from the address of CQ 3.1 will
inform CQ 3.2 and CQ 3.3.
CQ 3.2 What are the relationships between the biomechanical risk indicators in
fundamental gymnastics skills?
Fundamental skills are inherently linked through skill development. Use of the handstand
skill as a progression to the forward walkover within the sport of gymnastics (Mitchell et al.,
2002) is indicative of mechanical similarities between the two skills from a performance
perspective. The similarities are expected to translate to pain and injury mechanics, and thus,
the handstand skill BRIs are speculated to be reflected in forward walkover skill BRIs. The
outcomes of the skill similarity investigations are foreseen to provide insight into the extent
to which injury predisposition extends across skills. The knowledge gained is subsequently
anticipated to be informative for biomechanical screening protocols, providing initial insight
into whether one skill can be used to represent biomechanical risk of an addition skill. The
remaining chapter question (CQ 3.3) will be underpinned by the analysis of fundamental
skills in accordance with the respective chapter question outcome.
CQ 3.3 What are the associations between biomechanical risk indicators within isolated
fundamental gymnastics skills?
Ample independent associations of BRFs and BEDs with CSI (e.g. Grenier and McGill
(2007); Elphinston (2014)) are supportive of the anticipated strong relationships between
distinct BRI measures. Understanding of the association between BRIs through empirical
study is anticipated to be highly informative for the development of effective screening
approaches. Initial understanding of whether CSI risk is reflected in all BRIs to the same
extent, or if a number of BRIs are necessary to include within screening may be gained from
the investigation.
68
3.2
3.2.1
Methods
Participants and Study Design
To inform the research, female artistic gymnasts between the ages of nine and 15 years, who
trained regularly, enter at least one competition per year, and had no history of CBP or CSI
were selected. The participant criteria were formed to enable a susceptible population to be
monitored to inform injury screening approaches. Initial contact with potential participants
was made through gymnastics coaches and local gymnastics clubs. Participant and parental
recruitment sheets (Appendix A.1) were emailed or handed to the respective coaches, along
with details of the cohort criteria.
In addition to the emphasis of previous literature on the need for injury prevention focus on
competitive gymnasts (Kolt and Kirby, 1999), the associations of the competitive cohort
with high incidences of low back pain and spinal injuries (Meeusen and Borms, 1992)
informed the decision to ensure each of the participants were competitive. Focus on the
specified age range was decided upon in accordance with the National British Gymnastics
age criteria for Women’s Artistic Gymnastics competitions. As documented in the British
Gymnastics handbooks (Gymnastics, 2013, 2015), entry to National British Gymnastics
events was set at a minimum age of nine years. The minimum age was emulated in the
respective study. The upper age boundary was determined in accordance with the junior age
category (14–15 years). Use of the respective age range additionally aligned with previous
research by Brüggemann (2010) which found gymnasts between the ages of 12 and 13 years
to have the highest frequency of spinal disorders.
The gymnasts were required to be free from injury at the time of testing, therefore ensuring
a healthy cohort of gymnasts informed the respective research. Injury was defined as
pathologies which prevent the individual from training or competing (Caine et al., 1989;
Kolt and Kirby, 1999; Caine et al., 2003b). It was ensured that gymnasts had no history of
back pain or injury. As previous injury has been identified as a risk factor for CSI, the
inclusion criteria were customised in attempt to eliminate, to the greatest extent possible, the
influence of atypical mechanics as a result of previous back pain or injury. A secondary
purpose for recruiting a healthy group of female artistic gymnasts was to align with injury
prevention approaches, which aim to detect predisposing characteristics prior to the
development of pain or injury.
69
Using the participant criteria, the gymnastics coaches and clubs were asked to disseminate
the recruitment sheets to gymnasts and parents of gymnasts who sufficed the outlined
criteria. Parents of the gymnasts who wished to participate in the research made contact with
the lead researcher (via contact details on the participant recruitment sheet, Appendix A.1)
and data collections were subsequently organised. As is commonly the case within scientific
research, the inability to practically measure the target population in its entirety, necessitates
the examination of a representative sample (Fox et al., 2009). Adequate representation of
the population dictated by the outlined participant criteria, has been recognised to allow for
true population inferences (Mullineaux et al., 2001), thus permitting valid generalisation of
the research findings (Fox et al., 2009). To acquire suitable data for sample size calculation,
an enabling study was undertaken, the details of which are provided in Appendix A.2.
Sample size calculation revealed four gymnasts to be the minimum number necessary to
include in the respective research. The respective minimum sample size was exceeded by 10
gymnasts to increase the statistical power of the research (Suresh and Chandrashekara,
2012).
To address the respective chapter questions (CQ 3.1, 3.2 and 3.3), a cross-sectional approach
was considered to be most appropriate in developing initial insight into BRIs in a
predisposed gymnastics cohort. Fourteen female artistic gymnasts ranging from 9.3 to 15.0
years of age, with mean (SD) height and mass of 1.41 (0.12) m and 36.9 (11.0) kg
respectively, volunteered to take part in the cross-sectional study. The participant cohort
ranged from club to International level with mean (SD) training duration and weekly training
frequency of 5.3 (2.0) years and 16.5 (6.4) hours respectively. Given the prevalence of the
respective performance measures within the CSI eitology review (Chapter 2, Section 1.3),
details of each gymnasts’ performance was measured. As the main consideration of the
selected cohort was to resemble a typical female artistic gymnastics population, a decision
to monitor, rather than control, skill level, training duration and training frequency, was
taken. Participant performance details are presented in Table 3.1, for which age, training
duration, training frequency and performance level were reported by each gymnast.
70
Table 3.1. Individual gymnast and group mean (SD) details of age, training information and
performance level at the time of data collection
Gymnast
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Group
Age (years)
13.8
15.0
10.9
11.2
9.8
9.3
9.8
14.9
10.1
12.3
11.3
10.0
12.2
10.0
11.9 (1.9)
Training
duration
(years)
8
8
6
6
2
9
3
6
4
6
8
4
5
4
5.6 (2.1)
Training frequency
(hours/week)
Performance level
9
9
28
17
21
22
22
10
14
6
14
20
16
22
16.4 (6.4)
Regional
Regional
International
National
National
National
National
Club
Club
Club
National
Regional
National
National
-
Burt et al. (2013) recognised a high training frequency as six to 16 hours per week. With the
minimum training frequency of the gymnastics within the respective cohort being six hours
per week, the training frequency within the cohort as a whole was subsequently deemed to
be high and reflective of the high training demands which are inherent in competitive
gymnastics (Malina et al., 2013). The data within Table 3.1 identified that on average,
gymnasts began training at 6.2 years of age.
Full disclosure of the participant requirements, potential risks and benefits of taking part in
the study, as well as the right to withdraw from the study at any point, was provided to the
gymnast and their parents in both the recruitment sheet (Appendix A.1) and information
sheets (Appendix A.3 and A.4). Prior to participation in the research, written informed
consent was gained from the gymnasts’ parents/guardians (Appendix A.5). Gymnasts over
the age of 14 years additionally completed informed consent forms (Appendix A.6), while
gymnasts under the age of 14 years completed a participant assent form (Appendix A.7). To
ensure the gymnasts were in a suitable physical state for participation in the study, standard
pre-test health questionnaires were additionally completed by a parent/guardian (Appendix
A.8) and the gymnast (Appendix A.9). The health-based questionnaires were informed by
71
the American College of Sports Medicine pre-participation physical examination guidelines
(Roberts et al., 2014), with the specific questions (e.g. current medication) selected to ensure
an overview of the gymnasts’ physical states was gained and to highlight any factors which
may put the health of the gymnasts at risk as a result of participation in the study. To gain
further understanding of the gymnastics cohort, participant information, such as training
duration and training frequency, was gained through use of a participant question sheet
(Appendix A.10). Ethical approval was granted by Cardiff Metropolitan University prior to
the collection of data.
3.2.2
Experimental Protocol
Each collection took place in the National Indoor Athletics Centre situated in Cardiff
Metropolitan University, Cardiff. On arrival, the gymnasts and their parents/guardians were
shown the collection space and equipment. To ensure participant understanding of the
process required of them, the collection brief was inclusive of a simple demonstration of the
collection process, undertaken by the primary researcher.
Prior to data collection, the minimum number of trials necessary to produce stabilised
mechanical outputs was important to determine as a result of the mean reliability and validity
being compromised when the mean is not stable (James et al., 2007). To assist in ensuring
high validity of the respective research, the minimum number of trials necessary to produce
stabilised minimum lumbar angle outputs for the handstand and the forward walkover was
calculated using a cross-validation approach. As with the sample size calculation, the
minimum lumbar angle informed the respective study due to its prominence as a BRI
(posture). Details of the cross-validation calculations for 20 handstand and 20 forward
walkover trials of three gymnasts, are provided in Appendix A.11. Sixteen trials were
deemed necessary to obtain stabilised minimum lumbar angle output measures across the
three gymnasts. As the gymnasts who participated in the enabling studies did not find the 20
trial repetitions strenuous, a maximum number of 20 trials was set for each skill, thus
sufficing the cross-validation output, while assisting in establishing a true measure of each
variable (Rodano, 2002).
For each of the gymnasts, performance of the handstand and forward walkover skills were
undertaken in the preferred order of the individual. All trials of one skill were to be
completed in a blocked fashion, i.e. the completion of all trials of one skill before the
72
performance of the other skill. The method was selected in accordance with the protocol
used in the study undertaken by Slobounov and Newell (1996), which analysed handstand
performance in a competitive gymnastics cohort. The instructions provided for the
performance of each skill were minimal. Again aligning with the protocol developed by
Slobounov and Newell (1996), for the handstands, the gymnasts were asked to hold the
inverted stance for as long as possible, with a maximum time set at 15 seconds. For the
forward walkover, the only requirement was for the skill to be performed as it would be
within training, subsequently, no time restriction was dictated. The gymnasts were advised
that they could rest whenever desired, both within and between the skill ‘blocks’.
Each trial was visually monitored by the lead researcher to determine the success of the
performance. Aligning with Kerwin and Trewartha (2001)’s observation that good
handstand form dictates a rigid, straight position is held, the success criteria for the
handstand skill was established as attainment of a vertical inverted stance position with both
legs together. Successful forward walkover trials were determined as the completion of the
skill without loss of balance resulting in the participant falling out of ‘typical’ motion or
alignment. The success of each trial was ascertained during the data collection to the best of
the researcher’s ability, however, to ensure the effectiveness of the real-time skill appraisal,
post-processing analyses of trial success was additionally included. Collected trials which
did not meet the successful skill criteria were excluded from further analysis.
Prior to the collection of handstand and forward walkover trials, a self-directed warm-up
was undertaken by the gymnasts, during which, practice of the handstand and forward
walkover skills was undertaken as many times as the gymnast desired. Although not dictated
by the lead researcher, gymnasts typically chose to undertake static and dynamic stretches
prior to collection initiation. The acquisition of three static trials, for which, the gymnasts
were instructed to stand on a Kistler force plate (9287BA, Kistler, Swizerland) in a natural
stance, instigated the collection process. In addition to enabling the calculation of wholebody mass, the static trials served to inform the development of a model to inform data
processing and data analysis.
For the collection of kinematic data in the respective study, a spatial model was developed.
With primary focus on the ability to measure the selected BRIs, kinematic data were required
for 17 segments with subsequent positional data required for 48 anatomical landmarks
(Figure 3.1). A bilateral analysis approach was deemed necessary to sufficiently analyse
BRIs and inform the phase definitions. To obtain coordinate data for the whole-body
73
excluding the spine, seven markers were placed on each lower extremity, nine on each upper
extremity, four on the pelvis and one on the sternal notch.
Specifically, the markers placed on the lower extremities were located at the left and right
lateral base of the fifth metatarsal, cuboid, lateral malleolus, lateral shank, lateral femoral
epicondyles, lateral thigh and greater trochanter. Each upper extremity limb were defined
using markers at the third metatarsal, ulnar styloid process, lateral forearm, lateral humeral
epicondyles, lateral upper-arm, lateral shoulder, posterior shoulder, acromion process and
sternal notch. The pelvis was formed by markers on the left and right anterior superior iliac
spines and the left and right posterior superior iliac crest.
Figure 3.1. The anatomical positioning of each of the 48 markers for collection of bilateral
positional data.
Informed through previously literature, demonstrated in Error! Reference source not
found., spinal CODA motion markers were placed at: 7th cervical vertebrae (C7), 2nd
thoracic vertebrae (T2), the midpoint between the inferior angles of most caudal points of
the two scapulae (MAI), 10th thoracic vertebrae (T10), 1st lumbar vertebrae (L1), 3rd lumbar
vertebrae (L3), 5th lumbar vertebrae (L5), approximately 5 cm on either side of 2nd lumbar
vertebrae (L2L, L2R) and at the 4th lumbar vertebrae (L4L, L4R). The marker positioning
was informed by a number of existing biomechanical models, including Dempsey et al.
(2007); Ranson et al. (2008); Joyce et al. (2010); Crewe et al. (2011); Leardini et al. (2011).
74
Table 3.2. Established models supporting the placement of trunk and pelvic markers
Anatomical landmark
C7
T2
MIA
T10
Supporting models
Dempsey et al. (2007); Leardini et al. (2011)
Leardini et al. (2011)
Leardini et al. (2011)
Saunders et al. (2005); Dempsey et al. (2007); Joyce et al.
(2010)
L1
Saunders et al. (2005); Joyce et al. (2010); Crewe et al.
(2011); Leardini et al. (2011)
L2L and L2R
Crewe et al. (2011)
L3
Crewe et al. (2011); Leardini et al. (2011)
L4L and L4R
Crewe et al. (2011)
L5
Crewe et al. (2011); Leardini et al. (2011)
nd
Note: L2L is the left side of the 2 lumbar vertebrae, L2R is the right side of the 2nd lumbar
vertebrae, L4L is the left side of the 4th lumbar vertebrae, L4R is the right side of the 4th
lumbar vertebrae
The lumbar region of the spine has been identified as a focal anatomical segment within the
respective research (Chapter 2, Section 2.2.1); its pliancy, however, provokes difficulties for
its biomechanical quantification. Surface skin markers have been identified as a potential
source of error for the tracking of spinal motion (Cerveri et al., 2004), however, promising
results of the accurate representation of vertebral motion using such markers have been
confirmed through a number of pieces of research (Engsberg et al., 2008; Stinton et al.,
2010; Hashemirad et al., 2013). Further research by Gal et al. (1997) studied the accuracy
of vertebrae tracking through measure of anterior-posterior translations of vertebrae using
surface markers and bone pins. The research reported no significant differences between the
two methods, thus supporting the ability to utilise skin markers for vertebral motion tracking.
Marker placement on the fascia covering the spinous processes has been reported to increase
the accuracy of kinematic representation of the vertebral column as a reasonable
approximation of bone motion can be gained from skin movement (Gracovetsky et al., 1988;
Labesse et al., 1996); the respective research will adopt the approach of placing motion
analysis markers on the spinal process fascia. Kuo et al. (2008) presented findings that the
lumbar angle error of younger participants (21 mixed-gender participants with mean (SD)
age, height and mass of 18.6 (2.1) years, 1.70 (1.02) m and 62.7 (10.2) kg, respectively) was
less than half (3.0°) in comparison with older adults (6.5°). The female artistic gymnasts
used within the respective research are a young, athletic population, and therefore possess
75
features of high desirability for the minimisation of surface marker errors, such as low body
mass. To increase the accuracy of spinal marker placements, the primary researcher
underwent training with three qualified and experienced physiotherapists, thus ensuring a
full understanding of the correct manner for palpation and identification of the specific
spinous processes was achieved.
Although not definitive, the decision of maker placements played a vital role in the selection
of a motion capture system for the respective research. An overview of the possible motion
capture techniques, along with the strengths and limitations of each, was provided in Chapter
2 (Section 2.6.3); however, specific consideration of the most suitable system for the
collection of data within the respective study was additionally necessary. The primary
systems which were considered for use within the respective study were CODA motion
analysis (Charnwood Dynamics, Leicester, UK) and VICON (Vicon Motion Systems,
Oxford Metrics, Oxford, UK), in line with the findings from the review of previous research
(Chapter 2, Section 2.6.3). Although similar in their approaches to capture kinematic data,
the key difference between the two is the marker-types used, with CODA opting for an active
marker system and VICON utilising a passive marker approach. The primary challenge in
automated motion capture of the 48 markers lies in the close proximity of the spinous
processes (Zhang and Xiong, 2003). The VICON system requires markers to have a
minimum separation distance of 2 mm to enable distinction between the markers (Richards,
1999), therefore, use of VICON within the respective research is foreseen to be problematic
for marker capture throughout the performances of the fundamental skills. Although
potentially less problematic in the handstand skill, the close proximity of lumbar markers
during spinal hyperextension in the forward walkover skill is anticipated to lead to marker
tracking errors in VICON motion capture data. The active marker approach utilised by
CODA offers a solution to overcome the potential marker merging problem foreseen with
the VICON system; subsequently, the CODA motion system was selected for use in the
respective study.
Due to the motion of gymnastics skills, a challenging logistical aspect is to ensure that each
marker holds a high level of visibility for the skill duration. Using enabling study data, the
visibility of each CODA marker was determined throughout the handstand and forward
walkover skills. Each marker was found to average a minimum of 91% in-view for both of
the skills in their entirety across three participants. The marker visibility levels subsequently
allowed for joint and segment motions to be tracked without any obvious detrimental
76
oscillations. An additional enabling study was conducted to explore the test-retest reliability
of marker placement (Appendix A.12), through which, a maximum marker placement
difference was found to effect segment lengths <2%.
One potential problem which was inherent through use of a CODA motion analysis system,
was the need for drive boxes to be attached to each marker. To assess the extent to which
the drive boxes, in addition to the markers, compromised the gymnasts’ performance, a
number of enabling studies within which each participant performed 20 handstand and 20
forward walkover trials with the affixed full marker and drive box setup were undertaken.
The participant’s performances were deemed to be unaffected by the addition of a full marker
set-up as no discomfort was reported during performances of either skill as a result of the
motion analysis system.
3.2.3
Data Collection
Kinematic and Kinetic Data
Data were collected in the National Indoor Athletics Centre at Cardiff Metropolitan
University. A CODA motion analysis system was used for the collection of three
dimensional positional data. A total of 48 active CODA markers which were connected to
eight CODA motion drive boxes were placed and secured to specific anatomical landmarks
using adhesive tape. The gymnasts wore a pair of shorts and a cropped top to allow for
maximal contact of the CODA markers with the skin. Marker and drive box placement was
undertaken by the primary researcher on all occasions in attempt to overcome inconsistency
errors and to maximise reliability.
As exhibited in Figure 3.2, the data collection set-up consisted of four CODA motion Cx1
units (Charnwood Dynamics Ltd., Leicestershire, UK) operating at 100 Hz and one Kistler
force plate operating at 1000 Hz. As a result of the high number of CODA markers required
to effectively address the aim of the respective study, a reduction in the kinematic sampling
frequency to 100 Hz is mandatory for the use of the CODA system. Although not the case
for all gymnastics skills, the velocity of the selected fundamental skills enabled the
compromised sampling frequency not to be detrimental to the quality of the kinematic data
which was collected.
77
Figure 3.2. An aerial view diagram of the finalised data collection equipment setup; x is
indicative of the hand placement position on the force plate.
Coda Cx1 units
Force plate
30 cm
plyometric
box
Strobe units
Figure 3.3. CODA motion equipment setup and placement of the Kistler force plate for the
collection of kinematic and kinetic data during the completion of a handstand trial.
Kinematic and kinetic data were collected simultaneously for each trial of each skill. The
occasional oscillation of lumbar markers during the performance of the forward walkover
skill was highlighted through enabling studies; to overcome the respective error, numerous
Cx1 unit set-ups were explored. The final, optimal set-up for visibility of all markers is
demonstrated in Error! Reference source not found.. The inclusion of two strobe units
78
(Charnwood Dynamics Ltd., Leicestershire, UK), in addition to the horizontal placing of the
sagittal plane CODA motion units on 0.3 m plyometric boxes were found to increase marker
visibility through enabling study collections.
The challenges of recruiting the young gymnasts for participation in the research were
relatively substantial. The collection of data required the gymnasts to attend a specific
location, due to the inability to move all of the necessary equipment, and engage in data
collection for two to three hour periods.
Body Segment Inertial Parameters
To enable the calculation of body segment inertial parameters, an imaging approach
presented by Gittoes et al. (2009) was selected for use within the respective research. To
inform the approach, three whole-body frontal, left sagittal and right sagittal photographs
were taken using a Canon EOS 400 (Tokyo, Japan) digital camera. For the capture of each
image, the gymnasts were required to position themselves as directed by the lead researcher,
in accordance with the stance dictated by Gittoes et al. (2009). The participants were
positioned within a fixed wooden frame with six affixed calibration points of known
location.
3.2.4
Data Processing
Height and Mass Data
Whole-body height data were gained from digitisation of the inertia images using Peak
Motus software (Vicon Motus Systems, version 9.0.0.27-GM). Vertical positional data of
the top of head (taken from the frontal-plane image) subtracted from right and left lateral
heel average positional data (taken from the right and left sagittal plane images respectively)
informed the calculation of whole-body height. The height data obtained were presented in
Figure 3.4.
79
1.7
1.6
Height (m)
1.5
1.4
1.3
1.2
1.1
1
1
2
3
4
5
6
7
8
9
Participants
10
11
12
13
14
Figure 3.4. Whole-body height measured from inertia images.
Each gymnast’s mass was calculated from vertical ground reaction force data obtained
during three five second trials of quiet stance; a mean value was taken across each five
second trial, from which the mean data across the three trials were calculated for each
gymnast. Whole-body mass data are presented in Figure 3.5.
70
Body Mass (kg)
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
9
Participants
10
11
12
13
14
Figure 3.5. Mean body mass values for three standing trials for each participant with standard
deviation error bars.
80
Kinematic and Kinetic Data
The division of whole skills into a number of distinct phases is common practice within
sport-related biomechanical research (Bartlett and Bussey, 2012). Due in main to the
extended depth of biomechanical knowledge which the partitioning technique enables, skill
division additionally overcomes potential issues which may occur as a result of the whole
skill analyses, such as false representation of skill mechanics (Dufek et al., 1995). Within
the sport of gymnastics, a skill division approach is familiar and is favoured by many
researchers (Ferkolj, 2010; Gittoes et al., 2011).
The handstand and forward walkover skill phases of greatest interest for investigation within
the respective research were those during which CSI biomechanical risk was prominent. To
explore BRI within isolated phases of each skill, previous research which has used a phase
division approach for the analyses of fundamental gymnastics skills was initially considered.
A study conducted by Hars et al. (2005) divided the walkover skill into five distinct phases,
single support (phase one), triple support (phase two), double support (phase three), triple
support (phase four) and single support (phase five). The approach was subsequently
explored through use of handstand and forward walkover enabling study data to determine
the defining events of each phase. As the respective phase development approach was based
on the level of support of the gymnast (i.e. the number of limbs in contact with the ground),
a kinematic analysis was undertaken, as has been used previous research such as Slobounov
and Newell (1996); Ferkolj (2010). The phase initiation and phase termination events which
were found to be consistent across the female gymnasts included in the enabling study (n =
10), as well as being consistent across the handstand and forward walkover skill, are
identified in Table 3.3.
Table 3.3. Initiating and terminating phase boundary events for each of the five handstand
and forward walkover phases
Phases
Phase 1 – single support
Phase 2 – triple support
Phase 3 – double support
Phase 4 – triple support
Phase 5 – single support
Defining events
Phase initiation
Phase termination
Skill initiation
Hand placement
Hand placement
Training leg off
Trailing leg off
Leading leg touchdown
Leading leg touchdown
Hand release
Hand release
Skill end
81
To develop insight into the extent to which each phase encompassed BRIs, 10 gymnasts
were analysed during the performance of a minimum of 10 handstand and 10 forward
walkover trials. From the review of BRIs in each of the skill phases, the phase which was
identified to encompass posture (minimum lumbar angle), lumbo-pelvic range of motion,
CoPy, DPSI and DLPSI to the greatest extent, was the double support phase (phase 3), for
which, the gymnast is reliant on their hands for stability. Phase determination for both the
handstand and forward walkover was identified when the metatarsal phalangeal (MTP)
marker of trailing foot exceeded a 0.02 m vertical threshold. For the forward walkover, phase
termination was identified when the MTP marker of the first descending foot exceeded a
threshold of 0.02 m in the negative vertical direction.
With focus on the double support phase, the enabling study revealed inconsistency in the
amount of time for which the handstand skills were held between trials and gymnasts. As
such, some handstands were held for the duration of data collection (15 seconds), while other
handstands were descended within the time period. To ensure consistency across the
handstand trials, the phase termination for the handstand skill was altered to be calculated as
the point at which the CoM position exceeds the base of support (in the anterior-posterior
direction). The defining event was informed by Kerwin and Trewartha (2001), who
identified the maintenance of balance to be the preservation of the CoM within the boundary
limits of the base of support. Identification of the respective phase termination event within
the handstand skill analyses was undertaken in Microsoft Excel software, with input
positional data of the left and right ulnar styloid process (wrist), left and right MCP, and
whole-body CoM for each gymnast and each trial. Average positions for the MCP and wrist
markers were calculated across the double support phase, from which average wrist and
average MCP base of support positions were determined. Relation of the CoM data to the
average wrist position and the average MCP position were subsequently determined. From
the respective analyses, three scenarios of CoM in relation to the base of support position
were reported. The first scenario demonstrated the CoM to be within the base of support for
the duration of the double support phase, until the initiation of descent (Figure 3.6), in the
second scenario, the CoM position did not enter within the base of support. The third
scenario illustrated the CoM not to exceed the base of support (i.e. the descent phase of the
handstand does not occur).
Distance of CoM from base of
support landmarks (mm)
82
0.5
0.4
0.3
0.2
0.1
0
-0.1 1
-0.2
-0.3
-0.4
Anterior
direction
101
201
301
401
501
601
701
801
901
Posterior
direction
Data points
Figure 3.6 The CoM position in relation to the base of support for the handstand double
support phase; the dashed line represents CoM distance from the average wrist position and
the solid line shows the CoM distance from the average MCP position.
For the trials in which scenario a) occurred, the point at which the CoM exceeded the base
of support was identified as handstand phase termination. For scenario b), the point at which
CoM exceeded 29 mm from the average wrist position in the anterior-posterior direction
(determined in accordance with whole-body kinematic analyses across the enabling study
gymnasts), was accepted as phase termination. As the double support phase was ongoing in
scenario c), phase termination was determined at the end of the data collection of the
respective trial (i.e. 15 seconds).
The phases of interest for the handstand and forward walkover skills for the respective study
were subsequently determined as the phase of double support, as demonstrated in Figure 3.7
and Figure 3.8.
Phase of interest
Figure 3.7. A schematic demonstrating the phase of interest for the handstand skill.
83
Phase of interest
Figure 3.8. A schematic of the forward walkover, illustrating the phase of interest.
Using the findings of the handstand and forward walkover phases of interest, to identify the
optimal cut-off frequency, a residual analysis technique was undertaken in accordance with
Wells and Winter (1980). The method was selected in accordance with the review of relevant
literature (Chapter 2, Section 2.6.4). Residual analyses of enabling study data (detailed in
Appendix A.13) revealed the optimal cut-off frequency for kinematic, ground reaction force
and centre of pressure data to be 10 Hz, 120 Hz and 3 Hz, respectively. A fourth-order lowpass Butterworth filter was applied to the data accordingly.
Following data filtering, the sampling frequency of the filtered data was necessary to
determine, as reported in Chapter 2 (Section 2.6.4). Kinematic data were collected at 100Hz
and kinetic data were collected at 1000Hz, therefore a combination of interpolation and
decimation was necessary to resample the data sets and subsequently determine a sampling
frequency consensus between the two. Dainty (1987) reported reliance of the sampling
frequency on the skill frequency itself; sampling frequency was subsequently determined
through enabling study data reported in appendix A.14, rather than sampling frequency from
previous research. The respective approach determined an optimal sampling rate of 501 data
points for the exportation of all data within the respective research (Appendix A.14).
Body Segment Inertial Parameters
Subject-specific anthropometric measurements were determined through the image-based
approach outlined by Gittoes et al. (2009), informed by static photographs which were
obtained at each data collection. The photographs of the highest quality were selected,
determined in accordance with image clarity and the similarity of the standing position to
Gittoes et al. (2009)’s guidelines. The selected images were converted from .jpeg to .avi
format by use of ImageToAVI software (version 1.0.0.5). The left sagittal, right sagittal and
frontal plane .avi clips for each participant were imported to PeakMotus software (Vicon
Motus Systems, version 9.0.0.27-GM). Each clip was digitised three times by the lead
84
researcher, once for calibration and twice for anatomical landmarks locations as specified
by Yeadon (1990). Two dimensional coordinates were obtained for each of the 45 specified
landmarks. The output coordinate data were input into Yeadon (1990)’s inertia model by use
of MathCad software (MathSoft Inc., Cambridge, MA); as the original programme by
Yeadon (1990) was written in Fortran (Yeadon, 1984), an updated version of the inertia
model informed the respective analyses. The specific development of Yeadon (1990)’s
model for the gymnastics population, and subsequent use within numerous gymnasticsbased studies (e.g. Irwin and Kerwin (2007)), provided support for use of the model in the
current gymnastics-centred research. For each participant, a whole-body mass value was
determined as a product of the input coordinate data, Yeadon (1990)’s inertia model
calculations and Dempster (1955)’s density values.
To explore the influence of measurement issues on the inertial data outputs, a number of
accuracy and reliability tests were conducted (Appendix A.15). Rigorous reliability testing
identified a maximum reliability error of 2%. Mean calculation of the difference between
the whole-body mass calculated through use of the inertia model and the whole-body mass
measured for the force plate for the whole gymnastics cohort (n = 14) was 4.8 (3.5)%. The
respective error for use of an inertia model to predict whole-body mass was found to be 1.9%
higher than the findings of Gittoes et al. (2009).
Integration of density data as presented by Dempster (1955) to Yeadon (1990)’s
mathematical inertia model enabled the calculation of BSIP for individual gymnasts. As the
density data which informed the inertia parameters were not specific to the individual female
gymnasts, it was speculated that the density values were one of the primary factors which
induced error into the estimated BSIP outcomes. As whole-body mass was the only known
measure for each participant, the process of scaling Dempster (1955)’s density values to the
error outcomes from predicted whole-body mass in comparison with measured whole-body
mass, enabled the development of gymnast-specific measures density. The altered density
technique is further outlined in Appendix A.16.
85
3.2.5
Data Analysis
Kinematic and Kinetic Data
Raw coordinate and ground reaction force data were exported from CODA motion software
for each trial, skill and participant and input into Visual 3D software (C-motion, Rockville,
MD, USA). Kinematic and kinetic data from a static trial, in which the participant stood as
still as possible in their natural stance for a five-second period, was used for the creation of
gymnast-specific customised Visual 3D models (Error! Reference source not found.).
Individual gymnast models were created through use of a number of anatomical
measurements, obtained from the digitised images. Measurements for each gymnast
consisted of bilateral knee, ankle, toe, elbow and wrist widths, in addition to thorax depth
and whole-body height. Body mass data, obtained from averaged vertical ground reaction
force data (Figure 3.5), was additionally utilised for the development of each model.
Figure 3.9. Anterior and posterior views of static models created using Visual 3D software.
Each model consisted of 17 segments, six upper-body, six lower-body, one pelvis segment,
one thorax segment and three lumbar segments. The lumbar spine region was modelled as a
whole segment for the analysis of lumbo-pelvic angle (LPRoM and DLPSI). In order to
enable the examination of the curvature of the segment for the analyses of posture, upper
(L1 to L3) and lower lumbar segments (L3 to L5) were additionally created. The need for
lumbar posture to be modelled by two segments is illustrated in Figure 3.10.
86
Figure 3.10. An image demonstrating the two-segmented lumbar spine for the analysis of
posture angle.
Variables Analysed
Analysis of the BRIs, selected through the appraisal of previous literature, was undertaken
using Visual 3D software, with additional calculations carried out using Microsoft Excel
software (2007, Microsoft Inc., New Mexico, USA). For each trial, lumbar angle variable,
including ground reaction force, lumbo-pelvic angle and anterior-posterior centre of pressure
were calculated in Visual 3D. Continuous profiles of each of the respective variables
(exemplified in Figure 3.11) during the hand balance phase of the handstand and forward
walkover were subsequently exported from Visual 3D software and input into Microsoft
Excel software, within which, further calculations were undertaken.
87
0
-0.06
Medio-lateral centre of pressure (m)
-0.08
-0.04
-0.02
-0.02 0
0.02
0.04
-0.04
-0.06
CoPy range
-0.08
-0.1
-0.12
CoPx range
-0.14
-0.16
-0.18
-0.2
Anterior-posterior centre of pressure (m)
a)
Mean DPSI
160
140
120
DPSI
100
80
60
40
20
0
1
101
201
301
401
501
Time (%)
b)
Figure 3.11. Example of the continuous data and the discrete BRIs from a) handstand centre
of pressure trace; b) forward walkover DPSI profile.
The calculations of BRIs from the continuous data, exported from Visual 3D were
undertaken in Microsoft Excel; the process of the data analysis of each BRI is reported in
Table 3.4. To inform the calculation of DPSI (Chapter 2, Section 2.6.5), body weight was
obtained from vertical ground reaction force data, averaged across three trials for each
participant.
88
Table 3.4. An outline of the BRIs analysed in Visual 3D software, exported from the software
and the calculations undertaken using Microsoft Excel software
BRIs
Posture
V3D
Exported from V3D
Angle between L1 and Lumbar angle data (y-z)
L3 and L3 and L5 (y-z)
Microsoft Excel
Minimum lumbar
angle (°)
LPRoMy
Angle between pelvic
and lumbar segments
(y-z)
Lumbo-pelvic angle data Range of lumbo-pelvic
(y-z)
angle (°)
LPRoMx
Angle between pelvic
and lumbar segments
(x-z)
Lumbo-pelvic angle data Range of lumbo-pelvic
(x-z)
angle (°)
DPSI
Ground reaction force
(x, y and z)
Ground reaction force
(x, y and z)
Dynamic postural
stability index
(Wikstrom et al., 2005)
CoPy
Centre of pressure (y)
Centre of pressure (y)
Centre of pressure
range (y)
CoPx
Centre of pressure (x)
Centre of pressure (x)
Centre of pressure
range (x)
DLPSI
Lumbar spine to pelvis
angle (x, y and z)
Lumbo-pelvic angle (x, Dynamic lumbo-pelvic
y and z)
stability index (adapted
from Wikstrom et al.
(2005)
The interpretation of each of the BRIs was decided upon in line with previous literature and
will be kept consistent throughout the thesis. To exemplify the BRI interpretations, Table
3.5 demonstrates the BRI meanings if increased data were gained for each of the BRF and
BED. The literature which supported the interpretations is additionally provided.
89
Table 3.5. Biomechanical risk indicator interpretations informed by previous literature
BRIs
Posture
Increased Value Meaning
Reduced posture
BRI Meaning
Lower BRF
LPRoMy
Increased anteriorposterior lumbo-pelvic
range of motion
Increased medio-lateral
lumbo-pelvic range of
motion
Decreased general
stability
Increased anteriorposterior stability
Increased medio-lateral
stability
Decreased lumbo-pelvic
stability
Higher BRF
LPRoMx
DPSI
CoPy
CoPx
DLPSI
3.2.6
Literature Support
Tanchev et al. (2000); Kim and
Green (2011); Bugg et al. (2012)
Pacey et al. (2010); Konopinski et
al. (2012)
Higher BRF
Pacey et al. (2010); Konopinski et
al. (2012)
Higher BED
Wikstrom et al. (2005)
Lower BED
Slobounov and Newell (1996)
Lower BED
Slobounov and Newell (1996)
Higher BED
Wikstrom et al. (2005)
Statistical Analyses
Statistical analyses were undertaken with the intent of exploring the level of association
between individual gymnasts and the group BRIs, handstand and forward walkover skill BRI
relationships and BRF and BED relationships were additionally explored with assistance
from statistical analyses. As variable relationships were of interest, rather than the extent to
which a certain variable influenced another, correlation analyses were deemed the most
appropriate statistical approach for the respective study, allowing for a quantitative
expression of the interrelationships explored (Bartlett and Bussey, 2012; Riffenburgh, 2012).
Two-tailed bivariate correlations were utilised to address each of the chapter questions.
One of the main underlying assumptions of a statistical correlation is that the bivariate data
follow a normal distribution (Riffenburgh, 2012); as the sample size within the respective
study was less than 50, a Shapiro-Wilk test for normality was undertaken. Although the
assumptions of linearity and homoscedasticity were met (Levene’s test, p>0.05), the
parametric assumptions for normality were violated (Shaprio-Wilk test, p<0.05). Two-tailed
bivariate Spearman’s Rho correlation analyses were conducted as the non-parametric
alternative (Wikstrom et al., 2005).
90
The level of accepted statistical significance was set a priori to p<0.05, thus controlling the
Type I error rate to be no greater than 5%. The use of p-values is extensive within
biomechanical research, however, the statistical interpretation only provides the probability
of obtaining the results if it were due to chance (Mullineaux et al., 2001). In addition, there
is an accepted increased chance of attaining misleading inferential statistics as a result of
small sample sizes (Mullineaux et al., 2001). As a result of the p-value limitations, the
strength of the interrelationship between two specified variables in the way of the population
coefficient is often favoured within small sample size research (Mullineaux and Bartlett,
1997; Mullineaux et al., 2001; Ellis, 2010; Sullivan and Feinn, 2012).
Effect sizes are standardised measures of the magnitude of observed relationships (Field,
2009), allowing for statistical understanding to be gained independent of scale. The effect
size outputs (r) were interpreted in accordance with Cohen (1988)’s boundaries, which
indicated r-values of greater than 0.5 to be considered a large effect, r>0.3, to be a medium
effect and r>0.1 to be a small effect. A decision for focus on the large effect size outputs
(r>0.5) was made for interpretation within the respective research. The respective boundary
was decided upon as, to inform biomechanical screening approaches, it must be ensured that
the findings had notable practical implications (Payton and Bartlett, 2008). A large effect
size indicates that the independent variable explains a minimum of 25% of the variance of
the dependent variable (Mullineaux et al., 2001).
3.3
3.3.1
Results
Biomechanical Risk Indicators
Initial understanding of the cohort’s mechanical responses to the performance of handstand
and forward walkover skills was drawn from mean and standard deviation data of the whole
group (n=14) (Figure 3.12).
91
160
140
120
100
80
60
40
20
0
-20
-40
Handstands
Forward Walkovers
Posture LPRoMy LPRoMx DPSI
(°)
(°)
(°)
CoPy
(mm)
CoPx
(mm)
DLPSI
Figure 3.12. Whole group mean data with standard deviation error bars for each of the
handstand and forward walkover BRIs.
In accordance with the BRI meanings, presented previously in Table 3.5, greater overall
biomechanical risk was demonstrated for the forward walkover skill. The only
biomechanical risk indicators which showed disparity from this initial observation were
CoPy (42.41 mm greater for the forward walkover) and CoPx (12.40 mm greater for the
forward walkover). Although not compliant with the other variables in terms of
biomechanical risk, the centre of pressure findings were anticipated in the context, as the
forward walkover possesses a greater level of dynamic motion than the handstand skill. The
average group difference for posture was found to be -11.4°, in addition to LPRoM skill
differences of 10.6° for the anterior-posterior and 24.2° for the medio-lateral. Higher general
and lumbo-pelvic stability was determined in the handstand skill, with lower values of 26.76
and 4.38 respectively, in comparison with the forward walkover.
Although the whole group analyses provided initial understanding of the general
biomechanical indicator values, along with quantification of the mechanical differences
between the two skills, interrogation of individual mechanical outputs enabled a further
depth of understanding of the mechanical responses to be developed. The individual gymnast
mean and standard deviation data for each BRI for the handstand and forward walkover skill
are presented in Error! Reference source not found..
92
Table 3.6. Mean (SD) BRI values for each gymnast, along with the whole group for the handstand and forward walkover skills
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11
P12
P13
P14
Group
Posture
LPRoMy
LPRoMx
(°)
(°)
(°)
-1.0
(0.7)
7.20
(2.78)
-4.3
(0.6)
-3.7
(1.2)
8.2
(0.9)
1.1
(1.3)
-3.2
(2.1)
1.4
(1.2)
12.2
(2.5)
-8.4
(1.8)
-5.5
(2.0)
-12.1
(1.3)
-1.0
(1.6)
-5.9
(1.4)
-1.1
(6.7)
10.4
(0.8)
13.3
(2.5)
5.2
(1.7)
21.4
(3.0)
12.3
(1.4)
9.3
(2.1)
12.0
(3.9)
12.7
(1.1)
9.4
(2.1)
7.5
(3.5)
11.0
(4.4)
6.4
(1.0)
10.0
(1.5)
11.1
(1.6)
10.8
(3.9)
10.9
(2.6)
31.1
(10.7)
8.7
(3.1)
38.3
(7.7)
28.1
(3.8)
17.4
(6.9)
19.0
(7.9)
11.4
(3.8)
24.0
(21.0)
22.0
(7.9)
61.6
(11.3)
25.9
(9.2)
17.7
(7.2)
23.8
(9.8)
24.3
(13.5)
Handstands
DPSI
36.58
(10.10)
42.97
(12.35)
6.95
(1.83)
7.78
(2.25)
3.60
(1.53)
12.01
(5.92)
6.96
(1.33)
23.16
(8.57)
16.98
(7.06)
29.28
(9.24)
8.79
(3.20)
11.98
(4.82)
13.36
(6.37)
8.08
(2.56)
16.32
(12.14)
CoPy
(mm)
85.82
(29.91)
71.32
(18.16)
75.83
(10.01)
53.59
(6.77)
70.61
(18.13)
79.84
(50.99)
109.49
(113.90)
52.53
(22.33)
70.11
(28.82)
52.69
(41.25)
81.12
(51.33)
79.93
(17.45)
108.51
(76.48)
75.01
(21.61)
76.17
(17.57)
CoPx
(mm)
71.88
(26.37)
72.79
(25.07)
55.03
(15.61)
67.93
(15.03)
48.75
(12.70)
78.33
(23.53)
86.89
(76.29)
73.11
(67.22)
81.59
(47.79)
71.55
(62.90)
81.77
(47.87)
81.70
(22.45)
100.02
(85.89)
78.74
(31.47)
75.01
(12.69)
DLPSI
17.75
(1.91)
9.71
(1.13)
4.57
(1.65)
19.07
(4.54)
8.20
(1.88)
4.99
(1.96)
22.69
(1.52)
7.90
(1.99)
10.91
(1.63)
6.70
(2.58)
22.78
(7.17)
10.25
(3.77)
10.82
(2.93)
12.34
(3.14)
12.05
(6.13)
Posture
LPRoMy
(°)
(°)
-2.0
(1.9)
-27.0
(13.7)
-25.6
(5.2)
-10.3
(1.6)
-17.4
(17.6)
-11.9
(2.5)
-32.8
(5.1)
-7.2
(1.4)
7.9
(0.9)
-12.6
(5.9)
-2.7
(2.7)
-13.1
(0.4)
-7.1
(7.5)
-13.1
(6.3)
-12.5
(10.8)
13.5
(2.7)
35.8
(7.8)
15.7
(2.9)
25.0
(2.9)
31.1
(17.6)
24.4
(9.9)
22.3
(6.6)
23.1
(3.3)
14.6
(1.1)
28.3
(10.4)
20.2
(2.0)
8.3
(2.6)
24.4
(15.3)
13.4
(4.9)
21.4
(7.7)
Forward Walkovers
LPRoMx
DPSI
CoPy
(mm)
(°)
20.1
67.66
94.06
(11.4)
(4.05)
(57.78)
52.9
51.27
58.69
(9.9)
(3.54)
(13.28)
48.5
30.40
258.03
(10.3)
(4.03)
(161.29)
37.9
32.30
122.02
(22.1)
(2.90)
(45.09)
46.7
22.93
64.43
(16.9)
(4.69)
(12.56)
49.4
23.17
106.54
(17.9)
(4.24)
(95.65)
56.8
23.55
141.38
(12.5)
(4.48)
(136.69)
44.0
60.58
71.48
(14.5)
(4.62)
(43.70)
24.5
45.14
127.58
(5.2)
(3.16)
(144.47)
107.2
65.42
79.40
(48.6)
(6.86)
(47.90)
35.3
36.66
196.31
(8.1)
(2.91)
(233.35)
20.6
46.51
78.45
(8.3)
(3.65)
(38.02)
80.8
49.47
148.85
(16.2)
(6.73)
(99.09)
53.4
48.08
112.96
(14.5)
(3.14)
(77.92)
48.4
43.08
118.59
(23.3)
(15.34)
(55.25)
CoPx
(mm)
67.32
(22.18)
84.84
(20.72)
63.30
(22.64)
58.70
(19.83)
47.61
(16.60)
76.31
(31.38)
49.10
(16.15)
101.08
(14.60)
91.29
(70.55)
81.55
(41.90)
55.83
(47.57)
83.04
(57.50)
258.54
(122.78)
105.23
(80.51)
87.41
(52.51)
DLPSI
15.12
(1.46)
13.60
(1.09)
13.43
(2.96)
13.17
(2.52)
13.27
(1.87)
16.45
(4.50)
19.36
(1.98)
10.19
(0.85)
12.19
(1.67)
22.31
(6.57)
18.70
(1.64)
9.63
(1.61)
25.24
(4.58)
27.34
(3.02)
16.43
(5.46)
93
The analysis of posture in the handstand skill identified nine out of the fourteen gymnasts
(64%) to utilise lordotic posture (identified by a negative value) during the performance of
the skill. In the forward walkover, only one gymnast was exempt from the use of lordotic
posture, with the most extreme minimum lumbar angle displayed by gymnast 7 to be -32.8°.
The range of LPRoMx values across the gymnasts was far greater than for the LPRoMy.
Handstand LPRoMx values ranged from 8.7° to 61.6° and forward walkover LPRoMx range
values were between 20.1° and 107.2°; however, LPRoMy ranges were between 5.2°and
21.4° for the handstand and 8.3° and 35.8° for the forward walkover skill. Not only were the
data of a greater spread for LPRoMx than LPRoMy, but also, the values, on the whole were
higher, thus representing the increased use of lumbo-pelvic motion in the medio-lateral
direction, in comparison with the anterior-posterior for the handstand and the forward
walkover skills.
DPSI values ranged from 3.60 to 42.97 in the handstand and 22.93 to 67.66 in the forward
walkover skill. The increased DPSI data for the forward walkover, indicating decreased
general stability in comparison with the handstand, were consistent across all of the
gymnasts. The same was not true, however, for the centre of pressure range data in both the
anterior-posterior and the medio-lateral directions. Greater CoPy values were generally
measured for the forward walkover in comparison with the handstand skill, however, three
gymnasts (P2, P5 and P12) displayed opposing directional CoPy data, with higher
biomechanical risk identified in the forward walkover, as a result of high values of CoPy for
the handstand skill. CoPy exhibited increased mean data for the handstand and forward
walkover on the whole, in comparison with CoPx. Data for CoPx ranged from 48.75 mm to
100.02 mm for the handstand and 47.61 mm to 258.54 mm for the forward walkover. High
biomechanical risk for the medio-lateral centre of pressure range data were identified for the
forward walkover in six gymnasts, P1, P4, P5, P6, P7 and P11. Gymnast P5 used the smallest
medio-lateral centre of pressure range for both the handstand and forward walkover out of
the 14 gymnast cohort. Five gymnasts, P1, P4, P7, P11 and P12, displayed an increase in
lumbo-pelvic stability in the forward walkover skill in comparison with the handstand skill;
however, the respective gymnasts were found to have little difference in DLPSI values
between the two skills, with a maximum difference of 5.9 found for P4. For the remaining
nine gymnasts, an increased lumbo-pelvic stability was found in the handstand skill, in
comparison with the forward walkover, with a maximum skill difference of 15.61 for P10.
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Appraisal of whole group and individual mean BRI data offered initial insight and
understanding of the mechanical responses; the outcomes presented distinctions between the
group and individual responses, in addition to BRI disparity between the two skills. The
analyses, however, did not allow for conclusions of the extent to which whole group
responses reflected individual mechanics; subsequently further exploration through
statistical means was deemed necessary to support the descriptive findings.
3.3.2
Group and Individual Biomechanical Risk Indicator Correlations
A two-tailed bivariate Spearman’s Rho correlation analysis was undertaken to investigate
the first chapter question. A single large group correlation (r>0.5) between DLPSI and
LPRoMx in the forward walkover was reported (Table 3.7). Individual BRF and BED
correlation analyses for the 14 female artistic gymnasts revealed a greater quantity of large
relationships (r>0.5). The percentages of individuals who demonstrated large effect size
relationships for each BRF and BED correlation are additionally reported in Table 3.7. The
correlation analyses revealed disparity between the group and individual effect size outputs.
The single large group effect size (r = 0.60) was supported by 57% of the gymnastics cohort,
indicating that 43% (n = 6) of the gymnasts exhibited a medium or small relationship
between DLPSI and LPRoMx in the forward walkover. Although no large group effects were
found in the handstand skill, the greatest effect size at a group level was between DLPSI and
LPRoMx (r = 0.45). Underpinning the respective group relationship, 64% of the gymnasts
individually revealed large effect sizes. Excluding DLPSI and LPRoMx, between 0% and
29% of gymnasts individually exhibited large effects for the BRI correlations.
The results presented in Table 3.7 additionally revealed the significance of whole group
correlations. Sixty six per cent of handstand BRI correlations were found to be statistically
significant at a group level, in addition to 50% of forward walkover BRI. For DPSI, whole
group significance was found for all but one BRF (forward walkover LPRoMx) across the
two skills. The finding is matched for DLPSI, however, the BRF for which a significant
whole group correlation was not found was for LPRoMy for the forward walkover. Two
correlations which were identified to be significant at a group level, DLPSI and LPRoMy in
the handstand, and DPSI and posture in the forward walkover, were unsupported at an
individual level.
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Table 3.7. Whole group BRF and BED correlation outputs (r) and the percentage of
individual gymnasts with large effect (r>0.5) for BRI correlations in the handstand and
forward walkover skills.
Handstands
BEDs
DPSI
CoPx
CoPy
DLPSI
Group (r)
Individual (%)
Group (r)
Individual (%)
Group (r)
Individual (%)
Group (r)
Individual (%)
Posture
0.18*
29%
-0.00
14%
0.08
21%
-0.23*
21%
BRFs
LPRoMx
-0.37*
29%
0.12
14%
0.13*
21%
0.45*
64%
LPRoMy
-0.20*
7%
-0.01
14%
-0.14*
7%
0.29*
0%
BRFs
LPRoMx
0.03
21%
0.24*
7%
-0.02
7%
0.60*
57%
LPRoMy
-0.13*
21%
0.02
7%
-0.13*
14%
0.12
7%
Forward walkovers
Posture
BEDs DPSI
Group (r)
0.28*
Individual (%)
0%
CoPx
Group (r)
0.09
Individual (%)
0%
CoPy
Group (r)
-0.04
Individual (%)
7%
DLPSI
Group (r)
-0.34*
Individual (%)
29%
Grey shading indicates a large group effect size; * p<0.05
3.3.3
Fundamental Skill Biomechanical Risk Indicator Correlations
A quantitative examination of the relationship between handstand and forward walkover
BRIs was undertaken to investigate the second chapter question. Two-tailed bivariate
correlation analyses for each gymnast were conducted to allow the statistical examination of
the inter-skill BRI relationships. The percentage of gymnasts who displayed large correlation
effects were subsequently calculated. Of the seven BRIs, four (posture, DPSI, CoPy and
DLPSI) were found to demonstrate large correlation effects (r>0.5) between the handstand
and forward walkover at an individual level. The remaining BRIs, for which medium or
small effects were output from the statistical analyses, were CoPx, LPRoMx and LPRoMy.
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3.3.4
Biomechanical Risk Indicator Correlations
The final chapter question was informed through individual gymnast correlation analyses
between BRFs and BEDs. As in previous results within the respective chapter, interpretation
of findings were informed by large effect size outputs. The percentage of individual
gymnasts who displayed large effect sizes between BRFs and BEDs were calculated. Large
positive and negative correlation effects (r>0.5 or r<-0.5 respectively) were found for each
BED in relation to posture across the two skills (Table 3.8).
Table 3.8. Percentage of large positive (r>0.5) and negative (r>-0.5) correlations between
posture and BED for the handstand and forward walkover skills
Posture
Positive correlation
Negative correlation
Handstands
DPSI CoPy
DLPSI
29 %
0%
7%
0%
21 %
14 %
Forward Walkovers
DPSI
CoPy
DLPSI
0%
0%
7%
0%
7%
21 %
Overall, more gymnasts displayed large BRF and BED correlation effect sizes in the
handstand skill than the forward walkover skill. A minimum of two gymnasts reported large
effect sizes between posture and each of the BEDs in the handstand condition, whereas, in
the forward walkover, no gymnasts displayed large correlation effects with DPSI. In
addition, a large effect between posture and CoPy was only evident for one gymnast.
The negative relationship between CoPy and posture for the handstand (r = -0.53, -0.66 and
-0.74) and forward walkover (r = -0.55), showed that, for these gymnasts, higher levels of
anterior-posterior stability were associated with a greater level of lumbar lordosis for each
of the skills. The correlation findings for posture and DPSI reflected the CoPy findings, i.e.
greater lordosis correlates with greater general stability; this, however, was only true for the
handstand skill as no large effects were found between posture and DPSI within the forward
walkover outputs. Error! Reference source not found. illustrates a scatter plot for the
correlation between posture and DLPSI for one of the gymnasts, where r = 0.87; the
correlation had the highest effect size between posture and any BED.
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r = 0.87
Figure 3.13. Spearman's rho correlation analysis output for posture and DLPSI in the forward
walkover skill for one gymnast, where each data point represents a single trial.
Mixed-directional results were found for posture and DLPSI for both the handstand and
forward walkover, however, the majority of participants presented a negative correlation
(36% of the gymnastics cohort) between the two variables, indicating that more neutral
postures (values close to 0°) were associated with higher levels of stability. Opposing effects
were found for a single participant for the handstand skill (r = 0.68) and one participant for
the forward walkover (r = 0.56); for these two participants (14% of the cohort), increased
lordotic postures were associated with increased lumbo-pelvic stability.
3.4
Discussion
The chapter aim was to quantitatively investigate primary biomechanical risk indicator
measures in a female artistic gymnastics cohort performing fundamental gymnastics skills.
The aim was addressed using an empirical, cross-sectional approach, which was centred on
a cohort of 14 competitive female artistic gymnasts. The chapter findings offered original
quantification of the injury associated biomechanical variables through the performances of
fundamental skills. Descriptive and statistical analyses informed understanding of group and
individual gymnast mechanics. Initial insight into the relationship between handstand and
forward walkover BRIs was gained, in addition to knowledge of the relationships between
BRFs and BEDs. The high prevalence of biomechanical indicators for CSI risk in previous
literature (Chapter 2, Section 2.2.1), with sparse understanding of BRIs in the female
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gymnastics population, endorsed focus on extrinsic risk factors within the respective
research. To the researcher’s knowledge, a screening approach for CSI risk has yet to be
developed for female artistic gymnasts. To inform the development of such, certain
prerequisites including understanding of the underpinning risk factors, skills, and analyses
were outlined. The respective chapter subsequently endeavoured to provide initial insight
into the methods and analyses which may inform CSI screening approaches in female artistic
gymnasts.
The first chapter question (CQ 3.1) was concerned with the extent to which group-based
BRIs were reflected by individual mechanics within fundamental gymnastics skills. The
chapter question sought to develop understanding of the extent to which biomechanical risk
can be explored using a group approach, or if individual assessments proved necessary for
injury screening. An appraisal of descriptive BRI data indicated discordant individual BRI
outcomes through large BRI ranges across the gymnastics cohort and the fundamental skills.
The statistical effect size output for the grouped gymnastics cohort and the individual
gymnasts supported the mechanical descriptive findings. Three handstand and forward
walkover BRI correlations (12.5%) revealed agreement between group and individual
gymnast effects. For each of the correlations in which individual gymnast effect sizes were
reflective of the whole-group, the respective effect sizes were below the r>0.5 boundary for
large effects. Therefore, only medium or small group effect sizes supported the agreement
between individual and group responses. A single large relationship (r>0.5) for the wholegroup of gymnasts was identified for DLPSI and LPRoMx; the large group effect was
supported by 57% of the gymnasts, for whom large correlations between the respective BRIs
were evidenced. With only 12.5% of BRIs revealing agreement between group and
individual responses, the conclusion that screening should be individualised was made. If
musculoskeletal screening was undertaken on whole groups in neglect of the respective
research findings, i.e. generalised across the population, BRIs deemed to be important at a
group level may not be reflected at an individual level. As such, screening approaches may
be ineffective. Support was therefore provided for research by Gittoes and Irwin (2012);
DiFiori et al. (2014), which advocated the individual customisation of prevention strategies.
In addition to informing the extent to which group BRIs were reflected by individual
mechanical responses, the whole group and individual BRI correlations offered valuable
insight into the relationships between handstand and forward walkover skills. At a grouplevel, four of the 12 significant correlations (33%) were evident in both skills. In addition,
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the percentages of gymnasts for whom large correlations were identified were inconsistent
between the skills. More specifically, the percentage of large correlations for individual
gymnasts were not the same for any handstand BRI correlation as forward walkover BRI
correlation. Appraisal of the mechanical responses and correlation outputs for handstand and
forward walkover skills furthered understanding of the biomechanical risk responses of the
two skills and subsequently addressed the second chapter question. In line with the findings
from Irwin and Kerwin (2007), use of the handstand skill as a progression to the forward
walkover in gymnastics practice (Mitchell et al., 2002) was suggestive of mechanical
similarities between the two skills. As such, the second chapter question emerged in response
to the need for exploration of the relations between the two skills in the way of indicating
CSI susceptibility through BRIs. In addition to extending knowledge of CSI risk in the
respective population, understanding of the similarities of biomechanical risk between the
fundamental skills was anticipated to be highly informative for determining whether the
handstand skill can satisfactorily reflect the forward walkover, or if both skills are needed
for effective musculoskeletal screening of gymnasts.
Initial insight into the mechanical correspondence between the handstand and forward
walkover was gained from descriptive BRI data. Each gymnast was found to experience
heightened DPSI and LPRoMx for the forward walkover in comparison with the handstand
skill. The respective BRIs were exclusive in that each of the gymnasts within the cohort
experienced the same trend from handstand to forward walkover. Although bi-directional,
and therefore not conclusive across all gymnasts, increased biomechanical risk was
evidenced in the forward walkover for posture, LPRoMy and DLPSI, for 93%, 86% and
50% of the gymnasts, respectively. Opposing BRI responses were found for CoPx and CoPy
in comparison with the other BRI trends. Increased biomechanical risk for the handstand
was evidenced for CoPx and CoPy, with 57% and 79% of gymnasts respectively supporting
the trend. Although unanticipated, when put into the context of the skill requirements, the
need for use of greater ranges of anterior-posterior and medio-lateral CoP is reasonable as a
result of the increased dynamic motion of the forward walkover skill. Excluding CoPx and
CoPy responses, heightened biomechanical risk was identified for the forward walkover in
comparison with the handstand skill for the majority of gymnasts and BRIs. The forward
walkover is one of the most commonly associated gymnastics skills with chronic back pain
and chronic spinal injuries (Jackson et al., 1976; Hall, 1986; Kruse and Lemmen, 2009), thus
the heightened biomechanical risk increase in the respective skill was in agreement with the
previous literature outcomes. Varying trends of BRIs across gymnasts and between skills
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may be indicative of the potential influence of intrinsic risk factors on BRIs. The etiological
model which was developed by Meeuwisse et al. (2007) provided support for the suggestion
through the report that intrinsic risk factors, such as age and growth, lead to predisposed
athletes. The investigation of the contribution of prominent CSI intrinsic risk factors to BRIs
within the female gymnastics population was subsequently highlighted to potentially extend
insight into the evidenced inconsistent BRI responses.
Large correlation coefficient values for handstand BRIs and forward walkover BRIs
informed understanding of the fundamental skills relationship from a statistical perspective.
As biomechanical risk was identified to be individual for the gymnasts, the correlations were
undertaken and interpreted on an individual basis. The bivariate correlation outputs revealed
large correlation coefficient values (r>0.5) for 57% of the BRIs between the handstand and
forward walkover. Although large correlations were evidenced for posture, DPSI, CoPy and
DLPSI, only four gymnasts were found to contribute to the large inter-skill relationships.
The statistical outcomes furthered the descriptive support for the lack of cohesion in the
gymnastics cohort and the need for screening to be conducted on an individual basis was
subsequently concluded.
Through the analysis of BRIs, a lack of large effect sizes between the handstand and forward
walkover skills for CoPx and LPRoMx was evidenced. The dominance of sagittal plane BRIs
for the handstand and forward walkover skills was suggestive of the primary underpinning
of CSI by planar movement responses. The mechanical formation of the handstand and
forward walkover skills from a performance perspective (Figure 3.7 and Figure 3.8) was
supported by a dominance of sagittal plane analyses of gymnastics skills in previous research
(Kerwin and Trewartha, 2001; Irwin and Kerwin, 2005; Kong et al., 2011; Exell et al.,
2012b). Fine-grained understanding of sagittal-plane responses of the handstand and forward
walkover skills were subsequently deemed necessary, therefore justifying the exclusion of
CoPx and the LPRoMx within further analyses. From an injury screening perspective, a twodimensional approach is desirable and has been evidenced as the most typical technique to
inform musculoskeletal screening (Crewe et al., 2012a). The result of the correlation outputs
which informed the exclusion of CoPx and LPRoMx from further analyses additionally
identified LPRoPy to lack support for its continued inclusion in the respective research.
Further interrogation of posture, DPSI, CoPy and DLPSI was subsequently deemed to be
valuable for overall understanding of the biomechanical risk of the young gymnastics
population.
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The BRIs analysed in the respective chapter were selected in accordance with previous
research findings and refined in accordance with empirical relevance to fundamental
gymnastics skills. The inter-dependence of BRIs have been evidenced by researchers
including Hamill et al. (2012) and Whiting and Zernicke (2008) and therefore informed the
development of the third chapter question (CQ 3.3). To address the respective question,
novel insight into the inter-relations between specific BRFs and BEDs in a female
gymnastics cohort was gained. Comprehension of the relationships between BRIs may assist
in developing understanding of the relevance of each BRI to the respective population and
gymnastics skills. As the analysis was undertaken to directly inform injury screening
approaches, bivariate correlations were based on individual gymnasts. Appraisal of the
handstand and forward walkover skills in isolation was undertaken in accordance with
Section 3.3.3 findings.
The direction of the relationships between posture (the remaining BRF) and BEDs (CoPy,
DPSI and DLPSI) was of particular interest in understanding the BRI trends. Previous
associations of each BRI with CSI development were suggestive of the positive relationship
between the BRF and BEDs. Initial insight into the correlation coefficient values revealed
large effects for a maximum of 29% of gymnasts for each posture and BED correlation. The
quantity of large effect sizes output was lower than anticipated, considering the associations
of each BRI with CSI. Further support for the need for individualised understanding of
biomechanical risk of the gymnasts was subsequently provided. Although the BRI
relationship trends across the skills were similar for two of the BRF and BED correlations,
DPSI showed no similarity in BRF correlations for the handstand and forward walkover.
Therefore, as found in previous correlation outputs, the large effects were not reflected
across the skills. Although similar trends were observed for the percentage of gymnasts with
large correlation coefficients between posture and CoPy and posture and DLPSI, only large
negative correlations were found for CoPy for each of the skills. For the correlation analyses
between posture and DLPSI, a large positive correlation for both skills was identified by one
gymnast, while 14% and 21% of gymnasts revealed large negative correlation coefficients
for the handstand and forward walkover respectively. The lack of consensus across
individual gymnasts provided evidence for the need for multiple BRIs to be included in the
screening of female artistic gymnasts. In addition, support for the consideration of each skill
in isolation was provided by the diverse inter-skill responses. Subsequently, screening may
not adequately be centred on basic (handstand) skills to predict predisposition within
dynamic sports.
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3.5
Chapter Summary
The chapter presented initial insight into CSI biomechanical risk indicators for female
artistic gymnasts performing fundamental skills. Disparity between group and individual
BRI relationships was indicative of the need for CSI approaches to be individualised. The
findings subsequently aligned with previous research by Gittoes and Irwin (2012), among
others. Empirical data evidenced the inability of the handstand to predict predisposition in
dynamic gymnastics skills, therefore supporting the separate consideration of the handstand
and forward walkover skills in further research and injury screening approaches. Statistical
analyses of the interactions between BRIs provided valuable insight into the relationships of
BRFs and BEDs within each skill. As the majority of the BRI relationships were found to be
inconsistent across individuals, exploration of intrinsic risk factors was advocated in
accordance with by Meeuwisse et al. (2007)’s model, to develop potential insight into the
varying extrinsic risk factors.
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CHAPTER 4 - PHYSICAL DEVELOPMENT AND BIOMECHANICAL
RISK INDICATORS: A CROSS-SECTIONAL INVESTIGATION
4.1
Introduction
Meeuwisse et al. (2007)’s model of injury etiology acknowledged the contribution of
intrinsic risk factors to predisposition of a specific pathology. From the critical review of
relevant literature (Section 2.3), physical development was found to be a prominent intrinsic
risk factor for CSI development. Chronological ageing, maturation and growth have been
recognised as three distinct mechanisms of physical development within previous research
(Siatras et al., 2009). Although the mechanisms of physical development (chronological age,
maturation status and anthropometric growth status) have been individually appraised in
previous research (e.g. Beunen and Malina (2008)), empirical understanding of the
interaction of the mechanisms in the female artistic gymnastics population is scarce.
Interrogation of the physical development mechanism relationships may offer important
insight into the inclusion of each measure in musculoskeletal screening approaches.
The basis for targeted injury prevention strategy development is an understanding of
associated risk factors (Hume et al., 2013). The interactions of intrinsic and extrinsic risk
factors have been outlined as being essential to athlete susceptibility (Meeuwisse et al.,
2007). Translation of the respective knowledge to the injury prevention framework
(Donnelly et al., 2012), illustrates the significance in the consideration of intrinsic (physical
development) and extrinsic (BRIs) risk factors to inform musculoskeletal screening
development. Speculation that individual BRIs may manifest from unique physical
development pathways was induced from the recently developed insight that biomechanical
risk in female artistic gymnasts was individual (Chapter 3). Investigation of the influence of
physical development mechanisms on BRIs in female artistic gymnasts was subsequently
necessitated to develop initial insight into the role of physical development in CSI screening
approaches.
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4.1.1
Chapter Aim
The chapter aimed to explore measures of physical development and their influence on
biomechanical risk indicators in a female artistic gymnastics cohort using a cross-sectional
approach.
4.1.2
Chapter Questions
CQ 4.1 What relationships exist within physical development mechanisms in a female
gymnastics population?
Within clinical and sporting cohorts, physical development mechanisms have been identified
as prominent intrinsic risk factors for CSI (e.g. Kerssemakers et al. (2009)). Insights into the
interactions of the mechanisms within the female gymnastics population are sparse but
needed to increase understanding of the potential role of physical development in CSI
screening. Strong linear interactions between the mechanisms have been previously
documented (Stang and Story, 2005). The relationships between the physical development
mechanisms in a female gymnastics cohort were therefore anticipated to be strong, with the
implication of a single measure being adequate to represent physical development in
screening approaches.
CQ 4.2 How do measures of physical development influence biomechanical risk
indicators in fundamental gymnastics skills?
Although there is a lack of current understanding of the influence of physical development
risk in the female artistic gymnastics population, chronological age, maturation and growth
have previously been associated with CSI (Tanchev et al., 2000; Brüggemann, 2010; Kim
and Green, 2011). Physical development mechanisms were therefore speculated to have a
large influence on BRIs in a cohort of female artistic gymnasts.
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4.2
4.2.1
Methods
Participants and Study Design
To address the chapter aim, the same group of female artistic gymnasts (n = 14) that informed
Chapter 3, were selected to participate in the research. The cohort of gymnasts were of
chronological ages ranging from nine and 15 years, therefore encompass individuals at
varying stages along the physical development spectrum. The gymnasts were all of a
competitive level, with mean (SD) whole-body height and mass of 1.41 (0.12) m and 36.9
(11.0) kg respectively. A cross-sectional approach was taken, with each gymnast attending
a single data collection. Upon arrival, participant and parental/guardian written consent was
gained and standard pre-test health questionnaires were completed by the respective
parent/guardian and gymnast. Ethical approval for the study was granted by Cardiff
Metropolitan University. To address the chapter questions (CQ 4.1 and 4.2) and develop
understanding of physical development mechanisms within the gymnastics population, a
group design was employed.
4.2.2
Data Collection
Measures of Physical Development
To inform the calculation of chronological age, the date of birth for each gymnast was
reported through use of a participant information sheet (Appendix A.10), in addition, the
respective data of collection was recorded by the primary researcher. Within the predisposed
female gymnastics population, use of a self-assessed maturation approach, specifically PDS,
was deemed to be of greatest suitability in comparison with alternative methods of measuring
maturation status (Chapter 2, Section 2.6.2). In accordance with PDS, a rating of one to four
(1 = no development, 2 = beginning development, 3 = additional development, 4 =
development completed) for breast, body hair development, growth spurt and skin change
(Petersen et al., 1988), informed the maturation status questionnaire (Appendix A.17). The
questionnaire was completed by each of the gymnasts upon arrival, with further explanation
provided by the lead researcher when requested. The final measure of physical development,
growth, was quantified through measure of bicristal and biacromial breadths, to inform the
b-ratio. As a standard measure of anthropometric growth, the b-ratio is typically informed
106
through breadth measures taken using calipers (e.g. Daly et al. (2000)). To enable noninvasive calculations of bicristal breadth and biacromial breadth, use of the image-based
inertia approach presented by Gittoes et al. (2009) was explored. Frontal plane images of
each gymnast were taken in accordance with the procedure outlined in Chapters 2 and 3.
Biomechanical Risk Indicators
Use of a CODA motion analysis system, comprising of four Cx1 units (sample rate: 100 Hz)
and two strobe units were positioned in accordance with the setup reported in Chapter 3
(Figure 3.2). The additional use of a Kistler force plate (sample rate: 1000 Hz) allowed for
simultaneous capture of kinematic and kinetic data throughout the performances of
handstand and forward walkover skills. In replication of the methods of BRI capture in
Chapter 3, forty-eight CODA motion markers and eight CODA motion drive boxes were
secured to each gymnast at specific anatomical landmarks dictated by the spatial model
developed in Chapter 3. With the markers and drive boxes secured, a self-directed warm-up
was undertaken by each of the gymnasts, followed by the performance of a maximum of 20
handstand and 20 forward walkover skills by each gymnast. The trial protocol outlined in
Chapter 3 was replicated for the collection of relevant BRI data in the respective chapter.
4.2.3
Data Processing and Analysis
Measures of Physical Development
Chronological age was calculated as the difference between the individual gymnasts’ date
of birth and the date of attendance; the outcomes were reported in months. Maturation status
was accepted as a product of the ratings provided for each of the four features of maturation
included in the maturation status questionnaire (Appendix A.17). The overall scale of
maturation status ranged from four to 16, with a rating of four indicating no development for
any of the respective measures, and a rating of 16 interpreted at full development. To obtain
gymnast-specific bicristal and biacromial breadth data for the calculation of anthropometric
growth status, frontal-plane images were digitised using Peak Motus software. The twodimensional coordinates for the left acromion process, the right acromion process, the left
iliac crest and the right iliac crest landmarks (Figure 4.1) were determined.
107
Biacromial breadth (mm)
Bicristal breadth (mm)
Figure 4.1. A frontal plane image captured using the procedure outlined by Gittoes et al.
(2009), with indication of the biacromial and bicristal breadth measures.
The relevant coordinates were exported from the digitisation software and imported into a
Microsoft Excel spreadsheet. The diameter of the acromion process landmarks (left and
right) and of the two iliac crest locations (left and right) were calculated, producing a
measure of biacromial breadth and a value of bicristal breadth for each gymnast. Intersession reliability of bicristal breadth and biacromial breadth measures for a female artistic
gymnast revealed a maximum difference of 0.8% of mean bicristal breadth and 0.9% of
mean biacromial breadth (Appendix A.18). To explore the accuracy of the image-based
approach, measurements of the respective breadths were additionally taken using a caliper
measurement tool (Holtain Ltd., Dyfed, UK). Bicristal breadth error was found to be 0.3%
of the mean measured bicristal breadth; the difference between digitised and measured
biacromial breadth was 2.4% of the mean measured biacromial breadth.
The breadth values for each gymnasts were input into the anthropometric growth status
formula (4.1) reported by Malina et al. (2004), which produced a percentage of bicristal
breadth development in relation to biacromial breadth (b-ratio). The larger the percentage
data, the closer the bicristal breadth in relation to the biacromial breadth.
Anthropometric growth status (%) = (bicristal breadth/biacromial breadth) x 100
[4.1]
Reliability testing of b-ratio measurement identified a maximum difference of 0.8% across
three trials (Appendix A.18).
108
Biomechanical Risk Indicators
Handstand and forward walkover coordinate and ground reaction force data were exported
from CODA motion analysis software and input into Visual 3D software. Customised
gymnast Visual 3D models were applied for the analyses of each trial for each gymnast,
further details of which were provided in Chapter 3. Filtered lumbar angle, ground reaction
force (x, y and z), centre of pressure (y) and lumbo-pelvic angle (x, y and z) data were
exported from the Visual 3D software for the double support phase for each trial (defined in
Chapter 3). In accordance with the procedures outlined in Chapter 3, Microsoft Excel
software was utilised for the calculation of posture (°), DPSI, CoPy (mm) and DLPSI.
4.2.4
Statistical Analysis
Informed through the findings from Chapter 3, the handstand and forward walkover skill
data were analysed independently within the respective chapter. As the influence of physical
development on BRIs would be unable to be quantified through individual analyses, wholegroup responses were utilised to address the chapter aim, along with the respective chapter
questions. In line with the approach for interpreting the statistical outputs within the previous
chapter, the effect sizes formed the basis for interpretation of the results, with support from
the statistical p-value data (p<0.05).
Measures of Physical Development
To appraise the relationships between chronological age, maturation status and
anthropometric growth status, correlation coefficient analyses were conducted. Using SPSS
software, the normality of physical development data was tested. Chronological age and
maturation status data distributions satisfied the assumptions of normality (Shapiro-Wilk
test, p>0.05), however, anthropometric growth status data violated the parametric statistical
assumption (Shapiro-Wilk test, p<0.05). In line with the normality outputs, the association
between chronological age and maturation status was assessed through use of a two-tailed
Pearson’s correlation for the gymnastics cohort. Consequent to violation of the assumption
of normality for anthropometric growth status, a two-tailed Spearman’s Rho correlation was
conducted to explore the relationship between anthropometric growth status and
chronological age, and anthropometric growth status and maturation status.
109
Physical Development and Biomechanical Risk Indicators
Regression analyses were undertaken to explore the influence of each of the physical
development measures on BRIs in the female gymnastics cohort. Homoscedasticity was
accepted through visual appraisal of residual scatter plots. Theoretical understanding has
been highlighted to be the most important aspect of guidance for the decision of which type
of regression is of greatest substantive sense (Cohen et al., 2013). Within the respective
research context, a polynomial regression was considered to be of greatest suitability as
physical development is a non-linear process (Rogol et al., 2000; Courteix et al., 2001; Lloyd
and Oliver, 2012). The examination of physical development data trends in previous
literature and visual inspection of data informing the respective statistical analyses informed
the decision to undertake quadratic regression analyses. With no inflections, use of the
quadratic regression offers a plausible reflection of physical development trends (Sun and
Jensen, 1994; Baxter-Jones et al., 2003).
The quadratic regressions were formed by taking the independent variable (x) to successive
powers; the quadratic equation subsequently produces a parabola through a single bend in
the regression line (O'Donoghue, 2012). The polynomial regression equation in 4.2 informed
the respective quadratic regression analyses.
Quadratic regression = a+b1.x+b2.x2
[4.2]
a = Y-intercept
b1 = first constant
b2 = second constant
x = independent variable
In line with the previous chapter, the appraisal of effect size outputs (r) informed the
interpretation of the statistical outputs with the significance values additionally reported to
offer insight into the probability of obtaining the result due to chance (Mullineaux et al.,
2001). The effect size outputs were interpreted through use of Cohen (1988)’s boundaries
and the accepted level of statistical significance was set at p<0.05.
110
4.3
4.3.1
Results
Physical Development Mechanisms
Prior to the investigation of the inter-relations between physical development measures and
the exploration of the influence of physical development measures on BRIs, the crosssectional physical development measures of the female artistic gymnastics cohort were
appraised. The descriptive data are reported in Table 4.1.
Table 4.1. Descriptive data of the physical development measures for the female artistic
gymnastics cohort (n = 14)
Participant
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Chronological age
(months)
Maturation status
112
118
118
120
120
121
131
134
136
146
147
166
179
180
5
5
5
8
6
5
5
5
10
6
7
9
9
8
Anthropometric
growth status
(%)
75.0
73.2
76.6
73.2
71.8
81.8
72.7
76.9
73.9
80.4
84.7
83.0
86.3
83.1
The range in maturation status for the gymnastics cohort was five to 10, with six of the 14
gymnasts (43%) reporting a self-classification of five. A rating of five indicated the
respective gymnast considered themselves to have no development in three of the four
categories, with development beginning in one. Figure 4.2 provides initial insight into the
interaction between chronological age, maturation status and anthropometric growth status.
100
90
80
70
60
50
40
30
20
10
0
12
10
8
6
4
Maturation Status
Anthropometric growth status
(%)
111
2
0
112 118 118 120 120 121 131 134 136 146 147 166 179 180
Chronological Age (months)
Figure 4.2. Interaction of chronological age (months), maturation status (columns) and
anthropometric growth status (line) for a cross-sectional cohort of female artistic gymnasts.
Although not conclusive, visual appraisal of the relationships between the physical
development mechanisms provided indication of the similarity in trends between the three
mechanisms. The interaction of chronological age, maturation status and anthropometric
growth status were further explored through bivariate correlation analyses to address the first
chapter question. A two-tailed Pearson correlation between chronological age and
maturation status revealed a large effect size (r>0.3). The data informing the significant
correlation (p<0.05) for the 14 gymnasts is presented in Figure 4.3.
r = 0.65
Figure 4.3. Chronological age in association with maturation status data for the gymnastics
cohort (n = 14); p<0.05.
112
A positive relationship was revealed between maturation status and chronological age,
evidencing the general trend of increased age with increased maturation status. As a group,
the gymnastics cohort’s chronological ages explained 42% of the variance of the maturation
statuses (r2 = 0.42), and vice versa. The mean (SD) for chronological age and maturation
status were 137.71 (22.96) years and 6.64 (1.82) respectively.
A smaller effect size was revealed for the relationship between maturation status and
anthropometric growth status than the previous correlation output; a medium effect size
output of 0.37 was revealed, the informing data for which is displayed in Figure 4.4. The
relationship was found to be insignificant, with a statistical output of p>0.05.
r = 0.37
Figure 4.4. Maturation status in association with anthropometric growth status ratio data for
the gymnastics cohort (n = 14).
The participants with an overall maturation status of five had anthropometric growth statuses
which varied from 69.9% to 78.5%. Within the cohort, the b-ratios (bicristal to biacromial
ratios) were between 69.9% and 83.8%; the mean (SD) for anthropometric growth status was
76.1 (4.5)%.
113
The between chronological age and anthropometric growth status was subsequently explored
(Figure 4.5). The large effect size for the interrelation of the two mechanisms (r>0.5) was
supported by statistical significance (p<0.05). A positive relationship was revealed between
chronological age and anthropometric growth status within the study cohort, with each
explaining 47% of the variance of the other measure (r2 = 0.47).
r = 0.68
Figure 4.5. Chronological age in association with anthropometric growth status data for the
gymnastics cohort (n = 14); p<0.05.
4.3.2
Physical Development and Biomechanical Risk Indicators
For the single BRF (informed through Chapter 3 findings), quadratic regression analyses
revealed physical development to have the greatest influence on posture in the handstand
skill, as opposed to the forward walkover. Large effect sizes were reported for both
chronological age and anthropometric growth status in the handstand (r>0.5). No large effect
sizes were found from the forward walkover skill regressions; however, of the physical
development measures, the greatest effect size for the respective skill was produced for
anthropometric growth status and posture (r = 0.41). A medium relationship was additionally
shown to exist between maturation status and posture for the forward walkover skill, with
an effect size of >0.3. Chronological age was found to have a small effect on BRIs in the
forward walkover skill (r>0.1). Maturation status was found to have a greater influence on
posture in the forward walkover skill, accounting for 13% of posture variance (r2 = 0.13),
114
than in the handstand skill, for which maturation status accounted for only 1% of posture
variance (r2 = 0.01). None of the regression results for physical development measures on
posture were found to be statistically significant (Table 4.2). A positive linear relationship
with large effect size between chronological age and anthropometric growth status was
found in the previous analyses, however, the quadratic regression trends outputs for the
physical development measures on posture did not comply with the respective trend.
Chronological ages of 14 to 16 years, were found to be associated with increased BRF
through increased lordotic posture. However, the more extreme lordotic posture was
produced by the gymnasts with low (approximately 70%) and high (approximately 83.5%)
morphological ratios, as oppose to those of mid-range anthropometric growth status, as may
be predicted from the chronological age and anthropometric growth status correlation.
The strongest influence of physical development measures was found to be on DPSI; large
effect sizes of statistical significance (p<0.05) were output for each measure of physical
development and DPSI in the handstand and the forward walkover skills. With effect sizes
of up to r = 0.82, a maximum of 70% of variance in DPSI was shown to be explained by
physical development measures. Anthropometric growth status was the only large effect
output for CoPy in the handstand (r>0.5), however, all physical development measures
exhibited large effects with CoPy for the forward walkover skill. None of the physical
development and CoPy outputs were statistically significant. Large effects for maturation
status and anthropometric growth status were found for DLPSI in the handstand (r>0.5),
however, DLPSI in the forward walkover was the only BRI for which all physical
development measures were shown not to have large effects. The DLPSI statistical
significance levels were reflective of those produced for CoPy, with each p-value output
being greater than 0.05. Graphical representations of the quadratic curves for the large effect
outputs of the measures of physical development on BRIs are provided in Figure 4.6 for the
handstand skill and Figure 4.7 for the forward walkover skill.
115
r = 0.54
r = 0.69
r = 0.52
r = 0.75
r = 0.82
r = 0.51
116
r = 0.50
r = 0.51
Figure 4.6. Quadratic regression outputs for physical development and BRI large effects (r>0.5) in the handstand skill.
r = 0.74
r = 0.57
r = 0.83
r = 0.54
Figure 4.7. Forward walkover skill quadratic regression large effect outputs (r>0.5) for physical development measures and BRI.
117
r = 0.77
r = 0.56
118
The appraisal of Figure 4.6 and Figure 4.7 offered insight into the within-group regression
trends of the gymnastics cohort. The large effect outputs for chronological age and
anthropometric growth status on DPSI demonstrated agreement, with increases in both
physical development measures exhibiting decreased whole-body general stability. The
findings for DPSI were consistent for the handstand and the forward walkover skill.
Anterior-posterior whole-body stability was found to reduce (increasing biomechanical risk)
with anthropometric growth status increase, i.e. as the bicristal breadth broadened in relation
to the biacromial breadth, CoPy during the both the handstand and the forward walkover
skills decreased. Within the forward walkover skill, large effects were exhibited for
chronological age and maturation status on CoPy, for which lower anterior-posterior stability
was determined for the younger and older individuals, yet increased stability was found for
those with high and low maturation statuses. Lumbo-pelvic stability was found to be lower
for those at the points of high and low maturation, yet higher for the individuals with high
and low anthropometric growth statuses. For those variables in which large effects were
identified for both the handstand and forward walkover skills (CoPy and anthropometric
growth status, DPSI and all physical development measures), the trend outcomes were
shown to be consistent across the skills. The r- and p-values from the quadratic analyses of
each measure of physical development and BRI are documented in Table 4.2, signifying the
influence of each measure of physical development on BRIs for the female gymnasts.
Table 4.2. Effect size (r) and statistical significance (p) outputs for the influence of physical
development mechanisms on BRIs in the handstand and forward walkover skills
Chronological Age
r
p
Maturation Status
r
p
Anthropometric
Growth Status
r
p
Handstand
Posture
0.54
0.15
0.11
0.94
0.52
0.18
DPSI
0.82
0.00*
0.69
0.03*
0.75
0.01*
CoPy
0.25
0.71
0.11
0.93
0.51
0.19
DLPSI
0.29
0.62
0.50
0.20
0.51
0.19
Forward Walkover
Posture
0.21
0.80
0.36
0.48
0.41
0.37
DPSI
0.74
0.01*
0.83
0.00*
0.77
0.01*
CoPy
0.57
0.11
0.54
0.15
0.56
0.13
DLPSI
0.41
0.36
0.27
0.65
0.19
0.81
Note: dark grey shaded values indicate large effect sizes (r>0.5), light grey shading
represents medium effect sizes (r>0.3) and the r values with no shading are small effect sizes
(r>0.1); *p<0.05
119
The appraisal of Table 4.2 revealed chronological age and maturation status to have large
effects on 50% of BRIs, within and across the fundamental gymnastics skills.
Anthropometric growth status was identified to have a large effect on all BRIs in the
handstand skill and 50% of BRIs in the forward walkover skill; anthropometric growth status
was identified to have a large overall influence on 75% of BRIs across the two skills.
4.4
Discussion
The aim of the chapter was to explore measures of physical development and their influence
on biomechanical risk indicators in a female artistic gymnastics cohort using a crosssectional approach. To suffice the chapter aim, chronological age, maturation status and
anthropometric growth status were quantified for the gymnastics cohort. Kinematic and
kinetic capture techniques were utilised to determine measurements of BRIs which were
appraised through the performance of handstand and forward walkover skills.
Biomechanical insight into the inextricable interactions between biology and mechanics
have been deemed essential for injury prevention development (Nuckley, 2013). Therefore,
in addition to investigation of the interactions of the physical development measures, the
influence of each physical development mechanism on BRIs was quantified. An inferential
approach was adopted to inform valuable contributions to the overall thesis aim in providing
initial quantification and understanding of physical development in a female artistic
gymnastics cohort. Further appraisal of empirical data, which informed comprehension of
the influence of physical development mechanisms on extrinsic risk factors, was undertaken
using a biomechanical perspective.
The first chapter question (CQ 4.1) was formulated to gain initial understanding of the
interactions between the physical development mechanisms of a female artistic gymnastics
cohort. The extent to which physical development mechanisms were related was addressed
through chronological age, maturation status and anthropometric growth status data, which
was obtained for each gymnast using established measurement techniques. As the only
measurement technique for chronological age reported in previous literature (e.g. Claessens
et al. (2006)), calculation of the difference between the date of birth and the collection date
informed the respective research. Maturation status for each gymnast was determined
through use of the PDS, presented by Petersen et al. (1988). The techniques which have been
used to determine maturation status in previous research were appraised in Chapter 2,
(Section 2.6.2) and revealed strong relationships between self-assessed and physically
120
examined maturation (Daly et al., 2005). The ability to assess the maturation of predisposed
female gymnasts through use of a non-invasive technique was the primary basis for selection
of the respective approach; the ease of application was additionally considered to be
favourable to inform injury screening approaches. The sensitivity of approaches such as xray or radiograph imaging to determine maturation was accepted to be greater than that of
the PDS method. Use of the PDS approach enabled initial insight into maturation within the
female gymnastics cohort, which may be extended through the use of invasive methods in
future research. As a prominent anthropometric measurement technique, use of the b-ratio
has been advocated for the determination of anthropometric growth status by numerous
researchers such as Claessens et al. (1992); Malina et al. (2004); Claessens et al. (2006);
Armstrong and van Mechelen (2008); Siatras et al. (2009). Innovative use of the imagebased approach to determine b-ratio for each gymnast provided initial support for the
suitability of the approach in further research with vulnerable populations and for screening
approaches. The ability to quantify each physical development mechanism using noninvasive approaches which have minimal time requirements was highly valuable for the
ability to transfer knowledge to applied practice.
Empirical understanding of the interactions of chronological age, maturation status and
anthropometric growth status in the female artistic gymnastics cohort was gained to address
the first chapter question. Strong relationships were revealed between chronological age with
maturation status and anthropometric growth status (r>0.5), however, a weaker effect size
was identified between maturation status and anthropometric growth status (r>0.3). The
respective medium effect size was unexpected given previous identifications of strong sexual
maturation and growth correlations (Stang and Story, 2005). The correlation findings were
therefore supportive of previous findings which have identified the diversity of physical
development mechanisms (Ulijaszek et al., 1998; Caine and Maffulli, 2005; McLeod et al.,
2011; Lloyd and Oliver, 2012; Brooks-Gunn and Peterson, 2013). The strong relationship
between chronological age and maturation status was suggestive of the potential ability for
the inclusion of one of the respective measures in a screening approach to enable inferences
about the other. The same approach may be considered for chronological age and
anthropometric growth status, however, the research findings indicated that the inclusion of
both maturation status and anthropometric growth status would be necessary within a
screening approach to enable full representation of physical development.
As chronological age is essentially representative of time, the strong relationships with
chronological age may provide indication of the alteration of maturation and growth across
121
time. Speculation of the need for longitudinal monitoring of maturation and anthropometric
growth was subsequently induced and has been supported by previous gymnastics-based
research which advocated the assessment of physical development over longitudinal periods
(Tanner, 1962; Beunen and Malina, 2008; Malina et al., 2013).
Informed by Meeuwisse et al. (2007), the respective chapter focused on intrinsic and
extrinsic risk factors for CSI. The appraisal of physical development measures advanced the
recently developed understanding of CSI risk in a female artistic gymnastics cohort which
was attained in Chapter 3. Further exploration of the risk that each physical development
mechanism implemented on young female gymnasts’ predisposition was undertaken through
evaluation of the influence of physical development measures on BRIs. The respective
analyses addressed the second chapter question. As each measure of physical development
had previously established associations with CSI (Tanchev et al., 2000; Adams and
Roughley, 2006; Baranto et al., 2009b; Kerssemakers et al., 2009), chronological age,
maturation status and anthropometric growth status were anticipated to have large effects on
each BRI. The empirical study presented compelling support for the influence of physical
development on CSI susceptibility in the female gymnast cohort. With the exception of
posture and DLPSI in the forward walkover skill, at least one measure of physical
development was revealed to have a large influence (r>0.5) on each BRI within the
handstand and forward walkover skills. Seventy-five per cent of BRIs were found to be
influenced by a physical development mechanisms to a large extent, highlighting the
importance of physical development in influencing CSI biomechanical risk. The findings
provided valuable support for the role of physical development in CSI screening approaches.
Interrogation of the large quadratic regression outputs revealed decreased general stability
(DPSI) with increased chronological age in the handstand and the forward walkover skills
(r>0.5). The same chronological age trend was revealed for CoPy in the forward walkover
(r>0.5). The findings provided empirical support for compliance of the female gymnastics
population with the previously identified trend of increased injury risk with increased
chronological age (Emery, 2005). A large effect was additionally identified for chronological
age and posture in the handstand skill (r>0.5), however, unlike the outputs for the other
BRIs, a reduction in posture (increased biomechanical risk) was observed at midchronological ages, with a U-shaped curve presented as the group trend. The conflicting BRI
trends with chronological age may be reflective of the complex nature of pain and injury
mechanisms, along with the complex influence of intrinsic risk factors on BRIs (Meeuwisse
et al., 2007).
122
Gymnasts with higher and lower maturation statuses were found to have lower
biomechanical risk for whole-body stability, with increased whole-body general stability for
the handstand and forward walkover (r>0.5) and increased anterior-posterior whole-body
stability for the forward walkover (r>0.5). Lumbo-pelvic stability in the handstand was
illustrated to be lower for those gymnasts with maturation status closer to the low and high
maturation status extremes of five and ten (r>0.5). The heightened lumbo-pelvic stability
risk for the gymnasts with high and low maturation status may be underpinned by the
continuous attempt of the stabilising subsystem of the spine to adapt to the structural and
mechanical changes which are brought about by the maturation process (Donatelli and
Thurner, 2014).
As anthropometric growth status increased, a decrease in both whole-body and anteriorposterior stability (increasing biomechanical risk) was observed for DPSI and CoPy in the
handstand and forward walkover skills (r (CoPy) = 0.51 and 0.56, respectively and r (DPSI)
= 0.75 and 0.77, respectively). The influence of anthropometric growth status on DLPSI in
the handstand revealed decreased lumbo-pelvic stability at mid-growth (r>0.5). High and
low anthropometric growth statuses were identified to influence decrease lumbar angles
(increased biomechanical risk) in the respective gymnastics cohort for the handstand skill
(r>0.5). The overall findings for the influence of anthropometric growth status on BRIs were
particularly interesting as the two spinal-related BRIs (posture and DLPSI) were found to
follow separate trends to the whole-body stability BRIs (DPSI and CoPy). The findings may
be explained by the inconsistencies in whole-body and segmental growth (Malina et al.,
2004). Adaptation to segmental growth has been evidenced to present in a variety of ways,
including a reduction in joint flexibility (Kerssemakers et al., 2009). The postural changes
were supportive of previous research which has documented altering spinal curvatures
throughout the process of growth (Donatelli and Thurner, 2014). With the non-uniform
growth of various segmental body properties, the nervous system must learn to adapt
(McLester and Pierre, 2007). A temporary decline in motor coordination during the period
of growth is typical (Verhagen and van Mechelen, 2008) and may provide an explanation
for the increase in whole-body stability risk identified with increased anthropometric growth
status. Comprehension of growth-based biomechanics has informed speculation that the
interplay between whole-body and segmental growth may underpin the way in which
anthropometric growth status has been evidenced to influence BRIs.
Overall, anthropometric growth status exhibited large relationships with six BRIs (75%), an
overall increase of 25% in comparison with chronological age and maturation status.
123
Therefore, anthropometric growth status was distinguished as the physical development
mechanism with the greatest influence on CSI risk in the female artistic gymnastics
population. Growth is accepted as being a dynamic process, which is non-linear in nature
(Rogol et al., 2000; Courteix et al., 2001; Lloyd and Oliver, 2012). The non-linearity of
growth has been identified to partially account for increased injury susceptibility of
gymnasts (Meeusen and Borms, 1992) and therefore warrants empirical investigation in the
female gymnastics population. The use of a cross-sectional approach (Chapters 3 and 4) has
been highly beneficial in providing initial insight into the extrinsic (BRIs) and intrinsic
(physical development) risk factors for CSI. However, to satisfactorily explore the influence
of non-linear growth on CSI risk from a biomechanical perspective, longitudinal studies are
necessary (Beunen and Malina, 2008).
4.5
Chapter Summary
Fourteen female artistic gymnasts informed the study, for which an empirical cross-sectional
approach provided novel insights into the intra-physical development mechanism
relationships and the contribution of physical development mechanisms to BRIs. Large
relationships were found between chronological age and maturation status, and
chronological age and anthropometric growth status. The findings were suggestive of the
potential ability for a reduction of the number of physical development measures included
within CSI screening approaches. Of the physical development mechanisms, anthropometric
growth status was found to influence BRIs to the greatest extent. The contribution of
anthropometric growth status to lumbar-related BRIs was revealed to be different to that of
whole-body stability BRIs, potentially supporting the theory that segmental growth is
inconsistent. Exploration of the influence of non-linear growth on CSI risk through
longitudinal study is anticipated to provide valuable insight into the way in which growth
influences biomechanical risk. Understanding of the suitability of cross-sectional injury
screening of female gymnasts is anticipated to be gained from such research.
124
CHAPTER 5 - ANTHROPOMETRIC GROWTH AND
BIOMECHANICAL RISK INDICATORS: A LONGITUDINAL
INVESTIGATION
5.1
Introduction
As identified by Baxter-Jones et al. (2002), growth is defined by an increase in the size of
the body as a whole and of its segments. Whole-body measures, i.e. whole-body height and
mass, are standard for the quantification of anthropometric growth (W.H.O., 2006). As
reported in Chapter 2 (Section 2.6.2), the respective measures are insightful and highly
suitable for use within injury screening approaches, due to the ease of measurement. A
potentially important feature of growth is its non-uniform nature, as recognised by Williams
et al. (2012) among others. Understanding of segmental growth of female gymnasts is
speculated to be an important contributor to BRI trends, as indicated through Chapter 4
findings. Quantification of whole-body growth alone may prevent understanding of the
growth of individual segments, and subsequently, the role of segmental growth in injury
screening may be overlooked. As recognised by Williams et al. (2012), to develop insight
into intra-individual non-uniform growth, the consideration of proportional anthropometric
growth for example, growth of the upper-body relative to the lower-body, may be more
meaningful than absolute measures such as whole-body mass.
In addition to varying segmental growth, non-linear growth across time has been identified
to influence injury susceptibility (Baranto et al., 2009b; Brukner, 2012). To further insights
into the complex, dynamic nature of the influence of physical development on extrinsic risk
factors for CSI, the investigation of longitudinal anthropometric growth of female gymnasts
was warranted. Use of the under-investigated longitudinal approach may offer the potential
to extend the effectiveness of injury screening programmes. In addition, the longitudinal
approach will suffice the call for further comprehension of growth through longitudinalbased research (Tanner, 1962; Beunen and Malina, 2008; Malina et al., 2013).
125
5.1.1
Chapter Aim
The chapter aimed to quantitatively investigate the contribution of longitudinal changes
associated with anthropometric growth on biomechanical risk indicators in a female artistic
gymnastics population.
5.1.2
Chapter Questions
CQ 5.1 How do anthropometric growth status measures underpin biomechanical risk
indicators?
In addition to growth of the whole-body, non-uniform segmental growth has been evidenced
to increase injury predisposition (Hamill et al., 2012). Informed by the b-ratio measure,
anthropometric growth status of female artistic gymnasts was found to have a prominent
influence on BRIs in Chapter 4. Exploration of anthropometric growth status measures
which differ from b-ratio was subsequently deemed necessary to further explore the
influence of growth status on BRIs. Due to the previously evidenced prominent influence of
b-ratio on BRIs, it was speculated that b-ratio would have a greater contribution to
biomechanical risk than the other growth measures.
CQ 5.2 How does anthropometric growth and biomechanical risk modify across a
longitudinal time period?
Growth has been evidenced to be a non-linear process (Lloyd and Oliver, 2012), therefore
alterations of anthropometric growth with time were anticipated. The speculation was
supported by the large influence of chronological age on growth status in Chapter 4 (Section
4.3.1). Transient effects of anthropometric growth were subsequently predicted, along with
prominent influences of time on CSI biomechanical risk. The respective findings may
provide valuable insights into the need for screening of physically developing populations
to be conducted over an extended period of time.
126
5.2
Methods
5.2.1
Participants and Study Design
The female artistic gymnasts which informed Chapters 3 and 4 selected for participation in
the respective study, which was informed through a prospective cohort design. Written
informed consent was obtained from each of the gymnasts in addition to parental/guardian
consent. Each of the gymnasts were healthy and injury free at the time of each testing. Eleven
of the previous 14 gymnasts took part in the collection of data on three separate occasions,
referred to as initial, mid and final collections. Data were acquired for one gymnast at initial
and final collections only, due to the gymnast’s availability. The reduction of the cohort in
size from 14 to 12 was due to two gymnasts being unable to commit to participation in two
further collections. The monitoring of 86% of the original gymnastics cohort was highly
valuable in informing cross-sectional and longitudinal data comparisons and enabling a more
complete understanding of the process of growth to be gained.
Mean (SD) details of the gymnastics cohort at each collection stage are provided in Table
5.1. The unique sample which was selected to inform the respective chapter to ensure quality
data were gained, provided a challenging task in the monitoring of the gymnasts over an
extended period of time. Following initial data acquisition, the collections were repeated
subsequent to mean (SD) of 8.0 (1.3) months (mid) and a 12.0 (0.5) month interval (final).
The specific timescales were selected to represent the minimal screening strategy (six
months), as reported in the IOC positional statement (Roberts et al., 2014), and the preseason approach, favoured by the majority of practitioners who conduct injury screening (12
months) (Conley et al., 2014). In addition, previous gymnastics-centred research has
assessed physical development at zero, six and 12 month periods (Bradshaw et al., 2014).
Table 5.1. Mean (SD) chronological age and training information for the gymnastics cohort
at each collection point
n
Initial
Mid
Final
12
11
12
Chronological age
(years)
11.6 (2.0)
12.3 (2.1)
12.7 (2.0)
Training duration
(years)
5.4 (2.2)
6.2 (2.3)
6.8 (2.2)
Training frequency
(hours/week)
15.8 (5.9)
16.0 (6.4)
15.6 (6.6)
127
With a maximum mean difference of less than one hour, the data presented in Table 5.1
demonstrated the consistency of the gymnastics cohort in relation to the number of hours
they trained for per week. The respective observation was subsequently important in
minimising the potential influence of training differences on BRIs.
5.2.2
Data Collection
Measures of Anthropometric Growth Status
Whole-body height and whole-body mass have been recognised as the most traditional
measures of growth of the whole body (Rogol et al., 2000), leading to extensive use in
previous gymnastics-based research (Claessens et al., 1992; Georgopoulos et al., 1999;
Richards et al., 1999; Courteix et al., 2001; Ackland et al., 2003; Georgopoulos et al., 2004;
Claessens et al., 2006; Georgopoulos et al., 2012). Tanner (1962) reported a difference
between the maximum growth of the lower limbs and the trunk segments of up to 12 months,
with the lower limbs identified to precede the upper-body (Wikstrom et al., 2010). As such,
lower-body segment measures such as leg length have appeared to be popular and valuable
measures of growth (Richards et al., 1999; Hawkins and Metheny, 2001; Malina et al., 2004;
Stergiou, 2004; Claessens et al., 2006). Although more complicated to obtain, and therefore
less desirable for injury screening, the mechanical contribution of upper- and lower-body
mass to handstand and forward walkover biomechanics was deemed important to advance
the understanding of anthropometric growth of the respective cohort. As different parts of
the body grow at different rates and times, the importance of proportional growth
consideration was highlighted by Caine and Purcell (2015). To progress the understanding
of growth gained from segmental measures, the exploration of segmental interactions
through ratio measures was subsequently used.
The anthropometric growth status measures selected for inclusion within the respective
chapter were b-ratio, whole-body height, upper-body length, lower-body length, lower to
upper-body length ratio, whole-body mass, upper-body mass, lower-body mass and lower to
upper-body mass ratio. Other than whole-body mass, which was calculated through use of a
Kistler force plate (as reported in Chapter 3 Section 3.2.3 and Chapter 4 Section 4.2.2), each
measure was calculated through use of an image-based inertia approach (Gittoes et al.,
2009). Informed through the directions provided by Gittoes et al. (2009), three whole-body
images were obtained for each gymnast at each collection point (initial, mid and final). The
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full details of the inertia image collection process were previously documented in Chapter 3
(Sections 3.2.3 and 3.2.4).
Biomechanical Risk Indicators
Ground reaction force and coordinate data were collected simultaneously for the duration of
the performances of a maximum of 20 handstand and forward walkover skills for each
gymnast at each collection. A kistler force plate operating at a rate of 1000 Hz, in addition
to a CODA motion analysis system (sample rate: 100 Hz) allowed for the collection of the
biomechanical data to inform BRIs. The CODA motion analysis system comprised of four
Cx1 units, two strobe units, 48 CODA motion markers and eight CODA motion drive boxes.
The protocol outlined in Chapter 3 was followed for the collection of kinematic and kinetic
data to inform BRIs in the respective chapter.
5.2.3
Data Processing and Analysis
Data were processed individually, following which, anthropometric growth status measures
and BRIs were quantified for each gymnast in isolation. To inform the chapter aim and
subsequent chapter question, the analyses were undertaken at a group level. The approach in
which group-based understanding has been developed to inform individual injury screening
has been typical in previous research, as exemplified by Crewe et al. (2012a).
Measures of Anthropometric Growth Status
As was reported in Chapter 4, gymnast-specific bicristal and biacromial location data were
determined through digitisation of the frontal plane images. Using an Excel spreadsheet, the
respective coordinates informed bicristal breadth and biacromial breadth calculations for
each gymnast at each time point. Digitisation of frontal and left and right sagittal plane
whole-body images in Peak Motus software underpinned the calculation of whole-body
height, upper-body length and lower-body length. The coordinates, which informed the
whole-body height measure were the top of the head, and the left and right posterior heel
position. In accordance with Yeadon (1990)’s mathematical model (Error! Reference
source not found.), division of upper- and lower-body was determined at the hip joint. Using
Microsoft Excel software, coordinates of the respective landmarks informed calculation of
whole-body height, upper-body length and lower-body length; full-body digitising informed
the calculation of whole-body, upper-body and lower-body mass, in accordance with the
procedure outlined by Gittoes et al. (2009). Each of the mass-centred anthropometric growth
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measures were calculated in MathCad software using Yeadon (1990)’s mathematical model;
full details of the procedure were provided in Chapters 2 and 3. In addition to the digitised
coordinate data, Dempster (1955)’s density data were input to the mathematical model to
enable the calculation of mass measurements. The percentage whole-body mass accuracy
which was calculated from force plate measures, in comparison with Yeadon (1990)’s
mathematical model, informed the gymnast-specific adjustment of Dempster (1955)’s
density data set. Customised density data subsequently informed whole-body, upper-body
and lower-body mass calculations. Details of the altering of density data are provided in
Appendix A.16.
For both length and mass calculations, the whole-body, upper-body and lower-body were
inclusive of eight, five and three segments respectively, as detailed in Table 5.2. The
proximal point of the upper-body was the top of the head and the distal point was the midpoint between the hips. For the lower-body, the average hip position and the average
posterior heel position were determined as the proximal and distal lower-body boundaries,
respectively.
Table 5.2. Details of the segments included in whole-body, upper-body and lower-body
length and mass calculations
Segments included
Whole-body
Head, torso, upper arms, forearms, hands, thighs, shanks and feet
Upper-body
Head, torso, upper arms, forearms and hands
Lower-body
Thighs, shanks and feet
NOTE: thighs, shanks, feet, upper arms, forearms and hands are inclusive of left and right
Using the upper- and lower-body length and mass data, ratio measures were calculated to
quantify the length ratio (lower- to upper-body) and the mass ratio (lower- to upper-body).
The calculations, which were undertaken in Microsoft Excel software, used the formulae
outlined in equations 5.1 and 5.2.
Length ratio = (lower-body length/upper-body length) * 100
[5.1]
Mass ratio = (lower-body mass/upper-body mass) * 100
[5.2]
The measures of growth output from the above procedure were inclusive of b-ratio (%),
whole-body height (m), upper-body length (m), lower-body length (m), lower- to upper-
130
body length ratio (%), whole-body mass (kg), upper-body mass (kg), lower-body mass (kg)
and lower- to upper-body mass ratio (%). As the anthropometric growth status measures
were quantified at each time point (initial, mid and final), the meanings of the directional
changes of the ratio measures across time were important to establish to inform
interpretation. Table 5.3 provides interpretation of the positive responses of the three ratio
anthropometric growth status measures (b-ratio, length ratio and mass ratio) across time. A
negative response for each ratio measure across time would elicit the opposite interpretation
to positive responses.
Table 5.3. Interpretation of positive directional responses across time of the three ratio
measures of anthropometric growth status
Anthropometric growth
status measures
B-ratio
Length ratio
Mass ratio
Positive responses across time
Bicristal breadth growing at an increased rate to biacromial
breadth
Lower-body length growing at an increased rate to the upperbody length
Lower-body mass growing at an increased rate to the upperbody-mass
Biomechanical Risk Indicators
The BRI data were processed and analysed in accordance with the full procedure outlined in
Chapter 3. As in Chapter 4, BRIs included posture, DPSI, CoPy and DLPSI. Coordinate and
ground reaction force data were processed and analysed for handstand and forward walkover
trials of each gymnast at each collection. The total number of handstand and forward
walkover trials collected for the gymnastics cohort at initial, mid and final time points was
434, 436 and 453 respectively.
5.2.4
Statistical Analyses
To inform the first chapter question, anthropometric growth status and BRI data were
accumulated across the time points as the analyses were not time-relevant. Insight into the
relationship of b-ratio with the length- and mass-based anthropometric growth status
measures was gained. The influence of the growth status measures which were found to
respond differently to b-ratio on BRIs were additionally explored. To inform the second
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chapter question, which was centred upon the influence of time on anthropometric growth
status and BRIs, the gymnastics cohort responses were divided in accordance with time to
form three sub-categories (i.e. initial, mid and final time points). To align with the findings
from Chapter 3, the handstand and forward walkover skills were appraised separately
throughout the analyses.
Measures of Anthropometric Growth Status
Statistical appraisal of the inter-anthropometric growth status measure relationships was
informed by two-tailed bivariate correlation analyses. A Shapiro-Wilks test of normality
revealed normally distributed data (p>0.05) for all anthropometric growth status measures
other than length ratio, whole-body mass, upper-body mass and lower-body mass (p<0.05).
Pearson’s and Spearman’s Rho bivariate correlations were conducted for all normally
distributed and non-normally distributed growth measures respectively. B-ratio had
previously been established to have a large influence on BRIs (Chapter 4), therefore, the
anthropometric growth status measures which revealed medium or small correlation effects
with b-ratio were of further interest for exploration of the influence on BRIs.
The correlation outputs were appraised through use of effect size interpretations in
accordance with Cohen’s boundaries (Cohen, 1988), as was the procedure used in previous
chapters (Chapter 3 and Chapter 4). Statistical significance values (p) were reported to
support the understanding gained from the r-value outcomes.
Anthropometric Growth Status Measure Influence on Biomechanical Risk Indicators
To address the first chapter question, polynomial regression analyses were conducted to
assess the influence of anthropometric growth status measures carried forward from the
correlation analyses. Using longitudinal data, collected across the three time points,
quadratic regression analyses were conducted, mirroring the statistical approach which
informed Chapter 4 (Section 4.3.2). The statistical effect size (r) outputs were appraised
through use of Cohen’s effect size interpretations (Cohen, 1988) and were further informed
through use of p-values.
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Time Influence on Anthropometric Growth Status Measures and Biomechanical Risk
Indicators
The influence of time (initial, mid and final collections) on anthropometric growth status
measures and BRIs was investigated through use of repeated measures ANOVA analyses
for the normally distributed variables (O'Donoghue, 2012) and Friedman’s tests for the nonnormally distributed data (Meeuwisse et al., 2007). Mauchly’s test was used to assess
sphericity for the repeated measures ANOVA output, the findings of p>0.05 ensured
homogeneity of variance and covariance was assumed. The Greenhouse-Geisser procedure
to correct probability was used when sphericity was violated.
Partial eta square (n2) data were appraised to quantify the size of the effect of time on each
growth measure and each BRI for the repeated measures ANOVA analyses. In accordance
with Cohen’s boundaries, large, medium and small n2 were denoted as >0.14, >0.06 and
>0.01, respectively (McLeod et al., 2011). Nonparametric effect sizes from Friedman’s test
outputs were interpreted through use of Kendall’s coefficient of concordance (W) with large,
medium and small effect boundaries set at >0.5, >0.3 and >0.1, respectively (Kraska-Miller,
2013). Statistical significance outputs (p-values) were reported to further evaluate the
repeated measures outputs. For any anthropometric growth status or BRI measures identified
to be significantly influenced by time, Friedman’s post-hoc tests were conducted between
initial and mid, mid and final and initial and final to provide further understanding of the
time points between which significant differences of the respective measures were found.
For each of the statistical tests to explore the influence of time on growth measures and BRIs,
mean mid-point data for the gymnastics cohort was imputed for the gymnast who had
‘missing data’ at the mid collection point. The method was selected to avoid distortion of
the average group data as the actual values for the gymnast were unknown (Blaszczyk et al.,
2003). Although the mean substitution approach has received concern as it results in the
reduction of standard deviation and variance (Horak, 2006; Cotoros and Baritz, 2010), this
limitation has minimal implication in the respective research as the total amount of missing
data was 2.7% of the overall group data (n = 36). In accordance with Blaszczyk and
Michalski (2006), a small amount of missing data is defined as 20% or less, thus, the missing
data quantity in the respective study was substantially less than this threshold identified by
Blaszczyk and Michalski (2006).
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5.3
5.3.1
Results
Growth Measures across Time
Initial understanding of the anthropometric growth status measures of the female artistic
gymnastics cohort at initial, mid and final data collections was gained through the appraisal
of descriptive mean (SD) data reported in Table 5.4.
Table 5.4. Mean (SD) growth measure data for the gymnastics cohort at initial (n = 12), mid
(n = 11) and final (n = 12) collection time points
B-ratio (%)
Whole-body height (m)
Upper-body length (m)
Lower-body length (m)
Length ratio (%)
Whole-body mass (kg)
Upper-body mass (kg)
Lower-body mass (kg)
Mass ratio (%)
Initial
78.2 (5.1)
1.41 (0.12)
0.68 (0.07)
0.82 (0.08)
121.5 (8.9)
38.3 (12.9)
24.1 (7.8)
14.1 (5.2)
58.0 (6.6)
Mid
78.7 (6.0)
1.46 (0.09)
0.70 (0.07)
0.87 (0.06)
124.4 (10.1)
41.4 (11.8)
25.9 (7.3)
15.5 (4.8)
59.5 (7.7)
Final
79.5 (6.0)
1.42 (0.13)
0.68 (0.08)
0.87 (0.06)
128.5 (15.8)
42.7 (12.7)
26.6 (7.4)
16.2 (5.4)
60.2 (5.3)
Each anthropometric growth status measure was recognised to increase in value between the
initial and final collections, providing indication of the physically developing nature of the
gymnasts over the study duration. B-ratio values of less than 100% at each time point were
indicative of the cohort’s broad shoulders (biacromial breadth) relative to pelvic breadths
(bicristal breadth). An increase in b-ratio data from initial to final collections demonstrated
pelvis breadth increased relative to the shoulder breadth; a mean ratio increase of 1.3% was
identified from initial to final collections for the gymnasts.
At a group level, whole-body height was found to increase between the initial and final
collections by a mean of 0.01 m, while quantification of the whole-body mass revealed a
mean increase of 4.4 kg. As the most frequently used measures of growth in previous
literature and, to the researcher’s knowledge, applied practice, individual whole-body height
and whole-body mass initial and final collection data were explored to provide insight into
the extent to which the group findings were underpinned by individual responses. Figure 5.1
and Figure 5.2 illustrate the increase of each of the respective measures for each of the
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gymnasts within the cohort from the initial to final data collection. The appraisal of
individual whole-body height and mass data provided confirmation that each of the gymnasts
experienced anthropometric growth throughout the testing period. The individual findings
subsequently supported the use of the respective cohort to inform the respective research and
to subsequently underpin the development of knowledge of a physically developing
population.
Whole-Body Height (m)
1.8
1.6
1.4
1.2
1
0.8
Initial
0.6
Final
0.4
0.2
0
1
2
3
4
5
6
7
8
9
10
11
12
Gymnasts
Figure 5.1. Whole-body height of each gymnast at initial and final data collections.
Whole-Body Mass (kg)
70
60
50
40
30
Initial
20
Final
10
0
1
2
3
4
5
6
7
8
9
10
11
12
Gymnasts
Figure 5.2. Whole-body mass of each gymnast at initial and final data collections.
As illustrated in Figure 5.1, each of the gymnasts increased their whole-body height from
initial to final collections. Of the 12 gymnasts, 58% experienced an increased height in
excess of 6 cm over the average 12 month period, with the remainder of the gymnasts
increasing in whole-body height between 0.34 cm and 4.57 cm; the greatest height increase
135
was 6.8% of initial height. The increases in whole-body mass were found to range between
1.7 kg to 13.5 kg across the gymnastics cohort. The greatest increase in whole-body mass
was 40% of initial mass, from 28.8 kg to 40.3 kg.
With length ratio values in excess of 100% at each time point, the group were identified to
have greater lower-body lengths in relation to their upper-body lengths. The increased mean
ratio from initial to final collections was indicative of an overall 7% increase in lower-body
length, relative to the upper bodies of the group. The mean upper-body length of the group
was stagnant over the 12 month (average) period, with SDs of 0.07 m and 0.08 m at initial
and final collection points, respectively. At final collections, the group mean for lower-body
length was identified to be 0.05 m greater than at the initial collection; SD were 0.08 m
(initial) and 0.06 m (final).
Opposing the length ratio findings, the group data exhibited a greater upper-body mass in
relation to the lower-body, with mean mass ratio values of less than 100%. An increase in
mass ratio data between initial and final collections revealed the lower-body mass to increase
relative to the upper-body by 2.2%. Mean upper-body mass was found to increase by 2.4 kg
for the overall group across the 12 month period (approximately), with SDs of 7.8 kg at
initial collection and 7.4 kg at the final collection point. A 2.0 kg increase was found for the
whole group lower-body mass with SDs of 5.2 kg and 5.4 kg at the initial and final
collections respectively. Table 5.4 identified the highest growth measure values for b-ratio,
whole-body height, lower-body length and mass ratio to be at the mid collection point.
5.3.2
Growth Measure Relationships
To inform the first chapter question, bivariate linear correlation analyses were undertaken
for b-ratio and each of the anthropometric growth status measures. The correlation analyses
outputs revealed large effect sizes for the relationships between b-ratio and six of the eight
measures of anthropometric growth status (Table 5.5). B-ratio was shown to explain up to
54.8% of the variance of the respective anthropometric growth status metrics (lower-body
mass r2 = 0.55). The greatest relationships with b-ratio were identified for lower-body mass
(r = 0.74), whole-body mass (r = 0.71) and upper-body length (r = 0.70). All relationships
were positive with the exception of b-ratio and length ratio, for which a medium negative
relationship was revealed (r = -0.36). The negative relationship indicated that those
gymnasts with a smaller pelvis breadth relative to their shoulder breadth, additionally had
greater lower-body length relative to their upper-body; the gymnasts whose pelvis breadth
136
was closer to their shoulder breadth also tended to have a lower-body length that was closer
in length to their upper-body. The only measure to have a non-significant relationship with
b-ratio (p>0.05) was mass ratio; the linear relationships between b-ratio and each other
anthropometric growth status measure were found to be significant (p<0.05).
Table 5.5. Effect size and significance outputs for linear correlations between B-ratio and
anthropometric growth status measures
B-ratio
r
Whole-body height
0.64
Upper-body length
0.70
Lower-body length
0.54
Length ratio
-0.36
Whole-body mass
0.71
Upper-body mass
0.68
Lower-body mass
0.74
Mass ratio
0.26
Note: dark grey shading indicates large effect sizes (r>0.5), light grey
p
0.00*
0.00*
0.00*
0.04*
0.00*
0.00*
0.00*
0.13
shading represents
medium effect sizes (r>0.3), small effect sizes are not shaded (r>0.1); * p<0.05
Two anthropometric growth status measures, length ratio and mass ratio, were found to have
medium (r>0.3) and small (r>0.1) relationships with b-ratio for the gymnastics cohort. Bratio was identified to explain 12.9% (r2 = 0.13) and 6.8% (r2 = 0.07) of the variance of the
respective growth measures. As two anthropometric growth status measures were found to
elicit different growth trends in the gymnastics cohort, in comparison with b-ratio, the need
for further exploration of influence of length and mass ratio on BRIs was warranted.
5.3.3
Biomechanical Risk Indicators across Time
Previous exploration of BRIs within the female artistic gymnastics population was informed
by cross-sectional data (Chapters 3 and 4); the biomechanical risk of the gymnastics cohort
is therefore yet to be appraised across an extended period of time. Descriptive mean and SD
data for the handstand and forward walkover BRIs are presented in Table 5.6. The respective
data are reported for the whole gymnastics cohort at initial, mid and final time points.
137
Table 5.6. Descriptive mean (SD) BRI data for the gymnastics cohort at each collection
(initial, mid and final) for the handstand and forward walkover skills
Initial
Mid
Final
-1.9 (5.9)
17.05 (12.87)
76.71 (19.01)
12.77 (6.21)
-4.6 (5.9)
22.74 (13.95)
62.41 (13.80)
13.14 (8.44)
-2.0 (6.2)
30.71 (19.55)
61.80 (17.87)
12.22 (5.23)
-13.1 (9.1)
43.97 (16.20)
106.22 (40.81)
17.03 (5.69)
-14.3 (6.2)
48.19 (14.70)
100.03 (64.91)
18.86 (8.91)
-9.8 (6.6)
51.74 (19.66)
67.65 (31.03)
19.80 (10.57)
Handstands
Posture (°)
DPSI
CoPy (mm)
DLPSI
Forward Walkover
Posture (°)
DPSI
CoPy (mm)
DLPSI
The mean data presented in Table 5.6 and Table 5.4 identified a whole group increase in
biomechanical risk from initial to final collections for each BRI in the handstand and the
forward walkover. DLPSI in the handstand and posture in the forward walkover were the
only BRI which did not comply with the respective trend. Mean data for DLPSI (handstand)
was found to decrease by 0.55 (4.3% relative change) and posture (forward walkover)
increased by 3.3° (25.5% relative change) from initial to final collections. In the handstand
skill, mean whole group posture decreased by 0.1° between initial and final collections (SD
= 5.9° and 6.2° respectively). For DPSI, the greatest biomechanical risk increase was
identified in the handstand skill, with a mean increase of 13.66 (initial and final SD = 12.87
and 19.55, respectively) in comparison with a 7.77 increase (initial and final SD = 16.20 and
19.66, respectively) in the forward walkover for the gymnastics cohort. Decreased whole
group CoPy mean data were greater in the forward walkover (38.57 mm, 36.3% relative
change) than the handstand skill (14.91 mm, 19.4% relative change). Biomechanical risk for
DLPSI was identified to increase for the forward walkover, with a mean whole group
difference of 2.77 (SD = 5.69 at initial collections and 10.57 at final collections); a 16.3%
relative change was identified.
Greater biomechanical risk was displayed at the mid collection point in comparison with the
initial and final collection data for three BRIs: posture in the handstand and the forward
walkover and DLPSI in the handstand. Mean data for posture in the handstand skill was 2.6°
less for the mid collection than the initial or final collection; the minimum mean difference
for forward walkover posture was 1.2° between mid-collection data and initial and final
138
collections. Increased DLPSI data of 0.37 were identified at mid-point for the handstand
skill, in comparison with initial and final data.
5.3.4
Growth Measure Influences on Biomechanical Risk Indicators
Quadratic regressions were used to investigate the influence of b-ratio, length ratio and mass
ratio on BRIs. The selection of the respective growth measures was informed through the
finding of medium and small effect size relationships between b-ratio with length ratio and
mass ratio. R-value outputs from the quadratic regressions were assessed in accordance with
Cohen’s boundaries, as utilised in previous chapters (Chapters 3 and 4) and statistical
significance (p-values) were additionally presented in Table 5.7.
Table 5.7. Quadratic regression effect size (r) and significance (p) outputs for b-ratio, length
and mass measures on BRIs
r
B-ratio
p
Length ratio
r
p
Mass ratio
r
p
Handstand
Posture
0.38
0.09
0.16
0.66
0.13
0.77
DPSI
0.70
0.00*
0.23
0.43
0.36
0.11
CoPy
0.31
0.19
0.00
0.99
0.45
0.03*
DLPSI
0.22
0.45
0.08
0.91
0.00
1.00
Forward Walkover
Posture
0.19
0.54
0.31
0.20
0.18
0.60
DPSI
0.67
0.00*
0.06
0.94
0.48
0.02*
CoPy
0.21
0.48
0.29
0.25
0.28
0.28
DLPSI
0.04
0.97
0.43
0.04*
0.40
0.06
Note: dark grey shaded values indicate large effect sizes (r>0.5), light grey shading
represents medium effects (r>0.3) small effect are not shaded (r<0.1), * p<0.05
B-ratio was found to have a large effect on DPSI for both the handstand and the forward
walkover, accounting for 49.6% (r2 = 0.50) and 45.1% (r2 = 0.45) of the DPSI variance in
the skills respectively. The influence of b-ratio on DPSI in both skills was recognised to be
statistically significant (p<0.05). Statistical significance was additionally reported for the
influence of length ratio on DLPSI in the forward walkover (p<0.05), mass ratio on CoPy
in the handstand (p<0.05) and mass ratio on DPSI in the forward walkover (p<0.05).
139
The two large effects from the quadratic regression analyses (b-ratio and DPSI in the
handstand and forward walkover) were further explored through scatter diagrams (Figure
5.3). The respective figure enabled further understanding of the way in which the b-ratios of
female artistic gymnasts contributed to DPSI in the fundamental skills.
r = 0.70
a)
r = 0.67
b)
Figure 5.3. Quadratic regression analyses for b-ratio and DPSI in a) the handstand and b) the
forward walkover; each data point represents the DPSI mean of all trials for a single gymnast
at one data collection point (n = 35).
DPSI tended to be greater in magnitude for the forward walkover in comparison with the
handstand, however, common trends of the influence of b-ratio on DPSI were shown to exist
across the two skills (Figure 5.3). The gymnasts who had smaller pelvic breadths in relation
to their shoulders (lower b-ratio) were recognised to have greater general stability, indicated
by lower DPSI data.
140
5.3.5
Time Influence on Growth Measures and Biomechanical Risk Indicators
Descriptive mean (SD) data provided initial understanding of the trends of growth measures
and BRIs across the three data collection points (referred to as ‘time’). The ratio
anthropometric growth status measures were each found to increase from initial to final
collections. Relative percentage increases across the study period for b-ratio, length ratio and
mass ratio were 1.7%, 5.8% and 3.7%, respectively. The descriptive findings subsequently
revealed bicristal breadth, lower-body length and lower-body mass to increase relative to
biacromial breadth, upper-body length and upper-body mass, respectively. BRI descriptive
data indicated that, other than DLPSI in the handstand and posture in the forward walkover,
whole group biomechanical risk increased from initial collections to final collections.
Statistical understanding of the influence of time was of interest as a result of the findings
from descriptive data appraisal across time. The repeated measures ANOVA and Friedman’s
test effect size and statistical significance outcomes were presented in Table 5.8.
Table 5.8. Effect size (n2 and W) and statistical significance outputs (p) for the influence of
time on growth measures and BRIs for each skill
2
n
Time
W
p
Anthropometric Growth Measures
B-ratio
0.15
0.16
Length ratio
0.06
0.47
Mass ratio
0.06
0.51
Handstand
Posture (°)
0.10
0.31
DPSI
0.40
0.01*
DLPSI
0.01
0.92
CoPy (mm)
0.23
0.06
Forward Walkover
Posture (°)
0.11
0.27
DPSI
0.18
0.11
DLPSI
0.02
0.78
CoPy (mm)
0.15
0.17
2
Note: dark grey shading indicates large effect sizes (n >0.14), light grey shading represents
medium effects (n2>0.06; W>0.3), small effects are not shaded (n2<0.01; W>0.1); * p<0.05
Interpreted using parametric partial eta square values and Kendall’s coefficient of
concordance, time was identified to have a large influence on b-ratio (n2>0.14), CoPy in the
141
handstand (n2>0.14) and DPSI in the forward walkover (n2 >0.14). Posture for both the
handstand and forward walkover was found to be influenced by time to a moderate level
(n2>0.06). The only nonparametric effect size, for which time was identified to have a
medium effect, was for DPSI in the handstand (W>0.3).
Statistical analyses of the data revealed a single statistically significant result. The statistical
significance of time on DPSI in the handstand skill was found to be less than 0.05, indicating
significance between time points across the initial, mid and final collections. To explore
where the differences lie between the time points, a post-hoc test was conducted. As a result
of the deviation of handstand DPSI data from a normal response, a Friedman test was
undertaken to investigate the three time associations: initial and mid, mid and final and initial
and final. Kendall’s coefficient of concordance (W) and Friedman’s test significance (p)
outputs for the post-hoc test are reported in Table 5.9Error! Not a valid bookmark selfreference..
Table 5.9. Friedman's post-hoc test outputs for handstand DPSI
Time
W
Initial & Mid
0.25
Mid & Final
0.44
Initial & Final
0.44
Note: light grey shading represents medium effect sizes (W>0.3), no
small effect sizes (W>0.1); * p<0.05
p
0.08
0.02*
0.02*
shading represents
Interpretation of Kendall’s coefficient of concordance revealed medium effects for both mid
and final and initial and final times on DPSI in the handstand, where W = 0.44 for both; the
respective findings were significant (p>0.05). Initial and mid time points were revealed to
have a small and non-significant contribution to DPSI in the handstand (W>0.1; p>0.05).
5.4
Discussion
The aim of the chapter was to quantitatively investigate the contribution of longitudinal
changes associated with anthropometric growth on biomechanical risk indicators in a female
artistic gymnastics population. To address the chapter aim, b-ratio measures, along with
whole-body, segmental and ratio measures of length and mass were obtained for a female
gymnastics cohort at three time points (initial, mid and final). To the best of the researcher’s
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ability, the collections were conducted at zero, six and 12 month intervals, in accordance
with contemporary injury screening approaches (Conley et al., 2014; Roberts et al., 2014)
and previous physical development-centred gymnastics research (Bradshaw et al., 2014).
Although logistical reasons prevented all of the mid-point collections complying with the
predefined six month time point, the final collections across the gymnastics cohort were
undertaken at a mean (SD) of 12 (0.5) months from initial collections. The prominent role
of the initial and final collection data alleviated deviation between mid-collections as a
concern within the respective research. In addition to anthropometric growth measures, BRI
data were obtained for each gymnast at the outlined collection points. Investigation of
anthropometric growth and its contribution to CSI risk, in addition to the influence of time
on growth measures and BRIs, offered essential contributions to the overall thesis aim. The
chapter findings were additionally valuable to the overall research purpose through the
developed contribution to knowledge of the role of anthropometric growth and time to injury
screening.
Focus of the chapter on anthropometric growth was informed by the findings from Chapter
4, in which growth status was highlighted to be the most pertinent mechanism of physical
development for CSI biomechanical risk. Through the exploration of the influence of b-ratio
on BRIs in Chapter 4, the dynamic, non-uniform nature of growth was considered to provide
a potential explanation for the varying whole-body and segmental BRI trends. Subsequently,
the relationship between b-ratio and whole-body measures of height and mass, in addition
to upper- and lower-body lengths and masses, were explored. In line with its associations
with injury susceptibility, intra-individual non-uniform growth, as highlighted by Williams
et al. (2012), was further explored. The use of ratio measures for the investigation of
anthropometric growth have been inclusive in previous research studies, such as Damsgaard
et al. (2001); Malina et al. (2004); Claessens et al. (2006). Measures of lower-to upper-body
length and mass were subsequently explored to provide insight into proportional
anthropometric growth measures.
Each of the anthropometric growth measures for the group of gymnasts were found to
increase from initial to final time points, demonstrating the physically developing nature of
the cohort. As standard measures of growth (Rogol et al., 2000), individual whole-body
height and mass between initial and final collections were additionally appraised to confirm
the group trend was reflective of the anthropometric growth trend of each gymnast.
Verification that each of the gymnasts experienced anthropometric growth across the
longitudinal testing was necessary to enable the adequate address of the chapter questions.
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Further exploration of the anthropometric growth of the gymnastics cohort revealed the
gymnasts to have greater lower-body lengths in comparison with their upper-bodies, and
greater upper-body masses and breadths in comparison with their lower-bodies. The length,
mass and b-ratio proportional trends aligned with previous research based on athletic and
gymnastics-specific populations (Baxter-Jones et al., 2002; Malina et al., 2004). Empirical
data which demonstrated the intra-individual non-uniform growth of body segments and the
inter-individual non-linear growth across time was subsequently attained.
Further appraisal of the mean mid-point anthropometric growth data across the gymnastics
cohort revealed data values to be greatest at the respective time point for b-ratio, whole-body
height, lower-body length and mass ratio, in comparison with initial and final time points.
The findings may have been reflective of the non-linearity of growth, however, it is more
probable that the omission of a gymnast at the mid collection point may explain the
respective whole-group growth findings. Quantification of each of the measures for the
specific gymnast at initial and final collection revealed b-ratios of 76.9% (initial) and 77.9%
(final), whole-body height values of 1.23 m (initial) and 1.27 m (final), lower-body lengths
of 0.70 m (initial) and 0.76 m (final) and mass-ratio percentages of 58% (initial) and 60.9%
(final). Subsequently, the inclusion of the gymnast’s mid-point data would have reduced the
each of the mean anthropometric growth status values for the overall gymnastics group
cohort.
As b-ratio was determined to have a large influence on BRIs in the previous chapter,
correlations between the respective breadth ratio measure and whole-body, segmental and
ratio measures of length and mass were explored. The correlation analyses were undertaken
to identify the anthropometric growth measures which did not have relationship with b-ratio
to a large extent, subsequently indicating the potential differing influence on BRIs. To
address the first chapter question, it was anticipated that b-ratio would have large
relationships with each of the identified anthropometric growth status measures. The
statistical analyses revealed large effect outputs between b-ratio and 75% of the
anthropometric growth status measures, with medium and small effects for the relationships
between b-ratio and lower to upper-body length ratio (length ratio) and lower to upper-body
mass ratio (mass ratio). The findings were subsequently suggestive that the expression of
lower and upper-body length and mass as ratios revealed proportional differences which
were omitted when expressed in isolation. Indication that the underlying contribution of
physical development to CSI biomechanical risk may be centred on the proportional
anthropometric growth of the gymnasts was subsequently provided. Investigation of
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segmental inertial parameters in the female artistic gymnastics population may enable further
exploration of the contribution of growth to CSI biomechanical risk.
Appraisal of group BRIs at initial, mid and final time points identified increased
biomechanical risk between initial and final collections for each BRI, with the exception of
DLPSI in the handstand skill and posture in the forward walkover. As length ratio and mass
ratio were identified to differ from b-ratio trends in the gymnastics cohort, understanding of
the influence of each proportional growth measure on BRIs was necessary to explore. To
address the first research question and inform the chapter aim, quadratic regressions analyses
informed the statistical quantification of the influence of each ratio measure on isolated BRI
which were independent of time. Quadratic regression outputs revealed seven medium
effects in total, across the three growth measures, 57% of which were identified for the mass
ratio, and 14% for both the b-ratio and the length ratio. The only two large effects of
anthropometric growth status measures on BRIs, were highlighted for the influence of bratio on DPSI in the handstand and forward walkover; each of the large effects were
supported by a significance level of p<0.05. The statistical outcomes clearly evidenced the
distinguished influence of the bicristal breadth in relation to the biacromial breadth on CSI
risk, with particular emphasis on the contribution to DPSI. Gymnasts with larger b-ratios
(increased size of bicristal ratio in relation to biacromial ratio) were found to have lower
general stability than the gymnasts with a smaller b-ratio. The gymnasts with smaller b-ratios
had a reduced bicristal breadth in relation to biacromial breadth and consequently, were
speculated to have a lower CoM in inverted stance, with closer proximity to the base of
support. The reduced distance of the CoM in relation to the base of support may provide an
explanation for the increased whole-body stability experienced by gymnasts with smaller bratios throughout the handstand and forward walkover. Exploration of the centre of mass
and its alteration with longitudinal growth was therefore suggested to increase understanding
of the influence of b-ratio on DPSI. Previous research which has quantified anthropometrical
measurements of female gymnasts has identified an ‘ideal’ body shape for top-level
gymnasts to be characterised by narrow hips with relatively broad shoulders (Claessens and
Lefevre, 1998; Siatras et al., 2009). In addition to the respective anthropometry being
favoured from a performance perspective, the respective research provided novel support for
smaller b-ratios being advantageous from an injury perspective.
The need for comprehension of the influence of time on the growth and BRI data sets, was
informed by previous research which identified physical development to be a dynamic
process (Rogol et al., 2000), which requires longitudinal monitoring (Tanner, 1962; Beunen
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and Malina, 2008). Understanding of the contribution of longitudinal growth within the
predisposed gymnastics population has been under-represented in previous literature,
therefore, empirical investigation within the respective research was valuable in developing
initial knowledge of the influence of time on anthropometric growth and BRIs. To suffice
the requirement for extended insight into the temporal influence on growth measures and
BRIs, factorial repeated measures ANOVAs (or Friedman’s tests) were undertaken to inform
the final chapter question. Large effects were identified for b-ratio, CoPy in the handstand
and DPSI in the forward walkover, with a single significant output reported for DPSI in the
handstand skill. The disparity of the influence of time on BRIs within the handstand and
forward walkover provided support for the previous findings that the handstand did not
sufficiently reflect BRIs within the forward walkover skill (Chapter 3, Section 3.3.3). The
conclusion that CSI screening should be inclusive of both fundamental skills was reinforced.
The altering BRIs across the initial, mid and final time points may be reflective of the
adaptation of the nervous system to the commonly rapid dynamics of motion change
consequent to periods of increased growth (McLester and Pierre, 2007). The explanation is
speculative due to the consideration of anthropometric growth status in the respective
research, however, understanding of the influence of the process of growth may assist in
developing further insight into the contribution of growth rate to BRIs.
5.5
Chapter Summary
The respective research developed unique insights into anthropometric growth and
biomechanical risk of competitive female gymnasts over a longitudinal period. The large
influence of bicristal breadth in relation to biacromial breadth on BRIs highlighted the need
for consideration of the proportional development of gymnasts’ bodies within future CSI
research. The performance-based ‘ideal’ narrow bicristal breadth and broad biacromial
breadth which has been previously observed, was supported by findings of increased wholebody stability for gymnasts with small b-ratios. The respective chapter successfully
contributed to Thomis et al. (2005)’s suggestion of the need for longitudinal research in the
young gymnastics population, through which large influences of time on b-ratio, CoPy in
the handstand skill and DPSI in the forward walkover skill was identified. In order to
accommodate the evidenced non-linear growth over time, the monitoring of female
gymnasts over an extended period of time was advocated for future research and CSI
screening.
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CHAPTER 6 - PHYSICAL DEVELOPMENT CONTRIBUTIONS TO
RISK INDICATORS: A BIOMECHANICAL PERSPECTIVE
6.1
Introduction
Empirical understanding of the process of growth in previous gymnastics-based research
has, to the researcher’s knowledge, only considered anthropometric growth in reference to
body size (e.g. Camargo et al. (2014)). In accordance with Tanner (1962), in addition to the
size of growth, the shape of growth is necessary to consider in relation to injury development.
Previous research has demonstrated the asynchronous development of body proportions of
gymnasts (Ackland et al., 2003); in addition, unique b-ratio anthropometric growth patterns
have been evidenced for the female gymnastics population (Claessens and Lefevre, 1998;
Siatras et al., 2009). Time was found to have a large influence on proportional
anthropometric growth (b-ratio) in Chapter 5 (n2 = 0.15). The respective findings are
suggestive of the need for further understanding of the influence of proportional growth of
body segments (termed morphological growth rate, MGR) on biomechanical risk in female
artistic gymnasts. Consideration of the shape of growth (MGR) of female gymnasts may be
prominent to understanding the contribution of the process of growth to CSI biomechanical
risk and assisting the development of successful screening programmes.
Inertial changes have been demonstrated to underpin the process of growth in a number of
populations (Jensen and Nassas, 1988; Cappozzo and Berme, 1990; Jensen, 1993; Richards
et al., 1999; Hawkins and Metheny, 2001; Ackland et al., 2003). The mechanical
components of gymnastics skills have been identified to alter in accordance with rapid
increases in BSIP associate with growth (e.g. Ackland et al. (2003)). As two prominent
BSIP-driven measures with growth associations (Jensen and Nassas, 1988; Cappozzo and
Berme, 1990), understanding of I and CoM of physically developing female gymnasts may
provide increased insight into the influence of anthropometric growth on biomechanical
alterations in fundamental skill mechanics.
The potential contributions of BSIP to longitudinal anthropometric growth and BRIs may be
highly valuable in explaining the prominent influence of anthropometric growth which has
been evidenced in Chapters 4 and 5. To gain understanding of the preliminary success of
integrating experimental findings into screening practice, the evaluation of longitudinal
growth-related changes in the female artistic gymnastics population is necessary. As injury
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screening is typically conducted in a cross-sectional manner (e.g. Dennis et al. (2008)), an
evaluation of the extent to which the respective longitudinal-based research findings may
translate to inform ‘one-off’ measurements may provide novel and highly valuable advances
to current injury screening approaches.
6.1.1
Chapter Aim
The aim of the chapter is to gain quantitative understanding of the contribution of inertial
changes associated with the growth process to biomechanical risk in female artistic
gymnasts.
6.1.2
Chapter Questions
CQ 6.1 How does anthropometric growth which is underpinned by inertial changes
influence biomechanical risk in female artistic gymnasts?
From a performance perspective, narrow pelvic breadths relative to broad shoulder breadths
have been favoured for female gymnasts (Claessens and Lefevre, 1998; Siatras et al., 2009).
The respective trend opposes the typical process of growth within the general population, as
evidenced by Malina et al. (2004). As time has been identified to influence b-ratio to a large
extent (Chapter 5, Section 5.3.5), it was speculated that the process of b-ratio growth may
be a contributing factor to the high prevalence of CSI within the gymnastics population.
Investigation of the inertial changes which have been associated with growth (Jensen and
Nassas, 1988; Cappozzo and Berme, 1990) may further understanding of why
anthropometric growth status has been evidenced to influence certain BRI in the handstand
and forward walkover skills (Chapters 4 and 5).
CQ 6.2 To what extent can inertial responses associated with anthropometric growth
inform CSI screening?
Initial evaluation of the translation of longitudinal-based knowledge (from CQ 6.1) to inform
cross-sectional screening may provide preliminary evidence of the transfer of research-based
understanding to injury screening practice. Anticipated findings of the need for longitudinalbased injury screening would provide consistency with performance screening, which
identified problems with the collection of cross-sectional anthropometric measurements to
reflect dynamic growth (Till et al., 2013).
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6.2
6.2.1
Methods
Participants and Study Design
The cohort of 12 competitive female artistic gymnasts was consistent with Chapter 5. The
gymnasts each attended three separate collections (initial, mid and final), with the exception
of one gymnast who was present for an initial and final collection but not the mid-point
collection. The temporal details of the collections and further information about the
gymnastics cohort can be located in Chapter 5 (Section 5.2.1). Each of the data collections
were conducted at Cardiff Metropolitan University subsequent to granted ethical approval
from the University’s ethical committee.
As within Chapter 4 and 5, group-based analyses informed the development of empirical
knowledge. Individual analyses of the gymnastics cohort were undertaken for the evaluation
of the extent to which findings from the address of chapter question 6.2 may translate to
inform CSI screening practice. The need for screening to be undertaken on an individual
basis was evidenced in Chapter 3, in agreement with previous research, including Gittoes
and Irwin (2012); DiFiori et al. (2014).
6.2.2
Data Collection
Measures of Growth
The interactive study used b-ratio as the single measure of growth, taken forward from
Chapter 5 findings. As demonstrated in Chapter 5, b-ratio status calculation required image
capture in a specific position of stance in accordance with the image-based inertia approach
outlined by Gittoes et al. (2009). The same inertia images informed the evaluation study
within the respective chapter; BSIP measures (whole-body and segmental I and CoM) were
calculated from the obtained inertia images at each data collection. Further details of the
image capture approach were reported in Chapters 2 and 3.
Biomechanical Risk Indicators
At each collection, the gymnasts performed a maximum of 20 handstand and 20 forward
walkover skills during which, ground reaction force and coordinate data were
simultaneously collected. A Kistler force plate (sample rate: 1000 Hz) allowed for ground
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reaction force capture, while a CODA motion analysis system comprising four Cx1 units,
two strobe units, 48 CODA motion markers and eight CODA motion drive boxes, enabled
the capture of coordinate data for the duration of the skill performances. The collected
kinematic and kinetic data informed the BRIs which underpinned both the interactive and
evaluation studies.
6.2.3
Data Processing and Analysis
Growth Rate
B-ratio percentage values were obtained for each gymnast, at each time point (initial, mid
and final), providing discrete growth values at three specified instances across a 12 month
period (average). In order to assess the rate of growth, advancing from the longitudinal
anthropometric growth status measure included in Chapter 5, chronological age (calculated
as the difference between the gymnasts’ date of birth and date of attendance) and b-ratio
status at final and initial collections were inputted into the growth rate (GR) equation (6.1).
GR = (final growth status – initial growth status) x (-1)
(final chronological age – initial chronological age)
[6.1]
Morphological Growth Rate
In opposition to the calculation of GR, for which the directional component of b-ratio growth
was negated, the calculation of morphological growth rate (MGR) allowed for the expression
of directional growth, i.e. whether the bicristal develop at an increased rate to the biacromial
or the other way around. To address the first chapter question, the equation used for the
determination of GR was subsequently adapted to calculate MGR outputs (6.2).
MGR = (final growth status – initial growth status)
(final chronological age – initial chronological age)
[6.2]
Growth of the bicristal breadth at an increased rate to the biacromial breadth (Figure 6.1)
was termed positive MGR, whereas growth of the biacromial breadth at an increased rate to
the bicristal breadth was identified as negative MGR (Figure 6.2).
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Figure 6.1. Frontal plan images of a positive MGR gymnast at initial (left) and final (right)
time points, with indication of biacromial and bicristal positions by way of white dots.
Figure 6.2. Frontal plan images of a negative MGR gymnast at initial (left) and final (right)
time points, with indication of biacromial and bicristal positions indicated by white dots.
Growth Rate and Morphological Growth Rate Groupings
Values of GR and MGR were obtained for each of the 12 gymnasts; the GR outputs are
presented in Figure 6.3 and the MGR data are reported in Figure 6.4. To further explore the
cohort in respect to GR and MGR, sub-groups were formed through analysis of the whole
group data; the approach was selected in line with previous research, such as Ackland et al.
(2003). To the researcher’s knowledge, no standard division approach for a cohort divided
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according to GR has been established, therefore, the cohort was divided using two
approaches, the outcomes of which were appraised to determine the final division approach.
Initial sub-division of the cohort in accordance with GR was undertaken using an arbitrary
criterion, in line with previous approaches (Ackland et al., 2003). Appraisal of the cohort
GR data informed the divisional GR to be 0.1 b-ratio %/months. Gymnasts who had a GR
of less than 0.1 b-ratio %/months were grouped as ‘low’ GR, and the gymnasts with a GR in
excess of 0.1 b-ratio %/months were established as ‘high’ GR gymnasts (Figure 6.3). The
division criteria lead to six gymnasts being categorised into the low and high GR sub-groups.
a)
b)
Figure 6.3. Growth rate division approach (solid line); a) high growth rate group n = 6 and
low growth rate group n = 6, the dotted line is indicative of the 0.1 cut-off; b) high growth
rate group n = 5 and low growth rate group n = 7, the dotted lines represent inflection.
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Secondary division of the gymnastics cohort was informed through visual of the growth rate
trend from which the inflection of the data points was evidenced. Division according to the
clear inflection which was exhibited (Figure 6.3), determined seven gymnasts as having a
‘low’ GR and five as having a ‘high’ GR. As the latter divisional approach was informed
through data trends rather than being arbitrary, further analyses utilised the latter GR division
approach. Of the gymnastics cohort, three gymnasts displayed a negative MGR, while nine
presented a positive MGR. The MGR sub-groups were presented in Figure 6.4.
Figure 6.4. Morphological growth rate division approach (solid line), negative
morphological growth rate group n = 3 and positive morphological growth rate group n = 9.
Biomechanical Risk Indicators
The BRIs of interest for exploration in the respective chapter were consistent with those
included in the previous chapters: posture, DPSI, CoPy and DLPSI, for the double support
phase of the handstand and the forward walkover skills. The full details of the procedure
through which handstand and forward walkover coordinate and ground reaction force data
were processed and analysed to produce BRI measures are reported in Chapter 3.
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Body Segment Inertial Parameters
As described in Chapter 3 (Sections 3.2.3 and 3.2.4), the process through which gymnastspecific BSIP were calculated firstly required the digitisation of standing frontal and left and
right sagittal plane images using Peak Motus software. The coordinate outputs from the
digitisation process sufficed the BSIP analyses for the interaction study; the bicristal and
biacromial breadths were calculated using the respective coordinate data in a Microsoft
Excel spreadsheet. The coordinate output data from the digitisation process was
subsequently input into a Mathcad programme, informed through Yeadon (1990)’s inertia
model and combined with Dempster (1955)’s density values. Full details of the procedure
through which whole-body and segmental calculations of mass (kg), distance of vertical
centre of mass from proximal point (m), length (m) and I about centre of mass in the
transverse axis (Iy, kg.m2) were provided in Chapter 3 (Section 3.2.4). Accuracy testing of
the respective approach revealed whole-body mass differences in comparison to measured
mass of 4.8 (3.5)%. In addition, reliability of the model was calculated to be a maximum of
2% of the mean predicted whole body mass. The informing data and further reliability test
outputs are fully described in Appendix A.15.
The respective outputs, in addition to the digitised coordinates, which were extracted from
the Peak Motus software and imported into a Microsoft Excel spreadsheet, were utilised for
the calculation of the BSIP reported in Table 6.1. Application of the parallel axis theorem to
combined segment masses, segment centre of masses and absolute segment moments of
inertia allowed for the calculation of moment of inertia about the transverse axis (Iy). The
radii of gyration for each of the Iy measures were based on segment length data, calculated
form the anatomical landmark coordinates. To inform the evaluation study, a total of ten
BSIP were calculated for each gymnast at each time point (Table 6.1).
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Table 6.1. BSIP measure details including the point at which each measure is calculated
relative to and which segments are included in each measure
Measured relative to
Moment of inertia about the transverse axis (kg.m2)
Whole-body
Whole-body CoM
Upper-body
Upper-body CoM
Lower-body
Lower: upperbody Ratio
Torso
Lower-body CoM
Lower: upper-body
CoM ratio
Torso CoM
Centre of mass (m)
Whole-body
Segments included
Head, torso, thighs, shanks, feet,
upper arms, forearms and hands
Head, torso, upper arms, forearms
and hands
Thighs, shanks and feet
Head, torso, thighs, shanks, feet,
upper arms, forearms and hands
Torso
Whole-body length
Head, torso, thighs, shanks, feet,
upper arms, forearms and hands
Upper-body
Upper-body length
Head, torso, upper arms, forearms
and hands
Lower-body
Lower-body length
Thighs, shanks and feet
Lower: upperLower: upper-body
Head, torso, thighs, shanks, feet,
body Ratio
length
upper arms, forearms and hands
Torso
Torso length
Torso
Note: thighs, shanks, feet, upper arms, forearms and hands are indicative of the inclusion of
right and left sides of the body for each segment
Following the determination of discrete BSIP at each time point for each gymnast, the rate
of change of each parameter was calculated to represent the progression of inertial
parameters from initial to final collections (6.3). The rate of BSIP change was examined to
explore the underpinning of GR and MGR rate of change; the data subsequently contributed
to the evaluation study.
BSIP rate of change =
(final BSIPi – initial BSIPi)
(final chronological age – initial chronological age)
[6.3]
Independence between the BSIP measures and b-ratio was ensured as the bicristal and
biacromial points were digitised to solely inform the b-ratio measures and were therefore not
included in the calculation of BSIP measures.
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Evaluation of Indicators for Screening
The underlying biomechanical theory, with respect to growth in the female gymnastics
population, will be informed through the BSIP outputs from the appraisal of the previous
section. To emulate the conditions which a practitioner may experience in a ‘real-world’
injury screening setting, the data obtained for each of the gymnasts at the initial time point
will inform the evaluation of the developed theory. Broadening of the biacromial breadth in
relation to the bicristal breadth, indicating negative MGR, has been suggested to oppose
growth responses evidenced in the general population (Malina et al., 2004). As such, the
gymnasts who experienced negative MGR were the individuals who were of greatest interest
to develop understanding of whether it is possible to distinguish them from the remainder of
the gymnastics cohort using discrete time point data. To assess if the BSIP at the initial time
point is superior to the assessment of anthropometric growth status (b-ratio) in indicating
whether a gymnast will go on to experience a negative MGR, evaluation of b-ratio in relation
to positive and negative MGR will additionally be encompassed within the chapter.
6.2.4
Statistical Analyses
Growth Rate and Morphological Growth Rate Groupings
To explore the objective distinction between the respective sub-groups for GR and MGR,
statistical differences between the high and low GR sub-groups and the positive and negative
MGR sub-groups were investigated. The use of non-parametric statistics are advocated for
small sample sizes (Kraska-Miller, 2013). As the most powerful nonparametric alternative
to the t-test for independent samples (Hill and Lewicki, 2006), a Mann-Whitney U test
informed the respective statistical assessment. Each divisional approach, high and low GR
and positive and negative MGR, identified significant differences between the sub-groups
(p<0.05). Independence of high GR to low GR and positive MGR to negative MGR was
therefore established.
To assess the relative difference of BRIs between high and low GR sub-groups and positive
and negative MGR sub-groups, a relative group difference (RGD) was calculated. For the
RGD, each BRI was informed by the median value for each sub-group, which was a measure
of the median value across time for each gymnast. The calculations were undertaken using
Microsoft Excel; the median statistical descriptive was selected as a result of the low sample
sizes, particularly for the negative MGR group (n = 3). Summation of the individual BRI
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between the sub-groups, analysed separately for GR and MGR, provided indication of the
distinction between BRIs for each division approach (GR or MGR). The division approach
with the greatest BRI difference was subsequently selected for further analysis in order to
further explore why BRIs differentiated to such an extent between the respective sub-groups.
To explore the sub-groups of the RGD outcome in more depth, with the purpose of
investigating the differences between sub-group BRIs, a between sub-group difference was
undertaken using BRIs from each trial (a maximum of 20 handstands and 20 forward
walkovers) at each time point (three for all gymnasts apart from one who had two time
points) and each gymnast (overall, n = 12). The data were grouped in accordance with the
sub-groups established as an outcome of the RGD. In accordance with previous literature,
time is a risk factor for CSI rather than a mechanism; subsequently, in a similar fashion to
Ackland et al. (2003), the influence of time was eliminated from the sub-group data to assess
the biomechanical risk independent of time. A Shapiro-Wilk test of normality was firstly
applied, demonstrating the non-normal distribution (p<0.05) of the data. As a result, a MannWhitney U test was conducted between sub-groups for each handstand and forward
walkover BRIs.
Re-introduction of time allowed for the exploration of the influence of time on the handstand
and forward walkover BRIs which were significant outcomes from the non-parametric
difference test. A Shapiro-Wilk test produced significant outcomes for each variable,
demonstrating the deviation from a normality data trend. Subsequently, Friedman’s and
Kendall’s W tests were applied to the sub-grouped data sets. The Friedman’s test produced
a value of significance (p) for the influence of time on sub-grouped BRIs, while Kendall’s
W test was undertaken to gain quantification of the effect size (W). The significant BRI subgrouped outcomes (p<0.05) were further assessed using Friedman’s and Kendall’s W posthoc tests to examine the separate influence of initial and mid, mid and final and initial and
final on sub-grouped BRIs.
Interpretation of the nonparametric effect sizes (Kendall’s coefficient of concordance, W),
was informed by the large, medium and small effect size boundaries identified at >0.5, >0.3
and >0.1, respectively (Kraska-Miller, 2013). The interpretation boundaries were consistent
with those used in the previous chapter and were of high relevance to the research due to the
dominant role of effect sizes in interpreting the statistical results. As in previous chapters,
statistical significance supported the effect size interpretations; significant results were
recognised when p<0.05.
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Body Segment Inertial Parameters
Analysis of the influence of the BSIP rates of change on b-ratio rate of change (either GR or
MGR, determined from previous RGD outcome) was informed through use of linear
regression analyses. Linear regression analyses were selected as opposed to quadratic
analyses as the researcher knew of no reason for the expected trend in the rates of change
variables to be anticipated to be anything other than linear. Normality tests were conducted
on BSIP rates of change and b-ratio rates of change data; a Shapiro-Wilk test established
each variable to be normally distributed (p>0.05). Linearity and homoscedasticity was
assessed and accepted for each of the respective variables.
Evaluation of Indicators for Screening
The BSIP rates of change which were established to contribute to the underpinning of b-ratio
rate of change through linear regressions (Chapter 6, Section 6.3.2) informed the evaluation
study. Statistical exploration of the difference between the BSIP linear regression output
measure and b-ratio measure at initial time point in relation to the sub-groups was undertaken
using a Mann-Whitney U test.
6.3
6.3.1
Results
Growth Rate and Morphological Growth Rate Groupings
Quantification of the influence of two divisional approaches of the gymnastics cohort (GR
or MGR) on BRIs was undertaken through use of a RGD approach. The analysis outcome
revealed a greater sum of BRI relative differences (62%) using the MGR group division
approach (296%) in comparison with the GR group division approach (234%). The greater
difference in biomechanical risk between positive and negative MGR sub-group signified
the need for further exploration of the influence of MGR on handstand and forward walkover
BRIs.
Descriptive data mean (SD) data provided quantification of which MGR sub-group
displayed increased biomechanical risk for each of the BRI. The findings indicated that
gymnasts who experienced negative MGR had greater DPSI risk in the handstand skill, and
greater DPSI, DLPSI and CoPy risk in the forward walkover skill. The positive MGR group
demonstrated increased biomechanical risk for posture, DLPSI and CoPy in the handstand,
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as well as posture in the forward walkover skill. The findings from a Mann-Whitney U
statistical test revealed significant differences (p<0.05) between the positive and negative
MGR sub-groups for posture, DPSI and DLPSI in the handstand and DPSI in the forward
walkover (Table 6.2).
Table 6.2. Descriptive mean (SD) BRI data for positive and negative MGR sub-groups and
statistical significance test outputs (p) for the BRI differences between positive and negative
MGR sub-groups
Mean (SD)
p
Handstand
Posture (°)
Negative MGR
0.7 (4.7)
0.00*
Positive MGR
-3.83 (6.14)
DPSI
Negative MGR
29.58 (15.87)
0.00*
Positive MGR
21.41 (20.71)
DLPSI
Negative MGR
8.86 (4.64)
0.00*
Positive MGR
13.75 (7.82)
CoPy (mm) Negative MGR
69.84 (39.15)
0.44
Positive MGR
65.10 (39.09)
Forward Walkover
Posture (°) Negative MGR
-12.9 (7.5)
0.09
Positive MGR
-11.77 (9.96)
DPSI
Negative MGR
54.81 (22.01)
0.00*
Positive MGR
45.93 (15.06)
DLPSI
Negative MGR
18.43 (10.68)
0.40
Positive MGR
18.58 (8.90)
CoPy (mm) Negative MGR
75.48 (63.89)
0.14
Positive MGR
96.25 (92.35)
Note: * p<0.05; shaded values represent the greatest biomechanical risk between positive
MGR and negative MGR
The combination of descriptive and statistical analyses enabled comprehension of the
influence of MGR on BRIs, with findings that the positive MGR sub-group had a
significantly greater posture and DLPSI risk in the handstand skill and the negative MGR
sub-group had a significantly greater DPSI risk in the handstand and DPSI risk in the forward
walkover skills (p<0.05).
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The significant between-sub-group BRI differences (posture, DPSI and DLPSI in the
handstand and DPSI in the forward walkover) were explored in relation to time. Kendall’s
W test outputs revealed an absence of large effects for the influence of time on within-subgroup BRIs. A single medium effect was identified (W = 0.402) for the handstand DPSI for
the negative MGR group, however, the statistical analyses revealed the remainder of the
within-sub-group BRIs to be influenced by time to a small extent (W>0.01). Friedman’s test
outputs revealed significant differences (p<0.05) for all but one within-MGR sub-group
BRIs (Table 6.3).
Table 6.3. Friedman's and Kendall's W test outputs for the influence of time on BRIs within
MGR sub-groups
Time
W
p
0.09
0.11
0.40
0.28
0.17
0.01
0.02*
0.00*
0.00*
0.00*
0.00*
0.16
Handstand
Posture (°)
DPSI
DLPSI
Negative MGR
Positive MGR
Negative MGR
Positive MGR
Negative MGR
Positive MGR
Forward Walkover
DPSI
Negative MGR
0.27
0.00*
Positive MGR
0.06
0.00*
Note: light grey shading represents medium effect sizes (W>0.3) and the effct size outputs
with no shading are indivative of small effect (W>0.1); * p<0.05
Eighty-eight per cent of BRIs within the MGR sub-groups were significantly influenced by
time. The only intra-sub-group BRI which was not significantly influenced by time was
handstand DLPSI in the positive MGR sub-group (p>0.05). Further exploration of the
temporal impact was undertaken through use of Friedman’s and Kendall’s W post-hoc tests
for each of the significant outputs (Table 6.4).
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Table 6.4. Friedman's and Kendall's W post-hoc effect size (W) and significance (p) outputs
for the influence of specific time phases on BRIs within MGR sub-groups
Time
Handstand
Posture (°)
Negative MGR
Posture (°)
Positive MGR
DPSI
Negative MGR
DPSI
Positive MGR
DLPSI
Negative MGR
Initial & Mid
Mid & Final
Initial & Final
Initial & Mid
Mid & Final
Initial & Final
Initial & Mid
Mid & Final
Initial & Final
Initial & Mid
Mid & Final
Initial & Final
Initial & Mid
Mid & Final
Initial & Final
W
p
0.30
0.02
0.04
0.10
0.17
0.02
0.13
0.38
0.56
0.30
0.09
0.39
0.34
0.00
0.13
0.00*
0.31
0.19
0.00*
0.00*
0.06
0.00*
0.00*
0.00*
0.00*
0.00*
0.00*
0.00*
0.88
0.01*
Forward Walkover
DPSI
Negative MGR
Initial & Mid
0.42
0.00*
Mid & Final
0.06
0.08
Initial & Final
0.29
0.00*
DPSI
Positive MGR Initial & Mid
0.02
0.06
Mid & Final
0.08
0.00*
Initial & Final
0.06
0.00*
Note: dark grey shading represents large effect sizes (W>0.5), light grey shading represents
medium effect sizes (W>0.3) and no shading represents small effect sizes (W>0.1); * p<0.05
A single large effect was revealed from the post-hoc test for handstand DPSI in the negative
MGR, between initial and final collections (W = 0.56). Time was found to have a medium
effect on four within-MGR sub-group BRIs, with 71% of the BRIs, MGR sub-group and
time combinations tested identifying as statistically significant (p<0.05). The non-significant
outputs (p>0.05) were evidenced for each time combination, with mid & final exhibiting the
greatest quantity of non-significant outputs (n = 3), followed by initial & final (n = 2) and
initial and mid (n = 1). Handstand DPSI for both MGR sub-groups was the only BRI to have
a significant influence (p<0.05) of time between each of the time conditions (initial and mid,
mid and final, and initial and final).
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6.3.2
Body Segment Inertial Parameters
A linear regression analysis provided indication of the influence of each of the Iy and CoM
rate of change parameters on MGR. The effect sizes and significance levels for each BSIP
rate of change on b-ratio rate of change (MGR) are reported in Table 6.5.
Table 6.5. Effect size (r) and significance (p) linear regression outputs for the influence of
inertial rate of change on b-ratio rate of change (MGR)
Morphological growth rate
(%/months)
r
p
Whole-body Iy Rate of Change
0.08
0.80
2
(kg.m /months)
Upper-body Iy Rate of Change
0.56
0.056
2
(kg.m /months)
Lower-body Iy Rate of Change
0.13
0.68
2
(kg.m /months)
Lower: Upper-body Iy Ratio Rate of Change
0.39
0.22
(%/months)
Torso Iy Rate of Change
0.74
0.01*
2
(kg.m /months)
Whole-body CoM Rate of Change
0.20
0.55
(m/months)
Upper-body CoM Rate of Change
0.04
0.91
(m/months)
Lower-body CoM Rate of Change
0.09
0.79
(m/months)
Lower: Upper-body CoM Ratio Rate of
0.15
0.65
Change
(%/months)
Torso CoM Rate of Change
0.03
0.92
(m/months)
Note: dark grey shading represents large effect sizes (r>0.5), light grey shading represents
medium effect sizes (r>0.3) and no shading represents small effect sizes (r>0.1); * p<0.05
Torso Iy rate of change and upper-body Iy rate of change were found to have a large influence
on MGR (r = 0.74 and 0.56, respectively). The rate of change of the respective BSIP
variables were highlighted to explain 55% and 31% of the variance in MGR (r2 = 0.55 and
0.31, respectively). Each of the CoM measures were shown to have a small effect on MGR,
with upper-body CoM rate of change and torso CoM rate of change only explaining 0.1% of
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the variance of the b-ratio measure. Torso Iy was the only BSIP rate of change to have a
significant influence on MGR (p<0.05). The significant effect, which is indicated in Figure
6.5, evidenced gymnasts who experienced a negative MGR, to additionally have small torso
Iy rates of change. The torso Iy rate of change for one gymnast was identified to be negative
across the 12 month period (-0.004 kg.m2/months).
r = 0.74
Figure 6.5. Graphical representation of the linear influence of the torso moment of inertia
rate of change about the transverse axis on b-ratio rate of change (morphological growth
rate) for each of the gymnasts (n = 12); p<0.05.
6.3.3
Preliminary Evaluation of Indicators for Screening
The previous finding of the large and significant influence of torso Iy rate of change on MGR
translated to the assessment of discrete torso Iy at initial collection for each of the individual
gymnasts. To evaluate the use of cross-sectional torso Iy data in informing CSI screening
approaches, the respective BSIP data for the negative MGR sub-group gymnasts were
individually appraised in relation to the remainder of the gymnastics cohort (i.e. the positive
MGR gymnasts). Presented in size order, the torso Iy for each of the gymnasts at initial time
point (n = 12), with indication of the MGR sub-group of each individual is presented in
Figure 6.6.
163
1.00
Torso I (kg.m2)
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
1
2
3
4
5
6
7
Gymnast
8
9
10
11
12
Figure 6.6. Torso moment of inertia about the transverse axis of each of the gymnasts at
initial time point (n = 12).
When considered in relation to the gymnasts with a positive MGR, the negative MGR
gymnasts were found to have a tendency to have a greater torso Iy at initial collection.
Median (IQR) data for the gymnasts who experienced positive MGR was 0.27 kg.m2 (0.05),
in comparison with the negative MGR group, which had torso Iy median (IQR) of 0.69 kg.m2
(0.26). To evaluate the use of discrete b-ratio at initial collection for providing indication of
the potential for individual gymnasts to develop with negative MGR, the discrete b-ratio data
were plotted with identification of whether each gymnast experienced positive or negative
MGR (Figure 6.7).
B-Ratio (%)
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100
90
80
70
60
50
40
30
20
10
0
1
2
3
4
5
6
7
Gymnast
8
9
10
11
12
Figure 6.7. B-ratio of each of the gymnasts at initial time point (n = 12).
Although two of the negative MGR gymnasts were found to have a greater b-ratio at initial
collection (86.3% and 83.1%) in comparison with the positive MGR gymnasts (median
(IQR) = 76.6% (7.2)), the discrete b-ratio for the third negative MGR gymnast was lower
than the positive MGR sub-group median (75%). A Mann-Whitney U test found negative
MGR gymnasts to be non-significantly different (p>0.05) to the positive MGR sub-groups
for torso Iy (p = 0.08) and b-ratio (p = 0.21). Although not statistically supported,
interrogation of the dispersion of negative MGR gymnasts within the remainder of the
gymnastics cohort in Figure 6.6 and Figure 6.7 provided evidence for the prominent
associations between torso Iy at initial testing and b-ratio rate of change (MGR) in
comparison with b-ratio at initial testing.
6.4
Discussion
The aim of the chapter was to gain quantitative understanding of the contribution of inertial
changes associated with the growth process to biomechanical risk in female artistic
gymnasts. Two chapter questions were developed to underpin the chapter aim. Address of
each (CQ 6.1 and 6.2) contributed to understanding of the way in which longitudinal growth
influenced BRIs in the female artistic gymnastics population and subsequently, assisted in
informing the overall thesis aim. The chapter findings additionally enabled the development
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of novel insights into the way in which CSI screening approaches should be conducted;
therefore the respective research made significant contributions to the thesis purpose.
The need for understanding of the way in which injury susceptibility is influenced by the
process of anthropometric growth was supported by previous findings of heightened CSI
risk with increased rates of growth (Roussouly et al., 2006; Baranto et al., 2009b;
Kerssemakers et al., 2009). As a result of the prominence of proportional growth in
contemporary research (Chapters 4 and 5), the investigation of GR alone was anticipated to
provide limited understanding of anthropometric growth in the gymnastics population. The
proportional b-ratio has been identified to be unique for some female gymnasts (Claessens
and Lefevre, 1998; Siatras et al., 2009) in comparison with the general population (Malina
et al., 2004). Therefore, exploration of the influence of the shape of longitudinal growth
(MGR) of female gymnasts on biomechanical risk was warranted. Investigation of GR and
MGR led to the formation of four distinct sub-groups, low and high GR, and positive and
negative MGR (Section 6.2.4). In support of Chapter 5 findings, which evidenced the
importance of proportional anthropometric growth, MGR sub-groups were found to have a
greater influence on BRIs than high and low GR. The respective finding provided novel
empirical support for Tanner (1962)’s avocation of the importance of shape of growth
consideration, i.e. the rate of proportional growth, for complete understating if the process
of physical development. Although the ‘ideal’ gymnast body shape has been characterised
by previous researchers such as Claessens and Lefevre (1998); Siatras et al. (2009), to the
researcher’s knowledge, there has been no previous empirical evidence of the process of bratio growth, through which the desirable broad shoulders relative to narrow hips has been
gained. The quantification of both positive and negative MGR within the gymnastics
population was subsequently accepted as a novel feature of the respective research.
To address the first chapter question (CQ 6.1), insight into the contribution of positive and
negative MGR to BRIs was developed. Statistical appraisal of the influence of positive MGR
and negative MGR sub-groups on each of the handstand and forward walkover BRIs, in
combination with the descriptive mean (SD) data for each sub-group, informed a number of
prominent findings. The gymnasts who experienced positive MGR (bicristal breadth
developing at an increased rate relative to the biacromial breadth) had a significantly greater
biomechanical risk for handstand posture (decreased minimum lumbar angle, p<0.05), with
a reduced mean angle of 4.5° in relation to the negative MGR sub-group. The positive MGR
sub-group were additionally recognised to have increased biomechanical risk in the way of
handstand DLPSI (decreased lumbo-pelvic stability, p<0.05), with a DLPSI mean which was
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4.89 greater than the negative MGR sub-group. The gymnasts who experienced negative
MGR (biacromial breadth developing at an increased rate relative to the bicristal breadth)
were found to have significantly increased biomechanical risk for handstand DPSI (p<0.05)
and forward walkover DPSI (p<0.05). The descriptive mean data revealed increased DPSI
values for the negative MGR sub-group of 8.17 for the handstand and 8.88 for the forward
walkover skill, highlighting a decreased whole-body general stability across the fundamental
skills, in comparison with positive the MGR sub-group. The only significant BRI for the
forward walkover skill was found to be DPSI, therefore indicating that proportional growth
may have a greater influence on quasi-static skills, as oppose to dynamic skills. As only one
of each skill ‘type’ was analysed, extension of the analysis to additional skills is necessary
to confirm the suggested outcome.
The considerable BRI differences between MGR sub-groups within the gymnastics cohort
furthered support for the need for young gymnasts’ morphology to be monitored throughout
the period of growth. The outputs were additionally indicative of the need for sport scientists
and practitioners working with young female gymnasts to focus on the lumbar and lumbopelvic region of the gymnasts who experienced positive MGR and to concentrate efforts on
improving general stability of the gymnasts who experienced negative MGR. The empirical
data provided valuable understanding of the contribution of longitudinal anthropometric
growth of female gymnasts to biomechanical risk and may go some way in understanding
the multifactorial nature of pain and injury risk, as it is commonly denoted (Goldstein et al.,
1991).
Statistical analyses were undertaken to further understanding of the influence of time on the
BRIs which were found to be significantly influenced by MGR sub-group (posture, DPSI
and DLPSI in the handstand, and DPSI in the forward walkover). The within-MGR subgroup analyses revealed the greatest influence of time on BRIs to be a medium effect (W =
0.40). The lack of large effect sizes was unexpected as injury susceptibility has previously
been found to alter across time (Lonstein, 1999). It was therefore speculated that the subgrouping of gymnasts may have alleviated the time influence on BRIs. Therefore, routine
monitoring of anthropometric growth status over an extended period of time may be
necessary to establish the MGR trend of each individual gymnast, however, the BRIs of
intra-MGR sub-groups may not alter to a great extent over time. Therefore, the appraisal of
time on BRIs within MGR sub-groups is suggestive that the longitudinal monitoring of BRIs
may not be necessary and insight into biomechanical risk may be able to be gained by
establishing whether the gymnast experiences positive or negative MGR alone. Although no
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large effects were found, time was revealed to have a significant influence on all MGR subgroup BRIs (p<0.05), with the exception of handstand DLPSI for the positive MGR subgroup (p>0.05). The respective non-significant BRI was found to be unaffected by time, thus
suggesting that the DLPSI biomechanical risk in the handstand skill experienced by the
positive MGR sub-group is irrespective of the stage of growth of the positive MGR
gymnasts.
A post-hoc test revealed a large effect of time on handstand DPSI in the negative MGR group
between initial and final collections. The respective finding is supportive of the need for the
monitoring of biomechanical risk across a minimum period of 12 month in physically
developing female gymnasts. The finding was novel as time periods for which injury
screening should be undertaken within a specific population was, to the researcher’s
knowledge, not previously established through empirical investigation. Extended monitoring
of the female gymnasts over a period beyond 12 months, to build on the initial insights
provided in the respective research was advocated within future research.
To extend knowledge of the anthropometric growth patterns of the female gymnastics
cohort, the potential biomechanical mechanisms which may underpin MGR and
subsequently predispose gymnasts to a greater risk of specific BRIs were explored. BSIP
have been demonstrated to underpin the process of growth in a number of populations
(Jensen and Nassas, 1988; Cappozzo and Berme, 1990; Jensen, 1993; Richards et al., 1999;
Hawkins and Metheny, 2001; Ackland et al., 2003). In addition to its role as the most
prominent BSIP in previous growth-based research, the moment of inertia about the
transverse axis (Iy) was considered in the respective research due to its relevance to the
sagittal plane handstand and forward walkover BRIs. In addition, each of the skills require
rotation about the sagittal axis for successful performance. Iy has been commonly quantified
for the whole-body (e.g. Ackland et al. (2003)), however, it has additionally been evaluated
through use of segmental analysis (e.g. Jensen and Nassas (1988)). The exploration of
segmental data in ratio form has been supported by previous research (Damsgaard et al.,
2001; Malina et al., 2004; Claessens et al., 2006), in addition to the supporting findings from
Chapter 5 (Section 5.3.2). Associations between CoM and growth have additionally been
documented in previous research (Cappozzo and Berme, 1990). Quantification of CoM to
investigate the biomechanical underpinning of growth in a female gymnastics population
was anticipated to identify shape-specific biomechanical alterations, indicative of
proportional development, which may be masked by the Iy measure alone. However, upperbody Iy rate of change and torso Iy rate of change, were the two BSIP which were identified
168
to have large influences on MGR (r = 0.56 and 0.74, respectively). The upper-body Iy rate
of change was found to explain 31% of the variance in MGR, however, the torso Iy rate of
change provided indication of 55% of the variance of b-ratio rate of change and was
statistically significant (p<0.05). The respective findings did not support the dominant
influence of CoM on BRIs which was speculated. As the Iy measures were calculated in
relation to the respective CoM, the consideration of the mass of the respective segment(s) in
Iy measures was foreseen to account for the greater influence of Iy on MGR in comparison
with CoM.
Appraisal of the graphical output of the torso Iy rate of change and MGR linear regression
exemplified the positive linear relationship between the respective parameters. The gymnasts
who underwent a high torso Iy rate of change were found to additionally experience an
increased MGR across the 12 month period. As such, the gymnasts with a high torso Iy rate
of change experienced positive MGR; the opposite was true for the gymnasts who had a low
torso Iy rate of change, depicting them as having a negative MGR. In relation to the BRI
findings, the positive MGR gymnasts, with greater rates of change of torso Iy, were at
increased biomechanical risk for posture and DPSI in the handstand skill. The increased
changes in resistance of the torso to rotate about the sagittal axis may subsequently account
for the need for the respective gymnasts to utilise injurious spinal mechanics to assist the
whole-body control of the handstand performance. As the forward walkover is a dynamic
skill, the ability to adapt to a change in torso Iy is speculated to be less problematic than
within the handstand skill, for which high torso control is required for successful
performance. The negative MGR gymnasts were found to have relatively small changes in
torso Iy from initial to final collections. The respective gymnasts were previously identified
to be at increased biomechanical risk in the way of handstand and forward walkover DPSI.
The minimal changes in the resistance of the torso to rotate about the sagittal axis offered
little explanation for the respective BRI findings. It was subsequently speculated that the
process in which the gymnasts’ biacromial breadths developed at an increased rate relative
to bicristal breadths, may be accountable for the decreased stability experienced by the
negative MGR gymnasts. The hand balance requirements of the two skills further support
the potential explanation as, growth of the biacromial breadth may directly influence the
stability mechanisms employed by a gymnast to perform the hand-balance skills.
The contribution of anthropometric growth rates (positive MGR or negative MGR) to
biomechanical risk in handstand and forward walkover skills has been found to be influenced
by torso Iy to a large extent. The findings from the address of CQ 6.1 were informed by
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longitudinal data, however, understanding of the translation of the respective knowledge to
inform injury screening may be highly valuable. The development of screening programmes
in sport have typically neglected to evaluate transient effects on BRIs associated with growth
(e.g. Crewe et al. (2012a)). The conclusion of the respective chapter, and address of the
second chapter question (CQ 6.2) subsequently sought to evaluate the extent to which the
screening of female artistic gymnasts at a discrete point during growth may inform CSI risk.
Due to the atypical b-ratios which have been evidenced within the gymnastics population
(Claessens and Lefevre, 1998; Siatras et al., 2009), in comparison with the general
population (Malina et al., 2004), the gymnasts who experience negative MGR were of
particular interest to inform the second chapter question. Torso Iy of individual negative
MGR gymnasts was subsequently explored in relation to the remainder of the gymnastics
cohort (positive MGR). A tendency for the gymnasts who experienced negative MGR to
present with a greater torso Iy at initial data collections, in comparison with the positive MGR
sub-group, was revealed. The descriptive median (IQR) data supported the respective trend
with the torso Iy output for the positive MGR established to be 0.27 kg.m 2 (0.05), and the
negative MGR gymnasts found to have torso Iy of 0.34, 0.69 and 0.85 kg.m2. The respective
evaluation was considered to be preliminary; future research would ideally require the
development of a pool of time-related responses for individual sports performers over many
years and diverse sporting contexts. Although not conclusive, the discrete quantification of
Iy provided evidence to suggest that longitudinal growth-based knowledge may translate to
inform cross-sectional CSI screening.
Further appraisal of torso Iy and b-ratio at the initial time point was undertaken to investigate
which measure was of greatest suitability for providing indication of gymnasts’ MGR. The
use of cross-sectional b-ratio for the determination of the MGR sub-group revealed less
consistency in terms of the identification of negative MGR gymnasts in the cohort than the
use of torso Iy. The respective appraisal provided confirmation of the superiority of torso Iy
for enabling the prediction of individual gymnasts’ tendency to experience positive or
negative MGR. Although neither measure (torso Iy or b-ratio) proved to indicate significant
differences between the positive and negative MGR gymnasts (p = 0.078 and 0.209
respectively), the findings were valuable in distinguishing the important role of torso Iy in
CSI screening. In relation to BRIs, the cross-sectional screening of gymnasts using torso Iy
may offer initial insight into whether lumbo-pelvic mechanics in the handstand (posture and
DLPSI) or general stability in both fundamental skills (DPSI) should be of focus to assist
the endeavour for CSI susceptibility reduction.
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6.5
Chapter Summary
Use of longitudinal data informed the finding that MGR sub-grouping (positive and negative
MGR) had a greater influence on BRIs than GR sub-groups (high and low GR). Statistical
assessment revealed greater biomechanical risk for posture and DLPSI in the handstand skill,
with the negative MGR gymnasts found to have greater biomechanical risk for DPSI in the
handstand and forward walkover skills. Exploration of the influence of time on the respective
BRIs for the positive and negative MGR sub-groups revealed no large effects, suggesting
the ability to conduct biomechanical screening using the respective BRIs in a cross-sectional
manner. Of the BSIP considered, torso Iy rate of change was revealed to have the greatest
influence on MGR. An evaluation of torso Iy of female gymnasts at a discrete time point
provided preliminary support for the ability for cross-sectional injury screening of gymnasts
to provide insight into longitudinal anthropometric growth and subsequent biomechanical
risk.
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CHAPTER 7 - GENERAL DISCUSSION
7.1
Introduction
High prevalence rates of chronic back pain (CBP) and chronic spinal injury (CSI) in the
gymnastics population have been sustained across almost four decades (e.g. Jackson et al.
(1976); Donatelli and Thurner (2014)). In isolation, physical development mechanisms and
biomechanical risk indicators (BRIs) have been evidenced to contribute to the respective
pathologies (Tanchev et al., 2000; Brüggemann, 2010; Kim and Green, 2011). A
biomechanical approach was considered to be most suitable for the investigation of the
inextricable relationships between the biology and the mechanics of physical development
(Nuckley, 2013). Informed through a review of literature, the aim of the research was to
develop understanding of the contribution of physical development to biomechanical
indicators of CSI risk in female artistic gymnasts performing fundamental gymnastics skills.
Underpinning the overall research aim, a series of research questions were posed in Chapter
1; the general discussion (Chapter 7) encompasses an appraisal of the contribution of
previous research (Chapter 2) and empirical research (Chapters 3 to 6) to address each
research question. In addition, a discussion of the methodological approaches used to inform
the overall research, along with an overview of the contribution to applied research and
applied screening practice will underpin the respective chapter. Future directions for
extended research are proposed on conclusion of the chapter.
7.2
Addressing the Research Questions
RQ 1. Which quantitative measurement techniques and analyses are appropriate for
the identification and interrogation of biomechanical risk indicators in fundamental
gymnastics skills?
A review of previous literature revealed numerous biomechanical variables to be associated
with increased risk of CBP and CSI development. The etiological variables were most
commonly identified through clinical methods of approach, such as physical examination
(e.g. range of motion, Harreby et al. (1999)) and imaging techniques (e.g. posture, Bugg et
al. (2012)), or biomechanical methods of approach, including in-vitro (e.g. hyperflexion,
Adams et al. (1994)) and in-vivo methods (e.g. posture, Smith et al. (2008)). To address the
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research aim, fine-grained biomechanical data were required, however, to inform the
research purpose, an approach which could be used by gymnasts-specific sport scientists for
CSI screening was necessary. To suffice the research aim and purpose, use of contemporary
technology through an in-vivo approach was deemed to be of greatest suitability. The
synchronised use of a CODA motion analysis system, for which test-retest marker placement
reliability was found to be <2% of segment length (Appendix A.12), and a Kistler force
plate, enabled the capture of kinematic and kinetic data throughout the performance of
fundamental skills.
Focus on fundamental gymnastics skills for biomechanical screening has been advocated by
Bradshaw and Hume (2012); Hume et al. (2013). As two of the most fundamental skills in
gymnastics, the handstand and the forward walkover were selected to inform the respective
research. To explore CSI biomechanical risk throughout the performance of the handstand
and forward walkover, a review of literature informed the selection of five BRIs. Posture,
lumbo-pelvic range of motion, centre of pressure excursions, general stability and lumbopelvic stability were selected in accordance with their relevance to CSI and to gymnastics
practice. Due to the vital role of BRIs in addressing each of the thesis research questions, the
biomechanical measures were central to the research design development. One use of BRIs
in informing the research design was for the exploration of handstand and forward walkover
mechanics. From the skill-related BRI data, the extent to which the forward walkover
mechanics were reflective of handstand BRIs were examined. The common use of the
handstand skill as a progression to the forward walkover from a performance perspective
(Mitchell et al., 2002), informed the anticipated similarity in mechanical properties of each
of the skills from a pain and injury perspective. Handstand and forward walkover BRI
correlations revealed inter-skill BRIs resemblance to be limited, with only 29% of the
gymnasts contributing to the large correlation effects. Separate analysis of the handstand and
forward walkover skills subsequently informed the remainder of the research.
Through the analyses of handstand and forward walkover BRIs in Chapters 4, 5 and 6,
general stability (DPSI) was evidenced to be the only BRI which was mirrored in the
respective fundamental skills. Chapter 4 analyses revealed chronological age, maturation
status and anthropometric growth status to have large effects on DPSI in both the handstand
and forward walkover. In addition, Chapter 5 analyses revealed large effects for bicristal to
biacromial breadth ratio (b-ratio) on DPSI in the handstand and forward walkover skills,
with no other large effects of growth measures on BRIs. Finally, the only significant
difference between positive and negative morphological growth rate (MGR) sub-groups
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which was reflected in both the handstand and forward walkover skills was DPSI (Chapter
6). The skill comparison outputs demonstrated that physical development had consistently
large influences on DPSI in the handstand and in the forward walkover, whereas, the
influence of physical development mechanisms (chronological ageing, maturation status and
anthropometric growth status) varied across the fundamental skills for each of the remaining
BRIs.
In addition to providing insight into the mechanics of the fundamental skills, the handstand
and forward walkover BRI correlation analyses assisted in informing the refinement of the
BRIs which extended across the empirical chapter analyses. Prior to the respective analyses,
undertaken in Chapter 3, the selection of biomechanical risk measures was informed by
previous research associated with CBP and CSI, in addition to the applicability of the risk
indicators to fundamental gymnastics practice. Empirical analyses enabled the selection of
BRIs to be tailored to the respective research; the BRIs of greatest relevance to the female
artistic gymnastics population may directly transfer to inform CSI screening approaches.
The BRIs recognised to have large associations between the handstand and forward
walkover skills, and therefore the BRIs carried forward for further analyses, were posture,
anterior-posterior centre of pressure (CoPy), DPSI and lumbo-pelvic stability (DLPSI).
The lack of empirical support for the appropriateness of medio-lateral centre of pressure
(CoPx) and lumbo-pelvic medio-lateral range of motion (LPRoMx) in the respective
research dictated a sagittal plane analyses for the remainder of the research. The respective
empirical findings were subsequently indicative of the suitability of a two-dimensional
screening approach for CSI biomechanical risk, through the use of fundamental gymnastics
skills. Crewe et al. (2012a) reported the typical fulfilment of musculoskeletal screening
using two-dimensional approaches, therefore, the two-dimensional approach advocated by
empirical findings within the respective research was reflective of standard musculoskeletal
screening techniques. The lack of support for further exploration of the range of motion
variables (LPRoMy and LPRoMx) was of interest in relation to the way in which the
respective variables were selected from previous literature. It is important to consider the
weighting of the evidence-base which informed the selection of BRIs. Unlike posture, range
of motion had relatively little support as a BRF from previous literature (six pieces of
literature support), however, it was considered prominent from a gymnastics perspective.
The empirical findings were therefore supportive of the BRF which was prominent in the
previous CSI literature (posture), and less-so the selected BRF with less CSI-relevance.
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Address of the first research question (RQ 1) enabled the address of further research
questions through an approach which was customised to inform the exploration of BRIs in
female artistic gymnasts. The respective research provided insight into the BRI trends of
female artistic gymnasts across the handstand and forward walkover skills, in addition to
informing a methodological approach for the investigation of BRIs in the respective
population. In addition to gaining understanding of a suitable approach through which BRIs
can be reliably investigated, the novel insight may translate to CSI screening practice. Using
an in-vivo approach for the examination of five specified BRIs throughout the performance
of two fundamental gymnastics skills, the second research question (RQ 2) was addressed.
RQ 2. How does physical development influence biomechanical risk indicators in
female artistic gymnasts?
The identification of physical development mechanisms as being pertinent to CSI (Tanchev
et al., 2000; Brüggemann, 2010; Kim and Green, 2011) within Chapter 2, informed the
premise that physical development mechanisms would have large influences on BRIs in the
female artistic gymnastics population. Heightened periods of gymnastics training have been
found to typically coincide with the phases during which physical development is prominent
(Daly et al., 2001; Wesley, 2001), contributing to the significant associations between
physical development and CSI in gymnasts. The concerning CSI predisposition of the
respective population warranted the exploration of indicators of CSI risk which are specific
to a female artistic gymnastics population. Three customary physical development
mechanisms (chronological ageing, maturation and growth), which were emergent from
previous literature appraised in Chapter 2, were interrogated in relation to BRIs to address
the respective research question. Complex relationships have been found to exist between
physical development-related biology and mechanics (Nuckley, 2013), to investigate the
respective interactions, a biomechanical approach was assumed.
Through use of a cross-sectional approach, informed through physical development and
biomechanical data gathered on a cohort of female artistic gymnasts, Chapter 4 analyses
exposed chronological age to have a large influence on 50% of fundamental skill BRIs.
Maturation status and anthropometric growth status were found to have large influences on
50% and 75% of handstand and forward walkover BRIs, respectively. The distinct
contribution of physical development to CSI biomechanical risk was revealed, with findings
from Chapter 4 further indicating growth to have the greatest influence on biomechanical
risk during the performance of fundamental skills. The prominent contribution of
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anthropometric growth to biomechanical outputs may be theoretically explained as the
altering body dimensions directly affecting limb dynamics and muscle forces (Hawkins and
Metheny, 2001).
Interpretation of the large regression effect outputs for anthropometric growth status on BRIs
revealed the gymnasts with high anthropometric growth statuses (greater bicristal breadth in
relation to biacromial breadth) to have increased biomechanical risk for DPSI (handstand
and forward walkover) and CoPy (handstand and forward walkover). For DLPSI
(handstand), the gymnasts with the greatest lumbo-pelvic stability were found to be those
with the highest or lowest anthropometric growth statuses (Chapter 4). The anthropometric
growth status outputs for posture in the handstand skill revealed the gymnasts with high and
low anthropometric growth statuses to have increased biomechanical risk. It was suggested
that the respective findings were reflective of the non-uniform growth of body segments, as
documented by Malina et al. (2004), among others. Handstand posture and DLPSI were
found to be inversely related for anthropometric growth status; the gymnasts who had high
lumbo-pelvic stability, displayed minimum lumbar angles and had either a low or a high
anthropometric growth status. Modification of spinal curvature throughout the process of
growth has been found to be coupled with altered forces, which may heighten injury
susceptibility (Donatelli and Thurner, 2014). The interaction between posture and lumbopelvic stability may therefore provide empirical support for the compensatory mechanisms
which gymnasts employ to deal with the altered mechanics associated with anthropometric
growth of the spine. The calculation of spinal forces throughout the handstand and forward
walkover within future research may further understanding of potential compensatory
mechanisms.
The prominent influence of anthropometric growth status on BRIs in Chapter 4 was
indicative of the need for further exploration of measures of growth and their contribution
to biomechanical risk in the female artistic gymnastics population. Chapter 5 subsequently
appraised the influence of b-ratio, length ratio (lower to upper-body) and mass ratio (lower
to upper-body) on BRIs collected over a 12 month period. B-ratio was found to have a large
influence on handstand and forward walkover DPSI with length and mass ratios having no
large effects on BRIs. The prominent influence of anthropometric breadth growth on wholebody stability, in comparison with length and mass ratio measures, may be explained by the
biomechanical goal of stability maintenance through the fine adjustments to the centre of
mass in relation to the base of support (Cotoros and Baritz, 2010). The large contribution of
b-ratio to general stability additionally supported the findings from Chapter 4; the gymnasts
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who had smaller pelvic breadths in relation to their shoulder breadths were found to have
greater general stability for both fundamental skills. The gymnasts with increased bicristal
breadths in relation to their biacromial breadths were evidenced to have lower general
stability for the handstand and forward walkover skills. Broad shoulders in relation to narrow
hips has been previously documented to be favourable for performance in female gymnasts
(Siatras et al. (2009). The investigation of b-ratio influence of CSI biomechanical risk has
provided novel empirical evidence to support the favourable low b-ratio from an injury
perspective. The cross-sectional and longitudinal research on female artistic gymnasts
provided quantitative support for the consideration of proportional growth in CSI screening
approaches.
The respective research question (RQ 2) provided valuable insight into the influence of
physical development mechanisms on BRIs in female artistic gymnasts, irrespective of time.
As time has been identified to play a vital role in the process of physical development
(Malina et al., 2013), interrogation of longitudinal data were considered essential in order to
examine the dynamic influence of physical development on BRIs. A third research question
(RQ 3) was subsequently developed to extend the insights gained from the cross-sectional
study of physical development and BRIs.
RQ 3. How does physical development contribute to biomechanical risk indicators in
female artistic gymnasts across time?
Growth has been identified as a dynamic process (Lloyd and Oliver, 2012), which is nonlinear across time (Rogol et al., 2000). The respective attributes of growth have supported
the findings that the rate at which an individual grows has a pronounced influence on the
potential for CSI development (Baranto et al., 2006; DePalma and Bhargava, 2006; Baranto
et al., 2009b). Therefore, longitudinal analyses were anticipated to reveal greater influences
of anthropometric growth on BRIs than cross-sectional examinations of female artistic
gymnasts. To inform the research, longitudinal data from a cohort of female artistic gymnasts
was gathered over a 12 month period. Chapter 5 analyses of the influence of time on
handstand and forward walkover BRIs revealed large effects for CoPy in the handstand and
DPSI in the forward walkover skill. The output signified limited agreement with the
hypothesis, with 50% of BRIs identified to be influenced by time to a large extent. Although
not conclusive, the large influence of time on half of the BRIs examined provided initial
evidence of the contribution of time to biomechanical risk. The respective findings were
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speculated to be reflective of the influence of anthropometric growth on BRIs, therefore, the
need for further exploration of the longitudinal nature of growth was affirmed.
Examination of the longitudinal data within Chapter 5 informed the finding that the gymnasts
with lower b-ratios (decreased bicristal breadth in relation to biacromial breadth) had
increased DPSI (increased biomechanical risk) for the handstand and forward walkover in
comparison with those gymnasts with higher b-ratios. Sub-division of the gymnastics cohort
according to the rate of bicristal breadth growth in relation to biacromial breadth growth,
analyses revealed large effects for the influence of b-ratio growth on handstand and forward
walkover DPSI (Chapter 6). The gymnasts who had a negative MGR and therefore
experienced increased bicacromial breadth growth in relation to bicristal breadth growth and
were found to have a significantly lower general stability than the positive MGR sub-group.
Therefore Chapter 6 findings, which considered the relative rate of breadth growth, provided
an explanation for the general stability findings within Chapter 5. Continual adjustments to
hand balance coping strategies as a result of increased biacromial growth were subsequently
accountable for the lower general stability found for the negative MGR gymnasts in
comparison with the positive MGR gymnasts. The respective explanation may additionally
translate to the positive MGR gymnasts as the primary area of development for the respective
gymnasts was at the bicristal breadth. In hand balance positions, the increased distance of
the pelvis from the base of support, in comparison with the shoulders, indicated positive
MGR to have a reduced influence on the maintenance of general stability. Marriage of the
findings from Chapter 5 (anthropometric growth status) and Chapter 6 (rate of
anthropometric growth) led to the recognition that the process of increased biacromial
breadth growth may be the primary indicator in reducing general stability across time. The
respective finding further endorsed the inclusion of b-ratio in CSI screening programmes.
When the cohort was sub-divided according to MGR, there was a lack of large effects for
the influence of time on BRIs, as evidenced in Chapter 6 (Section 6.3.1). Chapter 5 output
revealed time to have large influences on the gymnastics cohort when it was appraised as a
whole, yet intra-MGR sub-group large BRI effects were absent; the stable BRI outcomes
during the period of growth may therefore reinforce the influence of MGR on BRIs.
Longitudinal analyses were therefore found to be necessary to establish whether a gymnast
experienced positive or negative MGR, however, once divided into sub-MGR groups, crosssectional assessments of BRI may be sufficient.
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The knowledge gained in RQ 1, 2 and 3 has provided valuable initial insight into physical
development and CSI biomechanical risk in female artistic gymnasts. The respective
knowledge was significant in informing the research aim and research purpose, however,
further understanding to explain the anthropometric growth trends and subsequently
conclude in addressing the research aim was warranted. In addition, the advances made to
inform CSI screening necessitated preliminary evaluation to appraise translation of the
developed knowledge to practice, and subsequently conclude in informing the research
purpose. A final research question, RQ 4, was addressed to develop the respective insights.
RQ 4. How do inertial changes contribute to anthropometric growth and predict
longitudinal biomechanical risk indicators in female artistic gymnasts?
The longitudinal assessment of breadths has been documented as one of the most basic
measures of growth by Lloyd et al. (2014). The b-ratio measure is therefore highly
appropriate for use within screening programmes due to the ease of measurement by
gymnastics practitioners. However, exploration of the measure alone offers limited
understanding of why gymnasts experience different rates of b-ratio change. Address of the
respective research question was anticipated to advance empirical understanding of the
biomechanical underpinning of proportional b-ratio growth within the respective population.
Inertial changes have been evidenced to underpin the process of growth (Jensen and Nassas,
1988; Cappozzo and Berme, 1990; Jensen, 1993; Richards et al., 1999; Hawkins and
Metheny, 2001; Ackland et al., 2003). Appraisal of I and CoM of the whole-body, upper and
lower-bodies, upper-to-lower body ratios, and the trunk were undertaken for the female
gymnastics cohort to provide explanation of why some gymnasts experienced positive MGR,
whereas other individuals in the cohort experienced negative MGR. In conjunction with the
established contributions of MGR to BRIs, the findings enabled further comprehension of
the individual gymnasts’ predisposition to specific BRIs.
In Chapter 6 (Section 6.3.2), the rate of change of selected BSIP were assessed by way of
the extent to which they influenced MGR. The change in torso moment of inertia in the
transverse axis (torso Iy rate of change) was established to underpin MGR to the greatest
extend, highlighting a positive linear influence of torso Iy rate of change on MGR. As such,
the gymnasts with a negative MGR were identified to have a smaller torso Iy rate of change,
and the gymnasts with the greatest positive MGR were underpinned by the greatest rates of
change in torso Iy. Interpretation of the findings in relation to BRIs were suggestive that the
gymnasts who experienced increased bicristal breadth growth in comparison with biacromial
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breadth growth make greater use of the lumbo-pelvic segments for the performance of the
handstand skill. Chapter 6 findings revealed positive MGR employed different strategies to
the negative MGR gymnasts to control the musculoskeletal system for the performance of
the handstand skill. The same gymnasts had a tendency to experience torso Iy at an increased
rate, therefore increased use of lordotic posture may be mechanisms employed by the
gymnasts as coping strategies for the changing torso Iy. A reduction in lumbo-pelvic stability
may therefore be a consequence of the increase in changing torso Iy. In answer of the
previous research question (RQ 3), an explanation of the negative MGR gymnasts’ reduction
in general stability, in comparison with the positive MGR gymnasts was provided. It was
suggested that the process of heightened growth of the gymnasts’ biacromial breadths may
contribute to the lower whole-body general stability established, due to the gymnasts’
nervous systems needing to adapt to the altered body dimensions (McLester and Pierre,
2007).
To this point in the exploration of the biomechanical underpinning of morphological growth
in the female gymnastics population, longitudinal data has informed the understanding
gained. The finding that the rate of change in torso Iy underpinned MGR is highly valuable
in developing conceptual knowledge, however, the application of the respective knowledge
to practice must be evaluated to enable its effectiveness in informing a screening approach.
Evaluation of the respective findings, for the consideration of CSI implications was
subsequently conducted. Cross-sectional appraisal of the BSIP measure was anticipated to
be unable to provide sufficient indication of which BRIs specific gymnasts were at greatest
risk of. Chapter 3 evidenced the need for screening approaches to be tailored to individual
gymnasts. Using individual gymnast data, an innovative approach was undertaken to
evaluate cross-sectional torso Iy to identify whether a gymnast will experience positive or
negative MGR. The approach utilised the initial time point data from the female gymnastics
cohort to simulated gymnasts presenting for injury screening, which is typically conducted
at discrete time points. Evaluation of the developed knowledge using a cross-sectional
approach was of particular importance as a result of the findings of time having a mixed
influence on BRIs (Chapters 5 and 6). Due to the MGR deviation from the general population
trend which was evidenced by Malina et al. (2004), individual analyses of the negative MGR
gymnasts informed the evaluation study. Evaluation of the torso Iy of the respective
individual gymnasts in relation to the remaining cohort (i.e. the positive MGR gymnasts)
provided preliminary evidence that the gymnasts who would go on to experience negative
MGR may be more likely to have a greater torso Iy at initial collection. The preliminary
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evidence for the ability to reflect longitudinal changes through cross-sectional measures
provided novel support to inform CSI screening approaches for female artistic gymnasts.
7.3
Appraisal of Methodological Approaches
The concerning prevalence of CSI in the gymnastics population was little different in 2014
than in 1976, as evidenced in Chapter 2 (Section 2.2.1). The respective findings signified the
need for prevention-focused research of CSI in the gymnastics population. To inform
empirical investigations, a cohort of female artistic gymnasts were recruited to participate in
the biomechanical research project. To inform the first empirical chapter (Chapter 3) and
gain understanding of CSI biomechanical risk in the respective population, a general cohort
of competitive female gymnasts who ranged in age from nine to 15 years were recruited.
The requirement for the population to be free from injury at the time of testing, free from
previous back pain previous spinal injuries was dictated to emulate typical injury screening
approaches, for which the majority of athletes are healthy. Consistency of the gymnastics
cohort across each of the chapters was highly advantageous in developing understanding of
the translation of cross-sectional and longitudinal knowledge to injury screening.
Other sport-related injuries, for example, anterior cruciate ligament injuries, have been
explored using injury prevention models such as the injury prevention framework presented
by Donnelly et al. (2012). Although considered necessary, in accordance with CSI
prevalence, to the researcher’s knowledge, the empirical exploration of CSI risk in the
female gymnastics population to inform injury prevention has been illusive in previous
literature. In order to develop specific understanding of the causes of CSI, an etiological
model of sports injury which was developed by Meeuwisse et al. (2007) informed the
respective research. Figure 7.1 highlights the way in which the respective research
contributed to knowledge of risk factors for CSI through use of Meeuwisse et al. (2007)’s
model.
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Figure 7.1. Exemplification of the focus of each chapter (indicated in the key) in relation to
Meeuwisse et al. (2007)’s etiological model.
In relation to Meeuwisse et al. (2007)’s etiology model, the first empirical chapter (Chapter
3) provided descriptive insight into the exposure of female artistic gymnasts to extrinsic risk
factors, referred to in the respective research as BRIs, through the performances of handstand
and forward walkover skills. An appraisal of previous literature (Chapter 2) informed the
BRIs of greatest relevance to the respective research, in addition to the prominent intrinsic
risk factors for CSI, referred to in the respective research as exposure factors (e.g. growth).
Meeuwisse et al. (2007)’s model highlight the multi-faceted nature of intrinsic risk to sports
injuries. Physical development mechanisms were identified as prominent intrinsic risk
factors which informed the predisposition of female gymnasts to CSI (Tanchev et al. (2000);
Brüggemann (2010); Kerssemakers et al. (2009)). The initial analyses for Chapter 4 explored
the intra-exposure factor relationships, with the remainder of the chapter focused on the
influence of the respective exposure factors on BRIs. Chapter 5 and Chapter 6 extended the
knowledge gained of the influence of intrinsic risk factors on extrinsic risk factors, providing
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insight into the possible transition of predisposed female gymnasts to susceptible female
gymnasts. The empirical research which appraised the contribution of physical development
to BRIs provided valuable support for the status of physical development as a predisposing
factor for CSI.
Meeuwisse et al. (2007)’s model provided a sound basis from which the respective research
was developed. Valuable contributions to the etiology model have subsequently been made
through empirical investigation of physical development and BRIs in a female gymnastics
cohort. As it is, Meeuwisse et al. (2007)’s model addresses the multifactorial nature of
intrinsic risk factors, however, no indication of different weightings of the respective factors
was provided. The respective research has provided evidence of the heightened influence of
anthropometric growth status in relation to chronological age and maturation status on BRIs.
The disproportionate influence of physical development mechanisms on CSI predisposition,
as evidenced in Chapter 4 (Section 4.3.2), supported the consideration of relative
contributions of intrinsic risk factors. In addition, the etiological model utilised a generic
approach, which was intended to extend across all athletes. Although such an approach
would be desirable for injury screening, whole group and individual BRI disparities were
substantiated in Chapter 3 (Section 3.3.2), therefore indicating the inability to represent
individual etiology through use of a single model. The empirical findings from the respective
research may be utilised to provide potential adaptations to Meeuwisse et al. (2007)’s model
and subsequently advance the ability for use of the model to inform injury prevention
approaches, such as injury screening.
7.3.1
Research Type and Design
In order to address the research aim, the thesis employed descriptive, inferential and
explanatory research approaches. An overview of the emergence of the research types, study
designs and how each empirical chapter informed the four thesis research questions is
presented in Figure 7.2.
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Figure 7.2. Illustration of the multi-faceted approach taken to address the thesis aim, with inclusion of details of research types, study designs, key areas of
focus and prominent outcomes from each chapter.
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The first empirical chapter (Chapter 3) provided initial quantification of BRIs experienced
by a cohort of female artistic gymnasts during the performance of two fundamental
gymnastics skills. To gain preliminary insight into BRIs in the respective population, a crosssectional approach informed the address of the descriptive chapter questions. Group and
individual gymnast approaches were undertaken and evaluated within the respective chapter.
The investigation of group and individual BRI responses informed the role of each within
subsequent chapters. Individual screening of female gymnasts was deemed necessary,
however, in compliance with previous research, the investigation through which the
development of injury screening was informed was based on grouped responses. The
descriptive research in Chapter 3 additionally enabled the understanding of BRIs with
greatest relevance to a female artistic gymnasts performing fundamental gymnastics skills
to be gained. Chapter 4 utilised the knowledge developed within Chapter 3 and adopted an
inferential approach by investigating the contribution of physical development to CSI risk.
As within Chapter 3, a cross-sectional approach was undertaken to inform Chapter 4 and a
group design was implemented to gain a base of knowledge for further development in future
chapters.
The study design for Chapter 5 was developed from Chapter 4 outputs, which exposed
anthropometric growth status to have a prominent influence on BRIs in comparison with
chronological age and maturation status. Chapter 5 was additionally a piece of inferential
research (Gray, 2013). The chapter utilised longitudinal data, which was collected at three
discrete time points. The collection of longitudinal data to satisfactorily explore the dynamic
nature of physical development in a unique population is an approach which is greatly
advocated (Tanner, 1962; Beunen and Malina, 2008; Malina et al., 2013). The challenging
nature of monitoring a unique, predisposed population over repeated collections increased
the value of the insight gained within the respective research. A group approach informed
Chapter 5 analyses, which explored the influence of different measures of anthropometric
growth status and the contribution of time to BRIs. Findings from Chapter 5 evidence the
large contributions of time on a number of BRIs, therefore underpinning the decision for the
final chapter analysis to be longitudinal in nature. The final empirical chapter (Chapter 6)
aimed to provide an explanation for the large influence of b-ratio on BRIs, which had been
established in previous chapters (Chapter 4 and Chapter 5). Therefore, the respective chapter
was informed through explanatory research, as identified by Gray (2013). A group approach
was utilised to address the former part of the respective chapter; in accordance with Chapter
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3 findings, the evaluation of the group-based findings were interrogated through individual
gymnast analyses.
7.3.2
Gymnastics Skill Selection
The integral role of the respective skills throughout the thesis warranted prudent skill
selection. As the nature of the study design dictated gymnasts of a range of chronological
ages and skill levels perform the respective skills, two of the most fundamental skills in
gymnastics, the handstand and the forward walkover, were selected for analyses (Chapter 2,
Section 2.5.1). Each of the gymnasts who informed the cohort had a minimum training
experience in the sport of two years (Chapter 5, Section 5.2.1), throughout which, the
handstand and forward walkover skills were routinely performed. Selection of the
fundamental skills therefore assisted in controlling the influence of performance on BRIs in
the respective research.
The selection of progressive skills, i.e. the use of one skill to inform the learning of another,
was an important element in the selection of the handstand and forward walkover skills.
Biomechanical similarities between progression skills have been stressed in previous
gymnastics research, for example, Irwin and Kerwin (2007) and therefore were additionally
considered necessary to explore from an injury prevention perspective. Focus of the
fundamental skills was additionally informed by the identification of repetitive movements
one of the prominent BRIs in previous literature (Kim and Green, 2011). Repetition has been
identified to be central to the learning of skill progressions (Mitchell et al., 2002) thus,
furthering the suitability of the investigation of progressive fundamental skills.
Bradshaw and Hume (2012) further advocated the use of fundamental skills within injury
prevention approaches, to enable screening to be conducted on each gymnast in the
population of interest. The prominent phase of each of the skills in the way of BRIs was
established to be that of double support, i.e. when the hands are the only segments in contact
with the floor. Innovative biomechanical investigation of the hand-balance phases of
fundamental gymnastics skills informed knowledge which is subsequently anticipated to be
highly informative for biomechanical approaches such as screening protocols. Initial insight
into whether one skill can be used to represent addition skill biomechanical risk, or if a
number of skills are required was gained from the respective research.
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7.3.3
Biomechanical Risk Indicator Measurements
In alignment with the findings from the review of literature (Chapter 2, Section 2.4), the
BRIs encompassed in the respective research were posture, lumbo-pelvic range of motion
(LPRoMx and LPRoMy), general stability (DPSI), centre of pressure range (CoPx and
CoPy) and lumbo-pelvic stability (DLPSI). The most appropriate method of analyses of the
respective biomechanical measures was deemed to be through use of an in-vivo approach.
Risk factors which are incorporated into injury screening approaches are typically informed
through basic quantitative science (e.g. functional movement screening (Chorba et al.,
2010)). Innovative insight into the extent to which more sophisticated biomechanical
measurements of BRIs may facilitate injury screening approaches was subsequently
developed from the respective research. The use of non-invasive contemporary technology,
specifically CODA motion and Kistler force plates, enabled the acquisition of fine-grained
mechanical data to inform novel understanding of BRIs in a female gymnastics cohort.
Use of an automated motion analysis system enabled the collection of three-dimensional
kinematic data without dictating a trial or marker limit. Through test-retest reliability testing,
the tracking of CODA motion markers was found to alter segment length by a maximum of
2% (Appendix A.12). The kinematic data informed the collection of posture, lumbo-pelvic
range of motion (LPRoMx and LPRoMy) and DLPSI for each trial of the handstand and
forward walkover. The non-invasive approach, which enabled tracking of vertebral motion
through the use of surface markers placed on the fascia covering the spinous processes,
underpinned spinal kinematic data and informed a large proportion of BRIs in the respective
research. The approach to estimate the in-vivo spinal motion has been used in a wealth of
previous research including Leardini et al. (2011) and Crewe et al. (2013a). An embedded
Kistler force plate permitted the collection of ground reaction force in the medio-lateral,
anterior-posterior and vertical directions (x, y and z, respectively), informing the measure of
general stability (DPSI) and centre of pressure range in two axes (CoPx and CoPy).
Continuous data profiles were collected for lumbo-pelvic angles (transverse plane (x-y),
sagittal plane (y-z) and frontal plane (z-x)), lumbar angle (x-y), ground reaction force (x, y
and z) and centre of pressure (x and y) and were used to inform each of the respective discrete
BRIs. Supporting continuous data were provided in Chapter 3 (Section 3.2.5). Discrete
biomechanical indicators were selected for analyses in the respective research due to the
intended application of the research. Discrete measures for CSI screening were deemed to
be of greatest suitability, as the appraisal of each measure enables quick and easy
187
interpretation by the practitioner, therefore reducing the amount of time necessary to screen
for CSI risk. It is anticipated that practitioners may have a limited time with each gymnast,
therefore requiring inclusion of important biomechanical measures for which the analyses
can be performed within a time constraint; the interpretation of continuous data is considered
to be less suitable for inclusion within a screening approach.
7.3.4
Physical Development Measurements
Three mechanisms of physical development, chronological ageing, maturation and growth,
informed the initial exploration of physical development, an intrinsic risk factor in relation
to Meeuwisse et al. (2007)’s model, in a female artistic development cohort. As
anthropometric growth status was established to have a prominent influence on BRIs in
comparison with chronological age and maturation status (Chapter 4, Section 4.3.2), eight
further measures informed the further exploration of anthropometric growth. The respective
measures were whole-body height, upper-body length, lower-body length, lower to upperbody length ratio, whole-body mass, upper-body mass, lower-body mass and lower to upperbody mass ratio. The image-based inertia approach outlined by Gittoes et al. (2009) was
used to calculate each of the anthropometric growth status measures. The use of an imagebased approach to quantify anthropometric measures is desirable as a practitioner is able to
obtain the images and digitise and analysed the respective data at a later time point, the
participant contact time which the method necessitates is minimal. The appropriateness of
the image-based measurement approach extends as a result of its non-invasive nature,
therefore overcoming potential ethical issues when working with predisposed female
gymnasts. The method was particularly appropriate for use within screening approaches.
Use of the image-based approach for the quantification of anthropometric data was an
innovative feature of the research design. Reliability testing of the bicristal breadth and
biacromial breadth measurements determined through digitisation of the frontal plane
whole-body image provided initial support for use of the technique (Appendix A.18). Intersession reliability for the digitised bicristal and biacromial breadth measurements revealed a
maximum difference of 0.8% of mean bicristal breadth across trials and 0.9% of mean
biacromial breadth across trials. A maximum b-ratio difference of 0.8% for each of the three
informing trials was evidenced. Using a caliper measurement tool, bicristal breadth accuracy
of the image-based approach was calculated, from which a 0.3% error of the mean measured
breadth was identified. The digitised to measured biacromial breadth difference was revealed
188
to be 2.4% of the mean measured breadth. Maintenance of a consistent group of gymnasts
across the chapters alleviated the respective accuracy as a concern within the respective
research. Additionally, as individual injury screening has been advocated, the reliability data
for breadth measures provided support for the use of the image-based technique to inform
anthropometric growth measures within CSI injury screening approaches.
Rigorous intra- and inter-digitiser, inter-condition and inter-session digitising reliability
testing of inertia model outputs were additionally assessed (Appendix A.15). Input of the
relevant digitised coordinates into Yeadon (1990)’s mathematical model, in combination
with Dempster (1955)’s density values enabled the calculation of predicted whole-body mass
for each of the participants and trials. The reliability outputs ranged from 0.3% to 2.0% of
the mean predicted whole-body mass For the gymnastics cohort at initial time point (n = 14),
appraisal of the predicted whole-body mass in comparison with the measured whole-body
mass through use of a Kistler force plate, revealed a mean (SD) difference of 4.8 (3.4)%.
The model error was in excess of the 2.9% reported by Gittoes et al. (2009). However, as
was concluded for the bicristal and biacromial breadth measures, the use of the same
gymnasts throughout the respective research diminished the potential errors which may have
been brought about as a result of the respective model accuracy findings. In addition to
measurement errors, the accuracy of the inertial models was speculated to be limited by the
uniform segment density data. To condense the influence of density inaccuracies on the
mathematical model outputs, customisation of Dempster (1955)’s density values was
undertaken for each gymnast using the approach outlined in Appendix A.16.
An additional measure which was determined for each gymnast using a non-invasive
approach was the PDS questionnaire which was used to calculate maturation statuses in
Chapter 4. Consideration of the use of the measure in screening practice strengthened the
rationale for the non-invasive measurement as opposed to a physical examination or use of
imaging techniques, as were explored in Chapter 2 (Section 2.6.2). Although superior in the
levels of accuracy which have been reported for imaging techniques (Baxter-Jones et al.,
2002; Verhagen and van Mechelen, 2008), use of the invasive technique would have required
routine radiographs deemed unacceptable in the immature population from an ethical
perspective (Mac-Thiong et al., 2004). The ability to collect sexual maturation data by a
practitioner in a field setting, rather than the gymnast attending a clinic, is a perceived
strength of the respective research. Non-invasive methods were used to determine each
physical development measure, and the respective research was therefore compliant with the
189
ability for the approaches to be readily employed in practice. The accessibility of the research
outputs was subsequently enhanced.
7.4
7.4.1
Contributions to Research and Practice
Contributions to Applied Research
Through the examination of a predisposed population, the respective research provided
novel contributions to advance understanding of the extent to which biomechanical risk was
influenced by specific intrinsic risk factors. Throughout the respective research, the
prominent contribution of anthropometric growth to BRIs was emergent, highlighting the
need for the consideration of growth in future injury-related biomechanical research. In
addition, the need for a greater emphasis on proportional growth was supported through the
empirical research findings. Understanding of the influence of physical development on
BRIs contributed to the etiological model developed by Meeuwisse et al. (2007), as
appraised in Chapter 2 Section 2.5. In addition, the respective research informed Donnelly
et al. (2012)’s injury prevention model. In relation to Donnelly et al. (2012)’s model, the
research was conducted on in-lab etiology and informed athlete screening, therefore
contributing to an established method of approach which may inform the advance of CSI
prevention in female artistic gymnasts.
Analyses of anthropometric growth status (Chapters 4 and 5) indicated the large influence
of time on a number of BRIs. However, further investigation of the rate of morphological
growth (MGR) revealed that following division into MGR sub-groups, time had small or
medium effects on BRIs. The research subsequently indicated that longitudinal measurement
of growth is necessary for the identification of whether gymnasts experienced positive or
negative MGR, however, cross-sectional measures of BRIs may be sufficient for CSI
screening approaches. As consideration of time in relation to BRIs had previously been
neglected, the respective findings made a pronounced contribution to knowledge of the
influence of the measure on injury risk factors. The respective understanding was informed
by data collections across a 12 month period, although valuable insight was gained, extended
longitudinal research of the predisposed gymnastics population is advocated.
The use of contemporary biomechanical technologies to advance understanding of CSI risk
factors was demonstrated within the empirical research. Measurement of biomechanical risk
indicators throughout the performance of fundamental skills enabled novel gymnastics-
190
specific BRI data capture. The innovative image-based method to quantify anthropometric
measures provided initial endorsement of its potential use within future applied research.
7.4.2
Contributions to Applied Practice
The intention of the respective research was to extend scientific knowledge of physical
development and CSI risk to applied practice, specifically CSI screening. The BRI
differences between MGR sub-groups (Chapter 6) provided confirmation of the importance
of the monitoring of young gymnasts’ morphology throughout the period of growth. The
respective chapter findings were further indicative of the need for sports scientists and
practitioners working with young female gymnasts to focus on the lumbar and lumbo-pelvic
region in the gymnasts who experience positive MGR. In addition, concentration of efforts
on improving general stability of the gymnasts who experience negative MGR was
advocated. An evaluation study which interrogated the effectiveness of cross-sectional torso
Iy in forecasting individual longitudinal MGR provided initial evidence for the incorporation
of the respective BSIP in CSI screening approaches. The research findings highlighted the
need for a baseline measure, and subsequent longitudinal monitoring in relation to the
baseline measure. In compliance with the findings from Chapter 3 (Section 3.3.2), the
screening approach should be conducted on individual gymnasts. An individual, longitudinal
approach for CSI screening is supported by a contemporary performance-based study (Till
et al., 2015) and injury prevention research (Hume et al., 2013; DiFiori et al., 2014).
The research findings in relation to the screening process are outlined in Figure 7.3. It is
suggested that upon initial presentation to a practitioner, quantification of the individual’s
torso Iy may provide indication of the type of MGR (positive or negative) the gymnast is
more likely to experience. Use of the image-based approach outlined in Chapter 6 (Section
6.2.3) would enable calculation of torso Iy with little gymnast contact-time; the respective
approach is therefore desirable for use in practice. Informed by the torso Iy data, the
practitioner will then be able to make a recommendation of which BRIs should be of focus
for the individual for assistance in CSI prevention. Using data collected on the respective
gymnast over an extended period of time, the gymnast can then be determined to be of
positive MGR or negative MGR, informed through measures of b-ratio and chronological
age. The respective MGR data is anticipated to enable the practitioner to further establish
the BRIs of focus for the specific gymnast in accordance with the MGR trend experienced.
191
Figure 7.3. Application of research findings to chronic back pain and chronic spinal injury screening in the female gymnastics population.
192
The research has made valuable contributions to applied practice, with an outcome approach
which can be readily implemented with little difficulty and relatively simplistic equipment.
One notable feature of the proposed screening process is the ability to take a cross-sectional
photograph at initial collection and acquire outcomes to begin working from preceding the
collection of longitudinal data. The respective feature is supportive of previous research
which has advocated the initiation of injury prevention as early as possible (Jackson et al.,
1981; Gerbino and Micheli, 1995; Sands et al., 2011).
With initial support and guidance in the equipment set-up, understanding the data collection
technique, and the process of calculating torso Iy, it is anticipated that sports practitioners
will be able to implement the screening approach and initiate CSI screening within individual
gymnasts. It is therefore foreseen that the approach outlined in Fig 7.3 can be followed by
sport physiotherapists or strength and conditioning coaches with little expertise in
biomechanics. Although the research was undertaken within the biomechanical discipline,
the application to practice dictates the need for multi-disciplinary effort. A biomechanist
would be required to train a sports practitioner, or run the screening collections and data
analysis, whereas a physiotherapist and strength and conditioning coach would be required
to focus efforts on the outcome BRIs. The application of the research findings to practice is
considered an initial step to further research and development of the approach, along with
regular evaluation of the screening method.
7.5
7.5.1
Limitations and Directions for Future Research
Limitations
Undertaking research with the intent of informing applied practice is typically challenging.
The need for the research findings and methodology to translate to applied practice often
dictates aspects of the research design which may have otherwise have been approached in
a different manner. While attempting to ensure the methodology could be replicated with
relative ease by gymnastics and sport science practitioners, several limitations transpired.
The selection of skills was primarily informed through the fundamental level of each skill
and the ability to reliably measure BRIs throughout its performance. The handstand and
forward walkover sufficed the criteria and analysis of the respective skills provided valuable
insight. However, had there been no constraints in the application of the research findings to
practice, it would have been of great interest to explore more skills which encompass a better
193
overlap of BRIs. Had it been suitable to appraise more complex gymnastics skills, rotationalbased skills would have been desirable to explore due to the support for rotation in previous
literature (Section 2.4.1).
As one of the most notable BRFs in previous literature, the inability to examine mechanical
spinal loading was a further limitation of the respective research. The favourable approach
for the analysis of mechanical spinal loading within previous research was through the
development of a biomechanical model (Appendix A.4). Due to focus on application to
practice the model-based approach was beyond the scope of what was feasible to explore
within the respective research, therefore, the knowledge gained is speculated to be somewhat
limited in the absence of the potentially important variable.
A limitation which was not dictated by the practical application of the research was the lack
of consideration of where the gymnasts’ hands were placed on the force plate during the
performances of the handstand and forward walkover skills. Although it was ensured that
both hands were placed within the boundary of the force plate, previous research has
identified greater errors in dynamic forces when application occurs beyond the perimeter of
the load cells, towards the corners of the plate (Kerwin, 1997). Care in monitoring of hand
placement to within the load cells would have overcome the respective limitation.
The etiological variables, specifically exposure factors and BRIs, were selected from
previous literature. As a result of the approach taken, understanding of the variables was
restricted by the evidence-base available. As not all variables had been previously researched
to the same extent (as demonstrated in Sections 2.3 and 2.4), the ability to decipher the most
pertinent exposure factors and BRIs to CSI were largely dependent of the frequency of
identification. Had each variable been included in the same quantity of research, the
exposure factor and BRI selections would have been solely dependent on the true
associations with CSI may have permitted a more reliable selection of variables.
Finally, the implications of the research must be consider in light of the time scale over
which the research was undertaken. Although the 12 month collections were advanced from
the typical cross-sectional research undertaken within the biomechanical discipline, it would
have been beneficial to have conducted the research over an extended period of time. The
ability to do so within the respective research was hindered by the time scale which was
permitted, nevertheless, the findings and subsequent implications of the research are limited
as a result of the inability to collect data over a greater period of time.
194
7.5.2
Directions for Future Research
The longitudinal monitoring of female artistic gymnasts across a 12 month period enabled
important insights into the role of anthropometric growth in respect to CSI risk to be gained.
Continued monitoring of the respective cohort beyond 12 months would have been
advantageous to extend understanding of physical development and BRIs and subsequently
inform injury screening. To advance the developed understanding, future studies should look
to investigate the contribution of physical development to BRIs over an extended period of
time. To inform talent identification, contemporary research has measured anthropometry
and physical characteristics of rugby league players over a six-year period (Till et al., 2015);
a similar approach, using anthropometry and BRIs, may be beneficial to informing pain and
injury screening programmes in female artistic gymnasts.
Analyses of the intra-physical development mechanism relationships within Chapter 4
(Section 4.3.1) evidenced a large relationship between anthropometric growth with
chronological age (r>0.5). A medium relationship was revealed between anthropometric
growth status and maturation status (r>0.3), therefore initial support for the necessary
consideration of maturation status in CSI screening was provided. Due to the dominant
influence of anthropometric growth status on BRIs, further exploration of maturation within
the female gymnastics population was not undertaken in the respective research. Future
investigation of the role of maturation in CSI screening is therefore warranted.
Knowledge of the contribution of physical development to CSI risk within a female artistic
gymnastics cohort was advanced from a biomechanical perspective. Although biomechanics
has been crucial in quantifying CSI risk and explaining how the shape of gymnasts’
proportional anthropometric growth influences BRIs, advanced understanding may be
gained from other scientific disciplines. The integration of other perspectives, for example,
physiological and psychological, may establish more holistic understanding of CSI in
physically developing gymnasts. Using a multidisciplinary approach the idealised notion, to
monitor physical development and BRIs of healthy gymnasts who develop CSI, would be
invaluable in establishing effective prevention strategies. Although such research would
require large sample sizes, longitudinal monitoring and may be expensive to run (McBain et
al., 2012), the insight would enable rigorous evaluation of the findings developed through
the exploration of intermediate outcomes of CSI, i.e. risk factors.
195
7.6
Final Note
Heightened predisposition to CSI development has been evidenced throughout the process
of physical development (Tanchev et al., 2000; Brüggemann, 2010; Kim and Green, 2011).
The complex interactions of biology and mechanics informed the need for biomechanical
knowledge development to inform injury prevention approaches (Nuckley, 2013). The
respective research aimed to develop understanding of the contribution of physical
development to biomechanical indicators of chronic spinal injury risk in female artistic
gymnasts performing fundamental gymnastics skills. The aim was achieved through the use
of unique multi-faceted approach informed by cross-sectional and longitudinal analyses of a
predisposed female gymnastics cohort. The monitoring of a population which is inherently
difficult to access and track enabled novel understanding of physical development and its
contribution to biomechanical risk to be gained. The rate at which the gymnasts’ bicristal
and biacromial breadths grew was revealed to be prominent to chronic spinal injury risk. The
evaluation of inertial underpinning of the growth-related changes provided preliminary
support for the inclusion of torso moment of inertia in screening approaches for predisposed
female gymnasts. Informed by the cross-sectional evaluation of longitudinal findings, a
baseline measure and subsequent longitudinal monitoring in relation to the baseline
measures was advocated for the screening of female artistic gymnasts. The research used
innovative approaches to develop knowledge to contribute to the development of
individualised gymnastics-specific CSI screening approaches.
196
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APPENDICES
A.1 - EPIDEMIOLOGY LITERATURE
Table A.1. Details of chronic back pain and chronic spinal injury epidemiological literature
Condition
Study
Year
Prevalence
rate
Diagnostic
criteria
Region of
pain/ injury
Age
(years)
Sport
Performance
Level
Back pain
prevalence
Gender
Sample origin
Number of
participants
(Jackson et
al.)
1976
30%
Any history of
previous back
pain
N/A
6 – 24
Gymnastics
Regional,
national and
International
N/A
Female
Volunteers
representing
their teams in
gymnastics
competition
without current
low back pain
100
(Szot et al.)
1985
49%
Spinal column
pain
Lumbosacral
segment
15 – 31
Gymnastic
National
N/A
Male
Members of the
National
gymnastics
team
41
(Swärd et
al.)
1990
85%
Previous or
present pain
with a
duration of
more than one
week or
recurrent pain
irrespective of
duration
Thoracic;
lumbar
16 – 25
Gymnastics
National
N/A
Male
Members of the
Swedish
National team
or the Swedish
National Junior
Team
26
Chronic back pain
237
66%
Previous or
present pain
with a
duration of
more than one
week or
recurrent pain
irrespective of
duration
Thoracic;
lumbar
14 – 25
Gymnastics
National
N/A
Female
Members of the
Swedish
National team
or the Swedish
National Junior
team
26
(Swärd et
al.)
1991
79%
Previous or
present pain
with a
duration of
more than one
week or
recurrent pain
irrespective of
duration
Thoracic;
lumbar
19 – 29
Gymnastics
Elite
N/A
Male
Present or
previous
members of the
Swedish
National team
24
(Dixon and
Fricker)
1993
50%
N/A
Lumbosacral
16.5
(average)
Gymnastics
(artistic)
Elite
N/A
Male
Artistic
gymnasts who
trained at the
Australian
Institute of
Sport between
1982 and 1991
42
(Hutchinson)
1999
86%
N/A
N/A
15 - 17
Gymnastics
(rhythmic)
Elite
N/A
Female
Members of the
United States
Rhythmic
Gymnastics
National Team
7
238
(Lundin et
al.)
2001
67%
Pain during
the last year or
pain causing
the inability to
work in the
past
Thoracic;
lumbar
28.9 –
37.7
Gymnastics
Previously
elite
N/A
Male
Previously
Swedish top
athletes with the
highest possible
international or
national ranking
24
three years
67%
Pain during
the last year or
pain causing
the inability to
work in the
past three
years
Thoracic;
lumbar
27.0 –
38.9
Gymnastics
Previously
elite
N/A
Female
Previously
Swedish top
athletes with the
highest possible
international or
national ranking
24
(Bennett et
al.)
2006
58%
Any low back
pain currently
limiting
training to any
degree; any
prior (but not
current) low
back pain that
previously
compromised
training
N/A
12 – 20
Gymnastics
Olympic
N/A
Female
Gymnasts who
were invited to
attend a specific
weeklong
national training
camp
19
(Donatelli
and Thurner)
2014
49%
Low back pain
was
determined
through the
Osaka City
Lumbar (L1
to S1)
19.7
(average)
Gymnastics
Pre-elite,
elite and
National
Back pain
was assessed
but was not
part of the
participant
Male and
female
Japanese
collegiate
gymnasts
104
239
University
questionnaire
selection
criteria
Degenerated disc
(Tertti et al.)
1990
9%
N/A
Lumbar (L12; L4-5);
Lumbosacral
(L5-S1)
8 - 19
Gymnastics
District,
national and
international
History of
back pain
was
recorded but
was not part
of the
selection
criteria
Male and
female
N/A
35
(Swärd et
al.)
1991
79%
Reduced disc
signal
intensity
Thoracic;
lumbar
19 – 29
Gymnastics
Elite
Back pain
was assessed
but was not
part of the
participant
selection
criteria
Male
Present or
previous
members of the
Swedish
National team
24
(Katz and
Scerpella)
2003
43%
N/A
Thoracic
(T7-12);
Lumbar (L45)
11 - 17
Gymnastics
Competitive
(pre-elite)
All
gymnasts
reported
back pain
Female
Pre-elite
gymnasts from
a single
gymnasium
7
(Bennett et
al.)
2006
63%
Mild –
Lumbar (L12; L3-4; L45);
lumbosacral
(L5-S1)
12 – 20
Gymnastics
Olympic
4 with
current pain,
7 with prior
history of
pain and 8
with no pain
Female
Gymnasts who
were invited to
attend a specific
weeklong
national training
camp
19
osteophyte
formation,
vertebral
endplate
marrow
changes
and/or
abnormal
240
decreased
signal within
the nucleus
pulposis.
Moderate –
the above
criteria with
disk space
narrowing
Severe –
loss of the
normal disc
space with
bone-on bone
articulation of
the vertebral
bodies
(Donatelli
and Thurner)
2014
40.4%
Decreased
signal
intensity of the
intervertebral
discs from
L1.L2 to
L5/S1
Lumbar (L1
to S1)
19.7
(average)
Gymnastics
Pre-elite,
elite and
National
Back pain
was assessed
but was not
part of the
participant
selection
criteria
Male and
female
Japanese
collegiate
gymnasts
104
(Jackson et
al.)
1976
11%
Bilateral pars
interarticularis
defects
Lumbar (L5)
6 – 24
Gymnastics
Regional,
national and
international
Previous or
current LBP
was
recorded but
it was not
Female
Volunteers
representing
their teams in
gymnastic
competition
100
Spondylolysis
241
part of the
selection
criteria
(Rossi)
1978
33%
A cleft in the
neural arch of
a vertebra at
the level of
isthmus
Lumbar
15 – 25
Gymnastics
N/A
The majority
suffered
from
significant
back pain
Male and
female
Competitive
athletes
137
(Tertti et al.)
1990
29%
N/A
Lumbar (L5)
8 – 19
Gymnastics
District,
national and
international
History of
back pain
was
recorded but
was not part
of the
selection
criteria
Male and
female
N/A
35
(Rossi and
Dragoni)
1990
16%
N/A
N/A
15 – 27
Gymnastics
N/A
Current LBP
Male and
female
Competitive
athletes
417
(Goldstein et
al.)
1991
9%
N/A
N/A
11.8
(average)
Gymnastics
Pre-elite
No current
LBP
Female
N/A
11
21%
N/A
N/A
16.6
(average)
Gymnastics
Elite
No current
LBP
Female
N/A
14
13%
N/A
N/A
25.7
(average)
Gymnastics
Olympic
No current
LBP
Female
N/A
8
7%
N/A
Lumbosacral
16.5
(average)
Gymnastics
(artistic)
Elite
Current pain
Male
Artistic
gymnasts who
trained at the
Australian
Institute of
42
(Dixon and
Fricker)
1993
242
Sport between
1982 and 1991
(Soler and
Calderón)
(Bennett et
al.)
2000
2006
14%
N/A
Lumbosacral
13.5
(average)
Gymnastics
(artistic)
Elite
Current pain
Female
Artistic
gymnasts who
trained at the
Australian
Institute of
Sport between
1982 and 1991
74
17%
N/A
N/A
14.34
(average)
Gymnastics
(artistic)
Elite
53% were
symptomatic
Male and
female
Gymnasts who
underwent
medical
checkups
between April
1998 and May
1997
112
10%
N/A
N/A
14.34
(average)
Gymnastics
(rhythmic)
Elite
67% were
symptomatic
Male and
female
Gymnasts who
underwent
medical
checkups
between April
1998 and May
1997
92
16%
A disruption
of the pars
interarticularis
Lumbar (L3;
L5)
12 – 20
Gymnastics
Olympic
4 with
current pain,
7 with prior
history of
pain and 8
with no pain
Female
Gymnasts who
were invited to
attend a specific
weeklong
national training
camp
19
243
Spondylolisthesis
(Jackson et
al.)
1976
6%
First degree
spondylolisthe
sis
Lumbosacral
(L5-S1)
6 – 24
Gymnastics
Regional,
national and
International
Previous or
current LBP
was
recorded but
it was not
part of the
selection
criteria
Female
Volunteers
representing
their teams in
gymnastic
competition
100
(Rossi)
1978
9%
A cleft in the
neural arch of
a vertebra at
the level of
isthmus with
the consequent
ventral gliding
over the body
of the vertebra
beneath
N/A
15 – 25
Gymnastics
N/A
The majority
suffered
from
significant
back pain
Male and
female
Competitive
athletes
137
(Bennett et
al.)
2006
16%
Anterior
slippage of the
superior
vertebral body
on the inferior
one (graded 1
to 5)
Lumbar (L34);
lumbosacral
(L5-S1)
12 – 20
Gymnastics
Olympic
4 with
current pain,
7 with prior
history of
pain and 8
with no pain
Female
Gymnasts who
were invited to
attend a specific
weeklong
national training
camp
19
(Donatelli
and Thurner)
2014
0.9%
Ventral
slippage of
one vertebral
body onto
Lumbar (L1
to S1)
19.7
(average)
Gymnastics
Pre-elite,
elite and
national
Back pain
was assessed
but was not
part of the
Male and
female
Japanese
collegiate
gymnasts
104
244
another
(measured by
the Meyerding
grading
system)
participant
selection
criteria
Scoliosis
(Tanchev et
al.)
2000
12%
Scoliotic
curves of 10°
or more
Thoracolumbar;
lumbar
10 - 16
Gymnastics
N/A
No previous
LBP that
was severe
enough to
undergo
radiograph
examination
s
Female
Girls who had
been training in
rhythmic
gymnastics for
more than 5
years
100
(Swärd et
al.)
1991
38%
Intervertebral
disc with a
lower height
than the discs
immediately
above
Thoracic;
lumbar
19 – 29
Gymnastics
Elite
Back pain
was assessed
but was not
part of the
participant
selection
criteria
Male
Present or
previous
members of the
Swedish
National team
24
(Katz and
Scerpella)
2003
29%
N/A
Thoracolumbar
(T12-L1);
Lumbar (L45)
11 - 17
Gymnastics
Competitive
(pre-elite)
All
gymnasts
reported
back pain
Female
Pre-elite
gymnasts from
a single
gymnasium
7
Disc height
reduction
Herniated disc
245
(Bennett et
al.)
2006
21%
Extension of
disc material
beyond the
interspace
Lumbar (L45);
lumbosacral
(L5-S1)
12 – 20
Gymnastics
Olympic
4 with
current pain,
7 with prior
history of
pain and 8
with no pain
Female
Gymnasts who
were invited to
attend a specific
weeklong
national training
camp
19
(Swärd et
al.)
1991
50%
A convex
extension of
the disc
beyond the
cortex of the
vertebra
Thoracic;
lumbar
19 – 29
Gymnastics
Elite
Back pain
was assessed
but was not
part of the
participant
selection
criteria
Male
Present or
previous
members of the
Swedish
National team
24
(Donatelli
and Thurner)
2014
7.7%
Circumferential symmetric
extension of
the discs
beyond the
interspace
Lumbar (L1
to S1)
19.7
(average)
Gymnastics
Pre-elite,
elite and
national
Back pain
was assessed
but was not
part of the
participant
selection
criteria
Male and
female
Japanese
collegiate
gymnasts
104
(Donatelli
and Thurner)
2014
28.8%
Focal or
asymmetric
extension of
the discs
beyond the
interspace
Lumbar (L1
to S1)
19.7
(average)
Gymnastics
Pre-elite,
elite and
national
Back pain
was assessed
but was not
part of the
participant
selection
criteria
Male and
female
Japanese
collegiate
gymnasts
104
Disc bulging
Disc protrusion
246
Spina bifida
occulta
(Jackson et
al.)
1976
38%
Presence of a
defect in the
closure in the
posterior wall
of the spinal
canal
Lumbar
(L5); sacral
(S1);
lumbosacral
(L5-S1)
6 – 24
Gymnastics
Regional,
national and
International
Previous or
current LBP
was
recorded but
it was not
part of the
selection
criteria
Female
Volunteers
representing
their teams in
gymnastic
competition
100
(Tertti et al.)
1990
3%
N/A
Sacral (S1)
8 - 19
Gymnastics
District,
national and
International
History of
back pain
was
recorded but
was not part
of the
selection
criteria
Male and
female
N/A
35
(Swärd et
al.)
1991
71%
A defect in the
vertebral endplate with
similar signal
intensity as the
rest of the disc
Thoracic;
lumbar
19 – 29
Gymnastics
Elite
Back pain
was assessed
but was not
part of the
participant
selection
criteria
Male
Present or
previous
members of the
Swedish
National team
24
(Katz and
Scerpella)
2003
14%
N/A
Lumbar (L5)
11 - 17
Gymnastics
Competitive
(pre-elite)
All
gymnasts
reported
back pain
Female
Pre-elite
gymnasts from
a single
gymnasium
7
Schmorl’s node
247
(Donatelli
and Thurner)
2014
5.8%
Areas of
endplate
irregularities
in which the
darkened rim
of the
vertebral
endplate had
indented the
vertebral body
Lumbar (L1
to S1)
19.7
(average)
Gymnastics
Pre-elite,
elite and
national
Back pain
was assessed
but was not
part of the
participant
selection
criteria
Male and
female
Japanese
collegiate
gymnasts
104
(Tertti et al.)
1990
9%
N/A
N/A
8 - 19
Gymnastics
District,
national and
International
History of
back pain
was
recorded but
was not part
of the
selection
criteria
Male and
female
N/A
35
(Szot et al.)
1985
7%
N/A
N/A
15 – 31
Gymnastics
National
49% of
gymnasts
reported
pain
Male
Members of the
national
gymnastics
team
31
(Szot et al.)
1985
7%
N/A
N/A
15 – 31
Gymnastics
National
49% of
gymnasts
reported
pain
Male
Members of the
national
gymnastics
team
31
Scheuermann’s
disease
Lumbarization
Sacralization
248
(Tertti et al.)
1990
43%
N/A
Lumbar (L5)
8 - 19
Gymnastics
District,
national and
International
History of
back pain
was
recorded but
was not part
of the
selection
criteria
Male and
female
N/A
35
(Katz and
Scerpella)
2003
14%
N/A
Thoracic
(T11-12)
11 - 17
Gymnastics
Competitive
(pre-elite)
All
gymnasts
reported
back pain
Female
Pre-elite
gymnasts from
a single
gymnasium
7
(Swärd et
al.)
1991
17%
Abnormalities
affecting the
region of the
vertebral ring
apophysis
Thoracic;
lumbar
19 – 29
Gymnastics
Elite
Back pain
was assessed
but was not
part of the
participant
selection
criteria
Male
Present or
previous
members of the
Swedish
National team
24
(Bennett et
al.)
2006
47%
Excavation of
the anterior
apophyseal
region with or
without a
persistent ring
apophysis
Thoracic
(T12);
lumbar (L2;
L4; L5);
sacral (S1)
12 – 20
Gymnastics
Olympic
Four with
current pain,
seven with
prior history
of pain and
eight with no
pain
Female
Gymnasts who
were invited to
attend a specific
weeklong
national training
camp
19
Disc space
narrowing and
anterior wedging
Apophyseal ring
injury
249
Schistorrhachis
(Szot et al.)
1985
20%
N/A
Sacral (S1)
15 – 31
Gymnastics
National
49% of
gymnasts
reported
pain
Male
Members of the
national
gymnastics
team
31
(Swärd et
al.)
1991
33%
Increased
anterioposterior
diameter
and/or
wedging
and/or
flattening of a
vertebral body
Thoracic;
lumbar
19 – 29
Gymnastics
Elite
Back pain
was assessed
but was not
part of the
participant
selection
criteria
Male
Present or
previous
members of the
Swedish
National team
24
(Donatelli
and Thurner)
2014
18.3%
Separate,
sclerotic,
triangular
ossicles
adjacent to but
separate from
the vertebral
endplate
Lumbar (L1
to S1)
19.7
(average)
Gymnastics
Pre-elite,
elite and
national
Back pain
was assessed
but was not
part of the
participant
selection
criteria
Male and
female
Japanese
collegiate
gymnasts
104
Abnormal
configuration of
vertebral body
Limbus vertebra
250
A.2 - EXPOSURE FACTOR METHODS (CLINICAL)
Table A.2. Details of previous literature which have used a clinical method of analysis to identify chronic spinal injury exposure factors
Exposure
Factor
Study
Year
Clinical assessment criteria
(general)
(Kujala et
al.)
1996
Questionnaire; MRI
(Feldman et
al.)
2001
(Battié et
al.)
(Richardson
et al.)
Clinical assessment
criteria (variablespecific)
Application (e.g.
sport)
Age
(years)
Number of
participants
Pain/injury status
Questionnaire
(Tanner’s stages of
maturity)
Young athletes and
non-athletes
10.313.3
116
No back pain that
forced training
interruption during
the preceding year
Questionnaire; physical
measurements (abdominal
muscle strength and trunk
and lower limb flexibility)
Growth of more than
5 cm in a 6-month
period was defined
as a high growth
spurt
High school
students
13.8
(mean)
502
Measured not
controlled
1995
MRI
Analysis of notable
concordance
between co-twins
Male twins, one of
which was a
smoker and the
other was not
36-60
40 (20 pairs)
Measured not
controlled
1997
Questionnaire (participant
and family member)
Concordance
between
Herniated lumbar
disk patients and
21-55
88
Growth
Genetics
Family history
Upper extremity
index patients had no
251
questionnaire
answers
patients with upper
extremity disorders
history of diagnostic
back pain
Physical activity
(Wojtys et
al.)
2000
Anthropometric
measurements;
questionnaire; interview;
standing photographs
Questionnaire and
interview
Athletic children
and controls
8-18
2270
No participants with a
history of traumatic
back injury, scoliosis
exceeding 20° or a
congenital spine
abnormality were
included
(Kovacs et
al.)
2003
Questionnaires (participants
and parents)
Questionnaire
School children
and their parents
13-15
16357
Measured not
controlled
(Auvinen et
al.)
2008
Questionnaire
Questionnaire
Children of mixedgender
15-16
5999
Measured not
controlled
(Bak et al.)
1994
One year injury registration
Elite or sub-elite
identification
Male and female
artistic and
tumbling gymnasts
8-25
117
Measured not
controlled
1985
Clinical examinations
(specific motor system
examinations and case
Case history
Male national team
gymnasts
15 to 31
41
Measured not
controlled
Skill level
Training duration
(Szot et al.)
252
history); pain complaints; xray photographs
Hours of practice
(McMeeken
et al.)
2001
Questionnaire
Questionnaire
Mixed gender
participants from
community,
secondary schools,
university and
ballet and
gymnastics
institutions
9-27
614
Measured not
controlled
(Mulhearn
and George)
1999
Questionnaire; pressure
biofeedback unit (postural
abdominal exercise test)
Abdominal postural
muscle endurance
(pressure
biofeedback unit)
National and
international
mixed-gender
gymnasts and
controls
12
(mean)17(mean
)
22 (n
controls not
specified)
Measured not
controlled
(Feldman et
al.)
2001
Questionnaire; physical
measurements (abdominal
muscle strength and trunk
and lower limb flexibility)
Hamstring and
High school
quadriceps flexibility students
(goniometer)
13.8
(mean)
502
Measured not
controlled
(Kim et al.)
2006
Neurological examination;
radiological examination of
low back; trunk muscle
measurement
Trunk muscle
strength (Isostation
B-200)
13-61
31
All subjects
experienced low back
pain
Musculature
Mixed-gender
patients managing
low back pain
253
(Ranson et
al.)
2008
MRI
Cross-sectional and
functional crosssectional area
measurements were
determined from the
scans (Image J
V1.36b software)
Male professional
fast bowlers and
controls
Fast
bowlers:
16
(mean);
controls:
25
(mean)
63 (17
controls)
No reported low back
pain in the 3 months
before testing
(Ota and
Kaneoka)
2011
Abdominal muscle
thickness were measured an
ultrasound imaging
Assessment of the
rectus abdominis,
external oblique,
internal oblique and
the transverses
abdominis (real-time
B-mode ultrasound
transducer)
Mixed-gender
healthy participants
and participants
with chronic low
back pain
Healthy:
30.2
(mean);
chronic
low back
pain:
31.5
(mean)
100 (50
healthy and
50 low back
pain patients)
No healthy
participants had
experienced low back
pain within the
previous 3 months; all
low back pain patients
had been experiencing
low back pain for
more than 3 months
(Punnett et
al.)
1991
Questionnaire; interview;
screening examination (26
back, neck and shoulder
ranges of motion
movements; ergonomic job
analysis (videotape and
analysis); postural analysis;
peak spinal force analysis
Interview and
examination
Employees of an
automobile
assembly plant
29-64
219
Previous injury
Monitored not
controlled
254
(Greene et
al.)
2001
Questionnaire; one year
back pain observation
Questionnaire and
observation
University varsity
athletes
representing 20
sport disciplines
19
(mean)
679
Measured not
controlled
(Bennett et
al.)
2006
History and physical
examination; MRI
History examination
outcome
Olympic-level
female gymnasts
12-20
19
Measured not
controlled
(Feldman et
al.)
2001
Questionnaire; physical
measurements (abdominal
muscle strength and trunk
and lower limb flexibility)
Questionnaire
High school
students
13.8
(mean)
502
Measured not
controlled
Smoking
255
A.3 - BIOMECHANICAL RISK FACTOR METHODS (CLINICAL)
Table A.3. Details of previous literature which have used a clinical method of analysis to identify chronic spinal injury biomechanical risk factors
Biomechanical
Risk Factor
Study
Year
Clinical assessment
criteria (general)
(Seidler et al.)
2003
Interview;
questionnaire; selfreported occupational
history; whole body
vibration measurement
(Kujala et al.)
1997
(Hardcastle et
al.)
1992
Clinical assessment
criteria (variable-specific)
Application (e.g.
sport)
Age
(years)
Number of
participants
Self-reported information
Male patients
from
neurosurgical and
orthopaedic
clinics
25-65
464 (197
controls)
Questionnaire;
measurements (lumbar
sagittal posture and
flexibility)
Motion measurements
taken (modified flexicurve
technique)
Athletes (ice
hockey, soccer,
gymnastics and
figure skating)
and non-athletes
10.313.3
116
Imaging (MR, plain
radiographs and CT
scans); filming of
bowling action
Imaging findings and
bowling action
classification
Fast bowlers and
batsmen
16-18
36 (24 fast
bowlers and
12 batsmen)
Pain/injury status
Flexion
All participants
had acute disc
herniation
Extension
No back pain
which interrupted
training during the
preceding year
Rotation
Measured not
controlled
256
Repetitive movements
(Hardcastle et
al.)
1992
Imaging (MR, plain
radiographs and CT
scans); filming of
bowling action
Imaging findings and
bowling action
classification
Fast bowlers and
batsmen
16-18
36 (24 fast
bowlers and
12 batsmen)
(Foster et al.)
1989
Muscle group torque
(Cybex II Isokinetic
Dynamometer);
shoulder and trunk
strength (Clarke and
Clarke, 1963);
flexibility (Leighton,
1942); muscular
endurance (60-second
sit-up test); postural
photographs; aerobic
capacity (15-minute
run)
Postural photograph
assessment
Club and/or
school male fast
bowlers
15-22
82
(Christie et
al.)
1995
Personal details; pain
intensity in sitting
(visual analogue scale);
postural photographic
slides (standing and
sitting)
Standing and sitting
postural photograph
assessment
Mixed-gender
participants from
medical
institutions and a
university campus
18-46
59 (20
controls)
Measured not
controlled
Posture
Injury-free
All participants
had acute or
chronic low back
pain; controls had
no history of low
back pain
257
(Mulhearn
and George)
1999
Questionnaire; muscle
contraction
measurement (pressure
biofeedback)
Standing plumline postural National and
assessment
international
mixed-gender
gymnasts and a
control group
12
(mean)17(mea
n
22 gymnasts
(number of
controls not
detailed but
assumed to
be 22 also)
(Tanchev et
al.)
2000
Documentation of
personal details;
physical examination of
the back
Physical examination of
the back
(Kim et al.)
2006
Neurological
examination;
radiographs; trunk
muscle measurement
(Isostation B-200)
(Bugg et al.)
2011
MRI; lumbar spine
radiographs
Measured not
controlled
Female rhythmic
gymnasts who
had trained for
more than 5 years
10-16
100
No past diseases or
congenital
abnormalities
Sacral angle and lumbar
lordosis measurements
from the radiographic
films (goniometer)
Mixed-gender
back pain patients
13-61
31
All subjects
experienced low
back pain
Standing lateral lumbar
spine radiographs
assessment (modified
Cobb angle measurement)
Archives from
mixed-gender
patients and
controls
9-63
58 (29
controls)
All participants
had bilateral L5
pars interarticularis
fractures, no other
abnormalities;
controls had
normal MRI results
but presented
symptoms
attributed to their
lumbar spines
258
Coupled posture and twisting
(Fazey et al.)
2006
MRI
Lumbar flexion, extension
and trunk rotation induced
by use of cylindrical
bolsters and foam wedge
cushions (MRI)
Female volunteers
27
(mean)
3
No current or
history of low back
pain during
previous year
(Kujala et al.)
1997
Questionnaire;
measurements (lumbar
sagittal posture and
flexibility)
Motion measurements
taken (modified flexicurve
technique)
Athletes (ice
hockey, soccer,
gymnastics and
figure skating)
and non-athletes
10.313.3
116
No back pain
which interrupted
training during the
preceding year
(Harreby et
al.)
1999
Questionnaire; physical
examination (hamstring
tightness and knee
extension)
Hypermobility physical
examination (numerical
scoring system)
Eight and ninth
grade pupils
92.4%
were 14
or 15
1389
Measured not
controlled
(Stuelcken et
al.)
2008
Questionnaire; range of
motion assessment
(plurimeter-V
inclinometer and
goniometer)
Lumbar range of motion
(plurimeter-V); range of
hip extension (long-armed
goniometer)
Mixed-gender fast
bowlers
Female
s: 22.5
(mean);
male:
21.5
(mean)
34
Range of motion
No male
participants had
low back pain at
the time of testing;
low back pain was
measured not
controlled in
females
259
Repetitive loading
(Ikata et al.)
1996
Plain lateral
radiographs and MRI
scans
Plain radiograph and MRI
Young athletes
9-18
77
All participants
had spondylolysis
or
spondylolisthesis
(Seidler et al.)
2001
Interview;
questionnaire; selfreported occupational
history; whole body
vibration measurement
Calculated using selfreported information
Male patients
from
neurosurgical and
orthopaedic
clinics
25-65
426 (197
controls)
Participants with
osteochondrosis or
spondylosis of the
lumbar spine
associated with
chronic complaints
or acute lumbar
disc herniation
(Seidler et al.)
2003
Interview;
questionnaire; selfreported occupational
history; whole body
vibration measurement
Calculated using the
Mainz-Dortmund dose
model (self-reported
information)
Male patients
from
neurosurgical and
orthopaedic
clinics
25-65
464 (197
controls)
All participants
had acute disc
herniation
2000
Documentation of
personal details;
observation; physical
examination of the back
Amount of playing time
(with implements) using
one-hand playing
(observation)
Female rhythmic
gymnasts who
have trained for
more than five
years
10-16
100
Cumulative loading
Asymmetrical loading
(Tanchev et
al.)
No past diseases or
congenital
abnormalities
260
A.4 - BIOMECHANICAL RISK FACTOR METHODS (BIOMECHANICAL)
Table A.4. Details of literature which has used a biomechanical method of analysis to identify chronic spinal injury biomechanical risk factors
Biomechanics Risk
Factor
Study
Year
Biomechanical
assessment criteria
(general)
Biomechanical assessment
criteria (variable-specific)
(Punnett et
al.)
1991
Interview; screening
examination (26 back,
neck and shoulder ranges
of motion movements);
ergonomic job analysis
(videotape and analysis);
postural analysis; peak
spinal force analysis
Videotape analysis
(Marras et
al.)
1993
Three-dimensional
lumbar motion monitor
(triaxial
electrogoniometer)
(Adams et
al.)
1994
Cadaveric; computercontrolled hydraulic
materials testing machine
Application (e.g.
sport)
Age
(years)
Number of
participants
Pain/injury status
Employees of an
automobile
assembly plant
29-64
219
Monitored not
controlled
Trunk motion and
workplace factor model
Industrial workers
N/A
235
Measured not
controlled
Computer-controlled
hydraulic materials testing
machine
Cadaveric lumbar
spines
19-74
19
No history of
spinal injury
Flexion
Hyperflexion
261
(Hall)
1986
Sagittal plane films taken
during the performance
of five skills onto a force
plate; side photographs
during relaxed standing
posture
Film and slide projection
measurements
University
women’s
gymnastics team
N/A
4
N/A
(Marras et
al.)
1993
Three-dimensional
lumbar motion monitor
(triaxial
electrogoniometer)
Trunk motion and
workplace factor model
Industrial workers
N/A
235
Measured not
controlled
(Schmidt et
al.)
2007
Three-dimensional finite
element L4-L5 model
(informed by CT scans);
MRI; historical
observations
Finite element model
N/A
N/A
N/A
N/A
(Roaf)
1960
Cadaveric; radiographs
and visual assessment;
forces (Denniston testing
machine)
Manual rotation
Mostly children
and young adult
spines
N/A
N/A
N/A
(Farfan et
al.)
1970
Cadaveric; torquestrength testing machine;
experimental models
Torque-strength testing
machine
N/A
N/A
66
Lateral bending
Rotation
Clinically and
roentogenographic
ally normal
262
(Marras et
al.)
1993
Three-dimensional
lumbar motion monitor
(triaxial
electrogoniometer)
Trunk motion and
workplace factor model
(Marras
and
Granata)
1995
EMG; biomechanical
model
(Burnett et
al.)
1996
(Dolan and
Adams)
(Burnett et
al.)
Industrial workers
N/A
235
Measured not
controlled
Axial twist performed,
Male
controlled by a
dynamometer, analysed by
biomechanical model
21-31
12
No history of low
back disorder
Sagittal and transverse
plane filming; MRI
Digitised footage data
School and club
level competitive
bowlers
13.6
(mean)
19
No bowler had any
knowledge of any
spinal abnormality
but some had low
back pain
complaints at times
1998
3-Space Isotrak
(electromagnetic
movement analysis
device); EMG; lifting of
a loaded handlebar
assessment
3-Space Isotrak and EMG
assessment as participants
lifted and lowered a 10 kg
disk 100 times
Mixed gender
30.6
(mean)
15
No previous
history of low back
pain
2008
Three-dimensional
lumbar kinematics (3Space Fastrak)
3-Space Fastrak
Adolescent
female rowers
14
(mean)
18
No current or
previous low back
pain
263
Flexion and rotation
(Schmidt et
al.)
2007
Three-dimensional finite
element L4-L5 model
(informed by CT scans);
MRI; historical
observations
Finite element model
N/A
N/A
N/A
N/A
2007
Three-dimensional finite
element L4-L5 model
(informed by CT scans);
MRI; historical
observations
Finite element model
N/A
N/A
N/A
N/A
(Burnett et
al.)
1996
Sagittal and transverse
plane filming; MRI
Digitised footage data
School and club
level competitive
bowlers
13.6
(mean)
19
(Schmidt et
al.)
2007
Three-dimensional finite
element L4-L5 model
(informed by CT scans);
MRI; historical
observations
Finite element model
N/A
N/A
N/A
Extension and rotation
(Schmidt et
al.)
Flexion or extension, side
bending and rotation
No bowler had any
knowledge of any
spinal abnormality
but some had low
back pain
complaints at times
N/A
264
(Burnett et
al.)
2008
Three-dimensional
lumbar kinematics (3Space Fastrak)
3-Space Fastrak
Adolescent
female rowers
14
(mean)
18
No current or
previous low back
pain
(Punnett et
al.)
1991
Interview; screening
examination (26 back,
neck and shoulder ranges
of motion movements);
ergonomic job analysis
(videotape and analysis);
postural analysis; peak
spinal force analysis
Videotape analysis
Employees of an
automobile
assembly plant
29-64
219 (215
male)
Back pain was
monitored not
controlled
(Adams et
al.)
1994
Cadaveric; computercontrolled hydraulic
materials testing machine
Computer-controlled
hydraulic materials testing
machine
Cadaveric lumbar
spines
19-74
19
No history of
spinal injury
(Smith et
al.)
2008
Lateral standing
photographs;
questionnaire
Digitisation of reflective
markers (Peak Motus
motion analysis system)
Mixed-gender
adolescents
14
(mean)
766
Measured not
controlled
(Marras et
al.)
1993
Three-dimensional
lumbar motion monitor
(triaxial
electrogoniometer)
Trunk motion and
workplace factor model
Industrial workers
N/A
235
Measured not
controlled
(Schmidt et
al.)
2007
Three-dimensional finite
element L4-L5 model
Finite element model
N/A
N/A
N/A
N/A
Posture
Coupled posture and twisting
265
(informed by CT scans);
MRI; historical
observations
(Drake and
Callaghan)
2008
Isolation of the lumbar
spine (custom jig);
kinematic motion capture
(22 infra-red emitting
diodes and Optotrak
system); EMG
Axial twist moment-angle
relationships (a custom
jig)
Male university
students
23.3
20
No low back pain
for at least a year
preceding the study
(Marras et
al.)
1993
Three-dimensional
lumbar motion monitor
(triaxial
electrogoniometer)
Trunk motion and
workplace factor model
Industrial workers
N/A
235
Measured not
controlled
(Marras
and
Granata)
1995
EMG; biomechanical
model
Biomechanical model
Male
21-31
12
No history of low
back disorder
(Callaghan
et al.)
2001
Video; biomechanical
model of the L4/L5
intervertebral joint
Biomechanical model
Males from the
university
population
26
(mean)
3
No low back pain
for a minimum of a
year
(Brüeggem
ann)
2010
Clinical examination;
Biomechanical model
MRI; muscle strength,
anthropometric variables;
biomechanical model
Elite female
gymnasts
10-22
135
Mechanical loading
N/A
266
(kinematics, kinetics and
EMG inputs)
(Yang et
al.)
2011
Lumbar motion monitor
(electrogoniometer);
motion characteristics
(magnetic-based motion
tracking sensors); threedimensional lumbar
spine model; blood
samples
External spinal loads
measured using a
biomechanical method
developed by Fathallah et
al. (1997)
Male participants
who had not had a
manual materials
handling job
within one year of
the study
24.3
(mean)
12
No history of back
pain or back injury
(Cyron and
Hutton)
1978
Cadaveric; hydraulic
servo-controlled testing
machine
Force distribution in 100
cycles (hydraulic servocontrolled testing
machine)
N/A
14-80
28
No known history
of bone disease and
no immobilisation
for long periods of
time
(Hall)
1986
Sagittal plane films taken
during the performance
of five skills onto a force
plate; side photographs
during relaxed standing
posture
Force plate
University
women’s
gymnastics team
N/A
4
N/A
Repetitive loading
267
Cumulative loading
(Norman et
al.)
1998
Questionnaire; videobased posture analysis;
two-dimensional quasidynamic biomechanical
model of L4/L5
Spinal load multiplied by
the total time spent in the
waiting phase
(biomechanical model)
Automotive
assemble facility
workers
41.1
(low
back
pain
particip
ants)
Over 10,000 Current low back
pain; controls
made no reports of
low back pain
; 41.5
(control
s)
(Fischer et
al.)
2007
Video data collection;
force gauge
Instantaneous force and
duration were multiplied
to calculate finite
cumulative loads which
determined task
cumulative loading
Mixed gender
workers from an
automotive parts
manufacturing
plant
Male:
35.4
(mean);
female:
43.9
(mean)
28
Measured not
controlled
(Roaf)
1960
Cadaveric; radiographs
and visual assessment;
forces (Denniston testing
machine)
Slow load application
(Denniston testing
machine)
Mostly children
and young adult
spines
N/A
N/A
N/A
(Marras
and
Granata)
1997
Three-dimensional
biodynamic model of the
trunk
Calculated from the vector
sum of validated muscle
forces (biodynamic
model)
N/A
N/A
N/A
N/A
Shear loading
268
(Norman et
al.)
1998
Questionnaire; videobased posture analysis;
two-dimensional quasidynamic biomechanical
model of L4/L5
Biomechanical model
Automotive
assemble facility
workers
41.1
(low
back
pain
particip
ants)
Over 10,000 Current low back
pain; controls
made no reports of
low back pain
; 41.5
(control
s)
Shear and compressive force
(Alderson
et al.)
2009
Questionnaire; Vicon
motion analysis system;
force plate
Shear force (customised
Bodybuilder 3D dynamic
model); compression
forces (3DSSP model)
Male dancers and
ballerinas
Male
dancers:
22.6
(mean);
ballerin
as 26.8
(mean)
13
Measured not
controlled
(Adams et
al.)
2000
Cadaveric; computercontrolled hydraulic
materials testing
machine; compressive
stress measurement
(miniature pressure
transducer)
Computer-controlled
hydraulic materials testing
machine
Cadaveric lumbar
motion segments
19-87
19
N/A but cause of
death was not
related to spinal
pathology
Flexion and loading
269
Hyperextension and loading
(Hall)
1986
Sagittal plane films taken
during the performance
of five skills onto a force
plate; side photographs
during relaxed standing
Simultaneous evaluation
of the force and lumbar
curvature
University
women’s
gymnastics team
N/A
4
N/A
2004
Three-dimensional finite
model
Finite model
N/A
N/A
N/A
N/A
(Roaf)
1960
Cadaveric; radiographs
and visual assessment;
forces (Denniston testing
machine)
Manual rotation with slow
loading application
(Denniston testing
machine)
Mostly children
and young adult
spines
N/A
N/A
N/A
(Chosa et
al.)
2004
Three-dimensional finite
model
Finite model
N/A
N/A
N/A
N/A
Extension and compression
(Chosa et
al.)
Rotation and compression
270
A.5 - PARTICIPANT RECRUITMENT SHEET
Project Title: Age-Related Spine Biomechanics of Female Gymnasts
Principle Investigator: Hannah Wyatt
Contact Details: [email protected]
Supervisors: Dr Marianne Gittoes and Dr Gareth Irwin
Contact Details: [email protected]
Purpose of this information sheet:
This document has been given to you to disclose information about the respective research
project; its aim is to give you insight into what will be asked of you should you agree to
participate/for your daughter or gymnast to participate in the research. Before an explanation
of the research is given, it is important for you to realise that participation in the study is
entirely voluntary and should you/your daughter or gymnast decide to participate, you/your
daughter or gymnast will have the right to withdraw at any time without the need to provide
a reason.
What participants are we looking to recruit for the study?
- Female gymnasts
- Train regularly and are of a ‘competitive’ level (enter at least one competition a year)
- Ages 9 to 15
- No current or previous back pain or injury
- Injury free (injuries which are preventing the individual from training or competing)
Research background:
The intense training regimes which gymnasts commonly undergo lead to the generation of
high biomechanical loads on the body, a recognised precursor to chronic injury. The
considerable back movements that the gymnasts are required to perform even during
fundamental gymnastics skills place large strains on the spine. As well as dealing with these
strains, the spine also changes during growth and, as a result, is more susceptible to injury.
As the lumbar (lower) spine is the main region for weight bearing, mobility and stability, it
is the most common site for chronic injuries to occur in the spine.
Aims of the research:
The aim of this research is to develop understanding of spine biomechanics in competitive
female gymnasts through an examination of fundamental gymnastics skills.
What will happen once you/your daughter or gymnast agrees to participate in the study?
Following your/your daughter or gymnast’s agreement to take part in the study, you/she will
be required to complete a pre-test health questionnaire which will allow the researchers to
make sure that you are/she is able to undertake the research requirements. Healthy volunteers
will then be asked to provide a few personal details such as age, height etc. Once this
procedure has been completed, the data collection can commence; this will involve each
participant performing 20 successful trials of each of the selected gymnastic skills
(handstand and forward walkover). Sufficient rest will be given between performances. In
order to obtain the necessary data, the skills will be performed onto a covered force plate
and several markers will be placed on the surface of specific anatomical landmarks; these do
not cause pain or discomfort, interfere with performance or increase the potential for injury
occurrence. It is likely that the duration of testing will be approximately 2.5 to 3 hours per
participant.
271
What are the risks of participating in the study?
The skills which the participants will be required to perform will be no more strenuous than
those which are executed in a normal training session; therefore the foreseen risks to the
participants are minimal. In addition, a qualified gymnastics coach will be available to
support any skills if the participant requires.
Benefits to the participant:
Both visual and written feedback of individual performances will be made available to each
participant; this will provide valuable information that can be used by both gymnast and
coach for technique evaluation.
Benefits to us, the research team:
The main benefit of completing this research is the advancement of understanding female
gymnasts’ predisposition to age-related chronic back pain and chronic spinal injuries. The
research will help to inform the research area, broaden the knowledge scope and facilitate
future biomechanical research into injury mechanisms.
What will happen to the data and information collected during the study?
The data will be analysed and stored using a coded format; therefore, all participants will be
anonymous within the data. Following analysis, copies of your/your daughter or gymnast’s
individualised results will be made available. Participant performance data will only be
accessible by the research team, and so total confidentiality will be sustained throughout the
research project. The coded copies of all data will be stored in a secure holding location for
five years, during which time, only the research team will be able to access it.
What next?
If you have any questions or concerns, please contact me on the e-mail address or phone
number provided. If you are happy to participate/for your daughter or gymnast to participate
in the study please let me know; if this is the case I will then forward the necessary forms
and contact you to arrange a convenient date and time for testing.
Yours sincerely,
Hannah Wyatt
272
A.6 - SAMPLE SIZE CALCULATION
Two female gymnasts performed 10 handstand trials with three CODA motion markers
secured to the skin adjacent to the lumbar spinous processes at L1, L3 and L5, using adhesive
tape. The marker positions were selected to allow for sagittal plane lumbar angle calculation.
Four CODA Motion Cx1 units were positioned around the gymnasts (anterior, posterior,
medial and lateral placements) to allow for tracking of each marker throughout the
performance of the handstand skills in their entirety. Using the formulae (A.1) adapted from
Cohen (1992), the obtained enabling study mean and standard deviation data informed the
calculation of an effect size. As has been reported by Cooper et al. (2009), Cohen’s d has a
tendency to overestimate the standardized mean difference in small samples, consequently
Hedge’s g equation (A.2) was utilised to produce an unbiased estimate of the standardized
mean difference.
Cohen’s d = mean1 – mean2
(SD1 + SD2)/2
Hedge’s g = Cohen’s d x (1 – (
[A.1]
3
)
4 (n1 + n2) – 9
[A.2]
Mean1 = Mean for the first gymnast
Mean2 = Mean for the second gymnast
SD1 = Standard deviation for the first gymnast
SD2 = Standard deviation for the second gymnast
n1 = Number of data points for the first gymnast
n1 = Number of data points for the first gymnast
Selected due to the prominence of posture in the respective research, the mean minimum
lumbar angles were calculated for each gymnast. The respective data, along with the
calculated standard deviation values are detailed in Table A. .
273
Table A. 5. Average minimum lumbar angle and standard deviation values for two gymnasts
across 10 handstand trials
Gymnast
1
2
Minimum lumbar angle
mean (°)
-2.2
1.6
Minimum lumbar angle standard
deviation (°)
1.1
0.8
Input of the mean and standard deviation data for each gymnasts into the outlined equations
(A.1 and A.2), produced an overall effect size of 3.97. Using the Cohen’s d effect size
categories outlined by Field (2009), for which >0.1 is a small effect, >0.3 is a medium effect
and >0.5 is a large effect, the specified effect size was identified to fall within the medium
category (0.3 to 0.5).
Using the calculated effect size, along with the accepted level of significance set at 95% and
the power level set at 80%, selected in accordance with statistical recommendations (Suresh
and Chandrashekara, 2012), the sample size was calculated by use of a priori analysis in
G*Power 3.1 software (Faul et al., 2007). The outcome of a t-test of two dependent matched
pair means determined four participants per group as the acceptable proportion of the target
population needed within the respective study.
274
A.7 - PARTICIPANT INFORMATION SHEET
Project Title: Age-Related Spine Biomechanics of Female Gymnasts
Principle Investigator: Hannah Wyatt
Contact Details: [email protected]
Supervisors: Dr Marianne Gittoes and Dr Gareth Irwin
Contact Details: [email protected]
Purpose of this information sheet:
This document has been given to you to disclose information about the respective research
project; its aim is to help you to reach a decision about whether or not you would like to
participate. Before an explanation of the research is given, it is important for you to realise
that participation in the study is entirely voluntary and should you decide to participate, you
will have the right to withdraw at any time without the need to provide a reason.
What type of participants are we hoping to use in the study?
The participants that the research requires are female gymnasts who regularly train at a
competitive level. The participants must be injury free at the time of testing and be between
the ages of nine and 15 years.
Research background:
A female gymnast who competes at international level is required to complete intense
training from a young age. This training puts large stresses on the gymnast’s body which can
lead to injury. The considerable back movements that the gymnasts are required to perform
during both fundamental and complex gymnastics skills place large strains on the spine. As
well as dealing with these strains, the spine is also changing during growth and so is more
susceptible to injury. As the lumbar (lower) spine is the main region for weight bearing,
mobility and stability, it is the most common site for overuse injuries to occur in the spine.
Aims of the research:
The aim of this research is to develop understanding of spine biomechanics in competitive
female gymnasts through an examination of fundamental and complex gymnastics skills.
What will happen once you agree to participate in the study?
Following agreement to take part in the study, each participant will be required to complete
a pre-test health questionnaire which will allow the researchers to make sure that they are
able to undertake the research requirements. Healthy volunteers will then be asked to provide
a few personal details such as age, height etc. Once this procedure has been completed, the
data collection can commence; this will involve each participant performing two gymnastics
skills with up to 20 repeated performances of each skill. Sufficient rest will be given between
performances. In order to obtain the necessary data, several surface markers will be placed
on each participant’s skin and the skills will be performed onto a covered force plate. Neither
of these factors will harm nor increase the potential for injury occurrence. It is likely that the
duration of testing will be approximately three hours per participant.
What skills will you be asked to perform?
The 2 skills are as follows: handstands (static on floor, held for 15 seconds) and forward
walkovers (on gymnastic floor). A high performance coach will be on hand for each testing.
275
What are the risks of participating in the study?
The skills which the participants will be required to perform will be no more strenuous than
those that are executed in a normal training session, therefore the foreseen risks to the
participants are minimal. In addition, a qualified gymnastics coach will be available to
support any skills if required by the participant.
Benefits to you, the participant:
Both visual and written feedback of individual performances will be made available to each
participant; this will provide valuable information that can be used by both gymnast and
coach for technique evaluation.
Benefits to us, the research team:
The main benefit of completing this research is the advancement of understanding of female
gymnasts’ predisposition to age-related chronic spinal injuries. The research will help to
inform the research area, broaden the knowledge scope and facilitate future biomechanical
research into injury mechanisms.
What will happen to the data and information collected during the study?
The data will be analysed and stored using a coded format; therefore, all participants will be
anonymous within the data. Following analysis, copies of your individualised results will be
made available to you. Participant performance data will only be accessible by the research
team, and so total confidentiality will be kept. The coded copies of all data will be stored in
a secure holding location for five years, during which time, only the research team will be
able to access it.
What next?
If you have any questions or concerns, please contact me on the e-mail address or phone
number provided. If you are happy to participate in the study please let me know and
complete the attached informed consent form. I will then contact you to confirm your
availability for specific test dates.
Many thanks,
Hannah Wyatt
276
A.8 - PARENTAL/GUARDIAN INFORMATION SHEET
Project Title: Age-Related Spine Biomechanics of Female Gymnasts
Principle Investigator: Hannah Wyatt
Contact Details: [email protected]
Supervisors: Dr Marianne Gittoes and Dr Gareth Irwin
Contact Details: [email protected]
Purpose of this information sheet:
This document has been given to you to disclose information about the respective research
project; its aim is to provide insight into what will be asked of your daughter should they
agree to participate in the research. Before an explanation of the research is given, it is
important for you to realise that participation in the study is entirely voluntary and should
you decide to allow your daughter to participate, she will have the right to withdraw at any
time without the need to provide a reason.
What type of participants are we hoping to use in the study?
The participants that the research requires are female gymnasts who regularly train at a
competitive level. The participants must be injury free at the time of testing and be between
the ages of nine and 15 years.
Research background:
A female gymnast who competes at international level is required to complete intense
training from a young age. This training puts large stresses on the gymnast’s body which can
lead to injury. The considerable back movements that the gymnasts are required to perform
during both fundamental and complex gymnastics skills place large strains on the spine. As
well as dealing with these strains, the spine is also changing during growth and so is more
susceptible to injury. As the lumbar (lower) spine is the main region for weight bearing,
mobility and stability, it is the most common site for overuse injuries to occur in the spine.
Aims of the research:
The aim of this research is to develop understanding of spine biomechanics in competitive
female gymnasts through an examination of fundamental and complex gymnastics skills.
What will happen once you agree for your daughter to participate in the study?
Following agreement for your daughter to take part in the study, you will be required to
complete a pre-test health questionnaire on your daughter’s behalf which will allow the
researchers to make sure that your daughter is able to undertake the research requirements.
Healthy volunteers will then be asked to provide a few personal details such as age, height
etc. Once this procedure has been completed, the data collection can commence, this will
involve each participant performing two gymnastics skills with up to 20 repeated
performances of each skill. Sufficient rest will be given between performances. In order to
obtain the necessary data, several surface markers will be placed on each participant’s skin
and the skills will be performed onto a covered force plate. Neither factors will harm nor
will they increase the potential for injury occurrence. It is likely that the duration of testing
will be approximately three hours per participant.
What skills will your daughter be asked to perform?
The 2 skills are as follows: handstands (static on floor, held for 15 seconds) and forward
walkovers (on gymnastic floor). A high performance coach will be on hand for each testing.
277
What are the risks of your daughter participating in the study?
The skills which the participants will be required to perform will be no more strenuous than
those that are executed in a normal training session, therefore the foreseen risks to the
participants are minimal. In addition, a qualified gymnastics coach will be available to
support any skills if required by the participant.
Benefits to the participant:
Both visual and written feedback of individual performances will be made available to each
participant; this will provide valuable information that can be used by both gymnast and
coach for technique evaluation.
Benefits to us, the research team:
The main benefit of completing this research is the advancement of understanding of female
gymnasts’ predisposition to age-related chronic spinal injuries. The research will help to
inform the research area, broaden the knowledge scope and facilitate future biomechanical
research into injury mechanisms.
What will happen to the data and information collected during the study?
The data will be analysed and stored using a coded format; therefore, all participants will be
anonymous within the data. Following analysis, copies of your daughter’s individualised
results will be made available to your daughter. Participant performance data will only be
accessible by the research team, and so total confidentiality will be kept throughout the
research project. The coded copies of all data will be stored in a secure holding location for
five years, during which time, only the research team will be able to access it.
What next?
If you have any questions or concerns, please contact me on the e-mail address or phone
number provided. If you are happy for your daughter to participate in the study please let me
know. If this is the case could you complete the attached informed consent form and your
daughter complete the attached participant assent form. I will then contact you to confirm
your gymnasts/daughters availability for specific test dates.
Many thanks,
Hannah Wyatt
278
A.9 - PARENTAL/GUARDIAN CONSENT FORM
Title of Project: Age-Related Spine Biomechanics of Female Gymnasts
Name of Researchers: Hannah Wyatt ([email protected])
Parent/guardian of participant to complete this section: Please initial each box.
1. I confirm that I have read and understand all of the information included
in the sheet titled ‘Parental/Guardian Information Sheet’. I have had the
opportunity to consider the information, ask questions and have had these
answered satisfactorily.
2. I understand that my daughter’s participation is voluntary and
that it is possible for her to stop taking part at any time without giving a
reason.
3. I understand that if this happens, our relationships with Cardiff
Metropolitan University and our legal rights will not be affected.
4. I understand that information from the study may be used for reporting
purposes, but that she will not be identified.
5. I give my consent for video footage to be taken of my gymnast/daughter
during the testing (your daughter’s participation in the study will not be
affected should you not agree to your daughter being videoed).
6. I have chosen and agree for my daughter to take part in this research.
Name (Participant)
Name (Parent/guardian)
_________________________________________
Signature (Parent/guardian)
_______________
Date
Name of person taking consent
Signature of person taking consent
_______________
Date
* When completed, one copy for participant and one copy for
researcher’s files.
279
A.10 - PARTICIPANT INFORMED CONSENT FORM
Title of Project: Age-Related Spine Biomechanics of Female Gymnasts
Name of Researchers: Hannah Wyatt ([email protected])
Participant to complete this section: Please initial each box.
1. I confirm that I have read and understand all of the information included
in the sheet titled ‘Participant Information Sheet’. I have had the
opportunity to consider the information, ask questions and have had these
answered satisfactorily.
2. I understand that the participation is voluntary and that it is possible to
stop taking part at any time without giving a reason.
3. I understand that if this happens, my relationship with Cardiff
Metropolitan University and my legal rights will not be affected.
4. I understand that information from the study may be used for reporting
purposes, but that I will not be identified.
5. I give my consent for video footage to be taken throughout the time that I
will be participating (your participation in the study will not be affected
should you not agree to being videoed).
6. I have chosen and agree to take part in this research.
__________________________________
Name
_______________________________
Signature
_______________
Date
_________________________________
Name of person taking consent
_______________
Date
_______________________________________________
Signature of person taking consent
* When completed, one copy for participant and one copy for researcher’s files.
280
A.11 - PARTICIPANT ASSENT FORM
Title of Project: Age-Related Spine Biomechanics of Female Gymnasts
Name of Researchers: Hannah Wyatt ([email protected])
Participant to complete this section: Please initial each box.
7. I understand what I need to do to be a participant in the study and have
asked all of the questions I want to.
8. I understand that I that it is not compulsory for me to take part in the study.
If I do choose to take part, I understand that I can stop the testing at any
point without giving a reason why I have decided this.
9. I am happy to allow video footage to be taken during the testing (if you
do not want to be videoed, you are still able to participate in the study)
10. I have chosen to and agree to take part in this research.
__________________________________
Name
_______________________________
Signature
_______________
Date
_________________________________
Name of person taking consent
_______________
Date
_______________________________________________
Signature of person taking consent
* When completed, one copy for participant and one copy for researcher’s files.
281
A.12 - PARENTAL/GUARDIAN PRE-TEST HEALTH
QUESTIONNAIRE
Please answer the following questions. The purpose of this questionnaire is to ensure that
your daughter is in a fit and healthy state to complete the respective data collection. Please
inform the researcher if she is unable to participate.
Name (of participant): ……………………………….……………………………………..
Date of Birth (of participant): ……/………/………
1. Has your daughter had to consult your doctor recently?
Yes/No
2. Is your daughter presently taking any form of medication?
Yes/No
3. If yes what medication is she taking? ……………………………………………...
4. Does your daughter suffer, or have ever suffered from:
 Asthma?
Yes/No
 Diabetes?
Yes/No
 Bronchitis?
Yes/No
 Epilepsy?
Yes/No
 High blood pressure?
Yes/No
5. Does your daughter suffer, or have you ever suffered from any form of heart
complaint?
Yes/No
6. Is there a history of heart disease in her family?
Yes/No
7. Is your daughter currently suffering from any form of muscle or joint injury?
Yes/No
8. Has your daughter had any cause to suspend her normal training in the last two
weeks?
Yes/No
9. Is there anything to your knowledge that may prevent your daughter from
successfully completing the tests that have been outlined to you?
Yes/No
I confirm that I have answered these questions truthfully to the best of my knowledge.
Parent/guardian’s signature: ........................................................................
Date: ...../........./.........
Researcher’s signature: ................................................................................
Date: ....../........./.........
282
A.13 - PARTICIPANT PRE-TEST HEALTH QUESTIONNAIRE
Please answer the following questions. The purpose of this questionnaire is to ensure that
you are in a fit and healthy state to complete the respective data collection. Please inform
the researcher if you are unable to participate.
Name (print): ……………………………….………………………………………………..
Date of Birth: ……/………/………
1. Have you had to consult your doctor recently?
Yes/No
2. Are you presently taking any form of medication?
Yes/No
3. If yes what medication are you taking? ………..…………………………………...
4. Do you suffer, or have ever suffered from:
 Asthma?
Yes/No
 Diabetes?
Yes/No
 Bronchitis?
Yes/No
 Epilepsy?
Yes/No
 High blood pressure?
Yes/No
5. Do you suffer, or have you ever suffered from any form of heart complaint?
Yes/No
6. Is there a history of heart disease in her family?
Yes/No
7. Are you currently suffering from any form of muscle or joint injury?
Yes/No
8. Have you had any cause to suspend her normal training in the last two weeks?
Yes/No
9. Is there anything to your knowledge that may prevent you from successfully
completing the tests that have been outlined to you?
Yes/No
I confirm that I have answered these questions truthfully to the best of my knowledge.
Participant’s signature: ........................................................................
Date: ...../........./.........
Researcher’s signature: ........................................................................
Date: ....../........./.........
283
A.14 - PARTICIPANT QUESTION SHEET
Age (years, months)……………………………….………………………………………....
Favoured gymnastics event ………………………………………………………………….
Highest performance level……………………………………...….…………………………
Current performance level…………..…………………..………….………………………..
Number of years trained for………………………………………………………………….
Average number of hours of training completed per week…………….……….………...….
Participation in any other sports……………………………………………………………..
Time since occurrence of last injury (weeks)……………………..…………….…….……..
Site of last injury……………………………………………………….…………….………
Have you had any previous back injuries?
Yes/no
If yes please detail….……………………………………….………………………….…….
Have any close family members suffered with low back injuries?
Yes/no
If yes please provide details ……….………………...………………………………………
284
A.15 - TRIAL SIZE DETERMINATION
The development of trial protocol for individual studies has been deemed necessary for the
establishment of a true measure of the selected study parameters (Rodano, 2002). To
determine the number of handstand and forward walkover skill trials necessary for each
gymnast to perform in the respective research, an enabling study was undertaken. Three
gymnasts took part in the enabling study, for which, each gymnast performed a minimum of
20 handstand and 20 forward walkover skills. The high number of repetitions which
gymnasts are accustom to as a result of the repetitive nature of typical gymnastics training
(Kolt and Kirby, 1999) allowed for the selection of a high number of trials for the enabling
studies. Using three CODA motion analysis markers and the CODA motion analysis setup
presented in Figure 3.2, lumbar angle data were collected for the duration of each handstand
and forward walkover trial. The respective lumbar angle data were exported from CODA
motion analysis software to Microsoft Excel software, within which, minimum lumbar
angles were calculated for each trial of each skill for the three gymnasts. A cross-validation
approach was subsequently utilised to establish the minimum number of trials necessary to
produce stabilised maximum lumbar angle outputs for the respective skills. An example of
the graphs produced for each gymnast in Microsoft Excel software is provided in Figure A.1.
Based on the generation of a cumulative mean from the addition of one trial at a time, the
cross-validation approach deemed a minimum of 16 trials to be necessary to obtain stabilised
lumbar angle output measures. To account for unnoticed errors which may occur during the
testing process, a requirement of 20 handstand and 20 forward walkover trials was
established for the respective research.
Minimum lumbar angle
(º)
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
-5
-10
-15
-20
Trial number
Figure A.1. An example of the cross-validation approach for one gymnast performing 22
handstand skills.
285
A.16 - MARKER PLACEMENT RELIABILITY
Defined as the consistency of a set of measurements or measuring instruments (Bartlett,
2007), the extent to which marker placement is reliable across data collections has important
implications on the value of the research findings. To quantify the reliability of marker
placement for the respective research, one Cx1 CODA motion unit was used to collect
sagittal plane anterior-posterior (y) and vertical (z) positional data for four markers during
three testing sessions. Test-retest reliability was determined using markers at the third
metacarpal phalange (MCP), the triquetrum, the styloid process of the ulna and the lateral
epicondyle of the humerous. Each marker was placed on the left-hand side of the body; the
triquetrum marker remained unchanged through the course of the reliability testing. A single
female participant with age, height and mass of 23 years, 1.72 m and 57.0 kg respectively
took part in the reliability testing. With the intention of increasing inter-participant
reliability, the primary researcher performed marker placements for each data collection
within the respective research, therefore, the same individual undertook each marker
placement for the reliability testing.
Through determination of marker separation, the quiet stance coordinate data were utilised
to calculate hand and forearm lengths at each of the three testing sessions. Raw kinematic
data were exported from CODA motion software for one trial for each of the three conditions
and input into a Microsoft Excel spreadsheet. All positional data were defined according to
the triquetrum marker which was named as the origin due to its positional consistency
through the collection process. An average for each of the three second periods of marker
separation and coordinate data were subsequently obtained. Maximum marker placement
differences and the percentage effect of the maximal differences on mean segment lengths
for the hand and forearm were calculated from the raw positional data (Table A.6).
286
Table A.6. Maximum marker placement differences for two segments and three anatomical
locations, in addition to the effect of the maximum error on mean segment length
Segments and body landmark
Hand segment length
Forearm segment length
Third MCP
Styloid process of the ulna
Lateral epicondyle of the humerous
Axis Maximum difference of Maximum difference effect
marker placement
on mean segment length
(m)
(%)
0.002
1.5
0.004
1.8
y
0.004
z
0.001
y
0.002
z
0.002
y
0.005
z
0.007
-
Test-retest reliability has been reported as an indication of the stability of a repeatedly
measured variable (James et al., 2007). From the test-retest collection, maximum marker
placement difference was found to be less than 2% of the segment length.
287
A.17 - RESIDUAL ANALYSIS
The positional data of three markers (L1, L3 and L5) were tracked during one handstand
trial and one forward walkover trial, performed by two female gymnasts. The phases of
interest were identified for each trial and, using the technique outlined by Winter (2009), a
residual analysis was performed on each data set. Raw data sets for the phases of interest for
each skill were utilised. As displacement data was selected for use, the frequency at which
the signal distortion was equal to the residual noise was taken as the optimal cut-off for that
particular data set (Winter, 2009). As a result of the optimally flat nature (Robertson et al.,
2014), a fourth order Butterworth filter was used. The Butterworth filter was applied to each
data set at frequencies ranging from 0 to 16 Hz, using 4 Hz increments (Figure A.2).
Following calculation of the root mean squared difference between the filtered and unfiltered
data, an average of the determined optimal cut-off frequencies for each data set was
calculated. The overall optimal cut-off frequency was determined to be 10 Hz. To reduce the
chance of error within the derived kinematic outputs of the research, a 10 Hz cut-off
frequency was applied to all raw kinematic data sets.
0.7
Residual (mm)
0.6
0.5
0.4
0.3
0.2
0.1
0
4Hz
8Hz
12Hz
Cut-Off Frequency (Hz)
16Hz
Figure A.2. A graph of the a lumbar marker residuals with a Butterworth filter applied at 4
Hz increments and the optimal cut-off frequency determined at 10Hz.
The residual analysis process was repeated for raw vertical ground reaction force data sets
and raw centre of pressure data sets using increments of 20Hz from 0 to 200Hz and 1Hz
from 0 to 10Hz respectively. The optimal cut-off frequency for ground reaction force data
was determined to be 120Hz and for the centre of pressure data, the cut-off frequency was
identified to be optimal at 3Hz.
288
A.18 - SAMPLING FREQUENCY
Sagittal plane lumbar angle, vertical ground reaction force and anterior-posterior centre of
pressure data were exported for the phase of interest for the handstand and forward walkover
skills and input into a Microsoft Excel worksheet. The average maximum value was
calculated for each using the collected data and for the data sets exported at a 101 data point
frequency to 1001 data points at 100 point increments. The difference between the average
maximum values between the collected data and data at each sampling frequency were
calculated. The number of data points for each measure with the smallest difference from
the collected data averaged maximum value were subsequently averaged between lumbar
angle, ground reaction force and centre of pressure for the handstand and forward walkover.
A sampling rate of 501 data points was determined to be optimal for the exportation of all
data.
289
A.19 - INERTIAL MEASUREMENT ISSUES
The development of customised inertia models through an image-based approach have
previously been based on male athletes (Gittoes et al., 2009). Appraisal of the accuracy of
inertia models for use within the female artistic gymnastics population was therefore
required. To compare the whole-body mass outputs of the mathematical models, whole-body
mass data were obtained from quiet standing using a Kistler force plate. Fourteen female
artistic gymnasts informed the initial inter-participant accuracy testing. The mean (SD)
predicted whole-body mass of the cohort was calculated to be 38.6 (12.4) kg; the measured
body mass was 36.9 (11.0) kg. The model error for a cohort of 14 female gymnasts was
calculated at 4.8 (3.5)%.
Intra-digitiser reliability testing was additionally undertaken, through digitisation of the
same images three times for one gymnast. The outcomes for which presented a maximum
predicted mass difference of 0.3 kg, with model error value of -0.9 (0.5)%. The intra-digitiser
reliability was determined at 0.9% of the mean predicted whole-body mass. Further
digitisation of the same three (frontal, left sagittal and right sagittal) images by two
researchers produced an inter-digitiser predicted mass difference of 0.6 kg and an accuracy
difference of 0.4%. The inter-digitiser reliability was determined at 2.0% of the mean
predicted whole-body mass.
As nine images were taken at each collection (three frontal, three left sagittal and three right
sagittal), examination of the impact of photograph selection on predicted body whole-body
mass and model accuracy was additionally important to evaluate. A difference in predicted
body mass from using two different photographs for each image plane produced predicted
whole-body mass differences of 0.7 kg and accuracy differences of 1.2%. The inter-session
reliability was determined at 1.1% of the mean predicted whole-body mass. Finally, to
examine the influence of the camera set-up on the data gained, images of one participant
were acquired, following which the equipment were put away and the participant moved out
of the set position. The process was then repeated, with subsequent full-body digitising of
the two conditions by the lead researcher. The inter-condition predicted mass difference was
0.9 kg with a 1.5% accuracy difference; inter-condition reliability was determined at 1.4%
of the mean predicted whole-body mass. Overall, throughout the various testing approaches,
the model error was not recorded to be in excess of the value of 2.9% reported by Gittoes et
al. (2009); the only exception to this was the whole-body mass error for 14 gymnast cohort.
290
A.20 - ALTERED DENSITY VALUES
Using the density data presented by Dempster (1955), model accuracy was determined
through the predicted whole-body mass and the measured whole-body mass similarity for
each participant (Gittoes et al., 2009). Dempster (1955)’s density values were subsequently
altered in accordance with the relative accuracy to provide revised density values for each
gymnast. Altered density values for a gymnast who, using Dempster (1955) densities,
revealed a 2.1% difference in whole-body mass between the inertia model calculation and
the force plate measurement are displayed in Table A.7 to exemplify the process. Once the
altered density values were obtained through calculation using Microsoft Excel Software,
the respective data were input into Yeadon (1990)’s model, in addition to the digitised
coordinate data, to obtain revised inertial parameters.
Table A.7. Example of density alterations from Dempster (1955)’s density values
Segments
Head-neck
Shoulders
Thorax
Abdomen-pelvis
Upper arm
Forearm
Hand
Thigh
Lower leg
Foot
Dempster (1955)
1.11
1.04
0.92
1.01
1.07
1.13
1.16
1.05
1.09
1.10
Altered density values
1.09
1.02
0.90
0.99
1.05
1.11
1.14
1.03
1.07
1.08
291
A.21 - MATURATION STATUS QUESTIONNAIRE
1 = no development
2 = beginning development
3 = additional development (development is well underway)
4 = development already passed (completed)
Please circle the following according to the above scale:
Body hair
1
2
3
4
Breast change
1
2
3
4
Skin change (onset of acne)
1
2
3
4
Growth spurt
1
2
3
4
Please identify yourself as one of the following:
Premenarcheal or postmenarcheal ………………………………………………………….
Note
Premenarcheal = before your first period; Postmenarcheal = after your first period
292
A.22 – ANTHROPOMETRIC GROWTH MEASUREMENT ISSUES
The inter-session reliability of bicristal and biacromial breadth measurements was
determined through use of the image-based approach outlined by Gittoes et al. (2009). In
accordance with Armstrong and van Mechelen (2008), biacromial breadth was identified as
‘the distance between the tips of the acromial processes’; bicristal breadth was measured as
‘the distance between the most lateral points of the iliac crests’. Digitising of the right and
left bicristal positions and right and left biacromial positions were undertaken within Peak
Motus software. The reliability testing was based on a frontal plane, whole-body standing
image of a female gymnast for which the respective landmarks were digitised three times,
represented at trials a, b and c. In consistency with the approach taken throughout the
respective research, the primary investigator undertook the digitisation for each trial. The
coordinate data were exported from Peak Motus software and input into a Microsoft Excel
spreadsheet. Subtraction of the left coordinate positions from the right coordinate positions
provided quantification of the bicristal and biacromial breadths for each trial. The bicristal
and biacromial breadths were measured to the nearest 0.1 cm and are provided in Table A..
Table A.8. Bicristal and biacromial breadth reliability testing data
Trial a
Trial b
Trial c
Bicristal breadth (cm)
24.7
24.5
24.7
Biacromial breadth (cm)
33.6
33.7
33.9
The reliability testing revealed a maximum bicristal breadth difference between trials of 0.2
cm (0.8% of the mean bicristal breadth across trials). A maximum biacromial breadth
difference between trials was calculated to be 0.3 cm (0.9% of the mean biacromial breadth
across trials). The calculation of b-ratio for each trial identified a maximum difference of
0.8% (73.5% - 72.7%).
As the respective image-based approach is not customary for anthropometric measurements
in previous research, the accuracy of the measures which inform the b-ratio calculation were
necessary to consider. The accuracy of biomechanical analyses has been outlined by Gittoes
et al. (2009) to depend on the extent to which the true anatomical structure is represented by
approximation of the body. Caliper measurements have been recognised as the most
common method of quantifying anthropometric breadth measures in previous research (e.g.
Barlow et al. (2014)). Therefore, a caliper measurement tool (Holtain Ltd., Dyfed, UK) was
293
used to ascertain bicristal and biacromial breadth data for a female participant, in addition to
quantification of the respective breadth measures through digitisation of the frontal plane
images. Three measures for each breadth measure were attained from use of the caliper and
the image-based approach; mean (SD) of the respective data were determined and is reported
in Table A..
Table A.9. Digitised (image-based approach) and mean (SD) measured (caliper approach)
bicristal breadth and biacromial breadth and the differences between the approaches
Bicristal breadth (cm)
Digitised
28.5
Measured
28.4 (0.2)
Digitised - measured
00.1
Biacromial breadth (cm)
29.8
30.5 (0.3)
-0.7
The bicristal breadth error was calculated at 0.3% of the mean measured breadth. The
digitised to measured biacromial breadth difference was revealed to be 2.4% of the mean
measured breadth.