University School of Physical Education in Wrocław
University School of Physical Education in Kraków
vol. 14, number 2 (June), 2013
University School of Physical Education in Wrocław (Akademia Wychowania Fizycznego we Wrocławiu)
University School of Physical Education in Kraków (Akademia Wychowania Fizycznego im. Bronisława Czecha w Krakowie)
Human Movement
quarterly
vol. 14, number 2 (June), 2013, pp. 93 – 190
Editor-in-Chief Alicja Rutkowska-Kucharska
University School of Physical Education, Wrocław, Poland
Associate EditorEdward Mleczko
University School of Physical Education, Kraków, Poland
Editorial Board
Physical activity, fitness and health
Wiesław Osiński
University School of Physical Education, Poznań, Poland
Applied sport sciences
Zbigniew Trzaskoma Józef Piłsudski University of Physical Education, Warszawa, Poland
Biomechanics and motor control
Tadeusz Bober
University School of Physical Education, Wrocław, Poland
Kornelia Kulig
University of Southern California, Los Angeles, USA
Physiological aspects of sports
Andrzej Suchanowski
Medical University of Bialystok, Białystok, Poland
Psychological diagnostics of sport and exercise
Andrzej Szmajke
Opole University, Opole, Poland
Advisory Board
Wojtek J. Chodzko-Zajko
Gudrun Doll-Tepper Józef Drabik
Kenneth Hardman
Andrew Hills
Zofia Ignasiak
Slobodan Jaric
Toivo Jurimae
Han C.G. Kemper Wojciech Lipoński
Gabriel Łasiński
Robert M. Malina Melinda M. Manore Philip E. Martin Joachim Mester Toshio Moritani
Andrzej Pawłucki John S. Raglin Roland Renson
Tadeusz Rychlewski
James F. Sallis James S. Skinner
Jerry R. Thomas
Karl Weber
Peter Weinberg
Marek Woźniewski
Guang Yue Wladimir M. Zatsiorsky Jerzy Żołądź
University of Illinois, Urbana, Illinois, USA
Free University, Berlin, Germany
University School of Physical Education and Sport, Gdańsk, Poland
University of Worcester, Worcester, United Kingdom
Queensland University of Technology, Queensland, Australia
University School of Physical Education, Wrocław, Poland
University of Delaware, Newark, Delaware, USA
University of Tartu, Tartu, Estonia
Vrije University, Amsterdam, The Netherlands
University School of Physical Education, Poznań, Poland
University School of Physical Education, Wrocław, Poland
University of Texas, Austin, Texas, USA
Oregon State University, Corvallis, Oregon, USA
Iowa State University, Ames, Iowa, USA
German Sport University, Cologne, Germany
Kyoto University, Kyoto, Japan
University School of Physical Education, Wrocław, Poland
Indiana University, Bloomington, Indiana, USA
Catholic University, Leuven, Belgium
University School of Physical Education, Poznań, Poland
San Diego State University, San Diego, California, USA
Indiana University, Bloomington, Indiana, USA
University of North Texas, Denton, Texas, USA
German Sport University, Cologne, Germany
Hamburg, Germany
University School of Physical Education, Wrocław, Poland
Cleveland Clinic Foundation, Cleveland, Ohio, USA
Pennsylvania State University, State College, Pennsylvania, USA
University School of Physical Education, Kraków, Poland
Translation: Michael Antkowiak, Tomasz Skirecki
Design: Agnieszka Nyklasz
Copy editor: Beata Irzykowska
Statistical editor: Małgorzata Kołodziej
Proofreading: Agnieszka Piasecka
Indexed in: SPORTDiscus, Index Copernicus, Altis, Sponet, Scopus, CAB Abstracts, Global Health
8 pkt wg rankingu Ministerstwa Nauki i Szkolnictwa Wyższego
© Copyright 2013 by Wydawnictwo AWF we Wrocławiu
ISSN 1732-3991
http://156.17.111.99/hum_mov
Editorial Office
Dominika Niedźwiedź
51-612 Wrocław, al. Ignacego Jana Paderewskiego 35, Poland, tel. 48 71 347 30 51, [email protected]
This is to certify the conformity with PN-EN-ISO 9001:2009
Circulation: 160
HUMAN MOVEMENT
2013, vol. 14 (2)
contents
ph y sic a l ac t i v i t y, f i t n e s s a n d h e a lt h
Anna Burdukiewicz, Jan Chmura, Jadwiga Pietraszewska, Justyna Andrzejewska, Aleksandra Stachoń, Jarosław Nosal
Characteristics of body tissue composition and functional traits in junior football players...............................................96
Anna Zwierzchowska
Gender-based dimorphism of aerobic and anaerobic capacity and physical activity preferences
in deaf children and adolescents ............................................................................................................................................102
applied sport sciences
Mauro Gonçalves, Anderson Souza Castelo Oliveira
Effects of elbow flexor muscle resistance training on strength, endurance and perceived exertion.................................110
Beata Makaruk, Henryk Sozański, Hubert Makaruk, Tomasz Sacewicz
The effects of resisted sprint training on speed performance in women ............................................................................ 116
Tomasz Tasiemski, Joanna Bauerfeind
Subjective assessment of sports success in wheelchair rugby – proposal of a new research tool........................................123
Piotr Kuczek
On the possibility of applying achievement goal theory in competitive sports...................................................................129
biomechanics and motor control
Emmanuel S. da Rocha, Álvaro S. Machado, Pedro S. Franco, Eliane C. Guadagnin, Felipe P. Carpes
Gait asymmetry during dual-task obstacle crossing in the young and elderly....................................................................138
Dalwinder Singh, Sukhwinder Singh
Effects of vertical and horizontal plyometric exercises on running speed...........................................................................144
Tomasz Niznikowski, Jerzy Sadowski, Andrzej Mastalerz
The effectiveness of different types of verbal feedback on learning complex movement tasks.........................................148
physiological aspects of sports
Marek Zatoń, Dariusz Dąbrowski
Differences in the direction of effort adaptation between mountain bikers and road cyclists..........................................154
Krzysztof Durkalec-Michalski, Małgorzata Woźniewicz, Joanna Bajerska, Jan Jeszka
Comparison of accuracy of various non-calorimetric methods measuring energy expenditure
at different intensities.............................................................................................................................................................. 161
psychological diagnostics of sport an d exercise
Monika Guszkowska, Katarzyna Sempolska, Agnieszka Zaremba, Marta Langwald
Exercise or relaxation? Which is more effective in improving the emotional state of pregnant women?.........................168
Linda Schücker, Norbert Hagemann, Bernd Strauss
Analogy vs. technical learning in a golf putting task: an analysis of performance outcomes
and attentional processes under pressure............................................................................................................................... 175
Publishing guidelines – Regulamin publikowania prac............................................................................................................ 185
95
HUMAN MOVEMENT
2013, vol. 14 (2), 96– 101
Characteristics of body tissue composition
and functional traits in junior football players
doi: 10.2478/humo-2013-0010
Anna Burdukiewicz *, Jan Chmura, Jadwiga Pietraszewska,
Justyna Andrzejewska, Aleksandra Stachoń, Jarosław Nosal
University School of Physical Education, Wrocław, Poland
Abstract
Purpose. The aim of this study was to examine the body tissue composition and functional traits of young football players.
Methods. Analysis was performed on 23 junior football players. Body mass and height were measured. Bioelectrical impedance
was used to assess the players’ body composition (fat mass, muscle mass, body cell mass and extracellular mass). The body mass
index, body cell mass index and the extracellular mass/body cell mass ratio were also calculated. Functional traits were assessed
by a one-on-one football game in an enclosed space with the objective to score the highest number of goals in a timed setting.
Measurements of HR rest, HR max and heart rate reserve were used to evaluate the efficiency of the subjects’ cardiovascular systems.
Results. Insignificant differences in body tissue composition and cardiovascular efficiency were found regardless of what
position was played. Overall, forwards were characterised by having the greatest height, the highest level of active body tissue
development and the most efficient cardiovascular systems. Defenders were characterised by having larger body build, while
midfielders displayed a significantly greater percentage of extracellular mass and EMC in relation to BCM. Conclusions. The
results reveal that trends exist in the body tissue composition and cardiovascular efficiency of football players depending on
which position they play. These differences reflect the varied physical efforts players perform during a match and should be
taken into consideration when designing training programmes.
Key words: body composition, heart rate, football
Introduction
The game of football requires players to perform periodically under high intensity by using aerobic energy
sources that sometimes involves overloading the neuromuscular and hormonal systems. The ability of the neuromuscular system to produce maximum power in the
lower extremities is particularly important for football players, since the ability to produce explosive efforts
at maximum power and force together with a high contraction velocity seems to be one of the main physiological features which differentiate players at different
training levels [1, 2]. On the other hand, the variation
of sprint activity among football players is reflected in
the variety of physiological responses players’ bodies
produce. Results have shown that high intensity aerobic
interval training leads to an increase in VO2max without
negative interference effects on strength, jumping ability
or sprint performance [3].
One of the most informative and easiest to examine
parameters is heart rate, which characterises the efficiency of the cardiovascular system [4]. Research has
shown that whole-day heart rate monitoring is an objective, unobtrusive method for measuring physical
activity at the age of puberty. For athletes in training,
these data are commonly collected from the monitoring
* Corresponding author.
96
of heart rate changes and used to prevent the occurrence
of fatigue [5]. It is commonly known that athletes performing to a high degree are characterised by an improved
lowering of their resting heart rate (HR rest). Furthermore, the correlations observed between maximum
heart rate (HR max), reflected as the highest heart rate
achieved during exercise, and HR rest have been used to
create an index that can compute VO2max [6]. This research revealed that the absolute and relative values of
maximum heart rate and oxygen absorption were higher
in young elite players in comparison to their peers at
a lower training level [7]. In amateur football, the recording of HR was confirmed to be useful for training
purposes and was also applied to characterise metabolic
expenditure during physical effort [8].
Furthermore, with regard to young players, the influence of puberty on body height and functional capacity have also been well substantiated. Children and
youth performing sports, in comparison to their nonexercising peers, displayed greater development of their
somatic features, body efficiency and physical fitness [9].
Studies performed on pubertal youths indicate that the
level of biological maturity influences the variation of
development regarding physical efficiency, velocity and
strength. The period of greatest body growth is frequently
followed by a significant rise in static and explosive
force development. Analogous changes in VO2max have
been found to accompany the pubertal spurt of body
height [10]. The application of multiple linear regres-
HUMAN MOVEMENT
A. Burdukiewicz et al., Body composition and functional traits
sion analysis revealed the existence of a significant relationship between maturity advancement, growth and
composite football skill scores in a group of football
players at the age of puberty [11]. Positive regression coefficients were obtained for the occurrence of puberty
and aerobic resistance. However, the coefficient for body
height was negative, indicating the role of a lower centre
of gravity in better football skill performance. However,
Philippaerts et al. [12] observed that the period during
the greatest height spurt coincides with the development
of maximum balance ability, explosive force, running
speed, upper-body muscular endurance, agility, cardiorespiratory endurance and anaerobic capacity. A plateauing of explosive force development, upper-body
muscular endurance and running speed was observed
after the pubertal height spurt, at which point body
flexibility increasingly developed.
Body tissue composition constitutes one of the factors that not only determine athletes’ motor fitness and
sport level but also plays a role in training. Moreover,
it varies tremendously across individuals in regards to
age and body build. In this regard, adolescence is a very
important phase in life due to various social factors
that adolescents face and the numerous neuro-hormonally determined changes that affect body tissue composition. This includes the influence of growth hormone,
which has, among others, been found to be of significant
importance in the maturation of lean mass and muscle
strength development at puberty and for young adults
in general [13]. The results of research also indicate that
a relationship exists between fat (determined by anthropometric measurement) and the beginning of puberty
in both genders. In the case of young football players,
development of choice body tissue components (lean
tissue) has been noted as the result of improved physical
performance [14, 15].
The development of adolescent boys is, in particular,
characterised by an overall decrease in fat tissue and
increase in BMI, which at this age reflects an increase in
lean mass [16]. Youth involvement in sport (e.g. football) has also been credited in stimulating bone mass
development. However, longitudinal research on a cadet
football league (youths aged 11–14) did not reveal any
acceleration in their morphological development, although it was revealed that muscle power, especially
agility and coordination, distinguished the young
football players from their untrained peers [17]. Therefore, in order further to investigate this issue, this study
examined the features of body tissue composition and
functional traits of a group of young 2nd league football
players.
Material and methods
Twenty-three junior football players playing on a 2nd
league team from Wrocław, Poland were recruited. The
players’ mean age was 16.2 years ( ± 0.70) and had mean
training period of 7.3 years (± 1.87). The university’s research ethics committee approved the study and all participants provided their written informed consent prior
to data collection, which took place at the end of the
2009 competitive season. Information regarding what
position they played in was obtained from their coach.
Body mass and height were measured and used to
calculate body mass index (BMI; body mass [kg]/body
height [m]2). Body composition was assessed by bioelectrical impendance with a BIA-101/S analyser (tetrapolar version, electrodes placed on the hand–foot) integrated with Bodyimage 1.31 software (Akern, Italy). Body
composition was measured before an exercise test, with
fat mass (FM), muscle mass (MM), body cell mass (BCM)
and extracellular mass (ECM) recorded. The components of body composition were expressed in kilograms
or percentage of body mass. Body composition measurements were used to compute the body cell mass index
(BCMI = BCM [kg] / body height [m]2) and the ratio of
ECM/BCM (extracellular mass/body cell mass).
The players’ functional abilities were measured in
special test conditions in order to promote high-intensity exercise: individual players participated in a threeminute game of one-on-one football within an enclosed,
circular cage (a diameter of 500 cm with 250 cm walls)
with goals located on both sides (Hattrick Cage, Ludus
Partner, Poland). The aim of the game was to score the
highest number of goals. Resting heart rate (HRrest) was
measured prior to the test, while maximum heart rate
(HR max) was measured immediately after each game.
Heart rate was monitored and analysed with a shortrange telemetry system (Polar Electro Oy, Finland). Heart
rate reserve (HRR) was computed by subtracting HRrest
from HR max.
Statistica version 9.0 for Windows (StatSoft Inc., USA)
was used for statistical analysis. Basic statistical characteristics were computed (mean, standard deviation). The
Shapiro-Wilk’s test was used to evaluate normal distribution. One-way between-groups analysis of variance
(ANOVA) with Tukey’s post hoc test was used to evaluate the variation of the values recorded for body tissue
composition and the physiological features among the
participants depending on their position (forwards
n = 7, midfielders n = 9, defenders n = 7). Statistical
significance was set at p 0.05.
Results
The anthropometric characteristics and functional
abilities of the football players are presented in Table 1.
The Shapiro-Wilk’s test indicates that body height and
mass and the studied components of body composition
and the players’ physiological response present normal
distribution. Analysis of variance, applied to evaluate
the variation of the analysed features between those playing as forwards, midfielders and defenders, did not reveal
any statistically significant differences (Tab. 2) except
97
HUMAN MOVEMENT
A. Burdukiewicz et al., Body composition and functional traits
for the percentage of extracellular mass between forwards and midfielders.
The results find that forwards are characterised by
the highest body height, body cell mass, muscle mass
and fat mass. HRmax and HRR values were also at a high
level. Furthermore, forwards displayed the lowest levels
of extracellular mass development, ECM/BCM and
resting heart rate. Furthermore, the BMI and BCMI indices indicate that forwards had the largest body build
as well as exhibiting the highest HRmax. When compared
Table 1. Physical characteristics of the junior football
players (N = 23)
Variable
Body mass (kg)
Body height (cm)
Fat mass (kg)
Body cell mass (kg)
Extracellular mass (kg)
Muscle mass (kg)
Fat mass (%)
Body cell mass (%)
Extracellular mass (%)
Muscle mass (%)
BMI (kg ∙ m–2)
BCMI (kg ∙ m–2)
ECM/BCM
HR rest (b ∙ min–1)
HR max (b ∙ min–1)
HRR (b ∙ min–1)
Mean
SD
66.0
173.7
10.4
32.6
22.9
39.7
15.7
49.5
34.8
60.3
21.9
10.8
0.7
78.96
181.26
102.30
5.62
3.69
2.64
2.92
2.25
3.41
3.29
2.90
2.53
3.17
1.36
0.88
0.07
12.05
10.40
13.43
with the other positions, their BCM percentage, muscle
mass and heart rate reserve were at lower levels. Overall,
midfielders displayed the smallest body size. This group
also exhibited the lowest level of body fat and BMI and
BCMI values. Their HRmax values were the lowest compared with the other positions. However, when compared with forwards and defenders, midfielders were
characterised by a significantly greater amount of extracellular body mass and larger values of the ECM/
BCM index.
Discussion
The specificity of modern sport necessitates taking
into consideration certain body build predispositions
in order to determine what somatic criteria ought to be
used when selecting potential athletes in given sport.
The optimum adaptation of an athlete to the requirements of the sport they play in is in large part the result
of their morphological structure and a targeted training regimen that modifies selected somatic parameters.
For young athletes, in addition to the above factors,
puberty also plays a large role in promoting significant
changes in body morphology and tissue composition [18].
This period is characterised by an increase in height,
mass, lean mass and bone mineral content. When compared with girls, the fat content of boys is at a lower
level, where this predisposition is also reinforced by the
large-scale involvement of young boys in sport. Although
the physical load youths undergo depends on the sport,
most training is sufficient enough to cause characteristic
changes in the development level of various body com-
Table 2. Physical characteristics of the junior football players grouped by playing position (mean ± SD)
Playing position
Variable
Body mass (kg)
Body height (cm)
Fat mass (kg)
Body cell mass (kg)
Extracellular mass (kg)
Muscle mass (kg)
Fat mass (%)
Body cell mass (%)
Extracellular mass (%)
Muscle mass (%)
BMI (kg ∙ m–2)
BCMI (kg ∙ m–2)
ECM/BCM
HR rest (b ∙ min–1)
HR max (b ∙ min–1)
HRR (b ∙ min–1)
Forwards
(n = 7)
Midfielders
(n = 9)
Defenders
(n = 7)
p
67.21 ± 5.46
175.53 ± 2.28
11.39 ± 2.24
33.51 ± 3.27
22.31 ± 2.23
40.70 ± 3.71
16.91 ± 2.76
49.88 ± 2.85
33.22 ± 2.16*
60.59 ± 2.99
21.83 ± 1.69
10.89 ± 1.10
0.67 ± 0.07
77.14 ± 14.75
196.00 ± 11.75
104.14 ± 17.33
63.77 ± 6.30
172.67 ± 4.47
9.06 ± 2.37
31.49 ± 2.95
23.22 ± 2.66
38.36 ± 3.52
14.11 ± 3.08
49.49 ± 3.16
36.42 ± 1.82
60.29 ± 3.58
21.40 ± 1.12
10.54 ± 0.69
0.74 ± 0.07
77.33 ± 7.43
191.33 ± 7.02
101.78 ± 7.85
67.57 ± 4.58
173.36 ± 3.54
11.19 ± 2.93
33.20 ± 2.39
23.19 ± 1.86
40.40 ± 2.79
16.40 ± 3.64
49.19 ± 3.02
34.38 ± 2.72
59.87 ± 3.25
22.49 ± 1.23
11.06 ± 0.90
0.70 ± 0.08
82.86 ± 14.70
193.00 ± 9.61
101.14 ± 16.53
0.332
0.302
0.140
0.333
0.700
0.331
0.189
0.914
0.028
0.921
0.299
0.513
0.174
0.612
0.668
0.914
* significantly different from midfielders (p < 0.05)
98
HUMAN MOVEMENT
A. Burdukiewicz et al., Body composition and functional traits
position and functional features. For example, a study
of young prepubertal football players revealed a decrease
in body fat and an increase in lean body and bone mineral content in comparison with their control group
peers [19]. A significant increase in bone mineral content
around the femur neck and lumbar spine areas was also
observed in male adults practicing recreational football for many years [20].
When comparing playing positions, body composition analysis on adult football players found observable
differences between goalkeepers and outfield players [21].
Regarding youth, all players aside from goalkeepers revealed little difference in the development of their body
composition. The results indicate that the lowest amount
of fat tissue is observed in midfielders, although similar
values were noted for forwards and defenders. However,
greater variation of fat tissue levels has been revealed
in adult players [22]. Significantly greater fat mass was
discernible in midfielders in comparison with forwards
and defenders.
Lean body mass consists of body cell mass, extracellular fluid and extracellular solids [23]. Body cell mass,
which is the mass of all metabolically active body cell
components, plays a significant role in physical training. Chronic diseases such as AIDS, tumours or cancers
and the ageing process all result in a decrease of BCM.
The metabolic activity of BCM and its significant role
in the human body is also evident in how diversified
its development is, although depending on the physical activity an individual performs and their training
level [24]. The results confirm previous studies that
have indicated an insignificant variation in the somatic
structure and body composition of outfield players in
relation to players in other positions [25]. The largest
BCM and muscle mass values are observed in forwards
while the lowest in defenders. Melchiorri et al. [26]
observed a similar trend by analysing the body composition of two professional male football teams from two
different divisions. The higher ranked team displayed
significantly lower levels of body fat in its defenders,
while higher BCM values were noted among the forwards from both teams. Players who were individually
ranked higher displayed greater cell mass, even though the
two teams differed in age, body mass, height and BMI.
The players analysed in this study did not display
significant differences in body mass and tissue composition. Previous research has confirmed a correlation
between athletes’ BMI and creatinine concentration
although this is dependent on the practiced sport, type
of training, involvement of aerobic and anaerobic meta­
bolism and the length of the competitive season [27].
Nevertheless, other research on athletes of both genders and people with eating disorders indicated that
body cell mass index, in comparison to BMI, is better
suited to monitor changes in the amount of muscle
mass [28]. This results from the fact that the body cell
mass index is more sensitive to changes in the nutri-
tional status of an individual. In the examined group
of footballers, the lowest values of both indices were
observed in midfielders, while defenders displayed the
greatest body mass and cell mass when taking body
height into consideration. The obtained results may
be further justified by the observed ascendency of the
mesomorphic somatotype of defenders [29].
Extracellular mass contains all the metabolically
inactive body tissues, and thus an increased ECM/BCM
index value is frequently interpreted as a sign of malnutrition. However, a different trend is observed among
football players, who feature a decrease in the relative
amount of extracellular mass [30]. This has been linked
to physical activity that requires larger power output,
such as in endurance running and cross country skiing.
In the group of football players examined in this study,
the overall ECM/BCM index was found to be 0.7, which
corresponds to those values in well-trained adult competitors [31]. When considering playing positions, the
lowest index value was observed in forwards, while
midfielders were characterised by the highest level of
extracellular mass in relation to cell mass.
The easiest way to measure the reaction of the cardiovascular system to effort is to determine the heart rate
index, which has been significantly correlated to VO2max
and blood lactate and saliva lactate levels. Heart rate
reserve is also used as an indirect measurement of the
intensity of metabolic changes and useful when comparing the endurance of players in different positions
on the pitch [32]. The group of youth football players
analysed in this study featured no statistically significant variation between resting heart rate, maximum
heart rate or heart rate reserve. However, it should be
emphasised that forwards displayed the lowest HR rest
and the highest HR max and HRR during the test. Defenders were characterised by the highest values of resting heart rate and the lowest values of maximum heart
rate and heart rate reserve. Based on the obtained results, it can be concluded that forwards are characterised
by the highest level of cardio-vascular efficiency. Research
conducted on 14–21 year-old football players revealed
that forwards were characterised by greater endurance,
velocity, agility and power, along with better muscle development and body leanness, than other players [33].
Goalkeepers, on the other hand, were characterised with
greater height, mass, body fat and the lowest aerobic
capacity. Midfielders displayed greater levels of agility
and endurance, while defenders were characterised by
the lowest body fat.
Conclusions
Analysis of the results revealed that there are certain
differentiating trends in body tissue composition and
cardiovascular efficiency among football players playing
in different positions. Forwards were characterised by
having the greatest height, highest levels of active body
99
HUMAN MOVEMENT
A. Burdukiewicz et al., Body composition and functional traits
tissue development and the most efficient cardiovascular
systems. Defenders displayed larger body build, while
midfielders were characterised by significantly higher
values of extracellular mass and EMC in relation to BCM.
These differences reflect the varied physical efforts players perform during a match and should be taken into
consideration when designing training programmes.
References
1. Wisloff U., Helgerud J., Hoff J., Strength and endurance
of elite soccer players. Med Sci Sports Exerc, 1998, 30,
462–467.
2. Gorostiaga E.M., Izquierdo M., Ruesta M., Iribarren J.,
González-Badillo J.J., Ibáñez J., Strength training effects
on physical performance and serum hormones in young
soccer players. Eur J Appl Physiol, 2004, 91, 698–707, doi:
10.1007/s00421-003-1032-y.
3. McMillan K., Helgerud J., Macdonald R., Hoff J., Physio­
logical adaptations to soccer specific endurance training
in professional youth soccer players. Br J Sports Med,
2005, 39, 273–277, doi: 10.1136/bjsm.2004.012526.
4. Tanaka H., Monahan K.D., Seals D.R., Age-predicted
maximal heart rate revisited. J Am Coll Cardiol, 2001,
37, 153-156, doi: 10.1016/S0735-1097(00)01054-8.
5. Bricout V.-A., Dechenaud S., Favre-Juvin A., Analyses of
heart rate variability in young soccer players: The effects
of sport activity. Auton Neurosci, 2010, 154, 112–116,
doi: 10.1016/j.autneu.2009.12.001.
6. Uth N., Sørensen H., Overgaard K., Pedersen P.K., Estimation of VO2max from the ratio between HRmax and HRrest –
the Heart Rate Ratio Method. Eur J Appl Physiol, 2004,
91, 111–115, doi: 10.1007/s00421-003-0988-y.
7. Strøyer J., Hansen L., Klausen K., Physiological profile
and activity pattern of young soccer players during match
play. Med Sci Sports Exerc, 2004, 36, 168–174, doi:
10.1249/01.MSS.0000106187.05259.96.
8. Esposito F., Impellizzeri F.M., Margonato V., Vanni R.,
Pizzini G., Veicsteinas A., Validity of heart rate as an in­
dicator of aerobic demand during soccer activities in amateur soccer players. Eur J Appl Physiol, 2004, 93, 167–172,
doi: 10.1007/s00421-004-1192-4.
9. Burdukiewicz A., Janusz A., Physical capacity and fitness
of children and youths as related to their somatic development. Biol Sport, 1995, 12, 175–188.
10. Malina R.M., Eisenmann J.C., Cumming S.P., Ribeiro B.,
Aroso J., Maturity-associated variation in the growth and
functional capacities of youth football (soccer) players
13–15 years. Eur J Appl Physiol, 2004, 91, 555–562, doi:
10.1007/s00421-003-0995-z.
11. Malina R.M, Ribeiro B., Aroso J., Cumming S.P., Characteristics of youth soccer players aged 13–15 years classified by skill level. Br J Sports Med, 2007, 41, 290–295,
doi: 10.1136/bjsm.2006.031294.
12. Philippaerts R.M., Vaeyens R., Janssens M., van Renter­
ghem B., Matthys D., Craen R. et al., The relationship
between peak height velocity and physical performance
in youth soccer players. J Sports Sci, 2006, 24, 221–230,
doi: 10.1080/02640410500189371.
13. Hulthén L., Bengtsson B.-Å., Sunnerhagen K.S., Hallberg L., Grimby G., Johannsson G., GH is needed for the
maturation of muscle mass and strength in adolescents.
J Clin Endocrinol Metab, 2001, 86, 4765–4770, doi:
10.1210/jc.86.10.4765.
100
14. Silvestre R., West C., Maresh C.M., Kraemer W.J., Body
composition and physical performance in men’s soccer:
A study of A National Collegiate Athletic Association
Division Iteam. J Strength Cond Res, 2006, 20, 177–183,
doi: 10.1519/R-17715.1.
15. Pantelis T. N., Anaerobic Power across Adolescence in
Soccer Players. Hum Mov, 2011, 12 (4), 342–347, doi:
10.2478/v10038-011-0039-1.
16. Maynard L.M., Wisemandle W., Roche A.F., Chum­
lea W.C., Guo S.S., Siervogel R.M., Childhood body
composition in relation to Body Mass Index. Pediatrics,
2001, 107, 344–350, doi: 10.1542/peds.107.2.344.
17. Mirkov D.M, Kukolj M., Ugarkovic D., Koprivica V.J.,
Jaric S., Development of anthropometric and physical
performance profiles of young elite male soccer players:
A longitudinal study. J Strength Cond Res, 2010, 24,
2677–2682, doi: 10.1519/JSC.0b013e3181e27245.
18. Bale P., Mayhew J.L., Piper F.C., Ball T.E., Willman M.K.J.,
Biological and performance variables in relation to age
in male and female adolescent athletes. J Sports Med
Phys Fitness, 1992, 32, 142–148.
19. Vicente-Rodríguez G., Ara I., Perez-Gomez J., SerranoSanchez J.A., Dorado C., Calbet J.A.L., High femoral
bone mineral density accretion in prepubertal soccer
players. Med Sci Sports Exerc, 2004, 36, 1789–1795.
20. Calbet J.A.L., Dorado C., Díaz-Herrera P., Rodríguez-Ro­
dríguez L.P., High femoral bone mineral content and density in male football (soccer) players. Med Sci Sports Exerc,
2001, 33, 1682–1687, doi: 10.1097/00005768-20011000000011.
21. Sutton L., Scott M., Wallace J., Reilly T., Body composition
of English Premier League soccer players: Influence of
playing position, international status, and ethnicity. J Sports
Sci,2009,27,1019–1026,doi:10.1080/02640410903030305.
22. Wittich A., Oliveri M.B., Rotemberg E., Mautalen C.,
Body Composition of Professional Football (Soccer) Players Determined by Dual X-Ray Absorptiometry. J Clin
Densitom, 2001, 4, 51–55.
23. Wang Z.M., Heshka S., Wang J., Gallagher D., Deurenberg P., Chen Z. et al., Metabolically active portion of fatfree mass: a cellular body composition level modeling
analysis. Am J Physiol Endocrinol Metab, 2007, 292, 49–53,
doi: 10.1152/ajpendo.00485.2005.
24. Andreoli A., Melchiorri G., Brozzi M., Marco A., Volpe S.L.,
Garofano P. et al., Effect of different sports on body cell
mass in highly trained athletes. Acta Diabetol, 2003,
40, S122–S125, doi: 10.1007/s00592-003-0043-9.
25. Hazir T., Physical characteristics and somatotype of soccer
players according to playing level and position. J Hum
Kinet, 2010, 26, 83–95, doi: 10.2478/v10078-010-0052-z.
26. Melchiorri G., Monteleone G., Andreoli A., Callà C.,
Sgroi M., De Lorenzo A., Body cell mass measured by bioelectrical impedance spectroscopy in professional football (soccer) players. Sports Med Phys Fitness, 2007, 47,
408–412.
27. Banfi, G., Del Fabbro M., Relation between serum creatinine and body mass index in elite athletes of different
sport disciplines. Br J Sports Med, 2006, 40 (8), 675–678,
doi: 10.1136/bjsm.2006.026658.
28. 28. Talluri A., Liedtke R., Mohamed E.I., Maiolo C.,
Martinoli R., De Lorenzo A., The application of body cell
mass index for studying muscle mass changes in health
and disease conditions. Acta Diabetol, 2003, 40, S286–
S289, doi: 10.1007/s00592-003-0088-9.
HUMAN MOVEMENT
A. Burdukiewicz et al., Body composition and functional traits
29. Orhan Ö., Sağir M., Zorba E., Kishali N. F., A comparison
of somatotypical values from the players of two football
teams playing in Turkcell Turkish super league on the
basis of the players’ positions. J Phys Educ Sport Manag,
2010, 1, 1–10.
30. Randáková R., Effect of regular training on body composition and physical performance in young cross-country skiers: as compared with normal controls. Acta Univ
Palacki Olomuc Gymn, 2005, 35, 17–35.
31. Bunc V., Body composition as a determining factor in the
aerobic fitness and physical performance of Czech children.
Acta Univ Palacki Olomuc Gymn, 2006, 36, 39–45.
32. Impellizzeri F.M., Rampinini E., Marcora S.M., Physio­
logical assessment of aerobic training in soccer. J Sports
Sci, 2005, 23, 583–592, doi: 10.1080/02640410400021278.
33. Gil S.M., GIL J., Ruiz F., Irazusta A., Irazusta J., Physio­
logical and anthropometric characteristics of young soccer
players according to their playing position: Relevance
for the selection process. J Strength Cond Res, 2007, 21,
438–445, doi: 10.1519/R-19995.1.
Paper received by the Editors: June 19, 2012
Paper accepted for publication: March 12, 2013
Correspondence address
Anna Burdukiewicz
Akademia Wychowania Fizycznego
al. I.J. Paderewskiego 35
51-612 Wrocław, Poland
e-mail: [email protected]
101
HUMAN MOVEMENT
2013, vol. 14 (2), 102– 109
Gender-based dimorphism of aerobic
and anaerobic capacity and physical activity
preferences in deaf children and adolescents
doi: 10.2478/humo-2013-0011
Anna Zwierzchowska
The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland
Abstract
Purpose. Research on the hearing impaired has revealed that the rate of change of physical fitness characteristics between
both genders may be different than that of the hearing. The aim of the study was to verify the gender-based differentiation of
aerobic and anaerobic capacity in a group of deaf children and adolescents (aged 10–18 years) and to evaluate their physical
activity preferences. Methods. A semi-longitudinal study was conducted, with data collected three times over a period of two
years. Aerobic capacity was measured by the PWC170 cycle test, anaerobic capacity by the Wingate test. A questionnaire was
used to evaluate the physical activity preferences and favored leisure activities of the participants. Results. Significant genderbased differences were found in the aerobic and anaerobic capacity of the deaf boys and girls. A moderate correlation was noted
for leisure time preferences. Conclusions. Deaf children feature no gender-based differences among their physical activity preferences.
Environment plays a major role in stimulating the behavior of deaf children and adolescents.
Key words: aerobic and anaerobic capacity, sexual (gender) dimorphism, deafness
Introduction
Disabilities, especially those that affect the musculoskeletal system, play a large role in reducing physical
activity levels. However, often at times individuals with
sensory impairments are not perceived as having the
same limitations in performing physical activity as those
with physical disabilities. This is especially so with hearing
impairments, which are usually not regarded as limiting
physical activity, although research on this subject has
provided contradictory results. Many researchers state
that the physical abilities of the deaf are highly differentiated and even sometimes lower than those found among
an average hearing population [1–5], concluding that
this may be the consequence of how physical education
is shaped and taught to the deaf. A study by Ellis [6] revealed that one of the most important factors motivating deaf youth in performing physical activity is the
emotional support and involvement of parents. A similar
conclusion was reached by Dummer et al. [2], stating
that there are no differences in the motor skills of deaf
children and their hearing peers. This group of authors
believes that the introduction of early intervention and
special education programs already at the preschool age
helped bridge any supposed impediment. Moreover, they
recognized that environmental factors (type of school,
lifestyle, parental attitude as well as their involvement
in physical activity, and the availability of free play opportunities) also play an important role in motor development. Liberman et al. [4, 7] drew attention to the importance of several environmental factors, in particular on
how physical education classes were conducted through
the use of special programs and the role of physical education teachers in providing a behavioral role model
102
for participation in physical education. An additional
factor noticed by auxologists and teachers of the deaf is
the difference in interest in various forms of physical
activity based on gender, which is believed to be a reflection of what physical activity can actually be performed [2, 8].
Research has confirmed that the gender difference
between males and females is already visible at the preschool age and includes not only interest in various forms
of physical activity but also motility [9]. The ontogenetic
development of motor and morphological skills has been
described as highly variable. Motor skills are largely the
result of environmental conditioning, hence dimorphic
variation cannot be as clearly defined as in the case of
somatic characteristics. Therefore, it is difficult to expect
that dimorphic traits in motility would not be present
even when a hearing impairment is present. However,
a few studies that have been conducted on the hearing
impaired found that the rate and pace of characteristics that can emerge to differentiate both genders may
be different than those among the hearing [3, 10–13].
Among girls, fewer differences were found to exist between those hearing and deaf than in the case of boys.
Comparative studies on the physical development of
deaf boys and girls have revealed significant differences
in favor of girls. One of many conclusions reached by
such studies was that deaf girls develop physical and
motor skills better than boys [10–15]. It was also noted
that deaf girls learn new motor skills quicker and show
little or no differences when compared with their hearing peers than in the case of deaf boys. In contrast,
deaf boys often showed significantly greater motor deficits than their hearing peers [2]. Haubenstricker and
Seefeldt’s findings [8] on the hearing helped theorize
HUMAN MOVEMENT
A. Zwierzchowska, Aerobic and anaerobic capacity and physical activity of the deaf
that the ability to learn basic motor skills is more similar
between deaf boys and girls than among their hearing
peers. Instead, the delay experienced by deaf boys in
learning new motor skills may be caused by them presenting a physical fitness level lower than among the
hearing.
The aim of this study was to verify what gender differences exist among a group of deaf children and adolescents (10–18 years old) in their ability to perform
aerobic and anaerobic tasks as well as what their physical
activity preferences. In light of the formulated objective,
the study was guided by the following research questions:
1. What is the preferred physical activity of deaf male
and female youth?
2. Is gender a factor that differentiates the deaf in
their ability to perform aerobic and anaerobic
tasks?
It was assumed that the preferred physical activity is
an important factor differentiating aerobic and anaerobic exercise capacity.
Material and methods
Students attending special education schools for the
deaf and hard of hearing from the Polish cities of Katowice, Kraków, and Racibórz comprised the target population. A sample was selected by adopting the criteria
used in modern audiology as based on Parving [16].
The main criterion for inclusion was for the student to
have been diagnosed of profound hearing loss (prelingual deafness) before the age of three and experiencing sensorineural hearing impairment. All cases where
the etiology of deafness was unknown were excluded
from the study. All of the participants had normal intelligence as well as showed no signs of any physical
disabilities that could impair movement.
The final sample included deaf students of both genders within the calendar age groups of 9.6–10.5 years,
12.6–13.5 years, and 15.6–16.5 years, where 17.7% had
inherited deafness, 55.4% were prenatal cases, and 26.9%
suffered a hearing impairment after the postnatal period up to age three. The study design was designed to
be semi-longitudinal in nature and divided into three
age groups within a 10–18 year old spread. It was conducted three times in 2004, 2005, and 2006 (all in the
month of October) on the same deaf students within
the mentioned three age groups, allowing the same
age groups to be observed (9.6–12.5, 12.6–15.5, and
15.6–18.5 years old) (Tab. 1).
A self-designed questionnaire was used to evaluate
the physical activity preferences of the participants. It
contained closed-ended questions with multiple-choice
answers on how they enjoyed spending their leisure time.
The questionnaire was completed with the help of a sign
language interpreter who also provided instructions on
how to complete the exercise tests measuring aerobic
and anaerobic capacity. Each exercise task was preceded
by a demonstration with a complete explanation of the
instructions and conducted by the same research team
each time.
The study was approved by the Bioethics Committee
of Scientific Research at the University School of Physi­
cal Education in Katowice, Poland as part of a project
funded in part by the State Committee for Scientific
Research. In addition, the legal guardians of the participants were informed of the nature of the experiment
and provided their written consent. The participants
were informed they may at any time leave the study
without providing any reason and reminded that their
personal information would remain private in accordance with all applicable data privacy laws.
Physiological data was collected by lung vital capacity as well as the aerobic and anaerobic capacity of the
participants was measured. Vital capacity (VC) was measured in l/min by use of Pony Graphic 3.7 spirometer
(Cosmed, Italy). Respiratory rates were measured twice
as per the manufacturer’s recommendation. Prior to taking
a measurement, the participant was asked to breathe
calmly for a short period of time and then inhale and
exhale as hard as possible, performing a maximum inhalation and maximum exhalation. After exhaling the
remaining residual air volume was measured.
Aerobic capacity was assessed by VO2max · kg–1 and the
PWC170 cycle test on an 828E cycle ergometer (Monark,
Sweden), which from a technical point of view was the
most accommodating for the participants due to their
impairment. The task was thoroughly explained to the
participants and motivation was provided throughout
the test. First, the workload on the cycle ergometer needed
to maintain a heart rate of 170 beats per minute was
calculated (a higher value in the PWC170 test denotes
that more work needs to be performed based on a correctly functioning cardiovascular system). It was determined that two five-minute trails at 30 and 60 W for
Table 1. Participants grouped by age and gender
Year
2004
2005
2006
10 (12)
13 (15)
16 (18)
2004–2006
Girls
Boys
Girls
Boys
Girls
Boys
n
6
6
6
16
14
15
6
6
6
6
6
6
12
10
10
10
9
10
56
51
53
Age in parentheses is the age of the participants at the conclusion of the study
103
HUMAN MOVEMENT
A. Zwierzchowska, Aerobic and anaerobic capacity and physical activity of the deaf
girls and 50 and 100 W for boys would be adequate.
Throughout the test the participants’ heart rate was
monitored. PWC170 was calculated by the formula:
PWC170 = N1 – N2 ·
170 – f1
f1 – f2
,
where:
N1– first test workload,
N2– second test workload,
f1 – heart rate at the fifth minute of the first test,
f2 – heart rate at the fifth minute of the second test.
Maximal oxygen uptake (VO2max) was then estimated
based on the Astrand-Ryhming nomogram by taking
into consideration steady heart rate at submaximal effort [17]. This provided two variables that could be
used to assess aerobic endurance: maximal aerobic
power (PWC170 [W/kg]) and and maximal oxygen uptake (VO2max [ml/kg x min]).
Anaerobic capacity was measured by the 30-second
Wingate Test, which is a non-invasive method that is
suitable for repeated use and considered to be a reliable
and accurate measure of anaerobic capacity, as anaerobic
processes meet almost 90% of the overall energy demands
of the test [18]. The test also registers the power output
of a participant as a function of time (throughout the
30 second period of the test) as it increases and then
decreases as the effects of fatigue set in. Analysis of
power output as a function of time indicates that humans produce maximum power between the first 3–6
seconds of the test, followed by steady decrease until
completion. This reveals the nature of the energy conversion process in the working muscles.
The test was performed with the use of a different
cycle ergometer (model 829, Monark, Sweden) that
measures the duration of each pedal revolution. After
receiving a visual cue, the participant’s task was to reach
a maximum pedaling frequency as fast as possible and
maintain this speed for 30 seconds. The load was matched
individually to each participant by taking into account
their body mass, age, and sex (75g per kg). Changes in
power output were determined by the duration of each
pedal revolution. The test was preceded by a five-minute
warm-up on the cycle ergometer with a load suitable
to reach a heart rate of 140–150 beat per minute.
Anaerobic capacity and power output were measured
with the following variables: maximal anaerobic power
– MAP [W], average anaerobic power – AAP [W], time to
reach maximal power – TMP [s], time under tension –
TUT [s], and the rate of power loss – RPL [%]. Data were
recorded and calculated by using MCE ver. 2.0 computer software.
All statistical analysis was performed with Statistica
v. 7.1 (Statsoft, USA) and Microsoft Excel software.
The mean ( ), median, minimums and maximums,
standard deviation (SD), and measures of skewness (SK)
104
and kurtosis (KU) were calculated for data that were
expressed as a ratio variable. Normal distribution was
assessed with the Shapiro-Wilk test. Univariate ANOVA
and correlation analysis using Spearman’s rank correlation coefficient (rs) was also used. The results were
treated as statistically significant at p < 0.05.
The sexual dimorphism of the participants’ somatic
characteristics were determined by the differences of
the mean values in each successive year. However, several
studies have shown that sexual dimorphism is more
accurately measured by indicators that define body proportions and not individual morphological characteristics. Developmental differences between the studied boys
and girls were determined by Mollison’s index of sexual
dimorphism (SDI) [19]:
SDI =
–
SD
,
where:
SDI – the indicator of sexual dimorphism,
– the arithmetic mean of the girls’ characteristics,
– the arithmetic mean of the boys’ characteristics,
SD – the standard deviation of the boys’ characte­
ristics.
Dimorphic differences were treated as significant when
the difference between the means ( ) was larger than
the standard deviation (SD) of the group of males. The
absolute value of the tested variable would indicate the
degree of differentiation: the larger the value the larger
its value of one standard deviation away from the mean
of the boys’ results. A positive value would indicate that
this characteristic is in favor of females.
Results
The responses obtained from the questionnaire found
that the boys were decidedly less physically active than
the girls, with a large majority of them preferring to spend
their leisure time passively by watching TV or playing
computer games (94.2% and 77.7%, respectively). However, the majority of boys reported that their more actively spent leisure time consisted of bicycling and team
sports (80.5%), which was in contrast with the girls who
preferred calmer activities such as playing outside and
taking walks (51.8%). The results of the questionnaire
indicated a lack of statistically significant differences
in the leisure activity preferences of the deaf boys and
girls. A moderate correlation was found between the
boys’ and girls’ preference for passive forms of physical
activity (rs = 0.629, p < 0.05) although no significant
relationships were found among active forms of physical
activity (Tab. 2, 3).
Physical fitness was analyzed by measuring aerobic
and anaerobic capacity. Analysis of the indicators of
aerobic capacity and vital capacity (VC) found a statistically significant difference between the boys and girls
only in PWC170 [W/kg]. Only the youngest group of girls
HUMAN MOVEMENT
A. Zwierzchowska, Aerobic and anaerobic capacity and physical activity of the deaf
Type of activity
Boys
n = 35
%
Girls
n = 27
%
Passive
TV
Computer
Reading books
Social games
8
25
1
2
22.8
71.4
3.2
5.7
12
9
2
4
44.4
33.3
7.4
14.8
Active
Table 2. Preferred leisure activities by the deaf girls and
boys
Bicycling
Swimming
Taking walks,
playing outdoors
Skiing
Team sports
20
3
57.7
8.6
9
3
33.3
11.1
4
7.1
14
51.8
2
8
7.4
22.8
4
4
14.8
11.4
Table 3. Spearman’s rank correlation coefficient (rs)
the deaf boys and girls with regard to their preferred
leisure activities
Passive leisure activities
Girls
Boys
Girls
Boys
x
0.629
0.629
x
Girls
Boys
x
0.143
0.143
x
Active leisure activities
Girls
Boys
achieved better results than the boys, with the later
tests finding that the boys achieved significantly better results up to the age of 18 (f = 5.6; p < 0.03). Gender
had no statistically significant effect on the rate of
maximal oxygen uptake, only age was a significant
factor differentiating both groups. A decline of VO2max
values was noticed in both the boys and girls. A some-
what different picture is seen in the case of VC, whose
values progressively rise over time, although no statistically significant differences were found between the
boys and girls (Tab. 4).
The sexual dimorphism index found dimorphic variation in favor of the males for PWC170 above the age of
12 and for VC above the age of 16. It is worth noting that
the dimorphism index was highly fluctuated showing no
clear trend. Furthermore, the dimorphism index calculated for VO2max pointed to no differences greater than
one standard deviation away from the boys’ mean, which
indicates that there is no significant variation between
genders (Tab. 4).
Analysis of the increases in PWC170 and VO2max finds
that gender has no statistically significant effect on these
values, with the only statistically significant difference
found in the rate of change of vital capacity between
10 and 12 years of age (Fig. 1).
The participants’ ability to perform brief anaerobic
effort was based on the following five measured variables: maximal anaerobic power – MAP [W], average
anaerobic power – AAP [W], time to reach maximal
power – TMP [s], time under tension – TUT [s], and
the rate of power loss – RPL [%]. Significant differences
between the boys and girls were found for MAP and AAP
(the oldest group composed of 16-, 17-, and 18-year-olds),
RPL (11- and 17-year-olds), and TMP (17-year-olds), all
in favor of the boys (Tab. 5). It should be noted that the
time needed to reach these values was significantly
higher than expected (3–6 seconds).
Anaerobic capacity assessed using the dimorphism
index indicates a regular progressive trend for MAP and
AAP from the age of 13 onwards, whereas the absolute
values point to significant differences between genders
in favor of the boys starting from the age of 16. A similar situation, although reversed, was found with RPL,
which measures the rate at which fatigue sets in. This
variable was found to largely characterize the girl participants (indicating a smaller tolerance to fatigue).
Table 4. Aerobic capacity and vital capacity of the deaf girls and boys
VC
Age
± SD
10
11
12
13
14
15
16
17
18
2.0 ± 0.5
1.9 ± 0.4
2.14 ± 0.6
2.9 ± 0.2
2.8 ± 0.4
3 ± 0.4
2.9 ± 0.3
2.7 ± 0.5
2.7± 0.6
± SD
2.5 ± 0.3
2.3 ± 0.4
2.7 ± 0.8
3.4 ± 0.9
2.9 ± 0.6
3 ± 0.7
3.8 ± 0.5
3.9 ± 0.4
3.6 ± 0.5
VO2max
PWC170
SDI
–1.5
–0.8
–0.7
–0.4
–0.2
0.1
–1.6
–2.7
–1.7
± SD
1.9 ± 0.8
1.6 ± 0.7
1.8 ± 0.3
1.5 ± 0.3
1.4 ± 0.2
1.9 ± 0.4
1.7 ± 0.7
1.5 ± 0.6
1.8 ± 0.6
± SD
1.7 ± 0.6
1.8 ± 0.7
2.4 ± 0.7
2.0 ± 0.4
1.7 ± 0.2
2.3 ± 0.6
2.5 ± 0.7
2.5 ± 1.2
2.4 ± 0.5
SDI
0.2
–0.2
–0.8
–1.2
–1.6
–0.7
–1.2
–0.9
–1.0
± SD
54.2 ± 18.8
48.5 ± 18.5
48.2 ± 13.2
40.1 ± 4.0
36.6 ± 4.0
40.4 ± 5.8
39.7 ± 9.4
39.2 ± 7.7
41.6 ± 9.3
± SD
50.2 ± 11.3
49.1 ± 12.3
50.9 ± 11.3
49.2 ± 10.1
43.2 ± 3.8
48.4 ± 11.0
44.6 ± 7.6
48.0 ± 13.7
46.1 ± 7.7
SDI
0.3
–0.1
–0.2
–0.9
–1.6
–0.7
–0.6
–0.6
–0.6
* statistically significant difference between genders at p < 0.05; SDI – Mollison’s sexual dimorphism index;
shaded values indicate a difference in dimorphic traits (SDI > SD )
105
106
9.5 ± 1.2
9.4 ± 1.8
8.0 ± 1.3
7.5 ± 1.1
–2.2
–1.2
–1.9
–1.2
–0.9
0.1
–0.8
–0.5
–0.8
SDI
5.2 ± 0.9
5.5 ± 1.1
5.09 ± 1.7
5.5 ± 0.5
5.2 ± 0.4
5.7 ± 0.5
4.4 ± 1.9
4.1 ± 1.9
3.6 ± 1.7
± SD
7.8 ± 0.7
7.4 ± 1.4
7.5 ± 0.7
7.2 ± 0.9
6.5 ± 0.9
6.1 ± 0.9
5.9 ± 1.7
4.9 ± 1.4
5.4 ± 1.6
± SD
AAP [W/kg]
–3.3
–1.4
–3.1
–1.7
–1.4
–0.5
–0.9
–0.5
–1.0
SDI
± SD
11.5 ± 4.5
10.2 ± 1.4 11.7 ± 4.0
10.2 ± 3.4 16.2 ± 6.6
10.2 ± 3.0 12.8 ± 5.8
12.5 ± 2.6 10.4 ± 2.8
13.5 ± 2.5 12.0 ± 5
12.5 ± 3.3 10.8 ± 2.8
9.9 ± 5.2
12.7 ± 5.5 16.1 ± 7.8
15.2 ± 5.4 11.6 ± 4.4
± SD
TMP [s]
0.3
–0.9
–0.4
0.7
0.6
0.6
–0.3
–0.4
0.8
SDI
2.6 ± 1.3
2.5 ± 1.5
1.3 ± 1.2
3.6 ± 2.2
2.7 ± 1.2
1.6 ± 0.7
3.3 ± 2.7
3.08 ± 2.2
1.6 ± 1.0
± SD
* statistically significant difference between genders at p < 0.05; SDI – Mollison’s sexual dimorphism index;
shaded values indicate a difference in dimorphic traits (SDI > SD )
6.8 ± 1.4
18
9.5 ± 1.2
7.4 ± 0.4
15
6.9 ± 2.2
6.7 ± 1.0
14
7.3 ± 1.7
7.7 ± 1.2
13
16
8.7 ± 1.0
5.7 ± 2.2
17
6.3 ± 1.5
5.5 ± 2.3
11
12
7.6 ± 2.1
6.9 ± 2.0
± SD
5.1 ± 1.7
10
± SD
Nonetheless, the SDI index was less than one standard
deviation away from the boys’ means, which suggests
that gender is not a differentiating factor here. The remaining variables assessing anaerobic capacity oscillated
between zero and the absolute value of one standard
deviation, indicating no significant differences between
the genders (Tab. 5).
Analysis on the rate of change of the variables measuring anaerobic capacity found that gender did have
a statistically significant effect on increased TMP in
the youngest age group. There were no statistically significant differences in the rate of change for the remaining variables between the two genders (Fig. 2).
Age
Figure 1. Rate of change for vital capacity
and the indicators measuring the aerobic capacity
of the deaf girls (G) and boys (B) among the three age
groups (10–12, 13–15, and 16–18 years old)
MAP [W/kg]
* statistically significant difference at p < 0.05
– denotes change as a unit of time (year) for VC, PWC170, and VO2max
Table 5. Anaerobic capacity of the deaf girls and boys
3.4 ± 2.8
2.0 ± 1.6
1.2 ± 2.5
3.5 ± 2.3
4.7 ± 4.6
1.0 ± 1.7
1.9 ± 0.8
2.1 ± 2.6
1.1 ± 1.3
± SD
TUT [s]
14.3 ± 8.1
16.8 ± 7.8
± SD
16.9 ± 9.5
21.7 ± 9.7
12.6 ± 5.8
14.2 ± 3.8
28.7 ± 17.1 17.6 ± 5.8
24.7 ± 7.4
17 ± 4.8
± SD
–0.3
20.4 ± 7.2
0.23 22.3 ± 7.9
13.6 ± 6.7
11.1 ± 7.8
–0.01 25.6 ± 17.7 15.7 ± 7.1
0.03 22.5 ± 12.5 15.3 ± 2.6
–0.4
–0.2
1.6
0.3
0.4
SDI
RPL [%]
1.0
1.4
1.4
2.7
0.7
1.9
1.9
1.2
0.1
SDI
HUMAN MOVEMENT
A. Zwierzchowska, Aerobic and anaerobic capacity and physical activity of the deaf
HUMAN MOVEMENT
A. Zwierzchowska, Aerobic and anaerobic capacity and physical activity of the deaf
* statistically significant difference at p < 0.05
– denotes change as a unit of time (year) for maximal anaerobic power (MAP)
AAP – average anaerobic power
TMP– time to reach maximal power
TUT – time under tension
RPL – the rate of power loss
Figure 2. The rate of change of variables measuring
anaerobic capacity for the deaf girls (G) and boys (B)
among the three age groups (10–12, 13–15,
and 16–18 years old)
Discussion
Lung vital capacity has been medically verified to
increase together with maturity, although it remains
highly variable not only due to age but also gender [21].
This study confirmed the progressive rise of vital capacity
in both females and males, with significant gender differences emerging after the age of 15. However, no significant sexual dimorphic differences in the rate of change
of this physiological variable were found to occur in
this group of deaf 10–18 year-olds.
The progressive variability of various somatic characteristics defining human development have been found
to determine individual exercise capacity. This was the
most visible in the oldest group of deaf participants
(16-, 17-, and 18-years-old), where gender was a factor
differentiating their aerobic and anaerobic capacity with
males showing a considerable advantage over their female peers. These findings correspond with the results
of able-bodied young adults, due in part that the physio­
logical adaption of children’s bodies to exercise significantly differs than in mature adults. These differences
are particularly noticeable in exercise performed at
maximal and supramaximal intensities that use predominantly anaerobic energy processes. This is due to
children having a less developed ability to resynthesize high-energy resources based on anaerobic energy
processes as well as a reduced ability to neutralize the
byproducts of anaerobic exercise. Hence, children obtain lower measures of maximal anaerobic power and
feature less tolerance to homeostatic imbalance during
physical effort [22, 23]. A study by Bar-Or [18] has also
shown that children’s lower levels of anaerobic capacity
may be caused by reduced capacity to use muscle glycogen during physical effort. This was evidenced by
a slower rate of anaerobic glycolysis and lower blood
lactate concentration levels in the working muscles when
compared to adults. This relationship was verified in
the present study of deaf children and youth, where the
potential for effort increased with age and which was
most visible among the group of deaf males. In terms
of the differentiation between boys’ and girls’ anaerobic
capacity, Cempla and Bawelski [24] were more critical
of the opinion that boys featured a greater increase in
107
HUMAN MOVEMENT
A. Zwierzchowska, Aerobic and anaerobic capacity and physical activity of the deaf
maximal anaerobic power (MAP) relative to girls, although the results obtained in this study do not confirm their assessment.
Research on the physical activity of disabled children
and youth has indicated that the hearing impaired do
not see themselves as individuals who are dysfunctional when compared to the rest of the population. This
group has been found to have very high self-esteem in
regards to their habits and ability to perform physical
exercise, while at the same time reporting that they do
not feel to have physical ability levels lower than their
hearing peers [25]. Among a group of disabled indivi­
duals, the hearing impaired presented a high level of phy­
sical fitness [26]. Nonetheless, these observations have
been contradicted by a number of empirical studies on
the aerobic and anaerobic capacity of the deaf in comparison with the non-disabled [11, 27, 28]. However,
few have concentrated on the gender-based differences
of the deaf’s aerobic and anaerobic capacity.
Shepard, Ward, and Lee [28] examined 15 boys and
14 girls (ages 12 to 15) finding that only 40% were found
to meet the norms for their age and sex. These authors
pointed out that age and gender did differentiate the
results, which followed a progressive trend together with
age, although these changes were statistically insignificant for the group of girls. They also drew attention to
the increased adiposity of the deaf, especially in the case
of females, which may have contributed to this finding.
Other researchers have stated that deaf children and
adolescents feature lower tolerance to effort during aerobic
and anaerobic testing [11, 27]. The results of this study
support this hypothesis especially in the case of females.
The variable measuring power loss (RPL) was significantly lower among boys in the oldest age group, which
reflects their higher (better) tolerance during short-term
anaerobic exercise (Tab. 5). Here, the sexual dimorphism
index had a positive value as the girls’ recovery process
required more time, but was at the same time less than
one standard deviation from the boys’ mean, finding that
RPL was not a characteristic that differentiates gender.
Of considerable interest is also one of the other analyzed variables, the time to reach maximal anaerobic
power (TMP). The time to reach maximal power has
been defined to occur at around 3–6 seconds. A surprising outcome in this study was that both the boys and
girls had difficulty in reaching their maximum heart
rate within this time frame. One of the only explanations for this result may be that this group was less
motivated (volition). Motivation is an important factor not only for succeeding in sports but also, above all,
guides individuals to engage in suitable fitness training.
The concept of motivation has been defined as a “hypothetical construct” [29], as a state of readiness to take
specific action stemming from both individual needs
and external factors and which possesses a certain significance that cannot be completely defined through
empirical evidence. Evidence of this fundamental problem can be found in the responses provided by the par108
ticipants in the questionnaire on their physical activity
preferences, which indicated that individual forms of
physical activity were highly preferred. Yet, it is common
knowledge that nothing better motivates individuals
than interpersonal relationships and healthy competition. It should be taken into account that deafness is
a mitigating factor in social behavior (feelings of strong
alienation from both able-bodied and disabled individuals)
and might have been reflected in the participants’ res­
ponses. For example, their preference for these forms of
physical activity are consistent with those found in
a group of deaf students in Karachi, Pakistan [30]. It is
worth noting that the deaf students from Karachi also
ranked individual sports and forms of recreation first,
while rating “improving health and the body” the least
motivating factor for their participation in physical activity. Therefore, it is difficult to expect that deaf individuals would present large differences in their prefe­
rences for various forms of physical activity as is the
case for the able-bodied. The findings of this study –
showing a moderate correlation between girls and boys
who prefer passive forms of leisure activities – allow
us to assume that deafness acts to limit both the prefe­
rences and motivation for physical activity and is an
issue that requires further investigation.
Conclusions
The ability to perform increasing amounts of aerobic
and anaerobic work was found to increase together with
age for both the deaf male and female participants.
Gender-based differences were noted for aerobic (from
the age of 12) and anaerobic capacity (from the age of 14).
In contrast, no statistically significant differences were
observed in the rate of developmental change that defines aerobic and anaerobic capacity.
The study found no differences in the physical activity
preferences of the deaf boys and girls, which is believed
to show that deafness is a factor that limits and, consequently, unifies what forms of physical activity the deaf
prefer to engage in. It is believed that the social environment plays a large role in stimulating the behavior of
deaf children and adolescents.
It was found that deaf boys perform aerobic and
anaerobic effort increasingly better as they get older when
compared with their female peers. Based on this study’s
findings (TMP) and observations made during the tests,
it is believed that motivation significantly affected the
attained results, possibly due to communication and interpersonal difficulties. This signifies the need for providing additional external motivation for the hearing
impaired when measuring exercise capacity and during
physical education classes, making this a challenge to be
met by both teachers and researchers. Such a conclusion
was also reached by Jonsson and Gustafsson [31], who
reported that motivation is an important criterion when
measuring the respiratory efficiency of the hearing
impaired.
HUMAN MOVEMENT
A. Zwierzchowska, Aerobic and anaerobic capacity and physical activity of the deaf
References
1. Butterfield S.A., Gross motor profiles of deaf children.
Percept Mot Skills, 1986, 62 (1), 68–70, doi: 10.2466/pms.
1986.62.1.68.
2. Dummer G.M., Haubenstricker J.L., Stewart D.A., Motor
skill performances of children who are deaf. Adapt Phys
Activ Q, 1996, 13 (4), 400–414.
3. Ellis M.K., Butterfield S.A., Lehnhard R.A., Grip strength
performances by 6- to 19- year old children with and
without hearing impairments. Percept Mot Skills, 2000,
90 (1), 279–282, doi: 10.2466/pms.2000.90.1.279.
4. Liberman L.J., Volding L., Winnick J.P., Comparing motor
development of deaf children of deaf parents and deaf
children of hearing parents. Am Ann Deaf, 2004, 149 (3),
281–289, doi: 10.1353/aad.2004.0027.
5. Zwierzchowska A., Żebrowska A. Evolution of anaerobic
efficiency of deaf children from special schools using
a Wingate test. Annals Universitatetis Mariae Curie-Skło­
dowska Sectio D Medicina, 2005, 60 (16), 452–456.
6. Ellis B.J., Essex M.J., Family environments, adrenarche,
and sexual maturation: A longitudinal test of a life history
model. Child Dev, 2007, 78 (6), 1799–1817, doi: 10.1111/
j.1467-8624.2007.01092.
7. Lieberman L.J., Dunn J.M., van der Mars H., McCubbin J., Peer tutors’ effects on activity levels of deaf students in inclusive elementary physical education. Adapt
Phys Activ Q, 2000, 17 (1), 20–39.
8. Haubenstricker J.L., Seefeldt V., Acquisition of motor skills
during childhood. In: Seefeldt V. (ed.), Physical activity
and well-being. AAHPERD, Reston 1986, 41–102.
9. Migasiewicz J., Kiczko A., Somatic build relationships and
overall physical fitness tests of motor performance in
children of younger and older school age [in Polish]. In:
Kowalski P, Migasiewicz J. (eds.), Sport swimming and
athletics at school [in Polish]. AWF, Wrocław 1999, 23–33.
10. Maszczak T., Somatic and motor parameters of deaf
children in Poland [in Polish]. PZGł, Warszawa 1977.
11. Zwierzchowska A., Gawlik K., A Comparative study of
motor abilities of deaf and hearing children. In: Plinta R.,
Kosińska M., Niebrój L. (eds.), Health Care: coping with
disability. Eukrasia 9, ŚAM Katowice 2005, 91–98.
12. Dziedzic J., Physical fitness of deaf children [in Polish].
Kultura Fizyczna, 1967, 8, 28–34.
13. Krawański A., Assessment of the physical development
of young deaf and normal. [in Polish]. WFiS, Warszawa
1974, 5.
14. Zwierzchowska A., Gawlik K., Grabara M. Energetic and
coordination abilities of deaf children. Human Kinetics,
2004, 11, 83–92.
15. Cieśla E., Assessment of the level of development morfofunctional children with hearing impairments and deaf.
[in Polish]. In: Kruk Lasocka J., Sekułowicz M. (eds.), Early diagnosis and treatment of children with difficulties
in development: an interdisciplinary problems [in Polish].
TWP, Wrocław 2004, 253–260.
16. Parving A., Hearing disorders in childhood some procedures for detection, identification, and diagnostic evaluation. Int J Pediatr Otorhinolaryngol, 1985, 9 (1), 31–57,
doi: 10.1016/S0165-5876(85)80003-3.
17. Malarecki J., Capacity and physical fitness in the light
of human physiology [in Polish]. Wychowanie Fizyczne
i Sport, 1970, 4.
18. Bar-Or O., The Wingate anaerobic test. An update on
methodology, reliability and validity. Sports Med, 1987,
4 (6), 381–394.
19. Arska-Kotlińska Z., Drozdowski Z., Manifestations of
sexual dimorphism in human recessive traits in the population and targeted groups of physical activity. [in Polish].
In: Socha S. (ed.), Materiały II Krajowej Konferencji Naukowej „Problemy dymorfizmu płciowego w sporcie”.
AWF Katowice–PTNKF, Katowice 1995, 79–82.
20. Stanisz A., Affordable rate statistics [in Polish]. Statsoft
Polska, Kraków 1998.
21. Manzke H., Stadlober E., Schellauf H.P., Combined body
plethysmographic, spirometric and flow volume reference values for male and female children aged 6 to 16
years obtained from “hospital normals”. Eur J Pediatr,
2001, 160 (5), 300–306, doi: 10.1007/s004310100724.
22. Klimek A., Physiological response of the respiratory
system during repeated physical activity against aerobic capacity and anaerobic adult children [in Polish].
Studia i Monografie, AWF, Kraków 2004, 28.
23. Inbar O., Bar-Or O., Anaerobic characteristics in male
children and adolescents. Med Sci Sports Exerc, 1986,
18 (3), 264–269.
24. Cempla J., Bawelski M., Development index reflecting
changes in the relationship of anaerobic to aerobic exercise
capacity [in Polish]. Antropomotoryka, 1998, 18, 49–56.
25. Longmuir P.E., Bar-Or O., Factors influencing the physical activity levels of youths with physical and sensory
disabilities. Adapt Phys Activ Q, 2000, 17 (1), 40–53.
26. Hattin H., Fraser M., Ward G.R., Shephard R.J., Are deaf
children unusually fit? A comparison of fitness between
deaf and blind children. Adapt Phys Activ Q, 1986, 3 (3),
268–275.
27. Cumming G.R., Goulding D., Baggley G., Working capacity of deaf and visually and mentally handicapped
children. Arch Dis Child, 1971, 46 (248), 490–494, doi:
10.1136/adc.46.248.490.
28. Shephard R., Ward R., Lee M., Physical ability of deaf
and blind children. In: Berridge M.E, Ward G.R. (eds.),
International perspectives on adapted physical activity.
Human Kinetics, Champaign 1987, 355–362.
29. Vallerand R.J. Thill E.E., Introduction to the concept of
motivation. In: Singer R.N., Hausenblas H.A, Janelle C.M.
(eds.), Handbook of sport psychology. Wiley, New York
1993, 389–416.
30. Sulman N., Naz S., Motivational factors influencing
the participation of deaf students in sports activities.
Interdiscip J Contemp Res Bus, 2012, 3 (12), 481–488.
31. Jonsson Ö., Gustafsson D., Spirometry and lung function in children with congenital deafness. Acta Pediat,
2005, 94 (6), 723–725, doi: 10.1111/j.1651-2227.2005.
tb01971.x.
Paper received by the Editor: December 10, 2012
Paper accepted for publication: April 26, 2013
Correspondence address
Anna Zwierzchowska
Zakład Korektywy i WF Specjalnego
Akademia Wychowania Fizycznego
im. Jerzego Kukuczki
ul Mikołowska 72a
40-066 Katowice, Poland
e-mail: [email protected]
109
HUMAN MOVEMENT
2013, vol. 14 (2), 110– 115
EFFECTS OF ELBOW FLEXOR MUSCLE RESISTANCE TRAINING
ON STRENGTH, ENDURANCE AND PERCEIVED EXERTION
doi: 10.2478/humo-2013-0012
Mauro Gonçalves 1, Anderson Souza Castelo Oliveira 2 *
1
2
Department of Physical Education, Bioscience Institute, São Paulo State University, Rio Claro, Brazil
Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
Abstract
Purpose. To verify the effects of resistance training at the electromyographic fatigue threshold (EMGFT) based on one-repetition
maximum strength (1RM), heart rate (HR), rate of perceived exertion (PE) and endurance time (EndT). Methods. Nineteen
subjects (training group [TG]: n = 10; control group [CG]: n = 9), performed 1-min bicep curl exercises sets at 25%, 30%, 35% and
40% 1RM. Electromyography (biceps brachii and brachiorradialis), HR and PE were registered. Biceps brachii EMGFT was used
to create a load index for an eight-week resistance training programme (three sets until exhaustion/session, two sessions/week)
for the TG. The CG only attended one session in the first week and another session in the last week of the eight-week training
period for EndT measurement. EndT was determined from the number of repetitions of each of the three sets performed in the
first and last training sessions. After training, 1RM, EMGFT, EndT, HR and PE at the different bicep curl load intensities were
again measured for both groups. Results. Increases in 1RM (5.9%, p < 0.05) and EndT (> 60%, p < 0.001) after training were found.
In addition, PE was reduced at all load intensities (p < 0.05), while no changes were found for HR and EMGFT after training.
Conclusions. Strength-endurance training based on the EMGFT improved muscular endurance and also, to a lesser extent, muscular
strength. Moreover, the reduced levels of physical exertion after training at the same intensity suggest that endurance training
exercises may improve comfort while performing strength exercises.
Key words: elbow flexion, electromyography, endurance, perceived exertion, training
Introduction
Increased muscular strength, muscular volume (hyper­
trophy), endurance and fat tissue loss are the usual adaptations of skeletal muscle tissue to resistance training
[1, 2]. In addition, neural drive facilitation measured
by analysing surface electromyography (EMG) is also
reported following resistance training, which is related
to increased EMG activity for agonist muscles and reduced activation for antagonist muscles [3–6]. However,
the literature on the topic shows controversial results
in terms of EMG activity following resistance training,
some reported increased EMG [3, 4], others an absence
of changes [5] and also one a reduction [4] among the
trained muscles. These contrasting observations relate to
different training protocols, such as training volume/
duration, session frequency and intensity [1, 4]. Unfortunately, training intensities based on EMG data have
been rarely studied, even though the use of the electromyographic fatigue threshold (EMGFT) has been previously discussed [7, 8] and suggested as an alternative
training index [9].
The determination of EMGFT was originally suggested
by using different load intensities performed until exhaustion, usually at one intensity per day [10]. However,
* Corresponding author.
110
Oliveira et al. [8] verified that by performing shorter
sets (30–60 seconds), the EMG slopes (EMG activity
vs. time) and subsequent EMGFT are similar to those
obtained after more exhaustive periods of exercise. Therefore, this allows accurate EMGFT to be determined within a single session. Previous investigations that applied
EMGFT as a training intensity found increased elbow
flexor strength and reduced EMG activity for the biceps
brachii (BB) and brachioradialis (BR) muscles and, concomitantly, reduced activity for antagonist muscles (triceps brachii) [9].
Resistance training has been associated with neuromuscular and also metabolic and/or psychological adaptations. Previous investigation has found reduced heart
rate (HR) following high-repetition lower limb resistance
training [11], which may suggest an attenuation in the
fatigue process during exercise. For the upper limbs, previous studies have reported increases in HR and perceived exertion (PE) at higher load levels [12–14]. Oli­
veira et al. [9] have verified on average HR at 140bpm and
PE at 8 (on a scale of 0 to 10) for bicep curls at the end
of a 1-min set at 40% one repetition maximum (1RM).
Thus, low load intensities can elicit significant effort
demands for smaller muscular groups.
Metabolic and psychological measurements such
as HR and PE have been well correlated to elbow flexor
EMG activity during fatiguing exercises [13, 14], which
may suggest similar modulation for neuromuscular and
metabolic/psychological properties during exercise
HUMAN MOVEMENT
M. Gonçalves, A.S.C. Oliveira, Endurance training at EMGFT
[12–14]. Based on the above, we hypothesized that resistance training focused on endurance performance,
such performed at the EMGFT, can enhance time to
exhaustion and reduce HR and PE. Such strength-endurance training may be aided by the use of individualized load intensities estimated from the EMGFT, which
could eventually optimize endurance. Therefore, the
aim of the present study was to investigate the effects
of individualized resistance training on muscular endurance and metabolic/psychological demands during
the bicep curl.
Material and methods
Nineteen healthy male (age 21 ± 1.1 years, height
174.2 ± 4.3 cm, body mass 71.4 ± 7.7 kg; mean ± SD)
volunteered for the experiment. The characteristics of
the participants are shown in Table 1. None had been
taking part in any systematic form of upper limb resis­
tance training six months prior to the beginning of the
study, and were asked to maintain their normal daily
activities throughout the investigation period. All subjects were informed of the procedures, the risks and be­
nefits associated with participating in the study and signed
an informed consent term previously approved by the
Local Ethics Committee.
The participants were randomly divided in two groups,
a training group (TG, n = 10) and a control group (CG,
n = 9), and tested over a 12-week period. The testing
procedure was as follows: Week 1 – dynamic 1RM test was
performed by both groups for the biceps curl; Week 2 –
EMGFT was determined during one day of testing; from
Week 3 to Week 10 – subjects in the TG took part in an
endurance training program conducted twice a week
for the elbow flexor muscles based on biceps brachii
EMGFT [8]; the CG did not participate in any resistance
training. The CG was asked not to participate in any re­
sistance training during the duration of the eight-week
training period, but required to attend one training
session in the first and last week (Weeks 3 and 10) of the
resistance training programme when endurance time
(EndT) for all sets was measured for both groups. An
additional 1RM test was performed at the beginning
of Week 7 in order to evaluate potential strength improvements. After the training period was completed, the test
procedures from the first two weeks were repeated for
both groups (in Weeks 11 and 12).
Table 1. Anthropometric characteristics of participants
in the control group (CG: n = 9) and training group
(TG: n = 10); mean ± SD
CG
TG
Age (years)
Mass (kg)
Height (cm)
20.8 ± 1.2
21.2 ± 1.4
73.76 ± 7.88
70.48 ± 7.73
177.95 ± 3.90
174.40 ± 5.50
1RM test and familiarization
The procedure to assess maximal strength during
the biceps curl exercise has been described elsewhere [9].
The initial load was set to 30kg and increased/decreased
if necessary. The participants needed to perform the
full range of motion, starting from a full extension in
order to avoid compensation by the shoulders or trunk.
Invalid trials were those in which the participant could
not perform the full range of motion and/or performed
trunk/shoulder compensative movements to raise the bar.
The participants were familiarized with the bicep
curl with a demonstration showing correct posture and
movement rhythm. They were instructed to remain
standing 1.5 m in front of a mirror with the trunk in
a fixed position; their execution of the exercise was assisted by a frame specially designed to avoid compensation [9]. The rhythm was fixed at 40 bpm by a metronome (1.5 seconds for the concentric and 1.5 seconds
for the eccentric phase of each repetition). In addition,
the subjects were familiarized with the OMNI physical
exertion scale [15], ranging from 0 (extremely easy) to
10 (extremely hard). This scale was positioned in front
of the subject, fixed at eye height on the mirror frame.
EMGFT determination, heart rate
and perceived exertion
The participants performed four sets of 1-min bicep
curl exercises at 25%, 30%, 35% and 40% 1RM in a randomly selected order, with a 10-min rest interval provided between sets. Verbal encouragement and feedback
on posture was constantly provided during movement
execution. The rhythm was fixed at 40 bpm, similar to
the one used in the familiarization session, and the range
of motion was fixed from approximately 15° to 125°
elbow flexion (0° = full elbow extension). EMG activity
was recorded for the biceps brachii (BB) and brachioradialis (BR) muscles at each load intensity by using
pairs of adhesive, pre-gelled silver/silver chloride MediTrace surface electrodes (Covidien, USA) with a 10 mm
caption area placed at an inter-electrode distance of
20 mm. Surface EMG signals were recorded (model
CAD 1026, Lynx, Brazil) at a 4000 Hz sampling frequency,
amplified (1.000x) and band pass filtered (20–500 Hz).
Further details about EMG acquisition and calculation
are available elsewhere [9]. Offline kinematic analysis,
synchronized with the surface EMG measurements,
were used to determine 90° elbow flexion for every concentric action. The root mean square (RMS) was calculated in a 250 ms time-window commencing at 90°
elbow flexion. Linear regressions between RMS vs. time
for each set were then calculated, from which the slopes
and intercepts were obtained. A new linear regression
model was calculated for slopes vs. load, and the intercept of this linear regression was defined as the EMGFT
for each participant [9, 10]. An illustration of the methods
used for EMGFT estimation is presented in Figure 1.
111
HUMAN MOVEMENT
M. Gonçalves, A.S.C. Oliveira, Endurance training at EMGFT
Figure 1. Determination of electromyographic
fatigue threshold (EMGFT): surface EMG signals
are recorded during bicep curls at different
load intensities (A); slope of the root mean
square (RMS) values for each repetition are
then extracted from the linear correlations (B);
second linear correlation between slopes
and load intensities generates a Y-intercept,
defining the EMGFT (C)
HR was recorded at 15 s into the set and at its end
(60 s) by using a heart rate monitor (model S150, Polar,
Finland). Concomitantly, subjects were asked to numerically rate how they felt their active muscles working
using the previously cited PE scale as a guide.
Training program based on EMGFT
The training group’s resistance training programme
was conducted during an eight-week period with two
sessions held each week. The training sessions consisted
of performing three sets of biceps curls exercise until
exhaustion (failure to maintain complete range of motion and/or movement velocity/rhythm), each set was
interspaced with 2-min rest. Training intensity (load) was
individually determined by the biceps brachii EMGFT
(%1RM). At the end of the fourth week, 1RM levels
were re-evaluated in order to adjust the training intensity
if necessary so as to maintain EMGFT as a percentage
of the current strength. Throughout the sessions and
during the sets the participants were strongly encouraged to give their maximum and maintain correct execution until exhaustion.
tween-subject factor) on the dependent variables: 1RM;
EMGFT for BB and BR; EndT for first, second and third
sets; HR; and PE. In addition, in order to verify the
effects of load intensity (25% x 30% x 35% x 40% 1RM)
and exercise duration (15 s x 60 s) on HR and PE as de­
pendent variables, two-way ANOVA was used. Tukey’s
post-hoc test was applied when necessary. The significance level was set at p < 0.05.
Results
Maximal strength and EMGFT
No changes in 1RM strength were found for the CG
throughout the test protocol (Week 1: 36.1 ± 3.9 kg,
Statistical analysis
Data was measured as mean ± SD for all variables.
Two-way mixed model ANOVA was used to verify the
effects of training protocol (PRE-training x POST-training – within-subject factor) and group (CG x TG – be112
Figure 2. Biceps brachii (BB) and brachioradialis (BR)
electromyographic fatigue threshold (EMGFT) before (PRE)
and after (POST) eight-week endurance training; mean ± SD
HUMAN MOVEMENT
M. Gonçalves, A.S.C. Oliveira, Endurance training at EMGFT
* denotes significant difference in relation to 60 s time for both PREand POST-training (p < 0.01)
† denotes significant difference in relation to 60 s time at 40% 1RM
for both PRE- and POST-training (p < 0.05)
Figure 3. Heart hate at the beginning (15 s) and the end
(60 s) of biceps curl exercise at different load intensities
for the Control Group and Training Group before (PRE) and
after (POST) eight-week endurance training; mean ± SD
Week 7: 36.7 ± 3.1 kg, Week 11: 37.1 ± 4.0 kg; p > 0.05).
On the other hand, resistance training increased 1RM
strength for the TG (Week 1: 36.9 ± 3.7 kg, Week 7: 38.9
± 4.1 kg, Week 11: 39.3 ± 4.3 kg; p < 0.05). No significant
effects of training were found for the TG on EMGFT
(Fig. 2), with only an increasing trend observed after
the training period (p = 0.08). No significant changes
were also verified between BB and BR EMGFT, as well
as between CG and TG (p > 0.05).
Heart rate and perceived exertion
Heart rate measurements performed before and after the study period found that both the CG and TG
showed lower HR at 15 s of exercise when compared
to 60 s for all load intensities (p < 0.05), except at 25%
and 30% 1RM for the CG (Fig. 3). Load intensity had
minor effects on HR; only for the TG by the end of the
exercise (60 s) was HR at 25% 1RM significantly lower than that at 40% 1RM. The training program did not
affect HR for any load intensity, moreover no significant differences between the CG and TG were found.
Similar to HR, PE (Fig. 4) was lower at the beginning
of the exercise (15 s) when compared to the end (60 s) for
* denotes significant difference in relation to 60 s time for both PREand POST-training (p < 0.01)
† denotes significant difference in relation to PRE-training (p < 0.05)
‡ denotes significant difference in relation to 60 s time at 40% 1RM
for both PRE- and POST-training (p < 0.05)
Figure 4. Perceived exertion at the beginning (15 s) and end
(60 s) of bicep curl exercise at different load intensities for
the Control Group and Training Group before (PRE) and
after (POST) eight-week endurance training; mean ± SD
both the CG and TG at all load intensities (p < 0.001).
In addition, at 60 s, PE at 25% 1RM was significantly
lower than at 40% 1RM (p < 0.01). Conversely to HR,
lower PE levels were verified for all load intensities
(p < 0.01) after training for 15 s and 60 s, except at 40%
1RM at 15 s. Due to this training effect, PE for the TG after
completing the 8-week training programme (Week 12)
was significantly lower than PE tested at the same time
for the CG (p < 0.05) for all load levels and test times
during the exercise.
Elbow flexor endurance time
EndT for the biceps curl was significantly lower from
the first set in relation to the second and third set (p < 0.01)
for both the CG and TG (Fig. 5). In addition to improving
muscular strength, resistance training also improved
EndT for the biceps curl exercise at the EMGFT (Fig. 5).
Significant increases were found from the first set (68.6
± 46%, p < 0.001) to the second set (81.9 ± 43%, p < 0.001)
and third set (78.9 ± 38%, p < 0.001) at the end of the
training period for TG, with no changes observed among
the CG.
113
HUMAN MOVEMENT
M. Gonçalves, A.S.C. Oliveira, Endurance training at EMGFT
* denotes significant difference in relation to POST-training (p < 0.05)
† denotes significant difference in relation to B2 and B3 (p < 0.05)
‡ denotes significant difference in relation to B3 (p < 0.05)
Figure 5. Endurance time of biceps curl exercise for the
first set (B1), second set (B2) and third set (B3) before
(PRE) and after (POST) eight-week endurance training;
mean ± SD
Discussion
The primary objective of the present study was to
verify whether individualized resistance training based
on EMGFT could improve muscular strength and endurance while reducing HR and PE, suggesting that
muscular adaptations to endurance training can also
reduce discomfort during resistance exercises. The main
results of the study did confirm an increase in muscular
strength with a reduction in perceived exertion. Moreover, resistance training based on EMGFT improved on
average at least 60% of EndT, therefore endurance improvements by training at the EMGFT intensity can attenuate the discomfort felt when performing the bicep
curl. Moreover, these results suggest that individualized
training intensities may be essential in optimizing endurance training outcomes.
Low-to-moderate intensities have been suggested in
resistance training aimed at improving endurance [1],
such as the one used in the present investigation (approxi­
mately 30% 1RM). Although substantial increases in
strength post-endurance training were not expected, we
found a ~6% increase in 1RM for the TG. This strength
gain may be predominantly credited to neural adaptations such as muscle fibre recruitment or neural drive [2],
which have also been previously related to increased EMG
activity after maximal endurance training [9, 11].
Upon completion of the training programme, the participants were able to perform bicep curls for a longer
period of time. It is noteworthy that the training protocol
was performed until exhaustion for every set, which
has been credited in inducing improvements in blood
flow and muscular capillarity [16]. Increased capillarity also enhances muscular oxygenation and reduces
metabolite accumulation (blood lactate, K+, inorganic
phosphate, etc.) [16, 17]. Another study performed by
this research group found reduced elbow flexor EMG
activity at a fixed load intensity (%max) after a training
114
program similar to the presented protocol [18], suggesting that motor unit recruitment is also enhanced
after endurance training [19]. In addition, increases in
muscular strength have also been corroborated with
maintaining fatiguing tasks for longer periods of time
[4, 17]. Therefore, the present results suggest that endurance training based on low-intensity exercises improves muscle function and attenuates the effects of
fatigue. Further investigations on the use of the EMGFT
index in training routines should be conducted on other
muscular groups such as the quadriceps and triceps surae.
Heart rate is a parameter often used in monitoring
workouts by measuring the effects of exercise intensity
on the cardiovascular system [9, 11, 20]. In this study, HR
measured when performing the bicep curls was higher
by the end of each set, although at 25% and 30% 1RM
the differences were found to be insignificant, which
suggests low demand on the cardiovascular system at
such low intensities [20]. Nonetheless, towards the end
of exercise it is necessary to increase oxygen availability
to the muscles and optimize metabolite removal, which
promotes increases in blood circulation and, consequently, in HR [20]. Increased cardiovascular demand
has been previously described in different types of elbow
flexor exercises [9], however the specific training protocol used in the present study was unable to reduce
HR levels. Training protocols applying high-repetition
sets for larger muscular groups such as the quadriceps
muscles were able to verify changes in HR [11]. Thus,
perhaps the use of endurance training based on the
EMGFT for larger muscular groups may elicit greater
changes in HR.
Physical exertion (PE) has previously been used to
predict load intensity for isometric exercises [13, 14], and
EMG activity for isometric tasks [12]. In this experiment,
PE scales were used to verify the psychological aspects
linked to metabolic and/or neuromuscular changes during
exercise [12], thus verifying whether resistance training
could influence PE during fatigue. In fact, reduced PE
was found after resistance training, which has been suggested as an indirect measure of muscle fatigue and exercise performance [15]. Therefore, the present investigation confirms that endurance training protocols (such
as those based on EMGFT) may be able to reduce discomfort caused by fatigue.
Although not shown in the present investigation,
elbow flexor EMG activity was reduced following this
specific training protocol [19]. Although muscle recruitment and PE are regulated by the central nervous system,
perhaps other peripheral contributions can alter force
output/EMG [21] and PE [12–14]. Therefore, the underlying mechanisms behind muscular activation and PE
may be somehow linked, since simultaneous inputs are
sent for both muscular activation and sensation during
exercise [21]. It can be suggested that the endurance
training used in this study was able to reduce muscular
activation and, consequently, produce less discomfort
while performing the bicep curl exercise.
HUMAN MOVEMENT
M. Gonçalves, A.S.C. Oliveira, Endurance training at EMGFT
Conclusions
Resistance training targeting elbow flexor endurance
improved 1RM strength and EndT during a bicep curl
exercise. In addition, reductions in PE suggest that the
exercise at the same training intensity was performed
with less discomfort during sets. The individualized load
intensities allowed substantial improvement in EndT,
suggesting that EMGFT may be a useful alternative for
prescribing a training program focused on improving
endurance.
Acknowledgements
We wish to thank the participants in this study and the Co­
ordenação de Aperfeiçoamento de Pessoal de Nível Superior
(CAPES) for their financial support. Oliveira A.S. is currently
supported by a CAPES international PhD fellowship
(No. 0293-09-1).
References
1. Kraemer W.J., Ratamess N.A., Fundamentals of resistance training: progression and exercise prescription.
Med Sci Sports Exerc, 2004, 36, 674-688.
2. Campos G.E.R., Luecke T.J., Wendeln H.K., Toma K., Ha­
german .FC., Murray T.F. et al., Muscular adaptations in
response to three different resistance training regimens:
specificity of repetition maximum training zones. Eur J
Appl Physiol, 2002, 88, 50–60, doi: 10.1007/s00421-0020681-6.
3. Colson S., Pousson M., Martin A., Gottlieb G.L., Isokinetic elbow flexion and coactivation following eccentric
training. J Electromyogr Kinesiol, 1999, 9, 13–20, doi:
10.1016/S1050-6411(98)00025-X.
4. Mcbride J.M., Blaak J.B., Triplett-Mcbride T., Effect of
resistance exercise volume and complexity on EMG,
strength, and regional body composition. Eur J Appl Physiol,
2003,90, 626–632, doi: 10.1007/s00421-003-0930-3.
5. Moore D.R., Burgomaster K.A., Schofield L.M. Gibala M.J.,
Sale D.G., Phillips S.M., Neuromuscular adaptations in
human muscle following low intensity resistance training
with vascular occlusion. Eur J Appl Physiol, 2004, 92,
399–406, doi: 10.1007/s00421-004-1072-y.
6. Pousson M., Amiridis L.G., Commetti G., Van Hoecke J.,
Velocity specific training in elbow flexors. Eur J Appl Physiol,
1999, 80, 367–372, doi: 10.1007/s004210050605.
7. Hug F., Nordes A., Guével A., Can the electromyographic
fatigue threshold be determined from superficial elbow
flexor muscles during an isometric single joint task? Eur
J Appl Physiol, 2009, 107, 193–201, doi: 10.1007/s00421009-1114-6.
8. Oliveira A.S., Gonçalves M., Cardozo A.C., Barbosa F.S.S.,
Electromyografic fatigue threshold of the biceps brachii
muscle during dynamic contraction. Electromyogr Clin
Neurophysiol, 2005,45, 167–75.
9. Oliveira A.S, Gonçalves M., Positioning during resistance
elbow flexor exercise affects electromyographic activity,
heart rate and perceived exertion. J Strength Con Res, 2009,
23, 854–862, doi: 10.1519/JSC.0b013e3181a00c25.
10. Housh T.J., deVries H.A., Johnson G.O., Housh D.J., Evans S.A., Stout J.R. et al., Electromyographic fatigue thresholds of the superficial muscles of the quadriceps femoris.
Eur J Appl Physiol, 1995, 71, 131–136, doi: 10.1007/
BF00854969.
11. Greco C.C., Oliveira A.S., Pereira M.P., Figueira T.R.,
Ruas V.D., Gonçalves M. et al., Improvements in metabolic
and neuromuscular fitness after 12-week Bodypump®
training. J Strength Cond Res, 2011, 25, 3422–3431, doi:
10.1519/JSC.0b013e3182160053.
12. Hummel A., Läubli T., Pozzo M., Schenk P., Spillmann S.,
Klipstein A., Relationship between perceived exertion
and mean power frequency of the EMG signal from the
upper trapezius muscle during isometric shoulder elevation. Eur J Appl Physiol, 2005, 95, 321–326, doi: 10.1007/
s00421-005-0014-7.
13. Hunter S.K., Critchlow A., Enoka R.M. Muscle endurance
is greater for old men compared with strength matched
young men. J Appl Physiol, 2005, 99, 890–897, doi:
10.1152/japplphysiol.00243.2005.
14. Hunter S.K., Rochette L., Critchlow A., Enoka R.M.,
Time to task failure differs with load type when old adults
perform a submaximal fatiguing contraction. Muscle
Nerve, 2005, 31, 730–740, doi: 10.1002/mus.20325.
15. Robertson R.J., Goss F.L., Rutkowskli J., Lens B., Dixon C.,
Timmer J. et al., Concurrent Validation of the OMNI
Perceived Exertion Scale for Resistance Exercise. Med
Sci Sports Exerc, 2003, 35, 333–341.
16. Westerblad H., Allen D.G., Recent advances in the understanding of skeletal muscle fatigue. Curr Opin Rheumatol,
2002, 14, 648–652.
17. Giannesini B., Cozzone P.J., Bendahan D., Non-invasive
investigations of muscular fatigue: metabolic and electromyographic components. Biochimie, 2003, 85, 873–883,
doi: 10.1016/S0300-9084(03)00124-X.
18. Oliveira A.S., Gonçalves M., EMG amplitude and frequency parameters of muscular activity: Effect of resistance
training based on electromyographic fatigue threshold.
J Electromyogr Kinesiol, 2009, 19, 295–303, doi: 10.1016/
j.jelekin.2007.07.008.
19. Kollmitzer J., Ebenbichler G.R., Sabo A., Kerschan A.,
Bochdansky T., Effects of back extensor strength training
versus balance training on postural control. Med Sci Sports
Exerc, 2000, 32, 1770–1776.
20. O’leary D.S., Heart rate control during exercise by baro­
receptors and skeletal muscle afferents. Med Sci Sports
Exerc, 1996, 28, 210–217.
21. Søgaard K., Gandevia S.C., Todd G., Petersen N.T., Taylor J.L., The effect of sustained low-intensity contractions
on supraspinal fatigue in human elbow flexor muscles.
J Physiol, 2006, 573, 511–523, doi: 10.1113/jphysiol.2005.
103598.
Paper received by the Editor: November 26, 2012
Paper accepted for publication: April 5, 2013
Correspondence address
Anderson S.C. Oliveira
Center for Sensory-Motor Interaction (SMI)
Department of Health Science and Technology
Aalborg University
Fredrik Bajers Vej 7 D-3
DK-9220 Aalborg, Denmark
e-mail: [email protected]
115
HUMAN MOVEMENT
2013, vol. 14 (2), 116– 122
THE EFFECTS OF RESISTED SPRINT TRAINING
ON SPEED PERFORMANCE IN WOMEN
doi: 10.2478/humo-2013-0013
Beata Makaruk 1 *, Henryk Sozański 2 , Hubert Makaruk 1, Tomasz Sacewicz 1
1
2
Biała Podlaska Faculty of Physical Education and Sport, Józef Piłsudski University of Physical Education, Warszawa, Poland
Józef Piłsudski University of Physical Education, Warszawa, Poland
Abstract
Purpose. The main aim of the study was to examine the effects of resisted and standard sprint training on the kinematics of sprintrunning acceleration in women. Methods. Thirty-six untrained but physically active female college students were randomly
assigned to one of three groups: a running resisted training group (RTG, n = 12), a standard training group (STG, n = 12), and
a control group (CON, n = 12). All participants in the experimental groups trained three times a week for four weeks, followed
by a 1-week training break, after which they trained again for four weeks. Pre-training, post-training and detraining (three weeks
after completing the training programs) measures of mean running velocity, stride length, stride frequency, knee angle at toe
off and footstrike, ground contact time, and flight time were analyzed by a 20 m sprint test. Results. The RTG improved mean
running velocity and increased stride length and knee angle at toe off. Simultaneously, the RTG featured decreased stride
frequency and increased ground contact time. The STG demonstrated an increase in mean running velocity due to higher stride
frequency and a decrease in ground contact time. All of the measured parameters did not significantly decrease after the three-week
detraining period. The control group featured no changes. Conclusions. Both resisted and standard sprint training improves
speed in sprint-running acceleration in women by improving different sprint kinematic parameters.
Key words: speed, acceleration performance, kinematics, stride length, stride frequency
Introduction
It is speed that to a large extent determines athletic
success in sports [1]. Running speed is in a large part
determined by running form, as it determines the body’s
movement as a function of time and space by the diagonal, cyclical stride of the lower limbs. Running stride
and therefore speed, from a mechanical point of view, is
determined by two antagonistic kinematic parameters,
stride length and stride frequency. This makes running
at the fastest speed possible only by exhibiting a combination of optimum stride length and frequency. They
are not, however, constant values; the contribution of
each in creating a “golden ratio” depends on running
gait phase as well as sex, age, competitive level. It has
been suggested that stride frequency is dependent on
nerve conduction velocity and thus strongly linked to
genetic factors. Hence, research has focused more on
the second parameter – stride length – and how it can be
improved by adapting existing training techniques [2].
One of the most basic ways used to lengthen running stride is through the use of resisted training, a type
of conditioning performed by adding external load by
pulling a sled, tire, or a specially designed parachute;
resisted bands; or by running uphill or against the wind.
The greatest benefits provided by such forms of condi-
* Corresponding author.
116
tioning are increases in the strength and power of the leg
extensor muscles at toe off, mainly in the first stage of
running – the acceleration phase [3, 4]. This relationship between increasing strength and power with running velocity has been observed by many researchers
[5–7]. On the other hand, Saraslanidis [8], among others,
did not find an increase in running velocity after an
eight-week resisted running program, although measurements were taken only after completing a run. In tests
carried out by Zafeiridis et al. [9], an eight-week program led to improvements in maximum velocity during
acceleration (0–20 m) and in stride frequency, but not
stride length. Similarly, Spinks et al. [10] noted a significant improvement in velocity when running short
distances (up to 15 m) but noted no significant changes
in stride length or frequency.
In view of the lack of clear results on the effects of
resisted sprint training on improving running velocity,
as well as a lack of research and recommendations that
take sex into account (all of the above mentioned tests
were performed only on males), the aim of present study
was to evaluate the effectiveness of resisted sprint training in women by measuring changes in running velocity
and other kinematic parameters. With this in mind,
the following research questions were formulated:
1. Does resisted running with the use of an external
load improve speed in physically active women?
2. Does the type of the sprint training program
differentiate the kinematic parameters of stride
in women?
HUMAN MOVEMENT
B. Makaruk, H. Sozański, H. Makaruk, T. Sacewicz, Resisted sprint training in women
3. What are the long-term effects of resisted sprint
training when compared with standard sprint
training?
Material and methods
The research group consisted of 36 female physical
education students who did not practice professional
sports. However, in light of the participants’ field of study
(8–10 h of physical activity per week), they can be specified as highly physically active individuals. The study
group was randomly divided into three sub-groups: the
first experimental group trained resisted running with
an external load (RTG, n = 12), the second trained under
normal sprint training technique (STG, n = 12), and the
third was a control group (CON, n = 12) who participated
only in the measurement sessions. Age, body height,
and body mass are presented in Table 1. All were asked
to refrain from participating in any physical activity
outside of their normal university classes. The participants were informed about the aim of the study and
research procedure, which was accepted by the Research
Ethics Committee at Józef Piłsudski University of Physical Education in Warsaw, Poland.
Testing
The participants’ sprint performance was tested on
three separate occasions: three days before the training programs for the experimental groups were to begin
(pre-training), three days after the programs were
completed (post-training), and then three weeks later
(detraining). The test consisted of a 20-m sprint (R 20),
run at the fastest speed possible. Participants began from
a standing start position with the legs in stride, the front
leg located just before the starting line and the rear leg
approximately 30 cm behind. They were checked for
proper starting posture, with a slight bend at the knees
Table 1. Characteristics of the participants
Group
RTG(n = 12)
STG(n = 12)
CON(n = 12)
Age
(years)
Body height
(m)
Body mass
(kg)
22.0 ± 0.9
22.3 ± 0.8
21.9 ± 0.7
1.67 ± 0.07
1.66 ± 0.06
1.68 ± 0.08
61.5 ± 4.7
61.3 ± 5.4
62.1 ± 4.9
RTG – running resisted training group
STG – standard training group
CON – control group
and the torso slightly bent forward. All participants
performed the task in appropriate sportswear (t-shirt
and shorts).
The participants completed two trials; they were
allowed to start at their leisure, no starting command
was given. The run with the highest mean velocity was
recorded for later analysis. All tests were performed at
a track and field stadium at an ambient air temperature of 21–23 degrees Celsius with minor wind (0.3–
0.6 m · s –1), measured by an electronic anemometer
(Slandi 2000, Poland) in the direction of track. Seven
to eight min of rest was provided between trials [11]:
the first four min consisted of absolute rest, the next
three or four min were spent preparing for the run by
performing dynamic stretching exercises, each of which
were followed by shaking the leg muscles.
A warm-up prior to measurement taking was performed, beginning with a low-intensity run (5 min) and
dynamic stretching exercises (7–8 min) of the most involved muscle groups when sprinting, i.e., hip, knee, and
ankle extensors and flexors [12]. The rest interval between each stretching exercise was 10–15 s. After the
general warm-up, skipping exercises were performed
(1 x 20 m) and another run at submaximal intensity
(1 x 40 m). The warm up was performed while wearing
a sweatshirt and sweatpants, which were removed just
before completing the sprint test.
The sprint test (R 20) was preceded by a pilot study
whose aim was to determine the reliability of the R 20
test as well as calculate the external load for the participants who would take part in the resisted sprint training
program. Previous studies have suggested an optimal load
that can reduce normal running velocity by 10% [14].
For this purpose, a sled was constructed from two 70-cm
circular runners held together by perpendicular tubing
45 cm in length. Located in the center of the sled was
a vertical shaft on which disc weights (plates) were placed.
A 5-m harness was used to connect the sled to a leather
belt worn above the hip bones. The total weight of sled
without additional plates was 3 kg. The participants
performed three runs with an external load of 5%, 7.5%,
and 10% body mass, performed in random order. The
procedure and conditions for this pilot test were the
same as when performing the R20 test. External load was
determined by multiplying body mass by the percent of
external load to be used (e.g., 5% body mass = 0.05),
subtracting the mass of the sled [15]. Based on the cri-
Figure 1. Resisted running
with an external load
117
HUMAN MOVEMENT
B. Makaruk, H. Sozański, H. Makaruk, T. Sacewicz, Resisted sprint training in women
teria for selecting resisted running load (10% reduction
in mean running velocity), a weight of 7.5% of body mass
was used for eight of the participants, while for the remaining four a weight of 10% of body mass was used.
Kinematic analysis
Two digital cameras were used to record the sprint
trials at a sampling rate of 100 Hz; video was later analyzed using SteamPix 3.34.0 software (Norpix, Canada).
The cameras were set perpendicularly to the track at
a distance of 24 m (Fig. 2), each filming a 10-m portion
of the track including 1 m before and 1 m after the start
and finish lines with an overlap of 2 m at the center.
Only every 3rd and 4th stride were considered for analysis.
Five tracking markers were placed on the right side
of the body during measurement taking: at the height
of anterior superior iliac spine, the greater trochanter
of the femur, the lateral condyle of the tibia, the lateral
malleolus, and the 5th metatarsal [15, 16]. Later, twodimensional kinematic analysis of the recorded test
runs was performed using APAS-XP marker-tracking
software (Ariel Dynamics, USA). The video was scaled
with the use of a flat calibration system.
The following kinematic parameters were measured
during the tests, all of which were performed only on
the right side of the body: mean running speed (m · s –1),
calculated by first adding ground contact time and
flight, then having this value divide stride length [16];
stride length (m), determined by the distance from the tip
of the front shoe at toe off to the tip of the opposite
shoe at footstrike; stride frequency, calculated on the basis
of the number of steps in a certain period of time; knee
angle at toe off and footstrike, measured by the angle between the thigh and lower thigh by a straight line passing
through the greater trochanter of the femur and the
lateral condyle of the tibia and a line passing through
the lateral condyle of the tibia and lateral malleolus;
ground contact time, as the time between footstrike and
toe off; and flight time, measured as the time between
toe off by one foot until footstrike by the opposite foot.
The reliability of the above-measured parameters, determined by intraclass correlation coefficients (ICC), was
found to be high and ranged between 0.79–0.92 [13].
Sprint training programs
Due to the intensive nature of the sprint training programs, the participants in the two experimental groups
(RTG and STG) concluded a three-week compensatory
physical fitness course, held twice a week, before their
actual training programs were to begin. Average duration of each class was approximately 50 min. The course
focused on basic exercises aimed at improving sprint
Table 2. Sprint training programs implemented
by both experimental groups
(resisted and standard sprint training)
Week
Training program
Set x repetition
x distance [m]
Rest intervals*
Set [min]
x repetition [s]
1
2
3
4
5
6
7
8
9
3 x 3 x 20
4 x 3 x 20
3 x 3 x 25
4 x 3 x 25
Rest
3 x 3 x 30
4 x 3 x 30
3 x 3 x 35
3 x 3 x 20
3 x 60
3 x 60
3 x 90
3 x 90
Rest
4 x 120
4 x 120
4 x 150
4 x 150
* rest provided in accordance with previous
recommendations [11]
Figure 2. Graphic representation of the track used to measure sprint velocity at a distance of 20 m (R 20)
118
HUMAN MOVEMENT
B. Makaruk, H. Sozański, H. Makaruk, T. Sacewicz, Resisted sprint training in women
performance, with particular attention placed on proper
execution. After completing the compensatory physical
fitness course, groups RTG and STG began a nine-week
speed training program (with a rest interval during the
fifth week where no training was performed) with classes
held three times per week (Tab. 2). The design of the
sprint training programs included periodization, as its
performance benefits have been scientifically verified
in a number of studies [1, 17]. The training programs
were conducted by a coach specializing in short-distance running. Time was measured with a 83520 stopwatch (Casio, Japan). Immediately after finishing a sprint,
the participants received feedback on their time as well
as motivational support, such as “maintain the same
time” or “try to run faster”. In addition, sprint technique
was continuously monitored, such as performing larger
extensions of the rear leg at the knee at toe off or maintaining correct posture along the axis made between
the ankle and hip of the propulsive leg (at toe off). The
RTG performed all runs with the sled, whose weight
was previously calculated for each individual; the STG
ran with no external load.
Pre-training
Post-training
Detraining
$
– significantly different (p < 0.05) from the control group
#
– significantly different (p < 0.05) from pre-training values
RTG – running resisted training group
STG – standard training group
CON – control group
Figure 3. Mean ± SD running velocity measured
pre-training, post-training, and three weeks
after completing training (detraining)
Pre-training
Post-training
Detraining
Statistical analysis
The collected data were summarized as mean and
standard deviation (SD). The Shapiro-Wilk test was used
to confirm whether the variables were normally distributed. Significant differences among the analyzed stride
kinematic parameters were analyzed by a two-way repeated measures ANOVA. Tukey’s t test was applied if
the results were statistically significant. Statistical significance was set at p 0.05. All statistical analysis was
performed using Statistica v. 5.1 PL software (Statsoft,
Poland).
Results
Figures 3–9 present the results as means ± SD for the
kinematic parameters measured during the R 20 test before (pre-training) and after (post-training) the training
programs as well as three weeks after completion (detraining). Statistical analysis found significant effects
between: group (RTG, STG, CON) x time (post-training,
post-training, and detraining), x mean running velocity
(F4.66 = 4.92; p < 0.01), x stride length (F4.66 = 8.47; p <
0.001), x stride frequency (F4.66 = 2.72; p < 0.05), x knee
angle at toe off (F4.66 = 3.42; p < 0.01); x knee angle at
footstrike (F4.66 = 4.42; p < 0.01). Both experimental
groups (RTG and STG) significantly increased their
mean running velocity upon completing their training
programs by 2.5% and 4.9% (p < 0.05), respectively,
with the velocity attained by the STG being significantly
higher (p < 0.05) than the control group. Mean running
velocity of both experimental groups three weeks after
completing training (detraining) did not significantly
differ (p > 0.05) from post-training velocity. For stride
$
– significantly different (p < 0.05) from the control group
#
– significantly different (p < 0.05) from pre-training values
RTG – running resisted training group
STG – standard training group
CON– control group
Figure 4. Mean ± SD stride length measured pre-training,
post-training, and three weeks after completing training
(detraining)
Pre-training
Post-training
Detraining
#
– significantly different (p < 0.05) from pre-training values
RTG – running resisted training group
STG – standard training group
CON– control group
Figure 5. Mean ± SD stride frequency measured
pre-training, post-training, and three weeks
after completing training (detraining)
119
HUMAN MOVEMENT
B. Makaruk, H. Sozański, H. Makaruk, T. Sacewicz, Resisted sprint training in women
Pre-training
Post-training
Detraining
RTG – running resisted training group
STG – standard training group
CON– control group
#
– significantly different (p < 0.05) from pre-training values
RTG – running resisted training group
STG – standard training group
CON– control group
Figure 6. Mean ± SD knee angle at toe off measured
pre-training, post-training, and three weeks
after completing training (detraining)
Pre-training
Post-training
Detraining
Figure 9. Mean ± SD flight time measured pre-training,
post-training, and three weeks after completing training
(detraining)
length, the only significant differences were observed
among the resisted training group (RTG) after completing the training program (by 5.9%; p < 0.05). In
turn, stride frequency changed significantly (p < 0.05) in
both experimental groups, for the RTG it decreased by
3.4%, while for the STG it increased by 3.3%. The RTG
featured a significant increase in knee angle at toe off at
post-training (by 2.3%; p < 0.05). Additionally, group
RTG was the only one with a significant increase in
ground contact time (by 7.2%; p < 0.05). For the control
group none of the measured parameters changed significantly (p > 0.05).
Discussion
RTG – running resisted training group
STG – standard training group
CON– control group
Figure 7. Mean ± SD knee angle at footstrike measured
pre-training, post-training, and three weeks
after completing training (detraining)
Pre-training
Post-training
Detraining
#
– significantly different (p < 0.05) from pre-training values
RTG – running resisted training group
STG – standard training group
CON– control group
Figure 8. Mean ± SD ground contact time measured
pre-training, post-training, and three weeks
after completing training (detraining)
120
Pre-training
Post-training
Detraining
The obtained results confirm the validity of using
resisted sprint training in increasing running speed.
The RTG, which trained for nine weeks by pulling an
external load, improved mean running speed during the
acceleration phase as well as increased stride length
despite a decline in stride frequency. In addition, the
effects of resisted sprint training were observable even
in measurements taken three weeks after completing
the training program (detraining).
An improvement in running speed was also observed
in the STG, suggesting that this form of training –
probably due to its specificity – is also effective in improving running velocity. However, the mechanisms
behind both groups’ velocity improvements proved to be
different. Running stride length increased only in the
RTG, which is an effect that has also been confirmed
by Delecluse [18] when studying resisted sprint training.
It is believed that increase in stride length is the result of
performing a fuller leg extension at the knee with each
additional step, as evidenced by the increasing rise in
the knee angle at toe off. Some researchers believe [3] that
this change indicates an increase in strength among hip
and knee extensor muscles. The results of this study did
confirm the findings of Zafeiridis et al. [9] or Spinks
HUMAN MOVEMENT
B. Makaruk, H. Sozański, H. Makaruk, T. Sacewicz, Resisted sprint training in women
et al. [10], who did not observe any change in stride length.
It is worth mentioning that the differences in the results
between these researchers and the present study’s may
stem from the use of different training techniques. Unfortunately, as is usual in scientific literature, details
on the types of solutions used to monitor correct technique during movement execution are rarely provided.
Therefore, if the methodology used was in fact the cause
of such a discrepancy, this could be the result of ineffective controls, such as when providing instructions on
extending the rear leg at the knee during toe off, this
could have lead participants to perform an even larger
bend at the knee than necessary, resulting in shorter stride
length. Furthermore, the increase in stride length as
observed in group RTG was found to be long-term, as
three weeks after completing the training program (detraining) not only were there no decreases in stride length
when compared with post-training values, but this para­
meter was still significantly higher than when measured pre-training.
Nonetheless, not all of the effects of resisted sprint
training were positive, such as the decrease in stride
frequency. These findings are in complete discrepancy
with those by Zafeiridis et al. [9], who observed an increase in stride frequency, and by Spinks et al. [10], who
observed no significant change. We believe that group
STG’s decrease in stride frequency may have been the
result of increased stride length, and thus prolonged
contact with the ground due to the larger distance that
the body’s center of mass needed to cross. This observation is in line with other researchers [16, 19], who
confirmed that stride length and stride frequency, determined by the ground contact time as well as flight
time, are antagonistic parameters. Although shortening
ground contact time is highly desirable in sprinting, it
should be noted that longer ground contact time (within
limits) is conducive to producing more force during toe
off; this is advantageous, as Weyand et al. [20] observed,
since running velocity is determined to a great extent
by the force developed by the legs during the stance
phase and not by the speed of the legs when in flight
(swing phase).
It is highly probable that the increase in stride frequency by group STG was due to shorter ground contact
time, although this result was not statistically significant
(p > 0.05). Shortened ground contact time has been
linked with increased stiffness of the muscle-tendon
unit, thus allowing for more efficient use of the extensioncontraction cycle [21], as Markovic et al. argued [22].
It also is worth emphasizing that no changes were
observed in knee angle at the moment when the foot
makes contact with the ground (footstrike) in either of
the experimental groups, which may indirectly indicate
poor technique during the forward swing phase. We assumed that greater knee angle is equivalent to increased
stride length, especially in regards to the distance between the body’s center of mass and the point of foot-
strike. Therefore, as this distance increases so does braking force [23], which consequently carries with it an
increased risk in straining the rear thigh muscles [24].
This type of injury is very common among individuals
who practice speed-strength sports [25].
One of the limitations of this study, besides the
small sample size and relatively short duration of the
training programs, is that only the lower limbs were subjected to analysis. However, the significance of omitting the upper limbs from analysis may be minimized by
taking into consideration the results of Spinks et al. [10],
who found no changes in the kinematic parameters of the
upper limbs after standard and resisted sprint training,
emphasizing the relatively minor role the upper limbs
play in improving running speed [26].
It needs to be highlighted that the implementation
of a resisted sprint training program requires carefully
research, especially during the competitive season, as
research has shown that this form of training significantly impacts a number of kinematic parameters that
form the core of running technique, such as by lowering
stride frequency. Significant changes introduced during
the running season may lead to instable locomotor patterns and thus adversely affect running times. On the
other hand, it would be desirable for future studies to
determine which solutions are suitable for developing
strength, especially when beginning training with the
use of resisted training. This is important in light of the
findings by Moira et al. [27], who noticed a decrease
in running speed and a reduction in stride frequency
(increased ground contact time and flight time) as the
result of strength training, where solutions based on
resisted training could provide an alternative to standard
strength-building exercises.
The above aspect also requires careful consideration
when choosing the correct external load. We found
that the weight used in this study (7.5% of body mass, but
also 10%) was adequate in terms of the exercise potential
of the relatively untrained, although physically active,
female students. However, depending on the desired
outcome, every situation requires an individual and
careful assessment as to best decide the most optimal
load. This includes taking into consideration not just sex,
age or physical fitness level, but also the movement and
functional specifics of a given sport and its requirements
as to speed.
Conclusions
1. The results of the present study indicate that resisted sprint training in women, by pulling an external
load, improves short-distance running velocity.
2. Resisted sprint training led to increases in stride
length and completing fuller leg extensions at the knee
joint during toe off but, concomitantly, caused an decrease in stride frequency and increase in ground contact
time. Standard sprint training was found to increase
121
HUMAN MOVEMENT
B. Makaruk, H. Sozański, H. Makaruk, T. Sacewicz, Resisted sprint training in women
stride frequency, without any significant changes in
stride length.
3. The long-term effects of the sprint training programs (resisted and standard) used in present study were
similar for both experimental groups. The three-week
detraining period following the completion of the training programs had no significant effect on any of the
running gait parameters.
References
1. Sozański H., Witczak T., Starzyński T., The basis of speed
training [in Polish]. COS, Warszawa 1999.
2. Maćkała K., Kowalski P., Sprint running training [in
Polish]. AWF, Wrocław 2007.
3. Donati A., The association between the development of
strength and speed. New Stud Athl, 1996, 2 (3), 51–58.
4. Murray A., Aitchison T.C., Ross G., Sutherland K., Watt I.,
McLean D., Grant S., The effect of towing a range of
relative resistances on sprint performance. J Sports Sci,
2005, 23 (9), 927–935, doi: 10.1080/02640410400023332.
5. Mero A., Komi P.V., Gregor R.J., Biomechanics of sprint
running. A review. Sports Med, 1992, 13 (6), 376–392,
doi: 10.2165/00007256-199213060-00002.
6. Hunter J.P., Marshall R.N., McNair P.J., Relationships
between ground reaction force impulse and kinematics
of sprint-running acceleration. J Appl Biomech, 2005,
21 (1), 31–43.
7. Ciacci S., Di Michele R., Merni F., Kinematic analysis
of the braking and propulsion phases during the support time in sprint running. Gait Posture, 2010, 31 (2),
209–212, doi: 10.1016/j.gaitpost.2009.10.007.
8. Saraslanidis P., Training for the improvement of maximum speed: flat running or resistance training? New
Stud Athl, 2000, 15(3/4), 45–51.
9. Zafeiridis A., Saraslanidis P., Manou V., Ioakimidis P.,
Dipla K., Kellis S., The effects of resisted sled-pulling sprint
training on acceleration and maximum speed performance. J Sports Med Phys Fitness, 2005, 45 (3), 284–290.
10. Spinks Ch.D., Murphy A.J., Spinks W.L., Lockie R.G.,
The effects of resisted sprint training on acceleration performance and kinematics in soccer, rugby union, and
Australian football players. J Strength Cond Res, 2007,
21 (1), 77–85, doi:10.1519/00124278-200702000-00015.
11. Prus G., Zając A., Sprint training. Practical applications
[in Polish]. Sport Wyczynowy, 1986, 2–3, 39–46.
12. Makaruk H., Makaruk B., Kędra S., Effects of warm-up
stretching exercises on sprint performance. Phys Educ
Sport, 2008, 52 (1), 23–26, doi: 10.2478/v10030-0080005-7.
13. Makaruk B., Makaruk H., Sacewicz T., Makaruk T.,
Kędra S., Długołęcka B., The accuracy and reliability of
kinematic measurements in tests measuring running
velocity [in Polish]. Pol J Sport Tourism, 2009, 16 (2),
85–92.
14. McFarlane B., Speed a basic and advanced technical model.
Track Technique, 1994, 96, 4016–4020.
15. Lockie R.G., Murphy A.J., Spinks C.D., Effects of resisted
sled towing on sprint kinematics in field-sport athletes.
J Strength Cond Res, 2003, 17 (4), 760–767.
122
16. Paradisis G.P., Cooke C.B., The effects of sprint running
training on sloping surfaces. J Strength Cond Res, 2006,
20 (4), 767–777, doi:10.1519/R-16834.1.
17. Hauschildt M.D., Integrating high-speed treadmills into
a traditional strength and conditioning program for speed
and power sports. Strength Cond J, 2010, 32 (2), 21–32,
doi: 10.1519/SSC.0b013e3181caddd9.
18. Delecluse C.H., Influence of strength training on sprint
running performance: Current findings and implications
for training. Sports Med, 1997, 24 (3), 147–156, doi:
10.2165/00007256-199724030-00001.
19. Mero A., Komi P.V., Force-, EMG-, and elasticity-velocity
relationships at submaximal, maximal and supramaximal
running speeds in sprinters. Eur J Appl Physiol Occup
Physiol, 1986, 55 (5), 553–561, doi: 10.1007/BF00421652.
20. Weyand P.G., Sternlight D.B., Bellizzi M.J., Wright S.,
Faster top running speeds are achieved with greater
ground forces not more rapid leg movements. J Appl
Physiol, 2000, 89 (5), 1991–1999.
21. Makaruk H., Sacewicz T., Effects of plyometric training
on maximal power output and jumping ability. Hum
Mov, 2010, 11 (1), 17–22, doi: 10.2478/v10038-010-0007-1.
22. Markovic G., Jukic I., Milanovic D., Metikos D., Effects
of sprint and plyometric training on muscle function
and athletic performance. J Strength Cond Res, 2007, 21
(2), 543–49, doi:10.1519/R-19535.1.
23. Corn R., Knudson D. Effect of elastic-cord towing on the
kinematics of the acceleration phase of sprinting. J Strength
Cond Res, 2003, 17 (1), 72–75.
24. Orchard J., Biomechanics of muscle strain injury. NZJ
Sports Med, 2002, 30, 92–98.
25. Witvrouw E., Danneels L., Asselman P., D’Have T., Cambier D., Muscle flexibility as a risk factor for developing
muscle injuries in male professional soccer players. A prospective study. Am J Sports Med, 2003, 31 (1), 41–46.
26. Hinrichs R.N., Upper extremity function in running. II:
Angular momentum considerations. Int J Sport Biomech,
1987, 3, 242–263.
27. Moir G., Sanders R., Button Ch., Glaister M., The effect
of periodized resistance training on accelerative sprint
performance. Sports Biomech, 2007, 6 (3), 285–300, doi:
10.1080/14763140701489793.
Paper received by the Editors: October 30, 2012
Paper accepted for publication: March 18, 2013
Correspondence address
Beata Makaruk
Wydział Wychowania Fizycznego i Sportu
w Białej Podlaskiej
Akademia Wychowania Fizycznego
Józefa Piłsudskiego
ul. Akademicka 2
21-500 Biała Podlaska, Poland
e-mail: [email protected]
HUMAN MOVEMENT
2013, vol. 14 (2), 123– 128
Subjective assessment of sports success in wheelchair rugby
– proposal of a new research tool
doi: 10.2478/humo-2013-0014
Tomasz Tasiemski *, Joanna Bauerfeind
University School of Physical Education, Poznań, Poland
Abstract
Purpose. The main purpose of this study was to design and perform a preliminary psychometric analysis of a measure in the
subjective assessment of sports success in wheelchair rugby (WR). An additional objective of this study was to assess potential
differences in the subjective assessment of sports success between rugby players who play in the first and second Polish Wheelchair Rugby League (PWRL). Methods. Thirty WR players who played in the first (n = 11) and second PWRL (n = 19) completed
the newly-created Subjective Assessment of Sports Success in Wheelchair Rugby (SASS-WR) scale and a questionnaire collecting
demographic, career, and sports training data. Results. The SASS-WR scale was found to be a valid and reliable measure of sports
success in WR. The final version consists of 12 items defining four dimensions of sports success: (1) Individual Sports Success of
the Player, (2) National Sports Success of the Team, (3) International Sports Success of the Team, (4) Social and Personal Success
of the Player. The players in the first league were significantly more focused on achieving individual sports success as well as
having their team achieve national and international sports success when compared with the players in the second league.
Social and personal success (the fourth dimension of the SASS-WR) was more important for the second league players than
first league players, although this difference was not significant. Conclusions. The findings suggest that the SASS-WR scale can
serve as a valuable diagnostic tool in assessing sports success among WR players.
Key words: wheelchair rugby, subjective sports success assessment, spinal cord injury
Introduction
Success in team sports is determined by many factors,
of which the most commonly mentioned are technical
and tactical training as well as physical and mental preparation [1–3]. Research in this area on able-bodied athletes has been undertaken, however, little is known
about the determinants of sports success among athletes with disabilities. This is especially so with regards
to the psychological aspects that play a role here, which
include the subjective perception of sports success by
athletes with disabilities.
Currently, the fastest growing team sport for individuals with disabilities in Poland is wheelchair rugby
(WR), with the Polish Wheelchair Rugby League (PWRL)
having grown to include already 20 official teams [4].
WR is intended mainly for individuals with cervical spinal
cord injuries (SCI), although it is open to those who feature other locomotor disabilities as long as they have at
least three limbs with functional deficits. However, WR
is still a relatively new sport for individuals with disabilities; hence research on this discipline and its players is
quite limited. Until now, most of the published scientific
work has concentrated on testing strength levels and classifying players [5–8] or analyzing the impact of training on the functional abilities of WR players [9–13].
Defining sports success in WR can begin by under* Corresponding author.
standing what motivations guide individuals with disabilities, especially those with SCI who decide to take up
physical activity. Previous research found that, first of
all, men differ from women with regards to their motivation for participating in sports [14]. Men with disabilities stressed the importance of achieving athletic status,
while women claimed that developing friendships was
much more important. Studies focused on individuals
with SCI found that the main reason for engaging in phy­
sical activity was to maintain physical fitness levels and
to improve upper-body strength [15, 16]. Among those
individuals with SCI who participate in sports, men
valued the opportunity to compete against one another
higher than women, while women rated the possibility
to control body weight higher than men [17]. In turn,
those with SCI who were intensively involved in sports
stressed that their main motivation was the pleasure
derived from playing sports, with health and other social motivations less relevant [18, 19]. However, the
motivations that guide professional athletes with disabilities, as is the case for those playing in the PWRL, are
particularly important as it is teamwork that allows them
to achieve sports success. Previous research found a significant relationship between team cohesion and sports
success [20, 21]. In the case of individual sports, however,
such a relationship was not found, with sports success
even having a negative impact on team cohesion [22–24].
Therefore, it seems that athletes focused on sports success would be motivated more by sports-related goals
than factors such as improving health.
123
HUMAN MOVEMENT
T. Tasiemski, J. Bauerfeind, Sports success in wheelchair rugby
Within this context, the main aim of the present study
was to design and perform a preliminary psychometric
analysis of a research tool that could be used in the subjective assessment of sports success in WR. An additional
goal of the study was to assess potential differences in
the subjective assessment of sports success between rugby players who play in the first and second PWRL. It
was assumed that players in the first league would be
more motivated in having their team achieve sports
success than players in the second league.
Material and methods
Creating a scale
Reliability of the scale
In order to choose what phrasing would be most
suitable in assessing players’ subjective perception of
sports success in WR, an interview was conducted with
25 randomly selected players from both the first and
second WR leagues in Poland. During the interview the
players were asked to specify a maximum of 20 responses
to the question: “How would you define sports success
in WR?” After collecting the replies, a five-person expert
panel was assembled consisting of a WR player, coach,
and referee and the two authors of the present study to
critically analyze the collected data. A consensus was
reached on deleting responses that were either identical
or closely related to each other, leading to the creation
of a 14-item scale that was then tested in a pilot study.
Participants in the pilot study
For the pilot study, 30 additional players were recruited (29 men and 1 woman) from the first (n = 11)
and second (n = 19) WR league (Tab. 1). The majority
of the players were individuals with SCI (n = 26), while
the remaining four were disabled due to other limb
impairments. The mean age of the first league players
was 34 years (SD = 4.51) while the mean for the second
league players was 39 years (SD = 9.69); the difference
between both groups for age was not statistically significant (t = –1.529; p = 0.137).
In terms of the functional abilities of the players,
among the first league group there were three athletes
who were low-pointers (point values from 0.5 to 1.5,
Table 1. Participants of the pilot study
Team name
Balian Poznań
Flying Wings Rzeszów
Jokers Bydgoszcz
Brave Snails Lublin
Piecobiogaz Poznań
TetraGryf Szczecin
124
based on the classification system by the International
Wheelchair Rugby Federation) and eight who were
high-pointers (2.0–3.5 points). In the group of second
league players, fourteen were low-pointers and five were
high-pointers. The difference between the groups in
terms of the players’ functional capacity was significant
( 2 = 6.111, p = 0.013). No significant differences were
noted between the first and second league players in
terms of basic data on the players’ experience and career,
such as the number of years spent playing WR, the number of days per week spent training WR, and the number of minutes per day spent training WR (Tab. 2).
Number
of partici­
pants
n
PWRL
Ranking
in 2012
5
3
3
5
8
6
I
I
I
II
II
II
1
3
9
12
13
15
The participating players (n = 30) completed the newlycreated Subjective Assessment of Sports Success in Wheelchair Rugby (SASS-WR) scale, subjectively rating the 14
items that were previously selected to describe the perception of sports success. The participants res­ponded to
the statement “Sport success in WR means to me…” by
choosing their responses to each item on a 5-point Likert
scale (1 = completely unimportant, 5 = extremely important). The reliability of the 14-point SASS-WR scale
was found to be good (Cronbach’s alpha = 0.728), although
after removing statements No. 12 and No. 14 the reliability of the scale increased to Cronbach’s alpha = 0.753
and Cronbach’s alpha = 0.747, respectively (Tab. 3). As
a result, these statements were removed to increase the
reliability of the scale, leaving 12 items.
Results
Extracting the SASS-WR scale’s factors
Factor analysis was performed in order to extract
the SASS-WR’s underlying dimensions. A scree plot
pointed to four dimensions, which were extracted by
Principal Component Analysis (Promax rotation with
Kaiser normalization). Four of the scale’s items had a
value greater than one, which accounted for 85% of
the total explained variance. Each of the scale’s four
dimensions consisted of three components (Tab. 4).
Each of the four dimensions of the SASS-WR scale
were named according to their semantic content, and
their reliability was found to be satisfactory: 1) Individual Sports Success of the Player (Cronbach’s alpha
= 0.775), 2) National Sports Success of the Team (Cronbach’s alpha = 0.861), 3) International Sports Success
of the Team (Cronbach’s alpha = 0.979), and 4) Social
and Personal Success of the Player (Cronbach’s alpha
= 0.617). The responses of the participants for each statement were summed separately for each of the scale’s
four dimensions (range: 3–15 points). A higher score indicated the greater importance of the component (dimension) for a player in their subjective assessment of
sports success in WR (see Appendix).
HUMAN MOVEMENT
T. Tasiemski, J. Bauerfeind, Sports success in wheelchair rugby
Table 2. Sports career and training frequency of the first and second WR league players
Sports experience
Years playing WR
Days spent training WR per week
Daily WR workout (min)
0.05; ** p
*p
I league (n = 11)
± SD
II league (n = 19)
± SD
t test
(p)
7.36 ± 4.56
2.50 ± 1.07
106.36 ± 61.64
8.39 ± 4.63
2.03 ± 1.08
93.95 ± 40.88
0.559
0.257
0.512
0.01
Table 3. Reliability of the SASS-WR scale after removing individual items
Cronbach’s alpha
after removing the item
No.
SASS-WR items
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Qualifying to be a player on my WR team’s roster
Selected by the coach to play in a game
Qualifying to be a player on the national WR team
Team being highly ranked in a PWRL tournament
Team being highly ranked in PWRL overall classification
Team advancing to a higher group in PWRL
Team winning first place in the European Championships
Team qualifying for the World Championships
Team winning first place in the World Championships
Achieving social prestige
Developing social contacts
Pleasure derived from playing sports
Improving my fitness level
Overcoming my own weaknesses
0.699
0.693
0.720
0.722
0.713
0.689
0.694
0.699
0.701
0.705
0.724
0.753
0.718
0.747
Values in bold denote those items, when removed, improved the scale’s reliability
Table 4. Matrix model of the SASS-WR scale
Components
No.
SASS-WR items
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
Qualifying to be a player on my WR team’s roster
Selected by the coach to play in a game
Qualifying to be a player on the national WR team
Team being highly ranked in a PWRL tournament
Team being highly ranked in PWRL overall classification
Team advancing to a higher group in PWRL
Team winning first place in the European Championships
Team qualifying for the World Championships
Team winning first place in the World Championships
Achieving social prestige
Developing social contacts
Improving my fitness level
1
2
3
4
0.960
0.935
0.753
0.858
1.021
0.769
1.002
1.012
0.904
0.833
0.894
0.478
Extraction method: Principal Component Analysis; rotation method: Promax with Kaiser normalization
Sport success based on the opinions
of WR players
First league players, compared against second league
players, placed more importance on achieving sports
success in three of the SASS-WR scale’s dimensions
(individual sports success and their team’s national
and international success), with the differences between the two groups statistically significant (Tab. 4).
This result confirmed the initial assumption that higher
classified players in the PWRL (first league) are more
motivated in achieving team success than players at
a lower classification (second league). In turn, social and
personal success (the fourth dimension of the SASS-WR)
was more important for second league players than first
league players, although this difference was not significant (Tab. 5).
125
HUMAN MOVEMENT
T. Tasiemski, J. Bauerfeind, Sports success in wheelchair rugby
Table 5. Sports success based on the opinions of the first and second league players
SASS-WR dimensions
Individual Sports Success of the Player
National Sports Success of the Team
International Sports Success of the Team
Social and Personal Success of the Player
*p
0.05; ** p
II league (n = 19)
± SD
14.36 ± 1.43
14.45 ± 1.03
14.73 ± 0.91
11.27 ± 2.32
10.89 ± 2.55
13.05 ± 1.84
12.26 ± 3.78
12.84 ± 2.19
t test
(p)
0.001**
0.028*
0.044*
0.075
0.01
Discussion
Preliminary psychometric analysis of the proposed
SASS-WR scale suggests that it can serve as a both relevant and reliable tool in assessing the motivations behind sports success in WR. The final scale consists of
12 items measuring four dimensions of sports success:
1) Individual Sports Success of the Player, 2) National
Sports Success of the Team, 3) International Sports Success of the Team, and 4) Social and Personal Success of
the Player. Noteworthy is the fact that the WR players,
despite being clearly asked to indicate what sports success means to them (when initially creating the SASS-WR
scale), pointed to a number of non-sports-related factors
such as developing social contacts and improving their
own fitness level. This implies that for individuals with
a disability, even for those who are professionally involved in sports, the motivation behind taking up a sport
is not only based on typical sports-related factors, such
as qualifying to be a player of the national team or
having their team in high standing. These results are
consistent with the findings of other authors. The participants in Wu and William’s study [18] – individuals
with SCI intensively practicing sports – placed “fun in
doing sports” as their main motivation, followed by
goals such as “physical fitness”, “health”, “competition”
and “social aspects”. Similarly, research conducted by
Furst, Ferr, and Megginson [19] on athletes with SCI
(triathlon competitors) found that the main reason for
them engaging in sports was “fun” and then mentioned
“physical development and improving health”, “enjoying competition”, and “social aspects”.
The participants of the present study, specifically the
players in the first league, were found to be significantly
more focused on achieving sports success than the players in the second league. The first league players obtained
results at the upper limit of the scale, i.e., 14.4–14.7
points (out of a maximum of 15 points) in three of the
scale’s dimensions, signifying their focus on both individual sports success as well as their team’s success,
with these results being significantly higher than those
of the second league players. This outcome can be interpreted in different two ways. On the one hand, it seems
quite obvious that players playing at a higher level (first
league) would be more focused on individual and team
success than players who were not as successfully competitive (second league). On the other hand, the results
126
I league (n = 11)
± SD
may have been influenced by the differences between
the first and second league teams in terms of the functional abilities the players possess. Among those playing
in the first league, the percentage of high-pointers to lowpointers was 73% to 27%, while in the second league
this was the opposite, i.e. 26% to 74%, respectively. Highpointers (who primarily play on offense) may be more
strongly motivated in achieving success in sports because
they are mainly responsible for scoring points, while
low-pointers (playing on defense) rarely leave the rear
of the court and have a much smaller opportunity to
score any points. The differences between the groups’
focus on different aspects of sports success should be
verified in future research on groups of first and second
league players although with a similar ratio of highpointers and low-pointers.
The results of the present study suggest a relationship
between team cohesion (understood as players being
focused on the same goal) and sports success (understood as a team’s ranking in the PWRL). Carron et al. [20]
demonstrated a very strong relationship between the
success of a sports team and team cohesion when analyzing eighteen basketball and nine soccer teams (154
women and 140 men). Similar results were obtained
by Mullen and Copper [21]. Generally speaking, the present study confirmed the positive relationship between
sports success and team cohesion in team sports. In disciplines such as volleyball or soccer, each player provides
a different skill set that is effective only when working
together as a team. On the other hand, individual sports
such as golf or bowling are entirely dependent on a player’s own skills and abilities, where the success of a team
depends more on each individual’s performance. In such
sports, team cohesion does not significantly affect sports
success, and can even have a negative impact on sports
success [22–24].
Certain limitations of the present study could have
influenced the final results. Firstly, the obtained results
were based on a relatively small sample of athletes (n = 30),
and the two sub-groups were not equinumerous. Almost
two-thirds of the surveyed players competed in the
second league with the remaining one-third belonged in
the first league. In addition, as was already mentioned
earlier, the different ratio of high-pointers to low-pointers
in both groups (first and second league) could have affected the final results of this study.
HUMAN MOVEMENT
T. Tasiemski, J. Bauerfeind, Sports success in wheelchair rugby
Conclusions
The findings suggest that the SASS-WR scale can
serve as a valuable diagnostic tool in assessing sports
success among WR players. The present-day process of
professionalizing sports for athletes with disabilities
in Poland, in particular WR, requires reliable tools to
assess not only the physiological, motor, and technical
parameters of its players but also assess their psychological make-up, as all of these factors constitute the
ability of team to achieve sports success. Future research
should confirm the SASS-WR scale’s psychometric
properties on a larger group of athletes and verify its sensitivity with regard to differences in the competitive
level of both the participants and their teams. It would
also be useful to assess whether the SASS-WR scale can
serve as a reliable tool for assessing WR players from
other European countries.
References
1. Stølen T., Chamari K., Castagna C., Wisloff U., Physiology
of soccer: an update. Sports Med, 2005, 35 (6), 501–536,
doi: 10.2165/00007256-200535060-00004.
2. Van-Yperen N.W., Duda J.L., Goal orientations, beliefs
about success, and performance improvement among
young elite Dutch soccer players. Scand J Med Sci Sports,
1999, 9 (6), 358–364, doi: 10.1111/j.1600-0838.1999.
tb00257.x.
3. Graczyk M., Portrait of a psychologist, its role and tasks
carried out in light of the opinion of Olympic coaches
[in Polish], Medycyna Sportowa, 2007, 5, 247–253.
4. Results of the 2012 Polish Wheelchair Rugby League.
January 2013. Available from URL: http://rugby.far.org.pl/
aktualnosc,112,Podsumowanie+sezonu.html.
5. Morgulec N., Kosmol A., Molik B., Specific fitness level
and classification system in wheelchair rugby. In: Żak S.,
Spieszny M., Klocek T. (eds.), Team games in physical education and sport [in Polish]. Studia i monografie AWF,
Kraków 2005, 335–337.
6. Szymczak Ł., Laurentowska M., An assessment of anaerobic performance of individuals with quadriplegia training wheelchair rugby [in Polish]. Medycyna Sportowa,
2009, 25, 35–36.
7. Morgulec-Adamowicz N., Kosmol A., Bogdan M., Molik B., Rutkowska I., Bednarczuk G., Game efficiency of
wheelchair rugby athletes at the 2008 Paralympic Games
with regard to player classification. Hum Mov, 2010, 11 (1),
91–95, doi: 10.2478/v10038-010-0002-6.
8. Sarro K.J., Misuta M.S., Malone L., Burkett B.L., Barros R.M.,
Correlation between functional and kinematical variables in elite wheelchair rugby players. In: Jensen R., Ebben W., Petushek E., Richter C., Roemer K. (eds.), XXVIII
International Symposium on Biomechanics in Sports:
Conference Proceedings Archive, July 19–23, 2010, Marquette, USA, 28, 124–125.
9. Adnan Y., McKenzie A., Miyahara M., Self-efficacy for
Quad Rugby skills and activities of daily living. Adapt
Phys Activ Q, 2001, 18 (1), 90–101.
10. Dallmeijer A.J., Hopman M.T.E., van As H.H.J., van der
Woude L.H.V., Physical capacity and physical strain in
persons with tetraplegia; The role of sport activity. Spinal
Cord, 1996, 34 (12), 729–735, doi:10.1038/sc.1996.133.
11. Furmaniuk L., Cywińska-Wasilewska G., Kaczmarek D.,
Influence of long-term wheelchair rugby training on the
functional abilities of persons with tetraplegia over a
two year period post-spinal cord injury. J Rehabil Med,
2010, 42 (7), 688–690, doi: 10.2340/16501977-0580.
12. Morgulec N., Kosmol A., Influence of sport activity on
the functional abilities of individuals with spinal cord
injury (SCI-C) [in Polish]. Lider, 2009, 12, 26–29.
13. Morgulec N., Skrzypczyk R., Development of wheelchair
rugby and its influence on the functional abilities of individuals with quadriplegia [in Polish]. Wychowanie Fi­
zyczne i Sport, 2003, 47, 535–544.
14. Fung L., Participation motives in competitive sports:
A cross-cultural comparison. Adapt Phys Activ Q, 1992,
9 (2), 114–122.
15. O’Neill S.B., Maguire S., Patient perception of the impact
of sporting activity on rehabilitation in a spinal cord injuries unit. Spinal Cord, 2004, 42 (11), 627–630, doi:
doi: 10.1038/sj.sc.3101651.
16. Tasiemski T., Bergström E., Savic G., Gardner B.P., Sports
recreation and employment following spinal cord injury
– a pilot study. Spinal Cord, 2000, 38 (3), 173–184.
17. Tasiemski T., Kennedy P., Gardner B.P., Blaikley R.A.,
Athletic identity and sports participation in people with
spinal cord injury. Adapt Phys Activ Q, 2004, 21 (4),
364–378.
18. Wu S.K., Williams T., Factors influencing sport participation among athletes with spinal cord injury. Med Sci
Sports Exerc, 2001, 33 (2), 177–181.
19. Furst D.M., Ferr T., Megginson N., Motivation of disabled athletes to participate in triathlons. Psychol Rep,
1993, 72 (2), 403–406, doi: 10.2466/pr0.1993.72.2.403.
20. Carron A.V., Bray S.R., Eys M.A., Team cohesion and team
success in sport. J Sports Sci, 2002, 20 (2), 119–126, doi:
10.1080/026404102317200828.
21. Mullen B., Copper C., The relation between group cohesiveness and performance: An integration. Psychol Bull,
1994, 115 (2), 210–227, doi: 10.1037/0033-2909.115.2.210.
22. Carron A.V., Chelladurai P., Cohesion as a factor in sport
performance. Int Rev Sport Sociol, 1981, 16, 2–41.
23. Landers D.M., Lüschen G., Team performance outcome
and cohesiveness of competitive coacting groups. Int Rev
Sport Sociol, 1974, 9, 57– 69.
24. Williams J.M., Widmeyer W.N., The cohesion–performance outcome relationship in a coaching sport. J Sport
Exerc Psychol, 1991, 13 (4), 364–371.
Paper received by the Editors: January 29, 2013
Paper accepted for publication: April 5, 2013
Correspondence address
Tomasz Tasiemski
Zakład Sportu Osób Niepełnosprawnych
Akademia Wychowania Fizycznego
im. Eugeniusza Piaseckiego
ul. Królowej Jadwigi 27/39
61-871 Poznań, Poland
e-mail: [email protected]
127
HUMAN MOVEMENT
T. Tasiemski, J. Bauerfeind, Sports success in wheelchair rugby
Appendix: SASS-WR scale
SUBJECTIVE ASSESSMENT OF SPORTS SUCCESS IN WHEELCHAIR RUGBY (SASS-WR)
This questionnaire is designed to assess how wheelchair rugby players perceive sports success.
Please rate how important the following statements are to you on a scale of 1 (completely unimportant)
to 5 (extremely important).
Sports success in wheelchair rugby means to me… (please circle your answer):
1. Qualifying to be a player on my wheelchair rugby team’s roster
1
2
3
4
5
2. Selected by the coach to play in a game
1
2
3
4
5
3. Qualifying to be a player on the wheelchair rugby national team
1
2
3
4
5
4. Team being highly ranked in a PWRL tournament
1
2
3
4
5
5. Team being highly ranked in PWRL overall classification
1
2
3
4
5
6. Team advancing to a higher group in PWRL
1
2
3
4
5
7. Team winning first place in the European Championships
1
2
3
4
5
8. Team qualifying for the World Championships
1
2
3
4
5
9. Team winning first place in the World Championships
1
2
3
4
5
10. Achieving social prestige
1
2
3
4
5
11. Developing social contacts
1
2
3
4
5
12. Improving my fitness level
1
2
3
4
5
* PWRL – Polish Wheelchair Rugby League
INSTRUCTIONS FOR RESEARCHERS
The SASS-WR has four dimensions, measuring:
I. INDIVIDUAL SPORTS SUCCESS OF THE PLAYER (Statements No. 1–3)
II. NATIONAL SPORTS SUCCESS OF THE TEAM (Statements No. 4–6)
III. INTERNATIONAL SPORTS SUCCESS OF THE TEAM (Statements No. 7–9)
IV. SOCIAL AND PERSONAL SUCCESS OF THE PLAYER (Statements No. 10–12)
The responses that fall under each dimension need to be summed up (their numerical value).
A higher score (range: 3–15 points) indicates the greater importance of this component (dimension)
for a player’s subjective perception of sports success in wheelchair rugby.
128
HUMAN MOVEMENT
2013, vol. 14 (2), 129– 137
On the possibility of applying achievement goal theory
in competitive sports
doi: 10.2478/humo-2013-0015
Piotr Kuczek
State Higher School of Vocational Education, Tarnów, Poland
Abstract
Purpose. There exist numerous empirical proofs as well as theoretical bases showing that task motivational orientation and
task climate allow students and athletes to function better and be more efficient. What is not certain is whether the same applies
to athletes competing at the professional level. The aim of this study was to analyze whether task orientation and task-oriented
climate help professional athletes avoid experiencing high levels of anxiety, thereby providing a favorable foundation for performance in high-level competition. Methods. Basketball players from the Polish II League (amateur) and Extraleague (professional)
were surveyed. Motivational orientation, motivational climate, and anxiety levels were measured by the Perception of Success
Questionnaire (POSQ), Perception of Significant Others’ Sport Success Criteria Questionnaire (PSOSSCQ), and Sport Anxiety
Scale (SAS), respectively. Results. The reliability of the research tools on a Polish population was confirmed. Motivational climate
was associated with motivational orientation; task orientation and a task-oriented climate were found to not reduce anxiety
levels. Conclusions. The results do not confirm the application of achievement goal theory in high-level competitive sports.
Key words: motivation, anxiety, motivational climate
Introduction
The motivation behind achievement and success as
well as the natural propensity for rivalry and competition has been one of the most significantly debated
issues in psychology for the last six decades. It should
come as no surprise that the use of this theory in recreational and competitive sports has garnered considerable
interest by researchers. This stems from the fact that
competitive sport is an arena where motivation is not
only of colossal significance in regard to its final outcome
but that it also plays a role in the phenomenon of persevering or quitting. Achievement goal theories are deeply
rooted in psychology and even in philosophy, being
a direct precursor of Lepper’s overjustification hypothesis
[1] and Deci’s Cognitive Evaluation Theory [2]. It has to be
emphasized, however, that these two theorists dealt with
the differences between external and internal motivation,
while goal orientation theory deals with two kinds of internal motivation that work in achieving various goals.
The motivational theories currently being used originate from work conducted in the field of education and
have been modified for use in sports [3–5]. Disregarding
the differences in terminology, all of these theories can
be distinguished by two types of guiding orientations
(attitudes). The first, termed task orientation, consists
of combining the effect of an action with effort. For individuals with such an orientation, spending a great deal
of energy in achieving a goal does not suggest that they
have poorer abilities. Instead, the effort placed in fostering one’s own self-development is treated as having
a value and, consequently, nullifying in some respect
the objective, or goal, itself. Such individuals are believed
to have no need to compare themselves with others or to
a set of specific standards; instead, they demonstrate
their abilities only to appease themselves, as something
convergent with their effort rather than the final effect.
This may be interpreted as saying those who work hard
and try to improve themselves have already achieved
success in some manner. However, Dweck and Leggett
[4], offered a slightly different definition of this phenomenon by defining it as perceiving the changeability or permanence of one’s own capacities. A task-oriented person
regards their capacities as a group of acquired features and
are changeable, subject to modeling and development.
The other orientation, as described by Nicholls, is ego
orientation [3], which consists of having effort and talent remain non-differentiated, where talent and effort are
notions that overlap each other. This approach emphasizes a stark contrast between effort and ability. Individuals with such an orientation need to prove their own
abilities in an entirely different way. They believe that
the more effort put into achieving a goal, the less talent they actually possess; hence the reason why their
aim is to achieve maximum capability with minimum
effort. Since the effort is treated as possessing no value
of itself, what remains important, in effect, is the final
outcome and preferably when it outranks the results
of others or by attaining a certain defined standard. To
someone who is ego-oriented, one’s own development
has little influence on their “sense” of success. According
to Dweck and Leggett [4], this stems from the fact that
individuals with such an orientation consider capacity
and talent as permanent features, resulting in the belief
that all and any attempts to improve them are performed
in vain.
129
HUMAN MOVEMENT
P. Kuczek, Motivational orientation theory and sports
As Nicholls asserts [3], the ontology of task orientation motivation presents itself earlier than ego orientation. Only when children reach the age of 12–13 do
they begin to notice the role of talent. They also start to
understand why their peers, who more often than not
try to do their best, do not feature the achievement level
as those who are lazy. Children begin to realize that
only effort lets them achieve their maximum capability
but that it cannot help overcome their own limitations.
The problem of the source and causes of ego orientation
motivation was investigated by Ames [5], who demonstrated that the process is affected by such factors as
the type of task that is to be performed, the evaluation
method, the level of an individual’s independence, task
grouping and division, and reward.
Both empirical evidence and theoretical analysis have
concluded that task orientation is the most beneficial
in the proper motivational and emotional development
of young students and athletes. Nicholls [3] emphasizes
that task-oriented individuals identify success with effort,
hence one of the reasons why they try to use effort itself
as the source as well as path leading to success. An egooriented individual believes that real success and satisfaction are derived from displaying the highest gained
capability with minimum effort. Consequently, it can be
expected that, given two athletes, the individual featuring higher task orientation would place more effort
in a given task.
A similar line of logic is present when choosing a task
based on its difficulty. It is believed that choosing a task
of medium difficulty, which still poses a challenge to
an individual, can ensure the notion of attaining real
success in sports. Accordingly, only such a choice is considered rational. Nicholls [3] posits that a task-oriented
individual would naturally choose a task of medium difficulty, as would an ego-oriented individuals that might
have better sense of their own competence. However,
as an individual begins experience more difficulty in
demonstrating competence in a given task, they begin
to gravitate towards less attractive goals as a way of avoiding failure. As a result, such individuals would then
choose a very easy or a very difficult (i.e., impossible) task.
In the first example, such an individual would definitely
be able to cope with and perform the task; in the other
case, they would simply treat themselves as just another
example of those unable to complete such a difficult task.
This defense strategy might even be considered efficient
were it not for the fact that, in sports, one has to choose
goals that are difficult but at the same time accomplishable.
There is a great deal of evidence to support the belief
that task-oriented individuals feel less anxiety than egooriented ones [5, 6]. The difference is especially seen in
individuals who are convinced that their competence
is low or when a difficult situation is encountered.
This is because when faced with a difficult situation,
accompanied by a lack of confidence in one’s own capabilities, one feels a threat to one’s self-image. As had
130
been previously stated, a goal-oriented individual regards
one’s features as capable of undergoing development.
Consequently, such an individual does not treat such
a situation as an attack on one’s integrity even in a state
of hopelessness, as they understand that they can always
improve and overcome their own weaknesses, progressing so as to eliminate the problems they are faced with
regardless of the fact that at the present time they are
faced with an insurmountable obstacle. An ego-oriented individual behaves in a completely opposite way.
Anxiety coincides with the feeling of satisfaction that
an individual derives from their effort, and is a facet
particularly visible in sports [7]. A student or athlete
can feel satisfaction regardless of the results they have
achieved. They feel satisfied with process of accepting
a challenge, attempting to meet it, and improving their
capabilities. For ego-oriented students and athletes, it
is victory that counts the most, which they can achieve
only when performing at an appropriate sports or educational level. Therefore, as Roberts asserts [8], such
athletes do derive satisfaction from practicing sports but
only when they regard their capacity as being high. Comparing one’s achievements with others, which for egooriented athletes is what determines worth, means that
only winners can feel truly satisfied.
The aforementioned reflections point to the benefits
of developing goal orientation motivation in young
athletes, enabling them to act as best they can in light
of the task at hand. This issue, however, is not so clear in
the case of athletes who have already achieved a high
level of competence.
In light of the findings of some authors [9, 10], it is
questionable whether motivational orientation theory,
which has been found to work well in the fields of education and youth sports, can be fully transferred and
efficiently applied to the realm of professional sports
and whether professional athletes may benefit as much
from task orientation and being embedded in a task
climate. Therefore, the aim of the present study was to
study this issue by sampling amateur and professional
athletes by the use of specially developed questionnaires
used to assess motivational orientation. In addition, as
these questionnaires were to be used for the first time
in the country of Poland, the reliability of the translations
was checked to see if did not weaken the strengths of
these tests.
Material and methods
A representative sample of basketball players from
the Polish Extraleague (professional) and the II League
(amateur) was used, numbering 65 (mean age 25.0 years)
and 47 individuals (mean age 22.66 years), respectively.
All players in the leagues were included regardless of their
nationality; however, representatives of other Central
and Eastern Europe (Lithuanians, Serbs, Croatians, Russians) were excluded due to potential language difficulties.
HUMAN MOVEMENT
P. Kuczek, Motivational orientation theory and sports
Data was collected before or after a training session
in the city/town the player represented in testing conditions that provided anonymity. The players were informed about the objective of the study and its scientific
character as well as the confidentiality of the results.
Instructions on how to fill out the questionnaires were
provided. The players were assured that there were no
right or wrong answers and asked to respond as honestly
as possible. They then began filling out the questionnaires, which took about 10–15 minutes to complete.
The questionnaires consisted of measuring the motivational climate, motivational orientation as well as the
anxiety level of the players. Players whose native language
was English (i.e., Americans) received the original version
of the tests in English [11, 12]. Polish players received
a translated version.
Motivational climate was measured by Roberts et al.’s
Perception of Significant Others’ Sport Success Criteria
Questionnaire (PSOSSCQ) [13]. The aim of this tool is
determine the motivational climate which an athlete
competes in. The questionnaire consists of 16 suggested
answers that pertain to the perceived motivational climate created by individuals who are important to the
athlete. It is preceded by the task orientated question
of “When playing my sport, my coach feels that I have
success when…”. The athlete then rated various responses
on a 1–5 Likert scale. Typical answers which testify to
the task climate include “I do my best” or “I overcome
difficulties”; answers reflecting the ego climate include
“I beat other individuals” or “I show other individuals
I am the best”.
Motivational orientation was assessed using Roberts
et al.’s Perception of Success Questionnaire (POSQ) [13].
This tool is designed to help researchers answer questions on the motivational attitude of athletes. It consists
of 12 suggested answers measured on a 1–5 Likert scale
to the question: “When playing sport, I feel most successful when…”. For the ego orientation, typical answers
included “I accomplish something others cannot do”
or “I am the best”; task orientated responses included
“I work hard” or “I show personal improvement”.
In order to measure anxiety, Smith et al.’s Sport
Anxiety Scale (SAS) [12] was used, which had been
designed specifically for the need of assessing athletes.
The test consists of 21 items athletes are supposed to agree
or disagree with on a four-point Likert scale (definitely
not = 1, rather not = 2, rather yes = 3, definitely yes = 4).
Answers that testify to the presence of somatic anxiety
include, for instance, statements such as “My body feels
tense”; answers that measure cognitive anxiety and concentration disruption include statements such as “I have
self-doubts” and “I’m concerned I won’t be able to concentrate”, respectively.
Additionally, with a view to establishing the value
and usefulness of particular players in a team, the participants’ coaches were asked to rank their players from
highest to lowest, a higher number for players they con-
sidered to be the most valuable whereas the lowest indicated those whose value they perceived in the team
to be lowest.
Data computation was performed with Statistica ver. 9
(Statsoft, USA).
Results
The results of the psychometric tests translated into
Polish measuring motivational climate and orientation
as well as anxiety level found that the translated versions of the tests are sufficiently reliable and applicable
for dissemination in Poland.
Cronbach’s alpha for the Perception of Significant
Others’ Sport Success Criteria Questionnaire (PSOSSCQ),
measuring motivational climate, was 0.7973 and 0.8276
for the task and ego climates, respectively. The deletion
of any of the questions did not considerably affect the
results. Escarti et al. [11] obtained similar results with
values of 0.87 and 0.92 for the task and ego climates,
respectively. The relatively minor difference between
these values notwithstanding, the translation of the
PSOSSCQ and the impact of any cultural differences
only slightly weakened the Polish version’s reliability.
The Perception of Success Questionnaire was found
with Cronbach’s alpha of 0.8274 and 0.8009 for task
and ego orientations, respectively. Deletion of any of the
questions did not considerably affect the results. The
results of the present study were found to be in line with
those provided by the creators of the test. For example,
while studying a population of athletically active Ameri­
can students (mean age 20.8 years), the creator’s obtained
values of 0.82 and 0.87 for the task and ego orientations, respectively [13]. Research on a group of both female and male American basketball players (mean age
19.5 years) by Kavusssanu and Roberts [14] obtained
Cronbach’s alpha equal to 0.88 in both the task and ego
orientations. The results presented above come quite
close to those obtained in the Task and Ego Orientation
in Sport Questionnaire scale (TESOSO), designed by
Duda et al. [15], for measuring motivational orientations
in sports, where Cronbach’s alpha for task and ego orientations were found to be 0.72 and 0.82, respectively.
The psychometric results of the Polish version of Roberts
et al.’s test show that the translated version is wellsuited for use in sports and that its reliability does not
considerably deviate from the original or from the results of similar tests.
Cronbach’s alpha for the Sports Anxiety Scale (SAS)
was, respectively, 0.8513, 0.8648, and 0.6178 for cognitive anxiety, somatic anxiety, and concentration disruption. Only Question #1 proved to be weak in the test
(correlation of 0.39), which may have resulted from a poor
translation. The original test used the word “nervous”,
which is semantically connected with anxiety, apprehension, fear, stage fright, etc. The Polish equivalent that
was used is more connected with being roused, over131
HUMAN MOVEMENT
P. Kuczek, Motivational orientation theory and sports
excited, and angry. The difference may have been large
enough so as to considerably weaken this question. Non­
etheless, similar values of reliability were obtained by
other researchers working with the original version of
the test, obtaining Cronbach’s values of 0.84, 0.90, and
0.71 for cognitive anxiety, somatic anxiety, and concentration disruption, respectively [16]. As can be seen, the
highest Cronbach’s alpha value was found measuring
somatic anxiety, the lowest for concentration disruption.
Even though the results shown above seem to testify
to the better reliability of the original English version,
the translated version is strong enough to be used in analyzing Polish athletes.
A comparison of the professional (Extraleague) and
amateur (II League) players found significant differences
only between two variables: task orientation and age,
with the amateur players being much younger than the
professionals and more task-oriented. The remaining
differences among the variables were found to be statistically insignificant (Tab. 1).
The results confirmed that task orientation decreased
with age and that the motivational climate recognized
by an individual is connected with their orientation.
What was not confirmed were reports on the positive influence of task orientation on anxiety level. Reversely,
it was ego orientation that correlated negatively with
anxiety level, with the strength of the correlation higher for Extraleague players than those in the II league
(Tab. 2).
Correlation analysis performed separately on the two
groups of athletes (amateur and professional) showed
different interactions between certain parameters. In
the group of II league players, age negatively correlated
with the level of each anxiety component, whereas it positively correlated with their value to the team (as measured by their coach) and ego motivational climate. Such
correlations were not found among the Extraleague
players (Tab. 3, 4).
Moreover, the item “value to the team” showed a correlation between ego climate and ego orientation among
II league players whereas no such correlation was found
among the Extraleague players; instead, a slightly negative correlation was observed.
In view of the relatively small number of American
Table 1. Groups characteristic and primary statistics
Age
Ego climate
Task climate
Ego orientation
Task orientation
Cognitive anxiety
Somatic anxiety
Mean
EX league
Mean
II league
p
N
EX league
N
II league
SD
EX league
SD
II league
25.00
3.62
3.95
3.85
4.25
2.13
1.82
22.66
3.65
4.19
4.01
4.54
2.26
1.94
0.0053
0.8473
0.0535
0.2510
0.0192
0.2689
0.3037
65
63
63
65
65
63
63
47
46
46
46
46
46
47
4.32
0.76
0.69
0.69
0.73
0.57
0.52
4.26
0.75
0.52
0.72
0.48
0.68
0.63
EX league – players from the Polish basketball Extraleague
II league – players from the Polish basketball II league
SD – standard deviation
1.00
0.09
0.01
0.12
0.04
–0.14
–0.06
–0.12
* Correlation is statistically significant at 0.05
132
1.000
0.151
0.558*
0.025
–0.024
–0.229*
1.000
0.341*
–0.067
–0.296*
–0.268*
1.000
0.144
–0.018
–0.201*
1.000
0.604*
0.415*
1.000
0.528*
Concentration
disruption
Task orientation
Ego orientation
Task climate
Ego climate
1.000
0.493*
0.467*
0.246*
–0.069
–0.153
–0.287*
Somatic anxiety
1.00
0.16
0.06
–0.11
0.05
–0.21*
–0.21*
–0.07
–0.09
Cognitive anxiety
Age
Team position
Ego climate
Task climate
Ego orientation
Task orientation
Cognitive anxiety
Somatic anxiety
Concentration disruption
Team position
Age
Table 2. Correlation analysis of a whole control group (Spearman Rank Order Correlations)
1.000
HUMAN MOVEMENT
P. Kuczek, Motivational orientation theory and sports
Ego orientation
Task orientation
1.000
0.356*
0.468*
0.055
–0.219*
–0.184
–0.326*
1.000
–0.024
0.443*
0.058
0.021
–0.330*
1.000
0.192
–0.130
–0.219*
–0.161
1.000
0.089
–0.065
–0.332*
1.000
0.742*
0.483*
1.000
0.541*
Concentration
disruption
Task climate
1.000
0.257*
–0.079
0.295*
0.040
–0.149
–0.124
–0.079
Somatic anxiety
Ego climate
1.000
0.203*
0.331*
0.129
0.132
–0.136
–0.317*
–0.373*
–0.270*
Cognitive anxiety
Team position
Age
Team position
Ego climate
Task climate
Ego orientation
Task orientation
Cognitive anxiety
Somatic anxiety
Concentration disruption
Age
Table 3. Correlation analysis for II league (Spearman Rank Order Correlations)
1.000
* Correlation is statistically significant at 0.05
1.000
0.159
–0.026
–0.192
Concentration
disruption
1.000
0.415*
–0.025
–0.391*
–0.384*
Somatic anxiety
1.000
0.236*
0.579*
–0.022
–0.081
–0.213*
Cognitive anxiety
1.000
0.586*
0.471*
0.353*
0.064
–0.127
–0.259*
Task orientation
Ego orientation
1.000
–0.035
0.042
–0.025
0.025
–0.144
–0.017
–0.168
Task climate
1.000
0.167
–0.121
–0.191
0.030
–0.188
–0.102
0.218*
0.086
Ego climate
Age
Team position
Ego climate
Task climate
Ego orientation
Task orientation
Cognitive anxiety
Somatic anxiety
Concentration disruption
Team position
Age
Table 4. Correlation analysis for Extraleague (Spearman Rank Order Correlations)
1.000
0.448*
0.339*
1.000
0.509*
1.000
* Correlation is statistically significant at 0.05
participants who completed the original English versions of the tests, the results of these players were subjected to only quantity analysis. Considerable differences
were found in the mean values of task climate, task orientation, and all of the anxiety components (Tab. 5). However, the limited amount of data collected from these
players prevented any conclusive statements from being
made. Nonetheless, the large differences among the
means of the studied parameters are definitely surprising. What is of particular interest is the fact that the
Americans who play in the Polish leagues and who had
been rated as among the best in their own teams by their
coaches showed considerably higher levels of task orientation. This is at odds with the results of the Polish players.
Although this issue lies outside the scope of the present
study, future research should attempt to explain this
difference.
Discussion
The results of the present study found that task climate and task orientation do not cause individuals from
experiencing less negative emotional states such as anxiety
or experiencing disruption in concentration. The positive impact of motivational orientation and the negative
impact of ego orientation on anxiety levels, the satisfaction derived from effort, choosing positive life strategies,
and the amount of effort put into completing tasks
have been verified in many studies, mainly in the field
of education [17]. Both theoretical and experimental
evidence also confirms the positive impact of a task climate and the negative impact of ego climate [6].
However, the results obtained in the present study
differed from these hypotheses and need to be explained.
The possibility and legitimacy of applying motivational
133
HUMAN MOVEMENT
P. Kuczek, Motivational orientation theory and sports
Table 5. Comparison of Polish and American players
Age
Ego climate
Task climate
Ego orientation
Task orientation
Cognitive anxiety
Somatic anxiety
Concentration disruption
Mean
EX league
Mean
Americans
p
N
EX league
N
Americans
SD
EX league
SD
Americans
25.80
3.68
4.05
3.88
4.33
2.01
1.78
1.72
28.10
4.21
4.61
3.96
4.92
1.35
1.16
1.16
0.1436
0.0577
0.0169
0.7338
0.0143
0,0016
0,0012
0.0021
84
81
81
84
84
82
82
81
10
9
9
10
10
10
10
10
4.66
0.76
0.67
0.70
0.74
0.62
0.58
0.55
4.75
0.88
0.38
0.96
0.14
0.42
0.26
0.26
EX league – players from the Polish basketball Extraleague,
Americans – American players from the Polish basketball Extraleague
SD – standard deviation
orientation in competitive sports has been under considerable criticism, including the use of practically all
research tools that are used in measuring these spheres
[18]. The literature on the subject is considerably more
indicative of the presence of higher anxiety or concentration disruptions in ego-oriented individuals or those
who perceive their climate as ego-related when compared
to task-oriented individuals or those who perceive their
climate as task-oriented. Such results are frequently found
in studies on young students or individuals participating
in youth sports, such as fencers (mean age 12.7 years)
[19], athletically active students [17], or athletes engaged
in various recreational sports (mean age 14.08 years) [6].
However, not all of these results confirmed the need of
developing and maintaining task-oriented goals for such
individuals. Instead, these results, similar to those in the
present study, did not show the preventive “positive”
influence of task orientation and task climate nor did
they display the supposed negative influence of ego orien­
tation and ego climate.
Newton and Duda [20] tested the relationship between motivational orientation and multi-dimensional
anxiety as well as expectations of success or failure. Studying a group of tennis players (mean age 20.2 years), these
researchers demonstrated that ego orientation negatively
correlated with self-confidence with the use of Martens’
Completive State Anxiety Inventory (CSAI-2) [21]. The
remaining anxiety components, including cognitive
anxiety, which is an important constituent from the
point of view of the final makeup of anxiety, did not
correlate with motivational orientation.
Hall et al. [22] examined neurotic and normal perfectionism in group of young 14-year-old runners.
They used the notion of motivational orientation by
checking its relationship with perfectionism as well as
with anxiety levels and self-confidence. The hypothesis
that “a dispositional task orientation would be a strong
predictor of confidence while a dispositional ego endorsement would predict cognitive anxiety” [22, p. 213] was
found to be without merit.
134
Similar results were found by Duda et al. [15] while
studying volleyball and basketball players (mean age
21.1 years) and tennis players (mean age 20.0 years). Task
orientation positively correlated with lower levels of
anxiety only among male volleyball players, while ego
orientation positively correlated only with the group
of tennis players. The remaining results were statistically insignificant, except for one unexpected result,
where female volleyball players with higher task motivational orientation claimed to feel higher levels of
anxiety, tension, and pressure than ego-oriented players.
The results obtained by ardent advocates and supporters of goal perspective theory, such as Newton and
Duda [23], are important in leading credence to the
results of present study, as they also did not find an anticipated correlation between multidimensional anxiety and motivational orientation. Martin and Gill [24]
carried out an examination on the relationship between
motivational orientation and self-confidence in mediumand long-distance runners aged 14–18 years. The results
did not confirm the belief that victory-oriented athletes
(which is a construct theoretically similar to ego orientation) had smaller values of self-confidence. On the
contrary, it was found that the runners who were more
oriented towards winning displayed greater self-confidence, although this correlation was insignificant. The
results obtained by these authors are consisted with those
presented in this study, where self-confidence, in line
with Martens et al.’s assumptions [21], is negatively correlated with cognitive anxiety, where the more self-confidence an individual has, the less cognitive anxiety.
One of the few experimental studies that showed
the influence of strong ego orientation and climate on
the frequency of dropping out of sports was found in
judokas, where those quitting the sport did not perceive
the climate as more ego-oriented than those who kept
up the sport [25]. This is important since task orientation and task climate were hypothesized as preventing
individuals from making such decisions too early in
their sports career.
HUMAN MOVEMENT
P. Kuczek, Motivational orientation theory and sports
Motivational orientation theory was originally developed in the field of education, hence the reason why
most empirical data on the subject is based on individuals at a young age. The findings of the present study as
well as assumptions made by other researchers demonstrated that the strength of motivational orientation
tends to decrease with age [3, 26]. In other words, what
may affect adolescent and child athletes does not necessarily imply the same for adults. Moreover, the strength
of various motivational correlations was indeed different depending on skill level of the participants, as was
seen with the “Concentration disruption” variable. In
the professional league (Extraleague), concentration disruption was negatively correlated with ego orientation,
whereas in the amateur league it was task orientation.
This further substantiates the belief that professional
sports operate under a completely different set of principles than amateur sports, and that results of research
one do not apply to the other.
Serious doubts as to the role of “task orientation”
and “ego orientation” have also been put forward by
Harwood and Swain [27]. In their opinion, the concept
of “motivational orientation” cannot be applied in the
same way to sports and to education, asserting that “the
overall message here is that we cannot afford to simply
assume that task and ego involvement mean exactly
the same thing in the sport domain as they do in the
education domain” [27, p. 245]. Harwood and Swain
found that a distinction between task and ego orientations in the realm of competitive sports to be totally
groundless. For example, an athlete who states that he
feels satisfied in sport if he really works hard does not
necessarily have to be task-oriented. Harwood and Swain
provided an example of an athlete who, on account of
his high ego and low task motivational orientation,
derived satisfaction from defeating others in competition, but who – in order to achieve this goal – had to
perform in a way that is characteristic of task orientation, including working hard, placing large emphasis
on effort, and the willingness to personally improve.
Harwood and Swain postulated on extending the two
orientations to include a third one. According to their
proposal, the currently existing notion of task orientation should pertain only to recreational sports, while
ego orientation should be defined either according to
one’s own standards or be based upon general standards and consist of comparing oneself to others.
The addition of one or even more orientation constituents has also been postulated by Elliot and Conroy [28]. They pointed out that ego orientation in itself
is not unhelpful. Instead, what is important is the recognition of whether an athlete is driven by the need to win
or by the need to avoid failure. The former was termed
performance-approach orientation, the latter performanceavoidance orientation. They suggested that task orientation should also give rise to two additional constituents,
mastery-approach orientation and mastery-avoidance
orientation. According to this theory, both orientations
are designed for achievement, regardless of whether it
is normative success or success gained from individual
progress. However, both orientations, guided by the
need to avoid failure or the loss of already acquired
skills, are also associated with negative expectations and
may therefore have an adverse effect on an athlete’s
emotional processes.
Hardy et al. [9] drew attention to the fact that task
and ego orientations are of an independent character,
stating that the same athlete can be simultaneously
strongly task-oriented and ego-oriented. Despite being
confirmed in another study [8], this issue has rarely
appeared in the literature on the subject. While comparisons between athletes with high task and high ego
orientations are frequent, no comparative studies on
athletes with high ego and low task orientation or high
task and low ego orientation have been noted. Following
Hardy et al.’s description [9], comparisons between task
orientation and ego orientation are said to be similar to
choosing between an apple and an orange and, therefore, of low theoretical value. After all, both motivations
can bring about the desired effects. Many studies credit
effective athletes with taking advantage of all possible
motivational attitudes in order to raise their motivation
and improve efficiency. When it comes to competitive
sports, Hardy et al. [9] object to regarding task orientation as more desirable or better. They assert that “ego
orientations are often denigrated by goal orientation
researchers, a position that is certainly not in accordance
with the view received from coaches and performers
that: «you don’t get to be a world champion by not
wanting to beat other individuals». Indeed, if goals
really do motivate one’s behavior (…), then it is difficult
to see how one could become a genuinely elite performer
without having a strong ego orientation” [9, p. 78].
In a similar vein, Weinberg et al. [10] also spoke on
this issue in a discussion on attitude and the choice of
goals: “In essence, we have recently begun to question
whether sport’s pervasive preoccupation with winning
may actually be responsible for many athletes’ anxiety,
motivation and self-confidence problems. It is not that
winning is unimportant; it is just that, for many athletes
and coaches, it has seemingly become the only goal worth
pursuing” [10, p. 284]. Weinberg et al. also questioned
the need for changing motivational orientation from
being ego-oriented to task-oriented, as Ames [5] had
promoted, although in the field of education. Weinberg et al. [10] claimed that “(…) in a sports environment,
athletes may not need to change their focus. Rather,
for some athletes, a winning orientation might produce
the best performance and greatest persistence. As noted
earlier, it may be the interaction of winning, performance, and fun orientations that is critical, rather than
simply saying that an athlete who has a winning orientation should change to performance orientation.”
[10, p. 285]
135
HUMAN MOVEMENT
P. Kuczek, Motivational orientation theory and sports
Many studies and theoretical constructs have emphasized the correlation between motivational climate
and motivational orientation [29, 30]. The results obtained in the present study also confirm this relationship. It is by no means certain, though, whether motivational climate affects orientation or whether it is the
other way round; that is, motivational orientation affects the perception of motivational climate [8, p. 46]. For
Nicholls [3], climate has an objective value, for Ames [5]
it maintains a subjective one, signifying that in this case
what is more important is what impressions an individual has rather than an analysis of the actual climate they perform in. Ames labeled this as “perceived
motivational climate”, emphasizing that the perception
of the climate in which an individual acts depends on
his/her attitudes and expectations..
The classical theory of motivational climate’s influence upon orientation believes that every athlete performs better in a task climate environment, and it also
has a positive effect on the development of an individual’s internal, or autonomous, motivation. This belief
has been confirmed by the results of one study [30]. However, according to matching theory, it is more important
for athletes to perform in a climate that is compatible
with their orientation than for the climate to be taskoriented. If a student is task-oriented, they will find
better opportunity to be satisfied when working in a similar climate. If, however, the same individual is placed in
an ego-oriented climate, then the goals stipulated in such
an environment are dissimilar to their own and can
have the student feel that their autonomy is threatened.
It is not the very process of the ego climate but the incompatibility with a student’s attitude that poses a problem [32, 33]. Since the present study did not demonstrate the beneficial influence of task attitude or a task
climate per se, the statement that it is more beneficial
or desirable cannot be confirmed.
The question stands whether this implies that coaches
and instructors should forgo developing and maintaining a higher proportion of task attitude among athletes.
The literature on the subject leads to the conclusion that
in the course of training youth, coaches should first
maintain task orientation and a task climate and only
with the passage of time augment other motivational
orientations, e.g. ego orientation. A more mature athlete,
engaged in competitive sports, must realize that fans
and sponsors expect him/her to win, and lead to the realization by both the coach and athlete to expect the
same. This also introduces the idea that coaches could
introduce slightly different training methods at various
times in the training process. A task motivational climate
may be advisable in the off-season so as to help the
athlete with the arduous training process and, as the
season approaches, switch to a more personal, egooriented form of motivation. The justification of this
method requires more detailed analysis and should be
taken under consideration in future research.
136
Conclusions
The psychometric tests translated into Polish were
found to be reliable. The obtained results confirm the
doubts of certain researchers on the application of
achievement goal theory in competitive sports. Neither
task orientation nor task climate was found to significantly lower the level of sports anxiety among basketball players regardless of their performance level. In
addition, the hypothesis that high ego orientation in
athletes is reflected by a poorer emotional state was not
confirmed.
References
1. Lepper M.R., Greene D., Nisbett R.E., Undermining
children`s intrinsic interest with extrinsic rewards: A test
of „overjustification” hypothesis. J Pers Soc Psychol, 1973,
28 (1), 129–137, doi: 10.1037/h0035519.
2. Ryan R.M., Control and information in the intrapersonal
sphere: An extension of cognitive evaluation theory. J Pers
Soc Psychol, 1982, 43 (3), 450–461, doi: 10.1037/00223514.43.3.450.
3. Nicholls J.G., Achievement motivation: Conception of
ability, subjective experience, task choice, and performance. Psychol Rev, 1984, 91 (3), 328–346, doi: 10.1037/
0033-295X.91.3.328.
4. Dweck C.S., Leggett E.L., A social-cognitive approach to
motivation and personality. Psychol Rev, 1988, 95 (2),
256–273, doi: 10.1037/0033-295X.95.2.256.
5. Ames C., Classrooms: Goals, structures, and student motivation. J Educ Psychol, 1992, 84 (3), 261–271, doi: 10.1037/
0022-0663.84.3.261.
6. 6. Vazou S., Ntoumanis N., Duda J.L., Predicting young
athletes’ motivational indices as a function of their perceptions of the coach and peer-created climate. Psychol
Sport Exerc, 2006, 7 (2), 215–233, doi: 10.1016/j.psychsport.2005.08.007.
7. Duda J.L., Nicholls J.G, Dimensions of achievement motivation in schoolwork and sport. J Educ Psychol, 1992,
84 (3), 290–299, doi: 10.1037/0022-0663.84.3.290.
8. Roberts G.W., Understanding the dynamics of motivation in physical activity: The influence of achievement
goals on motivational processes. In: Roberts. G.W. (ed.),
Advances in Motivation in Sport and Exercise. Human
Kinetics, Champaign 2001, 1–50.
9. Hardy L., Jones G., Gould D., Understanding Psycholo­
gical Preparation for Sport. Theory and Practice of Elite
Performers. Wiley, Chichester 1996.
10. Weinberg R.S., Burton D., Yukelson D., Weigand D.A.,
Goal setting in competitive sport: An exploratory inves­
tigation of practices of collegiate athletes. Sport Psychol,
1993, 7 (3), 275–289.
11. Escarti A., Roberts G.C., Cervello E.M., Guzman J.F.,
Adolescent goal orientations and the perception of criteria of success used by significant others. Int J Sport
Psychol, 1999, 30 (3), 309–324.
12. Smith R.E., Smoll F.L., Schulz R.W., Measurement and
correlates of sport-specific cognitive and somatictrait
anxiety: The sport anxiety scale. Anxiety Res, 1990, 2,
263–280.
13. Roberts G.C., Treasure D.C., Balague G., Achievement goals
in sport: The development and validation of the Perception
HUMAN MOVEMENT
P. Kuczek, Motivational orientation theory and sports
of Success Questionnaire. J Sports Sci, 1998, 16 (4), 337–347,
doi: 10.1080/02640419808559362.
14. Kavussanu M., Roberts G.C., Motivation in physical
activity contexts: The relationship of perceived motivational climate to intrinsic motivation and self-efficacy.
J Sport Exerc Psychol, 1996, 18 (3), 264–280.
15. Duda J.L., Chi L., Newton M.L., Walling M.D., Catley D.,
Task and ego orientation and intrinsic motivation in sport.
Int J Sport Psychol, 1995, 26 (1), 40–63.
16. White S.A., Zellmer S.R., The Relationship Between
Goal Orientation, Beliefs About the Sport Success, and
Trait Anxiety Among High School, Intercollegiate, and
Recreational Sport Participants. Sport Psychol, 1996, 10 (1),
58–72.
17. Sarrazin P., Roberts G., Cury F., Biddle S., Famose J-P.,
Exerted effort and performance in climbing among boys:
The influence of achievement goals, perceived ability, and
task difficulty. Res Q Exerc Sport, 2002, 73 (4), 425–436,
doi: 10.1080/02701367.2002.10609042.
18. Fogarty G.J., Tenenbaum G., Norrow K., Psychometric
Evaluation of Goal Orientation Measures in Sport. In:
Katsikitis M. (eds.), Proceedings of the 2006 Joint Conference of the APS and NZPsS. Auckland, 26–30 September, 2006, 120–124.
19. Hall. H.K., Kerr A.W., Motivational antecedents of precompetitive anxiety in youth sport. Sport Psychol, 1997, 11 (1),
24–42.
20. Newton M.L., Duda J.L., The interaction of motivational climate, dispositional goal orientation, and perceived
ability in predicting indices of motivation. Int J Sport
Psychol, 1999, 30, 63–82.
21. Martens R., Vealey R.S., Burton D., Competitive Anxiety in Sport. Human Kinetics, Champaign 1990.
22. Hall H.K., Kerr W.A., Matthews J., Precompetitive anxiety
in sport: The contribution of achievement goals and perfectionism. J Sport Exerc Psychol, 1998, 20 (2), 194–217.
23. Newton M.L., Duda J.L., Relations of goal orientations
and expectations on multidimensional state anxiety. Percept Mot Skills, 1995, 81 (3f), 1107–1112, doi: 10.2466/
pms.1995.81.3f.1107.
24. Martin J.J., Gill D.L., The relationship among competitive orientation, sport-confidence, self-efficacy, anxiety, and
performance. J Sport Exerc Psychol, 1991, 13 (2), 149–159.
25. Le Bars H., Gernigon C., Ninot G., Personal and contextual determinants of elite young athletes’ persistence or
dropping out over time. Scand J Med Sci Sports, 2009,
19 (2), 274–285, doi: 10.1111/j.1600-0838.2008.00786.x.
26. Chin N.S., Khoo S., Low W.Y, Sex, age group and locality
differences in adolescent athletes’ beliefs, values and goal
orientation in track and field. J Exerc Sci Fitness, 2009,
7 (2), 112–121, doi: 10.1016/S1728-869X(09)60014-9.
27. Harwood C.G., Swain A., Achievement goals in sport:
A critique of conceptual and measurement issues. J Sport
Exerc Psychol, 2000, 22 (3), 235–255.
28. Elliot A.J., Conroy D.E., Beyond the dichotomous model
of achievement goals in sport and exercise psychology.
Sport Exerc Psychol Rev, 2005, 1 (1), 17–26.
29. Ntoumanis N., Biddle S.J.H., Haddock G., The mediating role of coping strategies on the relationship between
achievement motivation and affect in sport. Anxiety
Stress Coping, 1999, 12 (3), 299–327, doi: 10.1080/1061580
9908250480.
30. Moreno J.A., Gimeno E.C., Coll D., Relationships among
goal orientations, motivational climate and flow in adolescent athletes: Differences by gender. Span J Psychol,
2008, 11, 181–191.
31. Brunel P.C., Relationship between achievement goal
orientations and perceived motivational climate on intrinsic motivation. Scand J Med Sci Sports, 1999, 9 (6),
365–374, doi: 10.1111/j.1600-0838.1999.tb00258.x.
32. Ntoumanis N., Biddle S., The Relationship Between Competitive Anxiety, Achievement Goals, and Motivational
Climates. Res Q Exerc Sport, 1998, 69 (2), 176–187, doi:
10.1080/02701367.1998.10607682.
33. Duda J.L., Achievement goal research in sport: Pushing
the boundaries and clarifying some misunderstanding.
In: Roberts G.W. (ed.), Advances in motivation in sport
and Exercise. Human Kinetics, Champaign 2001, 129–182.
Paper received by the Editors: January 31, 2012
Paper accepted for publication: April 30, 2013
Correspondence address
Piotr Kuczek
Zakład Wychowania Fizycznego
Państwowa Wyższa Szkoła Zawodowa
ul. Mickiewicza 8
33-100 Tarnów, Poland
e-mail: [email protected]
137
HUMAN MOVEMENT
2013, vol. 14 (2), 138– 143
GAIT ASYMMETRY DURING DUAL-TASK OBSTACLE CROSSING
IN THE YOUNG AND ELDERLY
doi: 10.2478/humo-2013-0016
Emmanuel S. da Rocha 1, Álvaro S. Machado 1, Pedro S. Franco 1,
Eliane C. Guadagnin 2 , Felipe P. Carpes 1, 2 *
1
2
Applied Neuromechanics Group, Laboratory of Neuromechanics, Federal University of Pampa, Uruguaiana, Brazil
Laboratory of Biomechanics, Federal University of Santa Maria, Santa Maria, Brazil
Abstract
Purpose. To evaluate gait asymmetry during obstacle crossing by young and elderly adults performing normal and dual-task
gait. Methods. Ten healthy young adults and ten elderly adults with mild cognitive impairment performed a gait protocol by
stepping over a foam obstacle during normal gait and while performing a secondary task (Stroop task). Sagittal kinematics of
the lead and trail limbs were analyzed. Statistical procedures involved analysis of variance and t tests at a significance of 0.05.
Results. Many of the kinematic variables presented a main effect for group (young adults vs. elderly adults), where the elderly
featured poorer gait performance. It was observed that gait velocity during obstacle crossing in normal and dual-task gait was
similar between the preferred and non-preferred limbs in both the young and elderly. However, the elderly were slower during
normal and dual-task gait. A main effect for the dual-task condition was observed. Kinematic asymmetries for obstacle crossing
were more frequent in the elderly and especially during the dual-task condition. Conclusions. The results suggest that the elderly may
require more compensatory adjustments after crossing an obstacle. The asymmetries observed among the elderly may contribute
to higher risk of falling during perturbed gait.
Key words: obstacle negotiation, ageing, functional lateralization, walking, secondary task, perturbed gait
Introduction
The difficulties that arise when stepping over an obstacle have been used to study locomotion in the elderly [1]. Previous studies have suggested that obstacles
encountered on a walking pathway may increase the risk
for tripping or slipping in the elderly [2, 3], as they require the adaptation of new gait strategies [4], more
precise swing control, and higher levels of inter-joint
coordination [5]. Additionally, protocols that combine
gait with a secondary task were used to address the relationship between cognitive performance and gait ability
in a context more similar to that experienced in daily
life. When submitted to a dual-task condition, the elderly
presented poorer gait performance than younger subjects [1]. Additionally, the negative effects of a secondary
task are more pronounced in the elderly with dementia [6], even in cases with mild cognitive impairment [7].
Assessing gait when stepping over obstacles in a dualtask situation has been suggested as a potential tool for
screening fall risk in the elderly [8]. However, there is
very little research addressing the presence of gait asymmetry when elderly engage in dual-task walking.
“Split-belt” experiments have suggested that gait
asymmetries lead to more failures in obstacle avoidance
and require more attention to be paid by the elderly [9].
The limitation in performing executive functions during
* Corresponding author.
138
walking may increase asymmetry, which could affect
dynamic stability [10]. As the elderly require adequate
time to adapt foot placement strategies in relation to
the obstacle as to avoid contact, dual-task gait would
have similar effects on time constraints and might put
the elderly in greater risk of contacting the obstacle [2].
Moreover, the elderly with strength asymmetry have
greater gait asymmetries and gait variability than those
without strength asymmetry [11].
The aim of this study was to address the presence
of gait asymmetry in the elderly during obstacle crossing in normal and dual-task gait conditions and compare
them with a population of young adults. It was believed
that kinematic asymmetries during obstacle crossing
in dual-task conditions would be more frequently observed in the elderly. This may suggest that while asymmetry may be part of regular gait performance, its impact on the risk of falling might differ between young
and elderly subjects.
Material and methods
Subjects
Ten adult university students (six males, four females),
aged 24.1 ± 3.6 years with a height of 1.69 ± 0.12 m
and body mass of 68 ± 14.5 kg, and ten elderly community-dwelling volunteers (four males, six females), aged
74.4 ± 5.2 years with a height of 1.57 ± 0.05 m and body
mass of 66 ± 9 kg, participated in our study. Subjects
HUMAN MOVEMENT
E.S. da Rocha et al., Gait asymmetries during obstacle crossing
with neurological diseases (e.g., Parkinson’s or Huntington’s disease or who had suffered a stroke), vestibular
or visual problems (clinically detectable), or with lower
limb prosthesis were excluded. All subjects signed
a written informed consent form approved by the local ethics committee (IRB no. #0102011).
Neuropsychological and mobility assessment
Cognitive status was assessed using the Mini-mental
State Examination (MMSE) [12]. Mental status was
assessed using the Geriatric Depression Scale [13]. The
Instrumental Activities of Daily Living Scale (IADL)
[14] was used to quantity the independence of the subjects. Leg preference was verified using the “Waterloo
inventory” [15].
Gait assessment
Participants performed a gait protocol with obstacles that had been previously used in other studies [3].
They performed the task while wearing their habitual
shoes (flat, rubber-soled walking shoes) and clothes at
self-selected comfortable speed along a 6 m walkway
that had a foam obstacle positioned at the halfway point
(height 15 cm, width 34 cm, length 11 cm). A familiarization trial with and without the obstacle was permitted.
Two valid trials were considered for each condition.
Movement was recorded in the sagittal plane with a digital video camera (S2000HD, FUJI, Japan) at a resolution
of 12.2 megapixels using a 1/2.3 inch CCD sensor, with
the video later analyzed at 60 Hz. Spherical reflexive
markers were placed as anatomical references at the hallux, the fifth metatarsal head, and calcaneous tuberosity
on both feet. A motion analysis tool (SkillSpector ver.
1.2.4, Video4coach, Denmark) was used to digitalize and
track the markers providing position data. The camera
was calibrated using a calibration frame with known dimensions. Estimated tracking error was 4 mm, therefore gait motion was filmed in both directions, with
the right limb and the left limb alternating in front of the
camera, in order to minimize the influence of recording
error. Toe clearance was calculated as the vertical dis-
tance between the toe marker and the obstacle when the
hallux marker of the swing limb was just crossing above
the obstacle [16]. A Butterworth filter with a low-pass
cut-off frequency of 6 Hz was applied to filter the data.
The kinematics parameters monitored were: pre-obstacle trail limb step length, pre-obstacle trail limb distance, lead limb toe clearance, post-obstacle lead limb
distance, lead limb stride length, and trail limb toe clearance (Fig. 1). Step variables were normalized for each
subject’s height and average velo­city was computed for
each trial.
The participants were requested to step over the obstacle during normal gait and while performing a variation of the Stroop task. In the dual-task condition, subjects were asked to verbally reply “yes” when the examiner
said “blue” and “no” when the examiner said “red”. For
any other color that was named, the participants had
to repeat the color (for example, if the examiner said
“yellow”, subject repeated “yellow”). For a trial to be
considered valid, the dual-task condition was to be performed without stopping. Two valid trials were considered for analysis.
Statistical analysis
Data normality was verified using the Shapiro-Wilk
test. Statistical comparisons were performed using analysis of variance in a 2 × 2 × 2 mixed model by analyzing
group (elderly and adult), leg (preferred and non-preferred), and condition (normal and dual-task gait) with
Bonferroni corrections for multiple comparisons. When
main effects or interactions were observed, kinematic
data were compared by applying paired t tests. Non-parametric data were compared using the Wilcoxon test.
The level of significance was set at 0.05.
Results
Neuropsychological and mobility
The cognitive status of the young adults, assessed
using the MMSE, was higher than 26 points indicating
cognitively normal status. The elderly were found to
(1) pre-obstacle trail limb step length (4) lead limb stride length
(2) pre-obstacle trail limb distance (5) trail limb toe clearance
(3) lead limb toe clearance
(6) post-obstacle lead limb distance; black arrow denotes the movement direction
Figure 1. Lower limb movements during the obstacle crossing task, where LL represents the lead limb
(the first limb to step over the obstacle) and TL the trail limb (the contralateral limb to step over the obstacle)
139
HUMAN MOVEMENT
E.S. da Rocha et al., Gait asymmetries during obstacle crossing
was observed for dual-task gait performance while crossing the obstacle with the preferred [Z = –2.524; p = 0.012]
and non-preferred limbs [Z = –2.193; p = 0.028]. Gait
velocity was similar when crossing the obstacle with the
preferred or non-preferred limb.
An effect for group in pre-obstacle trail limb step
length, post-obstacle lead limb distance, and lead limb
stride length was found, which were lower in the elderly
(Fig. 2). An effect for leg was observed in pre-obstacle
trail limb distance (greater in the preferred leg for both
groups) and post-obstacle lead limb distance (lower in
the preferred leg for both groups). An effect for condition was observed in both groups for pre-obstacle trail
limb step length and pre-obstacle trail limb distance,
which was greater in the dual-task condition. Significant interactions were observed between group and leg
for trail limb toe clearance, group and condition interaction for post-obstacle lead limb distance, and leg
and condition interaction for lead limb toe clearance.
The corresponding F and p values are presented in Tables 1 and 2.
While symmetry was observed in normal gait, the
dual-task condition elicited greater pre-obstacle trail
limb step length in the preferred leg in the elderly [t(9)
= –4.212; p = 0.002] and in the non-preferred leg in
the young adults [t(9) = –2.659; p = 0.026].
Pre-obstacle trail limb distance was found to be asymmetric among the young adults during normal gait
[t(9) = 3.22; p = 0.01] and for the elderly in the dual-
present mild cognitive impairment as based on MMSE
scores between 20 and 26 points [15, 16]. The results
from the Geriatric Depression Scale excluded the risk
of depression, and all subjects were classified as independent in regard to daily life tasks.
Gait analysis
When performing normal gait during obstacle crossing with the non-preferred limb, average gait velocity
was 0.91 ± 0.26 m/s for the elderly and 1.11 ± 0.13 m/s
for the young adults. When comparing the groups, it was
observed that gait velocity when crossing the obstacle
during normal [Z = –0.105; p = 0.917] and dual-task
gait [Z = –1.402; p = 0.161] was similar between the
preferred and non-preferred limbs among the young
adults. The same was observed for the elderly in normal
[Z = –0.663; p = 0.508] and dual-task gait [Z = –1.126;
p = 0.260]. During the dual-task condition while crossing an obstacle with the preferred limb, average velocity
was 0.71 ± 0.15 m/s for the elderly and 0.97 ± 0.16 m/s
for the young adults. When performing the dual-task
condition while crossing an obstacle with the non-preferred limb, average gait velocity was 0.78 ± 0.26 m/s for
the elderly and 1.04 ± 0.16 m/s for the young adults.
The elderly were slower than the young adults during
normal gait performance under obstacle-crossing conditions with the preferred [Z = –2.016; p = 0.044] and
non-preferred limbs [Z = –2.191; p = 0.028]. The same
Table 1. Statistical outcomes considering the effects of group, leg, and condition by analysis of variance (F and p values)
Group
Variable
Pre-obstacle trail limb step length
Pre-obstacle trail limb distance
Lead limb toe clearance
Post-obstacle lead limb distance
Lead limb stride length
Trail limb toe clearance
Leg
Condition
F
p
F
p
F
P
6.45
2.59
1.81
47.14
9.68
3.76
0.032*
0.14
0.21
> 0.001*
0.01*
0.08
0.09
17.75
0.79
10.24
4.64
1.95
0.76
0.002*
0.39
0.011*
0.06
0.19
6.05
5.85
3.10
0.08
0.29
0.21
0.03*
0.03*
0.11
0.77
0.60
0.65
* statistically significant at p < 0.05
Table 2. Statistical outcomes considering the interactions of group × leg; group × condition; leg × condition;
and group × leg × condition (F and p values)
Variable
Group × leg
F
Pre-obstacle trail limb step length
Pre-obstacle trail limb distance
Lead limb toe clearance
Post-obstacle lead limb distance
Lead limb stride length
Trail limb toe clearance
* statistically significant at p < 0.05
140
1.42
0.07
0.003
0.21
3.91
15.73
p
0.26
0.79
0.96
0.65
0.07
0.003*
Group × condition
Leg × condition
Group × leg
× condition
F
p
F
p
F
p
0.56
0.002
3.12
10.1
2.18
1.55
0.47
0.96
0.11
0.01*
0.17
0.24
0.09
0.41
14.31
0.001
0.35
0.25
0.76
0.53
0.004*
0.97
0.56
0.62
2.74
3.44
0.98
0.06
0.04
0.19
0.13
0.09*
0.34
0.80
0.83
0.67
HUMAN MOVEMENT
E.S. da Rocha et al., Gait asymmetries during obstacle crossing
Values normalized for subjects’
height except for toe clearance
N – normal gait
DT – dual-task condition
* indicates statistically significant
asymmetry (p < 0.05)
# indicates a difference between
the groups (p < 0.05)
† indicates differences between
normal and dual-task gait (p < 0.05)
Figure 2. Kinematic data for lead and trail limbs, where the white bars represent data from the preferred leg (P)
and black bars represent data from the non-preferred leg (NP)
task condition [t(9) = 3.42; p = 0.008]. For both groups, the
values were greater in the preferred leg. In the dualtask condition, the elderly increased the pre-obstacle trail
limb distance of the preferred leg [t(9) = –3.174; p = 0.011],
although this was not observed in the young adults.
The elderly presented asymmetry in the post-obstacle
lead limb distance in both normal [t(9) = –4.26; p = 0.002]
and dual-task conditions [t(9) = –3.51; p = 0.007]. Moreover, a condition effect was observed for this variable
in the elderly group, with lower values in the dual-task
condition than during normal gait for both the preferred [t(9) = 2.93; p = 0.017] and non-preferred legs
[t(9) = 3.47; p = 0.007]. Increased lead limb toe clearance
was observed in the elderly performing the dual-task
condition with the non-preferred limb when compared
with the young adults [t(9) = –2.803; p = 0.021], but no
asymmetry was detected.
The elderly in the dual-task condition presented asymmetry in trail limb toe clearance, which was greater
with the preferred leg [t(9) = 2.39; p = 0.04]. Despite
this asymmetry, during the dual-task condition there
were no significant changes in the magnitude of trail
limb toe clearance in the elderly during the normal
and dual-tasking conditions (Fig. 2).
Discussion
Dual-task conditions are often observed in daily life,
such as when walking while talking to other individuals.
When crossing obstacles, the elderly are known to require
141
HUMAN MOVEMENT
E.S. da Rocha et al., Gait asymmetries during obstacle crossing
a longer period of time for stepping over an obstacle [17].
Previous studies on the elderly have suggested an increased risk of tripping or falling when attention is divided during walking in the presence of expected [18, 19]
or unexpected obstacles [1]. However, little attention
has been paid to the issue of asymmetry in locomotion.
Here, the asymmetry in the kinematics of gait over
obstacles during normal and dual-task conditions for
young adults and the elderly were studied. In general,
our data suggest that gait during obstacle-crossing
situations in a dual-task condition elicits asymmetries
primarily in the elderly. Additionally, the results on
the elderly suggest that the non-preferred leg seems to be
more affected in a dual-task condition than the preferred leg.
Lower extremity asymmetries may increase the risk
of falling in the elderly, as had been suggested in a study
about asymmetry in leg extension strength and power
in individuals with and without a history of falls [20, 21].
Additionally, lower extremity strength asymmetry was
recently correlated with gait asymmetry and variability
in the elderly, which was reinforced when they were
performing near maximal capability [11].
The findings of our study demonstrate that the obstacle-approaching phase differs between the young and
elderly. The elderly presented asymmetry in the trail
limb kinematics with effects found for both groups and
the dual-task condition. The differences, asymmetries,
and effects of a dual-task condition in the studied groups
suggest that the elderly may require more compensatory adjustment after crossing an obstacle, since the trail
limb presented effects for both groups and the dual-task
condition, with asymmetric toe clearance in the dualtask condition.
Based on these observations, it can be assumed that
the elderly may prefer a more consistent gait pattern by
shortening step and stride length [22]. However, this
did not result in them producing similar movements for
the preferred and non-preferred leg. The asymmetry
presented by the elderly during obstacle crossing is consistent with the asymmetric propulsion pattern found
in the muscle moments and power patterns of the elderly [23].
In the group of elderly, asymmetry was observed in
the toe clearance of the trail limb but not in the lead
limb. This suggests that the elderly may place greater
attention on the first limb when crossing an obstacle,
but feature impaired control of gait symmetry when
the trail limb crosses. Asymmetry in foot clearance was
observed among the elderly with a high risk of falling
[10] and may reflect functional asymmetries that are
needed for gait stability [24]. Indeed, most of studies on
gait asymmetry in the elderly considered the measure
of toe or foot clearance [25]. The influence of the dualtask condition on lead limb toe clearance may suggest
that the elderly place more attention on the lead limb
as it is the first leg to cross an obstacle, but then pay
less attention during the second crossing with the trail
142
limb, which was noted to feature significant asymmetry
in the dual-task condition.
When considering the foot-ground clearance presented by older adults, a recent study reported asymmetries regardless of whether they performed treadmill
and overground walking [25]. In both cases, minimum
toe clearance was greater with the non-preferred foot.
The authors suggested that the non-preferred limb works
primarily in the stabilization of gait [25]. Our data on
step and stride kinematics suggest that obstacle crossing may induce significant asymmetries in the elderly.
Even considering this study’s small sample size, this
could generally indicate that gait performed over obstacles may rely on specific responses depending on which
leg is first used by the elderly.
The protocol presented here suggests that a bilateral
assessment of gait during obstacle crossing requires further attention. One of the limitations of this study was
the use of a single camera for recording movement,
although it may be useful for clinics and hospitals when
addressing elderly mobility without the possibility of
using several cameras simultaneously.
Conclusions
The results suggest that the elderly feature kinematic
asymmetries during obstacle crossing especially in a dualtask condition. The group differences, asymmetries, and
effects of dual-task gait suggest that the elderly may use
additional compensatory adjustment after crossing an
obstacle.
Acknowledgments
This study was funded by the Brazilian National Counsel of
Technological and Scientific Development (CNPq) by grant
No. 476163/2010-2. The authors would like to thank Dr. Jacques
Duysens for his comments in preparing the manuscript.
References
1. Hegeman J., Weerdesteyn V., van den Bemt B., Nienhuis B., van Limbeek J., Duysens J., Dual-tasking interferes with obstacle avoidance reactions in healthy seniors.
Gait Posture, 2012, 36 (2), 236–240, doi: 10.1016/j.gaitpost.2012.02.024.
2. Galna B., Peters A., Murphy A.T., Morris M.E., Obstacle
crossing deficits in older adults: A systematic review. Gait
Posture, 2009, 30 (3), 270–275, doi: 10.1016/j.gaitpost.
2009.05.022.
3. Chen H.L., Lu T.W., Wanga T.M., Huang S.C., Biomechanical strategies for successful obstacle crossing with the
trailing limb in older adults with medial compartment
knee osteoarthritis. J Biomech, 2008, 41 (4), 753–761, doi:
10.1016/j.jbiomech.2007.11.017.
4. Zhang C., Mao D., Riskowski J.L., Song Q., Strategies
of stepping over obstacles: The effects of long-term exercise in older adults. Gait Posture, 2011, 34 (2), 191–196,
doi: 10.1016/j.gaitpost.2011.04.008.
5. Wang T.M., Yen H.C., Lu T.W., Chen H.L., Chang C.F.,
Liu Y.H. et al., Bilateral knee osteoarthritis does not
affect inter-joint coordination in older adults with gait
HUMAN MOVEMENT
E.S. da Rocha et al., Gait asymmetries during obstacle crossing
deviations during obstacle-crossing. J Biomech, 2009, 42
(14), 2349–2356. doi: 10.1016/j.jbiomech.2009.06.029.
6. Beauchet O., Allali G., Berrut G., Hommet C., Dubost
V., Assal F., Gait analysis in demented subjects: Interests and perspectives. Neuropsychiatr Dis Treat, 2008, 4
(1), 155–160.
7. Ijmker T., Lamoth C.J.C., Gait and cognition: The relationship between gait stability and variability with executive function in persons with and without dementia.
Gait Posture, 2012, 35 (1), 126–130, doi: 10.1016/j.gaitpost.2011.08.022.
8. Beauchet O., Annweiler C., Dubost V., Allali G., Kressig R.W., Bridenbaugh S. et al., Stops walking when talking: a predictor of falls in older adults? Eur J Neurol, 2009,
16 (7), 786–795, doi: 10.1111/j.1468-1331.2009.02612.x.
9. McFadyen B.J., Hegeman J., Duysens J., Dual task effects
for asymmetric stepping on a split-belt treadmill. Gait
Posture, 2009, 30 (3), 340–344, doi: 10.1016/j.gaitpost.
2009.06.004.
10. Di Fabio R.P., Kurszewski W.M., Jorgenson E.E., Kunz R.C.,
Footlift asymmetry during obstacle avoidance in highrisk elderly. J Am Geriatr Soc, 2004, 52 (12), 2088–2093,
doi: 10.1111/j.1532-5415.2004.52569.x.
11. Laroche D.P., Cook S.B., Mackala K., Strength asymmetry increases gait Asymmetry and variability in older
women. Med Sci Sports Exerc, 2012, 44 (11), 2172–2181,
doi: 10.1249/MSS.0b013e31825e1d31.
12. Folstein M.F., Folstein S.E., McHugh P.R., “Mini-mental
state”. A practical method for grading the cognitive state
of patients for the clinician. J Psychiatr Res, 1975, 12 (3),
189–198, doi: 10.1016/0022-3956(75)90026-6.
13. Yesavage J.A., Brink T.L., Rose T.L., Lum O., Huang V.,
Adey M. et al., Development and validation of a geriatric
depression screening scale: a preliminary report. J Psychiatr Res, 1982, 17 (1), 37–49, doi: 10.1016/0022-3956
(82)90033-4.
14. Lawton M.P., Brody E.M., Assessment of older people:
self-maintaining and instrumental activities of daily
living. Gerontologist, 1969, 9 (3 Part 1), 179–186, doi: 10.
1093/geront/9.3_Part_1.179.
15. Elias L.J., Bryden M.P., Bulman-Fleming M.B., Footedness is a better predictor than is handedness of emotional lateralization. Neuropsychologia, 1998, 36 (1), 37–43,
doi: 10.1016/S0028-3932(97)00107-3.
16. Liu M.W., Hsu W.C., Lu T.W., Chen H.L., Liu H.C., Patients
with type II diabetes mellitus display reduced toe-obstacle clearance with altered gait patterns during obstaclecrossing. Gait Posture, 2010, 31, 93–99, doi: 10.1016/j.
gaitpost.2009.09.005.
17. Uchiyama M., Demura, S., Sugiura, H. The mobility performance of the elderly before, during and after cross-
ing over an obstacle. Hum Mov, 2012, 13 (4), 297–302,
doi: 10.2478/v10038-012-0034-1.
18. Muir S.W., Speechley M., Wells J., Borrie M., Gopaul K.,
Montero-Odasso M., Gait assessment in mild cognitive
impairment and Alzheimer’s disease: The effect of dualtask challenges across the cognitive spectrum. Gait Posture,
2012, 35 (1), 96–100, doi: 10.1016/j.gaitpost.2011.08.014.
19. Harley C., Wilkie R.M., Wann J.P., Stepping over obstacles: Attention demands and aging. Gait Posture, 2009,
29 (3), 428–432, doi: 10.1016/j.gaitpost.2008.10.063.
20. Perry M.C., Carville S.F., Smith I.C., Rutherford O.M.,
Newham D.J., Strength, power output and symmetry of
leg muscles: effect of age and history of falling. Eur J Appl
Physiol, 2007, 100 (5), 553–561, doi: 10.1007/s00421006-0247-0.
21. Skelton D.A., Kennedy J., Rutherford O.M., Explosive
power and asymmetry in leg muscle function in frequent fallers and non-fallers aged over 65. Age Ageing,
2002, 31 (2), 119–125, doi: 10.1093/ageing/31.2.119.
22. Chen H.C ., Ashton-M iller J.A., A lexander N.B.,
Schultz A.B., Stepping over obstacles: gait patterns of
healthy young and old adults. J Gerontol, 1991, 46 (6),
M196–203, doi: 10.1093/geronj/46.6.M196.
23. Sadeghi H., Prince F., Zabjek K.F., Labelle H., Simultaneous, bilateral, and three-dimensional gait analysis of
elderly people without impairments. Am J Phys Med Rehabil,
2004, 83 (2), 112–123, doi: 10.1097/01.PHM.0000107484.
41639.2C.
24. Nagano H., Begg R.K., Sparrow W.A., Taylor S., Ageing
and limb dominance effects on foot-ground clearance
during treadmill and overground walking. Clin Biomech
(Bristol, Avon), 2011, 26 (9), 962–968, doi: 10.1016/j.
clinbiomech.2011.05.013.
25. Barrett R.S., Mills P.M., Begg R.K., A systematic review
of the effect of ageing and falls history on minimum foot
clearance characteristics during level walking. Gait Posture, 2010, 32 (4), 429–435, doi: 10.1016/j.gaitpost.2010.
07.010.
Paper received by the Editors: October 2, 2012
Paper accepted for publication: April 16, 2013
Correspondence address
Felipe P. Carpes
Federal University of Pampa
Laboratory of Neuromechanics
BR 472 km 592
Po box 118 - ZIP 97500-970
Uruguaiana, RS, Brazil
e-mail: [email protected]
143
HUMAN MOVEMENT
2013, vol. 14 (2), 144– 147
Effects of vertical and horizontal plyometric exercises
on running speed
doi: 10.2478/humo-2013-0017
Dalwinder Singh 1 *, Sukhwinder Singh 2
1
2
Panjab University, Chandigarh, India
University College, Dhilwan, India
Abstract
Purpose. The aim of the present study was to compare the effects of vertical, horizontal and a combination of both vertical
and horizontal plyometric exercises (depth jumping) on running speed. Methods. A purposively selected sample of 80 male
students were randomly assigned into either a control group or groups training the vertical depth jump, horizontal depth jump
or a combination of both. The experimental groups trained twice weekly for 10 weeks, performing 6 sets of 10 repetitions per
session. Drop height was increased from 20 to 40 cm according to the step method. Running speed was measured by a 45.72 m
dash test before and after the 10-week period. Results. Analysis of covariance was applied to compare scores. A pair-wise
comparison was performed using Scheffe’s post-hoc test at a 0.05 level of confidence. The results showed significant improvements among the three experimental groups as compared with the control group, whereas a comparison between the three
experimental groups was found to be insignificant. The percentage of performance increase from pre-test to post-test running
speed was 2.23%, 2.96% and 3.57% for the groups training vertical, horizontal and both vertical and horizontal depth jumps,
respectively. Conclusions. A combination of both vertical and horizontal depth jumping, with a slightly larger emphasis on
horizontal plyometric training, can aid sprinters’ performance.
Key words: amortization phase, stretch reflex, vertical depth jump, horizontal depth jump, step progression
Introduction
The term “plyometrics” first made its appearance in
sports methodology literature in V.M. Zaciorskij’s 1966
work “Fiziceskie Kacestva Sportsmena”. Zaciorskij used
this term to indicate the greater tension present in a group
of muscles when an exercise involved a quick stretching
phase followed by an equally quick concentric contraction. In this process, the tension expressed by working
muscle (metron) measured externally is found to be higher
(plio) than the tension expressed using any other exercise (isometric, isotonic, auxotonic) [1]. An American
track and field coach named Fred Wilt offered an explanation of the term in 1975, where, based on its Latin
origins, plyometrics was interpreted as “measurable increases”. Plyometrics rapidly became known to coaches
and athletes as exercises or drills aimed at linking strength
with speed of movement in order to produce more power.
Plyometric training became essential to athletes who
jumped, lifted, or threw. During the late 1970s and into
the 1980s, athletes in other sports also began to see the
applicability of this concept in their own movement
activities. Throughout the 1980s, coaches in sports such
as volleyball, football and weightlifting began to use
plyometric exercises and drills to enhance their training
programmes. If there was any drawback to this enthusiasm, it lay with the lack of expertise that American
* Corresponding author.
144
coaches and athletes had in administering plyometric
programmes and a faulty belief that more must be better. Since these early years, however, practitioners have
learned through applied research as well as trial and error
to establish realistic procedures and expectations [2].
The term “plyometrics” remains to be known as a specific group of exercises that encompass a rapid stretching of a muscle that is undergoing eccentric stress followed
by a concentric, rapid contraction of that muscle for the
purpose of developing a forceful movement over a short
period of time [3]. For example, as an athlete lands on
the ground during a plyometric exercise, a stretch occurs
in the involved muscle fibres. Proprioceptors within the
muscle tissue immediately sense this stretch and send
a message to the spinal cord through an afferent or sensory neuron. The spinal cord sends a message immedia­
tely back to the muscle fibre via an efferent or motor
neuron, telling it to contract to keep it from overstretching. This is known as the “stretch reflex” and is one of
the body’s built-in protection mechanisms for preventing muscle tissue injuries. Plyometric drills can be used
to train the body to emit these sensor signals in a shorter
period of time, causing the affected muscle to react ever
more quickly [4].
Researchers have focused on using depth jumping
as a form of plyometric training [5]. Two types of depth
jumping in particular were described by Chu [2]. The
first is the vertical depth jump, performed by stepping
off a box and landing on both feet all the while trying
to anticipate the landing. As soon as contact is made,
HUMAN MOVEMENT
D. Singh, S. Singh, Vertical and horizontal plyometrics for speed
it is necessary to spring up as quickly as possible so as
to keep the body from “settling” on the landing, keeping
ground contact to a minimum. The second is termed as
the horizontal depth jump, also performed by taking
a step off a box and landing on both feet. However, upon
landing, the participant jumps immediately as far forward as possible, again landing on both feet.
The aim of this study was to investigate the influence
of both vertical and horizontal depth jump training,
as well as a combination of both vertical and horizontal
depth jumping, on running speed, by calculating an
optimal drop height and applying a depth jumping training programme.
Material and methods
Purposive sampling was used to select eighty (n = 80)
male physical education students aged between 18 to
21 years. All participants were full time students attending classes according to their college curriculum. All were
deemed medically fit to undergo the study’s training
programme and signed an informed consent form prior
to participation. The Joint Research Board of the university approved all procedures for the study.
The eighty participants were randomly assigned
into three experimental groups and one control group.
Group VP (n = 20) performed only the vertical depth
jump twice a week, Group HP (n = 20) performed only
horizontal depth jumping twice a week, Group CP
(n = 20) trained with vertical depth jumping on one day
and horizontal depth jumping on another day, while
Group CG (n = 20) served as the control group. Those
participating in the training sessions attended an instruction session before the first test to ensure proper
technique and an understanding of the testing process. A demonstration was also provided by a trained
athlete. The participants were tested for proper execution of the vertical depth jump from a drop height of
45 cm (44.3 ± 5.13 cm). To ensure data uniformity, the
subjects were always tested in the morning by the same
group of examiners.
A pilot study was conducted to determine training
intensity and load progression. Ten participants were
randomly selected from the original sample and performed first a standing vertical jump and then the depth
jump from a height of 10, 20, 30, 40, 50 and 60 cm.
Mean maximum vertical jump height was measured
at 46.25 cm. Mean maximum depth jump height was
found to be 48.64 cm taken from a step height of 20 cm,
with depth jump performance remaining above the initial vertical jump height up to a step height of 40 cm
(Fig. 1). Therefore, a drop height of 20 cm, where depth
jump performance was at a maximum and higher than
vertical jump performance, was taken to be the initial
training intensity [2]. Drop height was then increased
across the training sessions according to the step method
from a height of 20 cm up to 40 cm (Tab. 1).
Figure 1. Mean vertical and depth jump performance
in the pilot study
Table 1. Progression of drop height during the experiment
Week
Drop Height
(cm)
I
II III IV V VI VII VIII IX X
20 25 30 25 30 35 30 35 40 35
Each of the experimental groups trained twice a week
for 10 weeks at identical intensities and volumes. The
training sessions were administered by dividing each
group into four smaller subgroups. After a brief warm-up,
the group was trained simultaneously on four stations,
with the five participants of each subgroup performing
in rotation one by one at a station. Each of the participants performed 6 sets of 10 repetitions per session [6].
Fifteen seconds of rest was provided as recovery between
repetitions by performing a short walk to a cone placed
11 m in front of the station when training the vertical
depth jump, or 12.1 m when training the horizontal
depth jump [7, 8]. Rest between sets was completed by
a 1.5–2 min slow jog to a cone placed 220 meters from
the first cone [9]. After training, the participants engaged in a cool down.
The running speed of each participant was measured before and after the 10-week period according to
the recommendations by the American Alliance for
Health, Physical Education and Recreation [10]. Two
lines were marked 45.72 m apart and parallel to each
other on an area that included enough room for stopping after crossing the finish line. Two subjects ran at
the same time, both starting from a standing position.
The commands “Are you ready?” and “Go!” were given.
At “Go!” the starter dropped his arm so that the time
keepers at the finish line could begin timing. The elapsed
time from the starting signal until the runner crossed
the finish line was recorded to the nearest hundredth
of a second.
Analysis of covariance was applied to find a significant difference among the groups. Pre-test scores were
used as the covariate and post-test scores, adjusted for
covariance, were the dependent measures. When a sig145
HUMAN MOVEMENT
D. Singh, S. Singh, Vertical and horizontal plyometrics for speed
nificant F-value was found, a pair-wise comparison
was performed using Scheffe’s post-hoc test to identify
significant differences between groups. The alpha level
was set at 0.05.
Results
Discussion
The mean pre-test and post-test running speeds of
the three experimental groups and control group are
presented in Figure 2. The significant differences among
the various groups at an F-value of 113.29 was found to
be greater than the tabulated value of 2.73 for degrees of
freedom (3, 75) at the 0.05 level of confidence (Tab. 2).
Furthermore, Scheffe’s post-hoc test was employed to
study the direction and significance of differences between the paired adjusted final means. Significant imgroups
VP – vertical depth jump group, HP – horizontal depth jump group,
CP – vertical and horizontal depth jump group, CG – control group
Figure 2. Pre-test and post-test running speed means (s)
of the experimental and control
Table 2. Analysis of covariance for the experimental
groups and control groups
Source of
variation
Between-group
Within-group
provements in the three experimental groups as compared to control group were found (Tab. 3). However,
the differences for the remaining paired means were
found to be insignificant.
Mean
Sum of
Degrees
sum of
squares of freedom
squares
0.578
0.127
3
75
0.1927
0.0017
F-value
113.29*
* Significant at the 0.05 level F0.05 (3, 75) = 2.73
Analysis of the data revealed that vertical and horizontal plyometric training, as well a combination of
both jumps, is effective in bringing about a significant
increase in running speed. Similar findings pertaining
to running speed performance have been reported by
Gemer [11], Tamrakar and Singh [12] and Polhemus
and Osina [13]. Luhtanen and Komi [14] recognized
the effects of eccentric-concentric coupling on running speed. They partitioned the total contact time of
the feet on the ground as either negative contact time
or positive contact time, where, assuming that the
initial foot plant is at a position with the lowest centre
of gravity, the contact leg’s extensor muscles contract
eccentrically and perform negative work. The later
portion of contact time consists of concentric contractions with a rise in the centre of gravity, making the
work being performed positive [14]. This was further
supported by Lundin and Berg [15], who attributed
improved efficiency of running at higher speeds to the
effects of the stretch reflex and use of elastic energy.
In the light of above descriptions by Luhtanen and
Komi [14] and Lundin and Berg [15], the findings of the
present study are in line with the observations presented
by Chu [2], who explained that plyometric depth jumping
is an activity that acts to increase the neuromuscular
system’s ability to more effectively perform concentric
contraction, as the forces encountered in plyometric
exercises lead to greater motor unit synchronous activity and earlier recruitment of larger motor units via
the myotatic reflex [2]. Furthermore, Miller et al. [16]
concluded that six weeks of plyometric training reduced
the time spent on the ground when compared with a control group. In another study, six weeks’ plyometric training
significantly reduced the rebound time in the depth
jump [17]. According to Pettitt [18], plyometric training
leads to physiological adaptations such as a reduction
Table 3. Significance of differences of paired adjusted final means for the experimental and control groups
Means (s)
Group VP#
Group HP#
6.674
6.674
6.674
6.6113
Group CP#
Group CG#
6.5784
6.8009
6.6113
6.6113
6.5784
6.5784
6.8009
6.8009
Difference
between means
0.0627
0.0956
0.1269*
0.0329
0.1896*
0.2225*
* Significant at 0.05; VP# – vertical depth jump group, HP# – horizontal depth jump group,
CP# – vertical and horizontal depth jump group, CG# – control group
146
Scheffe’s critical
differences
0.1177
0.1177
0.1177
0.1177
0.1177
0.1177
HUMAN MOVEMENT
D. Singh, S. Singh, Vertical and horizontal plyometrics for speed
of the amortization phase and greater cross-sectional
recruitment and threshold elevation for the inverse
stretch reflex.
Analyses of the differences between the paired adjusted
final means of three experimental groups showed no
statistically significant result. However, the percentage
of performance increase from pre-test to post-test running
speed was 2.23%, 2.96% and 3.57% for the groups
training vertical (VP), horizontal (HP) and both vertical
and horizontal (CP) depth jumps, respectively. Thus, the
results are in favour of the CP group, which Chu [2] had
originally posited that training involving a horizontal
and vertical component could be the most successful in
contributing to an improvement in running performance.
This is further supported by Dintiman et al. [19], who
evaluated the stride length and stride rate of athletes.
These authors recommend drills emphasizing vertical
displacement for athletes who present a weakness in
stride rate or in the vertical jump and, conversely, drills
with a larger horizontal displacement component for
athletes who present a larger weakness in stride length or
in tests such as the standing long jump. Furthermore,
a comparison of groups VP and HP showed a trend in
favour of group HP, which is in conformity with results
of Mach et al. [20], who believe that stretch-shortening
drills performed horizontally can improve the speed
component of athletes’ speed-power properties.
Conclusions
A combination of both vertical and horizontal depth
jumping can aid running performance, especially in the
case of sprinters in training. However, horizontal plyo­
metric training in itself was more effective in improving
running speed performance than solely vertical plyometric training. Thus, while planning a plyometric training programme for sprinters, coaches and physical education teachers should provide slightly more emphasis
on horizontal plyometric training.
Acknowledgements
We express our gratitude to Bhagoo Majra of the S.K.R. College of Physical Education in Punjab, India for his permission to conduct the study and for providing encouragement
throughout its course. We are also thankful to the students
who volunteered to participate in this study.
References
1. Zanon S., Plyometrics: past and present. New studies in
athletics, 1989, 1 (4), 7–17.
2. Chu D.A., Jumping into plyometrics. Human Kinetics,
Champaign 1998.
3. Chu D., Plyometrics: The link between strength and speed.
NSCA Journal, 1983, 5, 20–21.
4. Freeman W., Freeman E., Plyometrics: complete training
for all sports. Championship books, Ames 1984.
5. Zanon S., Plieometry in jumping. Die lehre der leichtathletik, 1974, 16, 1–13.
6. de Villarreal E.S.S., González-Badillo J.J., Izquierdo M.,
Low and moderate Plyometric training frequency produces greater jumping and sprinting gains compared
with high frequency. J Strength Cond Res, 2008, 22 (3),
715–725, doi: 10.1519/JSC.0b013e318163eade.
7. Lima J.C., Marin D.P., Barquilha G., da Silva L.O., Puggina E.F., Pithon-Curi T.C., Hirabara S.M., Acute effects of drop jump potentiation protocol on sprint and
countermovement vertical jump performance. Hum Mov,
2011, 12 (4), 324–330, doi: 10.2478/v10038-011-0036-4.
8. Read M.M., Cisar C., The influence of varied rest interval
lengths on depth jump performance. J Strength Cond Res,
2001, 15 (3), 279–283.
9. Verkhoshansky Y., Perspectives in the improvement of
speed-strength preparation of jumpers. Yessis Review of
Soviet Physical Education and Sports, 1969, 4, 28–34.
10. American Alliance for Health, Physical Education and
Recreation, Youth Fitness Test Manual. AAHPER Publication, Reston 1976.
11. Gemar J.A., The effects of weight training and Plyometric
training on vertical jump, standing long jump and forty-meter sprint. J Phys Educ Sports, 1988, 12 (2), 22–26.
12. Tamrakar A., Singh K., Effects of weight training, plyometric training and their combination on selected motor
components. J Sports Sports Sci, 2001, 24 (4), 5–14.
13. Polhemus R., Osina M., The effects of plyometric training
with ankle and vest weights on conventional weight training programme for men. Track Field Q Rev, 1984, 80 (4),
59–61.
14. Luhtanen P., Komi P.V., Mechanical factors influencing
running speed. In: Asmussen E., Jorgensen K. (eds.), Biomechanics VI. University Park Press, Baltimore 1978,
23–29.
15. Lundin P., Berg W., A review of plyometric training. National Strength and Conditioning Association Journal, 1991,
13 (6), 22–30.
16. Miller M.G., Herniman J.J., Ricard M.D., Cheatham C.C.,
Michael T.J., The effects of a 6-week plyometric training
program on agility. J Sports Sci Med, 2006, 5, 459–465.
17. Makaruk H., Sacewicz T., Effects of plyometric training on
maximal power output and jumping ability. Hum Mov,
2010, 11 (1), 17–22, doi: 10.2478/v10038-010-0007-1.
18. Pettitt R., The role of Plyometrics in the scope of a periodized training model. J Perform Enhan, 1999, 1 (1), 11–20.
19. Dintiman G., Ward R., Tellez T., Sports speed (2nd edn.).
Human Kinetics, Champaign 1997.
20. Mach G., Tschiene P., McNab T., McWatt B., Lydiard A.,
Ocean coaches clinic. Auckland, New Zealand, January
4–12, 1979.
Paper received by the Editors: July 11, 2012
Paper accepted for publication: April 16, 2013
Corresponding address
Dalwinder Singh
Department of Physical Education
Panjab University
Chandigarh, India
e-mail: [email protected]
147
HUMAN MOVEMENT
2013, vol. 14 (2), 148– 153
THE EFFECTIVENESS OF DIFFERENT TYPES OF VERBAL FEEDBACK
ON LEARNING COMPLEX MOVEMENT TASKS
doi: 10.2478/humo-2013-0009
Tomasz Niznikowski *, Jerzy Sadowski, Andrzej Mastalerz
Faculty of Physical Education and Sport in Biala Podlaska, Józef Piłsudski University of Physical Education, Warsaw, Poland
Abstract
Purpose. The purpose of the study was to assess the effectiveness of different types of verbal feedback in the learning of a complex movement task. Methods. Twenty university students took part in a six-week training course learning how to correctly
execute the vertical jump. The participants were randomly assigned to one of three groups: Group E&P received verbal feedback on
errors made during movement execution and on how to improve, Group P obtained verbal feedback only when they correctly
performed the task, and Group E was provided with verbal feedback only when an error was made. Performance was measured
on three separate occasions, before the training course (pre-training), one day after (post-training) and seven days after completing the course (retention) by executing the vertical jump in front of three gymnastic judges who scored their performance
on a scale of 1 to 10. Jump kinematics were also measured pre-training and post-training by recording landing force and flight
time on a force platform. Results. Post-hoc comparison indicated that a significant improvement in performance was observed
only in the group receiving verbal feedback on errors (E). Judges’ scores received in post-training were significantly higher than
those measured pre-training (10.3 %; p < 0.0003) and further increased to 14.4 % in the retention test (p < 0.0001). Judges’
scores for the groups receiving verbal feedback on errors and correctness (E&P) and only correctness (P) improved insignificantly. Conclusions. Providing too much verbal feedback when learning the vertical jump turned out to be less effective than
providing limited verbal feedback only when errors were made.
Key words: training sessions, verbal feedback, vertical jump, complex task
Introduction
One of the most important factors in the motorlearning process is the feedback provided to a learner
attempting to acquire a new motor skill. Many researchers
have attempted to find the most appropriate methods
of providing information through feedback to aid the
learning and refinement of motor skills or body position
control [1–5]. Supplementary information on how a task
was completed, when coming from a source external to
the performer, e.g. a teacher or a coach, is known as extrinsic or augmented feedback. There is a bulk of research
providing experimental evidence on such factors as the
frequency of feedback, organization of feedback, types
of augmented feedback, forms of knowledge of result
(KR), or knowledge of performance (KP) [6, 7].
Because augmented feedback is such an important
part of motor skill learning, teachers and coaches should
understand what types of information as well as how
often and how precise it should be provided to facilitate
the process of learning new skills. Feedback that is too
precise is as useless as that which is too vague [8]. Some
researchers [9] postulated that the amount and precision
of KR are often too overwhelming, with the learner
unable to correct a certain response due to the fact that
individuals can effectively process only a limited amount
* Corresponding author.
148
of information at a time. Hence, it has been argued that
coaches should develop the form and content of how
augmented feedback would be presented ahead of time.
Furthermore, the complexity of a motor task is believed to determine which learning method or feedback
strategy would be most successful, where, for example,
numerous sources of task-related information are considered to be beneficial for learning complex tasks [10, 11].
As many studies have revealed, learning principles derived from the study of simple skills are not transferable
to that of learning more complex skills [12]. Schmidt
and Lee [13] claim that further research is required to
establish relationships between the level of motor task
complexity and forms and types of feedback. Currently
there is a lack of data on how different types of feedback
affect the effectiveness of completing tasks at varying
levels of complexity [2, 11, 14–16].
However, some researchers have highlighted the fact
that it may be extremely difficult to establish the influence of different types of content and form of feedback
on task performance, due in part to the numerous intricate mechanisms that occur during this process [14, 17].
Others stress the necessity of such research in order to
develop guidelines for learning motor tasks at different
levels of complexity [11, 18–20].
Therefore, in order to contribute to the literature on
the subject, the purpose of this study was to assess the
effectiveness of different types of verbal feedback in the
learning of a complex movement task.
HUMAN MOVEMENT
T. Niznikowski, J. Sadowski, A. Mastalerz, The effectiveness of verbal feedback
Material and methods
Twenty students were recruited and randomly assigned to one of three groups. Each of the groups differed
in terms of the feedback they were to receive when executing a motor task. The groups were as follows: group
E&P obtained information on the errors they made and
on how to correct them (n = 7, height 177 cm ± 5.0 cm,
body mass 81.2 kg ± 3.8 kg, age 20.3 ± 1.1 years),
group P received feedback only when they correctly
executed the task (n = 6, height 177 cm ± 5.0 cm, body
mass 81.2 kg ± 3.8 kg, age 20.3 ± 1.3 years), and group E
obtained information only on the errors they made
(n = 7, height 178 cm ± 4.0 cm, body mass 79.4 kg ±
3.6 kg, age 20.4 ± 1.2 years) (Tab. 1).
A six-week experiment was conducted, with 60-min
training sessions held three times per week (on Mondays,
Wednesdays, and Fridays), with each subject participating in a total of 16 sessions. The subjects learned how
to correctly execute the vertical jump by swinging the
arms forward and upward, pulling the knees up to the
chest while grabbing the shins followed by a half-squat
landing with the arms extended sidewards. All of the
subjects were unfamiliar with this type of task. The pro-
gressive-part method was applied to the training process,
i.e., the task was divided into parts. The subjects mastered the preparatory phase during training sessions 1–4;
sessions 5–8 were devoted to acquiring the main phase,
while sessions 9–12 focused on learning how to perform the final phase. Sessions 13–16 were devoted to
performing the entire movement. Each training session
involved performing 20 sets of 5 repetitions each of the
aspect being taught. After each set the subjects received
feedback (knowledge of results).
Performance was measured on three separate occasions, before beginning the training course (pre-training),
after (post-training) and seven days after completing
the course (retention). The assessment tests began with
a standard warm-up followed by the participants perform a single execution of the movement task. Three
gymnastic judges rated their performance on a scale
of 1 to 10 based on the criteria of the International Gymnastics Federation (FIG). Each minor error received a deduction 0–0.3 pts., for a medium error 0.4–0.6 pts.
were deducted, while a major error cost the participant
0.7–1 pts. The inter-rater reliability of the experts’
scores was confirmed by the concordance correlation
coefficient (= 0.94).
Table 1. Examples of feedback: Group E&P – verbal feedback on errors and on how to improve, Group P – verbal feedback
only on correct movement execution, and Group E – verbal feedback only on errors
(Group E&P)
– you performed the jump learning
too far forward, jump straight up
– you performed the jump leaning
too far backwards, jump straight up
– you did not perform the jump while
simultaneously swinging the arms up,
try it again with arms swinging up at
the same time
– you drew the knees up to your chest
too early, pull your knees at the end
of the rising phase
– you drew your knees up to your chest
too late, pull your knees at the end of
the rising phase
– you did not grab your shins with your
hands, hold your shins next time
– tucking done too early, do it during
the ascending flight phase
– tucking done too late, do it during
the ascending flight phase
– untucking done too early, do it during
the beginning of the descending phase
– untucking done too late, do it during
the beginning of the descending phase
– landing without bending the knee
and hip joints, do it by cushioning
the landing at the knee and hip joints
– you did not keep your arms in front
of you out to the side, hold your arms
out next time
(Group P)
– good vertical jump while simulta­
neously swinging the arms up
– you drew knees at the right time
– hands correctly grabbed the shins
– correct body tuck during the rising
flight phase
– correct body tuck during the beginning of the descending flight phase
– correct landing by absorbing impact
with the ankles, knees, and hips
– direction of the arms to the side and
up in front was correct
(Group E)
– jump was performed leaning too
far forward
– jump was performed leaning too
far backwards
– you did not simultaneously swing
your arms up
– you drew your knees to your chest
too early
– you drew your knees to your chest
too late
– hands grabbed the shins too early
– hands grabbed the shins too late
– tucking done too early
– tucking done too late
– untucking done too early
– untucking done too late
– landing done without bending the
knee and hip joints
– you kept your arms to the side
149
HUMAN MOVEMENT
T. Niznikowski, J. Sadowski, A. Mastalerz, The effectiveness of verbal feedback
Results
ANOVA with repeated measures for the judges’ scores
revealed a significant effect of Test Time (F(2,32) = 8.30;
p = 0.001). There were no interaction effects of Group
(F(2,16) = 2.15; p = 0.809) as well as Group x Time of
Measurement (F(4, 32) = 1.91, p = 0.133). Means and
standard deviations are presented in Table 2. The rate
of how the scores improved is displayed in Figure 2.
Fisher’s LSD test (p < 0.05) was performed for posthoc pairwise comparison, indicating that a significant
improvement in performance was observed only in the
group that received verbal feedback about errors (E).
Table 2. Means (± SD) of judges’ scores at pre-training,
post-training, and retention for Groups: E&P – verbal
feedback on errors and how to improve, P – verbal
feedback on correctness, and E – verbal feedback on errors
Figure 1. Example of ground reaction vertical force (GRVF)
measure with flight time and landing force marked
A jumping and landing assessment was performed
at pre-training and post-training by quantifying jump
performance by flight time and recording vertical ground
reaction force (VGRF). The participants performed two
maximal height jumps, a countermovement jump (Test A)
and a vertical jump by pulling the knees up to the chest
(Test B). Kinematic data were measured on a Type
2812A1-3 Force Plate System with BioWare software v.
3.23 (Kistler, Switzerland) at a sampling rate of 400 Hz.
Body mass was also measured on the force plate, which
was calibrated prior to each measurement. Participants
were instructed to begin from a standing position and
jump as high as they could. No other specific instructions were provided during the test as to not influence
performance. Three jumps were completed for each test,
with ample rest provided between each trial. Only the
jump attaining the greatest height was selected for analy­
sis, with landing force and flight time (Fig. 1) evaluated
to estimate jumping and landing effectiveness.
Statistical significance was assessed with ANOVA.
Normality of distribution and homogeneity of variances
were tested with the Shapiro-Wilk test. If normal distribution was verified, the studied variables were then
analyzed by two-way mixed-factor analysis of variance,
Group (3) x Time of Measurement (3) for the judged
jumps and Group (3) x Time of Measurement (2) for
the force measurements, with the three experimental
groups representing a between-subjects factor and the
three testing periods representing a within-subjects
factor. Statistical significance was set at p < 0.05. Posthoc Fisher’s LSD test was used for pairwise comparison.
The results were analyzed using Statistica v. 7.1 software
(StatSoft, USA).
150
E&P
P
E
Pre-training
Post-training
Retention
7.15 ± 0.16
7.19 ± 0.15
6.8 ± 0.16
7.27 ± 0.22
7.47 ± 0.2
7.5 ± 0.22
7.35 ± 0.22
7.5 ± 0.2
7.78 ± 0.22
Figure. 2. Percent increase in judges’ scores at pre-training,
post-training, and retention by Groups: E&P – verbal
feedback on errors and how to improve, P – verbal
feedback on correctness, and E – verbal feedback on errors
Scores received in post-training were significantly
higher than in pre-training (10.3 %; p < 0.0003) and
further increased to 14.4 % in the retention test (p <
0.0001), pointing to an improvement in task performance. Judges’ scores observed in the group with verbal
feedback on errors and correctness (E&P) and correctness (P) improved insignificantly.
ANOVA on the groups’ jumping performance revealed significant interaction effects of Group x Time
of Measurement for landing force (VGRF) in Test A
(F(4, 32) = 3.24, p = 0.066). There were no effects of
Group (F(2, 16) = 2.0101, p = 0.16642) as well as Time of
Measurement (F(1, 16) = 0.00551, p = 0.94174). There
were no interaction effects of Group, Time of Measurement, and Group x Time of Measurement for flight
time (T) in Tests A and B as well as for landing force in
Test B. Means and standard deviations are presented
in Table 3.
HUMAN MOVEMENT
T. Niznikowski, J. Sadowski, A. Mastalerz, The effectiveness of verbal feedback
Table. 3. Means (± SD) of flight time (T) and landing force (LF) recorded during Tests A (countermovement jump)
and B (vertical jump) across test times (pre-training and post-training) for the groups receiving verbal feedback
on errors and how to improve (E&P), verbal feedback only on correctness (P), and verbal feedback on errors (E)
Pre-training
E&P
Post-training
E&P
Pre-training
P
Post-training
P
Pre-training
E
Post-training
E
T [s]
A
B
0.49 ± 0.02
0.54 ± 0.02
0.49 ± 0.02
0.57 ± 0.02
0.49 ± 0.02
0.54 ± 0.02
0.51 ± 0.02
0.57 ± 0.02
0.52 ± 0.02
0.55 ± 0.02
0.52 ± 0.02
0.59 ± 0.02
LF [N/kg]
A
B
31.4 ± 6.1
28.5 ± 3.9
37.4 ± 5.7
35.9 ± 4.0
26.4 ± 6.4
32.8 ± 4.1
33.7 ± 6.1
31.9 ± 4.2
45.1 ± 5.9
30.8 ± 3.8
33.6 ± 5.6
30.9 ± 3.9
The highest flight time was recorded for the group
receiving verbal feedback on errors (E), although the
results were very similar among all three groups. Post-hoc
comparisons indicated a significant decrease in landing
force in Test A only for Group E. Landing force in posttraining was significantly higher than in pre-training
(25.5 %; p < 0.06) for the other two groups, although
the increase in landing force was statistically insignificant for Group E&P for both Tests A and B in post-training. A decrease in landing force was only observed in
Group P for Test B, although this result was statistically
insignificant (2.74%, p > 0.05).
Discussion
The purpose of the study was to assess the effectiveness of different types of verbal feedback in the learning
of a complex movement task, finding that the effectiveness of learning the task was different among the groups
receiving different feedback. Providing verbal information on what errors were made and what should be improved (E&P) as well as feedback only when the task was
correctly performed (P) turned out to be the least effective strategies. Instead, participants from Group E, who
received feedback only when they made an error, obtained the best results.
This may stem from the fact that too much verbal
feedback particularly at the initial stage of learning
a complex movement task is not beneficial. Some researchers believe that providing too much feedback is
too overwhelming for learners, making them unable to
effectively process new information. Moreover, it is believed that providing too much information causes learners to become overdependent on extrinsic sources of information. As a result, the use of intrinsic information
becomes more limited, which leads to difficulties in performing a task once the amount of extrinsic information is reduced [4].
In line with the above assumptions, although the subjects from Group E received 22% and 15% less feedback
than those from Group E&P and Group P, they exhibited
better learning effects. Similar results were obtained by
Sadowski et al. [21], who noted that providing feedback
both on errors and how to improve performance was not
as effective as providing feedback only on the correctness
of performing a complex movement task. Conversely,
Kernodle et al. [18] claim that when a task is complex
and difficult, it is advisable to provide feedback both on
errors and on how to improve. Williams and Hodges [20],
Tzetzis et al. [10], and Wulf et al. [15] also maintain
that the simultaneous employment of prescriptive and
descriptive feedback brings about better learning results.
It appears that it is still hard to state unequivocally which
type of feedback is the most effective in learning complex movement tasks. Our findings, however, are in line
with those of Wulf and Shea [12], and Williams and
Hodges [20], who found that learning effects depended,
inter alia, on types of feedback on knowledge of result
(KR) or knowledge of performance (KP).
In our opinion, learning effects depend not only on
the content of feedback but also on the complexity of
the movement skills needed to perform a task. Similar
observations were made by Tzetzis et al. [16], Tzetzis and
Votsis [19], and Tzetzis et al. [10], who investigated correlations between the correctness of task performance
and feedback quality as well as the complexity of a skill.
Tzetzis et al. [10] found that in the process of learning
the difficult backhand-clear in badminton (a high return stroke on the non-dominant side of the body that
carries the shuttlecock deep into the backcourt), the group
receiving positive feedback, correction cues, and feedback on errors performed better than the group receiving
only correction cues and positive feedback or the group
that received only feedback on errors. It ought to be
emphasized that Tzetzis et al. [10] conducted their study
on badminton players with already some form of experience, whereas our investigation involved students
with no prior experience of the task. This may indicate
that, in the learning of movement tasks, the learning
effect is determined not only by the content of feedback and task complexity but also by athletes’ experience. Therefore, the findings of the above-mentioned
researcher are not generalizable to athletes other than
badminton players. Similarly, our findings refer to the
learning of movement tasks that were new and unfamiliar and this may account for the fact that different
types of feedback were found to be more effective
than others.
The training method employed in our study may
also account for the differences between our results
151
HUMAN MOVEMENT
T. Niznikowski, J. Sadowski, A. Mastalerz, The effectiveness of verbal feedback
and the findings of the aforementioned researchers. The
progressive-part method was used, where task learning
is divided into consecutive parts. This allows for a relatively complex task to be simplified and, in the present
study, did not require the use of extremely extensive or
very precise feedback. Winstein and Schmidt [7] proved
that too much feedback was no more effective than
little feedback, while Janelle et al. [22] showed that, in
learning an overhead throw, only 11% of total feedback
provided was utilized by learners. Guadagnoli et al. [23]
stated that longer summaries are better in the learning
of simple movement tasks, while shorter summaries are
more appropriate in complex tasks. Magill and Schoen­
felder-Zohdi [24] claimed that multiple sources of taskrelated information are redundant for simple tasks as
single sources already provide enough adequate information for the development of cognitive representations
and overt performance.
In turn, Laguna [11] proved that during the observational learning process the performance of a complex
task benefited from a combination of task-related information (model demonstration and knowledge of performance). However, it should be noted that the task
adopted in Laguna’s study used arm movement only, while
the present study applied a task using the whole body.
Also of interest was the fact that Group E finished
their jumps in a better landing position, with landing
force generated by Group E being lower than in Groups P
and E&P. Other researchers observed a decrease in
landing force after physical [25, 26] and technical
training [27] as well as after being provided with specific instructions. Prapavessis and McNair [28] noted
a decrease in landing force (19%) immediately upon
providing technical guidelines regarding the kinematics
of the lower limbs. These researchers drew similar conclusions when applying imagery-based feedback. In other
studies, providing augmented feedback during landings
decreased landing force by 13% to 19% already after
one session [29, 30].
Our results confirmed that applying verbal feedback on errors not only determines better learning effects than verbal feedback on task error and improvement cues and only on execution correctness, but that
it may help in the prevention of injuries by decreasing
landing force. Therefore, the results of such verbal feedback may aid in developing appropriate guidelines and
principles when learning complex motor skills.
One of the limitations of this study that need attention is the fact that the participants demonstrated
similar levels of general physical fitness and development and did not reveal any significant differences
regarding maximal vertical jump performance. As a result, the findings should not be directly compared to
athletes for example, as these individuals feature high
levels of physical fitness and motor skill development.
Moreover, the force-velocity potential of athletes is much
higher than in less active people. To further clarify the
152
most appropriate methods in learning complex tasks,
future investigations should be carried out on subjects
having different motor competences and testing carried out with the use of various motor tasks and types
of feedback.
Conclusions
The learning effects of new complex movement tasks
depend on the content of providing feedback on task
performance (KP). Providing too much verbal feedback
when learning the vertical jump turned out to be less
efficient than limited verbal feedback focused only on
the errors being made when performing the task.
The learning effects depend on the type of feedback,
its amount and the content of information as well as the
complexity of the task. The progressive-part method is
recommended in the motor learning of new complex
tasks, providing short cues on what errors are made.
Further research is needed to determine the principles behind learning complex motor skills. It is advisable to carry out studies on complex motor tasks with
varying degrees of freedom. The complexity of the task
should be estimated using a clear task-characteristic scale
to avoid ambiguous results.
References
1. Adams J.A., Historical review and appraisal of research
on the learning, retention, and transfer of human motor
skills. Psychol Bull, 1987, 101, 41–74, doi: 10.1037/00332909.101.1.41.
2. Salmoni A.W., Schmidt R.A., Walter C.B., Knowledge
of results and motor learning: a review and critical reappraisal. Psychol Bull, 1984, 95, 355–386, doi: 10.1037/00332909.95.3.355.
3. Swinnen, S.P., Information feedback for motor skill learning: A review. In: Zelaznik H.N. (ed.), Advances in motor
learning and control. Human Kinetics, Champaign 1996,
37–66.
4. Wulf G., Shea C.H., Understanding the role of augmented
feedback: The good, the bad and the ugly. In: Williams A.M.,
Hodges N.J. (eds.), Skill acquisition in sport: Research
theory and practice. Routledge, London 2004, 121–144.
5. Zemková E., Hamar D., The effect of task-oriented sensorimotor exercise on visual feedback control of body
position and body balance. Hum Mov, 2010, 11 (2),
119–123, doi: 10.2478/v100038-010-0013-3.
6. Schmidt R.A., Lee T.D., Motor Control and Learning.
A Behavioral Emphasis. Human Kinetics, Champaign
1999.
7. Winstein C.J., Schmidt R.A., Reduced frequency of knowledge of results enhances motor skill learning. J Exp Psychol
Learn Mem Cognit, 1990, 16, 677–691, doi: 10.1037/02787393.16.4.677.
8. Wright D.L., Smith-Munyon L., Sidaway B., How close is
too close for precise knowledge of results? Res Q Exerc
Sport, 1997, 68 (2), 172–176
9. Lee T.D., Carnahan H., When to provide knowledge of
results during motor learning: Scheduling effects. Hum
Perform,1990,3,87–105,doi:0.1207/s15327043hup0302_2.
HUMAN MOVEMENT
T. Niznikowski, J. Sadowski, A. Mastalerz, The effectiveness of verbal feedback
10. Tzetzis G., Votsis E., Kourtessis T., The effect of different
corrective feedback methods on the outcome and self-confidence of young athletes. J Sports Sci Med, 2008, 7 (3),
371–378.
11. Laguna P., Task complexity and sources of task-related
information during the observational learning process.
J Sports Sci, 2008, 26, 1097–1113, doi: 10.1080/026404
10801956569.
12. Wulf G., Shea C.H., Principles derived from study of
simple skills do not generalize to complex skill learning.
Psychonomic Bull Rev, 2002, 9, 185–211, doi: 10.3758/
BF03196276.
13. Schmidt R.A., Lee T.D., Motor Control and Learning:
A Behavioural Emphasis. 3rd edition. Human Kinetics,
Champaign 1998.
14. Franks I.M., The use of feedback by coaches and players.
In: Reilly T., Bangsbo J., Hughes M. (eds.), Science and
football III. E & FN Spon, London 1997, 267–278.
15. Wulf G., Shea C.H., Matschiner S., Frequent feedback
enhances complex motor skill learning. J Mot Behav,
1998, 30 (2), 180–192, doi: 10.1080/00222899809601335.
16. Tzetzis G., Kioumourtzoglou E., Laios A., Stergiou N.,
The effect of different feedback models on acquisition and
retention of technique in basketball. J Hum Mov Stud,
1999, 37, 163–181.
17. Kernodle M.W., Carlton L.G., Information feedback and
the learning of multiple-degree-of-freedom activities. J Mot
Behav, 1992, 24 (2),187–196,doi:10.1080/00222895.1992.
9941614.
18. Kernodle M.W., Johnson R., Arnold D.R., Verbal instruction for correcting errors versus such instructions
plus videotape replay on learning the overhead throw.
Percept Mot Skills, 2001, 92, 1039–1051, doi: 10.2466/
pms.2001.92.3c.1039.
19. Tzetzis G., Votsis E., Three feedback methods in acquisition and retention of badminton skills. Percept Mot Skills,
2006, 102, 275–284, doi: 10.2466/pms.102.1.275-284.
20. Williams A.M., Hodges J.N., Practice, instruction and
skill acquisition in soccer: Challenging tradition. J Sports
Sci, 2005, 23 (6), 637–650, doi: 10.1080/026404104000
21328.
21. Sadowski J., Mastalerz A., Niźnikowski T., Wiśniowski W.,
Biegajło M., Kulik M., The effects of different types of
verbal feedback on learning a complex movement task.
Pol J Sport Tourism, 2011, 18, 4, 308–310, doi: 10.2478/
v10197-011-0026-2.
22. Janelle C.M., Barba D.A., Frehlich S.G., Tennant L.K.,
Cauraugh J.H., Maximizing performance feedback effectiveness through videotape replay and a self-controlled
learning environment. Res Q Exerc Sport, 1997, 68 (4),
269–279.
23. Guadagnoli M.A., Dornier L.A., Tandy R., Optimal
length for summary of results: the influence of task related
experience and complexity. Res Q Exerc Sport, 1996,
65, 250–257.
24. Magill R.A., Schoenfelder-Zohdi B., A visual model
and knowledge of performance as sources of information for learning a rhythmic gymnastic skill. Int J Sport
Psychol, 1996, 27, 7–22.
25. Hewett T.E., Stroupe A.L., Nance T.A., Noyes F.R., Plyometric training in female athletes. Decreased impact forces and increased hamstring torques. Am J Sports Med,
1996, 24, 765–773, doi: 10.1177/036354659602400611.
26. Irmischer B.S., Harris C., Pfeiffer R.P., DeBeliso M.A.,
Adams K.J., Shea K.G., Effects of a knee ligament injury
prevention exercise program on impact forces in women.
J Strength Cond Res, 2004, 18 (4), 703–707.
27. Dufek J.S, Bates B.T., The evaluation and prediction of
impact forces during landings. Med Sci Sports Exerc, 1990,
22, 370–377
28. Prapavessis H., McNair P.J., Effects of instruction in
jumping technique and experience jumping on ground
reaction forces. J Orthop Sports Phys Ther, 1999, 29,
352–356.
29. McNair P.J., Prapavessis H., Callender K., Decreasing landing forces: Effect of instruction. Br J Sports Med, 2000, 34,
293–296, doi:10.1136/bjsm.34.4.293.
30. Onate J.A., Guskiewicz K.M., Sullivan R.J., Augmented
feedback reduces jump landing forces. J Orthop Sports
Phys Ther, 2001, 31, 511–517.
Paper received by the Editor: August 16, 2012
Paper accepted for publication: February 19, 2013
Correspondence address
Tomasz Niźnikowski
Wydział Wychowania Fizycznego i Sportu
w Białej Podlaskiej
ul. Akademicka 2
21-500 Biała Podlaska, Poland
e-mail: [email protected]
153
HUMAN MOVEMENT
2013, vol. 14 (2), 154 – 160
DIFFERENCES IN THE DIRECTION OF EFFORT ADAPTATION
BETWEEN MOUNTAIN BIKERS AND ROAD CYCLISTS
doi: 10.2478/humo-2013-0018
Marek Zatoń, Dariusz Dąbrowski *
University School of Physical Education, Wrocław, Poland
Abstract
Purpose. Different forms of cycling require the use of different abilities and skills. The aim of this paper was to attempt to
identify differences in the directions and dynamics of the body’s adaption to training in road (ROAD) and mountain (MTB)
cyclists. Methods. Research was performed on a group of competitive road (n = 25) and mountain (n = 25) cyclists, mean age
16.96 ± 0.78 years presenting maximal oxygen uptake values of 4.45 ± 0.47 L/min –1. Body composition and physiological and
biochemical parameters at rest, during exercise, and during restitution (cool down) were determined. Exercise was performed
on a cycle ergometer in the form of a progressive load test. Analysis of the results included cluster analysis and basic statistical
methods. Results. Cluster analysis indicated that the amount of work performed during the progressive load test was a universal
indicator of physical fitness. The level of base excess (BE) in the 3rd min of restitution had a large influence on the remaining
parameters in both groups. Training adaptation in MTB were manifested through increased values of maximum heart rate, blood
oxygen saturation, oxygen partial pressure, and lactate and BE levels in the blood, as well as a reduction in blood pH and body
mass. Conversely, in ROAD, adaptation to effort was evidenced by increased maximum values of oxygen uptake, minute ventilation, cardiac output and the rate of carbon dioxide elimination as well as an increase in hematocrit count and lean body
mass. Conclusions. Adaptation to training by road cyclists is primarily evident in the development of aerobic capacity. Mountain
biking induces adaptive changes in the development of anaerobic capacity by increasing the buffer capacity of the blood and
muscles, as well as the development of the oxygen transportation system.
Key words: road cycling, mountain biking (MTB), effort adaptation, physical ability
Introduction
Road cycling competitions are commonly held as single or multi-stage races. Examples of the latter include
the Tour de France, which lasts 21 days (~100 hours of
exercise) and is performed over a distance of over
3500 km [1]. Road cycling competition demands prolonged effort while forcing competitors to perform at
high intensity levels – upwards of 90% of maximum
oxygen uptake ( O2max) – well above the anaerobic
threshold [1]. This finds road cyclists to be characterised by impressive aerobic capacity, reaching maximum
aerobic power levels of 370–570 W, maximal oxygen
uptake of 4.4–6.4 L min–1 and aerobic power at the onset
of blood lactate accumulation (OBLA) at 300–500 W [2].
As a result, training in road cycling is based mainly
on the development of power and aerobic capacity. In
order to develop oxygen capacity, the continuous training is commonly used. It employs exercise performed at
average intensity ( 60% O2max) for long duration
(up to 6 h). Although a road race is mostly performed
at constant speed, cyclists often perform 20–70 accele­
rations during a race at levels above maximum aerobic
power [3] whose energy cost is covered via the glyco-
* Corresponding author.
154
lytic and phosphogenic pathways. Hence, road cycling
training also integrates interval and variable training
to further improve aerobic capacity and glycolytic capacity as well as develop anaerobic performance.
Mountain bike races are usually performed on natural terrain and rely on overcoming obstacles [4]. This
form of cycling competition is performed at high intensity for the majority of a race, some of which last up to
two hours. Impellizzeri et al. [5] analysed the work intensity during a race, finding mean heart rate (HR) values
of 171 beats min–1 ( 90% HRmax). Frequent uphill runs
and accelerations also require high anaerobic fitness,
with the glycolytic and phosphogenic pathways used to
meet the required energy demand, causing significant
concentrations of lactate in the blood (10–11 mmol L –1
during the first 45 minutes of a race) [6]. This makes
the buffer capacity of muscle and blood an important
determinant in mountain biking performance.
Furthermore, mountain bikers are also characterised
by high levels of aerobic capacity, with O2max values
of 72.1 ± 7.4 mL kg–1 min–1 [7] and aerobic power at
OBLA at 366–417 W [8]. However, mountain biking
training is much more diverse than that in road cycling,
where repetitive training, among others, is used to develop the ability to perform in conditions of alternating
phosphogenic and glycolytic pathway use [4]. However,
in order to develop glycolytic power and capacity, variable
and interval training is also used (as in road cycling).
HUMAN MOVEMENT
M. Zatoń, D. Dąbrowski, Adaptation differences in cyclists
In order to better understand the differences between
road and mountain cyclists’ conditioning, the aim of the
present study was to determine the directions and dynamics of adaptation to training by analysing differences in
body composition and physiological and biochemical
parameters at rest, during exercise (progressive load test)
and during restitution (cool down).
Material and methods
The study involved fifty men (N = 50) engaged in
road (n = 25) and mountain cycling (n = 25) with 2–5
years of competitive riding experience (championship
winners and national team members in their respective
categories). The age of participants was between 16–18
years (mean age 16.96 ± 0.78 years). Mean body mass was
68.21 ± 6.71 kg, mean body height 178.44 ± 6.41 cm.
Later testing found the participants to present maximum oxygen uptake values of 4.45 ± 0.47 L min–1, or
65.88 ± 6.56 mL kg-1 min-1 relative to body mass, indicating the high physical fitness of the participants.
The study was approved by the Human Research
Ethics Committee at the University of Physical Education
in Wrocław, Poland. The participants (or guardian, in
the case of minors) signed an informed consent form
which outlined the study’s aims and procedure.
The subjects were asked to refrain from heavy exercise 24 hours before measurement taking as well as to
not eat earlier than 3–4 hours before the actual test.
At rest, the participants were measured for body
mass (m) by a WPT-200 (Radwag, Poland) medical scale,
body height (h) with an anthropometer (GPM, Switzerland) and resting blood pressure (RRsp); blood pressure
over 150/90 mmHg resulted in exclusion from further
participation in the study.
Body composition was assessed by a 6100/XL analyser (Futrex, England), measuring body water (Water%)
and fat percentage (Fat%), body fat (Fatkg) and fat free
body mass (FFMkg) expressed in kilograms, and body
water content expressed in litres (WaterL).
Acid-base balance was measured by collecting 80 µl
of arterial blood from the fingertip into heparin tubes
and immediately examined in a blood gas analyser
(model 248, Bayer, USA), determining -log [H+] (pHsp),
partial oxygen pressure (pO2sp), base excess (BEsp) and
blood oxygen saturation (O2SATsp). Peripheral blood
morphology was determined by drawing capillary blood
into EDTA tubes and stirring the sample for 2 min. The
samples were then entered into an ABX Micros 16 OT
(Horiba, USA) haematological analyser for hematocrit
(HCTsp) count. A hematocrit count over 50% disqualified a participant from the study. In addition, blood
lactate (LAsp) levels were determined by taking 10 µl
samples of capillary blood from the fingertip with Dr.
Lange LKM 140 test vials (Hach-Lange, Germany) and
analysed on a LP 400 spectrophotometer (Hach-Lange,
Germany).
An exercise test with progressive load was then administered to the participants on an Excalibur Sport
cycle ergometer (Lode, Netherlands). The ergometer
was calibrated before each test as well as individually
adjusted for each participant. The test began with a load
of 50 W, increased by 50 W every 3 min. The subjects
were instructed to maintain a cadence of no less than
60 rpm. The test was performed until exhaustion or
when an increase in load did not result in increased
oxygen consumption, after which the participants
continued to pedal with no load for 5 min as restitution (cool down).
The cycle ergometer was connected to a computer
that recorded power output, heart rate, time and speed
allowing the total work (Wz) done during the test to be
calculated. Heart rate (HR) was measured with a S810
heart rate monitor (Polar, Finland).
Respiratory variables were assessed with a Quark
metabolic cart (Cosmed, Italy). Measurement of exhaled
air began 2 min before the start of the progressive load
test and continued for 5 min after it was completed. The
metabolic cart was calibrated with atmospheric air and
reference gases before each trial. Variables that were
recorded included: maximal oxygen uptake ( O2max)
and maximum oxygen uptake relative to body mass
( O2max kg-1), maximum rate of carbon dioxide elimination ( CO2max), maximum oxygen content (FeO2max)
and carbon dioxide (FeCO2max) in exhaled air, maximum ventilation per minute ( Emax), maximum respiratory rate (RQmax) and maximum cardiac output
(Qmax).
The recorded values were averaged over 30-s periods
and cardiac output was then estimated [9]. At the 3rd min
of restitution, systolic (RRs3min) and diastolic (RRr3min)
blood pressure was measured in addition to the abovementioned parameters of acid-base balance and blood
morphology (a suffix of 3min denotes that they were
measured during restitution).
In total, 26 physiological parameters and five somatic variables were analysed. Basic statistics of the parameters, including arithmetic means, standard deviations
and minimum and maximum values, were calculated.
The Student’s t test for independent groups was used to
calculate the significance of differences between the
mean values of the parameters of the road and mountain cyclists.
An additional step included cluster analysis in order
to classify the tested parameters and better indicate
the differences between the two groups. This method
groups a set of variables into subsets (clusters), where
parameters located in one cluster are more closely related, in a certain sense, to each other than to those in
other clusters or located further away. The clusters are
plotted on a dendogram, creating a hierarchy of clusters
that merge with each other at certain distances. The
larger the Euclidean distance (abscissa) of a cluster or
individual parameter, the greater the influence the pa155
HUMAN MOVEMENT
M. Zatoń, D. Dąbrowski, Adaptation differences in cyclists
The road cyclists were characterised by higher values
of all the analysed physiological parameters than the
mountain bikers except for O2max kg–1. However, most
of the differences were not statistically significant except
for O2max, Wz, Emax, CO2max, and Qmax (Tab. 1).
Biochemical parameters were found to be very similar
in both groups, although there was a slight trend of exhibiting higher values was observed among the mountain bikers. However, most differences were also found
to be statistically insignificant. Significant differences
between the groups were found only among pH3min,
BE3min and LA3min (Tab. 2).
Analysis of the somatic parameters found mountain
bikers characterised by lower mass and body height
and, consequently, less lean body mass and body fat
as well as lower total water content. Significant differences were recorded for FFMkg and Fatkg as well as
WaterL (Tab. 3).
Cluster analysis of the two groups’ physiological and
somatic characteristics found that three parameters in
the mountain bike cyclist group (MTB) and four in the
road cyclist group (ROAD) deserved attention (Figs. 1
and 2). However, in both groups, the last or one of the
last clusters containing all the other parameters was the
amount of work done in the progressive load test (Wz).
Among mountain bikers, maximum heart rate was
found to be at a greater Euclidean distance than in road
cyclists. The obvious relationship between maximum
heart rate (HRmax), maximum lung ventilation ( Emax)
and systolic blood pressure (RRs3min) in both groups
bound these parameters into a single cluster. However,
less clear is the relationship among diastolic blood pressure (RRr3min) and two other clusters it is connected
with containing additional biochemical, physiological
and constitutional parameters.
O2max, expressed both in relative and absolute
terms, had no decisive influence on the other parameters
(small Euclidean distance). However, in MTB, maximal
oxygen uptake relative to body mass ( O2max kg–1) is
farther on the abscissa when compared with road cyclists.
Furthermore, BE3min was clustered in both groups,
underpinning the importance of this parameter.
Figure 2. Cluster analysis of the measured variables
in the group of road cyclists (ROAD);
dendogram depicting the weighted connections
by their Euclidean distances
Figure 1. Cluster analysis of the measured variables
in the group of mountain bike cyclists (MTB);
dendogram depicting the weighted connections
by their Euclidean distances
rameter or parameters have on the others. All statistical
calculations were performed using Statistica v. 10.0
software (Statsoft, USA).
Results
156
HUMAN MOVEMENT
M. Zatoń, D. Dąbrowski, Adaptation differences in cyclists
Table 1. The significance of differences among the analysed physiological parameters of mountain bike (MTB)
and road (ROAD) cyclists
Parameter
MTB
Wz [kJ]
O2max [L min–1]
O2max kg–1 [mL kg–1 min–1]
Emax [L min–1]
CO2max [L min–1]
HRmax [bpm min–1]
Qmax [L min–1]
RQmax
FeO2max [%]
FeCO2max [%]
RRssp [mmHg]
RRrsp [mmHg]
RRs3min [mmHg]
RRr3min [mmHg]
*p
0.05, ** p
249.92 ± 50.39
4.26 ± 0.49
66.03 ± 7.53
154.32 ± 18.44
4.73 ± 0.52
194.24 ± 9.29
26.5 ± 3.12
1.64 ± 0.16
18.3 ± 0.71
5.38 ± 0.48
125.8 ± 13.05
77.0 ± 9.35
147.4 ± 18.04
70.8 ± 14.41
ROAD
354.88 ± 25.1
4.64 ± 0.38
65.73 ± 5.58
181.32 ± 16.32
5.45 ± 0.44
196.8 ± 7.04
28.9 ± 2.05
1.66 ± 0.13
18.44 ± 0.44
5.49 ± 0.49
132.6 ± 11.38
81.0 ± 7.64
154.4 ± 15.02
68.0 ± 13.23
t
–9.32**
–3.10*
0.16
–5.48**
–5.28**
–1.10
–3.21*
–0.61
–0.83
–0.78
–1.96
–1.66
–1.49
0.72
0.001
Table 2. The significance of differences among acid-base balance, lactate concentration
and hematocrit count of mountain bike (MTB) and road (ROAD) cyclists
Parameter
pHsp
pH3min
pO2sp [mmHg]
pO23min [mmHg]
O2SATsp [%]
O2SAT3min [%]
BEsp [mmol L –1]
BE3min [mmol L –1]
HCTsp [%]
HCT3min [%]
LAsp [mmol L –1]
LA3min [mmol L –1]
*p
0.05, ** p
MTB
ROAD
t
7.41 ± 0.02
7.2 ± 0.05
70.02 ± 5.42
95.67 ± 4.27
94.0 ± 1.35
95.78 ± 0.72
–0.04 ± 1.28
–14.4 ± 2.39
46.05 ± 2.52
48.57 ± 2.67
0.63 ± 0.29
12.09 ± 2.36
7.41 ± 0.02
7.16 ± 0.04
70.05 ± 5.55
95.52 ± 4.85
93.97 ± 1.52
95.38 ± 0.75
0.37 ± 1.53
–16.41 ± 1.76
45.92 ± 1.83
48.51 ± 2.27
0.75 ± 0.3
13.5 ± 1.43
0.10
3.00*
–0.02
0.11
0.06
1.92
–1.03
3.38*
0.21
0.09
–1.43
–2.56*
0.001
Table 3. The significance of differences among the somatic variables of mountain bike (MTB) and road (ROAD) cyclists
Parameter
m [kg]
h [cm]
Fat% [%]
Fatkg [kg]
FFMkg [kg]
WaterL [L]
Water% [%]
*p
0.05, ** p
MTB
ROAD
64.84 ± 5.79
175.6 ± 6.1
8.66 ± 2.14
5.67 ± 1.71
59.16 ± 4.68
43.43 ± 3.46
67.03 ± 1.48
71.58 ± 5.89
181.28 ± 5.5
9.41 ± 2.66
6.86 ± 2.33
65.15 ± 4.26
47.86 ± 3.18
66.53 ± 1.78
t
–4.08**
–3.47*
–1.09
–2.06*
–4.74**
–4.71**
1.07
0.001
In the ROAD group, HCT, Water% and Qmax were
found to have a significant impact on the other parameters (large Euclidean distance). In MTB, cluster analysis
indicated that blood oxygen saturation (O2SATsp) and its
partial pressure in the 3rd minute of restitution (pO23min)
have a significant impact on the other parameters.
Discussion
Cluster analysis found that the amount of work done
during the progressive load test was the last or one of
the last clusters containing all the other parameters.
This indicates that the work performed during an ergo157
HUMAN MOVEMENT
M. Zatoń, D. Dąbrowski, Adaptation differences in cyclists
metric test can be used as a universal measure of exercise
capacity and a key indicator of performance in mountain
biking and road cycling. This can also make it a worthwhile tool in objectively assessing training by measuring
improved work efficiency, understood as a reduction in
energy cost and physiological work (evidenced by a decrease in submaximal values of O2, HR, E and postexercise lactate concentration), as performance in cycling is largely determined by the cost-effectiveness of
work performed at a submaximal intensity [10–11].
Another parameter that was found to play an important role in mountain biking was maximum heart rate,
as evidenced by its greater influence on other variables
than in the road group. Its increase is evident of adaptation to anaerobic effort, which is especially dominate
in cross-country racing [5]. Road cyclists, on the other
hand, feature lower maximum heart rate values, primarily through the use of long-duration training, stimulating the parasympathetic branch of the autonomic
nervous system [12].
Additional training effects in both road and MTB
cyclists were observed during the exercise test, where
increased oxygen demand and an increased rate of carbon dioxide elimination produced during the oxidation
of energy substrates were found to provoke increased
minute ventilation, heart rate (via decreased parasympathetic nervous system activity and increased sympathetic activity) and systolic blood pressure [13], as
was evidenced by the linking of these parameters into
a single cluster in both groups.
In both research groups, diastolic blood pressure decreased relative to resting values (6.2 mmHg in the MTB
group and 13 mmHg in the road group). Although diastolic blood pressure during exercise may show a slight
increase, no change or even a decrease, Cornelissen and
Fagard [14], after a meta-analysis of the available literature, indicated that endurance training reduces average
resting blood pressure. Therefore, the post-workout reduction in diastolic blood pressure found in the present
study is believed to be largely the effect of cycling training.
Furthermore, diastolic blood pressure was found to
combine two clusters of various biochemical, physiological and constitutional variables into one.
In the group of MTB riders, diastolic blood pressure
(both at rest and at the 3rd minute of restitution) was
combined with HCTsp, HCT3min, WaterL (one group of
variables) and pO2sp, Water%, FFMkg and O2max kg–1
(the second group of variables). The cause of these parameters being clustered together may stem from the
fact that water content and blood cell count are known
to affect blood pressure through changes in the quantity of plasma and blood viscosity [15].
The MTB cyclists had less body mass and were shorter than road cyclists. They also had featured significantly less lean body mass (MTB: 59.16 ± 4.68 kg versus
ROAD: 65.15 ± 4.26 kg, p 0.05) and fatty tissue
(MTB: 5.67 ± 1.71 kg versus ROAD: 6.86 ± 2.33 kg,
158
p 0.001) and less total body water content (MTB: 43.43
± 3.46 L versus ROAD: 47.86 ± 3.18 L, p 0.001). These
results are in line with those by other authors such as
Penteado et al. [16]. Furthermore, Lee et al. [17] also confirmed mountain bikers are characterised by lower body
mass (65.3 ± 6.5 kg, p = 0.01) and fatty tissue (sum of
seven skin-folds: 33.9 ± 5.7 mm, p = 0.01) than road
cyclists (74.7 ± 3.8 kg and 44.5 ± 10.8 mm, p = 0.01,
respectively). Furthermore, Lucía et al. [1] confirmed
that competitors specializing in individual time trials on
flat terrain are generally taller and heavier than those
who specialize in mountain riding. However, they indicated no statistically significant differences in the percentage of body fat in MTB or road cyclists.
These differences in the somatic features of cyclists
specializing in different types of riding can be explained
by the specificity of the sport and the body’s adaptation
to training. Success in road cycling is known to be determined mainly by aerobic fitness and the ability to
generate and maintain high performance throughout
an entire race [2]. Training programmes designed to
develop maximum power during cycling result in increased muscle mass, although the development of aerobic metabolism favours a shift toward oxidation of free
fatty acids [4] and can cause an increase in muscle triglyceride levels [18]. In contrast, performance in mountain biking requires both strong aerobic and anaerobic
fitness, as frequent uphill biking and accelerations rely
on energy from the glycolytic and phosphogenic pathways and can lead to significant concentrations of lactate
in the blood [6]. As a result, repetition, interval and
variable training methods are used by this group in order to maximize aerobic and anaerobic abilities. These
forms of training lead to further reductions in body mass,
which can also contribute to a reduction of aerodynamic drag and rolling resistance, thus having a major impact on competitive success [19–20].
Other parameters of interest in the MTB group were
blood oxygen saturation (both at rest and during restitution) and partial oxygen pressure in the 3rd min of restitution, which were both grouped over a large Euclidean distance. Mean blood oxygen saturation in the
3rd minute of restitution and partial oxygen pressure in
the blood were higher in the MTB group (95.78% and
95.67 mmHg, respectively) than in the group of road
cyclists (95.38% and 95.52 mmHg, respectively). This
may have come about due to increased oxygen saturation
and energy without significant increases in haemoglobin
concentration and red blood cell count (possibly with
changes in erythrocytic indices, although this parameter
was not tested). This notion was confirmed by Mørkeber
et al. [21], who demonstrated that haemoglobin concentration (Hb) and hematocrit count (HCT) decrease under
the influence of aerobic training. They found that, during
the off-season, Hb and HCT in elite cyclists were 15 g dL–1
and 43.2%, respectively, while during the competitive
season they fell to 14.1 g dL –1 (Hb) and 40.9% (HCT).
HUMAN MOVEMENT
M. Zatoń, D. Dąbrowski, Adaptation differences in cyclists
This was attributed to an increase in plasma, where,
during exercise, blood volume decreases due to a loss
of water as a result of thermoregulation function, and
to maintain blood oxygen capacity by reducing glycogen
levels. A reduction of plasma volume during exercise
has also been linked to intercellular and extracellular
fluid shifts [4, 15].
The importance of developing aerobic capacity
among road cyclists was seen by the large Euclidean
distances of maximum cardiac output, hematocrit count
and total body water content, indicating a number of
cardiovascular adaptations in this group. The first of
these being, namely, an increase in blood density combined with an increase in the amount of the morphological elements of the blood. This factor is believed to
contribute to achieving high performance in endurance
sports [15]. Secondly, endurance efforts cause an increase
in stroke volume and, consequently, an increase in cardiac output, which together with the morphological
changes in the blood, further improve aerobic capacity
[1, 2, 22].
It is interesting that O2max, expressed in both relative and absolute terms, was not influenced as strongly
by the other parameters (based on the small Euclidean
distance), even though it is traditionally considered to
be one of the most important determinants of cycling
performance. This may be explained by the fact that
while maximal oxygen uptake efficiency improves the
supply of oxygen to the mitochondria, it does not address the efficiency of cellular metabolism [8]. This indicates that this is just one of many parameters that
can be used for evaluating performance in cycling and
may in fact better serve in the selection of athletes who
already are successfully competitive by predicting their
development, and not by strictly assessing the effectiveness of a training programme [4].
Also worthy of attention is the use of absolute or relative O2max values when selecting cyclists to perform
in various cycling competitions. Researchers have noted
that O2max relative to body mass is more useful in assessing the fitness of MTB cyclists [6, 11, 23]. In the present study, average absolute maximum oxygen uptake
was higher among road cyclists than mountain bikers
(4.64 ± 0.38 L min–1 versus 4.26 ± 0.49 L min–1, respectively). However, after calculating oxygen consumption
relative to body mass, the opposite was found, with MTB
cyclists showing higher values (MTB: 66.03 ± 7.53 mL
kg–1 min–1 versus ROAD: 65.73 ± 5.58 mL kg–1 min–1).
Tolerance to acidity is the ability of muscles to perform contractions with high concentrations of lactate
and hydrogen ions [10]. In mountain biking, this parameter is of particular interest as riders are provided
with rest when going downhill after intensive uphill
climbs [11], underpinning the interval nature of this
kind of effort. The adaptation of the body to increased
metabolic acidosis occurs through increased buffer capacity of blood and muscle [24–26] and, as post-exertion
BE partially describes the buffering capacity of the blood,
explains the link of these two factors in both groups and
indicating their importance (higher in MTB group).
Furthermore, the road cyclists were characterised
by higher values of Wz, Emax, CO2max (p 0.001),
O2max and Qmax (p 0.05) than MTB. This suggests
that training adaptation in road cycling primarily occurs
in the respiratory tract (increased Emax, CO2max and
O2max) and the circulatory system (increase in Qmax),
as other authors also reported an increase in maximal
respiratory values under the influence of training in
a group of elite cyclists [1, 8, 22].
Among the statistically significant (p 0.05) differences in the biochemical parameters between both MTB
and ROAD, higher concentrations of lactate and consequently a higher base excess along with lower blood
pH values were found in road cyclists. Contradictory
results were obtained by Lucía et al. [23], who compared riders specialising in individual time trials (ITT)
with those specialising in mountain stages (C). They
found that the C riders featured higher average pH values,
maximum concentration of lactate and bicarbonate
concentration in venous blood, while the ITT group
achieved greater absolute power output. Furthermore,
other researchers have also noted the significant impact
of mountain biking training on the buffer capacity of
the blood and muscles and anaerobic capacity, which
include LAmax and post-exercise BE and pH values in
the blood and muscle [4, 6, 11]. The differences in these
test results may stem from differences in training, diet,
motivation to perform at maximum effort during an
exercise test or a combination of all these variables. These
aspects, which were not analysed in the present study,
should be included in future research on training adaptation.
Conclusions
Adaptation to training among road cyclists is expressed through increased aerobic capacity and an increase in lean body mass.
Mountain biking induces adaptive changes in the direction of development of anaerobic capacity by increasing the buffer capacity of the blood and muscles, as well
as the development of the system transporting oxygen
from the lungs to the tissues. This discipline also promotes changes in body composition leaning towards
lower body mass and reduced body fat.
The amount of work done in a progressive exercise
test and the concentration of base excess in the blood
in the third minute of restitution after maximum effort
are useful indicators in monitoring changes in physical
fitness of road cyclists and mountain bikers.
References
1. Lucía A., Hoyos J., Chicharro J.L., Physiology of Professional Road Cycling. Sports Med, 2001, 31 (5), 325–337.
159
HUMAN MOVEMENT
M. Zatoń, D. Dąbrowski, Adaptation differences in cyclists
2. Mujika I., Padilla S., Physiological and performance characteristics of male professional road cyclists. Sports Med,
2001, 31 (7), 479–487.
3. Ebert T.R., Martin D.T., Stephens B., Withers R.T., Power
output during a professional men’s road-cycling tour.
Int J Sports Physiol Perform, 2006, 1 (4), 324–335.
4. Zatoń M., Hebisz R., Hebisz P., Physiological basis of mountain biking training [in Polish]. AWF, Wrocław 2011.
5. Impellizzeri F., Sassi A., Rodriguez-Alonso M., Mognoni P., Marcora S., Exercise intensity during off-road cycling competitions. Med Sci Sports Exerc, 2002, 34 (11),
1808–1813.
6. Impellizzeri F.M., Marcora S.M., The Physiology of Mountain Biking. Sports Med, 2007, 37 (1), 59–71.
7. Impellizzeri F., Marcora S., Rampinini E., Mognoni P.,
Sassi A., Correlations between physiological variables and
performance in high level cross country off road cyclists.
Br J Sports Med, 2005, 39 (10), 747–751.
8. Faria E.W., Parker D.L., Faria I.E., The Science of Cycling
Physiology and Training – Part 1. Sports Med, 2005, 35 (4),
285–312.
9. Stringer W.W., Hansen J.E., Wasserman K., Cardiac output estimated noninvasively from oxygen uptake during
exercise. J Appl Physiol, 1997, 82 (3), 908–912.
10. Joyner M.J., Coyle E.F., Endurance exercise performance:
the physiology of champions. J Physiol, 2008, 586 (1),
35–44, doi:10.1113/jphysiol.2007.143834.
11. Costa V., De-Oliveira F., Physiological variables to predict
performance in cross-country mountain bike races. J Exerc
Physiol Online, 2008, 11 (6), 14–24.
12. Leicht A.S., Allen G.D., Hoey A.J., Influence of intensive
cycling training on heart rate variability during rest and
exercise. Can J Appl Physiol, 2003, 28 (6), 898–909, doi:
10.1139/h03-064.
13. Hu M., Finni T., Zou L., Perhonen M., Sedliak M., Alen M.
et al., Effects of strength training on work capacity and
parasympathetic heart rate modulation during exercise in
physically inactive men. Int J Sports Med, 2009, 30 (10),
719–724, doi: 10.1055/s-0029-1225329.
14. Cornelissen V.A., Fagard R.H., Effects of endurance training on blood pressure, blood pressure-regulating mecha­
nisms, and cardiovascular risk factors. Hypertension, 2005,
46(4),667–675,doi:10.1161/01.HYP.0000184225.05629.51.
15. Wirnitzer K.C., Faulhaber M. Hemoglobin and hematocrit during an 8 day mountainbike race: A field study.
J Sports Sci Med, 2007, 6 (2), 265–266.
16. Penteado V.S.R, Castro C.H.M., Pinheiro M.M., Santana M., Bertolino S., Mello M.T. et al., Diet, body composition, and bone mass in well-trained cyclists. J Clin
Densitom, 2010, 13 (1), 43–50, doi:10.1016/j.jocd.2009.
09.002.
17. Lee H., Martin D.T., Anson J.M., Grundy D., Hahn A.G.,
Physiological characteristics of successful mountain bikers
160
and professional road cyclists. J Sports Sci, 2002, 20 (12),
1001–1008, doi: 10.1080/026404102321011760.
18. Dobrzyn P., Pyrkowska A., Jazurek M., Szymanski K.,
Langfort J., Dobrzyn A., Endurance training-induced accumulation of muscle triglycerides is coupled to upregulation of stearoyl-CoA desaturase 1. J Appl Physiol, 2010,
109 (6), 1653–1661, doi: 10.1152/japplphysiol.00598.2010.
19. García-López J., Rodríguez-Marroyo J.A., Juneau C.E.,
Peleteiro J., Martínez A.C., Villa J.G., Reference values and
improvement of aerodynamic drag in professional cyclists.
J Sports Sci, 2008, 26 (3), 277–286, doi: 10.1080/0264041
0701501697.
20. Kyle C.R., The effect of crosswinds upon time trials.
Cycling Sci, 1991, 3(3-4), 51-56.
21. Mørkeberg J.S., Belhage B., Damsgaard R., Changes in
blood values in elite cyclist. Int J Sports Med, 2009, 30 (2),
130–138, doi: 10.1055/s-2008-1038842.
22. Abbiss C.R., Laursen P.B., Models to explain fatigue during prolonged endurance cycling. Sports Med, 2005, 35 (10),
865–898.
23. Lucía A., Joyos H., Chicharro J.L., Physiological response
to professional road cycling: climbers vs. time trialists.
Int J Sports Med, 2000, 21 (7), 505–512, doi: 10.1055/s2000-7420.
24. Weston A.R., Myburgh K.H., Lindsay F.H., Dennis S.C.,
Noakes T.D., Hawley J.A., Skeletal muscle buffering capacity and endurance performance after high-intensity
interval training by well-trained cyclists. Eur J Appl Physiol
Occup Physiol, 1997, 75 (1), 7–13, doi: 10.1007/s00421
0050119.
25. Böning D., Klarholz C., Himmelsbach B., Hütler M., Ma­
assen N., Extracellular bicarbonate and non-bicarbonate
buffering against lactic acid during and after exercise.
Eur J Appl Physiol, 2007, 100 (4), 457–467, doi: 10.1007/
s00421-007-0453-4.
26. Böning D., Rojas J., Serrato M., Reyes O., Coy L., Mora M.,
Extracellular pH defense against lactic acid in untrained
and trained altitude residents. Eur J Appl Physiol, 2008,
103 (2), 127–137, 10.1007/s00421-008-0675-0.
Paper received by the Editors: January 3, 2013
Paper accepted for publication: February 19, 2013
Correspondence address
Dariusz Dąbrowski
Katedra Fizjologii i Biochemii
Akademia Wychowania Fizycznego
al. Paderewskiego 35
51-612 Wrocław, Poland
e-mail: [email protected]
HUMAN MOVEMENT
2013, vol. 14 (2), 161– 167
COMPARISON OF ACCURACY OF VARIOUS NON-CALORIMETRIC
METHODS MEASURING ENERGY EXPENDITURE AT DIFFERENT
INTENSITIES
doi: 10.2478/humo-2013-0019
KRZYSZTOF DURKALEC-MICHALSKI *, MAŁGORZATA WOŹNIEWICZ,
JOANNA BAJERSKA, JAN JESZKA
Poznań University of Life Sciences, Poznań, Poland
Abstract
Purpose. The purpose of the study was to evaluate the accuracy of three non-calorimetric methods’ measurements of energy
expenditure (EE) in laboratory conditions and to compare the results obtained by each method in free-living condition in a group
of adult subjects. Methods. Measurement was performed on 20 individuals aged 19–39 years. An assessment of EE at different
intensities of physical activity was conducted by: monitoring heart rate with a S-610 Polar Sport Tester (HRM), measuring body
movement by an ActiGraph GT1M accelerometer (AM), and recording METs (MR) by a physical activity questionnaire, for different
activities (leisure and exercise) at various intensities in laboratory and free-living conditions. Indirect calorimetry (Cosmed K4b2
respirometer) was used as a reference standard. Results. The most reliable tool for EE assessment was HRM (100% accurate).
AM overestimated EE (about 60%) for activity at moderate-intensity and underestimated EE (about 40%) at vigorous-intensity.
MR overestimated the results, with measurement errors increasing with an increase in physical activity intensity (about 40–120%).
Conclusions. Although AM and MR provided less accurate results than HRM in laboratory conditions, there were no significant
differences between the three methods (HRM, AM and MR) when total daily energy expenditure was calculated for the participants in free-living condition.
Key words: energy expenditure, method accuracy, exercise, energy metabolism/physiology
Introduction
Research indicates that 50% of health is determined
by lifestyle, including making healthy diet choices and
performing enough physical exercise [1, 2]. Unfortunately, irregular lifestyles, low physical activity levels,
and poor nutrition are observed ever more frequently
among today’s youth, placing this generation at a greater
risk for developing adverse health effects in the future
[3–5].
In order to better screen and identify at-risk individuals, some preventive medicine strategies include measuring daily energy expenditure (EE) and physical activity
levels (PAL) [6, 7]. Such methods can quickly assess
whether one’s energy supply is properly balanced, preventing excess weight gain and obesity as well as other
lifestyle diseases [8]. However, an accurate assessment
of energy expenditure and physical activity level requires
the use of measurement tools that need to take into account a wide variety of human activities, functions, and
lifestyles. Furthermore, the various methods available for
calculating daily energy expenditure possess a number
of limitations. Although direct calorimetry is considered
to be the gold standard in EE measurement, it is a costly
technique and requires the use of special calorimetry
chambers that prevent analysis in natural conditions,
such as during normal activities of daily living [9, 10].
* Corresponding author.
Similarly, methods using doubly labeled water are also
quite expensive, and these techniques are also unable to
determine energy expenditure during various life activities and forms of physical activity [9, 11]. Advances in
technology have made it possible to construct portable
respirometers that continuously measure oxygen consumption and exhaled carbon dioxide (a form of indirect
calorimetry), allowing energy expenditure to be precisely
determined in a wider range of daily activities [9, 10,
12, 13].
However, this method also features a number of drawbacks, such as the high cost of the measuring apparatus
and the need to always breathe through a special mask
or mouthpiece, making it difficult to conduct research
over longer periods of time. Nonetheless, the high accuracy coupled with the relatively low cost of indirect
calorimetry has allowed it to be used as a new reference
standard in evaluating simpler, non-calometric methods that can measure energy expenditure and phy­sical
activity.
Some of the most commonly used non-calometric
methods include monitoring heart rate and body movement (heart rate monitors and accelerometers). Questionnaires and physical activity logs are also used, paired with
tables that can estimate the energy cost of various activities. The greatest advantage of the above methods is
their low cost and noninvasiveness. In addition, these
measurement tools are designed to be easy to use and
allow participants to go about their natural lives with no
restrictions on the type of activity, its duration or inten161
HUMAN MOVEMENT
K. Durkalec-Michalski et al., Accuracy of energy expenditure assessing
sity [12, 14]. However, the accuracy of such instruments
is of critical importance, as they may provide incorrect
results or be unsuitable for various research purposes.
In addition, the various non-calometric methods available for estimating energy expenditure have been frequently evaluated only by comparing one of them against
a reference method [13, 15, 16], with few studies having
conducted a simultaneous assessment of several measurement methods at the same time. This is unfortunate,
as analysis performed under similar research conditions
could help eliminate factors that may distort the results,
especially when comparing similar measurements. Moreover, in order to obtain results of a practical nature it is
important to validate measurements performed both in
laboratory and “free-living” conditions. Assah et al. [17]
believe that the measuring procedures used in laboratory
testing do not fully reflect the physical activity patterns
observed in uncontrolled, natural conditions. The authors also added that testing performed in “free-living”
conditions allows for observation of a wider range of
differentiated forms of physical activity.
With the above in mind, the purpose of this study was
to assess and compare the energy expenditure values
measured by various non-calometric methods with
those attained by indirect calorimetry when performing different forms of physical activity in laboratory
conditions, and to also compare the results of the noncalorimetric methods in more natural, “free-living”
conditions.
Material and methods
Twenty individuals participated in the study, 11
women, aged 26.0 ± 4.5 years, and 9 men, aged 26.5 ±
5.0 years, all of whom led normal life activities. Before
the start of the study, the participants’ body mass and
height were measured to calculate their body mass index
(BMI) and metabolic body weight (body mass raised
to the power of 0.73). Basal metabolic rate (BMR) was
calculated after fasting and in stress-free conditions by
indirect calorimetry using the K4b2 portable gas respiro­
meter (Cosmed, Italy). The participants were found to
be in good health with none showing signs of obesity
(BMI 30 kg/m2).
Three non-calometric methods used to measure
energy expenditure were assessed in laboratory conditions: by monitoring heart rate (HR) with a S-610 Polar
Sport Tester heart rate monitor (Polar Electro Oy, Finland), by measuring body movement with a GT1M triaxial accelerometer (ActiGraph, USA), and by recording
physical activity in the form of questionnaire used to calculate the metabolic cost (MET, Metabolic Equivalent of
Task) of the performed activities. Indirect calorimetry
was assessed with the previously-mentioned K4b2, continuously analyzing (breath-by-breath) the volume of
inhaled and exhaled air as well as changes in gas concentration between oxygen uptake and expiration. The port162
able gas analyzer was calibrated each time before use with
a reference gas mixture of: CO2 – 5%, O2 – 16%.
The taking of measurements by the three non-calorimetric methods and indirect calorimetry as a reference
were performed in laboratory conditions, beginning each
time in the morning after the participants had consumed
an easy to digest breakfast (average energy value of
600 kcal, composed of 13% protein, 57% carbohydrates,
and 30% fat) and continued for five hours in successive stages reflecting an average breakdown of the participants’ normal daily routine, which had been previously recorded during a pilot study. The breakdown
came to: sleep, lasting 94 min and 30 s, which accounted
for 31.5% of the day; light-intensity activity (i.e. reading,
writing, working at a computer, or watching TV) performed in a seated position, lasting 133 min and 30 s,
which accounted for 44.5% of the day; moderate-intensity activity (simulated by walking, various laboratory
exercises, preparing meals, or washing dishes), lasting
69 min, accounting for 23.0% of the day; and vigorousintensity activity (performed on a CX1 cycle ergometer
[Kettler, Germany] at an intensity of 70% VO2max at
150 W so as to reach heart rate values of 170–180 bpm),
lasting 3 min, which accounted for 1.0% of the day.
The first 15 min of the test were treated as an adaptation period and were omitted from the final results.
In addition, 10-min pauses were introduced between each
activity phase and also not recorded. The calibration
techniques recommended by Livingstone et al. were
adhered to during measurement taking [18]. The obtained data were than proportionally calculated and
presented as total daily energy expenditure.
Energy expenditure measured with monitoring heart
rate is based on the physiological relationship between
heart rate and metabolic rate, which is a result of the
body’s need to deliver sufficient levels of oxygen and
energy substrates. The heart rate monitor (S-610 Polar
Sport Tester) used in this method consisted of a cardiac
pulse transmitter placed on the chest around the area
of the heart by a rubber strap and a wrist monitor that
recorded the amount of beats per minute. A correct estimate of EE was preceded by first performing regression
analysis between oxygen uptake (VO2) and heart rate
(HR) – VO2/HR – for each individual. This assessment
was aided by the values recorded during indirect calorimetry at various intensity levels (lying, sitting, standing, walking, and riding the ergometer). In accordance
with the procedure recommended by Bradfield et al. [19]
and Livingstone et al. [18], the HR values obtained from
earlier VO2/HR calculations were used to delineate threshold heart rate, or HR-FLEX (as the average between
the highest heart rate at rest and the lowest exercising
heart rate), creating a individually established threshold
between activities performed at light intensity and those
of at least moderate physical activity. After HR-FLEX
was calculated for each participant, the heart rate recorded throughout the measurement period was cate-
HUMAN MOVEMENT
K. Durkalec-Michalski et al., Accuracy of energy expenditure assessing
Table 1. Estimating individual total daily energy expenditure based on heart rate measurement
Type of activity
Equation
Legend
EE during sleep
EE = ST/1400 × BMR
ST – Sleep time
BMR – Basal metabolic rate
EE at light-intensity activity
(HR < HR-FLEX)
EE = BMR/1440×DEp × 1.4
DE p – duration of effort at light intensity (min)
EEmin = (ax + b) × 4.9
EE min – energy expenditure (kcal/min)
a – linear regression slope coefficient (VO2/HR)
x – HR (bpm)
b – linear regression intercept coefficient (VO2/HR)
4.9 – energy equivalent for oxygen (kcal/dm3)
EE at moderate- and vigorous-intensity
activity (HR HR-FLEX)
BMR calculated by indirect calorimetry using the K4b2 respirometer.
ogrized into four types of activity depending on their
intensity [18], with energy expenditure calculated using the procedure described in Table 1.
The second non-calometric method for measuring
energy expenditure (EE) was performed with a accelero­
meter, which provides an objective assessment of all phy­
sical activity performed over a period of time as well as
its duration and intensity [20]. The accelerometer is
a lightweight, waterproof, and noninvasive tool, and it
can record body movement in the vertical, lateral, and
longitudinal directions. With this method, energy expen­
diture (EE) associated with physical activity is calculated
by the use of an intrasystemic algorithm, composed of
two equations [21]. For light-intensity activity (counts
< 1952, i.e., < 3.0 METs; with 1 count equal to 16.6 mg/s
at 0.75 Hz), the Work-Energy Theorem is used: EE (kcal/min)
= counts/min × 0.0000191 × body mass (kg). For moderateand vigorous-intensity activity (counts 1952, i.e.
3.0 METs), Freedson’s equation is used: EE (kcal/min) =
0.00094 × counts/min + 0.1346 × body mass (kg) – 7.37418.
Total daily energy expenditure (kcal) at different intensities of physical activity is then calculated by:
EE = EEpa + BMR × Dpa,
where
EEpa– energy expenditure associated with physical
activity,
Dpa – duration of physical activity at a given intensity.
Energy expenditure during sleep was calculated
the same way as in the method monitoring heart rate
(Tab. 1).
The third non-calometric method used for measuring
energy expenditure involved a physical activity questionnaire used to register the type and duration of all activity
the participants performed. The activities were crossindexed to compute their MET values (kcal/h/kg BM),
where one MET represents 1 kcal burned per 1 kg of body
mass (BM) in 1 h at complete rest, and it is equivalent
to oxygen uptake of about 3.5 ml/kg of body mass.
The three non-calorimetric methods were also assessed in “free-living” conditions, with the participants
leading their normal life activities over two 24-h periods
over three days (two weekdays and one day off from
work), excluding the time spent sleeping. All three methods
were used at the same time, meaning the participants
wore the Polar Sport Tester heart rate monitor and the
ActiGraph accelerometer and logged their physical activity by use of the questionnaire. The data collected
were used to calculate total daily energy expenditure
Statistical analysis of the collected data was performed using Statistica v. 7.1 (Statsoft, USA), which included checking for straight-line correlations, one-way
analysis of variance (ANOVA) post hoc Tukey’s test if
the results were statistically different; the level of significant was set at = 0.05.
Results
The anthropometric characteristics of the group are
presented in Table 2. An assessment of the participants’
nutritional status whith BMI index found that both the
males and females generally featured adequate BMI levels
(24.4 ± 3.7 kg/m2 and 21.9 ± 3.4 kg/m2 , respectively),
although the results were largely dispersed with a number
of individuals who were underweight (BMI < 18.4 kg/m2)
or overweight (BMI > 25 kg/m2). Large differences among
the group were also found after calculating the physical
activity level (PAL) for each participant, based on the
ratio of total metabolic rate to basal metabolic rate, and
ranged from 1.02 to 2.42.
Table 3 contains the energy expenditure values of
physical activity performed at different intensities from
using the three non-calorimetric methods and indirect
calorimetry. Significant differences (p < 0.05) in measured energy expenditure were found between the physical activity questionnaire (measuring METs) and the accelerometer (ActiGraph GT1M) at rest; between indirect
calorimetry (K4b2 gas analyzer) or heart rate (S-610 Polar
Sport Tester) and the accelerometer at moderate-intensity
activity; between indirect calorimetry or heart rate measurement or the physical activity questionnaire and the
accelerometer at vigorous-intensity activity. Although
the total daily energy expenditure (TDEE) values measured by the various methods in laboratory conditions
did not significantly differ, the discrepancy in TDEE
163
HUMAN MOVEMENT
K. Durkalec-Michalski et al., Accuracy of energy expenditure assessing
Table 2. Anthropometric characteristics and basal metabolic rate (BMR) of the participants
Women n = 11
Parameter
± SD
Age [years]
Basal metabolic rate (BMR)1 [kcal/day]
Body mass [kg]
Body height [cm]
BMI [kg/m2]
Metabolic body weight [kg0.73]
Physical activity level (PAL)2
26.0 ± 4.5
1135.2 ± 192.6
60.1 ± 9.1
165.9 ± 5.4
21.9 ± 3.4
19.9 ± 2.2
1.61 ± 0.42
Men n = 9
Min – max
± SD
19.0 – 36.0
960.2 – 1441.5
65.0 – 100.0
166.0 – 191.0
18.2 – 29.9
21.1 – 28.8
1.07 – 2.42
1
BMR calculated by indirect calorimetry, 2 PAL during “free-living” conditions:
1.55–1.6 – moderately active lifestyle; 1.75 – vigorously active lifestyle [22]
26.5 ± 5.0
1240.1 ± 87.1
77.7 ± 14.0
178.0 ± 8.4
24.4 ± 3.7
23.9 ± 3.1
1.52 ± 0.39
Min – max
23.0 – 39.0
1114.4 – 1458.6
47.0 – 83.0
152.0 – 173.0
17.7 – 29.4
16.6 – 25.2
1.02 – 2.07
1.4 –sedentary lifestyle;
Table 3. Assessment of energy expenditure measured by the three non-calorimetric methods versus indirect calorimetry
at different physical activity levels
Activity intensity level
Method
Indirect calorimetry
(K4b2)
Questionnaire
(METs)
Accelerometer
(ActiGraph)
Heart rate
(Polar Sport Tester)
Sleep
[kcal/kg BM/
duration]*
Light intensity
[kcal/kg BM/
duration]
Moderate intensity Vigorous intensity Total daily energy
[kcal/kg BM/
[kcal/kg BM/
expenditure
duration]
duration]
[kcal/kg BM/day]
7.7 ± 1.7a
14.6 ± 3.5a
14.0 ± 4.3a
1.0 ± 0.3a
37.3 ± 8.3a
6.8 ± 0.5a
17.9 ± 7.5b
18.3 ± 5.2ab
2.2 ± 0.8b
45.2 ± 10.9a
7.5 ± 0.6a
12.5 ± 2.8c
22.5 ± 14.6b
0.6 ± 0.3c
43.1 ± 17.4a
7.5 ± 0.6a
14.1 ± 3.6a
13.3 ± 4.0 a
1.0 ± 0.3a
36.0 ± 6.7a
a, b, c
– inscriptions denote statistically significant differences ( = 0.05) of the values in the columns
* time spent at each physical activity level, including: sleep – 454 min (7.6 h) = 31.5% of the day (the resting metabolic rate
was adopted as the energy expenditure of sleep for the accelerometer and heart rate methods)
light-intensity activity: 641 min (10.68 h) = 44.5% of the day; moderate-intensity activity: 329 min (5.48 h) = 23.0% of the day
vigorous-intensity activity: 16 min (0.27 h) = 1.0% of the day
values assessed by the K4b2 portable gas analyzer or by
monitoring heart rate, and the physical activity questionnaire or the accelerometer, was between 15–20%.
An assessment of EE measurement accuracy by the
three non-calometric methods and compared against
those recorded by indirect calorimetry are presented
in Figure 1. On the basis of the results, it was found that
monitoring heart rate with the Polar Sport Tester was
260
Mets - Questionnaire
Actigraph - Accelerometer
Polar-Sport - HR monitor
240
220
b
200
Accuracy [%]
180
160
b
b
b
140
120
a
a
a
100
a
a
80
60
40
20
Light intensity
Moderate intensity
Vigorous intensity
a, b
– inscriptions denote statistically significant differences ( = 0.05)
of the values
Figure 1. Accuracy of energy expenditure measurements
by three different non-calorimetric methods
164
the most reliable tool in measuring EE at rest as well as
at moderate and vigorous physical activity (accuracy
of 95%–100%). The method using the accelerometer
(ActiGraph) overestimated EE at moderate intensity by
60% and underestimated EE by 40% at vigorous intensity (the exercise test on the cycle ergometer). However,
the accelerometer was 86% accurate in measuring EE
at light intensity in relation to the values measured by
indirect calorimetry. Out of all the methods used to
measure energy expenditure, the least accurate was that
using the physical activity questionnaire and correlating
the answers with MET values. This method overestimated energy expenditure with ever larger measurement errors as the physical activity intensity increased:
being 20%–30% off for activity at light intensity to 120%
off for vigorous intensity.
Statistical analysis of the results confirmed that the
physical activity questionnaire (calculating METs) was
significantly less accurate (p < 0.05) than the other measurement methods by overestimating energy expenditure
at light and vigorous intensity levels. Furthermore, energy
expenditure assessed by the accelerometer (ActiGraph)
HUMAN MOVEMENT
K. Durkalec-Michalski et al., Accuracy of energy expenditure assessing
2200
a
TDEE [kcal/day]
2100
a
a
2000
1900
1800
1700
1600
1500
Mets - Questionnaire
Actigraph - Accelerometer
Polar-Sport - HR monitor
Figure 2. Comparison of total daily energy expenditure
values recorded in “free-living” conditions by the three
different non-calorimetric methods
and physical activity questionnaire (METs) at moderate
intensity were significantly less accurate (p < 0.05) than
by monitoring heart rate.
Figure 2 provides a comparison of total daily energy
expenditure by the three non-calorimetric methods in
“free-living” conditions. In comparison with EE based
on monitoring heart rate (Polar Sport Tester), whose
measurement accuracy in laboratory conditions was
confirmed by indirect calorimetry, both the accelerometer
and questionnaire methods overestimated total daily
energy expenditure by 5%. However, no statistically significant differences in total daily energy expenditure
were found among the three non-calorimetric methods.
Discussion
The present study estimated energy expenditure by
monitoring heart rate, using a accelerometer, and recording physical activity on questionnaire to calculate METs,
and compared the results with those attained by indirect calorimetry using a K4b2 respirometer. The most accurate of the non-calometric methods was monitoring
heart rate using the S-610 Polar Sport Tester heart rate
monitor, with an accuracy close to 100%. These results
are in line with previous observations on the reliability
of this method by other researchers. Goodie et al. [23]
found a high correlation (r = 0.98, p < 0.001) between
heart rate measurements using a Polar Vantage XL heart
rate monitor and those obtained using electrocardio­
graphy (ECG). In turn, Maffeis et al. [16] validated this
measurement technique and its accuracy in non-obese
children by comparing it with energy expenditure measured using doubly labeled water. Garet et al. [24] conducted a study on verifying measurement of energy expenditure by heart rate with the gold standard of EE
assessment, direct calorimetry. They found that measurement of physical activity levels and energy expenditure by heart rate was quite accurate and did not differ
with respect to the data obtained using the reference
method.
An assessment of the accuracy of the ActiGraph
accelerometer found it overestimated energy expenditure
up to 60% during moderate-intensity effort when com-
pared to the reference values from indirect calorimetry,
and that the obtained results showed the highest intragroup variability. This may be explained by the findings
of Hustvedt et al. [25], who claimed that some accelero­
meters can wrongly interpret even small variations in
walking gait (from 2 to 3 km/h), classifying it as mode­
rate-vigorous instead of light intensity. Furthermore,
Le Masurier et al. [26] noted that accelerometers may
incorrectly record non-walking movement as walking,
overestimating energy expenditure especially among
individuals who often travel by car.
What was also of interest was that the accelerometer
used in this study underestimated energy expenditure
during vigorous-intensity activity by 40% in comparison
with EE obtained by indirect calorimetry. This could be
linked to this study’s use of the cycle ergometer to simulate vigorous exercise, as a accelerometer works primarily
by measuring torso movement, and its placement on the
waist may have only recorded movement made by the
upper body and not the cyclical work performed by the
lower limbs. A more correct measure of this type of effort
may require the use of additional sensors in accelero­
meters, or the development of even more accurate intrasystemic algorithms in order to correctly estimate energy
expenditure during cycling. Therefore, the conclusions
set forth by Trost et al. [27] seem to be corroborated,
they stated that the algorithms used in accelerometers
to assess energy expenditure do not take into account
all the various types of physical activity humans perform. However, it is worth mentioning that the predictive accuracy of the most commonly used methods
to calculate energy expenditure on the basis of various
physiological parameters was highest by measurements
taken by an ActiGraph accelerometer with Freedson’s
equation (73% accurate) [28].
In the present study, the least accurate data on energy
expenditure at different exercise intensities in laboratory conditions were collected by the use of the physical
activity questionnaire that calculated METs. This may be
due to the need, among other factors, of maintaining a very
detailed log of the duration and type of physical activity
performed as well as problems connected with properly
classifying and interpreting their impact on energy
expenditure. Sergi et al. [29], in a study on an elderly
population, found that this method underestimated
the values of activity performed at light- or moderateintensity in comparison with the results attained by
indirect calorimetry. It is believed that changes in body
composition and the functioning of the cardiovascular
system may explain for these differences. Seale et al. [30]
found that the use of a physical activity questionnaire
to calculate METs (4.60 MJ/d for men and 3.42 MJ/d for
women) significantly overestimated total daily energy
expenditure when compared with the doubly labeled
water method. Similarly, Milani et al. [31] performed
a study on patients after coronary events, finding that
energy expenditure estimated by a questionnaire was
165
HUMAN MOVEMENT
K. Durkalec-Michalski et al., Accuracy of energy expenditure assessing
far less reliable than a reference method (indirect calorimetry). They found that this method overestimated
the results of an exercise test both before and after
a 12-week cardiac rehabilitation program. Similar measurement errors were found in the present study, and,
more interestingly, the magnitude of error increased
with higher exercise intensity, indicating this is a systemic error. Thus, due to their low accuracy, both the
physical activity questionnaire calculating MET values
as well as the accelerometer methods should be limited
to general population studies attempting to obtain only
indicative total daily energy expenditure and physical
activity levels.
As was mentioned, the physical activity questionnaire and accelerometer methods overestimated total daily
energy expenditure at different activity intensities by
15% and 20%, respectively, than those recorded using
the reference method. Although these results were found
to be statistically insignificant, the size of this error may
have a number of physiological repercussions, as the use
of these methods to assess total daily energy expenditure may lead to erroneous assumptions on individuals’
daily energy requirements.
In light of the results attained in laboratory settings,
the data obtained in “free-living” conditions showed
a surprisingly high convergence among the non-calometric methods. As monitoring heart rate in laboratory
conditions deviated to a considerably lesser extent from
the indirect calorimetry method in comparison with the
other methods, it was expected that a similar variation
would have been observed in natural conditions. However, the differences between the mean values of the three
non-calorimetric methods amounted to only 5%, which
corresponds to a deviation of approximately 100 kcal/d
(0.4 MJ/d) between the evaluated methods. This is likely
to be attributable to the fact that there was a greater
range of different forms of physical activity being performed and at more varied intensities, hence the measurement errors could have mutually compensated each
other [17]. As a result, it can be assumed that in many
cases the practical application of any of the three methods can provide relatively close assessment of total daily energy expenditure, although attention needs to be
paid to the questionnaire and accelerometer methods from
overestimating the results even in “free-living” conditions. However, the results of this study should be used
with some caution in regard to the population being
studied, where in this case it comprised of individuals
who were sedentary or moderately physically active.
Conclusions
Monitoring heart rate is a reliable method for assessing energy expenditure at different intensity levels.
Energy expenditure measurements based on a accelero­
meter and in particular with the use of a physical activity
questionnaire calculating MET values are less accurate,
166
where the questionnaire method showing a tendency
towards measurement overestimation, with error increasing with activity intensity. On the other hand, the accelerometer both overestimated or underestimated results
depending on the intensity or type of physical effort being
performed. Nonetheless, each of the evaluated non-calometric methods can be successfully used to meaningfully assess total daily energy expenditure in more natural,
“free-living” conditions, especially in a young sample
group with low to average physical activity levels.
References
1. Wojtyniak B., Goryński P., Health Status of the Polish
Population and the National Health Program 2006–2015
[in Polish]. Reumatologia, 2007, 45 (1), supl.1, S5-S17.
2. POL-HEALTH. Ministry of Health, Department of Health
Policy. National program of overweight and obesity prevention and chronic noncommunicable diseases by improving nutrition and physical activity for 2007–2011
[in Polish]. Warszawa 2009. Available from: http://www.
mz.gov.pl/w w wfiles/ma_ struktura/docs/ot ylosc_
06012010.pdf.
3. Durkalec-Michalski K., Suliburska J., Jeszka J., The evaluation of eating habits and nutritional status in young
men according to their physical activity [in Polish]. Standardy medyczne/Pediatria, 2011, 8, 100–106.
4. Czeczelewski J., Raczyński G., Food intake, somatic traits
and physical activity of adolescents. Hum Mov, 2006, 7 (1),
58–64.
5. Howe C.A., Freedson P.S., Feldman H.A., Osganian S.K.,
Energy expenditure and enjoyment of common children’s
games in a simulated free-play environment. J Pediatr,
2010, 157 (6), 936–942, doi: 10.1016/j.jpeds.2010.06.041.
6. Speck R.M., Schmitz K.H., Energy expenditure comparison: a pilot study of standing instead of sitting at
work for obesity prevention. Prev Med, 2011, 52 (3–4),
283–284, doi: 10.1016/j.ypmed.2011.02.002.
7. Norton K., Norton L., Sadgrove D., Position statement
on physical activity and exercise intensity terminology.
J Sci Med Sport, 2010, 13 (5), 496–502, doi: 10.1016/j.
jsams.2009.09.008.
8. Allender S., Scarborough P., Peto V., Rayner M., Leal J.,
Luengo-Fernandez R. et al., European cardiovascular
disease statistics edition 2008. Available from: http://
www.ehnheart.org/publications/annual-reports.html.
9. Hageman P.A., Norman J.F., Pfefferkorn K.L., Reiss N.J.,
Riesberg K.A., Comparison of two physical activity monitors during a 1-mile walking field test. J Exerc Physiol,
2004, 7 (3), 102–110.
10. Seale J.L., Rumpler W.V., Conway J.M., Miles C.W., Comparison of doubly labeled water, intake-balance, and directand indirect-calorimetry methods for measuring energy
expenditure in adult men. Am J Clin Nutr, 1990, 52 (1),
66–71.
11. Rising R., Harper I.T., Fontvielle A.M., Ferraro R.T.,
Spraul M., Ravussin E., Determinants of total daily energy
expenditure: variability in physical activity. Am J Clin
Nutr, 1994, 59 (4), 800–804.
12. White K., Schofield G., Kilding A.E., Energy expended by
boys playing active video games. J Sci Med Sport, 2011,
14 (2), 130–134, doi:10.1016/j.jsams.2010.07.005.
HUMAN MOVEMENT
K. Durkalec-Michalski et al., Accuracy of energy expenditure assessing
13. Nieman D.C., Trone G.A., Austin M.D., A new handheld device for measuring resting metabolic rate and
oxygen consumption. J Am Diet Assoc, 2003, 103 (5),
588–592, doi: 10.1053/jada.2003.50116.
14. Bourrilhon C., Philippe M., Chennaoui M., Van Beers P.,
Lepers R., Dussault C. et al., Energy expenditure during
an ultraendurance alpine climbing race. Wilderness Environ Med, 2009, 20 (3), 225–233, doi: 10.1580/08-WEMEOR-217R3.1.
15. Campbell K.L., Crocker P.R., McKenzie D.C., Field evaluation of energy expenditure in women using Tritrac
accelerometers. Med Sci Sports Exerc, 2002, 34 (10),
1667–1674
16. Maffeis C., Pinelli L., Zaffanello M., Schena F., Iacumin P., Schutz Y., Daily energy expenditure in free-living
conditions in obese and non-obese children: comparison of doubly labelled water (2H2(18)O) method and
heart-rate monitoring. Int J Obes Relat Metab Disord,
1995, 19 (9), 671–677.
17. Assah F.K., Ekelund U., Brage S., Wright A., Mbanya J.C.,
Wareham N.J., Accuracy and validity of a combined heart
rate and motion sensor for the measurement of free-living
physical activity energy expenditure in adults in Cameroon. Int J Epidemiol, 2011, 40 (1), 112–120, doi: 10.1093/
ije/dyq098.
18. Livingstone M.B., Robson P.J., Totton M., Energy expenditure by heart rate in children: an evaluation of calibration
techniques. Med Sci Sports Exerc, 2000, 32 (8), 1513–1539.
19. Bradfield R.B., Chan H., Bradfield N.E., Payne P.R., Energy
expenditures and heart rates of Cambridge boys at school.
Am J Clin Nutr, 1971, 24 (12), 1461–1466.
20. Tryon W.W., Williams R., Fully proportional actigraphy:
a new instrument. Behav Res Meth Instr Comp, 1996,
28 (3) 392–403, doi: 10.3758/BF03200519.
21. Rothney M.P., Neumann M., Béziat A., Chen K.Y., An
artificial neural network model of energy expenditure using
nonintegrated acceleration signals. J Appl Physiol, 2007,
103 (4), 1419–1427, doi: 10.1152/japplphysiol.00429.2007.
22. World Health Organization, Obesity: Preventing and
managing the global epidemic. report of WHO consultation. Technical report series 894. WHO, Geneva 2000,
1–253.
23. Goodie J.L., Larkin K.T., Schauss S., Validation of the
Polar Heart Rate Monitor for Assessing Heart Rate During
Physical and Mental Stress. J Psychophysiol, 2000, 14 (3),
159–164, doi: 10.1027/0269-8803.14.3.159.
24. Garet M., Boudet G., Montaurier C., Vermorel M., Coudert J., Chamoux A., Estimating relative physical workload using heart rate monitoring: a validation by wholebody indirect calorimetry. Eur J Appl Physiol, 2005, 94
(1–2), 46–53, doi: 10.1007/s00421-004-1228-9.
25. Hustvedt B.E., Christophersen A., Johnsen L.R., Tom­
ten H., McNeill G., Haggarty P. et al., Description and
validation of the ActiReg: a novel instrument to measure physical activity and energy expenditure. Br J Nutr,
2004, 92 (6), 1001–1008, doi: 10.1079/BJN20041272.
26. Le Masurier G.C., Tudor-Locke C., Comparison of pedometer and accelerometer accuracy under controlled conditions. Med Sci Sports Exerc, 2003, 35 (5), 867–871.
27. Trost S., Way R., Okely A., Predictive validity of accelero­
meter prediction equations for energy expenditure (EE)
during overland walking and running in children and
adolescents. Med Sci Sports Exerc, 2004, 36 (5), S197.
28. Trost S.G., Way R., Okely A.D., Predictive validity of three
ActiGraph energy expenditure equations for children.
Med Sci Sports Exerc, 2006, 38 (2), 380–387.
29. Sergi G., Coin A., Sarti S., Perissinotto E., Peloso M.,
Mulone S. et al., Resting VO2, maximal VO2 and metabolic
equivalents in free-living healthy elderly women. Clin
Nutr, 2010, 29 (1), 84–88, doi: 10.1016/j.clnu.2009.07.010.
30. Seale J.L., Klein G., Friedmann J., Jansen G.L., Mitchell D.C.,
Smiciklas-Wright H., Energy expenditure measured by
doubly labeled water, activity recall, and diet records in
the rural elderly. Nutrition, 2002, 18, 568–573, doi: 10.­
1016/S0899-9007(02)00804-3.
31. Milani R.V., Lavie C.J., Spiva H., Limitations of estimating metabolic equivalents in exercise assessment in
patients with coronary artery disease. Am J Cardiol, 1995,
75 (1), 940–942, doi: 10.1016/S0002-9149(99)80693-6.
Paper received by the Editor: September 27, 2011
Paper accepted for publication: April 25, 2013
Correspondence address
Krzysztof Durkalec-Michalski
Zakład Dietetyki
Katedra Higieny Żywienia Człowieka
Uniwersytet Przyrodniczy
ul. Wojska Polskiego 31
60-624 Poznań, Poland
e-mail: [email protected]
167
HUMAN MOVEMENT
2013, vol. 14 (2), 168– 174
Exercise or relaxation? Which is more effective
in improving the emotional state of pregnant women?
doi: 10.2478/humo-2013-0020
Monika Guszkowska*, Katarzyna Sempolska,
Agnieszka Zaremba, Marta Langwald
Józef Piłsudski University of Physical Education, Warszawa, Poland
Abstract
Purpose. The aim of this study was to compare the changes in emotional states in pregnant women after completing a relaxation
session and a one-off physical exercise class. Methods. The study included 139 pregnant women between the ages of 22 to 34 years
(28.59 ± 2.99 years) in their second and third trimesters of pregnancy who were participating in one of three programs: a progressive relaxation course, a physical exercise program, or a traditional childbirth education program (control group). Emotional
states were assessed by McNair, Lorr, and Droppleman’s Profile of Mood States immediately before (pre-test) attending their seventh
respective class and then immediately after finishing it (post-test) Results. The pre-test emotional state of participants in all
three groups was similar. Post-test, both the exercise and relaxation groups featured significantly improved emotional states,
with the changes greater than those of women who attended the prenatal class. Pregnant women who participated in physical
exercise were less troubled and tense and characterized by a higher level of vigor than the control group. Conclusions. Physical
exercise may be especially helpful in coping with fatigue and feeling tired, while relaxation training is more beneficial for women
with elevated levels of anxiety and depression.
Key words: exercise, relaxation, pregnancy, emotions
Introduction
Research shows that performing physical exercise at
a moderate intensity by healthy pregnant women not
only poses no threat to the mother or fetus, but actually
contains a number of important health and physical
benefits for both the mother and child [1–4]. As explained
by Melzer et al. [5] in a recent literature review on this
subject, regardless of the physiological changes that occur
during pregnancy, pregnant women benefit the same
from regular physical activity as non-pregnant women.
Physical activity is therefore an important component of a healthy pregnancy, with today’s health recommendations advising expectant mothers without pregnancy-related risks or complications to perform at least
30 minutes of moderately intensive, preferably aerobic,
physical exercise as often as possible, even every day
[1–2, 6].
Unfortunately, research has shown that the amount
of physical activity performed either during work or for
leisure significantly decreases during pregnancy [7–9].
The proportion of pregnant women participating in
moderate to vigorous intensity physical exercise or sport
falls steadily as the pregnancy advances, where significantly more moderate to high intensity physical exercise
is performed in the first and second trimester than in
the third. Moreover, the duration and intensity of the
* Corresponding author.
168
exercise performed during pregnancy is significantly
reduced. Although the number of women who are lightly
active remains stable during pregnancy, the proportion
of women leading a sedentary lifestyle increases significantly. Studies conducted on pregnant women in the
United States found that most spent more than half their
day engaged in sedentary behavior and did not follow
minimum physical activity recommendations [10].
The positive effects of performing proper amounts
of exercise during pregnancy not only have a major impact on physical health but are also known to have a number of potential benefits on mental health. The mental
well-being of expectant mothers is known to have a significant impact on pregnancy, childbirth, motherhood,
and the newborn and its later development in life. Pregnant women under large amounts of stress are more at
risk for miscarriages or premature birth as well as perinatal complications [11, 12]. Stress during pregnancy is
linked to giving birth to small for gestational age babies
in poorer psychophysical states and found to increase their
risk of developmental disorders in later life [12–14].
Since prenatal stress may significantly contribute to
pregnancy complications and developmental disorders,
particular importance has been placed on reducing the
stress levels of pregnant women. One such solution is
physical exercise, at least for those women whose pregnancy is progressing normally. Numerous studies conducted on a general population have repeatedly confirmed the positive emotional changes experienced after
a one-off workout. Therefore, instead of reviewing the
results of individual studies, attention was focused on
HUMAN MOVEMENT
M. Guszkowska et al., Exercise and relaxation for pregnant women
analyzing meta-analyses of the available literature. It was
found that the majority of studies focused on the possibility of using physical exercise in lowering negative
emotional states, such as fear and depression. Starting
from 1981, more than 40 systematic reviews have been
published, all of which confirmed that physical exercise
reduces anxiety and depression levels [15–17]. One of the
first studies of this type was performed by Yeung [18],
who noted that the majority of the results show an improvement in mood after a one-off workout, regardless of gender or age or even disability. Positive effects
were observed after both aerobic and anaerobic exercise
of varying duration and intensity, and were usually felt
up to 3–4 hours after completion. Interestingly, among
the studies analyzed by Yeung [18], the one group which
featured a deterioration in mood after exercise were
pregnant women.
Another aspect that needs to be considered is the approach proposed by positive psychology, which places
emphasis on the determinants of mental well-being. By
adopting this perspective, it needs to be asked whether
one-off exercise can in fact improve the indicators of positive mood such as positive affect, vigor, energy level, joy,
and euphoria. Reed and Ones [19] completed a metaanalysis on this issue, addressing over 158 studies conducted between 1979 and 2005, although only two
involved pregnant women. The results of their metaanalysis found significant increases in the indicators
of positive arousal immediately after aerobic exercise,
particularly when the levels of pre-exercise were below
average. Furthermore, the positives effects of exercise at
low intensity, lasting up to 35 minutes, and performed
from a low to moderate frequency (intensity × duration)
were unilaterally confirmed.
Unfortunately, few of the analyzed studies investigated the effect of one-off exercise on the emotional
states of pregnant women. The few did observed a decrease in the psychological indicators of depression
and anxiety after a one-off aerobic workout [20]. Similar
decreases in anxiety and depression levels were observed
in postpartum women after participating in a 60-minute
aerobic training session at moderate intensity; in addition, they also exhibited an improvement in mood and
an increase in vigor [21]. A study performed on pregnant
women participating in aqua-aerobics classes indicated
that this type of exercise causes increases in positive
mood, better well-being, a decrease in negative emotions, and a reduction in feeling tired or fatigued [22].
Similarly, after a one-off workout from the 9 Months
Active program, a group of pregnant women featured
beneficial emotional changes, particularly with noted
decreases in anxiety and tension levels [23].
Nonetheless, relaxation techniques are by far the
most commonly applied methods for alleviating stress
and anxiety. A meta-analysis by Manzoni et al. [24] found
that such techniques are moderately to highly effective
in reducing anxiety. Particularly effective in reducing
anxiety was meditation, although a number of other
techniques (autogenic training, progressive relaxation,
and mixed techniques) were also helpful. However, none
of the studies covered by this meta-analysis involved
pregnant women and so far little is known about the
effectiveness of relaxation therapy in this population.
Outside of the available meta-analyses on this subject,
of some interest were the findings of Urech et al. [25],
who compared the acute effects (i.e., the effect of a oneoff class) of three relaxation techniques (progressive relaxation, imagery relaxation, and passive relaxation),
on pregnant women. In all cases, a significant reduction in cortisol, noradrenaline, and ACTH levels was
observed, with progressive and imagery relaxation also
found to decrease the expectant mothers’ heart rates.
After further analysis, this study concluded that imagery
relaxation was more effective than the other two methods in reducing cardiovascular activity as well as inducing a subjectively perceived state of relaxation.
Another study [26] on pregnant women found that
an active relaxation session helped decrease anxiety
levels and heart rate better than a passive relaxation
method. However, passive relaxation was found to cause
more significant decreases in noradrenaline levels, which
did not occur in the group of pregnant women who
performed active relaxation. No change in the level of
adrenaline was observed in either group, although both
groups featured a significant decrease in the level of
cortisol.
In light of the relatively few studies that have addressed this issue, the aim of the present study was to
compare the changes in the emotional states of expectant
mothers who were attending an exercise training program or a relaxation course against a control group attending a traditional childbirth education course immediately before attending one of their classes and after
it was completed.
Material and methods
The study included 139 pregnant women between
the ages of 22 to 34 years (mean 28.59 ± 2.99 years) in
their second (n = 54) or third trimester (n = 85), or between 20–34 weeks of gestation (27.96 ± 4.01 weeks).
All of the participants had completed higher education and lived in a capital city; 78.4% (n = 109) were
married, the remaining 21.6% (n = 30) were unmarried.
All of the participants were professionally active before
becoming pregnant, although at the time of the study
56.1% (n = 78) had stopped working. The study was
approved by the Senate Ethics Committee at the Józef
Piłsudski Univeristy of Physical Education in Warsaw,
Poland.
The participants were recruited from women who
had signed up to attend a traditional childbirth education program in two different hospitals. Those who met
the initial entrance requirements (being in good health
169
HUMAN MOVEMENT
M. Guszkowska et al., Exercise and relaxation for pregnant women
and that this was their first single pregnancy) were
allowed to choose whether they wanted to participate in
a prenatal program that included relaxation sessions
or a completely independent program focused solely on
physical exercise. An additional condition to participate
in either of these courses was medical approval declaring
no contraindications for participation. Those who met
the original study prerequisites but did not want to
voluntarily participate in either of the research groups
were used to create a control group. Additional exclusion
criteria included individuals who trained systematically
at home or who were already participating in organized
exercise or relaxation classes as well as women with multiple pregnancies or with any pregnancy complications.
The relaxation group involved 42 women attending
prenatal classes with an emphasis on practical relaxation techniques. The course lasted six weeks, with relaxation exercises held twice a week. The participants
in this course performed various relaxation techniques
which included breathing technique, autogenic training,
imagery relaxation, and relaxation methods performed
in pairs (often with their partner). The duration of each
course varied depending on the subject, but ranged between 30 to 50 minutes.
The second group consisted of 56 women who completed an aerobic-strength physical exercise course based
on the eight-week 9 Months Active program. The course
was conducted by qualified instructors and held twice
a week, with each class lasting 50 minutes. The program
focused on general fitness by increasing strength, muscle
elasticity, and joint mobility with elements taken from
Pilates, yoga, and exercise-ball training.
The last group was a control group, composed of
41 women participating in traditional childbirth education classes held twice a week for a period of six weeks.
This program focused on educating future parents on
issues surrounding pregnancy, the postpartum period,
childbirth, and postnatal care. The duration of each
class varied depending on the subject, but ranged between 45 to 60 minutes.
The emotional state of the participants was measured immediately before attending one of their respective classes (pre-test) and then again upon its completion
(post-test) by the Profile of Mood States (POMS) by
McNair, Lorr, and Droppleman, [27], used to assess five
negative affective states (anger, tension, confusion, depression, and fatigue) and vigor. This tool was originally developed to monitor the effects of psychotherapy
due to its sensitivity to mood changes, but has often been
used to study the effects of physical exercise on emotional
states. A Polish version of this assessment tool was
used, which met all basic psychometric requirements.
A one-off measurement was performed during the
seventh class the participants were attending. In the
relaxation group, the class on that day focused on Jocobson’s progressive relaxation technique, while the
exercise group performed typical exercise activities as
170
part of the 9 Months Active program. The control group
attended a typical prenatal class that had no physical
fitness, breathing, or relaxation exercises. Furthermore, none of the class content that day involved issues
surrounding childbirth, as such a topic could lead to
the participants experiencing anxiety and contribute
to a worse emotional state. Only pregnant women were
allowed to attend the exercise and relaxation classes that
day, while those in the prenatal classes were allowed
to attend the class with their partners. All of the classes
lasted 50 minutes, an analysis was only performed on
those participants who had attended all of the preceding six classes in their respective programs.
A self-designed questionnaire was also administered,
collecting data on each participants (age, marital status,
education, and employment status before and during
pregnancy) and their pregnancy history as well as a selfassessed health and physical fitness survey (items rated
on a 5-point scale, from 1 – very bad to 5 – very good).
As the selection of the participants was not randomized, the participants were compared by the use of
socio-demographic variables by one-dimensional analysis of variance (ANOVA) and the chi-square test ( 2).
A priori analysis found significant differences among the
groups in terms of the stage of pregnancy (F = 10.31,
p < 0.001), with a post hoc Bonferroni correction finding that the participants in the exercise group were at
a significantly earlier stage (26.12 ± 4.58 weeks) than
the control group (29.27 ± 3.20 weeks, p < 0.001) or those
participating in the relaxation class (29.14 ± 3.60
weeks, p = 0.001); the last two groups did not significantly differ from each other (p > 0.1). Differences
were also noted in the proportion of women in their
second or third trimester in each group ( 2 = 9.25, p =
0.010). The number of women in their second or third
trimester participating in the physical exercise class was
similar (53.6% and 46.2%, respectively), while women
in their third trimester formed the majority in the relaxation group (n = 28, 66.7%) and control group (n = 31,
75.6%). No statistically significant differences were found
between the groups in terms of age (F = 0.677, p > 0.1),
marital status ( 2 = 3.831, p > 0.1), being employed while
pregnant ( 2 = 2.270, p > 0.1), or self-assessed physical
fitness levels (F = 1.035, p > 0.1). However, self-assessed
health levels were found to differ (F = 3.070, p = 0.055),
with pregnant women who signed up to participate in
the exercise course evaluated their health slightly better
(4.27 ± 0.59 points) than the control group (3.95 ± 0.67
points, p = 0.054), although they did not differ from
those that were to participate in the relaxation group
(4.05 ± 0.58). No other statistically significant differences were found between the groups (p > 0.1).
Results
Means and standard deviations of the participants’
pre-test and post-test results from all three groups are
HUMAN MOVEMENT
M. Guszkowska et al., Exercise and relaxation for pregnant women
Table 1. Pre- and post-test scores measuring emotional states of the relaxation group, exercise group, and the control group
Relaxation group (n = 42)
Variables
Anger
Confusion
Depression
Fatigue
Tension
Vigor
Exercise group (n = 56)
Control group (n = 41)
Pre-test
M (SD)
Post-test
M (SD)
Pre-test
M (SD)
Post-test
M (SD)
Pre-test
M (SD)
Post-test
M (SD)
4.43 (5.32)
6.83 (3.93)
5.24 (5.78)
6.07 (4.78)
6.48 (4.62)
17.74 (5.55)
2.64 (4.15)
4.90 (3.56)
3.14 (4.68)
4.24 (4.54)
3.10 (3.37)
18.60 (5.95)
4.68 (5.10)
6.16 (3.72)
5.46 (7.74)
5.11 (4.19)
5.59 (4.91)
18.55 (4.54)
1.79 (3.04)
3.89 (2.56)
2.45 (5.48)
3.50 (3.38)
3.38 (3.54)
20.32 (4.42)
4.46 (4.69)
6.90 (4.19)
4.46 (5.42)
4.66 (3.38)
6.49 (5.40)
17.68 (3.83)
3.56 (4.94)
6.27 (4.21)
3.80 (4.81)
4.39 (3.88)
5.54 (5.27)
18.39 (4.84)
presented in Table 1. Analysis of the pre-test POMSmeasured emotional states of the participants found no
significant differences in the levels of anger (F(2,136)
= 0.036, p < 0.1), confusion (F(2,136) = 0.545, p > 0.1),
depression (F(2,136) = 0.289, p < 0.1), fatigue (F(2,136)
= 1,261, p > 0.1), tension (F(2,136) = 0.538, p > 0.1), and
vigor (F(2,136) = 0.543, p > 0.1), pointing to a similar
emotional state among the three groups. This allowed
further analysis to be performed without control of the
baseline outcome measures.
Repeated measures analysis of variance (Group ×
Time of measurement) was performed to determine the
changes in emotional state and how they differed depending on the type of course that was attended. Since
the groups significantly differed in terms of the stage
of pregnancy, this variable was treated as a covariate.
The post-test results were also compared using onedimensional analysis of variance.
Here, it was found that the level of anger in the entire
study group significantly decreased (F(1,136) = 5.080,
p = 0.026, 2 = 0.036) and did not depend on the type
of group (effect of Group was insignificant F(2,136) =
0.329, p > 0.1). There was, however, a significant interaction between the groups and time of measurement
(F(2,136) = 3.342, p = 0.038, 2 = 0.047). Within-group
repeated measures analysis of variance found that all
participants experienced a significant reduction in anger,
although the effect was stronger in the exercise group
(F(1,55) = 28.533, p < 0.001, 2 = 0.342) and relaxation
group (F(1,41) = 16.586, p < 0.001, 2 = 0.288) than in the
control group (F(1,40) = 5.308, p = 0.026, 2 = 0.117).
No significant effect of trimester was found (F(1,136) =
0.046, p > 0.1). In the post-test, significant intergroup
differences were found (F(2,136) = 2.328, p = 0.101).
The level of confusion of the entire group did not
change significantly (F(1,136) = 2.091, p > 0.1) and did
not vary depending on the group (F(2,136) = 1.523,
p > 0.1). However, a significant interaction between
the groups and time of measurement was observed
(F(2,136) = 3.832, p = 0.024, 2 = 0.054). Significant
decreases in confusion were noted for the relaxation
group (F(1,41) = 25.95, p < 0.001, 2 = 0.388) and exercise group (F(1,55) = 31.123, p < 0.001, 2 = 0.361),
with no statistically significant changes found in the con-
trol group (F(1,41) = 2.429, p > 0.1). A trend was observed
with the effect of trimester (F(1,136) = 2.946, p = 0.088,
2
= 0.024), where confusion increased at more advanced
stages of pregnancy. The groups were significantly differentiated in the post-test (F(2,146) = 5.709, p = 0.004),
with the level of confusion significantly higher in the
control group than the pregnant women who were exercising (p = 0.003).
A similar case was found for depression, with no
significant main effect found (F(1,136) = 0.359, p > 0.1).
Participation in any of the groups did not determine its
level (main effect of group was insignificant F(2,136)
= 0.016, p > 0.1). However, a significant interaction between group and time of measurement was observed
(F(2,136) = 3.805, p = 0.025, 2 = 0.053), stemming from
the fact that a significant decrease in the level of depression occurred in the relaxation group (F(1,41) = 22.391,
p < 0.001, 2 = 0.353) and exercise group (F(1,55) =
17.810, p < 0.0001, 2 = 0.245), with no statistically
significant changes in the control group (F(1,40) = 1.619,
p > 0.1). The effect of trimester was also statistically
insignificant (F(1,41) = 22.391, p < 0.001, 2 = 0.353). In
the post-test no significant intergroup differences were
found (F(2,136) = 0.863, p > 0.1).
The level of fatigue did not significantly change among
the entire group (F(1,136) = 0.080, p > 0.1). Analysis
performed on each of the individual groups confirmed
a significant decrease in fatigue again for the exercise
group (F(1,55) = 8.009, p = 0.016, 2 = 0.127) and relaxation group (F(1,41) = 5.234, p = 0.027, 2 = 0.113), with
insignificant changes in the control group (F(1,40) = 0.072,
p > 0.1). Neither was the main effect of group (F(2,136)
= 0.579, p > 0.1) or the interaction between group and
time of measurement (F(2,136) = 1.671, p > 0.1) found to
be statistically significant. However, a significant effect
of trimester was found (F(1,136) = 4.572, p = 0.034,
2
= 0.033), with women at a more advanced stage of
pregnancy featuring higher levels of fatigue. The groups
did not differ significantly in the post-test measurement
(F = 0.740, p > 0.1).
The entire study sample exhibited a significant decrease in tension (F(1,136) = 5.899, p = 0.016, 2 = 0.042).
Within-group analysis pointed to a significant decrease
in tension in the relaxation group (F(1,41) = 29.036, p <
171
HUMAN MOVEMENT
M. Guszkowska et al., Exercise and relaxation for pregnant women
0.001, 2 = 0.415) and exercise group (F(1,55) = 18.890,
p < 0.001, 2 = 0.256) with no significant changes in the
control group (F(1,40) = 2.215, p > 0.1). The mean tension
level did not vary depending on the group (F(2,136) =
1.42, p > 0.1), although the interaction between group
and time of measurement was statistically significant
(F(2,136) = 3.670, p = 0.028, 2 = 0.052). The effect of
trimester was also statistically insignificant (F(1,136)
= 0.094, p > 0.1). In the post-test, significant intergroup
differences were noted (F(2,136) = 4.566, p = 0.012);
the tension level was higher in the control group than the
relaxation group (p = 0.022) and exercise group (p = 0.033),
with neither of the last two groups differing significantly from each another.
Among the last studied parameter, the level of vigor
did not significantly change in the entire group (F(1,136)
= 1.233, p > 0.1). Analysis on each of the individual groups
confirmed a significant increase in vigor only in the participants in exercise program (F(1,55) = 17.190, p < 0.001,
2
= 0.238). No statistically significant changes were found
in the relaxation group (F(1,41) = 1.023, p > 0.1) or control group (F(1,40) = 1.582, p > 0.1). Neither was the
main effect of group (F(2,136) = 0.639, p > 0.1) or the
interaction between group and time of measurement
(F(1,136) = 0.718, p > 0.1) found to be statistically significant. A significant effect of trimester was found
(F(1,136) = 5.744, p = 0.018, 2 = 0.041), with vigor higher
in women in the second trimester. No statistically significant differences between the groups were found in
the post-test measurements (F(2,136) = 2.209, p > 0.1).
Discussion
The results of the mood assessment scale found that
pregnant women who participated in an exercise or relaxation session improved their emotional state. These
improvements in both groups were larger than for women
participating solely in traditional prenatal classes. In this
last group, only a significant decrease in anger was observed. For pregnant women participating in physical
exercise, an improvement in mood was seen in all indicators of emotional state, with the same seen for women
in the relaxation group except in the case of vigor.
As of yet, few comparisons have been made on the
effectiveness of physical exercise and relaxation as two
concurrent effects on emotional well-being. The few
studies that do exist compared the anti-anxiety effects
of different forms of physical exercise and relaxation
techniques, finding relatively minor differences between
these two methods in terms of a decrease in anxiety
and tension [28–30].
This study found that the emotional benefits of relaxation and physical exercise to be slightly different
from each other, as evidenced when comparing the size
of the effects. The levels of tension, anger, confusion,
and depression were found to be smaller, albeit minimally, after completing the exercise session than the
172
relaxation class, although a larger decrease was noted in
fatigue. Furthermore, only the exercise group presented
a significant increase in vigor. On the other hand, relaxation resulted in a profound drop in negative emotional states, although the positive state of vigor did
not change. Similar results were obtained by Saklofske
et al. [31] when comparing the effects of walking and
relaxation on the affective sphere. Both conditions caused
a significant drop in tension, although only the physically active group featured increased energy levels. It
seems that physical activity is at least no less effective
than relaxation techniques in reducing negative emotional states, while being more effective in inducing
positive states.
The results of the present study were found to be in
line with those presented earlier, in which it was concluded that one-off workouts improve the emotional
well-being of different groups [15–19], including pregnant women [20, 22, 23]. The effectiveness of physical
exercise appears to be particularly high in bringing
about those states associated with arousal, where, in the
present study, only the exercise group presented a significant increase in the amount of vigor while at the
same time featuring the largest decrease in fatigue.
However, the effectiveness of various relaxation
methods in reducing anxiety levels has also been confirmed in the meta-analysis carried out by Manzoni et al.
[24]. Additionally, a study focused on pregnant women
also confirmed that participation in relaxation classes
lowered anxiety levels [32]. In the present study, the
relaxation class on the day of measurement was based
on progressive relaxation, which had been proven to be
particular effective in both the general population [24]
as well as pregnant women [25, 32]. Although the effectiveness of relaxation methods in reducing negative
emotional states was slightly greater than by physical
exercise, no significant change was noted in vigor. This
cause and effect relationship seems to be quite logical.
Relaxation in itself is based on reducing overall arousal
levels. This results in a decrease in the intensity of negative emotions, but it may also be the cause of the decrease in vigor and increase in lethargy.
The obtained results seem to confirm Russell’s circumplex model of affect [33]. According to this author,
there exist two independent dimensions of affect, one
related to valence (pleasure–displeasure) and the other
to arousal (alertness–somnolence). This allows four categories of affect to be distinguished: positive arousal (a
pleasant state, feeling joyful excitement, energy, and
vigor), positive low arousal (associated with feelings of
inner peace and contentedness), negative arousal (suffering), and negative low arousal (sadness and depression). Relaxation leads to positive low arousal, causing
a decrease in anxiety and tension since these states are
associated with negative stimulation (or at least stabilizes their levels, as was found in the present study), and
also caused a decrease in overall arousal, and, conse-
HUMAN MOVEMENT
M. Guszkowska et al., Exercise and relaxation for pregnant women
quently, an increase in feelings of pleasure. Physical
exercise also led to increase in hedonistic feelings but
in different way, primarily by increasing the general
level of arousal that is felt as a rise in vitality, vigor, and
vital energy.
None of the three analyzed groups differed from one
another in terms of their emotional well-being before
beginning their respective classes (pre-test). Upon finishing, however, certain intergroup differences were noted
especially between those participating in the childbirth
education program and the 9 Months Active exercise
program. The pregnant women taking part in physical
exercise revealed lower levels of confusion and tension.
No significant differences were found between the exercise and relaxation groups, however.
One of the more interesting results of the present
study was the decrease in the level of anger in the control group. Prenatal classes held in traditional childbirth schools provide expectant mothers with various
information and help them prepare for childbirth. This
may have contributed to their improvement in mood
due to not only the subject matter (infant care) but
also that the fathers/partners of the child were also
allowed to attend, providing an important source of support. It should also be remembered that simply being in
a peaceful environment is enough to improve one’s emotional state [18].
The study did have a number of limitations, beginning with the fact that the sample population was
rather homogeneous: all of the participants were welleducated, living in a capital city, had a steady job before pregnancy, and had above-average incomes, all of
which limits the generalization of the results. Secondly,
the selection process was not randomized, as the participants chose which class they would like to take based
on their own personal preferences. As a result, women
in less advanced stages of pregnancy and slightly better
assessing their own health naturally gravitated towards
the course focusing on physical exercise. However, the
effect of trimester, or how advanced the pregnancy was,
was statistically controlled. The week of gestation significantly differentiated the levels of fatigue and vigor,
with women in their third trimester having lower energy
levels and felt more tired. This effect was not found
among the other negative states (anger, confusion, depression, or tension), which is in contrast to previous
studies that suggest more advanced pregnancies have
increased feelings of anxiety and depression [34, 35].
Thirdly, as fathers were allowed to attend the traditional prenatal class (control group), this may have also significantly affected the emotional states of the expectant mothers.
Conclusions
Despite these limitations, the results are suggestive
that a one-off exercise and relaxation session can in-
crease levels of emotional comfort in pregnant women
more effectively than attending a traditional prenatal
class. Physical exercise can cause an increase in energy
levels, while progressive relaxation techniques can help
reduce the level of negative affect. Physical exercise
may be particular useful when dealing with fatigue
and feeling tired, which are often experienced during
pregnancy, while relaxation techniques are likely to bring
greater benefits to pregnant women with elevated levels
of anxiety or depression.
Acknowledgements
The research was financed under project no. NN 404 017838,
“The influence of pregnant women’s physical activity on their
mental and physical health, the course of pregnancy, and childbirth,” by the Ministry of Science and Higher Education in
Poland.
References
1. Davies G.A., Wolfe L.A., Mottola M.F., MacKinnon C.,
Arsenault M.Y., Bartellas E. et al., Exercise in pregnancy
and postpartum periods. J Obstet Gynecol Can, 2003,
25 (6), 516–529.
2. Royal College of Obstetricians and Gynaecologists, Exercise in pregnancy (RCOG Statement 4), 2006. Available
from: URL: http://www.rcog.org.uk/index.asp/PageID=1366.
3. Reimann M.K., Kanstrup Hansen I-L., Effects on the
foetus of exercise in pregnancy. Scand J Med Sci Sports,
2000,10(1),12–19,10.1034/j.1600-0838.2000.010001012.x.
4. Weissgerber T.L., Wolfe L.A., Davies G.A.L., Mottola M.F.,
Exercise in the prevention and treatment of maternal–
fetal disease: a review of the literature. Appl Physiol Nutr
Metab, 2006, 31 (6), 661–674, doi: 10.1139/h06-060.
5. Melzer K., Schutz Y., Boulvain M., Kayser B., Physical
activity and pregnancy. Cardiovascular adaptations, recommendations, and pregnancy outcomes. Sports Med,
2010, 40 (6), 493–507, doi: 10.2165/11532290-00000000000000.
6. Artal M., O’Toole M., Guidelines of the American College of Obstetricians and Gynecologists for exercise during
pregnancy and the postpartum period. Brit J Sports
Med, 2003, 37 (1), 6–12, doi: 10.1136/bjsm.37.1.6.
7. Evenson K.R., Savitz D.A., Huston S.L., Leisure-time physical activity among pregnant women in the US. Paediatr
Perinat Epidemiol, 2004, 18 (6), 400–407, doi: 10.1111/
j.1365-3016.2004.00595.x.
8. Poudevigne M.S., O’Connor P.J., A review of physical
activity patterns in pregnant women and their relationship to psychological health. Sports Med, 2006, 36 (1),
19–38, doi: 10.2165/00007256-200636010-00003.
9. Rousham E.K., Clarke P.E., Gross H., Significant changes
in physical activity among pregnant women in the UK
as assessed by accelerometry and self-reported activity.
Eur J Clin Nutr, 2006, 60 (3), 393–400, doi: 10.1038/
sj.ejcn.1602329.
10. Evenson K.R., Wen F., Prevalence and correlates of objectively measured physical activity and sedentary behavior
among US pregnant women. Prev Med, 2011, 53 (1–2),
39–43 doi: 10.1016/j.ypmed.2011.04.014.
11. Andersson L., Sundstrom-Poromaa I., Wulff M., Astrom M., Bixo M., Implications of antenatal depression and
173
HUMAN MOVEMENT
M. Guszkowska et al., Exercise and relaxation for pregnant women
anxiety for obstetric outcome. Obstet Gynecol, 2004, 104 (3),
467–476, doi: 10.1097/01.AOG.0000135277.04565.e9.
12. Martini J., Knappe S., Beesdo-Baum K., Lieb R., Wittchen H-U., Anxiety disorders before birth and self-perceived
distress during pregnancy: Associations with maternal
depression and obstetric, neonatal and early childhood
outcomes. Early Hum Dev, 2010, 86 (5), 305–310, doi:
10.1016/j.earlhumdev.2010.04.004.
13. Parcells D.A., Women’s mental health nursing: depression, anxiety and stress during pregnancy. J Psychiatr Ment
Health Nurs, 2010, 17 (9), 813–820, doi: 10111/j.1365-2850.
2010.01588.x.
14. Field T., Diego M., Hernandez-Reif M., Figueiredo B.,
Deeds O., Ascencio A. et al., Comorbid depression and
anxiety effects on pregnancy and neonatal outcome. Infant
Behav Dev, 2010, 33 (1), 23–39, doi: 10.1016/j.infbeh.
2009.10.004.
15. Biddle S.J.H., Emotion, mood and physical activity. In:
Biddle S.J.H., Fox K.R., Boutcher S.H. (eds.), Physical
activity and psychological well-being. Routledge, London 2000, 63–87.
16. Biddle S.J.H., Mutrie N., Psychology of physical activity.
Determinants, well-being and interventions. 2d ed. Routledge, London 2008.
17. Landers D.M., Arent S.M., Physical activity and mental
health. In: Singer R.N., Hausenblas H.A., Janelle C.M.
(eds.), Handbook of sport psychology. Wiley, New York
2001, 740–765.
18. Yeung R.R., The acute effect of exercise on mood state.
J Psychosom Res, 1996, 40 (2), 123–141.
19. Reed J., Ones D.S., The effect of acute aerobic exercise on
positive activated affect: A meta analysis. Psychol Sport
Exerc, 2006, 7 (5), 477–514, doi: 10.1016/j.psychsport.2005.
11.003.
20. Koltyn K.F., Mood changes in pregnant women following an exercise session and a prenatal information session. Women’s Health Issue, 1994, 4 (4), 191–195.
21. Koltyn K.F., Schultes S.S., Psychological effects of an
aerobic exercise session and a rest session following pregnancy. J Sport Med Phys Fitness, 1997, 37 (4), 287–291.
22. Lox C.L., Treasure D.C., Changes in feeling states following aquatic exercise during pregnancy. J Appl Soc
Psychol, 2000, 30 (3), 518–527, 10.1111/j.1559-1816.2000.
tb02494.x.
23. Guszkowska M., Bernatek P., Changes in emotional states
inpregnant women after one-time physical exercise [in
Polish]. Polskie Forum Psychologiczne, 2010, 15, 14–24.
24. Manzoni G.M., Pagnini F., Castelnuovo G., Molinari E.,
Relaxation training for anxiety: a ten-years systematic
review with meta-analysis. BMC Psychiatry, 2008, 8 (6),
41, doi: 10.1186/1471-244X-8-41.
25. Urech C., Fink N.S., Hoesli I., Wilhelm FH, Bitzer J.,
Alder J., Effects of relaxation on psychobiological wellbeing during pregnancy: A randomized controlled trial.
174
Psychoneuroendocrinology, 2010, 35 (9), 1348–1355, doi:
10.1016/j.psyneuen.2010.03.008.
26. Teixeira J., Martin D., Prendiville O., Glover V., The
effect of acute relaxation on indices of anxiety during
pregnancy. J Psychosom Obstet Gynecol, 2005, 26 (4),
271–276, doi: 10.1080/01674820500139922.
27. Dudek B., Koniarek J., Adaptation of Profile of Mood
States (POMS) by D.M. McNair, M. Lorr and L.F. Droppleman [in Polish]. Przegląd Psychologiczny, 1987, 3,
753–762.
28. Crocker P.R.E., Grozelle C., Reducing induced state
anxiety: effect of acute aerobic exercise and autogenic
relaxation. J Sports Med Phys Fitness, 1991, 31 (2), 277–282.
29. Glazer A.R., O’Connor P.J., Mood improvements following exercise and quiet rest in bulimic women. Scand
J Med Sci Sports, 1993, 3 (1), 73–79, doi: 10.1111/j.1600-0838.
1993.tb00365.x.
30. Brown D.R., Morgan W.P., Raglin J.S., Effects of exercise
and rest on the state anxiety and blood pressure of physically challenged college students. J Sports Med Phys Fitness, 1993, 33 (3), 300–305.
31. Saklofske D.H., Blomme G.C., Kelly I.W., The effect of
exercise and relaxation on energetic and tense arousal.
Personality and Individual Differences, 1992, 13 (5), 623–
625, doi: 10.1016/0191-8869(92)90204-3.
32. Newham J.J., Westwood M., Aplin J.D., Wittkowski N.,
State-trait anxiety inventory (STAI) scores during pregnancy following intervention with complementary therapies. J Affect Disord, 2012, 142 (1), 22–30, doi: 1016/j.
jad.2012.04.027.
33. Russell J.A., A Circumplex model of affect. J Pers Soc Psychol, 1980, 39 (6), 1161–1178, doi: 10.1037/h0077714.
34. DiPietro J.A., Mendelson T., Williams E.L., Costigan K.A.,
Physiological blunting during pregnancy extends to induced relaxation. Biol Psychol, 2012, 89 (1), 14–20, doi:
10.1016/j.biopsycho.2011.07.005.
35. Hoffman S., Hatch M., Depressive symptomatology during
pregnancy: Evidence for an association with decreased
fetal growth in pregnancies of lower social class women.
Health Psychol, 2000, 19 (6), 535–543.
Paper received by the Editors: September 20, 2012
Paper accepted for publication: April 24, 2013
Correspondence address
Monika Guszkowska
Zakład Podstaw Społeczno-Kulturowych
w Turystyce
Wydział Turystyki i Rekreacji
Akademia Wychowania Fizycznego
ul. Marymoncka 34
00-968 Warszawa, Poland
e-mail: [email protected]
HUMAN MOVEMENT
2013, vol. 14 (2), 175– 184
Analogy vs. technical learning in a golf putting task:
an analysis of performance outcomes and attentional
processes under pressure
doi: 10.2478/humo-2013-0021
Linda Schücker 1 *, Norbert Hagemann 2 , Bernd Strauss 1
1
2
Institute of Sport and Exercise Sciences, University of Muenster, Muenster, Germany
Institute of Sports and Sport Science, University of Kassel, Kassel, Germany
Abstract
Purpose. It is assumed that analogy learning helps prevent individuals from choking under pressure by limiting the conscious
control of movements when performing in high-pressure situations. The aim of the study was to extend the application of analogy
learning to golf putting and include an assessment on the proposed mechanisms of analogy learning and performance under
pressure. Methods. Golf novices learned a putting task either by technical instructions or with analogy. After the learning phase,
the participants were tested under low- and high-pressure conditions. Attentional focus was measured using a dual-task paradigm
based on a skill and an externally focused task. Results. Both groups showed an increase in putting accuracy under pressure
while performance in both dual-tasks decreased under pressure. Despite a difference in verbal knowledge, no group differences
were found in putting or dual-task performance. Conclusions. The results suggest that it does not matter if the skill is learned
technically or by analogy with regard to performance under pressure.
Key words: motor learning, implicit learning, attentional mechanisms
Introduction
The phenomenon of choking under pressure has
been frequently studied over the past several decades.
Besides its underlying mechanisms, strategies to prevent choking under pressure are of considerable interest
to researchers. Choking under pressure has been defined
as performing more poorly than expected given one’s
skill level in situations with high performance pressure
[1, 2]. Cases of choking have occurred across a wide
range of sports and even those performed by highly
skilled athletes. Missing a seemingly easy putt in an
important golf tournament is just one famous example
where choking can be observed among professional
athletes. However, it is important to keep in mind that
not every performance failure can be equated with
choking. Random fluctuations in skill level are common; only significantly less than optimal performance
as a response to a high pressure situation can be considered as choking [2, 3]. Furthermore, according to
Baumeister [1], an additional definition of choking
under pressure is where the individual desire to perform in an optimal way is the highest, yet, despite
this optimal motivation and the athletes’ strive to perform at their best, their performance drops to a suboptimal level.
Researchers attempting to study this phenomenon
are required to induce pressure experimentally, which
poses one of the challenges in this field of research.
* Corresponding author.
Pressure is defined as the presence of a situation in which
the incentive for optimal performance is highest and
subjectively perceived as such [4]. Furthermore, pressure
relies on the contingency of rewards or punishment on
performance outcome, it can include the presence of
an evaluative audience and other competitors, is dependent on how personally important a performance
outcome really is, and in situations when the event is
thought to be unrepeatable [4].
Besides individual differences in susceptibility to
choking, such as dispositional reinvestment, two different
attentional theories have been proposed to explain the
paradoxical performance effects in high pressure situations. Distraction theories assume that pressure creates
a distracting environment that impairs attentional resources necessary to successfully execute the task [e.g.,
2, 5, 6]. Distractions can include concentrating on taskirrelevant stimuli or being apprehensive about a given
situation [4, 6–8]. A specific distraction theory currently
being discussed is attentional control theory (ACT),
which has been proposed by Eysenck et al. [6]. It assumes that under pressure processing resources are disturbed by task-irrelevant stimuli culminating in poorer
processing efficiency. Eysenck et al. further assumed that
efficiency is disturbed more than effectiveness (performance outcome). Therefore, while performance quality
might be stable, more resources are needed to attain
a given performance level, or, in other words, one has to
invest more effort for the same performance outcome
while under pressure. Studies on distraction theories
have confirmed it in tasks that require high demands
on working memory [9, 10].
175
HUMAN MOVEMENT
L. Schücker, N. Hagemann, B. Strauss, Analogy learning in golf putting
On the other hand, self-focus theories (also termed
explicit monitoring theories) have gained support when
accounting for the phenomenon of choking during
sensorimotor tasks [1, 11]. In this case, it is assumed that
pressure causes a redirection of attention to the actual
execution of movement, leading to conscious control of
usually automated processes and consequently to a breakdown in performance [e.g., 1, 11, 12].
Explicit monitoring theories
Many studies have been designed using explicit monitoring theories as a theoretical guideline. There is ample
evidence showing that directing attention to the execution of well-learned motor tasks leads to performance
decrements. The detrimental effect of internally focused
attention has been studied in a number of different sports.
In many of these studies, attentional focus was treated
as the independent variable whereas pressure was taken
out of the equation. Beilock, Carr, MacMahon, and
Starkes [13] conducted two experiments manipulating
attentional focus by including an internal skill-focus
condition and an external dual-task condition during
movement execution. In two different sports, golf putting
and soccer dribbling, they found better performance in
the external rather than the internal focus of attention
in a group of experienced players. However, novice performers as well as experts performing a less familiar
task (dribbling with the non-dominant foot in soccer)
profited from monitoring the step-by-step execution
of the movement, which indicated the importance of
skill level for the attentional focus effect. These results
were replicated in field hockey, with the slowest performance found when participants monitored the position of their hand while the fastest under dual-task
conditions [14]. Similar effects were also found in
baseball [15], again in soccer [16], and in golf pitching
performance [17]. In running, as a cyclic endurance
task, an external focus was superior to two internal
focus conditions in terms of movement economy [18].
Wulf et al. conducted a series of experiments on the
effects of attentional focus [see 19 for a review]. They
explained the detrimental effects of an internal focus
of attention by constraining the motor system and interfering with automatic control processes. EMG studies lend support to this constrained action hypothesis
[20, 21]. According to the reinvestment theory [22], an
inward focus of attention implies conscious control
over the movement with explicit knowledge. This, in
turn, leads to a deterioration in performance as the skill
no longer functions automatically [23]. Reinvestment
of declarative knowledge of how a skill works [22] as
well as explicitly monitoring a skill [11] and constrained
action [19] all consistently conclude that focusing on
the execution of well-learned motor tasks has a negative
effect on performance by interfering with automatic
movement control.
176
It is assumed that the consistently reported negative effect of an internal focus of attention mirrors the
attentional processes induced by pressure [2]. Indirect
evidence was found in training studies, which showed
that practice with dealing with an internal focus of
attention reduced choking under pressure by letting
participants adapt to the attentional focus they experience under pressure [see 7, 11]. More direct evidence
about the attentional mechanisms involved in choking
was presented by Gray [12]. Using a simulated baseball batting task, he assessed attentional focus by using
a dual-task paradigm. A short tone was presented during
movement execution. Skill-focused attention was measured by judging the direction of bat movement upon
hearing the tone while externally-focused attention
was measured by judging the pitch of the tone. When
placed under pressure, participants demonstrated a higher
level of skill-focused attention (better performance in
the skill-focused dual task, meaning higher accuracy in
judging bat movement) compared with a control group
without pressure. The tone-judgment task was found not
to be affected by pressure. Also important was the fact
that an increase in skill-focused attention was related
to a deterioration in batting performance and changes
in batting kinematics. This frequently cited study was
the first to directly demonstrate that pressure does induce
an inward shift of attentional focus as Baumeister [1] had
proposed 20 years earlier. Results from an experimental
study that included measurement of the “quiet eye”
go in line with strengthening the importance of attentional focus under pressure [24]. This study showed that
individuals who did not choke under pressure were able
to direct visual attention externally, as was indicated by
a longer quiet eye period. Further support for the explicit monitoring theory was demonstrated in an experiment by Gucciardi and Dimmock [25], where they
directly compared self-focus to distraction theories on
a group of experienced golfers. They showed degraded
performance under pressure when they relied on explicit
knowledge, while focusing on task-irrelevant cues as well
as the swing thought condition did not cause choking.
Attempts to prevent choking under pressure
Different kinds of strategies to prevent choking have
been reported in the literature on the subject. One approach is to let participants adapt to the kind of focus
they experience under pressure. Studies have found that
training under self-focus conditions reduces a deterioration of performance when under pressure [7, 11]. In
a recent study, Oudejans and Pijpers [26] demonstrated
that training under mild levels of anxiety reduced performance decrements under subsequently higher levels
of anxiety. It has to be noted, however, that anxiety
(induced in this case by different heights on a climbing
wall) is not the same as pressure. Pre-performance routines have also been discussed as a way to alleviate
HUMAN MOVEMENT
L. Schücker, N. Hagemann, B. Strauss, Analogy learning in golf putting
choking. It was assumed that they enable the motor
response to run automatically without conscious control [27, 28]. Another approach involved participants
thinking of a global cue rather than detailed explicit instructions when performing under pressure [29]. Choking
was reduced in this swing thought condition, and similar
results were shown by Gucciardi and Dimmock [25].
Opposed to the aforementioned strategies is the approach promoted by Masters [23]. He assumes that
explicit knowledge about movement execution is reinvested under pressure and causes detrimental performance effects. It follows that the avoidance of the buildup of explicit knowledge is a way to prevent choking. So,
rather than implementing a strategy to help athletes
deal with the pressure situation, Masters [23] favors an
intervention during the skill acquisition phase. In his
experiment, he showed that participants who had only
acquired a small amount of explicit knowledge (through
implicit learning) were less susceptible to choking under
pressure [23]. However, implicit motor learning incorporates several problems (such as it being a lengthy
process) that makes it difficult to implement in sports
training contexts outside a laboratory setting. As an alternative, Masters [30] suggested analogy learning as it
operates with biomechanical metaphors instead of declarative knowledge and technical know-how. Here, he
proposed that only one rule which consists of a general
analogy ought to be provided and should include all the
technical aspects necessary to execute the skill successfully [30]. Liao and Masters [31] designed an experiment
to test whether analogy learning shows similar characteristics as implicit learning. Table tennis novices were
instructed to learn the topspin forehand either implicitly,
explicitly, or by analogy (drawing a right-angled triangle
with a table tennis paddle). The results confirmed the
implicit characteristics of analogy learning with less explicit knowledge and its robustness when performing
under dual-task conditions. In a second experiment, Liao
and Masters [31] showed that analogy learners’ performance was not negatively affected by pressure as opposed to that of the explicit learning group. Using the
same analogy learning paradigm, Law et al. [32] showed
that supportive audiences (under the notion that supportive audiences induce stress) brought about performance decrements only in the explicit learning group.
It was believed that analogy learners acquire less explicit
knowledge about a movement, which leads to less consciously controlled movement execution under stress.
Contrasting results were shown in another study using
analogy learning in the table tennis forehand [33]. In
this study, a large number of repetitions (10,000) were
implemented during the learning phase in an analogy
and explicit learning group. Performance was assessed
after 1,400 and 10,000 repetitions under pressure conditions. Despite the fact that the explicit learning group
accumulated more explicit rules, neither of the groups
showed performance decrements when under pressure.
These findings did not confirm the fact that the amount
of explicit knowledge is related to performance decrements under pressure. As table tennis had been predominantly used in analogy learning, Lam, Masters and
Maxwell [34, 35] conducted two studies using a new
motor task that involved taking basketball shots from
a seated position. In one study, it was shown that performance did not degrade for the analogy condition in
a dual-task transfer test but did for both explicit and
control conditions [35]. The other study involved a pressure manipulation to test Masters’ [23] theory of explicit
knowledge reinvestment under pressure [34]. After two
days of learning with a total of 480 trials, the third day
consisted of a test phase in an A–B–A (low-pressure, highpressure, low-pressure) design. Probe reaction times (PRT)
to assess allocation of attention and shooting performance were treated as the dependent variables. No difference in performance was found for the analogy group,
while the explicit learning group displayed a significant
drop in performance in the high-pressure condition.
PRT did not show any differences, suggesting an equal
attentional load in both groups. As the analogy learners
reported less explicit rules about their movement, the
results were interpreted as evidence for the presence of
conscious processing. However, as the authors noted in
their discussion, this evidence was rather incidental,
where a direct measure of cognitive processes under
different pressure conditions would be more helpful in
finding better evidence on how level analogy learning
actually operates. Schücker, Ebbing, and Hagemann [36]
conducted a study incorporating two kinds of learning
instructions (analogy vs. technical) and linked them to
a measure of skill-focused attention under low- and highpressure. The results revealed higher amounts of skillfocused attention for the technical learning group
compared with the analogy learning group during the
high-pressure condition. However, these differences were
not related to differences in performance and a manipulation check for pressure was missing. Furthermore, the
method of analogy learning differed considerably from
that of Masters [30], as it worked with a whole set of
analogies instead of using a single metaphor encompassing all technical aspects of the movement.
The present study
To this day, explanations for the positive effect of
analogy learning in preventing choking under pressure
have mostly been deduced rather indirectly. Differences
in the amount of explicit knowledge between learning
groups are taken as evidence for the conscious processing hypothesis [34]. This study aims to relate different learning methods to an assessment of attentional
processes under pressure by means of a dual-task para­
digm. Several studies proved that analogy learning is
helpful in avoiding performance decrements when under
pressure compared with classic learning paradigms based
177
HUMAN MOVEMENT
L. Schücker, N. Hagemann, B. Strauss, Analogy learning in golf putting
on technical instructions [e.g., 31, 32, 34]. Gray [12] successfully used a dual-task paradigm and showed an increase in skill-focused attention under pressure.
The aim of this study was to combine these two approaches to show the efficacy of analogy learning in
alleviating choking under pressure, on the one hand, and
to assess its functioning by implementing a skill-focused
dual task on the other hand. As analogy learning has
not yet been implemented in studying different movement skills, it was decided to test this method on a golf
putting task. As the movement in putting has been
commonly represented through a pendulum analogy
[e.g., 37], it was decided to use this analogy as it incorporates the essential aspects of the movement. In line
with previous research, it was expected that analogy
and technical learning groups would improve performance equally in the learning phase, but that only the
analogy learning group would retain performance under
pressure while the technical learning group would show
the choking effect. Performance in a skill-focused dual
task was used as an indicator for the amount of skillfocused attention. It was assumed that the technical
learning group would show an increase in skill-focused
attention under pressure when compared with the analogy
learning group.
Material and methods
Participants
Apparatus
The putting task was performed on an artificial grass
putting mat 4 m in length and 1.5 m in width. Standard
golf balls were placed 2 m from the target, which was
indicated by a red circle with a diameter of 10 cm. A grid
with 5 cm squares was plotted on the mat around the
target to allow for quick assessment of putting performance by scoring vertical and horizontal error. All participants used the same standard putter. A Casio EX-F1
digital camera (Casio, Japan) was used to record the
putting movement at a rate of 30 frames per second.
To assess focus of attention, a dual-task design similar
to the one in Gray’s [12] experiments was used. A single
100 ms auditory tone (produced at 800 or 1000 Hz) was
presented while the participants performed the putt.
After completing the putt the participants were randomly asked to either judge the pitch of the tone or at
what movement phase the tone was sounded (“Which
tone was it?” or “Which picture was it?”). The movement phase was judged by pictures showing the whole
putting movement, the participants were asked to indicate which picture best corresponded to the point in
time when the tone was heard during the movement
(Fig. 1). The tone was linked to a light signal so the
actual point in time when the tone was heard could be
identified later during video analysis.
Procedure
Forty-one undergraduate students (23 males, 18 females) volunteered to take part in this study. Their mean
age was 21.44 years (SD = 2.98). None had any previous
golf experience nor had received any kind of formal instruction before. Participants were randomly assigned
to either an analogy (n = 20, 9 females and 11 males)
or a technical (n = 21, 9 females and 12 males) learning group. Three participants in each group were lefthanded. Written informed consent was obtained before the beginning of the experiment. The study was
conducted according to the ethical guidelines of the
American Psychological Association (APA).
Participants were tested individually by performing
a series of 300 putts during the learning phase followed by another but shorter series of putts in the test
phase. They were instructed to try to place the ball as
close as possible to the middle of a red circle from
a distance of 2 m. The participants were given an information sheet either with the analogy instructions or a set
of six technical instructions according to their group.
Both groups also received a picture demonstrating the
starting position. The analogy group’s instructions included the metaphor of performing the movement
like a pendulum, which was visually demonstrated to
Figure 1. Pictures used for the skill-focused dual task showing the whole putting movement
178
HUMAN MOVEMENT
L. Schücker, N. Hagemann, B. Strauss, Analogy learning in golf putting
Table 1. Technical instructions for putting, adapted
from Poolton, Maxwell, Masters, and Raab [38]
Technical instructions
1. Move your arms and the club back a short distance
2.Swing your arms and the club forward with a smooth
action along a straight line
3. Allow your arms and the club to continue swinging
a short distance after contact with the ball
4. Adjust the speed of your arms and the club so that
the correct amount of force is applied
5. Adjust the angle of your arms and the club to attain
the correct direction
6. Focus on the ground for a few seconds after hitting
the ball
them (swinging a weight on a cord). The technical instructions were based on those by Poolton et al. [38].
However, in this study, we did not differentiate between
an internal and an external focus of attention as this
was not the focus of our experiment (Tab. 1).
After each set of 50 putts the participants rested
for a period of 1–2mins. During the break they were
reminded of their specific learning instructions. After
completing 300 putts both learning groups were asked
to write down the rules they had actually used during
the learning phase.
After the learning phase was completed, the participants had to complete four blocks of 20 trials under dual-task conditions. The first series of 20 putts
was used for familiarization with the dual-task procedure followed by an A–B–A (low-pressure, high-pressure, low-pressure) design. The second set of 20 putts was
used as the first low-pressure baseline and appeared to
the participants as just another series of putts. The third
series of putts formed the high-pressure condition followed by a second low-pressure baseline. A scenario that
has been frequently used before [e.g., 11, 12] was introduced to increase pressure. After completing the first
baseline, participants were told that the putting performance of their last 20 putts was to be calculated.
They were then told that during the following series
of 20 putts they had a chance of winning an additional
10€ (apart from the 5€ participation fee) by improving
their putting performance by 20%. They were also given
a team scenario where they were paired with another
participant of the experiment and both had to improve
their performance to win the extra money. They were
then told that their partner had successfully completed
their portion of the trial. After giving this pressure scenario, the experimenter calculated the actual putting
performance (total distance from target) of the last 20
putts and told the participant by how many centimeters
they had to improve in the next series to reach the 20%
criterion. Participants were reminded that they still had
to provide answers for the dual-task condition. At the end
of the experiment putting performance in the high pres-
sure condition was calculated and those who actually
reached the 20% criterion received the extra reward
money. After data collection was completed, all participants were fully debriefed.
To assess whether the introduction of pressure manipulation was successful, a German version of the cognitive and somatic anxiety subscales of the CSAI-2R [39]
was administered before each series of 20 putts in the
low- and high-pressure situations. A pressure rating scale
from 1 (no pressure) to 7 (extreme pressure) was admini­
stered after each pressure condition [see 40].
Data Analysis
Putting performance in the learning phase was recorded as the horizontal and vertical distance from
target in 5 cm increments. This allowed for a quick assessment during the 300 putts. Total distance from the
target was calculated at a later time. In the test phase,
the horizontal and vertical distance from target was
measured more precisely at 1 cm increments, with total
distance also calculated a later time. Video recordings of
each participant were analyzed frame by frame with
Premiere CS3 Pro (Adobe, USA) to determine the actual
point in time at which the tone was sounded during the
movement. To assess inter-rater reliability, 10% of the
video sample were analyzed by a second independent
rater. Intraclass correlation revealed an inter-rater reliability of r = 0.99 at p < 0.001. As was mentioned previously, the difference between the picture which actually corresponded with the tone signal and the picture
the participants selected was used as an indicator for
measuring performance of judging the movement phase.
Tone pitch judgments were calculated as a percentage
of the amount of correct judgments. To analyze the verbal
protocol, two independent raters who were blind to the
learning conditions of the participants counted the
number of explicit rules. Statements were only counted
as explicit rules if they referred to the technical or mechanical aspects of the movement (e.g., swing with little
acceleration/force). They were excluded if they were
irrelevant to movement execution (an example of one
is “don’t make the concentration phase too short”).
Inter-rater reliability was deemed sufficient at r = 0.84,
p < 0.001. Average scores were computed to show the
number of rules reported by each participant.
Statistical Analysis
All data analyses were computed with PASW Statistics 18 (SPSS, USA) software. The significance level
was set at p < 0.05. For effect sizes, ŋ 2p or d were calculated. Analysis of variance (ANOVA) was used for the
main analysis and violation of the assumption of sphericity was corrected by Greenhouse-Geißer adjustments.
179
HUMAN MOVEMENT
L. Schücker, N. Hagemann, B. Strauss, Analogy learning in golf putting
Results
Manipulation check
To assess whether pressure was induced successfully,
a two factor ANOVA with the within-subject factor pressure and the between-subject factor group was computed for the somatic and cognitive anxiety subscales
of the CSAI-2R [39] and the pressure scale. There was no
difference between pressure conditions for the somatic
anxiety subscale. However, the cognitive anxiety subscale showed significantly higher values under pressure
than under the two low-pressure conditions, F (2, 78)
= 25.73, p < 0.001, ŋ 2p = 0.40 (first low-pressure M =
16.00, SD = 4.82, high-pressure M = 20.88, SD = 5.73,
second low-pressure M = 15.95, SD = 6.30). The pressure
scale showed a large effect for pressure as well, F (2, 78)
= 91.91, p < 0.001, ŋ 2p= 0.70 (first low-pressure M = 2.68,
SD = 1.08, high-pressure M = 4.29, SD = 1.23, second
low-pressure M = 2.22, SD = 0.99). There was no significant effect of group and no significant interaction
effect of group × pressure. The results of the manipulation check lead to the conclusion that pressure was induced successfully by the cover story.
Figure 2. Mean distance from target of technical
and analogy learning groups throughout the learning
and test phases
Learning phase
The 300 putts in the learning phase were split into
fifteen blocks of 20 putts in order to examine learning
progress. For these blocks the mean and within-subject
variation (as a measure of putting performance consistency) of total distance (cm) from the target were calculated. To determine whether initial putting performance
was equal in the analogy and technical learning group,
a one-way ANOVA was computed for the first block of
20 putts. No group differences were found for mean distance from target at F (1, 39) = 1.62, p = 0.69 and withinsubject variation in the first block F (1, 39) = 0.00, p = 0.98.
Distance from target was found to be far (M = 48.82,
SD = 10.15) and within-subject variation high (M = 35.28,
SD = 5.67), indicating that the participants were unfamiliar with the task. A 2 × 15 ANOVA (group × block)
with repeated measures for the factor block was calculated for the two dependent measures to assess performance throughout the learning phase. For distance-totarget, a significant effect of block was found, F (8.7,
339.25) = 45.65, p < 0.000, ŋ 2p = 0.54, but not of group,
F (1, 39) = 0.13, p = 0.72, and no interaction effect,
F (8.7, 339.25) = 0.76, p = 0.65. The results for withinsubject variation revealed the same pattern, a significant
effect of block, F (14, 546) = 39.64, p < 0.000, ŋ 2p = 0.5,
no effect of group, F (1, 39) = 0.02, p = 0.88, and no interaction effect, F (14, 546) = 0.47, p = 0.95. This shows
that both groups improved their putting performance
equally throughout the learning phase (see Fig. 2 – mean
distance, Fig. 3 – within-subject variation).
180
Figure 3. Within-subject variation in distance from target
of the technical and analogy learning groups throughout
the learning and test phases
Test phase: Putting performance
A 2 × 3 (group × pressure condition) ANOVA with
repeated measures on pressure condition was computed
to analyze for differences between the three pressure
conditions. For mean distance-to-target, the effect of
pressure was significant at F (2, 78) = 3.72, p = 0.03,
ŋ 2p = 0.09. Post-hoc paired sampled t tests with Bonferroni adjustments revealed that performance was significantly improved from the first low-pressure to highpressure condition (p = 0.02, see Fig. 2). There was no
effect of group, F (1, 39) = 0.00, p = 0.97, and no interaction effect, F (2, 78) = 0.05, p = 0.95. The same pattern
was found for within-subject variation. There was a significant effect of pressure, F (2, 78) = 6.56, p = 0.002,
ŋ 2p = 0.14. Post-hoc tests showed that performance was
significantly improved from the first low-pressure to
HUMAN MOVEMENT
L. Schücker, N. Hagemann, B. Strauss, Analogy learning in golf putting
high-pressure condition (p = 0.005), and also from first
low-pressure to second low-pressure (p = 0.03, see Fig. 3).
There was no effect of group, F (1, 39) = 0.04, p = 0.84,
and no interaction effect, F (2, 78) = 0.19, p = 0.83.
Test phase: secondary task performance
Tone pitch judgments
To analyze performance in the tone pitch judgment task, 2 × 3 (group × pressure condition) ANOVA
with repeated measures on pressure condition was
computed with the dependent measure of correct tone
judgments measured as a percentage. Analysis revealed a significant effect of pressure, F (2, 78) = 4.11,
p = 0.02, ŋ 2p = 0.1. Post-hoc paired sampled t tests with
Bonferroni adjustments showed that tone recognition
under high-pressure was significantly worse than in
the first low-pressure condition (p = 0.03, see Fig. 4).
There was no effect of group, F (1, 39) = 0.1, p = 0.76,
and no interaction effect, F (2, 78) = 0.23, p = 0.80.
Movement phase judgments
Performance in movement phase recognition was
calculated by the difference between the picture which
actually corresponded to when the tone was sounded
and the picture the participants selected. The mean spread
to the correct picture was analyzed by 2 × 3 ANOVA
to look at differences in the pressure conditions and
the learning groups. The effect of pressure was found
to be significant, F (2, 78) = 11.54, p < 0.001, ŋ 2p = 0.23.
As Figure 5 shows, the post-hoc test revealed that performance in picture recognition degraded from the first
low-pressure to high-pressure condition (p < 0.01).
There was a trend for improvement in picture recognition from high-pressure to second low-pressure (p = 0.06).
As for the tone judgment task, there was no effect of
group, F (1, 39) = 1.32, p = 0.26, and no interaction
effect, F (2, 78) = 0.98, p = 0.38.
Verbal knowledge
An independent samples t test revealed that the number of explicit rules was significantly higher for the
technical learning group (M = 3.38, SD = 1.23) than for
the analogy learning group (M = 2.0, SD = 1.01), t (39)
= 3.9, p < 0.001, d = 1.6.
Discussion
Figure 4. Tone judgments in the externally-focused
dual task
Figure 5. Picture recognition in the internally-focused
dual task
In this study, we examined two methods of learning
a golf putting task (analogy vs. technical) with regard
to the stability of performance under pressure and the
attentional processes that were involved. In line with
our expectations, both learning groups improved performance equally throughout the learning phase, indicating
that the pendulum analogy for golf putting is as effective
in learning as receiving traditional technical instructions. As shown in other studies, the analogy learning
group reported fewer technical instructions than the
technical learning group. However, our assumptions
about performance under pressure were not supported by the results. Firstly, despite a significant increase
in pressure as evidenced in both manipulation checks
(CSAI-2R and pressure scale), there were no performance
decrements for either of the two learning groups. On the
contrary, both groups showed an increase in performance
from the low-pressure to high-pressure conditions. This
finding goes in line with a recent study conducted by
Ehrlenspiel, Wei, and Sternad [41], where participants
in the stressed group did not choke either but instead
improved task performance in a rhythmic ball bouncing
task. In Koedijker et al.’s longitudinal study [33] on
analogy learning in table tennis, both learning groups
did not show any decrements in performance, however,
181
HUMAN MOVEMENT
L. Schücker, N. Hagemann, B. Strauss, Analogy learning in golf putting
there were no increases in performance either. Our study
did not show an advantage of analogy compared to technical learning, and therefore is not consistent with
studies that did find a positive effect of analogy learning
in preventing choking under pressure when compared
with traditional learning [e.g. 31, 34]. However, it is important to keep in mind that choking under pressure
did not occur in either group.
The results of the two dual-tasks are not consistent
with the previously-stated assumptions either. Firstly,
contrary to Schücker et al.’s findings [36], the dual-task
designed to measure the amount of skill-focused attention did not reveal any differences between the two
learning groups. In all pressure conditions, both groups
showed an equal response to the two dual tasks despite
a different amount of technical verbal knowledge. We
would have expected a higher amount of skill-focused
attention in the explicit learning group when under
pressure. In the dual task, both groups showed similar
results in the external focus of attention as well. Secondly, the skilled focus as well as the external focus of
the dual task showed decreased accuracy under pressure, which could signify that the amount of attention
devoted to the secondary task decreased in general under
pressure. Lam et al.’s [34] findings of probe reaction
time results under pressure for analogy and explicit
learning groups did not reveal any differences between
them either, despite a difference in performance under
pressure. Allocation of attentional resources during
movement execution was equal in all conditions in Lam
et al.’s study [34]. In our study, both groups increased
putting performance and decreased accuracy in both
dual tasks.
The participants in our study did not show any form
of performance decrement under high pressure, thus no
evidence of choking under pressure was found. It seems
that the significant increases in CSAI-2R scores were
not powerful enough to produce performance deficits.
The scores of the cognitive anxiety subscale and pressure scale were similar to those reported previously by
other researchers [e.g., 40]. However, despite the expected decrease in performance, both groups showed
increases in performance under pressure, which was not
expected at all. One theory that has been discussed with
regard to increases in performance is the social facilitation theory originally postulated by Zajonc [42]. Performance in simple motor tasks might actually improve
under pressure as induced by social evaluative audiences [5]. The question then stands: is golf putting a simple
motor task? Golf putting is a complex movement which
needs to be performed very accurately in order to lead to
good performance outcomes. It is doubtful that it had
been so well learned by the groups that social facilitation effects could explain for their increase in performance. This is also intersecting considering the fact that
researchers looking at the choking phenomenon have
previously used the golf putting task [e.g., 11, 23]. There
182
were no evident differences between the analogy and
technical learning group under pressure, where even
though the skill had been instructed differently in the
learning phase, both groups showed the same putting and
dual-task performance under pressure. This means that
the same mechanisms are applied when the skill is executed under stress. However, as participants knew they
had a fifty percent chance of being asked about their
movement execution in the secondary task, the participants of both groups might have directed their attention to movement execution because of the nature
of the dual-task.
An explanation of the results (increase in putting performance and decrease in dual-task performance under
pressure) can also be considered from the perspective
of the attentional control theory [6]. It is possible that
the participants invested extra effort so as to improve
their performance in the golf-putting task (primary task)
and neglected the dual-task portion to some degree as
it was not part of the pressure manipulation.
In general, the findings of our study do not lend credence to the assumptions made in the reinvestment
theory and the usefulness of analogy learning in preventing choking under pressure. Despite a difference in
verbal knowledge, no differences were found in performance (both groups did not show decreases in performance) nor in attentional processes under pressure.
The results do not go in line with explicit monitoring
theories, as these would have predicted an increase in
skill-focused attention under pressure as per Gray [12].
The results of this study lead to the conclusion that it
does not matter how a skill was learned (either by analogy or by technical instructions) when it comes to performance under pressure and limits the conclusions
on performance after a short learning interval. However, Koedijker et al. [33] found similar results in a longterm learning interval but different results in table
tennis after a short learning interval [43]. Both studies
conducted by Koedijker et al. did not include an online
measure of attentional focus and included a fast externally paced task in contrast to the slower and self-paced
task of golf putting.
Some limitations weaken the conclusion of this study
and need to be discussed. First, the issue of pressure
manipulation requires further discussion. The results
of the manipulation check showed that pressure was
induced successfully albeit the observed changes were
relatively small. In a laboratory setting, it is very difficult to induce pressure similar to that in real competition.
The ecological validity of these types of studies is limited
to producing generally smaller levels of stress. However,
our results showed that participants did feel more under
pressure in the high-pressure condition, allowing comparisons between the pressure conditions to be valid.
Nonetheless, it should be considered whether the small
changes found in some studies’ performance levels should
be interpreted as signs of choking.
HUMAN MOVEMENT
L. Schücker, N. Hagemann, B. Strauss, Analogy learning in golf putting
Apart from the discussed attentional explanation for
the observed pattern of performance outcomes, the role
of motivation should also be considered. A greater amount
of motivation in the high-pressure condition could explain for the participants’ better putting performance.
According to Baumeister [1] only extremely motivated
people choke when under pressure; it may be that the
participants were motivated just enough to perform well.
Future studies on choking should include an assessment of motivation in addition to measures of anxiety
and pressure.
Another issue that is of importance is the length of the
learning interval, which was relatively short in this study
with only 300 repetitions. In this early stage of skill
acquisition, focusing on the skill might not be detrimental to performance as movement execution is far from
being completely automated. The inclusion of a dualtask at so early of a stage could have caused performance
decrements of the primary task. Our results are limited
as they are based on a short learning interval. A similar
study but implementing a longer learning interval as in
Koedijker et al. [33] should be conducted in the future.
Finally, a critical assessment of the secondary task
as a measure of internal and external focus of attention
is needed. The aim was to design a task to measure the
amount of internal and external focus of attention. The
question is whether the dual-task approach is a valid
measure for focus of attention. As had been shown before [see 12], designing secondary tasks relating to movement execution and external stimuli is a valid measure.
However, it should be questioned whether there are variables that might overlay the results of this measure.
Secondary tasks do require at least some allocation of
attentional resources. If a secondary task on skill execution is not answered correctly this may not only be
due to the fact that attention was not focused on the
skill but also that attention was not allocated to the
secondary task itself. Therefore, the decrease in secondary task performance under pressure as was observed in
this study could also signify that more attention was
allocated to do well in the primary task under pressure
and that the amount of skill and externally focused
attention was not measured precisely by the dual task.
In the future, the design of valid measures of attentional focus should be emphasized.
References
1. Baumeister R.F., Choking under pressure: Self-consciousness and paradoxical effects of incentives on skillful performance. J Pers Soc Psychol, 1984, 46 (3), 610–620, doi:
10.1037/0022-3514.46.3.610.
2. Beilock S.L., Gray R., Why do athletes choke under pressure? In: Tenenbaum G., Eklund R.C. (eds.), Handbook of
Sport Psychology. John Wiley & Sons, New Jersey 2007,
425–444.
3. Strauss B., Choking under pressure: positive public expectations and decreases in performance [in German]. Zeit­
schrift für experimentelle Psychologie, 1997, 44, 636–655.
4. Baumeister R.F., Showers C.J., A review of paradoxical
performance effects: Choking under pressure in sports and
mental tests. Eur J Soc Psychol, 1986, 16 (4), 361–383, doi:
10.1002/ejsp.2420160405.
5. Strauss B., Social facilitation in motor tasks: a review of
research and theory. Psychol Sport Exerc, 2002, 3 (3),
237–256, doi: 10.1016/S1469-0292(01)00019-X.
6. Eysenck M.W., Derakshan N., Santos R., Calvo M.G.,
Anxiety and cognitive performance: Attentional control
theory. Emotion, 2007, 7 (2), 336–353, doi: 10.1037/1528
-3542.7.2.336.
7. Lewis B.P., Linder D.E., Thinking about choking? Attentional processes and paradoxical performance. Pers Soc
Psychol Bull, 1997, 23 (9), 937–944, doi: 10.1177/014616
7297239003.
8. Wine J., Test anxiety and direction of attention. Psychol
Bull, 1971, 76 (2), 92–104, doi: 10.1037/h0031332.
9. Beilock S.L., Carr T.H., When high-powered people fail:
Working memory and “Choking under pressure” in math.
Psychol Sci, 2005, 16 (2), 101–105, doi: 10.1111/j.09567976.2005.00789.x.
10. Beilock S.L., Kulp C.A., Holt L.E., Carr T.H., More on
the fragility of performance: Choking under pressure in
mathematical problem solving. J Exp Psychol: Gen, 2004,
133 (4), 584–600, doi: 10.1037/0096-3445.133.4.584.
11. Beilock S.L., Carr T.H., On the fragility of skilled performance: What governs choking under pressure? J Exp
Psychol: Gen, 2001, 130 (4), 701–725, doi: 10.1037/00963445.130.4.701.
12. Gray R., Attending to the execution of a complex sensorimotor skill: Expertise differences, choking, and slumps.
J Exp Psychol: Appl, 2004, 10 (1), 42–54, doi: 10.1037/
1076-898X.10.1.42.
13. Beilock S.L., Carr T.H., MacMahon C., Starkes J.L., When
paying attention becomes counterproductive: Impact
of divided versus skill-focused attention on novice and
experienced performance of sensorimotor skills. J Exp
Psychol: Appl, 2002, 8 (1), 6–16, doi: 10.1037/1076-898
X.8.1.6.
14. Jackson R.C., Ashford K.J., Norsworthy G., Attentional
focus, dispositional reinvestment, and skilled motor performance under pressure. J Sport Exerc Psychol, 2006,
28 (1), 40–68.
15. Castaneda B., Gray R., Effects of focus of attention on
baseball batting performance in players of differing skill
levels. J Sport Exerc Psychol, 2007, 29 (1), 60–77.
16. Ford P., Hodges N.J., Williams A.M., Online attentionalfocus manipulations in a soccer-dribbling task: Implications for the proceduralization of motor skills. J Mot
Behav, 2005, 37 (5), 386–394, doi: 10.3200/JMBR.37.5.
386-394.
17. Bell J.J., Hardy J., Effects of attentional focus on skilled
performance in golf. J Appl Sport Psychol, 2009, 21 (2),
163–177, doi: 10.1080/10413200902795323.
18. Schücker L., Hagemann N., Strauss B., Völker K., The effect of attentional focus on running economy. J Sports Sci,
2009,27(12),1241–1248,doi:10.1080/02640410903150467.
19. Wulf G., Attentional focus and motor learning: A review
of 10 years of research. E-Journal Bewegung und Training,
2007, 1, 1–64.
20. Vance J., Wulf G., Töllner T., McNevin N., Mercer J., EMG
activity as a function of the performer’s focus of attention. J Mot Behav, 2004, 36 (4), 450–459, doi: 10.3200/
JMBR.36.4.450-459.
183
HUMAN MOVEMENT
L. Schücker, N. Hagemann, B. Strauss, Analogy learning in golf putting
21. Zachry T., Wulf G., Mercer J., Bezodis N., Increased movement accuracy and reduced EMG activity as the result of
adopting an external focus of attention. Brain Res Bull,
2005, 67 (4), 304–309, doi: 10.1016/j.brainresbull.2005.
06.035.
22. Masters R.S.W., Maxwell J.P., The theory of reinvestment.
Int Rev Sport Exerc Psychol, 2008, 1 (2), 160–183, doi:
10.1080/17509840802287218.
23. Masters R.S.W., Knowledge, knerves and know-how:
The role of explicit versus implicit knowledge in the breakdown of a complex motor skill under pressure. Br J Psychol,
1992, 83 (3), 343–358, doi: 10.1111/j.2044-8295.1992.
tb02446.x.
24. Vickers J.N., Williams A.M., Performing under pressure:
The effects of physiological arousal, cognitive anxiety,
and gaze control in biathlon. J Mot Behav, 2007, 39 (5),
381–394, doi: 10.3200/JMBR.39.5.381-394.
25. Gucciardi D.F., Dimmock J.A., Choking under pressure
in sensorimotor skills: Conscious processing or depleted
attentional resources? Psychol Sport Exerc, 2008, 9 (1),
45–59, doi: 10.1016/j.psychsport.2006.10.007.
26. Oudejans R.R.D., Pijpers J.R., Training with mild anxiety may prevent choking under higher levels of anxiety.
Psychol Sport Exerc, 2010, 11 (1), 44–50, doi: 10.1016/j.
psychsport.2009.05.002.
27. Cohn P.J., Preperformance Routines in Sport: Theoretical Support and Practical Applications. Sport Psychol,
1990, 4 (3), 301–312.
28. Lonsdale C., Tam J.T.M., On the temporal and behavioural consistency of pre-performance routines: An intra-individual analysis of elite basketball players’ free throw
shooting accuracy. J Sports Sci, 2008, 26 (3), 259–266,
doi: 10.1080/02640410701473962.
29. Jackson R.C., Willson R.J., Using ‘swing thoughts’ to prevent paradoxical performance effects in golf putting.
In: Farrally M.R., Cochran A.J. (eds.), Science and golf
III: Proceedings of the 1998 world scientific congress of
golf. Human Kinetics, Leeds 1999, 166–173.
30. Masters R.S.W., Theoretical aspects of implicit learning
in sport. Int J Sport Psychol, 2000, 31 (4), 530–541.
31. Liao C., Masters R.S.W., Analogy learning: A means to
implicit motor learning. J Sports Sci, 2001, 19 (5), 307–319,
doi: 10.1080/02640410152006081.
32. Law J., Masters R., Bray S.R., Eves F.F., Bardswell I., Motor
performance as a function of audience affability and metaknowledge. J Sport Exerc Psychol, 2003, 25 (4), 484–500.
33. Koedijker J.M., Oudejans R.R.D., Beek P.J., Table tennis
performance following explicit and analogy learning
184
over 10.000 repetitions. Int J Sport Psychol, 2008, 39 (3),
237–256.
34. Lam W.K., Maxwell J.P., Masters R., Analogy learning
and the performance of motor skills under pressure.
J Sport Exerc Psychol, 2009, 31 (3), 337–357.
35. Lam W.K., Maxwell J.P., Masters R.S.W., Analogy versus
explicit learning of a modified basketball shooting task:
Performance and kinematic outcomes. J Sports Sci, 2009,
27 (2), 179–191, doi: 10.1080/02640410802448764.
36. Schücker L., Ebbing L., Hagemann N., Learning by Analogies: Implications for performance and attentional processes under pressure. Hum Mov, 2010, 11 (2), 191–199,
doi:10.2478/v10038-010-0025-z.
37. Letzelter H., Letzelter M., Golftechniken: Wieso, weshalb,
warum? [in German]. Philippka, Münster 2002.
38. Poolton J.M., Maxwell J.P., Masters R.S.W., Raab M.,
Benefits of an external focus of attention: Common coding or conscious processing? J Sports Sci, 2006, 24 (1),
89–99, doi: 10.1080/02640410500130854.
39. Cox R.H., Martens M.P., Russell W.D., Measuring anxiety
in athletics: The Revised Competitive State Anxiety Inventory-2. J Sport Exerc Psychol, 2003, 25 (4), 519–533.
40. Kinrade N.P., Jackson R.C., Ashford K.J., Dispositional
reinvestment and skill failure in cognitive and motor
tasks. Psychol Sport Exerc, 2010, 11 (4), 312–319, doi:
10.1016/j.psychsport.2010.02.005.
41. Ehrlenspiel F., Wei K., Sternad D., Open-loop, closed-loop
and compensatory control: performance improvement
under pressure in a rhythmic task. Exp Brain Res, 2010,
201 (4), 729–741, doi: 10.1007/s00221-009-2087-8.
42. Zajonc R.B., Social facilitation. Science, 1965, 149 (3681),
269–274, doi: 10.1126/science.149.3681.269 .
43. Koedijker J.M., Oudejans R.R.D., Beek P.J., Explicit rules
and direction of attention in learning and performing
the table tennis forehand. Int J Sport Psychol, 2007, 38
(2), 227–244.
Paper received by the Editor: February 22, 2013
Paper accepted for publication: April 25, 2013
Correspondence address
Linda Schücker
Institute of Sport and Exercise Sciences
University of Muenster
Horstmarer Landweg 62b
48149 Münster, Germany
e-mail: [email protected]
HUMAN MOVEMENT
PUBLISHING GUIDELINES – Regulamin publikowania prac
The Editorial Office of Human Movement accepts original
empirical as well as comparative research papers on sport science from sports medicine, exercise physiology, biomechanics,
kinesiology, sociology, psychology. The Journal also invites
such contributions as letters to the Editor, reports from scientific conferences and book reviews. The publication of submitted contributions to Human Movement is free of charge.
The original version of the journal is offered in print form.
All proposals should be prepared using the guidelines set
forth below and sent electronically to: [email protected]
The author is also obliged to submit a signed declaration (downloadable from our website) that the submitted
work has not been and will not be published in any other
publications without the consent of the Editorial Office
and that they agree for their work to be published in Human Movement. Articles with more than one author need
only one declaration, signed by the principal author on behalf of all the co-authors.
The Editorial Office will not accept articles that were
“ghostwritten” or feature “guest authorship”, and any irregularities will be reported and disclosed by the Editorial Office.
Articles submitted for publication in the quarterly Human Movement are peer-reviewed. The peer-review procedure
used at Human Movement is in accordance with the guidelines set out by the Polish Ministry of Science and Higher
Education. The author may provide the names of potential
reviewers, but the Editorial Office reserves the right in their
selection of reviewers. Reviewers will not know the author’s
name nor will the authors know the reviewer’s name. Based
on the reviewers’ assessment of the submitted work, the Editorial Office will decide whether an article is to be published
or not. The Editorial Office’s decision is final.
Authors are not remunerated for published works.
Redakcja kwartalnika Human Movement przyjmuje do publikacji oryginalne prace empiryczne oraz przeglądowe z nauk
o kulturze fizycznej ograniczone do problematyki medycyny
sportu, fizjologii wysiłku fizycznego, biomechaniki, antropomotoryki, psychologii. Przyjmowane są również listy do Redakcji, sprawozdania z konferencji naukowych i recenzje książek. Publikowanie prac w Human Move­ment jest bezpłatne.
Wersją pierwotną czasopisma jest wersja papierowa.
Wszystkie prace powinny być przygotowane wg opisanych niżej zasad i przesłane w wersji elektronicznej na adres:
[email protected].
Autor jest zobowiązany ponadto do przesłania pod­pi­sa­
ne­go oświadczenia (formularz do pobrania ze strony in­ter­
ne­to­wej), że treść artykułu nie była i nie będzie publiko­
wana w tej formie w innych wydawnictwach bez zgody
Redakcji czasopisma Human Movement oraz że zgadza się
na ogło­sze­nie jej w tym kwartalniku. Przy pracach zespo­
łowych oświad­czenie w imieniu wszystkich współautorów
składa główny autor.
Redakcja nie przyjmie artykułu, w którym występują zja­
wiska „ghostwritting” i „quest authorship”, a wszelkie nie­
prawidłowości będą ujawniane przez Redakcję.
Artykuły zamieszczane w kwartalniku Human Movement są recenzowane. Procedury recenzowania są zgodne
z wytycznymi Ministerstwa Nauki i Szkolnictwa Wyższego,
umieszczonymi na stronie: http://pbn.nauka.gov.pl. Autor
może podać nazwiska potencjalnych recenzentów, lecz Redakcja zastrzega sobie prawo ich doboru. Recenzenci nie znają
nazwisk autorów ani autorzy nie znają nazwisk recenzentów. W zależności od oceny recenzentów Redakcja podejmuje
decyzję, czy artykuł zostanie opublikowany czy nie. Decyzja
Redakcji jest ostateczna.
Autorzy nie otrzymują honorarium za opublikowanie pracy.
Detailed guidelines for submitting articles
to Human Movement
Szczegółowe zasady przygotowania artykułu
do Human Movement
1. The article should be written in English.
2.Empirical research articles, together with their summary and any tables, figures or graphs, should not exceed
20 pages in length; comparative articles are limited to
30 pages. Page format is A4 (about 1800 characters with
spaces per page). Pages should be numbered.
3. Articles should be written using Microsoft Word with the
following formats:
– Font: Times New Roman, 12 point
– Line spacing: 1.5
– Text alignment: Justified
– Title: Bold typeface, centered
4. The main title page should contain the following:
– The article’s title
– A shortened title of the article (up to 40 characters in
length including spaces), which will be placed in the
running head
1. Redakcja publikuje prace w języku angielskim. Jedynie autorzy zatrudnieni na uczelniach wydających czasopismo
mogą nadsyłać prace w języku polskim.
2. Tekst prac empirycznych wraz ze streszczeniem, rycinami i tabelami nie powinien przekraczać 20, a prac przeglądowych – 30 stron znormalizowanych formatu A4
(ok. 1800 znaków ze spacjami na stronie). Strony powinny być ponumerowane.
3. Artykuł należy przygotować w edytorze tekstu Microsoft
Word według następujących zasad:
– krój pisma: Times New Roman, 12 pkt;
– interlinia: 1,5;
– tekst wyjustowany;
– tytuł zapisany pogrubionym krojem pisma, wyśrodkowany.
4.Strona tytułowa powinna zawierać:
– tytuł pracy;
185
HUMAN MOVEMENT
Publishing guidelines – Regulamin publikowania prac
– The name and surname of the author(s) with their affiliations written in the following way: the name of the
university, city name, country name. For example: The
University of Physical Education, Wrocław, Poland
– Address for correspondence (author’s name, address,
e-mail address and phone number)
5. The second page should contain:
– The title of the article
–An abstract of approximately 250 words divided into
the following sections: Purpose, Methods, Results, Con­
clusions
– Three to six keywords to be used as MeSH descriptors
(terms)
6. The third page should contain:
– The title of the article
– The main text
7. The main body of text in empirical research articles should
be divided into the following sections:
–skrócony tytuł artykułu (do 40 zna­­ków ze spacjami),
który zostanie umieszczony w żywej paginie;
– imię i nazwisko autora (autorów) z afiliacją zapisaną wg
następującego schematu: nazwa uczelni, nazwa miejscowości, nazwa kraju, np. Akademia Wychowania Fizycznego, Wrocław, Polska;
– adres do korespondencji (imię i nazwisko autora, jego
adres, e-mail oraz numer telefonu).
5. Następna strona powinna zawierać:
– tytuł artykułu;
– streszczenie (około 200 wyrazów) składające się z następujących części: Purpose, Methods, Results, Conclusions;
– słowa kluczowe (3–6) – ze słownika i w stylu MeSH.
6. Trzecia strona powinna zawierać:
– tytuł artykułu;
– tekst główny.
7. Tekst główny pracy empirycznej należy podzielić na następujące części:
Introduction
The introduction prefaces the reader on the article’s subject, describes its purpose, states a hypothesis, and mentions
any existing research (literature review)
Wstęp
We wstępie należy wprowadzić czytelnika w tematykę
artykułu, opisać cel pracy oraz podać hipotezy, stan badań
(przegląd literatury).
Material and methods
This section is to clearly describe the research material
(if human subjects took part in the experiment, include their
number, age, gender and other necessary information), discuss the conditions, time and methods of the research as well
identifying any equipment used (providing the manufacturer’s
name and address). Measurements and procedures need to be
provided in sufficient detail in order to allow for their reproducibility. If a method is being used for the first time, it
needs to be described in detail to show its validity and reliability (reproducibility). If modifying existing methods, describe what was changed as well as justify the need for the
modifications. All experiments using human subjects must
obtain the approval of an appropriate ethnical committee by
the author in any undertaken research (the manuscript must
include a copy of the approval document). Statistical methods should be described in such a way that they can be easily
determined if they are correct. Authors of comparative research articles should also include their methods for finding
materials, selection methods, etc.
Materiał i metody
W tej części należy dokładnie przedstawić materiał badawczy (jeśli w eksperymencie biorą udział ludzie, należy podać ich liczbę, wiek, płeć oraz inne charakterystyczne cechy),
omówić warunki, czas i metody prowadzenia badań oraz opisać wykorzystaną aparaturę (z podaniem nazwy wytwórni
i jej adresu). Sposób wykonywania pomiarów musi być przedstawiony na tyle dokładnie, aby inne osoby mogły je powtórzyć. Jeżeli metoda jest zastosowana pierwszy raz, należy ją
opisać szczególnie precyzyjnie, przedstawiając jej trafność
i rzetelność (powtarzalność). Modyfikując uznane już metody, trzeba omówić, na czym polegają zmiany, oraz uzasadnić
konieczność ich wprowadzenia. Gdy w eksperymencie biorą
udział ludzie, konieczne jest uzyskanie zgody komisji etycznej na wykorzystanie w nim zaproponowanych przez autora
metod (do maszynopisu należy dołączyć kopię odpowiedniego
dokumentu). Metody statystyczne powinny być tak opisane,
aby można było bez problemu stwierdzić, czy są one poprawne. Autor pracy przeglądowej powinien również podać metody poszukiwania materiałów, metody selekcji itp.
Results
The results should be presented both logically and consistently, as well as be closely tied with the data found in
tables and figures.
Wyniki
Przedstawienie wyników powinno być logiczne i spójne
oraz ściśle powiązane z danymi zamieszczonymi w tabelach
i na rycinach.
Discussion
Here the author should create a discussion of the obtained results, referring to the results found in other literature (besides those mentioned in the introduction), as well
as emphasizing new and important aspects of their work.
Dyskusja
W tym punkcie, stanowiącym omówienie wyników, autor
powinien odnieść uzyskane wyniki do danych z literatury (innych niż omówione we wstępie), podkreślając nowe i znaczące
aspekty swojej pracy.
Conclusions
In presenting any conclusions, it is important to remember
the original purpose of the research and the stated hypotheses,
and avoid any vague statements or those not based on the
results of their research. If new hypotheses are put forward,
they must be clearly stated.
Wnioski
Przedstawiając wnioski, należy pamiętać o celu pracy oraz
postawionych hipotezach, a także unikać stwierdzeń ogólnikowych i niepopartych wynikami własnych badań. Stawiając
nowe hipotezy, trzeba to wyraźnie zaznaczyć.
186
HUMAN MOVEMENT
Publishing guidelines – Regulamin publikowania prac
Acknowledgements
The author may mention any people or institutions that
helped the author in preparing the manuscript, or that provided support through financial or technical means.
Podziękowania
Należy wymienić osoby lub instytucje, które pomogły autorowi w przygotowaniu pracy bądź wsparły go finansowo
lub technicznie.
Bibliography
The bibliography should be composed of the article’s citations and be arranged and numbered in the order in which
they appear in the text, not alphabetically. Referenced sources
from literature should indicate the page number and enclose it in square brackets, e.g., Bouchard et al. [23].
The total number of bibliographic references (those found
only in research databases such as SPORTDiscus, Medline)
should not exceed 30 for empirical research papers (citing
a maximum of two books); there is no limit for comparative research papers. There are no restrictions in referencing
unpublished work.
Bibliografia
Bibliografię należy uporządkować i ponumerować według
kolejności cytowania publikacji w tekście, a nie alfabetycznie. Odwołanie do piśmiennictwa należy oznaczyć w tek­­ście
numerem i ująć go w nawias kwadratowy, np. Bouchard
et al. [23].
Bibliografia (powołania zawarte tylko w bazach danych,
np. SPORTDiscus, Medline) powinna się składać najwyżej
z 30 pozycji (dopuszcza się powołanie na 2 publikacje książ­
kowe), z wyjątkiem prac przeglądowych. Niewskazane jest
cytowanie prac nieopublikowanych.
Citing journal articles
Bibliographic citations of journal articles should include:
the author’s (or authors’) surname, first name initial, article title, abbreviated journal title, year, volume or number,
page number, doi, for example:
Opis bibliograficzny artykułu z czasopisma
Opis bibliograficzny artykułu powinien zawierać: naz­
wisko autora (autorów), inicjał imienia, tytuł artykułu, tytuł czasopisma w przyjętym skrócie, rok wydania, tom lub
numer, strony, numer doi, np.
Tchórzewski D., Jaworski J., Bujas P., Influence of long-lasting balancing on unstable surface on changes in balance.
Hum Mov, 2010, 11 (2), 144–152, doi: 10.2478/v10038-0100022-2.
Tchórzewski D., Jaworski J., Bujas P., Influence of long-lasting balancing on unstable surface on changes in balance.
Hum Mov, 2010, 11 (2), 144–152, doi: 10.2478/v10038-0100022-2.
If there are six or less authors, all the names should be
mentioned; if there are seven or more, give the first six and
then use the abbreviation “et al.”
If the title of the article is in a language other than English, the author should translate the title into English, and
then in square brackets indicate the original language; the
journal title should be left in its native name, for example:
Gdy autorami artykułu jest sześć lub mniej osób, należy
wymienić wszystkie nazwiska, jeżeli jest ich siedem i więcej,
należy podać sześć pierwszych i zastosować skrót „et al.”.
Tytuł artykułu w języku innym niż angielski autor powinien przetłumaczyć na język angielski, a w nawiasie kwadratowym podać język oryginału, tytuł czasopisma należy
zostawić w oryginalnym brzmieniu, np.
Jaskólska A., Bogucka M., Świstak R., Jaskólski A., Mechanisms, symptoms and after-effects of delayed muscle soreness (DOMS) [in Polish]. Med Sport, 2002, 4, 189–201.
Jaskólska A., Bogucka M., Świstak R., Jaskólski A., Mechanisms, symptoms and after-effects of delayed muscle soreness (DOMS) [in Polish]. Med Sport, 2002, 4, 189–201.
The author’s research should only take into consideration articles published in English.
W pracy powinny być uwzględnianie tylko artykuły publikowane ze streszczeniem angielskim.
Citing books
Bibliographic citations of books should include: the author (or authors’) or editor’s (or editors’) surname, first
name initial, book title translated into English, publisher,
place and year of publication, for example:
Opis bibliograficzny książki
Opis bibliograficzny książki powinien zawierać: nazwisko autora (autorów) lub redaktora (redaktorów), inicjał imienia, tytuł pracy przetłumaczony na język angielski, wydawcę,
miejsce i rok wydania, np.
Osiński W., Anthropomotoric [in Polish]. AWF, Poznań 2001.
Osiński W., Anthropomotoric [in Polish]. AWF, Poznań 2001.
Heinemann K. (ed.), Sport clubs in various European countries. Karl Hofmann, Schorndorf 1999.
Heinemann K. (ed.), Sport clubs in various European countries. Karl Hofmann, Schorndorf 1999.
Bibliographic citations of an article within a book should
include: the author’s (or authors’) surname, first name initial, article title, book author (or authors’) or editor’s (or
editors’) surname, first name initial, book title, publisher,
place and year of publication, paga number, for example:
Opis bibliograficzny rozdziału w książce powinien za­wie­
rać: nazwisko autora (autorów), inicjał imienia, tytuł roz­
działu, nazwisko autora (autorów) lub redaktora (redaktorów), inicjał imienia, tytuł pracy, wydawcę, miejsce i rok wydania, strony, np.
McKirnan M.D., Froelicher V.F., General principles of exercise testing. In: Skinner J.S. (ed.), Exercise testing and
exercise prescription for special cases. Lea & Febiger, Philadelphia 1993, 3–28.
McKirnan M.D., Froelicher V.F., General principles of exercise testing. In: Skinner J.S. (ed.), Exercise testing and
exercise prescription for special cases. Lea & Febiger, Philadelphia 1993, 3–28.
187
HUMAN MOVEMENT
Publishing guidelines – Regulamin publikowania prac
Citing conference materials
Citing conference materials (found only in international
research databases such as SPORTDiscus) should include:
the author’s (or authors’) surname, first name initial, article title, conference author’s (or authors’) or editor’s (or editor’s) surname, first name initial, conference title, publisher,
place and year of publication, page number, for example:
Opis bibliograficzny materiałów zjazdowych
Opis bibliograficzny materiałów zjazdowych (umieszczanych tylko w międzynarodowych bazach danych, np.
SPORTDiscus) powinien zawierać: nazwisko autora (autorów), inicjał imienia, tytuł, nazwisko autora (autorów) lub
redaktora (redaktorów), tytuł pracy, wydawcę, miejsce i rok
wydania, strony, np.
Rodriguez F.A., Moreno D., Keskinen K.L., Validity of a twodistance simplified testing method for determining critical swimming velocity. In: Chatard J.C. (ed.), Biomechanics and Medicine in Swimming IX, Proceedings of the IXth
World Symposium on Biomechanics and Medicine in Swimming. Université de St. Etienne, St. Etienne 2003, 385–390.
Rodriguez F.A., Moreno D., Keskinen K.L., Validity of a twodistance simplified testing method for determining critical swimming velocity. In: Chatard J.C. (ed.), Biomechanics and Medicine in Swimming IX, Proceedings of the IXth
World Symposium on Biomechanics and Medicine in Swimming. Université de St. Etienne, St. Etienne 2003, 385–390.
Citing articles in electronic format
Citing articles in electronic format should include: author’s (or authors’) surname, first name initial, article title,
abbreviated journal title, year of publication, journal volume
and number, website address where it is available, doi number, for example:
Opis bibliograficzny artykułu w formie elektronicznej
Opis bibliograficzny artykułu w formie elektronicznej
po­winien zawierać: nazwisko autora (autorów), inicjał imienia, tytuł artykułu, tytuł czasopisma w przyjętym skrócie,
rok wydania, tom i numer, adres strony, na której jest dostępny, numer doi, np.
Donsmark M., Langfort J., Ploug T., Holm C., Enevold­sen L.H., Stallknech B. et al., Hormone-sensitive lipase
(HSL) expression and regulation by epinephrine and exercise in skeletal muscle. Eur J Sport Sci, 2 (6), 2002. Available
from: URL: http://www.humankinetics.com/ejss/bissues.
cfm/, doi: 10.1080/17461391.2002.10142575.
Donsmark M., Langfort J., Ploug T., Holm C., Enevold­sen L.H., Stallknech B. et al., Hormone-sensitive lipase
(HSL) expression and regulation by epinephrine and exercise in skeletal muscle. Eur J Sport Sci, 2 (6), 2002. Available
from: URL: http://www.humankinetics.com/ejss/bissues.
cfm/, doi: 10.1080/17461391.2002.10142575.
8. The main text of any other articles submitted for consideration should maintain a logical continuity and that the
titles assigned to any sections must reflect the issues discussed within.
8. Tekst główny w pracach innego typu powinien zachować
logiczną ciągłość, a tytuły poszczególnych części muszą
odzwierciedlać omawiane w nich zagadnienia.
9. Footnotes/Endnotes (explanatory or supplementary to the
text). Footnotes should be numbered consecutively throughout the work and placed at the end of the main text.
9.Przypisy (objaśniające lub uzupełniające tekst) powinny
być numerowane z zachowaniem ciągłości w całej pracy
i umieszczone na końcu tekstu głównego.
10. Tables, figures and photographs
– Must be numbered consecutively in the order in which
they appear in the text and provide captions
–Should be placed within the text
– Additionally, figures or photographs must be attached
as separate files in .jpg or .pdf format (minimum resolution of 300 dpi)
– May not include the same information/data in tables
and also figures
– Illustrative materials should be prepared in black and
white or in shades of gray (Human Movement is published in such a fashion and cannot accept color)
–Symbols such as arrows, stars, or abbreviations used in
tables or figures should be clearly defined using a legend.
10. Tabele, ryciny i fotografie
– należy opatrzyć numerami i podpisami;
– należy umieścić w tekście artykułu;
–dodatkowo ryciny i fotografie trzeba dołączyć w postaci osobnych plików zapisanych w formacie *.jpg lub
*.pdf (gęstość co najmniej 300 dpi);
–nie można powtarzać tych samych wyników w tabelach i na rycinach;
–materiał ilustracyjny powinien zostać przygotowany
w wersji czarno-białej lub w odcieniach szarości (w taki
sposób jest drukowane czasopismo Human Movement);
– symbole, np. strzałki, gwiazdki, lub skróty użyte w tabelach czy na rycinach należy dokładnie objaśnić w legendzie.
Manuscripts not prepared as per the requirements set forth
in “Publishing Guidelines” will be returned to the author for
correction. The Editorial Office reserves the right to make
any language corrections or remove abbreviations found in
the manuscript. Once the Editorial Office accepts an article
for publication, a proof will be sent to the author for approval.
It is the author’s responsibility to accept any changes or submit any corrections within one week of receiving the proof.
Praca przygotowana niezgodnie z wymogami „Regulaminu publikowania prac” zostanie odesłana autorowi do
poprawy. Redakcja zastrzega sobie prawo usuwania usterek
językowych oraz dokonywania skrótów. Artykuł po opracowaniu redakcyjnym zostanie przekazany autorowi do akceptacji. Obowiązkiem autora jest przesłanie ewentualnych
uwag i poprawek w ciągu jednego tygodnia.
Prior to printing, the author will receive their article in
.pdf format. It is the author’s responsibility to immediately
inform the Editorial Office if they accept the article for publication. At such a point in time, only minor corrections can be
accepted from the author.
188
Przed drukiem autor otrzyma swój artykuł do akceptacji w formie pliku pdf. Obowiązkiem autora jest niezwłoczne przesłanie do Redakcji Human Movement informacji
o akceptacji artykułu do druku. Na tym etapie będą przyjmowane tylko drobne poprawki autorskie.
HUMAN MOVEMENT
Publishing guidelines – Regulamin publikowania prac
The Journal is subject to copyright as per the Berne Convention and the International Copyright Convention, except
where not applicable pursuant to a country’s domestic law.
The Editorial Office accepts advertising in Human Movement, which may be located on the second or third page of
the cover or as additional separate pages. Ad rates are negotiated separately.
Authors should contact the Editorial Office of Human
Movement only by email.
Publikacje podlegają prawu autorskiemu wynikającemu
z Konwencji Berneńskiej i z Międzynarodowej Konwencji
Praw Autorskich, poza wyjątkami dopuszczanymi przez prawo krajowe.
Redakcja przyjmuje zamówienia na reklamy, które mogą
być umieszczane na 2. i 3. stronie okładki lub na dodatkowych kartach sąsiadujących z okładką. Ceny reklam będą
negocjo­wane indywidualnie.
Autorzy powinni się kontaktować z Redakcją Human Move­­
ment wyłącznie za pośrednictwem poczty elektronicznej.
SUBSCRIBING to THE HUMAN MOVEMENT JOURNAL
ZASADY PRENUMERATY CZASOPISMA HUMAN MOVEMENT
The price of annual subscription (four issues) for individual sub­scribers is PLN 54 and PLN 110 for institutions.
All subscriptions are payable in advance. Subscribers are
requested to send payment with their order whenever
possible. The orders should be sent to the Editorial Office:
e-mail: [email protected] or
Human Movement Editorial Office
University School of Physical Education
al. I.J. Paderewskiego 35
51-612 Wrocław, Poland
Cena rocznej prenumeraty (cztery numery) dla odbiorców in­dy­w idualnych w kraju wynosi 54 zł brutto, dla
instytucji 110 zł brutto. Zamówienie wraz z potwierdzeniem dokonania wpłaty należy przesłać na adres mailowy:
[email protected] lub
The issues of the journal are sent by post after receiving
the appropriate transfer to the account:
Numery czasopisma wysyłamy pocztą po otrzymaniu od­
po­w ied­niej wpłaty na konto:
BPH PBK S.A. O/Wrocław
42 1060 0076 0000 3210 0014 7743
Akademia Wychowania Fizycznego
al. Paderewskiego 35, 51-612 Wrocław, Poland
with the note: Human Movement subscription.
BPH PBK S.A. O/Wrocław
42 1060 0076 0000 3210 0014 7743
Akademia Wychowania Fizycznego
al. Paderewskiego 35, 51-612 Wrocław
z dopiskiem: Prenumerata Human Movement.
We ask the subscribers to give correct and clearly written addresses to which the journal is to be sent.
Prosimy zamawiających o bardzo wyraźne podawanie adresów, pod które należy wysyłać zamawiane egzemplarze
czasopisma.
Pojedyncze egzemplarze można zamówić w ten sam sposób, wpłacając 16 zł brutto (odbiorca indywidualny) i 30 zł
brutto (instytucja) na podane konto.
Single copies can be ordered in the same way, by transferring PLN 16 (individual subscribers) and PLN 30 (institutions) to the above mentioned account.
Redakcja czasopisma Human Movement
Akademia Wychowania Fizycznego
al. I.J. Paderewskiego 35
51-612 Wrocław
189