Developing and Validation of Movement and Activity in Physical Space (MAPS)
Scores in Concussion Recovery
A thesis presented to
the faculty of
the College of Health Sciences and Professions of Ohio University
In partial fulfillment
of the requirements for the degree
Master of Science
James L. Farnsworth II
June 2012
© 2012 James L. Farnsworth II. All Rights Reserved.
2
This thesis titled
Developing and Validation of Movement and Activity in Physical Space (MAPS) Scores
in Concussion Recovery.
by
JAMES L. FARNSWORTH
has been approved for
the School of Applied Health Sciences and Wellness
and the College of Health Sciences and Professions by
Brian G. Ragan
Assistant Professor of Athletic Training
Randy Leite
Dean, College of Health Sciences and Professions
3
ABSTRACT
FARNSWORTH, JAMES L., II, M.S., June 2012, Athletic Training
Developing and Validation of Movement and Activity in Physical Space (MAPS) Scores
in Concussion Recovery
Director of Thesis: Brian G. Ragan
Current concussion assessment tools measure impairments and do not evaluate
total functional level. The Movement and Activity in Physical Space (MAPS) system
provides a way to measure the different components of function, as described by the
World Health Organization. Purpose: To develop and validate MAPS scores as a measure
of function following a concussion. Design: Repeated measures matched-pair. Methods:
31 participants (n = 19 concussion, n = 12 matched pair) were monitored using
accelerometers and GPS. Participants filled out travel diaries, and symptom
questionnaires. Results: The ANOVA results for MAPS scores indicated there was no
difference between groups. There existed a small inverse relationship between
concussion symptoms and MAPS scores (p < .05). Conclusion: MAPS scores are an
objective way to measure function in a patient following injury. The results of this study
indicated that symptom duration may be a key factor in determining the effects of
concussion on patient function.
Approved: _____________________________________________________________
Brian G. Ragan
Assistant Professor of Athletic Training
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ACKNOWLEDGMENTS
I would like to thank my family, and friends who have all helped support me in
my academic endeavors. Without their help none of this would have been possible.
This thesis could not have been completed without the assistance of Dr. Brian
Ragan who has served as a wonderful mentor and challenged me to constantly improve
myself throughout this experience. He and my committee members, Dr. Chad Starkey,
Dr. Cheryl Howe, and Shannon David have all been an invaluable help. Thank you to Dr.
Guarav Sinha for your expertise and assistance with GIS and GPS applications.
In addition, I would like to think Shannon Nickels for her assistance with
processing and analyzing physical activity data. Together we spent countless hours
reviewing information and interpreting the activity and travel of our participants.
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TABLE OF CONTENTS
Page
Abstract ............................................................................................................................... 3
Acknowledgments............................................................................................................... 4
List of Tables .................................................................................................................... 10
List of Figures ................................................................................................................... 11
Chapter 1: Introduction ..................................................................................................... 13
Statement of the Problem .............................................................................................. 15
Purpose.......................................................................................................................... 15
Significance of Study .................................................................................................... 16
Research Questions ....................................................................................................... 16
Null Hypothesis ............................................................................................................ 16
Delimitations ................................................................................................................. 17
Limitations .................................................................................................................... 17
Definition of Terms ...................................................................................................... 17
Chapter 2: Literature review ............................................................................................. 19
Concussion .................................................................................................................... 19
Concussion Signs ..................................................................................................... 20
Concussion Short-Term Effects ............................................................................... 20
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Concussion Long-Term Effects ............................................................................... 21
Current Concussion Measures ...................................................................................... 22
Concussion Symptom Scales ................................................................................... 22
Standardized Assessment of Concussion ................................................................. 23
Balance Error Scoring System ................................................................................. 24
Sport Concussion Assessment Tool 2 ...................................................................... 25
Neuropsychological Test ......................................................................................... 26
Construct Issues With Current Concussion Measures .................................................. 28
World Health Organization International Classification of Function, Disability, and
Health (WHOICF) ........................................................................................................ 31
Why Measure Function? .......................................................................................... 34
Physical Activity ...................................................................................................... 35
Physical Activity Applications ................................................................................ 35
Physical Activity Measures...................................................................................... 36
Geomatics ................................................................................................................ 39
The Global Positioning System ............................................................................... 39
Remote Sensing ....................................................................................................... 42
Geographic Information Systems ............................................................................ 44
Applications of Geospatial Research ....................................................................... 46
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Movement and Activity in Physical Space .............................................................. 48
Specific Aims ........................................................................................................... 50
Chapter 3: Methods ........................................................................................................... 51
Design ........................................................................................................................... 51
Participants.................................................................................................................... 51
Power Analysis ............................................................................................................. 52
Inclusion and Exclusion Criteria................................................................................... 53
Instruments.................................................................................................................... 54
Injury History Form ................................................................................................. 54
Concussion Symptom Questionnaire ....................................................................... 54
Standardized Assessment of Concussion ................................................................. 55
Accelerometer .......................................................................................................... 55
On-Person Global Position System Receivers ......................................................... 57
Geographic Information Systems ............................................................................ 59
Travel Diary ............................................................................................................. 59
Procedure ...................................................................................................................... 60
Data Processing............................................................................................................. 62
Movement and Activity in Physical Space Score Formula ..................................... 62
Processing Procedure ............................................................................................... 63
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Data Analysis ................................................................................................................ 65
Chapter 4: Results ............................................................................................................. 68
Monitor Wear Time Validity ........................................................................................ 68
MAPS Scores ................................................................................................................ 68
Reliability of MAPS Scores .......................................................................................... 70
Known Group Difference ............................................................................................. 71
Changes in Function over Time .................................................................................... 73
Relationship between MAPS Score and Symptom Score ............................................ 76
Chapter 5: Discussion ....................................................................................................... 79
Influence of Symptom Duration on Function ............................................................... 79
Symptom Duration ................................................................................................... 79
Symptoms and Function .......................................................................................... 82
Validity of MAPS Scores ............................................................................................. 85
Patient Population .................................................................................................... 85
Application of MAPS Scores ........................................................................................ 86
Limitations .................................................................................................................... 88
Potential Issues with MAPS Formula ........................................................................... 89
Clinical Application ...................................................................................................... 90
References ......................................................................................................................... 92
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Appendix A: Recruitment Script ................................................................................ 111
Appendix B: Approval Notice for Alternate Data Collection Sites........................... 113
Appendix C: Informed Consent .................................................................................. 115
Appendix D: Injury History Form .............................................................................. 117
Appendix E: Modified Standard Assessment of Concussion ..................................... 118
Appendix F: Concussion Symptom Questionnaire ..................................................... 119
Appendix G: Travel Diary .......................................................................................... 129
Appendix H: Study Feedback Questionnaire.............................................................. 130
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LIST OF TABLES
Page
Table 1: Common Concussion Symptoms ......................................................................21
Table 2: Concussion Assessment Tools ..........................................................................29
Table 3: Participant Demographics .................................................................................52
Table 4: Pilot Data Effect Sizes ......................................................................................53
Table 5: MAPS Data Sheet Example ..............................................................................66
Table 6: Mean MAPS Scores (Concussion vs. Control).................................................69
Table 7: Percent Change Between Phase 1 and Phase 2 .................................................70
Table 8: Correlation of Symptom and MAPS Scores .....................................................76
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LIST OF FIGURES
Page
Figure 1: World Health Organization International Classification of Function,
Disability, and Health Model ..........................................................................................32
Figure 2: Satellite triangulation ......................................................................................40
Figure 3: Varying accuracy of GPS ................................................................................42
Figure 4: Aerial photograph ............................................................................................43
Figure 5: GPS track layered into GIS program ...............................................................45
Figure 6: MAPS ..............................................................................................................48
Figure 7: Actigraph GT3X+ ...........................................................................................56
Figure 8: Accelerometer with clip and GPS on participant ............................................57
Figure 9: GPS Tracking Key Pro© .................................................................................58
Figure 10: Travel diary example .....................................................................................60
Figure 11: Data collection procedure ..............................................................................61
Figure 12: Data quality check .........................................................................................64
Figure 13: Location identification procedure .................................................................65
Figure 14: Mean MAPSI and MAPSV scores .................................................................72
Figure 15: Group by time interaction MAPSI and MAPSV scores .................................74
Figure 16: Daily mean MAPSI scores .............................................................................75
Figure 17: Daily mean MAPSI and total symptom scores ..............................................77
Figure 18: MAPSI and total symptom scores correlation ...............................................78
Figure 19: Symantec differential scaling ........................................................................84
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Figure 20: Case study......................................................................................................87
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CHAPTER 1: INTRODUCTION
A concussion is a biomechanically induced transient disturbance of neurologic
function (Giza & Difiori, 2011). An estimated 1.6 to 3.8 million concussions occur in the
United States annually as a result of sports participation (Langlois, Rutland-Brown, &
Wald, 2006). Concussions have documented consequences of short-term cognitive
impairments (Bleiberg et al., 2004; McCrea et al., 2003), and postural control deficits
(Sosnoff, Broglio, Shin, & Ferrara, 2011).
Concussions present athletic trainers (ATs) and health care professionals with
unique problems because they are unseen injuries that can be difficult to diagnose,
making it even harder to track their recovery. Because these injuries are so complex, it is
recommended that a single test battery is insufficient when making clinical decisions;
rather, a combination of measures should be used (McCrory et al., 2009).
Current methods of concussion assessment include symptom questionnaires,
sideline concussion screening, balance assessment tools, and neuropsychological testing.
These tools help determine the effects of a concussion by evaluating subjective
symptoms, and cognitive and balance impairments. However, these tools do not address a
patient's level of function. Presently there are no established methods of evaluating
function in a patient following concussion.
The World Health Organization International Classification of Function,
Disability, and Health (WHOICF) describes function as a complex paradigm that
encompasses body function and structures, activity and participation, environmental
factors, and personal factors (Schneidert, Hurst, Miller, & Ustun, 2002). The components
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of the WHOICF are interrelated and affect one another equally (Strucki, Cieza, &
Melvin, 2007). Body function and structures refer to anatomical abnormalities. Activity is
described as a simple task that has meaning, while participation is a patient's involvement
in situations. Environmental factors describe the external influences on an injury and may
be physical, social, cultural, or institutional in nature (Rosenbaum & Stewart, 2004;
Snyder et al., 2008). Personal factors describe patient demographics such as age, height,
weight, and past injury history (Snyder et al., 2008).
The Movement and Activity in Physical Space (MAPS) system is a new approach
to measuring function, which evaluates an individual's activity and participation within
their environment. This patient-centered approach to evaluation of function is a shift from
impairment based disease-oriented rehabilitation which focuses only on restoring
limitations [impairments]. Patient-centered medicine involves using a more
individualized approach to evaluation and rehabilitation that focuses on the uniqueness of
each individual. This is accomplished using accelerometers, and the global positioning
system (GPS), to create a MAPS score. The use of a MAPS score can provide a more
detailed picture of function following concussion.
After a concussion, a negative correlation exists between physical activity and
concussion symptoms, because symptoms subside as physical activity increases
(Majerske et al., 2008). Accelerometers are small portable electronic devices that can
measure intensity, frequency and duration of physical activity. GPS are navigation
systems that use satellite radio signals to triangulate location. Physical activity measures,
when combined with GPS monitoring, can provide ATs with objective tools for detecting
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an individual's level of function following injury (Oliver, Badland, Mavoa, Duncan, &
Duncan, 2010).
The MAPS score (a component of the MAPS system) integrates measures of
activity, time, and location. This score represents an individual's activity and participation
within the environment. It is reasonable to assume a patient suffering from an injury
would be less likely to participate in their normal daily activities. This decrease in
activity and participation would be demonstrated with a lower MAPS score indicating a
lower level of function. Using MAPS scores as an objective measure of function may
provide ATs with a more detailed assessment of health status following injury.
Statement of the Problem
A shift from disease-oriented impairment-based rehabilitation to patient-centered
care will help to improve quality health care of patients. Patient-centered care has been
associated with better recovery following injury (Stewart et al., 2000). Current
concussion assessment tools focus on measuring a patient’s impairments following injury
without addressing overall function. Presently there are no established methods for
evaluating function in a patient following concussion.
Purpose
The purpose of this thesis is to develop and validate the use of the Movement and
Activity in Physical Space (MAPS) scores in concussion recovery. The MAPS score
quantifies function by combining physical activity and geospatial measures.
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Significance of Study
This thesis intends to validate the development and use of MAPS scores as a
measure of function following concussion. Adding functional outcome measures may
enhance current assessment techniques and provide a more detailed picture of patient
health status. This thesis also assessed the relationship between concussion symptoms
and MAPS scores. Objective monitoring of patients post-concussion would allow a more
evidence based approach in making decisions on safe return to play. It is imperative that
ATs have the best resources possible to provide quality care for their athletes.
Research Questions
The research questions guiding this study are:
1. Can MAPS scores be used to identify differences in function between concussed
individuals and healthy matched controls?
2. Are MAPS scores sensitive to changes in function throughout the concussion
recovery process?
3.
Are there relationships between MAPS scores and concussion symptom scores?
Null Hypothesis
H01
There is no difference in MAPS scores between concussed and healthy matched
controls.
H02
There is no difference in MAPS scores in participants immediately following
concussion and once recovery has occurred.
H03 There is no relationship between MAPS and concussion symptom scores.
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Delimitations
Delimitations of this study include:
1. Participants in this study were college-aged club sports and intercollegiate
athletes.
2. Participants were free of head injury for the past 6 months.
3. Participants in the study were included with 48 hours of being diagnosed with a
concussion.
4. Participants reported no other health disorders, history of learning disability,
seizure disorder, attention deficit disorder, or other mental/physical disability.
Limitations
Limitations of this study include:
1. The unpredictable nature of concussions could limit the number of participants.
2. Participants must wear the accelerometer device, and GPS device for a minimum
of 10 hours for the day to be included in analysis.
3. Manufacturer limitations of the devices not being water-proof could result in lost
data with athletes who participate in sports involving water activities.
4. Techniques for concussion evaluation vary between independent athletic trainers.
Definition of Terms
Accelerometer. A small electronic device used to measure physical activity
intensity and step counts.
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Athletic trainer (AT). Health care professionals who collaborate with physicians
to optimize activity and participation of patients and clients. ATs specialize in prevention,
assessment, treatment, and rehabilitation of injuries and illnesses.
Concussion. A biomechanically induced transient disturbance of neurologic
function that may or may not be associated with loss of consciousness (Giza & Difiori,
2011).
Function. A combination of an individual's body function and structures, activity
and participation, environmental factors, and personal factors (Strucki et al., 2007).
Geographic information system. A computer-based tool for capturing, managing,
analyzing, and displaying all forms of geographically referenced data (Environmental
Systems Research Institute, 2011).
Global positioning system. A small portable electronic receiver that triangulates
location based upon satellite radio signals.
Impairment. Decreased quality, strength, or effectiveness of a bodily system.
Physical activity. Bodily movement produced by skeletal muscles that requires
energy expenditure above rest (Caspersen, Powell & Christenson, 1985).
Symptom. The subjective deviation from normal function or feeling, indicating the
presence of a disease or abnormality.
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CHAPTER 2: LITERATURE REVIEW
This chapter provides a review of the literature for the proposed research. Information
is included regarding: concussion assessment, World Health Organization International
Classification of Function, physical activity, and geospatial technology.
Concussion
A concussion is a biomechanically induced transient disturbance of neurologic
function that may or may not be associated with loss of consciousness (Giza & Difiori,
2011). It can be difficult to diagnose and determine the effects of these injuries because
there are no universal standards of assessment (Guskiewicz, Weaver, Padua, & Garret,
2000; Notebaert & Guskiewicz, 2005). Definitions can vary greatly from clinician to
clinician, because, currently there is no clear universal definition (Giza & Difiori, 2011;
Lovell, Collins, & Bradley, 2004).
There is no simple test that can be performed to identify a concussion.
Diagnosis depends largely upon patient reports (Notebaert & Guskiewicz, 2005) and
each case presents differently (Broglio & Puetz, 2008). Therefore, for the purpose of this
thesis, concussions were established by a force or impact to the head that resulted in one
or more concussion-related symptoms.
An estimated 1.6 to 3.8 million concussions occur in the United States annually as
a result of sports participation (Langlois et al., 2006). The National Electronic Injury
Surveillance System reports that concussions account for approximately 7% of ice
hockey related emergency room visits (Deits, Yard, Collins, Fields, & Comstock, 2010).
In high school football concussions make up approximately 5.6% of all injuries while
20
collegiate football player have a rate of 4.4 to 5.5% (Carter, Westerman, & Hunting,
2011; Guskiewicz et al., 2000). These values may be underestimated due to under
reporting of concussions (Guskiewicz et al., 2000).
Concussion Signs
Patients suffering from concussion may appear dazed, stunned or move clumsily.
They may be uncertain of game details such as the score, or show confusion about plays
and assignments. Other signs may include; loss of consciousness, behavior or personality
changes, a decreased reaction time, or retro and anterograde amnesia (McCrory et al.,
2009).
Concussion Short Term Effects
The short term effects, or symptoms, of a concussion typically last for a short
duration and resolve over a period of seven to ten days (Bleiberg et al., 2004; Guskiewicz
et al., 2000; Lovell et al., 2003; McCrea et al., 2003; McCrory et al., 2009). These
symptoms represent a deviation from normal function. Common concussion symptoms
are listed in table 1 and fall within a range of domains including clinical symptoms,
physical signs, behavior, balance, sleep and cognition (McCrory et al., 2009).
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Table 1
Concussion Symptoms
Headache
Feeling more emotional
Nausea
Numbness or tingling
Emesis
Confusion
Balance Problems
Feeling slowed down
Pressure in head
Sensation of being "in a fog"
Feeling "dinged" or "dazed"
Don't feel right
Sensitivity to light
Difficulty with concentrating
Sensitivity to noise
Difficulty with memory
Sadness
Visual Problems
Nervousness
Trouble falling asleep
Dizziness
Sleeping more than usual
Fatigue or low energy
Sleeping less than usual
Irritability
Drowsiness
Neck Pain
Concussion Long-Term Effects
Chronic Traumatic Encephalopathy (CTE) is a condition of neurodegeneration
that is characterized by symptoms of disordered memory and executive functioning,
behavioral and personality disturbances, Parkinsonism, and occasionally, motor neuron
22
disease (Gavett, Robert, & McKee, 2011). At the present there are no formal diagnostic
criteria for CTE, however it is believed to occur as a result of repetitive head injuries
(McKee et al., 2009). In addition to CTE, examination of retired athletes has indicated
that there may be a link between concussions and late-life cognitive impairments such as
depression (Guskiewicz et al., 2007), dementia (Guskiewicz et al., 2005), and
Parkinsonism (Stern, 1991).
Current Concussion Measures
Because these injuries are so complex, it is recommended that a single test battery
is insufficient when making clinical decisions; rather, a combination of measures should
be used (McCrory et al., 2009). The following sections discuss some of the current
concussion measures.
Concussion Symptom Scales
Concussion symptom scales show the largest degree of change immediately postconcussion (Broglio & Puetz, 2008). As a result they are one of the most commonly
used tools for concussion assessment (approximately 85% of cases) by clinicians
(Notebaert & Guskiewicz, 2005; Randolph et al., 2009). Concussion symptom scales
measure a patient's impairment following injury; most are based on a 7-point Likert
scale ranging from 0 (not present) to 6 (severe). Lower scores on the symptom scales
indicate less impairment. A number of symptom scales have been developed by various
researchers with from 9 to 34 symptoms included in each scale (Lovell et al., 2006;
Piland, Motl, Ferrara, & Peterson, 2003; Randolph et al., 2009).
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The Concussion Symptom Inventory (CSI) is an empirically driven scale
currently used that meets all the scientific criteria for scales (Eckner & Kutcher, 2010;
Randolph et al., 2009). The CSI examined sensitivity and analyzed receiver-operating
characteristics of the 27 concussion symptoms to create a 12-symptom computerized
scale. Symptoms include: headache, nausea, balance problems/dizziness, fatigue,
drowsiness, feeling "in a fog," difficulty concentrating, difficulty remembering,
sensitivity to light, sensitivity to noise, blurred vision, and feeling slowed down (Eckner
& Kutcher, 2010).
Headaches represent one of the most common symptoms of concussion occurring
in approximately 70%-86% of all cases (Guskiewicz et al., 2000; Lovell et al., 2004). A
study of 1,003 football-related concussions revealed dizziness (67%) was the second
most reported symptom, followed by mental confusion (49%), disorientation (48%),
blurred vision (35.5%), and 28.6% experienced a positive Romberg Test (Guskiewicz et
al., 2000)
Standardized Assessment of Concussion
The Standardized Assessment of Concussion (SAC) is an orally administered
concussion screening tool that measures impairments in a patient’s cognitive function
(immediate and delayed memory, orientation, and concentration) (McCrea, Kelly,
Kluge, Ackley, & Randolph, 1997; McCrea et al., 1998). The SAC has a total of 30
possible points with each correct response worth 1 point. The SAC is intended to be
used as a quick sideline assessment to help identify cognitive impairments following
injury. The test is administered before injury to obtain a baseline score and again after
24
injury. A decrease in score of 3 points indicates a concussion (McCrea, 2001a).
Reliability and validity evidence has been established by multiple studies (Barr &
McCrea, 2001; McCrea, 2001a, 2001b; Valovich McLeod, Barr, McCrea, &
Guskiewicz, 2006).
The test is most effective when compared to an individual-centered standard
baseline as opposed to criterion-referenced standards (Ragan & Kang, 2007). An
individual-centered standard means that each individual is scored independently.
Player motivation levels during baseline testing can lead to inaccurate scores giving a
potentially false negative concussion assessment (Echemendia & Julian, 2001). Authors
of the SAC claim that the test is "relatively free of significant ceiling effects" despite
reporting that 7% of normal controls achieve a perfect score. This ceiling effect can have
a negative impact on testing results specifically when tracking recovery during serial
testing complicated by known practice effects (Echemendia & Julian, 2001; Ragan,
Herrmann, Kang, & Mack, 2009). In addition the SAC has a mean score of 26.58 out of
30 possible points, giving it a skewed or uneven distribution of scores (Echemendia &
Julian, 2001).
Balance Error Scoring System
The Balance Error Scoring System (BESS), developed at the University of North
Carolina (Guskiewicz, Ross, & Marshall, 2001), is a tool commonly used by clinicians
when evaluating balance and postural deficiencies as a result of a concussion. The BESS
consists of three stances: double-leg stance (hands on the hips and feet together), singleleg stance (standing on the nondominant leg with hands on the hips), and a tandem stance
25
(nondominant foot behind the dominant foot) in a heel-to-toe fashion. The stances are
performed on a firm surface and on a foam surface while the eyes are closed, with errors
counted during each 20-second trial. An error is defined as opening the eyes, lifting the
hands off of the hips, stepping, stumbling, or falling out of position, lifting the forefoot,
or heel, abducting the hip by more than 30°, or failing to return to the test position after a
fault in less than 5 seconds. Each error is recorded with a maximum of 10 errors possible
for each stance. The BESS is administered before injury to obtain a baseline score. The
BESS is administered again after injury to identify balance impairments. The average
BESS score for healthy population is 8 compared to a score of 17 in concussed patients
(Guskiewicz et al., 2001).
The BESS has indicated a moderate level of reliability and validity (Guskiewicz
et al., 2001) for measuring balance impairments. Factors such as fatigue, dehydration,
ankle instability, training background, and neuromuscular training have indicated an
impact and can easily influence scores (Bell, Guskiewicz, Clark, & Padua, 2011).
Sport Concussion Assessment Tool 2
The Sport Concussion Assessment Tool 2 (SCAT2) was developed during the 3rd
International Conference on Concussion in Sport held in Zurich, in November 2008
(McCrory et al., 2009). The SCAT2 combines aspects of several concussion tools into
eight components used to assess concussion symptoms, cognition, and some neurological
signs (Eckner & Kutcher, 2010). The test uses an individual-centered standard for
grading similar to the SAC and is scored out of 100 possible points. A score card is
provided with the SCAT2 to keep track of scores and monitor resolution of impairments.
26
The components of the SCAT2 include a 22-item graded symptom scale, the SAC, a
modified Maddocks questionnaire, the Glasgow coma scale (GCS), assessment of
physical signs, a modified BESS, and an examination of coordination. The SAC is
separated into two separate components with delayed memory a separate component than
the rest of the test.
The length of the SCAT2 compared with other sideline assessment tools requires
longer time to complete. For this reason, larger teams or those with limited recourses may
find using the SCAT2 difficult. In addition, its length may limit its use on the sideline
(Eckner & Kutcher, 2010). Evaluation of baseline scores in high school athletes indicated
significant variability in scores across grade levels (Valovich McLeod, Bay, Lam, &
Chhabra, 2012). This suggests that baseline scores should be re-evaluated annually to
ensure that baseline measures are most accurate. At the time of writing this thesis there
were no published articles examining the reliability or the validity of the SCAT2 in its
entirety.
Neuropsychological Test
Neuropsychological testing is another method used to identify a concussion.
Immediate Post Concussion Assessment and Cognitive Testing (ImPACT), CogSport,
and HeadMinder are examples of computer-based neuropsychological tests (Schatz &
Zillmer, 2003). Most neuropsychological tests were designed to assess gross changes in
cognitive function, and as a result have limited usefulness in the sport setting (Collie et
al., 2003).
27
The CogSport test battery involves eight different tasks to assess cognitive
function. Of the eight tasks, simple reaction time, complex reaction time, one-back, and
continuous learning were determined to be the most effective for making return to play
decisions. These tasks measure psychomotor function/information processing, decision
making, working memory, and new learning, respectively. The CogSport test has shown
to have high correlations with similar test batteries (Collie et al., 2003). This
computerized test battery measures impairments to function.
The ImPACT is another commonly used neuropsychological test battery.
ImPACT includes six test sublets, each with its own score: Word Memory, Design
Memory, X's and O's, Symbol Match, Color Match, and Three Letters. The tests sublets
are designed to test impairments in memory, speed, reaction time, and impulse control.
These components are scored individually with no overall score. ImPACT takes
approximately 20-25 minutes to complete and includes a self-reported symptom scale at
the end of the examination (Randolph, McCrea, & Barr, 2005).
The computer-based neuropsychological tests have indicated no evidence that
they are more sensitive than traditional measures (Belanger & Vanderploeg, 2005). In
addition, the results are best interpreted by a neuropsychologist (McCrory et al., 2009)
making everyday use difficult. The ImPACT (version 2.0), has been shown that it is not
very sensitive to the effects of concussion, particularly after symptom resolution has
occurred (Randolph, 2011).
28
Construct Issues with Current Concussion Measures
Monitoring the recovery of patients suffering from concussion is vital to ensure
quality health care and limit negative consequences associated with the injury. During the
first 7 to 10 days following concussion, patients are at an increased risk of cerebral
vulnerability (Majerske et al., 2008; McCrea et al., 2003, 2009; McCrory et al., 2009).
Current assessment techniques used by health care professionals attempt to evaluate the
effects of a concussion by looking at patient impairments following injury. Examples of
concussion evaluation tools are summarized in Table 2.
29
Table 2
Concussion Assessment Tools
Test
Domain measured
Description
Concussion
Symptom Scales
Symptom
Impairment
Checklist or scales of varying word
lengths that measure the presence
of concussion-related symptoms.
Standard
Assessment of
Concussion (SAC)
Cognitive
Impairment
This is an oral administered side-line
screening tool. Administrators
require patients to memorize a list
of words and numbers repeated
them back in serial fashion or
reverse order respectively (McCrea
et al., 1997).
Balance Error
Scoring System
(BESS)
Balance
Impairment
Patients stand in six different stances
either on the ground or a foam pad
for bouts of twenty seconds. Errors
are recorded up to a maximum
value (Guskiewicz et al., 2001).
Sport Concussion
Assessment Tool 2
(SCAT2)
Cognitive
Impairment,
Balance
Impairment,
Symptom
Impairment
This is a combination of multiple test
including; graded symptom
checklist, standard assessment of
concussion, balance error scoring
system, maddocks score, and
physical signs (McCrory et al.,
2009).
CogSport
Cognitive
Impairment
This computerized test battery
requires 15 - 20 minutes to
complete. It contains measures of
speed, accuracy, and consistency
for responses within domains of
psychomotor, decision making,
problem solving, and memory.
There are eight scores produced by
the test with no overall score
(Collie et al., 2003).
30
Table 2 (continued)
Immediate Post
Concussion
Assessment and
Cognitive Testing
(ImPACT)
Cognitive
Impairment,
Symptom
Impairment
This computerized test battery
requires 20 - 25 minutes to
complete and examines 6 sublets,
each with multiple associated
scores: Word Memory, Design
Memory, X's and O's, Symbol
Match, Color Match, and Three
letters. There is no overall score. A
self-reported symptom scale is also
included (Randolph et al., 2005).
Note. The above table displays some common concussion assessment tools, gives
a brief description of the tool and identifies the domains measured with each test.
It is important to note that each of the test measure impairments in function and
do not evaluate the total functional level of a patient.
Symptom resolution is a large factor in determining return to play status in
concussed athletes. The Zurich concussion guidelines suggest normal activity can resume
once a patient is symptom free and has followed a graduated protocol (McCrory et al.,
2009). This protocol focuses around patient-reported symptoms as they progressively
increase in activity intensity before returning to full participation. However,
neurocognitive impairments have been found in concussed athletes despite being
completely symptom free (Broglio, Macciocchi, & Ferrara, 2007).
Notebaert and Guskiewicz (2005) reported that greater than 80% of concussion
evaluations rely on clinical examinations, physician recommendations, return-to-play
guidelines, and symptom scales. The subjective nature of symptom scales and a desire for
quick recovery have caused symptom scores to be biased and underreported (Broglio &
Puetz, 2008; Majerske et al., 2008; Valovich McLeod et al., 2006). These results suggest
31
that either patients are lying about their symptoms or that symptom resolution alone is not
enough to accurately gauge recovery (Broglio et al., 2007; Echemendia & Julian, 2001).
Current concussion assessment tools focus on measuring impairments of patients
following injury without addressing overall function. A shift from disease-oriented
impairment-based rehabilitation to patient-centered care would help to improve quality
health care of patients. Patient-centered care has been associated with better recovery
following injury (Stewart et al., 2000). Presently there are no established methods for
evaluating function in a patient following concussion. An objective measure of function
can provide a more detailed picture of health status. To provide the best quality care for
patients, it is imperative that evidence-based practices are used for measuring recovery
following concussion.
World Health Organization International Classification of Function, Disability, and
Health (WHOICF)
The following section defines function as described by the World Health
Organization and presents a novel approach to measuring function following concussion.
The World Health Organization International Classification of Function, Disability, and
Health (WHOICF) describes function as a paradigm of multiple interrelated components
(see Figure 1) including, functioning and disability, and contextual factors (Cieza et al.,
2002; Rosenbaum & Stewart, 2004; Snyder et al., 2008; Strucki et al., 2007). The
WHOICF model explains that management of injuries requires a broader scope than
merely impairment-based treatments and requires a focus on the components of health
rather than the consequences of disease (Rosenbaum & Stewart, 2004).
32
Figure 1. World Health Organization International Classification of Function, Disability,
and Health Model. Adapted from “The World Health Organization International
Classification of Functioning, Disability, and Health: A Model to Guide Clinical
Thinking, Practice and Research in the Field of Cerebral Palsy” by P. Rosenbaum and D.
Stewart, 2004, Seminars in Pediatric Neurology, 11, p. 6. Copyright 2004 by Elsevier
Inc.
Functioning and disability as well as contextual factors are broken down into
separate components; Body Function and Structures, Activity and Participation,
Environmental Factors, and Personal Factors. Each of these components combines to
create a picture of a patient's total level of function. These terminology reflect a change in
the use of negative terms such as "impairment," "disability," and "handicap" to the
neutral terms "body function and structure," "activity," and "participation," respectively
(Rosenbaum & Stewart, 2004). In addition, "disability" is a social construction involving
an interaction of the person and community or society between themselves and the
environment (Rosenbaum & Stewart, 2004). The WHO view, shared by rehabilitation
33
medicine, is that patient functioning and health are associated with, and not just a
consequence of, a condition (Snyder et al., 2008; Strucki et al., 2007). The components of
the WHOICF are intended to be a universal health model and not just limited to patients
with disabilities (Rosenbaum & Stewart, 2004).
Body Function and Structures refer to the physiological functions of body systems
and the anatomical parts of the body, such as limbs and their components. Irregularities in
function/structure are referred to as impairments, which are defined by loss in range of
motion, muscle weakness, and pain and fatigue (Snyder et al., 2008; Strucki et al., 2007).
Activity and Participation are often represented as a single component. Activity
represents the act of completing a task and is related to a patient's perception of function.
Participation refers to the involvement of a patient in real world situations. Limitations in
activity and participation are demonstrated in mobility dysfunctions or difficulties with
walking, climbing steps, grasping, or carrying (Snyder et al., 2008; Strucki et al., 2007).
Environmental Factors, describe the external influences affecting the health status
of a patient. Environmental Factors may be physical, social, cultural, or institutional in
nature (Rosenbaum & Stewart, 2004; Snyder et al., 2008). Personal Factors describe the
past history of a patient and demographic information such as age, height, weight, and
personal history.
The dynamic interaction between the components of the WHOICF can provide
health care professionals with a broader picture of a patient's level of function. This
model represents a transition from diseased-based clinical practice to one including an
equal focus on patient-centered care (Snyder et al., 2008). In addition, having a greater
34
understanding of the influences of an injury on the whole person can help with
establishing a more individualized approach to rehabilitation and recovery that focuses on
responding to the needs of the individual.
Why Measure Function?
Patient feedback is an important assessment tool used by health care professionals
to determine when a patient is safely able to resume normal athletic participation.
However, patient feedback is subjective and can be influenced by motivation levels.
Patients with a strong desire to return to normal activity may attempt to do so before they
are physically able, increasing their risk of reinjury or causing further harm. It is
imperative for health care professionals to have a means of objectively monitoring a
patient's health status.
Health status is typically measured by an impairment-based or disease-oriented
approach that involves restoring limitations. Impairments represent only one aspect of a
patient's level of function. Examination of patient function as a whole can provide a more
detailed picture of health status. The WHOICF accomplishes this by examining the
impairments caused by an injury (body function and structures) and how it affects a
patient's level of physical activity (activity) and their daily interaction (participation)
within their environment (environmental factors).
Currently there are very few well-established standards for measuring functional
level of a patient through the injury recovery process. Functional measures can provide
health care professionals with valuable patient centered outcomes. A better understanding
35
of a patient's functional level can help to limit unnecessary harm caused by returning to
participation too soon or waiting too long.
Physical Activity
Physical activity is defined as "any bodily movement produced by the skeletal
muscles that result in energy expenditure above rest" (Caspersen, Powell, & Christenson,
1985). A growing body of literature has established physical activity as an effective
measure for determining recovery patterns (Belza et al., 2001; Busse, Pearson, Van
Deurson, & Wiles, 2004; Pearson, Busse, Van Deursen, & Wiles, 2004). Studies have
indicated a positive correlation between physical activity and a patient's level of function.
Patients who are not healthy would be less physically active than patients who are well
(Herrmann & Ragan, 2008a). The following sections discuss examples of physical
activity measures and their applications in health care.
Physical Activity Applications
Physical activity provides health care professionals with a tool for objectively
measuring patient activity levels. Physical activity research has been used to track
recovery of patients, and to identify differences in health status between different
populations.
Evaluation of step counts in stroke victims 3 months post rehabilitation indicated
a strong relationship when compared with other mobility outcome instruments
(Shaughnessy, Michael, Sorkin, & Macko, 2005). Physical activity monitoring of patients
with peripheral artery disease (McDermott et al., 2002) and neurological disease (Busse
et al., 2004) revealed lower activity levels than healthy matched controls. In addition, as
36
the health of the patients with neurological disease improved, so did the number of steps
per day. Kop et al. (2005) found that patients with fibromyalgia and chronic fatigue
syndrome had lower activity levels than controls and spent less time performing high
intensity activities. In addition, activity levels of patients were inversely related to
concurrent ambulatory pain and fatigue.
Monitoring of physical activity has also been used to examine differences in
activities of daily living and mobility of patients with chronic congestive heart failure,
(Van den Berg-Emons, Bussmann, Balk, Keijzer-Oster, & Stam, 2001) chronic
obstructive pulmonary disease, (Steele, Belza, Ferris, Lakshiminaryan, & Buchner, 2000)
and the effects of fatigue on function in patients with Parkinson's disease. There exists an
inverse relationship between fatigue, leisure activity levels, frequency of activity, and
time spent moving about each day (Garber & Friedman, 2003).
Physical Activity Measures
Physical activity measures include both criterion and field-based measures. These
measures are evaluated using four criteria when determining effectiveness in
epidemiologic studies. (a) The instrument used must measure what it is intended to
measure. (b) The instrument must consistently give the same results under the same
circumstances. If the instrument is reliable and valid, it is also accurate. (c) The
instrument must have acceptable costs to both the investigator and the patient. (d) The
instrument must not alter a patient's normal physical activity (Laporte, Montoye, &
Caspersen, 1985).
37
Criterion Measures
Criterion measures of physical activity include direct and indirect calorimetry.
Direct calorimetry measures energy expenditure through the production of heat while
indirect calorimetry measures the consumption of oxygen that closely correlates with heat
production. Doubly labeled water is a form of indirect calorimetry and estimates physical
activity by measuring "integral CO2 production for up to three weeks from the difference
in elimination rates of deuterium and 18O from labeled body water” (Schoeller, 1988).
These measures are impractical for use in epidemiologic studies because they change
normal physical activity patterns and can be extremely costly for use with large
populations (Laporte et al., 1985).
Field Measures
Field measures of physical activity evaluate activity in a free-living environment
and are separated into two categories: subjective and objective measures. Subjective
measures of physical activity include self-reporting or interviews given over a
predetermined period of time. Objective measures involve the use of instruments such as
pedometers and accelerometers.
Self-reported measures of physical activity include activity diaries, logbooks, and
voice recorders. These methods of activity monitoring rely on an individual to recall
activities from previous time periods. Self-reported measures of physical activity are low
cost (printing cost & distribution) and easy to apply to large populations. The accuracy of
self-reported measures is limited as a result of biases either through difficulty of reporting
or the desire to conform to social norms (Pober, Staudenmayer, Raphae, & Freedson,
38
2006). Self-reported measures of physical activity were both lower and higher than direct
observation, with low to moderate correlations (Prince et al., 2008).
Pedometers are small electronic devices that measure the number of steps a
person takes throughout the day and range in price from $10-$200 (Schneider, Crouter, &
Basset, 2004). These devices are very easy to use and can be worn on the hip, wrist,
ankle, or carried in a purse or backpack. Despite this, their uses are limited because of
their inability to measure activity intensity as well as their inability to account for other
movement besides walking (e.g., carrying a load, upper body movements).
Accelerometers are electronic devices that measure accelerations and
decelerations of the body which is used to determine activity intensity, frequency, and
duration. These devices have the capability of providing accurate measurements over a
wide variety of exercise intensities (King, Torres, Potter, Brooks, & Coleman, 2004).
The accelerometer is able to convert different magnitudes of accelerations from up to
three axes into significant data. The activity data can be downloaded to a computer and
analyzed using accelerometer software. The software programs can be used to visualize
data in graphs and charts as well as track changes in activity over time. Monitoring of
physical activity with the accelerometers have yielded strong positive relationships (r =
.93) with laboratory-based treadmill exercise and non-laboratory lifestyle activities such
as sweeping, vacuuming, and shoveling (Trost, Mciver, & Pate, 2005).
The ability to objectively monitor a patient's level of health status is a vital tool
for health care professionals. While physical activity measures provide a crude measure
of patient function it lacks the contextual factors to identify the differences between
39
Activity and Participation described by the WHOICF. To obtain a more detailed picture
of function, patient environmental factors must be considered as well.
Geomatics
Geomatics refers to the science of acquiring, integrating, managing, analyzing,
visualizing and disseminating geospatially referenced information to support decision
making (National Oceanic and Atmospheric Administration Global Earth Observation
Integrated Data Environment, 2011). Geomatic technologies include; The Global
Positioning System (GPS), Remote Sensing, and Geographic Information Systems (GIS).
The following sections will describe these technologies and their research applications.
The Global Positioning System
The Global Positioning System (GPS) is a satellite based radio-navigation system
developed by the United States Department of Defense. GPS was originally intended
strictly for military applications and consists of three segments: space, control, and user.
The Space Segment contains 24 operational satellites in six circular orbits above the
earth. These satellites orbit the earth twice a day and are positioned so that six satellites
would be viewable to users at any location in the world. The Control Segment includes a
master control station in Colorado Springs, five monitor stations, and three ground
antennas. The User Segment consists of the commercial receivers, processors, and
antennas that allow operators to receive the GPS satellite broadcasts and compute
position, velocity, and time (U.S. Department of Homeland Security, 2012).
GPS receivers detect the radio signal projected by orbiting satellites to triangulate
its precise location. To create a 2-D image of longitude and latitude the GPS must acquire
40
signal from a minimum of three satellites (see Figure 2) or four to create a 3-D position
(Federal Aviation Administration, 2011). The GPS is separated into two service levels.
The first level, intended for civilian use, is the Standard Positioning Service (SPS). The
Precise Positioning Service (PPS) is primarily intended for use by the Department of
Defense and U.S. allies. The SPS has no restrictions and is available worldwide. SPS
provides reasonable accuracy of 100m with Selective Availability (SA). The accuracy of
PPS is within 22m and is not affected by SA (Federal Aviation Administration, 2011;
U.S. Department of Homeland Security, 2012).
Figure 2. Satellite triangulation. This figure represents the triangulation of three satellites
to determine location.
41
Selective Availability
Selective Availability (SA) has limited the use of the GPS in the past because the
Department of Defense intentionally implemented a random error (approximately +/100m) to prevent threats to national security. In May of 2000, SA was disabled to help
improve the accuracy of geospatial technologies for public interests. The U.S.
Government reserves the right to activate SA at any time (White House Office of the
Press Secretary, 2002).
Global Positioning System Augmentation Systems
To further aid the accuracy of GPS the Federal Aviation Administration (FAA)
has developed a satellite based differential GPS (dGPS) called the Wide Area
Augmentation System (WAAS) and the Ground Based Augmentation System (GBAS)
which provide assistance for horizontal and vertical measures. Differential GPS enhances
the accuracy of GPS receivers by transmission of error correction signals from groundbased radio beacons at known locations (Witte & Wilson, 2005).
The WAAS and GBAS have improved geospatial technology accuracy to
measures within one meter (Federal Aviation Administration, 2011). A comparison of
WAAS enabled GPS receivers, with non-WAAS enabled GPS receiver indicated median
absolute offset of 0.37 m for the WAAS-enabled unit and 4.8 m for the non-WAAS unit
(Witte & Wilson, 2005).
The deactivation of SA has led to an increase in public interest of GPS. As a
result they have become smaller, easier to manage and much more affordable (Stopher,
Greaves, & FitzGerald, 2005). The increased availability of portable GPS units, and
42
advances in technology have made them a viable option for public and research use.
Figure 3 provides an illustration of the differences in GPS accuracy with the addition of
WAAS and with SA turned off.
Figure 3. Varying accuracy of GPS. The figure illustrates the different degrees of
accuracy with the Global Positioning System (GPS) when selective availability is turned
on/off. The figure also shows the enhanced accuracy of GPS since the development of the
Wide Area Augmentation System (WAAS), differential GPS (dGPS), and the Ground
Based Augmentation System (GBAS).
Remote Sensing
Remote sensing is another geospatial tool and refers to techniques that use sensors
to acquire data and information about objects not in contact with the viewing instrument
(Short, 2011). Common techniques involve a large collection of aerial and satellite
images to provide data about the surface of the earth for global and detailed analysis,
43
change, and detection monitoring (Benz, Hofmann, Willhauck, Lingenfelder, & Heynen,
2004). Image detail has increased drastically over the past decade, as a result of the U.S.
Commercial Remote Sensing Policy (White House Office of the Press Secretary, 2003),
allowing for very detailed and accurate images. The quality of remote sensing has made it
possible to observe spatial resolutions at or better than 1m (Blaschke, 2010; Yu et al.,
2006). An example of an aerial photograph is shown in Figure 4.
Figure 4. Aerial photograph. The image is an aerial photograph of Ohio University in
Athens, OH. The photograph was accessed from Google Earth on February 5th, 2012
(http://www.google.com/earth/index.html).
44
Geographic Information Systems
The Environmental Systems Research Institute defines GIS as a computer-based
tool for capturing, managing, analyzing, and displaying all forms of geographically
referenced data (Environmental Systems Research Institute, 2011). Advancements in
technology have allowed for great improvements in GIS, combining the computation
power of computers, remote sensing and GPS to allow for analysis of data and
information layered over complex aerial maps, and satellite images (Kistemann,
Dangendorf, & Schweikart, 2002). GIS technology has an impact on literally every field
that can manage and analyze earth's surface data. It has its uses in agriculture,
archaeology, environment, public health and epidemiology, forestry, navigation,
marketing, real estate, regional and local planning, road and railway, site evaluation, and
costing, social studies, tourism, and utilities (Nayak, Thorat, & Kalyankar, 2009).
In the area of public health, GIS has been used as a tool for analyzing access to
healthcare (Love & Lindquist, 1995; Phillips, Kinman, Schnitzer, Lindbloom, &
Ewigman, 2000), mapping disease rates (Jarup, 2004), and to study environmental
influences on physical activity (Brownson, Hoehner, Day, Forsyth, & Sallis, 2009;
Yeom, Jung, & Choi, 2011). With the use of an accurate GIS program and GPS data it
may be possible to determine trip purpose without the use of travel diaries (Wolf,
Guensler, & Bachman, 2001).
GIS programs allow for GPS data to be examined in an easy to understand format.
Figure 5 shows an example of the mapping power of GIS combined with GPS data.
Programs can be used in conjunction with GPS data for the purposes of examining travel
45
behavior and patterns (Environmental Systems Research Institute, 2011; Herrmann &
Ragan, 2008b; Kwan, 2000).
Figure 5. GPS track layered into GIS program. The figure is an illustration of a Global
Position System track layered into a Geographic Information System (GIS). These two
images represent the same general location. The top aerial photograph was accessed
using Google Earth on February 7th, 2012 (http://www.google.com/earth/index.html). The
bottom image displays the same track and location with LandAirSea Past-Track GIS
Software. A star has been placed on the map to serve as a point of reference identifying
identical locations.
46
Applications of Geospatial Research
Geospatial technologies have provided a method for directly observing natural
phenomena without being influenced by researchers or laboratory settings. The ability to
observe populations without causing unnecessary environmental influences provides
promising research possibilities. Geospatial technologies have been used in public health
(Ali, Emch, Ashley, & Streatfield, 2001; Creange et al., 2007; Durand, Andalib, Dunton,
Wolch, & Pentz, 2011), agriculture (Anderson, Everitt, Escobar, Spencer, & Andrascik,
1996), sport and exercise (Cunniffe, Proctor, Baker, & Davies, 2010; Gavett et al., 2011;
Wisbey, Montgomery, Pyne, & Rattray, 2010), transportation (Morabia et al., 2009;
Schatz & Stigell, 2009), and for evaluating animal and human spatial behaviors (Aharoni
et al., 2009; Brosh et al., 2006; Cooper et al., 2010; Wieche et al., 2008; Yamazaki et al.,
2008).
A study of multiple sclerosis patients found GPS-observed maximal walking
distance to have strong correlations with the estimated walking capacity of patients
(Creange et al., 2007). In sports, GPS have been used to monitor speed, acceleration, and
distance of athletes during competition (Wisbey et al., 2010). These results demonstrate
the ability of objective observation of patients in a free-living environment.
Geospatial technology has made the observation of animals in their natural
environment possible. Using activity-sensing GPS collars, researchers were able to
estimate daily activity of Japanese black bears (Yamazaki et al., 2008). The GPS has also
been used to observe cattle and identify grazing patterns (Aharoni et al., 2009).
47
Improvements in geospatial technologies have increased the utility of these tools
for research and clinical practice. The GPS provides health care professionals with a
method of evaluating patients in a free-living environment. Wieche et al. (2008) used
GPS-enable cell phones to track travel patterns of adolescents during their daily activity.
The study identified that objective monitoring of patients using small portable GPS
devices did not influence natural travel behaviors. The ability to observe environmental
factors can help to provide context and understanding to activity.
Cooper et al. (2010) combined the use of accelerometers and geospatial
technologies to evaluate travel behaviors of school-aged children to and from school. The
study found significant differences in activity level based upon travel mode. By including
geospatial data with activity measures researchers were able to identify environmental
influences on activity levels of children during their morning commute.
The combination of accelerometers, GPS, and GIS allows for a novel approach to
studying a patient's behavior following injury. GPS receivers allow for the tracking of a
patient within their free-living environment. This incorporation of geospatial technologies
into physical activity measures may reveal a more complete picture of a participant's
level of function following injury. Figure 6 illustrates the integration of physical activity,
geospatial technologies. Geographic Information Systems (GIS) allow for the
interpretation of geospatial data by layering Global Positioning System (GPS) data over
satellite imagery and aerial photos to observe spatial behavior. By including physical
activity data with geospatial data health care professionals can assess patient activity and
participation within their environment
48





GIS
Combining Data Layers
Data Analysis
Movement and
Activity in
Physical Space
(MAPS)
GPS
Latitude
Longitude
Speed
Remote Sensing


Satellite Imagery
Aerial Photos
Physical Activity


Activity Counts/min (Intensity)
Step Count (Volume)
Figure 6. MAPS. This figure illustrates the integration of physical activity and geospatial
technologies. Geographic Information Systems (GIS) allow for the interpretation of
geospatial data by layering Global Positioning System (GPS) data over satellite imagery
and aerial photos to observe spatial behavior. By including physical activity data with
geospatial data health care professionals can assess patient activity and participation
within their environment.
Movement and Activity in Physical Space
The Movement and Activity in Physical Space (MAPS) System collects the
duration, volume, and intensity of physical activity at different locations (i.e., physical
space) throughout the day. The MAPS system is designed for evaluating continuous
accelerometer data (steps and physical activity counts/min), precise location data (from
GPS records), and travelogues (self-recorded) to create a detailed picture of function with
the MAPS score. The MAPS score is a reflection of a patient's activity and participation
49
within their environment. The MAPS score has been used to quantify function with postsurgical knee patients (Herrmann et al., 2011) and patients diagnosed with differing
severity of multiple sclerosis (Snook, Herrmann, & Ragan, 2012).
Herrmann et al. (2011) found significant differences between patients following
knee surgery and healthy matched controls. After 2 months, the control group was reevaluated with no significant difference from baseline scores. The postsurgical knee
group revealed a significant time and group interaction after 2 months. These results
suggest that MAPS scores are able to effectively identify changes in function over time.
As a patient's health status improves they are more likely to travel longer durations and to
a higher number of discretionary (non-essential) locations (Herrmann & Ragan, 2008b).
Current concussion measures focus primarily on participant impairments and do
not address a patient's level of function. The use of the MAPS system can give health
care professionals an objective measure for functional status, following concussion,
which has been lacking in the medical community. The use of a MAPS score has
demonstrated positive results at identifying recovery patterns with other injuries.
Therefore the purpose of this thesis is to validate the use of MAPS as an objective
measure for observing patients following a concussion, and to identify key relationships
between concussion symptoms and MAPS scores. This score was used to quantify
function as a combined measure of physical activity and environmental interaction.
50
Specific Aims
1. To validate the development and use of MAPS scores as a measure of function
following concussion.
2. To determine if there is a relationship between concussion symptoms and MAPS
scores.
51
CHAPTER 3: METHODS
This study examines the efficacy of using Movement and Activity in Physical
Space (MAPS) scores as a functional outcome measure following concussion. The study
involves two phases of data collection following a diagnosed concussion. This chapter
will describe the design of the study, participants involved, instrumentation, procedures,
and data analyses.
Design
This study was a longitudinal matched-pair design that examined athletes
following diagnosis of a concussion by a Licensed Athletic Trainer (AT) or other health
care professional, as compared to healthy control subjects.
Participants
A total of 31 participants were included in the study. The participants were
separated into two groups: concussion (n = 19) and healthy controls (n = 12). Participant
demographics are listed in Table 3. Intercollegiate and Club Sports athletes were
recruited (see Appendix A) from multiple universities (see Appendix B) to volunteer for
the study.
52
Table 3
Participant Demographics
Characteristics
Concussion group
Control group
Total
M(SD) (n = 19)
M(SD) ( n = 12)
M(SD) (N = 31)
Age (years)
19.8 (1.0)
19.6 (1.3)
19.7 (1.0)
Height (kg)
84.2 (27.9)
85.8 (24.0)
85.0 (25.2)
Weight (cm)
176.1 (10.7)
174.0 (9.7)
173.2 (8.9)
Power Analysis
Power analysis was performed based upon pilot data of disability in the upper and
lower extremity using MAPS as a functional outcomes measure. The pilot data (see Table
4) indicated that for activity and step counts of the upper and lower extremity, minimal
effect sizes of 1.3, 1.2, 1.9, and 1.5 respectively were needed to detect changes. 1.2 is the
smallest effect size observed using MAPS as a functional outcomes measure therefore;
based upon these numbers a projected power = 0.8, alpha = 0.05, effect size = 1.2, and
Beta = 0.2, a minimum sample size of N = 24 (n=12 concussion, n=12 control) was
needed.
53
Table 4
Pilot Data Effect Sizes
Upper Extremity
Lower Extremity
Physical Activity Counts
1.3
1.9
Step Counts
1.2
1.5
Inclusion and Exclusion Criteria
Participants were selected for the concussion group based upon the following
criteria. (a) Participants were physically capable of wearing a GPS and accelerometer unit
at all times. (b) Participants reported no other health disorders, history of learning
disability, seizure disorder, attention deficit disorder, or other mental/physical disability
that would hinder their participation in this study. (c) Participants in the concussion group
reported no additional head injuries six months prior to their inclusion in the study.
Once a participant had been recruited for the concussion group, a control
participant was chosen as a matched-pair based upon age, gender, perceived activity
level, and sport participation. Control participants had to meet all of the same inclusion
criteria as concussion participants. Selection of controls occurred shortly after inclusion
of a participant in the concussion group to limit variables in travel behavior. All
participants completed an informed consent form approved by Ohio University’s
Institutional Review Board (see Appendix C).
54
Instruments
This study used multiple instruments in data collection. Prior to receiving a
concussion all participants recruited filled out an Injury History Form and were
administered the SAC. Following concussion diagnosis and inclusion in the study
participants were re-evaluated with the SAC, and were given symptom questionnaires, an
accelerometer, and a GPS receiver to collect data for MAPS and symptom scores.
Injury History Form
The injury history form (see Appendix D) collected demographic information
from participants including age, height, weight, gender, history of head injury, sport
participation, and perceived levels of health and physical activity. Perceived levels of
health were measured with a 5-point Likert scale (1, excellent; 5, poor) and physical
activity was rated on a 5-point Likert scale (1, not active at all; 5, extremely active).
Concussion Symptom Questionnaire
The Symptom Questionnaire (see Appendix F) used in this study was a 22-item
survey based upon the theory of unpleasant symptoms. This theory defines symptoms as
having three major components: the symptoms that an individual is experiencing; the
influencing factors that cause the symptom; and its consequences (Lenz, Pugh, Milligan,
Gift, & Suppe, 1997). Each item in the survey measured the frequency, severity, and
bothersomeness of a concussion symptom. Frequency was measured with a 4-point Likert
scale (0, never; 1, occasionally; 2, often; 3, always). Severity was measured with a 3point Likert scale (0, not at all; 1, somewhat; 2, a great deal), and Bothersomeness was
measured with a 5-point Likert scale (0, not at all; 1, a little bit; 2, moderately; 3, quite a
55
bit; 4, extremely). Participants were given the option of filling out the survey by hand or
online via Patient Reported Outcomes Measurement Information System. The concussion
symptom questionnaire was available online for participants at:
https://www.assessmentcenter.net/ac1/Assessments/Concussion_Symptoms
Standardized Assessment of Concussion
The SAC (see Appendix E) is an orally administered concussion screening tool
that assesses patient orientation (5), immediate memory (10), concentration (5), and
delayed memory (10). The SAC was scored out of thirty points (one point for each
correct response) and completed by participants prior to competition to identify a baseline
score and again following injury. Health care professionals compare baseline score to the
post-injury score to identify decreases in performance, suggesting presence of a
concussion.
Accelerometer
An accelerometer is an electronic device used to measure physical activity of
participants in the field. Accelerometers are the preferred method of activity monitoring
because they are an objective field measure and not effected by recall bias that can occur
with traditional surveys and questionnaires. Accelerometers have indicated to be a
reliable way to accurately measure physical activity of participants (Welk, Blair, Wood,
Jones, & Thompson, 2000).
The Actigraph GT3X+ (size = 4.6cm x 3.3cm x 1.5cm, weight = 19g) (see Figure
7) is capable of measuring changes in acceleration ranging from 0.25 to 2.5 Hz. This
range allows the device to capture human motion. The GTX3+ has a storage capacity of
56
512MB allowing for up to 40 days of raw data and a battery life of approximately 31
days on the lowest settings. Data was processed with the ActiLife software, provided by
the manufacture, which allowed for the initialization of the unit, selection of power
settings, recording times, and epoch (data intervals).
For this study, data were processed using an epoch of 1-minute intervals. The
accelerometer was worn over the right hip (over the anterior superior iliac spine). This
device allowed for acquisition of activity intensity, frequency and duration of participants
throughout the day. To ensure proper placement of the device, a small belt clip was
attached to the device with double sided tape. This small clip did not interfere with data
collection and allowed for the device to remain upright and in the proper position (see
Figure 8). Participants were asked to wear the device during waking hours, but not during
grooming activities.
Figure 7. Actigraph GT3X+ accelerometer.
57
Figure 8. Accelerometer with clip and GPS on participant.
On-Person Global Position System Receivers
Global Position Systems or GPS are navigation systems that use 24 satellites
orbiting the earth. The GPS detects a small radio signal projected by orbiting satellites to
triangulate its position. To create a 2-D image the GPS must acquire signal from a
minimum of three satellites, or four to create a 3-D position (Federal Aviation
Administration, 2011). GPS have demonstrated to be an acceptable and reliable way of
measuring position and movement of participants (Larsson, 2003).
GPS data was collected with the GPS Tracking Key Pro© (7.6cm x 5.0cm x
3.6cm; 158g) created by LandAirSea Systems (LAS). These units are approximately the
size of a small cell phone and run on two “AA” batteries (Figure 9). On the back of the
device is a high powered magnet that was removed, because it was not necessary for the
purposes of this study, nor did it interfere with collection of data. On the side of the
device are two LED lights and an on/off button. The LED lights indicate remaining
58
battery life, and signal activity. The battery light will remain green while the device in
turned on. Once the battery life begins to get low the light will change to yellow. GPS
battery life will last approximately 80 hours depending upon the quality of battery and
physical activity variation in individual participants. The signal activity light will blink
intermittently with a green light when the GPS unit is recording data from GPS satellites.
This particular unit is capable of communicating with up to 16 satellites. The GPS
Tracking Key Pro© is equipped with a motion detector device that places the unit into
sleep mode after 2 minutes of inactivity to improve battery life.
For the study the GPS was placed into a small cell phone case (see Figure 9) to
improve ease of use. Participants were required to have the device with them while
traveling.
Figure 9. GPS Tracking Key Pro©.
59
Geographic Information Systems
The LAS Past-Track software program was used to process GPS data. GIS allow
users to view GPS data over digital maps or satellite images. In addition to the software
program, plotting of data points occurred with Google Earth
(http://www.google.com/earth/index.html). These two programs allowed researchers to
determine travel locations, distance traveled, and time spent at each location.
Travel Diary
The travel diary (see Figure 10) was used by participants to record their daily
activities. The survey included locations traveled, time of arrival and departure at each
location, and type of activity performed at that location. Participants were provided an
example travel diary (see Appendix G), and asked to fill out a travel diary for each day
with instructions to be as specific as possible. The data obtained from the travel diaries
was used to validate the objective information collected from GPS receivers.
60
Date:
Location (#)
Example
Location/Place
Time (minutes)
Type of activity
Home, work,
8:30 - 10:20
Shopping, exercise,
grocery store
10:45 - 11:15
social,
1
2
3
Figure 10. Travel diary example.
Procedure
The general scheme of the project has been illustrated in Figure 11. Participants
were baseline tested using the SAC as part of their annual physical examination and
given a consent form to read and sign while in a nonconcussed state. Participant consent
was achieved with approval from the Ohio University Institutional Review Board.
Concussions were diagnosed by an independent AT at each clinical site. Following
diagnosis the athlete was referred to the research staff and a meeting was set up to
distribute equipment and review collection procedures.
Participants wore the accelerometer and GPS for up to 10 days with the exception
of grooming periods and sleeping. This time period was chosen because majority of
concussed individuals experience symptom resolution within less than 1 week (average
4.2 ± 0.4 days) (McCrea et al., 2009).
61
Figure 11. Data collection procedure. The figure illustrates the general procedure for data
collection. The colored section indicates differences in symptom resolution between
participants. The blue section represents having symptoms (Sx) to, asymptomatic (No
Sx), where the middle area represents an area where participants may or may not have
symptoms.
Phase 1 (Symptomatic Period)
Participants were given instructions to wear the GPS and accelerometer units at
all times as well as fill out the concussion symptom questionnaire and travel diaries daily.
Data collection occurred for a period of up to 5 days (phase one). The participant's
preferred method of contact was documented (phone call, text message, or e-mail) for
daily reminder messages.
Participants were given instructions to place accelerometers upon the right hip. A
small metal clip was attached to the back of the device (using double-sided tape) to
ensure proper upright orientation of the device and to maintain consistency. The GPS was
placed in a small cell phone case with a clip that could be attached to a belt, purse, back
pack, etc. The device could have also been placed inside a jacket or pocket. Participants
62
were asked to keep the device on their person while traveling. GPS devices were given to
the participants with a fully charged set of rechargeable AA batteries (Duracell 2400mA).
The devices were worn for a period of 5 days. At the end of this time period,
participants met with researchers to return the GPS, accelerometer, updated symptom
checklist, and travel diaries as well as receive a second set of equipment for phase 2.
Phase 2 (Symptom Free Period)
Following the first phase participants were asked to wear the GPS and accelerometer
for another 5-day period (phase 2). During the meeting participants were given a second
set of travel diaries, a new GPS, and freshly charged accelerometer.
Participants met with the researcher at the end of the 5-day period to turn in the travel
diaries, GPS, and accelerometer device. Participants were given a t-shirt as compensation
for their time in the study and filled out a study feedback questionnaire (see Appendix H)
once all equipment was returned.
Data Processing
Data processing was separated into two sections. In the first section the
computation of MAPS score are explained. In the second section, the procedures for
processing data are explained.
Movement and Activity in Physical Space Score Formula
The MAPS score is calculated as an average of daily activity scores normalized
by time spent at locations. The formula for MAPS is expressed as:
 n  AL 
  
T 1  L 1  t L 
MAPS Score 
T
m

63
Where tL is the time spent at the location L (determined from GPS and travelogue
analysis), A is a measure of activity (from accelerometer readings), and T is the number
of days used to average daily activity scores. Depending on whether A is measured in
activity counts (intensity) or step counts (volume), the system can, respectively, yield a
MAPS intensity score (MAPSI) score or a MAPS volume score (MAPSV). While
physical activity is expected at home it does not represent a person interacting with their
environment. Therefore only locations other than home are calculated.
Calculating Missing Swim Data
The accelerometer device used in this study is unable to acquire activity counts
when used with swimming and diving. To account for these missing activity data, a
formula was used to calculate activity counts based upon known metabolic equivalent
values. The Compendium of Physical Activities list a range of validated swimming
metabolic equivalents from 4.8 (recreational swimming) to 13.8 (vigorous, breast stroke)
(Ainsworth et al., 2011). For this study a metabolic equivalent of 6.0 was selected for
moderate intensity swimming. The formula: cnts x min-1 = (METs - 1.439008)/0.000795
was validated for use by Freedson et al. (1998). It is reasonable to assume that an athlete
would not be physically active for the entire duration of practice. As a result a
conservative estimate was selected for 50% activity during practice. By using this
formula missing data from swimming participants can be adjusted, limiting skewed data.
Processing Procedure
Data were examined to verify quality of data (see Figure 12) and to ensure that
participants wore the devices for a minimum of 10 hours each day. All data were
64
validated with consensus from at least two of the three data sources (GPS,
Accelerometer, and Travel Diary). Once a location was verified, the type of location and
coordinates (longitude/latitude) were documented. The procedure for processing data is
illustrated in Figure 13.
Data Quality
Check
Subject wore the
device for 10
hours or more
Locations verified
with consensus
from all three
data types
Record location
and repeat quality
check for next
location
Subject did NOT
wear device for
10 hours or more
Consensus not
achieved with all
three data types
Data for that day
is not usable.
Consensus
achieved with two
of three data
types
No consensus
achieved
Record location
and repeat quality
check for next
location
Data invalid
Figure 12. Data quality check. The protocol for checking the quality of data and
verification of locations is outlined in the figure.
65
1
2
3
4
5
6
• Identify location
• Record Location Type/Latitude/Longitude
• Identify time spent at location (verified with two of three data sources)
• Record PA (Intensity) and Step Count (Volume) count for location
• Identify new location, record time between locations, PA, and Step Count
• Repeat Steps 1 - 4 for new location
Figure 13. Location identification procedure. The figure explains the data processing
procedure. Once a location was identified, the physical activity (PA), and step counts
were recorded for the duration of time spent at that location. Travel times, PA, and step
counts between locations were recorded and the process was repeated for each location of
the day.
Data Analysis
Physical activity and geospatial data were examined and analyzed by two separate
researchers. This was done to increase validity of the interpretations of findings. A
consensus was reached by the two researchers while evaluating the data. Geospatial data
and physical activity data were stored on separate computers allowing for data to be
analyzed simultaneously and viewed side by side. MAPS data was entered into a data
sheet (see Table 5) to allow for computation of figures.
66
Table 5
MAPS Data Sheet Example
Date:
Type of Latitude Longitude
3/15/2007 place
(x)
(y)
location
(#)
Time
(min)
Physical
Steps
Travel
activity (volume) time
count
(min)
(intensity)
1
Home
42.52
-92.47
0:00 07:45
(465)
4200
475
2
Work
42.48
-92.06
8:00 11:30
(210)
42584
2125
3
Lunch
42.47
-91.91
11:38 12:22 (44)
3583
358
4
Work
42.48
-92.06
12:40 17:00
(260)
65125
2556
5
Grocery
42.53
-92.02
17:10 18:00 (50)
38725
1895
6
Home
42.52
-92.47
18:05 19:20 (75)
4115
402
5
7
Walk
-
-
19:20 19:50 (30)
82014
2400
0
8
Home
42.52
-92.47
19:50 24:00
(270)
1821
224
0
1384
242,167
10,435
56
TOTAL
15
8
18
10
Note. This table represents sample data from a single participant over a 1-day period. The
table displays location information from a GPS receiver, the physical activity and step
count data from an accelerometer, and the location verification through GIS. The table
provides information on where the participant traveled the intensity of their activity at
each location. MAPS data is calculated from locations other than home and are shaded in
the table above.
67
Statistical analysis was based upon the following specific aims.
Specific Aim 1:
To establish validity evidence of MAPS scores with known group mean
differences between the concussion participants and their healthy matched controls
following a concussion. An independent t-test was performed with Bonferroni adjustment
(α = .05 / 2) for multiple comparisons (MAPSI and MAPSV scores). Alpha level was set a
priori at .05.
Specific Aim 2:
The sensitivity of MAPS score to identify and measure differences throughout the
recovery process was examined by a 2 x 2 repeated measures analysis of variance
(ANOVA) with the between group factor concussion (concussed & matched control) and
the within-participant factor time (symptom v.s. symptom free). Alpha level was set a
priori at .05
Specific Aim 3:
To determine if there is a relationship between concussion symptoms and MAPS
scores. This relationship was assessed using a Pearson Product Moment Correlation.
Alpha level was set a priori at .05.
68
CHAPTER 4: RESULTS
This chapter contains the results of the study with major sections including the
descriptive statistics, reliability of the measure, and hypothesis. This study intended to
develop and validate the use of the Movement and Activity in Physical Space (MAPS)
scores to objectively measure a patient's level of overall function following concussion.
Monitor Wear Time Validity
Each day was evaluated, prior to analysis, to ensure accelerometers were worn for
a minimum of 10 hours per day (Colley, Gorber, & Tremblay, 2010). Continuous activity
counts were examined with nonwear periods established by a period of 0 activity counts
for a continuous bout of 60 minutes. Days that did not meet the criteria for minimum 10
hour wear time were excluded from analysis as incomplete days.
MAPS Scores
The descriptive statistics for MAPS scores and self-reported symptom scores are
displayed in Table 6. During the data collection period the athletic training staff reported
19 concussions. 12 of the 19 concussions were excluded from analysis due to
noncompliance or drop out. Following the symptomatic period another participant was
lost due to dropping out of the study. The average duration of symptoms in the
concussion group was 1.3 ± 1.5 days. The percent change in MAPS scores between phase
1 and phase 2 for the concussed group is proved in Table 7.
69
Table 6
Mean MAPS Scores (Concussion vs. Control)
Concussion M (SD)
Outcome
measure
MAPSI
MAPSV
Healthy control M (SD)
Phase 1
Phase 2
Phase 1
Phase 2
(n = 7)
(n = 6a)
(n = 7)
(n = 7)
2,231.8 (1,314.4)
2,562.6 (1,985.5)
1,523.4 (1,068.9) 2,742.3 (968.3)
46.0 (40.0)
68.7 (27.0)
50.3 (25.0)
50.6 (33.0)
Note. Phase 1 indicates the first recording period and occurs while patients in the concussion
group have a symptom score greater than 0. MAPSI = Movement and Activity in Physical Space
intensity score; MAPSV = Movement and Activity in Physical Space volume score.
a
One participant was lost during phase 2 due to dropout.
70
Table 7
Percent Change Between Phase 1 and Phase 2
Number of
Participant symptomatic
days
MAPSI Scores
MAPSV Scores
Phase 1
Phase 2
Percent
change
Phase 1 Phase 2
Percent
change
1
1
2905.0
2284.3
-27.1 %
120.0
102.0
-17.6 %
2
2
2382.0
2125.23
-12.1 %
50.1
42.1
-19.0 %
3
2
394.9a
4011.8
90.2 %
13.6a
108.6
88.0 %
4
2
2644.7
3039.9
13.0 %
53.3
62.0
13.7 %
5
4
865.0
1486.9
42.0 %
19.8
33.4
40.7 %
6
4
900.9
2735.1
67.1 %
24.3
58.0
58.1 %
7
4
789.5
-
-
23.0
-
-
Note. This table displays the MAPSI and MAPSV scores for each of the concussed participants
ordered by number of days the patient experienced symptoms. The percent change represents the
percent decrease in function in phase 1 from phase 2. Negative percentages represent a decrease
in score from phase 1 to 2. Participant 7 does not have data for phase 2 due to drop out.
a
The participant score was extremely low due to insufficient wear time for the first recording day.
As a result this day was excluded from analysis. The second symptom day was on a Sunday,
which typically has very low activity counts resulting in the low score.
Reliability of MAPS Scores
The reliability of MAPS scores was assessed. The Reliability of MAPS scores
using an average of five days had an acceptable reliability of ICC [3, 1] = .82. Reliability
of MAPS scores with a three day average yielded ICC [3, 1] = .39.
71
Known Group Difference
The independent t-test, t(12) = -1.11, p = .29, and t(12) = -0.36, p = .73, indicated
the difference between the concussion and control groups during phase 1 for MAPSI and
MAPSV were not significant, respectively. Figure 14 displays the comparison between the
two groups.
72
a.
3000
2500
MAPSI Score
2000
1500
1000
500
0
Concussion
Control
b.
60
MAPSV Score
50
40
30
20
10
0
Concussion
Control
Figure 14. Mean MAPSI and MAPSV scores. This figure displays the mean MAPSI (a.)
and MAPSV (b.) scores for the concussion and control groups during phase 1. Phase 1
indicates time while participants were experiencing concussion symptoms. Bars represent
standard error.
73
Changes in Function over Time
The 2 x 2 repeated measures ANOVA, F(1, 11) = 1.56, p = .24 and F(1, 11) =
2.11, p = .17, indicated that there was not a significant time-group interaction between
MAPSI and MAPSV respectively. Figure 15 displays the mean MAPS scores between
groups for the symptomatic and symptom-free period. The mean MAPS scores for each
day are displayed in Figure 16.
74
a.
3000
MAPSI Score
2500
2000
Concussed
1500
Control
1000
500
0
Symptomatic
Symptom Free
b.
80
70
MAPSV Score
60
50
40
Concussed
30
Control
20
10
0
Symptomatic
Symptom Free
Figure 15. Group by time interaction MAPSI and MAPSV scores. This figure displays the
change in mean MAPSI (a.) and MAPSV (b.) scores over time. The blue line represents
the concussion group, while the red line represents the control group. The mean score for
the symptomatic period and asymptomatic period are plotted with trend lines drawn
between to indicate changes in function over time.
75
a.
3,500
MAPSI Score
3,000
2,500
2,000
1,500
1,000
500
0
b.
4,000
3,500
MAPSI Score
3,000
2,500
2,000
1,500
1,000
500
0
Figure 16. Daily mean MAPSI scores. This figure displays the mean MAPSI score by day
for the concussion group (a.) and the control group (b.) The left axis represents MAPSI
scores. Sx = Symptom Free.
76
Relationship between MAPS Score and Symptom Score
There is evidence to support the inverse relationship of MAPS scores, and selfreported symptom scores. As symptoms improved, MAPS scores increased. Outliers
were identified as values greater than 4 standard deviations away from the mean, and
excluded from analysis. One outlier was identified (4.5 standard deviations away from
the mean) and removed. Correlation data has been displayed in Table 8. Figure 17
displays the mean MAPSI scores and mean Total Symptom scores by day. Figure 18
displays a comparison of MAPSI and Total Symptom scores.
Table 8
Correlation of Symptom and MAPS Scores
1. MAPSV
2. MAPSI
3. Total Symptom Score
4. Symptom Severity
5. Symptom
“Bothersomness”
1
2
3
4
5
6
--
.83*
- .33*
- .31*
- .32*
- .33*
--
- .32*
- .30*
- .32*
- .32*
--
.98*
.99*
.99*
--
.96*
.98*
--
.98*
6. Symptom Frequency
Note. MAPSV = Movement and Activity in Physical Space score using activity volume;
MAPSI = Movement and Activity in Physical Space score using activity intensity.
*p < .05.
--
3,500
70
3,000
60
2,500
50
2,000
40
1,500
30
1,000
20
500
10
0
Total Symptom Score
MAPSI Score
77
Symptom Score
MAPSI
0
Figure 17. Daily mean MAPSI and total symptom scores. This figure shows the Mean
MAPSI scores for each day compared with the mean total symptom score for each day.
The blue line displays the mean MAPSI scores for each day; the red bars indicate the
mean Total Symptom Score for each day. The left axis represents MAPSI scores, and the
right axis represents Total Symptom Score. Sx = Symptom Free.
78
70
Total Symptom Score
60
50
40
30
20
10
0
0
1000
2000
3000
4000
5000
6000
MAPSI Score
Figure 18. MAPSI and total symptom scores correlation. This figure displays the
relationship between MAPSI scores and total symptom scores.
79
CHAPTER 5: DISCUSSION
This chapter will interpret and discuss the findings of the study and their clinical
significance. The major themes include the influence of symptom duration on function,
the validity of MAPS scores, and clinical relevance. Limitations and future directions will
also be included.
Influence of Symptom Duration on Function
Following concussion, patients experience a wide variety of short lasting
symptoms. The results from this study indicate that the duration of symptoms are a key
factor in determining the effects of a concussion on overall function.
Symptom Duration
In this study, participants who experienced symptom duration greater than 3 days
displayed large changes in their MAPS scores during recovery following concussion,
while participants with a shorter duration of symptoms (less than 3 days) had little or no
change. In Table 7, participants 5, 6, and 7 each had symptom duration for greater than 3
days. For participants 5 and 6, their MAPS scores were on average 50% lower than their
symptom-free scores. The symptom-free MAPS scores for participant 7 were lost due to
drop out. It is expected that based upon the duration of symptoms experienced,
participant 7 would exhibit similar patterns to those seen with participants 5 and 6. It is
interesting to note that the MAPS scores of participants with symptom duration greater
than 3 days were similar to those found in patients diagnosed with multiple sclerosis,
1314.9 ±1632.7 (Snook et al., 2011).
80
Understanding the effects of symptom duration on overall function following
concussion is critically important. Symptom evaluation plays a significant role in
determining return to play status of concussed athletes. During concussion evaluation
greater than 80% of ATs rely on concussion symptom scales to evaluate athletes after
injury (Notebaert & Guskiewicz, 2005). Symptom severity, location and frequency of
head impacts, and diagnostic criteria can all have an impact on duration of symptoms
following concussion.
Concussion Severity
The duration of symptoms following concussion may be affected by the severity
of the concussion. Currently there are no grading scales for measuring the severity of
concussions (McCrory et al., 2009). Due to the complexity of concussions and the
variability in their presentation, the original grading scales were removed. As a result,
there was no way to determine the extent to which “severity” of a concussion had on each
individual. These differences in severity may have influenced MAPS scores. Patients
with a more severe concussion would likely exhibit lower MAPS scores, higher symptom
scores, and longer symptom duration compared with a less severe concussion.
Frequency and Location
Ocweieja et al. (2012) identified that location of head impact can influence the
severity of a concussion. The frequency and location of head impact are unique to each
sport and influenced by a number of factors such as sport, team, position, and session
type (Crisco et al., 2010). The two factors, frequency and location, are often overlooked
in concussion diagnosis. In this study the location of head impact was not noted; as a
81
result no conclusions can be made about the effect that location had on the duration of
symptoms.
Concussion Diagnostic Criteria
The diagnostic criteria for concussions are loosely defined and vary greatly from
one health care professional to the next. With the rise in emphasis being placed on
recognizing concussions, it is a possibility that concussions are being over/under
diagnosed. It was the responsibility of the AT at each data collection site to determine if a
participant was concussed. The criteria were established as a blow to the head that
resulted in one or more concussion related symptoms. This means that any impact to the
head that caused any symptom of a concussion indicated a concussion. Headaches
represent one of the most common concussion symptoms; however, this symptom is also
seen in many other conditions. It is possible that some participants may have been
misdiagnosed with having a concussion, when in fact none had occurred.
Time Frame of Inclusion
The average duration of symptoms in this study (1.3 ± 1.5 days) conflicts with
those found in the literature (4.2 ± 0.4 days) (McCrea et al., 2003, 2009). Based upon ZScore calculations from the population mean for symptom resolution, approximately 99%
of concussions should result in symptom duration of between 3 and 5 days. This indicates
that the average symptom duration from the sample in this study has a 1% probability of
occurring. The time frame for inclusion in this study was a 48-hour window following
concussion. If the average duration of symptoms were to be adjusted to account for the
82
48-hour window (i.e., adding two days to the average symptom score) then the values
would be similar to those found in the literature.
MAPS Reliability
The single day reliability of MAPS scores is relatively low. There exists a large
degree of variability in daily assessment of participants. Figure 16 displays the daily
mean MAPSI scores. The day to day scores displayed vary by as much as 1,000 points
per day. In this figure one can clearly see when the number of available days of
observation is limited scores can be skewed. When calculating MAPS scores it is
recommended that a minimum of 3 days are used to calculate overall patient function
(Herrmann et al., 2011). As a result, MAPS scores may not be a sufficient measure of
overall function in patients whose symptom resolution occurs in a period of less than 3
days.
Symptoms and Function
There was a small inverse relationship between MAPS and symptom scores.
Figure 17 displays a comparison of the daily mean MAPSI and total symptom scores. As
expected, when symptom scores were high, MAPS scores were low. As symptoms
decrease, function improved demonstrated by the increased MAPS score. The methods
for symptom evaluation, rehabilitation protocols, and the number of symptom free days
compared with symptomatic days could have influenced the relationship between MAPS
and symptom scores.
83
Symptom Evaluation
Concussion symptom scales rate the symptoms of a concussion using Likert
scales. These scales determine the presence (or absence) of a symptom based upon the
self-reported severity of a patient. Figure 19 displays an example of how the evaluation
method for symptoms could have influenced the relationship between MAPS and
symptom scores. As a patient recovers from injury their symptoms indicated by the blue
line eventually reached a score of 0, indicating they were symptom free. In addition their
MAPS scores would increase. These scales are limited by their inability to assess a
symptom once it has become absent.
Semantic Differential Scaling would be more appropriate, but, to date, these
measurement designs have not been developed for concussion. Semantic Differential
Scales suggest that there exist a continuum between two interrelated polar variables
(Osgood, 1957). An example of this relationship would be water temperature. The
temperature of the water is described as a continuum between hot and cold. At a certain
temperature the water no longer becomes hot and changes to cold. Negative hot doesn’t
make sense. Application of this type of scaling to concussion symptom evaluation could
potentially enhance the quality of information obtained.
84
Figure 19. Semantic differential scaling. The figure highlights a limitation of symptom
scales. The horizontal axis represents MAPS scores; the vertical axis represents symptom
scores. The blue line represents the relationship between MAPS and symptom scores.
The line is stacked above the horizontal axis to improve visibility not indicating a score
above 0. The dotted blue line represents the theoretical continuation of symptom scores
beyond 0. Symptom scales measure the presence and severity of a symptom. Once the
symptom becomes absent the relationship can no longer be assessed. Thus the
relationship between these two variables becomes diminished as MAPS scores continue
to improve symptom scores remain absent.
Rehabilitation Protocol
The relationship between MAPS and symptom scores may have been affected by
current rehabilitation protocols. The standard rehabilitation protocol for concussed
patients involves refraining from activity until symptom resolution has occurred
(McCrory et al., 2009). Activity levels following concussion affect symptoms and
neurocognitive recovery (Majerske et al., 2008). Participants with symptoms for greater
than 3 days all demonstrated similar MAPS scores (MAPSI = 865.0, 900.9, and 789.5). It
is possible that although these participants may have been affected differently, because of
the established protocol they all remained relatively inactive until they had become
symptom free influencing their MAPS scores.
85
Number of Symptom Days vs. Symptom Free Days
Lastly, each participant was asked to wear the devices for a period of 10 days.
The average duration of symptomatic days was 1.3 ± 1.5. This resulted in the number of
symptom days compared with symptom free days not being proportional. The high
number of symptom free days diminishes the ability for a true relationship to be found
between MAPS and symptom scores.
Validity of MAPS Scores
The MAPS system is designed to create a detailed picture of function by
examining 14 different variables, including the MAPS score. MAPS scores were used to
monitor overall function as patients recovering from concussion. In this study, the
differences in MAPS scores between the two groups (concussion and healthy control)
were not significant.
The validity of MAPS scores was affected by the small sample size. Due to
participant drop-out and noncompliance, of the 19 concussions identified, only 7
provided useful data for analysis. The unpredictable nature of concussions, and the
limited time frame allotted for this study limited the number of participants available for
data collection. Despite the limited sample size MAPS scores still provides the potential
as a useful clinical tool.
Patient Population
An interesting observation noted in this study was the lack of change between
MAPSV scores of participants regardless of health status. Figure 14 displays the mean
MAPS scores for participants during the symptomatic period. This figure shows a large
86
degree of change in MAPSI scores with little to no change in MAPSV scores. A possible
explanation for this is the sample population observed. The sample population in this
study was comprised of college students. College students spend majority of their day
traveling to and from classes. To accommodate practice and game times, most athletes
have similar class schedules. As a result both groups would likely share the same number
of step counts traveling during the day.
Application of MAPS Scores
This novel approach to evaluating injured patients represents a shift from diseaseoriented impairment based rehabilitation to patient-centered care. Patient-centered care
involves using a more individualized approach to evaluation and rehabilitation that
focuses on the uniqueness of each individual, and has been linked to better recovery
following injury (Stewart et al., 2000). The combination of accelerometers and GPS
allows for a novel approach to studying a patient's behavior that may reveal a more
complete picture of a patient's level of function following injury.
Figure 20 displays the relationship between one athlete and their symptoms over a
10-day period. This example highlights the application of MAPS scores in an individual.
An obvious trend can be seen between the athletes MAPS and symptom scores. In this
case the patient’s symptom totals for the 4 symptomatic days were 82, 39, 49, and 36
(mean 51.5). The patient reported a total symptom score of 0 beginning day 5 indicating
that she was symptom free. Her daily MAPS scores, while symptomatic, for days 1 - 4
were 83.6, 865.2, 815.4, and 1,022.1. The first day of data collection occurred on an
incomplete day. This can be seen in the extremely low score for day 1. Her mean MAPS
87
functional score (incomplete days were excluded) while symptomatic was 900.9 and
while asymptomatic was 2,734.9 representing a three-fold increase. An interesting
observation was that on day 3 she reported attended a volleyball game which increased
her symptoms. This increase in symptoms was also reflected in her MAPS score for that
day which decreased from 865 to 815. The use of MAPS scores may present a way to
evaluate the validity of, or raise doubts, about self-reported symptom scores.
90
5000
Spectator at
Volleyball
Match
80
70
4500
4000
Sx Score
3000
*
50
Cleared
by
physician
40
2500
2000
30
MAPS I Score
3500
60
Sx Score
MAPS I
1500
20
1000
Begins return to
play protocol
10
500
0
0
1
2
3
4
5
6
7
8
9
10
Days
Figure 20. Case study. This figure illustrates the relationship between an athlete’s MAPSI
and symptom score over a ten day period. MAPSI = Movement and Activity in Physical
Space Intensity; Sx = Symptom. The participant began a return to play progression
beginning day 6. She was cleared by team physician to return to play on day 9.
*The athlete reported going to a volleyball game which exacerbated her symptoms. This
increase in symptoms was reflected in her MAPSI score for that day which decreased
from 865 to 815.
88
Limitations
This study is not without limitations. One limitation is that the sample population
was relatively small, and comprised of university students. In addition participant
compliance was problematic. Despite daily reminders, some participants had difficulty
remembering to wear both the GPS and accelerometer. This resulted in lost data.
Throughout the 2-year study, 19 concussions were reported, of which 12 were excluded
due to noncompliance. It is expected that motivation was a limiting factor in this case. In
the clinical setting patients desire to resume normal participation could enhance
motivation for proper wear.
The compliance of clinical staff in reporting concussions to the research staff was
problematic. Retrospectively, it was identified that at least 16 concussions were not
reported to the research staff as a result of miscommunication between the clinical and
research staff. As a result, a significant number of concussions were not available for
analysis. This resulted in the population sample to be much smaller than originally
anticipated.
Another limitation is that with aquatic or high-contact sports (where concussions
are likely to occur) there is a risk of damaging the equipment from impact or water
exposure. Athletes involved in these sports must remove the devices while participating.
This represents a flaw with the devices as they are not water proof nor can they withstand
a high impact force. However, because the goal in using the devices is to monitor
recovery, it is likely that once the patient has returned to participation, the devices would
89
no longer be needed. Thus this is strictly a research limitation and should not affect their
clinical use.
Acquiring MAPS scores requires an understanding of pedometers and
accelerometry along with a basic understanding of geospatial technology, which can
require training. This can be a limitation for using this technology in clinical practice. In
addition, there is a significant cost involved in purchasing the necessary equipment (i.e.,
GPS devices and GIS software, accelerometers, and computers capable of analyzing and
storing data). Lastly, the time requirement in analyzing the physical activity and
environmental data can be very extensive. The average time needed to evaluate a 5-day
data collection period was approximately 3 hours.
Potential Issues with MAPS Formula
The MAPS formula was created to evaluate a patient’s activity and participation
as well as provide context to the environmental factors that influence overall function.
This concept of examining overall function has been missing from previous concussion
assessment tools which currently assess limitations in cognition in balance.
The formula has one issue and how it is scored at each location. For example, a
person at a location for a very short duration but is highly active would receive a high
MAPS score. In addition, a person who is at a location for a long duration and is
moderately active could receive a low MAPS score.
This can be seen in some participants who went to the library. One participant
went to the library for 11 minutes and received a MAPSI score of 135.4 for the location;
however, the participant’s activity count was only 1489. Despite having a low activity
90
count because the time spent at that location was very short, their MAPS score is
excessively high for that location. The opposite is also true; a person who spent a large
portion of the day at work without leaving received a low MAPSI score, 55.4. Despite the
low score, they received a total activity count of 50,158 for that location.
These two individuals were not a matched pair and cannot be compared too
closely. However, it may be necessary to adjust the mathematical formula, and/or
incorporate a type of weighting system for each type of location.
Conclusions
Symptom duration is a key factor in determining the effects of concussion on
function. This study identified a relationship between concussions and symptom scores.
More research is needed to understand the relationship between MAPS and symptom
scores. Future studies should address some of the limitations associated with using
MAPS scores. Motivation and timing of inclusion seem to be significant contributors to
the quality of MAPS data. Furthermore, research needs to be done to consider the
potential role physical activity measures and MAPS may play as tools for monitoring
recovery following concussion. Other areas of research could include using MAPS as a
tool for goal setting, intervention during rehabilitation, or in application with other patient
populations that do not have ambulatory impairments; such as those with diabetes or
cancer.
Clinical Application
MAPS scores have shown to be a reliable method for measuring function in
postsurgical knee patients (Herrmann et al., 2011). By combining measures of physical
91
activity with environmental interaction, MAPS provides the potential for a greater
understanding of the patient’s functional status. Current concussion assessment tools
focus on symptoms and impairments and are largely subjective in nature. Adding MAPS
scores may enhance current assessment techniques and provide a more detailed picture of
athlete function following concussion, which may be used as part of the return to play
decision.
92
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111
APPENDIX A: RECRUITMENT SCRIPT
RECRUITMENT SCRIPT
for participants
Title of project:
Developing and Validation of Movement
Scores in Mild Traumatic Brain Injury Recovery
and
Activity
in
Physical
Spaces
(MAPS)
Principal Investigator(s):
Brian Ragan, PhD; Shannon David, Melissa Bartholomew, Danielle Mc Elhiney, Shannon Nickels, James
Farnsworth
Script:
You are invited to participate in a research project that the Master Athletic Training Program at Ohio
University is conducting on sports related concussions. We are asking for your participation in a study to
track and measure the symptoms and recovery of concussion.
Often, concussions are evaluated and monitored by many different assessment tools and self-reporting
symptom checklist. Today’s concussion assessment tools are gaining popularity in an attempt to measure
the effects of concussion. The purpose of this research project is to investigate the use of GPS and
accelerometer as an assessment tools for concussions, along with a symptom questionnaire to assess how
often, how serve, and how much the symptom bothers you. The results will help improve existing methods
and help with the development of a better concussion tool.
All students, male or female, are eligible and may participate. We ask that as many students as possible
participate in the baseline measurements, post-injury questionnaires, and functional movement
measurement, should they experience a concussion.
If you choose to participate, you will be asked to report to your Athletic Trainer and they will inform you
about times for a baseline testing session.
Your participation is voluntary. If you choose to participate you will receive a T-Shirt for compensation.
Your assistance with completing the data collection process is greatly appreciated. We look forward to
working with you to provide symptom feedback and functional movement measurements. If you are
interested in participating give Dr. Brian Ragan, Shannon David, Melissa Bartholomew, Danielle Mc
Elhiney, or James Farnsworth a call at (740) 597 – 1876, or email [email protected] Thank
you for your time.
Thank you
112
APPENDIX A: RECRUITMENT SCRIPT
113
APPENDIX B: APPROVAL NOTICE FOR ALTERNATE DATA COLLECTION
SITES
114
APPENDIX B: APPROVAL NOTICE FOR ALTERNATE DATA COLLECTION
SITES
115
APPENDIX C: INFORMED CONSENT
Ohio University Consent Form
Title of Research: Development of improved functional assessment for concussions.
Researchers: Brian Ragan, PhD; Shannon David, Melissa Bartholomew, Danielle Mc Elhiney, Shannon
Nickels, James Farnsworth, Chad Starkey, PhD; Andrew Krause, PhD
You are being asked to participate in research because you participate on an athletic team at Ohio
University. For you to be able to decide whether you want to participate in this project, you should
understand what the project is about, as well as the possible risks and benefits in order to make an informed
decision. This process is known as informed consent. This form describes the purpose, procedures, possible
benefits, and risks. It also explains how your personal information will be used and protected. Once you
have read this form and your questions about the study are answered, you will be asked to sign it. This will
allow your participation in this study. You should receive a copy of this document to take with you.
EXPLANATION OF STUDY
This study is being done to get a better understanding of the injury recovery processes following
concussions. The goal of this study is to create and validate an updated symptom checklist as well as
validate the use of total activity counts, movement, and physical activity scores (MAPS), for assisting in
return to play decisions following Mild Traumatic Brain Injury (MTBI) or concussions.
Upon agreeing to participate in this study, you will first complete a baseline concussion assessment
including the Modified Standardized Assessment of Concussions and health history form.
If you experience a concussion or are identified as a match to another participant that has a concussion,
you will be asked to complete an online symptom questionnaire daily following a concussion received during
the athletic season until you have returned to baseline or asymptomatic, and to wear GPS, and
accelerometer devices for a period of five days following injury. Once you become symptom free you will
meet with researchers to wear the device for another five days. To help test the accuracy of the instruments
you will be asked to fill out a travel diary that discusses where you have traveled, what you did at those
locations, and at what times you were at those locations. It will take you approximately 10 minutes each day
to fill out the symptom questionnaire, a total of 5 minutes throughout the day to fill out the travel diary.
Wearing the GPS and accelerometer takes the same amount of time it takes to clip a cell phone to your
waist. You will be asked me meet with researchers a total of five times to receive and return equipment
throughout the study. It is also possible that if you do not experience a concussion you might not be asked to
serve as a healthy match, which means you have no further participation in the study.
You should not participate in this study if you have experienced a concussion in the last six months, are
not physically able to wear a GPS and accelerometer device, or have a history of learning disability, seizure
disorder, attention deficit disorder, or other mental/physical disability that would hinder your participation in
this study.
Your participation in the study will last approximately 10 days following a concussion or as a healthy
matched participant.
Declining or withdrawing from this study will not affect status within the school or on an athletic team.
116
Risks and Discomforts
The GPS and accelerometer devices are approximately the size of a small cell phone and should not
have any effect on your daily activities. You can easily attach the devices to a belt or place them inside
your pocket. There are no anticipated risks associated with this study.
Benefits
Through this study the academic community will gain useful information about the reliability of
determining when an athlete can return to play. The population that will benefit from this research is wide
spread, including, coaches, allied health professionals, and most importantly the athletes. It could help
change the way athletes are evaluated for concussions and help determine better protocols for return to
play. You will benefit from this study by receiving a physical activity assessment.
Confidentiality and Records
Your study information will be kept confidential by assigning codes to your information. The codes will
be matched with a master’s list kept in a locked cabinet. Your personal information will not be used in the
study. Additionally, while every effort will be made to keep your study-related information confidential, there
may be circumstances where this information must be shared with:
* Federal agencies, for example the Office of Human Research Protections, whose responsibility is
to protect human subjects in research;
* Representatives of Ohio University (OU), including the Institutional Review Board, a committee that
oversees the research at OU;
Compensation
As compensation for your time/effort, you will receive a t-shirt upon return of the research equipment at
the end of the study.
Contact Information
If you have any questions regarding this study, please contact James Farnsworth (919)244-1003, Danielle
Mc Elhiney (413)221-3187, Shannon Nickels (904)327-5005, Shannon David (740)593-0935, Melissa
Bartholomew (707)372-9786, or Dr. Brian Ragan (740)597-1876
If you have any questions regarding your rights as a research participant, please contact Jo Ellen Sherow,
Director of Research Compliance, Ohio University, (740)593-0664.
By signing below, you are agreeing that:

you have read this consent form (or it has been read to you) and have been given the
opportunity to ask questions and have them answered

You have been informed of potential risks and they have been explained to your satisfaction.

you understand Ohio University has no funds set aside for any injuries you might receive as a
result of participating in this study

you are 18 years of age or older

your participation in this research is completely voluntary

You may leave the study at any time. If you decide to stop participating in the study, there will
be no penalty to you and you will not lose any benefits to which you are otherwise entitled.
Signature
Date
Printed Name
Version Date: 12/15/2010
117
APPENDIX D: INJURY HISTORY FORM
Injury History Form
Name: ____________________________________
Ht.__________
Wt.__________
Date: ____________________
Age: __________ Gender:
M
F
1. Do you participate in a club sport or intercollegiate sport at Ohio University? If yes,
please list sports in which you participate:
________________________________________________________________________
________________________________________________________________________
2. Do you have a history of concussions? If yes please explain: Yes_____ No_____
________________________________________________________________________
________________________________________________________________________
________________________________________________________________________
3. Do you have a history or diagnosis of a learning disability, seizure disorder, attention
deficit disorder, or other mental disability? If yes, please explain: Yes______ No ______
________________________________________________________________________
________________________________________________________________________
________________________________________________________________________
4. Do you currently have any other injury or condition that limits your activity level or
hinder your participation in this study? If so please explain: Yes____ No____
________________________________________________________________________
________________________________________________________________________
________________________________________________________________________
5. Are you currently physically active usually exercising 30 minutes per day, 3-5 days per
week?
Yes____
No_____
6. How would you rate your overall health status?
1 = excellent
2
3
4
5 = poor
7. How would you rate your general level of physical activity?
.
1 = not active
at all
2
3
4
5 = extremely
active
118
APPENDIX E: MODIFIED STANDARD ASSESSMENT OF CONCUSSION
119
APPENDIX F: CONCUSSION SYMPTOM QUESTIONNAIRE
Symptom Questionnaire
The following questions ask about symptoms that are commonly associated with
concussions. For each symptom you will be asked how frequently it occurs (How often
you have the symptom), how severe it is (the intensity of the symptom), and how much it
bothers you (the amount of distress it causes you) If your response to the frequency of
the symptom question as never you may skip the questions regarding the severity and
how much the symptom bothers you and proceed to the next symptom. Please consider
your experience with each symptom and circle the number that is the best response.
1. HEADACHE
How OFTEN did you experience a HEADACHE?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your HEADACHE?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your HEADACHE BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
2. PRESSURE IN YOUR HEAD
How OFTEN did you experience PRESSURE IN YOUR HEAD?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was the PRESSURE IN YOUR HEAD?
Not at
all
0
Somewhat
1
A Great
Deal
2
Extremely
4
120
How much did the PRESSURE IN YOUR HEAD BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
3. Neck Pain
How OFTEN did you experience NECK PAIN?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your NECK PAIN?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your NECK PAIN BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
4. NAUSEA OR VOMITING
How OFTEN did you experience NAUSEA OR VOMITING?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your NAUSEA OR VOMITING?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your NAUSEA OR VOMITING BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
121
5. DIZZINESS
How OFTEN did you experience DIZZINESS?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your DIZZINESS?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your DIZZINESS BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
6. BLURRED VISION
How OFTEN did you experience BLURRED VISION?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your BLURRED VISION?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your BLURRED VISION BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
7. BALANCE PROBLEMS
How OFTEN did you experience BALANCE PROBLEMS?
Never
0
Occasionally
1
Often
2
Always
3
Extremely
4
122
How SEVERE was your BALANCE PROBLEMS?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your BALANCE PROBLEMS BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
8. SENSITIVITY TO LIGHT
How OFTEN did you experience SENSITIVITY TO LIGHT?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your SENSITIVITY TO LIGHT?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your SENSITIVITY TO LIGHT BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
9. SENSITIVITY TO NOISE
How OFTEN did you experience SENSITIVITY TO NOISE?
Never
0
Occasionally
1
Often
2
How SEVERE was your SENSITIVITY TO NOISE?
Not at
all
0
Somewhat
1
A Great
Deal
2
Always
3
Extremely
4
123
How much did your SENSITIVITY TO NOISE BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
10. FEELING SLOWED DOWN
How OFTEN did you experience FEELING SLOWED DOWN?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was FEELING SLOWED DOWN?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did FEELING SLOWED DOWN BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
11. FEELING LIKE “IN A FOG”
How OFTEN did you experience FEELING LIKE “IN A FOG”?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was FEELING LIKE “IN A FOG”?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did FEELING LIKE “IN A FOG” BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
124
12. NOT FEELING RIGHT
How OFTEN did you experience NOT FEELING RIGHT?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was NOT FEELING RIGHT?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did NOT FEELING RIGHT BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
13. DIFFICULTY CONCENTRATING
How OFTEN did you experience DIFFICULTY CONCENTRATING?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your DIFFICULTY CONCENTRATING?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your DIFFICULTY CONCENTRATING BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
14. DIFFICULTY REMEMBERING
How OFTEN did you experience DIFFICULTY REMEMBERING?
Never
0
Occasionally
1
Often
2
Always
3
125
How SEVERE was your DIFFICULTY REMEMBERING?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your DIFFICULTY REMEMBERING BOTHER you?
Not at all
A little bit
0
1
15. FATIGUE OR LOW ENERGY
Moderately
2
Quite a bit
3
Extremely
4
How OFTEN did you experience FATIGUE OR LOW ENERGY?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your FATIGUE OR LOW ENERGY?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your FATIGUE OR LOW ENERGY BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
16. CONFUSION
How OFTEN did you experience CONFUSION?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your CONFUSION?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your CONFUSION BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
126
17. DROWSINESS
How OFTEN did you experience DROWSINESS?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your DROWSINESS?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your DROWSINESS BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
18. TROUBLE FALLING ASLEEP
How OFTEN did you experience having TROUBLE FALLING ASLEEP?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your TROUBLE FALLING ASLEEP?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your having TROUBLE FALLING ASLEEP BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
19. EMOTIONAL
How OFTEN did you experience being MORE EMOTIONAL?
Never
0
Occasionally
1
Often
2
Always
3
Extremely
4
127
How SEVERE were your EMOTIONS?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your EMOTIONS BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
20. IRRITABLITY
How OFTEN did you experience IRRITABLITY?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your IRRITABLITY?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your IRRITABLITY BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
21. SADNESS
How OFTEN did you experience SADNESS?
Never
0
Occasionally
1
Often
2
How SEVERE was your SADNESS?
Not at
all
0
Somewhat
1
A Great
Deal
2
Always
3
Extremely
4
128
How much did your SADNESS BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
22. NERVOUS OR ANXIOUS
How OFTEN did you experience being NERVOUS OR ANXIOUS?
Never
0
Occasionally
1
Often
2
Always
3
How SEVERE was your NERVOUSNESS OR ANXIOUSNESS?
Not at
all
0
Somewhat
1
A Great
Deal
2
How much did your NERVOUSNESS OR ANXIOUSNESS BOTHER you?
Not at all
0
A little bit
1
Moderately
2
Quite a bit
3
Extremely
4
129
APPENDIX G: TRAVEL DIARY
130
APPENDIX H: STUDY FEEDBACK QUESTIONNAIRE
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