This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use:
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I confirm that the work contained within this thesis is the author’s own, that the thesis has been composed solely by the author, and that no aspect of the work has been submitted for any other degree or professional qualification.
Andrew M Murray
27-May-2020
Date
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Recovery is important to both athletes and coaches as a key component of training programmes and a determinant of optimal performance. At times the subsequent performance is the ultimate measure of the success of the recovery. However, the measurement of a quality should not affect its outcome measure and assessment without incurring additional load, perhaps even without the knowledge of the athlete, would be the ideal approach. This thesis was designed to examine recovery monitoring methods in situ to meet the need of practitioners and athletes. Following an initial survey of athletes from the UK (n=53), Asia (n=112) & USA (n=152), athletes from collegiate
American Football (males, n=63), collegiate soccer (females, n=17) and professional rugby (males, n=47) were monitored across a full competitive season to measure the associations of various recovery markers with gait-related variability measured at the trunk.
The research process involved initially establishing how athletes consider recovery relative to its importance and their subsequent practice. Survey studies of distinct groups of athletes on three continents established athlete and coach perceptions around recovery and showed that their perceptions did not necessarily match their behaviours or their beliefs (for example, just over half of athletes rated sleep as the most impactful recovery intervention but did not mirror that belief in their actions; whereas in contrast
~40% of athletes believed in and utilised cold water immersion). Subsequently the benefit of measuring recovery within the sport activity and without additional external load was chosen as the preferred approach. A discussion of movement variability assessment
iii using existing accelerometry information collected for the management of external load was undertaken with decisions made on an appropriate measure of gait-related variability to track across longitudinal monitoring of athletes. This was in conjunction with a variety of subjective and objective markers already collected in their own sport.
Primarily a quality assurance assessment of the chosen tool to identify high speed running sections, occurring naturally within training and competition, matched to ensure they occurred in a linear direction, and the subsequent marker of variability (coefficient of mean determination), was undertaken. It was shown to be a stable and reliable marker for both treadmill and over-ground running in the vertical and anterior-posterior axis. In addition, it showed that analysis can occur interchangeably on data identified from GPS
(outdoor sports) and accelerometer-based assessments (indoor sports).
Having established the standard statistical approach for the investigations, three different sporting models were examined using existing measures within their sport and season to investigate if recovery can be effectively monitored without additional requirements on the athlete. Firstly, American Football collegiate athletes were monitored longitudinally to examine any associations between gait-related variability, external load and subjective markers of wellness within their regular sessions.
Associations between gait-related variability and fatigue (p<0.026) and 7-day acute training load (p<0.001) were established, supporting potential use in this context.
Secondly, in an additional population in the same collegiate environment, but in a different sport, objective markers of flexibility within a female collegiate soccer
iv population were examined, but showed no relationship between gait-related variability and flexibility measured electronically. This suggests that these objective markers cannot be associated with variability changes in this context.
Finally, a group of professional male rugby union athletes were monitored across their playing season for a relationship between gait-related variability and additional objective measures of recovery (sprint speed, manually measured flexibility and additional screening measures). This population showed that changes in hours of sleep, sprint performance, general flexibility, volume and intensity of sessions were associated with a change in variability. This suggests a combination of subjective and objective measures can be associated with variability in this context.
In conclusion, there is a disconnect between athlete perceptions and beliefs of recovery behaviours; it is possible, through monitoring of accelerometry load alongside subjective markers, to get an indication of recovery status of an individual athlete to optimize any intervention to their training plan. This may drive further education of athletes around recovery practices and suggestions for practitioners to establish a parsimonious monitoring system with a combination of subjective and objective markers that could be supplemented by movement variability measures.
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Declaration i Abstract iii Contents vii List of abbreviations xiv List of figures xvi List of tables xix List of publications resulting from this thesis xxi
1. General introduction: the PhD journey 1
1.0 Reflections 1 1.1 Report: Alignment of career and PhD progression 3 1.1.1 Position Summary: Pre PhD 4 1.1.1.1 Heart of Midlothian, UK: 2006-2008 4 1.1.1.2 sportscotland institute of sport, UK: 2009-2014 5 1.1.2 Position Summary: PhD 5 1.1.2.1 Aspire Academy, Qatar: 2014-2016 5 1.1.2.2 Relevant published research papers - Aspire 7 1.1.2.3 University of Oregon, USA: 2016-2018 7 1.1.2.4 Relevant published research papers - Oregon 8 1.1.2.5 Additional published research papers - Oregon 8 1.1.2.6 National Basketball Association (NBA), USA: 2018- 10 present 1.1.2.7 Additional published research papers - NBA 11 1.2 Relate: Connecting the dots 11 1.3 Reason: How different perspectives contribute to PhD thesis 13 1.3.1 Environment 13 1.3.2 Direction 14 1.4 Reconstruct: what if I started again tomorrow? 15
2. Literature review: recovery in sport 17
2.0 Recovery in sport: for who and by whom 17 2.0.1 Training programmes 19 2.0.2 Adaptations 20 2.0.3 Periodized programmes 22 2.0.3.1 Judging impact and success of programmes 23 2.0.4 Recovery approaches 25 2.1 General physiological effects of training 26 2.1.1 Exercise-Induced Muscle Damage 26 2.1.2 EIMD…so what? 29 2.2 Recovery methods 30 2.2.1 Sleep & Recovery: why is it needed? 31
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2.2.1.1 Sleep deprivation 31 2.2.1.2 Causes of performance decrement 32 2.2.1.3 Benefits of sleep 33 2.2.1.4 Consequences of not getting enough sleep 35 2.2.1.5 Constraints for athletes 35 2.2.1.6 Student athlete subset 38 2.2.1.7 Sleep Monitoring 40 2.2.2 Hydration 41 2.2.3 Active v Passive recovery strategies 42 2.2.4 Electrical 45 2.2.4.1 Markers of recovery 46 2.2.5 Stretching 48 2.2.6 Massage 49 2.2.7 Foam rolling 50 2.2.8 Water based 51 2.2.9 Compression 54 2.2.10 Psychological relaxation 55 2.2.10.1 Psychological and physiological interaction 55 2.2.11 Longitudinal monitoring 57 2.3 Monitoring stress & recovery 57 2.3.1 Challenges 58 2.3.1.1 Individual variation 58 2.3.1.2 Markers 59 2.3.1.3 Time course 60 2.4 Invisible monitoring 61 2.5 Summary 65
3. Recovery Practices: An international investigation of athlete beliefs 67 and behaviours
3.0 Place of recovery within training programmes 67 3.1 How recovery methods are chosen: evidence or experience? 67 3.1.1 Short-term or long-term? 68 3.1.2 Physiological or psychological? 69 3.1.3 Real or placebo? 71 3.2 Incorporating beliefs into recovery 71 3.3 Student athlete challenges 72 3.4 Aims 73 3.5 Methods 73 3.5.1 Demographics 78 3.5.2 Open questions 78 3.5.3 Closed questions 78 3.6 Statistical Analyses 79 3.7 Results 79 3.7.1 Demographics 79
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3.7.1.1 Coaches 79 3.3.1.2 Athletes 80 3.7.2 Effectiveness 83 3.7.3 Use 83 3.7.4 Belief 84 3.7.5 Assessment of recovery 88 3.7.6 Reasons 88 3.7.7 Ways / Means 91 3.8 Discussion 92 3.8.1 Sleep 93 3.8.1.1 Student athletes & possible factors 94 3.8.2 Cold water immersion 95 3.8.3 Foam rolling 96 3.8.4 Compression 97 3.8.5 Contextual factors 98 3.8.5.1 Accessibility 99 3.8.5.2 Training age 99 3.8.5.3 Influence of coaches 100 3.8.6 Environmental factors 102 3.8.7 Perception 102 3.8.8 Limitations 104 3.9 Conclusion 105 3.9.1 Next Steps 105
4. Literature Review: movement variability 109
4.0 Why movement variability 109 4.1 Movement Variability 110 4.1.1 So what…using variability in a meaningful way 112 4.2 Wearables: accelerometers 113 4.2.1 Accelerometers: movement & recovery 114 4.2.2 Running economy 115 4.2.3 Gait 116 4.2.4 Gait: best practice monitoring 119 4.3 Gait-related variability & pathology 118 4.4 Controlling constraints: identifying corresponding sections within 122 sessions 4.5 Measuring movement variability 123 4.5.1 Entropy 130 4.5.2 Entropy – running economy 132 4.6 Correlations 133 4.7 Detrended fluctuation analysis 134 4.8 Lyapunov exponent 135 4.9 Multivariate statistics 136 4.10 Co-efficient of multiple determination 137 4.10.1 Benefits 137 4.10.2 Drawbacks 138
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4.10.3 Use case 139 4.11 Summary 140 4.12 Aims & objectives 141
5. General Methods 143
5.0 Data collection/load monitoring in sports using MEMS 143 5.1 Data collection/load monitoring in sports using accelerometers 145 5.2 Field based monitoring 146 5.3 Aim 147 5.4 Methods 147 5.4.1 Dealing with outliers 151 5.4.2 Statistical analysis 156 5.5 Results 157 5.5.1 A: Validity: Treadmill v Outdoor Run 157 5.5.2 B: Reliability: Within modality across speeds – individual outdoor 159 run & minutes 5.5.3 C: Reliability: Within modality across bouts and speeds – blocks 160 of outdoor runs v treadmill 5.5.4 D: Reliability: Analysis tool methods – GPS v accelerometer 161 5.6 Discussion 161 5.6.1 Validity & reliability 161 5.6.2 Conditions & method 162 5.6.3 Treadmill v over ground 163 5.6.4 Speed 164 5.6.5 Axis 165 5.6.6 CMD 166 5.6.7 Statistical approaches 167 5.7 Summary 168
6. Analytical Approach: Coefficient of Multiple Determination 169
6.0 Variability in practice 169 6.1 Monitoring variability in sport 170 6.2 Fatigue changes 171 6.3 CMD measure 173 6.4 Proposed approach 175 6.5 Standard methods 176 6.5.1 Accelerometry (indoor sports) 176 6.5.2 GPS (outdoors) 177 6.6 Statistical Analysis 177
7. Variability within American football: is there an association between 179 movement variability, external load and subjective markers in male collegiate American football athletes?
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7.0 Introduction 179 7.1 Methods 180 7.1.1 Participants 180 7.1.2 Design 180 7.1.3 Methodology 180 7.1.3.1 Accelerometry 180 7.1.3.2 Wellness 181 7.1.3.3 Load 181 7.2 Aim 182 7.3 Statistical analysis 182 7.4 Results 183 7.4.1 Wellness 185 7.4.2 Comparing load and wellness 189 7.4.3 Compromised training 189 7.5 Discussion 190 7.5.1 Load & wellness 191 7.5.2 Compromised training 194 7.6 Limitations 194 7.6.1 Practical applications 195 7.7 Conclusions 196
8. Variability within soccer: is there an association between movement 197 variability and objective markers of flexibility in female collegiate soccer athletes?
8.0 Why Soccer 197 8.1 Introduction 198 8.1.1 Movement screens 200 8.2 Aim 202 8.3 Methods 203 8.4 Statistical analysis 206 8.5 Results 206 8.5.1 CMDwthn-vert 212 8.5.2 CMDbtwn-vert 212 8.5.3 CMDwthn-fwd 212 8.5.4 CMDbtwn-fwd 213 8.6 Standardized scores 213 8.7 Discussion 216 8.8 Limitations 218 8.9 Practical applications 219 8.10 Conclusions 220
9. Variability within rugby: is there an association between movement 221 variability and objective recovery markers in professional male rugby athletes?
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9.0 Why rugby? 221 9.1 Introduction 222 9.1.1 Monitoring fatigue 223 9.1.2 Monitoring readiness 224 9.1.3 Injury risk/monitoring 224 9.2 Aim 225 9.3 Methods 225 9.4 Statistics 227 9.5 Results 227 9.6 Discussion 234 9.6.1 Player Load 234 9.6.2 Sleep 236 9.6.3 Sprints 237 9.6.4 Accelerations 238 9.6.5 Fatigue changes 240 9.6.6 Flexibility 241 9.7 Limitations 243 9.8 Practical applications 243 9.9 Conclusions 244
10. Synthesis of findings 245
10.1 Introduction 245 10.2 Aims and objectives 247 10.2.1 Summary of outcomes 247 10.2.1.1 Stating the problem 247 10.2.1.2 What athletes do for recovery does not match what 247 they believe in 10.2.1.3 Best to monitor athletes within session and educate 248 them out with 10.2.2 Determining the utility of wearables to quantify stride variability 248 in different settings 10.3 Sport specific outcomes 249 10.3.1 American football 249 10.3.2 Soccer 250 10.3.3 Rugby 251 10.3.4 Comparisons of standardized metrics 252 10.4 General discussion 258 10.4.1 Perceptions and beliefs around recovery in athletes 258 10.4.2 Measuring recovery in situ 259 10.4.3 Team sport differences 261 10.4.3.1 Periodization differences 262 10.5 Limitations 266 10.5.1 Challenges of wearable sensors 266 10.6 Future Research Directions 268 10.6.1 Recommendations for future research 268 10.6.1.1 Efficacy of a recovery intervention 269
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10.6.1.2 Biomechanical loading 269 10.6.1.3 Intensified training 270 10.6.1.4 Relationship between variability changes and overuse 270 training risk 10.6.1.5 Application of movement variability assessment 271 10.6.1.6 Real-time feedback 272 10.7 Conclusions 273
11. References 276
12. Appendices 324
Appendix A: Survey ethics approval 324 Appendix B: Survey ethics application 325 Appendix C: Survey ethics information sheet 336 Appendix D: Consent Aspire 338 Appendix E: Consent Oregon 342 Appendix F: Variability ethics approval 348 Appendix G: Variability ethics application 349
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ACC Acceleration ACL Anterior Cruciate Ligament ACWR Acute-Chronic Work Ratio ADP Adenosine Diphosphate ApEn Approximate Entropy ATP Adenosine Triphosphate AU Arbitrary Units CK Creatine Kinase CL Confidence Limits CMC Coefficient of multiple correlation CMD Coefficient of multiple determination
CMDbtwn-fwd Coefficient of multiple determination between strides in the forward axis
CMDbtwn-vert Coefficient of multiple determination between strides in the vertical axis
CMDwthn-fwd Coefficient of multiple determination within strides in the forward axis
CMDwthn-vert Coefficient of multiple determination within strides in the vertical axis CoM Centre of Mass CV Coefficient of variation CWI Cold Water Immersion D1 Division 1 DOMS Delayed Onset Muscle Soreness EIMD Exercise Induced Muscle Damage ES Electromyographic stimulation EWMA Exponentially Weighted Moving Average
F0 Theoretical Maximal Force F-OR Functional over reaching FIFA Fédération Internationale de Football Association GPS Global Positioning System Hz Hertz ICC Intraclass correlation coefficient IL-6 Interleukin-6 IMU Inertial Measurement Unit IOC International Olympic Committee LFM Linear fit method LoA Limits of Agreement MAS Maximal Aerobic Speed MAV Mean absolute variability MDC Minimal Detectable Change MEMS Micro-electromechanical systems
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MRI Magnetic Resonance Imaging NBA National Basketball Association NBAPA National Basketball Association Players Association NCAA National Collegiate Athletic Association NFOR Non-functional overreaching OR Over reaching OT Over training
Pmax Maximal Sprinting Power r Pearson Correlation Coefficient REM Rapid Eye Movement RGB Red Green Blue RMSD Root mean square deviation ROM Range of Motion RPE Rating of Perceived Exertion S&R Sit & Reach SampEn Sample Entropy SEM Standard Error of the mean SLHS Straight line high speed SOS Sum of Squeeze SWC Smallest Worthwhile Change TEE Typical Error of the Estimate TEM Total error of the mean TENS Transcutaneous electrical nerve stimulation UK United Kingdom UN United Nations USA United States of America
V0 Theoretical Maximal Velocity
VO2 Rate of oxygen uptake
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Chapter 1 1.1 Fast or Slow, Practitioner or Researcher (Coutts, 2016) 3 1.2 An Applied Research Model for the Sport Sciences. From Bishop (2008) 12 1.3 The continuum of research and practice, accessing on-field and off-field 16 brains to develop practice, applying the principles of Pasteur's quadrant. From Jones et al. (2019) Chapter 2 2.1 Numbers of original publications and reviews around recovery in sport. The 19 orange marks the initial exponential increase in each case. Review articles began to increase around 10 years after the original research (data from Pubmed, October 2019). 2.2 Schematic of training cycle from adaptation to overtraining. Taken from 22 (Peake, 2019) 2.3 Adaptation of the athlete – hitting the bullseye 24 2.4 Banister Training Load equations. From Clarke & Skiba (2013) 65 Chapter 3 3.1 Experience levels of athletes and coaches by region 81 3.2 Belief and use of modalities by athletes (-/- = No belief or use by athlete; - 86 /+ = No belief but use by athlete; +/- = Belief but no use by athlete; +/+ = Belief and use by athlete) 3.3 Clusters of recovery modalities within training. NMES=Neuromuscular 89 electrical stimulation. CWI=Cold Water immersion 3.4 Clusters of recovery modalities within competition. NMES=Neuromuscular 90 electrical stimulation. CWI=Cold Water immersion 3.5 How athletes know they have recovered 91 3.6 Why athletes recover the way they do 92 Chapter 4 4.1 Sources of variability in gait measurements – adapted from Chau, Young, & 111 Redekop (2005) 4.2 Publications in wearable technology & accelerometry over the years. Data 114 from Pubmed search (October 2019) using terms Wearable Technology or accelerometer. 4.3 Average stride waveforms for an individual during high speed linear 119 running, measured at the trunk by axis
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Chapter 5 5.1 Subsection of treadmill running highlighting extracted wave forms as 150 vector sum and individual accelerometry components 5.2 Calculation of straight-line sections. As a player moves in a game he/she is 152 continuously monitored via GPS. Briefly, the instantaneous bearing was calculated using the latitude and longitude between the ‘start’ and ‘end’ (1.5s apart). Δbearing was calculated by determining the difference between ‘start’ and ‘end’ points (i.e. each 1.5s). If the average Δbearing during the subsequent 5 s was between ±0.05 rad then that point was a valid straight-line running point. 5.3 Schematic of analysis of acceleration data. Data from the nine outdoor runs 155 and three treadmill runs is shown in its entirety to represent the analysis. Subjects followed a randomized order of allocation over three consecutive days. The order was the same for both treadmill and outdoor. A: Compares treadmill to blocks of outdoor runs for validity. B: Compares within modalities across speeds (i.e. treadmill v treadmill or outdoor v outdoor) for reliability. C: Compares within modalities across bouts of activities and speeds for reliability. D: (not shown on diagram) Outdoor run data was analysed in blocks of 3 using both accelerometry and GPS data for reliability. Chapter 7 7.1 Within- and between-stride CMD over the season for individuals, with group 184 mean in red bold for vertical (A & B) and blue bold for forward (C & D) axes 7.2 Standardized (z-scored) effects of wellness and load on CMD. Coefficients 188 show the effect of a 1 standard deviation change in exposure and allow comparisons across variables. Chapter 8 8.1 Movements and measures from the marker-less motion capture system 205 8.2 Ankle Dorsiflexion during ‘knee-to-wall’ test by participant and group 208 average across season. Mean response in red for left and blue for right with associated confidence intervals. 8.3 Y-balance measures by participant and group average across season. 209 Mean response in red for left and blue for right with associated confidence intervals. 8.4 Overhead squat by participant and group average across season. Mean 210 response in red for left and blue for right with associated confidence intervals.
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8.5 Standardized scores for outcome variables by axis. Coefficients show the 215 effect of a 1 standard deviation change in exposure and allow comparisons across variables. Chapter 9 9.1 Standardized (z-scored) effects of variables on outputs. Coefficients show 232 the effect of a 1 standard deviation change in exposure and allow comparisons across variables. Chapter 10 10.1 Schematic showing progression throughout thesis. A = Aim; W&W = What 246 & Whom; M = Measures; O = Outcomes; ROM = range of motion; CMD = coefficient of multiple determination; acc = average acceleration. 10.2 All standardized metrics across sports 253 10.3 Load based standardized metrics across sports 254 10.4 Subjective and objective measures not related to load across sports 255 10.5 Parsimonious monitoring system in practice. An example of how teams 275 may incorporate measures of external load on training days alongside subjective and objective markers early in the training week to assess recovery from and preparation for the next game (based on 1 game a week). Sensible training design is the most important element of the system. Elements are chosen based on the results from this thesis (left column) and placed on the most important days (centre column; i.e. MV from main training sessions and fatigue measures as markers of recovery from previous game). These surround the athlete with a protective ‘early warning system’ (right column). MV=movement variability measured in session, PL=player load from accelerometry, Flex=Flexibility measures, Sleep=measures of sleep quality/quantity, Sprint=measure of sprint speed/acceleration.
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Chapter 3 3.1 Questionnaire detail 77 3.2 Belief in efficacy of treatments for athletes. Overall rating is a numerical value 82 out of 5 based on 5=most benefit, 1=least). For belief groups the % of the overall sample and response count is given. Groups of recovery interventions that do not share a letter are significantly different (i.e. sleep alone has the greatest belief in its efficacy (A), Normatec, foam rolling and compression have similar belief levels (D)). CWI=Cold Water Immersion. 3.3 Use of treatments. For each modality, the response count and the % of the 84 overall sample who used the treatment are given. Psych Relaxn=Psychological Relaxation. 3.4 Belief of treatments relative to use. For each modality, the response count and 87 the % of the overall sample who used the treatment are given. CWI = Cold Water Immersion. Chapter 4 4.1 Linear and non-linear methods. Linear methods only investigate the magnitude 126 of variability rather than the structure, hence ignoring the deterministic nature of the data Chapter 5
5.1 Treadmill v sprint variability for each speed and condition. TEEraw=Typical Error 158 of estimate. CV=TEE as a CV%. r=Pearson correlation. LOA=95% Bland-Altman limits of agreement. Effect sizes for standardized variables $Trivial, *Small, ^Large, !Very large, !!Extremely large 5.2 Reliability metrics across all individual sprints and all speeds. Effect size = 159 $Trivial, *Small, ^Large, !Very large, !!Extremely large 5.3 Reliability metrics across all minutes on treadmill. Effect size = $Trivial, *Small, 159 ^Large, !Very large, !!Extremely large 5.4 Reliability metrics across all sprint bouts and all speeds. Effect size = $Trivial, 160 *Small, ^Large, !Very large, !!Extremely large 5.5 Reliability metrics across all treadmill bouts and all speeds. Effect size = 160 $Trivial, *Small, ^Large, !Very large, !!Extremely large 5.6 Analysis of sprint bouts with GPS and accelerometry data approaches. Effect 161 size = $Trivial, *Small, ^Large, !Very Large, %Almost Perfect
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Chapter 7 7.1 Descriptive statistics for the 63 American football athletes. *denotes median 183 and interquartile range rather than mean and standard deviation (SD). AU = arbitrary units. 7.2 Linear Mixed Model Outputs. Significant relationships marked as bold. 187 Coefficients represent percentage change in CMD from a one unit change in the variable. +/- = 95% confidence interval of coefficient. 7.3 Results from a generalized linear mixed model of flagged events 190 7.4 Summary of variability changes from variables. wv=within stride-vertical axis. 191 wf=within stride-forward axis. bv=between stride-vertical axis. bf=between stride-forward axis. Chapter 8 8.1 Descriptive Statistics of 17 female soccer athletes. *denotes mean and 207 interquartile range rather than median and standard deviation. o = degrees; AU=arbitrary units; bpm=beats per minute; h= hours. 8.2 Linear Mixed Model Outputs. Significant relationships marked in bold. 211 Coefficients represent percentage change in CMD from a one unit change in the variable. +/- = 95% confidence interval of coefficient. 8.3 Summary of variability changes from variables. wv=within stride-vertical axis. 216 wf=within stride-forward axis. bv=between stride-vertical axis. bf=between stride-forward axis. Chapter 9 9.1 Descriptive statistics for the 47 elite male rugby athletes. *denotes Median 228 rather than mean and interquartile range rather than standard deviation. h = hours; AU = arbitrary units; cm = centimetres; s = seconds; mmHg = millimetres of mercury; m = meters; m/s2 = meters per second per second. 9.2 Linear Mixed Model Outputs. Significant relationships marked in bold. 231 Coefficients represent percentage change in CMD from a one unit change in the variable. +/- = 95% confidence interval of coefficient. 9.3 Summary of variability changes from variables. wv=within stride-vertical axis. 234 wf=within stride-forward axis. bv=between stride-vertical axis. bf=between stride-forward axis.
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Basis for Chapter 2 content Murray, AM and Cardinale, M. Cold applications for recovery in adolescent athletes: a systematic review and meta-analysis. Extrem Physiol Med 4: 17, 2015. Murray, A, Fullagar, H, Turner, AP, and Sproule, J. Recovery practices in Division 1 collegiate athletes in North America. Phys Ther Sport 32: 67–73, 2018. Basis for Chapter 3 content Murray, AM, Turner, AP, Sproule, J, and Cardinale, M. Practices & attitudes towards recovery in elite Asian & UK adolescent athletes. Phys Ther Sport 25: 25–33, 2016. Basis for Chapter 4 content Murray, AM, Ryu, JH, Sproule, J, Turner, AP, Graham-Smith, P, and Cardinale, M. A Pilot Study Using Entropy as a Non-invasive Assessment of Running. Int J Sports Physiol Perform 12: 1119–1122, 2017. Basis for Chapter 7 content Murray, A, Buttfield, A, Simpkin, A, Sproule, J, and Turner, AP. Variability of within-step acceleration and daily wellness monitoring in Collegiate American Football. J Sci Med
Sport 22(4): 488-93, 2019. Not reproduced in thesis Murray, AM, Jones, T, Horobeanu, C, Turner, A, and Sproule, J. Sixty seconds of foam rolling does not affect functional flexibility or change muscle temperature in adolescent athletes. Int J Sports Phys Ther 11: 765, 2016.
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Executive Summary
This chapter outlines the transitional elements that have changed during the course of this external part-time PhD. The aim is to establish the context that drove and inspired the research questions against the backdrop of applied practice.
1.0 Reflections
Reflective practice is a core component of the supervised experience and accreditation process to become a sport scientist with the British Association of Sport and Exercise
Science (Huntley, Cropley, Knowles, & Miles, 2019). This is adopted as practice-based problems are rarely encountered or solved with solely domain-specific expertise, requiring the application of the expertise in contextually specific forms. Indeed, applied practice can at times be driven by a ‘hurry-up mentality’, emphasizing action rather than critically reflective thought (Morton, 2014). Reflective practice provides an opportunity for critical thought to emphasize learnings.
Reflective practice can be defined as;
“A purposeful and complex process that facilitates the examination of experience by questioning the whole self and our agency within the context of practice. This examination transforms experience into learning, which helps us to access, make sense of and develop our knowledge-in-action in order to better understand and/or improve practice and the situation in which it occurs” (Knowles, 2014)
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There are a range of frameworks that support reflective practice, but engaging in the process is more important than the model per se. Bain and colleagues recommend a five-point scale for reflection, encompassing: reporting; responding; relating; reasoning; and reconstructing (Bain, Ballantyne, Packer, & Mills, 1999). While they see these levels as progressive in nature, I will attempt to use them to frame and reconstruct the journey which accompanies my research using a modified version of these steps to view the experience from a range of perspectives (Carrington & Selva, 2010).
This thesis encompasses the reality and associated issues of being an applied practitioner, balancing research demands and an associated level of academic integrity and rigor whilst simultaneously servicing the needs of athletes and coaches (Figure 1.1;
Coutts, 2016). Indeed the ‘research does not always apply’ in the field (Burgess, 2017) and some teams employ practice plans and schedules and succeed despite, rather than because, of them. For example, training times may not be optimal for performance but may be at a particular time dictated by, or balanced with, practical situational context.
Alternatively, cold-water immersion post-game for recovery may occur, but not be long- enough in duration to lower muscle temperature and have a physiological benefit
(Buchheit, 2017b). The challenge of operating in both worlds is to become a ‘type-3’ practitioner (Buchheit, 2017c), one who is comfortable being uncomfortable and is prepared to challenge the status quo to grow and develop in the role of a critical friend.
Supporting athlete preparation decisions with an underpinning scientific basis (the 'slow' applied research; figure 1.1) gives confidence to any decisions taken. The conscientious research scientist ensures practices are progressive yet grounded in evidence and
2 ethical in nature (McCall et al., 2016). The perception of benefit depends on the particular viewpoint as coaches may not see an immediate direct benefit from research and simply view it as an invasive distraction, whereas researchers may not align with coaches on what is of importance (Fullagar, McCall, Impellizzeri, Favero, & Coutts, 2019).
Figure 1.1: Fast or Slow, Practitioner or Researcher (Coutts, 2016)
1.1 Report: Alignment of career and PhD progression
No researcher comes to a project as a tabula rasa—a clean slate. The preconceptions and implicit biases from their career to date undoubtedly shape the direction, focus and design of their research. For the last 15 years I have been involved in elite sport. My involvement has been as an applied practitioner concerned with the physiological preparation of athletes. Within that environment I have supported Commonwealth and
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Olympic sports to best prepare for, and recover from, competitions, from domestic to international level. For me, when I moved from soccer to a multi-sport environment
(section 1.1.1.2), I saw little value in individual sports but working with swimming, one of the sports measured in centimetres, grams or seconds; this expanded my horizons on workload management, progression and early specialization. This natural evolution inevitably occurs to an extent for all researchers and practitioners, however my own work as an applied practitioner in sport also significantly impacted on the research during this external part-time PhD.
1.1.1 Position Summary: Pre PhD
1.1.1.1 Heart of Midlothian, UK: 2006-2008
Initially my involvement in sport was within soccer as the Head of Sport Science with
Heart of Midlothian Football Club in the Scottish Premier League. As a physiologist I was directly responsible for the 1st team squad. The job encompassed: systematic monitoring of work rates through GPS & heart rate monitoring; creating individual strength & conditioning programs for players; aiding preparation for & recovery from competition; match analysis of opposition and own teams performance; monitoring fitness and implementing new fitness regimes; end-stage rehabilitation of players; education for players & coaches; overseeing aspects of nutrition (i.e. menus and individual supplementation programs) and overseeing transition of academy players to professionals. Due to changes in approach at the ownership level the contract with the club finished after two years.
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1.1.1.2 sportscotland institute of sport, UK: 2009-2014
From soccer I went to Olympic sport with the sportscotland institute of sport, initially as a physiologist team member and then progressing to Senior and Head roles. To begin with, as part of the physiology team, I was directly responsible for team sports (both hockey national programs and the Scottish Rugby Academy and IRB 7’s programs) and swimming; providing advanced level support to targeted coaches and swimmers. From there I progressed into a Senior role which was similar but with additional responsibility of line management, budget responsibility and strategic planning & decision making across the 37 sports the Institute supported. I was involved in the development and implementation of a data management system to facilitate a World Class System and assumed Head of Physiology responsibility from July 2013, which created more exposure to strategic decision making.
After dual Home Games (London 2012 & Glasgow 2014) the opportunity to move abroad arose. It was unlikely that successive Home Games would occur for me again within my career if I remained in Scotland and so I was looking for additional opportunities.
1.1.2 Position Summary: PhD
1.1.2.1 Aspire Academy, Qatar: 2014-2016
In 2014 I moved to work with an adolescent population within Qatar and specifically at the Aspire Academy1. Its mandate is to ‘provide sports training and education to students aged 12 to 18 with sporting potential, in an exceptional learning and sporting
1 https://www.aspire.qa/Default.aspx
5 environment’ (Sulayem, 2013). It has around 200 students who specialize in football, athletics, squash, table tennis, sailing, judo, gymnastics, swimming, tennis, fencing, rowing, shooting and golf. Alongside this change in role I decided to undertake this PhD to maintain a research-based approach to my practice and to set up long term career prospects within academia.
The initial PhD investigation started with looking at a specific factor in a specific location as I moved to this new job in the Middle East; yet I wanted to continue my professional development to enhance future career development. This opportunity was viable and discussed with my new employer as a sensible approach to the new environment – one that would underpin and support the work at Aspire supporting young athletes. My personal experience as a practitioner across many sports and athlete groups, as well as the needs of the Aspire athletes and staff, pointed towards the area of recovery as one that was under-researched in such populations and certainly not well understood or applied in the practical environment.
The first series of studies of the thesis were developed to try and develop a line of enquiry in this area utilizing the array of technologies and equipment available whilst working with the population to advance their performance. This involved establishing and defining the problem of how much research happens in early adults either in an undergraduate population or adult athletes. Very little occurs in well-trained adolescent athletes as we encountered daily at Aspire. This environment was ideal to try and establish evidence to further address this problem and define some of the demands of sports on adolescent populations.
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1.1.2.2 Relevant published research papers - Aspire
- Murray, AM and Cardinale, M. Cold applications for recovery in adolescent athletes: a systematic review and meta-analysis. Extrem Physiol Med 4: 17, 2015. - Murray, AM, Turner, AP, Sproule, J, and Cardinale, M. Practices & attitudes towards recovery in elite Asian & UK adolescent athletes. Phys Ther Sport 25: 25–33, 2016. - Murray, AM, Jones, T, Horobeanu, C, Turner, A, and Sproule, J. Sixty seconds of foam rolling does not affect functional flexibility or change muscle temperature in adolescent athletes. Int J Sports Phys Ther 11: 765, 2016. - Murray, AM, Ryu, JH, Sproule, J, Turner, AP, Graham-Smith, P, and Cardinale, M. A Pilot Study Using Entropy as a Non-invasive Assessment of Running. Int J Sports Physiol Perform 12: 1119–1122, 2017.
1.1.2.3 University of Oregon, USA: 2016-2018
After 2 years the next stage in my career involved a transition to the United States to take up a position at the University of Oregon as the Director of Performance & Sports Science.
This was a progressive move career-wise and exciting from a personal point of view as I would live on my third continent. The new environment had many of the same technologies as Aspire but was different in one major way – the population at a collegiate level. Whilst still elite most of the population could not be considered adolescent. The main sport that drove the appointment was American football though I had responsibilities with all 19 sports at the University. Given the change in population and sport, the next stage was to replicate the recovery questionnaire we had completed in
Aspire to establish if the context and nature of the problem were similar. An important step at this stage was also to further develop the movement variability analysis
7 completed as pilot work within runners at Aspire and this was then applied to collegiate team sport athletes.
One of the challenges during my time at Oregon was to establish a scientific support process that was grounded in scientific theory and lent itself to accurate and reliable data collection, against a backdrop of methodological constraints of support from the administration and creating buy in-with coaches (figure 1.1). This approach produced several papers that are not directly relevant to this PhD during my time at Oregon, as well as some that are.
1.1.2.4 Relevant published research papers – Oregon
- Murray, A, Fullagar, H, Turner, AP, and Sproule, J. Recovery practices in Division 1 collegiate athletes in North America. Phys Ther Sport 32: 67–73, 2018. - Murray, A, Buttfield, A, Simpkin, A, Sproule, J, and Turner, AP. Variability of within-step acceleration and daily wellness monitoring in Collegiate American Football. J Sci Med Sport 22(4): 488-93, 2019.
1.1.2.5 Additional published research papers - Oregon
- Gibson, N. E., Boyd, A. J., & Murray, A. (2016). Countermovement jump is not affected during final competition preparation periods in elite rugby sevens players. Journal of Strength and Conditioning Research, 30(3), 777–783. - Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P., Kellmann, M., Varley, M. C., … Cable, N. T. (2017). Monitoring Athlete Training Loads: Consensus Statement. International Journal of Sports Physiology and Performance, 12(Suppl 2), S2- 161-S2-170.
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- Fowler, P. M., Murray, A., Farooq, A., Lumley, N., & Taylor, L. (2017). Subjective and Objective Responses to Two Rugby 7’s World Series Competitions. Journal of Strength and Conditioning Research, 33(4), 1043-55. - Fullagar, H., Govus, A., Hanisch, J., & Murray, A. (2017). The Time Course of Perceptual Recovery Markers After Match Play in Division I-A College American Football. International Journal of Sports Physiology and Performance, 12(9), 1264-66. - Fullagar, H., McCunn, R., & Murray, A. (2017). An Updated Review of the Applied Physiology of American Collegiate Football: The Physical Demands, Strength/Conditioning, Nutritional Considerations and Injury Characteristics of America’s Favourite Game. International Journal of Sports Physiology and Performance, 32(10), 1–27. - Govus, A. D., Coutts, A., Duffield, R., Murray, A., & Fullagar, H. (2018). Relationship Between Pretraining Subjective Wellness Measures, Player Load, and Rating-of-Perceived-Exertion Training Load in American College Football. International Journal of Sports Physiology and Performance, 13(1), 95–101. - McCunn, R., Fullagar, H., Williams, S., Halseth, T. J., Sampson, J. A., & Murray, A. (2017). Playing Experience and Position Influence Injury Risk Among NCAA Division I Collegiate Footballers. International Journal of Sports Physiology and Performance, 12(10), 1297-1304. - Murray, A. (2017). Managing the training load in adolescent athletes. International Journal of Sports Physiology and Performance, 12, 42–49. - Sampson, J. A., Fullagar, H., & Murray, A. (2017). Evidence is needed to determine if there is a better way to determine the acute:chronic workload. British Journal of Sports Medicine, 51(7), 621–622. - Delaney, J. A., McKay, B. A., Thornton, H. R., Murray, A., & Duthie, G. M. (2018). Training efficiency and athlete wellness in collegiate female soccer. Sport Performance and Science Reports, 19–21. - Jones, T. W., Williams, B. K., Kilgallen, C., Horobeanu, C., Shillabeer, B. C., Murray, A., & Cardinale, M. (2018). A review of the performance requirements of squash. International Journal of Sports Science & Coaching, 13(6), 1223-1232.
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- Murray, A., Fullagar, H., Delaney, J. A., & Sampson, J. (2018). Bradford Factor and seasonal injury risk in Division I-A collegiate American footballers. Science and Medicine in Football, 2(3), 173-176. - Sampson, J. A., Murray, A., Williams, S., Halseth, T., Hanisch, J., Golden, G., & Fullagar, H. (2018). Injury risk-workload associations in NCAA American college football. Journal of Science and Medicine in Sport, 16(1), 17–31.
1.1.2.6 National Basketball Association (NBA), USA: 2018-present
Finally, for now, a further career move came to move across the US to the NBA. These progressive appointments have been influenced by both my experiences as a practitioner and my emerging research expertise working with young athletes. The NBA role came with a responsibility of overseeing the performance aspects of their International
Academy programmes within one sport. There are 7 academies distributed around the globe (3 in China and single academies in India, Mexico, Senegal and Australia) in a young and dynamic program for adolescent athletes that sees them in full-time residential academic programs, receiving both physical and technical training alongside their schooling.
This work returned to the original study population of developing potential within elite adolescent athletes with the end-goal of becoming the world’s best – one of 450 basketballers in the NBA. The infancy of the program and shift to a more senior leadership role did not lend itself to further data collection for the topic of choice within this context – especially due to the remote locations. To that end the decision was made to obtain further data through existing contacts to continue the line of enquiry developed at Oregon (chapter 9).
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1.1.2.7 Additional published research papers - NBA
- Sampson, J. A., Murray, A., Williams, S., Sullivan, A., & Fullagar, H. (2019). Subjective Wellness, Acute: Chronic Workloads, and Injury Risk in College Football. Journal of Strength and Conditioning Research, 33(12), 3367-3373. - Fullagar, H., Sampson, J. A., Delaney, J., McKay, B., & Murray, A. (2019). The relationship between objective measures of sleep and training load across different phases of the season in American collegiate football players. Science and Medicine in Football, 3(4), 326-332. - Fullagar, H., Delaney, J., Duffield, R., & Murray, A. (2019). Factors influencing home advantage in American collegiate football Factors influencing home advantage in American collegiate football. Science and Medicine in Football, 3(2), 1–6.
1.2 Relate: Connecting the dots
Whilst the topic of investigation and the population have changed from when the research project began, this is the reality for an applied multisport practitioner and similar to challenges within an Olympiad working across different sports and within single sports, working across different training cycles and phases. As I moved through my career and as I have progressed professionally I have hopefully avoided being promoted to my level of incompetence!
The approach followed throughout is to have applied research questions (stakeholder- driven by input from coaches as well as consultations with athletes) that operate within different environments as described in a recent editorial (Sandbakk, 2018). This research mindset and setup have seen repeated outputs in the form of original contributions to the international peer-reviewed literature, but the work is importantly underpinned by speaking the same ‘language’ as the coach to ensure the rationale and direction
11 underpinning the research is valid and supports performance through service provision
(Halson, Hahn, & Coutts, 2019).
Sport science is about using the best available evidence, at the right time in the right environment, with the right individual, to improve performance (D. Bishop, 2008). In this journey through research and across locations I have looked to move through the stages of an Applied Research Model as described in Figure 1.2.
Figure 1.2: An Applied Research Model for the Sport Sciences. From Bishop (2008)
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1.3 Reason: How different perspectives contribute to PhD thesis
“No man ever steps in the same river twice, for it’s not the same river and he’s not the same man” Heraclitus (n.b.)
During this research there were two new jobs (and continents) and the study population changed repeatedly. Subsequently at each of these points the experience of me as the investigator changed and so did my perspective and outlook.
When this PhD began I had never worked outside the UK. I had developed within a fairly narrow sporting system which essentially had a singular way of thinking and limited diversity in its approach to sports research and development. The focus was on medals at major games and the associated ‘marginal gains’ (Slot, 2017). Whilst athletes differed between sports, they had the same cultural upbringing and similar outlooks on their sport.
Moving to a Muslim, Arabic speaking country with an entirely new culture, and athletes who had a different upbringing and history and indeed different motivations for sport changed me both as a practitioner and a person.
1.3.1 Environment
Within the Middle East, athletes had excellent facilities and support, but not necessarily the ability or experience in comparison to their counterparts in the UK. The target for us as practitioners creating a World Class Sporting System was the same as in the UK, but the missing pieces were starkly contrasting and almost opposing (athlete ability vs. facilities). Moving to Oregon I again encountered a sense of entitlement within athletes
– they had no struggle to overcome to be successful, as everything was provided.
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This creates an interesting discussion on whether the arms race in D1 NCAA colleges is beneficial in sheltering athletes or if talent needs trauma (Collins & MacNamara, 2012).
Indeed, this likely reflects the colleges focus on winning now, rather than some of the sports who treat the NCAA as a development pipeline for their athletes and ultimately want to ‘win later’. The challenge within the US came more with perceptions of the industry. Sports science was viewed in a reductionist manner as only being reports of external load from the Global Positioning System (GPS). I was moving to command a new facility with new multiple technologies that supplemented the previously known GPS; force plates, motion capture and many other items were now at coaches’ disposal but not currently in use. I had to challenge perceptions whilst educating coaches and encountering entirely new sports with highly contrasting demands (i.e. baseball and football).
1.3.2 Direction
During discussion and debate on direction with my PhD supervisors an interesting suggestion arose to explore entropy as a potential marker of recovery following other pilot research they were involved in. This was a feasible consideration as within the setting at Aspire I was working with experienced biomechanists as part of our inter- disciplinary service team. This collaboration led to developing this analysis in runners and a proof of concept design paper (Murray et al., 2017). This initial interest continued in a new environment with the move to Oregon as I looked to continue the applied research question (section 1.2). Here I continued to look to develop a non-invasive method to identify recovery in real time or at least daily – the outcome of this was taking
14 a non-linear analysis tool and showing that it could be linked to routinely-used subjective methods in American Football athletes (chapter 7). The research question then evolved to seeing if it was possible to validate its link to objective measures currently used within other sports (soccer and rugby; chapters 8 & 9).
1.4 Reconstruct: what if I started again tomorrow?
If I were starting this journey again tomorrow I am not sure I would do anything differently.
To not change jobs because I was pursuing a PhD would be unreasonable. This may make for a more challenging and complex story but overall, I believe a more rewarding journey. This process reinforces that sports are not as different and individual as people within them believe. The same principles can be applied regardless of the sport to understand and deconstruct the determinants of performance.
The ultimate goal and outcome of a PhD is research that is original in nature and makes an important addition in advancing the knowledge in the field of study and whilst I do believe there are novel findings within this thesis there have also been plenty of novel experiences along the way. At the end of the journey I am at a different stage of my career than I was when undertaking the initial research. I now have a different background as an applied practitioner. I have a clear identity about what and who I am and am proud of this body of work. I am also clear about how it integrates into an applied research model
(figure 1.3) bridging both research and practice, though I have performed a number of the proposed roles over the course of this research which may explain why this is the case!
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Figure 1.3: The continuum of research and practice, accessing on-field and off-field brains to develop practice, applying the principles of Pasteur's quadrant. From Jones et al. (2019)
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Executive Summary
This chapter explores and appraises recent research relating to recovery in sport, whilst establishing the context for research in this area. As part of this the purpose of recovery within periodized training programmes is discussed in relation to the effects of muscle damage and approaches taken to recover from them in athletes. Discussing the monitoring and key concepts related to managing stress and recovery provide an empirical basis for monitoring without the athlete’s knowledge or additional load.
2.0 Recovery in sport: for who and by whom
'Alle Ding sind Gift und nichts ohn' Gift; allein die Dosis macht, das ein Ding kein Gift ist’
‘All things are poison and nothing (is) without poison; only the dose makes that a thing is no poison’ Paracelsus (1493 – 1541)
Elite performance is a polygenic trait (R. Tucker & Collins, 2012), defined as ‘consistently superior performance on a specified set of representative tasks for a domain’ (Ericsson,
2014). In sporting terms it can be argued that ‘highly elite athletes’ are gold medallists at the Olympic Games, or World Championships, ‘elite athletes’ are bronze or silver medallists at the same levels, whereas ‘sub-elite athletes’ are those who finish outside the podium places at the international level (Eynon et al., 2013). This does not account for time in the sport, whereby athletes have been described as elite with just two years of
17 accumulated practice (Welch & Tschampl, 2012). The term ‘elite’ is not well defined in the literature and so it is possible to question the validity of research referring to an
‘expert’ or ‘elite’ population (Swann, Moran, & Piggott, 2015).
World-class high performance encompasses several factors, one of these is recovery.
Recovery has been practically defined as "the whole set of processes that result in an athlete's renewed ability to meet or exceed a previous performance" (Hausswirth &
Mujika, 2013). The interest in recovery has grown exponentially in recent years. Recovery itself can occur both within (e.g. between drills in sessions or bouts of activity) and between (i.e. days off) sessions and games. It can occur from external and internal stressors on athletes (i.e. travel, relationships, job or academic commitments) and from an injury. Recovery is now something that athletes consciously engage in, rather than something that just happens between sessions. A structured post-exercise recovery routine is now commonplace with athletes as they look to maximize their performance.
This has been reflected by increases in both original articles and review papers in the literature (figure 2.1).
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1990: Start of exponential increase in research
2000: Start of exponential increase in reviews
Figure 2.1: Numbers of original publications and reviews around recovery in sport. The orange marks the initial exponential increase in each case. Review articles began to increase around 10 years after the original research (data from Pubmed, October 2019).
2.0.1 Training programmes
Within a periodized programme there are periods when the goal of training is to disrupt homeostasis. Thereby forcing the body to adapt and subsequently super compensate during recovery periods, as proposed in the general adaption syndrome (Selye, 1946; Viru,
1994). Adequate recovery is sometimes overlooked as a requirement for adaptation to
19 increasing overload, intensity and volume. The principle of hormesis states that a low level of a toxin (training) can produce a favourable biological adaptation. Too much of the toxin would lead to an allostatic state – a chronic overactivation of regulatory systems and the alterations of body set points expressed as pathophysiology of overtraining (OT) or under recovery (Margaritis, 2019).
Periodized training programmes can be considered to be exercises in stress management (Kiely, 2018); as such they consist of alternating relatively high loads and intensities, with periods of moderate to low load and intensity. These may take differing formats of how the loads are sequenced (linear, block or undulating), but all have time frames to develop specific fitness adaptations (Kiely, 2012). The literature discusses if the manipulations of loads via training frequency, volume and intensity are programming, that deals with phases of training (Cunanan et al., 2018), rather than periodisation
(Matveev, 1975). Regardless of nomenclature, it is agreed that the process of managing stress and overloading the body to create favourable progressions is the basis of training program design.
2.0.2 Adaptations
Fitness adaptations, after the imposed stress of training, are greatly influenced by the makeup of the individual upon which training stimuli are overlaid. The neurobiological context, along with the psycho-emotional state and expectations and perceptions of the athlete, determine how they react in recovery periods between training stress (Kiely,
2018) and whether these recovery periods facilitate positive adaptation (Issurin, 2010).
A positive adaptation usually coincides with the removal of short-term fatigue. Fatigue
20 can be defined as a manifestation of limited physical and intellectual function due to synergies between perceived and performance fatigability (Enoka & Duchateau, 2016).
Training programmes are based on various pedagogical and biological assumptions related to progressive overloading. If done correctly there will be an increased tolerance or capacity in the athlete’s fitness (positive adaption; figure 2.2). Overloading or not providing adequate recovery will see non-functional overreaching (NFOR) and or overtraining (OT; figure 2.2) ensue. Functional overreaching (F-OR) is defined as a short- term decrease in performance due to increased training volume or intensity. It is a common part of the training process for athletes, with planned recovery restoring performance within 1-2 days (Meeusen et al., 2006). Deliberately employing F-OR can be beneficial during a taper period pre-competition (Aubry, Hausswirth, Louis, Coutts, & Le
Meur, 2014). NFOR is also transient; may take several days or a few weeks to restore, but is also often considered a normal outcome for elite athletes (Halson & Jeukendrup,
2004). OT is a performance decrement which can persist for several weeks or months and may seriously harm the athlete’s health (Halson & Jeukendrup, 2004; Meeusen et al.,
2006). Around a fifth of athletes experience NFOR &/or OT (Matos, Winsley, & Williams,
2011). Identifying when an athlete has F-OR is important so that training load can be adjusted to facilitate positive adaptations and prevent NFOR. One example of this possibility may be through monitoring motor control strategies, particularly in running athletes (J. T. Fuller et al., 2017).
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Figure 2.2: Schematic of training cycle from adaptation to overtraining. Taken from Peake (2019)
2.0.3 Periodized programmes
The most common example of a training programme or ‘stress management cycle’ consists of cycles of progressively intensified training within a periodized plan. While historically this may have been time based (e.g. 4-6 weeks), coaches’ practical application has outpaced the theoretical evolution of the literature and cycles are not always constrained by the calendar. It is clear now that coaches create a plan based on the athlete and their context, (i.e. training age, level, experience, etc), to maximise the training outcomes. Incorporating enough variation to avoid staleness but not so much as to cause injury (Kiely, 2018); there is no ‘one plan fits all’ and each year the individual athlete is different (section 1.3) and so applying the same plan year to year is also unlikely to work. The key is having a plan and regularly measuring to assess the progress of that plan relative to its defined outcome. This information can then be used as knowledge that creates a deeper level of understanding that can impact the outcomes (Murray,
22
2017) and creates a load for the athlete which is ‘just right’ (M. J. Cross, Williams,
Trewartha, Kemp, & Stokes, 2016).
2.0.3.1 Judging impact and success of programmes
Regarding the adolescent athlete there is an IOC consensus that mentions the need for
‘development of healthy, capable and resilient young athletes while attaining widespread and enjoyable participation for all levels of athletic achievement’ (Bergeron et al., 2015;
Brenner, 2007). Many institutions and/or national systems that develop talents look to utilise a broad talent pool and are comfortable with a “survival of the fittest” approach
(Capranica & Millard-Stafford, 2011). While this has seen overuse injuries in young people become increasingly common (DiFiori, 1999) there is evidence for a relationship between training load, and injury and illness in young athletes (Drew & Finch, 2016) but those that can tolerate the loads emerge from adolescence with fewer injuries and a greater accrued training volume (Bahr, 2014).
Muscle damage is one of the many well-known physiological effects of increased loading, in particular when resistance exercise and/or repeated eccentric actions are employed (Armstrong, 1990; Nosaka & Clarkson, 1995; Nosaka & Newton, 2002;
Sorichter, Puschendorf, & Mair, 1999). When considering training planning, the extent of the ‘damage’ can be placed on a continuum from excessive (injury) to the minimum dose needed to cause a training effect. Balancing these two strands of training and recovery is key in establishing the adaptation of the athlete (figure 2.3).
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Serenely injured Too much ‘Injury’ Enforced rest Athlete doesn’t adapt Athlete doesn’t adapt as always resting or as training to intense injured or injured
Too much Too little ‘Serenity’ Recovery ‘Disarray’
Serenely rested Disorganised rest
Athlete doesn’t adapt Load Training Athlete doesn’t adapt as always resting as not training Too little ‘No change’ intensely enough
Figure 2.3: Adaptation of the athlete – hitting the bullseye
The accumulation of small periods of missed training can be just as devastating as long periods out. This consistent absence can be considered an absence of consistency in training and could potentially lead to underperformance. For example, within Australian football, players who participated in >85% of preseason training sessions were likely to have increased in-season availability (Babijczuk, 2003). One way to measure this may be with the Bradford Factor (Murray, 2017; Murray, Fullagar, Delaney, & Sampson, 2018) that adds weight to the number of absences.
Within elite sport the terminology around the organization of the training process itself has shifted to a colloquialism of ‘load management’; reflecting how sessions are
24 manipulated to get athletes to peak for games. This is revealed in the literature by research associating athlete wellness before a session with their physical output within it (Gallo, Cormack, Gabbett, & Lorenzen, 2015). The management of athlete readiness to ensure that athletes can perform at their best is a large area of research. This shift may reflect the linguistic power of labelling something, making it more obvious to people, for example with colours; if someone is told a hue or shade is green it becomes easier to notice all the greens in the world (Regier & Xu, 2017). Coverage in the mainstream press and popularisation by journalists can create a phenomenon that takes on its own life.
2.0.4 Recovery approaches
Even though recovery is a recognized need and athletes attempt to ‘hit the bullseye’
(figure 2.3), a paucity of data exists on the implications of various recovery activities.
There is no definition of the most ‘appropriate’ modality, protocol and timing according to the level of the athlete and their training goals (Barnett, 2006). Given the diversity in athletes and the demands on them, it is surprising that we do not know how a marathon runner should recover for optimal outcomes as compared to a sprinter. On the other hand, if we consider team sport athletes in rugby, forwards and backs have different demands playing the same game and so likely need differing modalities for recovery. In another team sport such as soccer with more uniform training and competition demands in outfield players, it is still common for athletes to choose a variety of different modalities for recovery, all with their own associated levels of evidence.
Previous work has highlighted that most recovery interventions aim to restore psychological (Vaile, Halson, Gill, & Dawson, 2008), physiological (Gill, Beaven, & Cook,
25
2006; Montgomery et al., 2008), and performance variables (Duffield et al., 2008) to the
‘pre-exercise’ level or to baseline conditions identified in the absence of fatigue. There have been several reviews in recent years on the use of recovery modalities within sport.
The main focus for these has been on therapies utilizing different temperature mediums; cold - in the form of water immersion and whole body cryotherapy (Bieuzen, Bleakley, &
Costello, 2013; Bleakley, Bieuzen, Davison, & Costello, 2014; Poppendieck, Faude,
Wegmann, & Meyer, 2013; White & Wells, 2013) and heat via saunas and water immersion
(Cochrane, 2004; Versey, Halson, & Dawson, 2013).
These reviews show conflicting findings that are often dependent on the recovery strategy protocol, the type and intensity of the fatiguing stimulus and the recovery outcome variables used in the experiment. However, one review still supported the idea that such interventions can be useful enough for competitive athletes, provided
‘appropriate’ methodologies can be utilized (Versey et al., 2013). A recent narrative review in volleyball highlights the non-consensus aspect of this and the issues in defining what is ‘appropriate’ (Closs, Burkett, Trojan, Brown, & Mulcahey, 2019). Various physical, perceptual and physiological variables have been employed to test the extent of a recovery strategy – though the majority of practitioners in team sports still rely on subjective markers (R. Cross, Siegler, Marshall, & Lovell, 2019).
2.1 General physiological effects of training
2.1.1 Exercise-Induced Muscle Damage
Exercise can induce muscle damage and inflammation and the severity of this inflammation depends on the type, duration and intensity of exercise relative to a
26 participant’s age and training history (Douglas, Pearson, Ross, & McGuigan, 2017); the mechanisms of EIMD may be mechanical, metabolic or oxidative and the older you are the worse EIMD can be (Fernandes, Lamb, & Twist, 2019). Exercise protocols capable of creating muscle damage have predominantly been used as the model to understand the effectiveness of recovery modalities, as muscle damage is a well-known physiological outcome of increased loading.
The main rationale for muscle damage being the capacity of such a stimulus to induce marked changes in the skeletal muscles of individuals unaccustomed to resistance and/or eccentric exercise; from alterations at the cellular level to observed reductions in indices of muscle function (Clarkson & Sayers, 1999). Furthermore, such models have shown marked acute increases in inflammatory markers as well as muscle soreness
(Clarkson & Tremblay, 1988; Proske & Morgan, 2001) and stiffness and swelling
(Chatzinikolaou et al., 2010) in adult subjects. Such physiological responses have been shown to parallel a reduced ability to produce force (Clarkson & Tremblay, 1988) which was shown to be dissociated from motor unit recruitment issues (Newham, Jones, &
Clarkson, 1987).
The discomfort and soreness associated with muscle damage generally peaks between
24 to 72 hours following the exercise/training bout causing it, before subsiding (if there is no further training) over the next 5 to 7 days (Howatson & Van Someren, 2008) as the injury is gradually repaired (D. Jones, Newham, Round, & Tolfree, 1986; Newham, Mills,
Quigley, & Edwards, 1983). Initial damage to the muscle cell structure starts an inflammatory process and impairs the muscle’s acute ability to contract effectively. The
27 objective of any inflammatory process, is to repair injury and restore tissue function
(Malm, 2001), (for a review of the mechanisms involved in muscle damage see Clarkson
& Hubal (2002)).
Despite initiating events that elicit further muscle damage (Järvinen, Järvinen,
Kääriäinen, Kalimo, & Järvinen, 2005), certain cells (e.g., macrophages) also secrete factors that enable repair and provide a structure for satellite cells to begin the formation of new myofibers, thus facilitating regeneration (Järvinen et al., 2007). This is one of the reasons why coaches prescribe varied forms of exercise, where the intensity and volume of the activities and the modalities of contractile activity are such that they create micro- trauma to foster muscle tissue remodelling. This training approach is aimed at triggering an adaptive response such that the muscle is more damage resistant and any that does occur is repaired at a faster rate. It is clear that a relatively small insult will produce this adaptation (Clarkson & Tremblay, 1988; Ebbeling & Clarkson, 1989; L. Smith et al., 1994).
However, as inflammation and pain reduce the ability to train or compete, various approaches are taken to reduce the symptoms.
In an attempt to reduce the negative symptoms of inflammation, recovery techniques have been mainly targeted to affect skeletal muscles’ temperature and blood flow
(Bleakley et al., 2014; Eston & Peters, 1999; Gulick & Kimura, 1996; Paddon-Jones &
Quigley, 1997; Sellwood, Brukner, Williams, Nicol, & Hinman, 2007; Swenson, Swärd, &
Karlsson, 1996; Yanagisawa, Niitsu, Takahashi, Goto, & Itai, 2003). Strong correlations between functional recovery and blood markers of muscle damage (e.g., creatine kinase;
CK) may indicate that patterns of recovery partly depend on the extent of EIMD (Horita,
28
2000). A loss in muscle function may result in compensatory neuromuscular alterations
(e.g., altered kinematics, increased ground-contact times and reduced movement velocity); these can be significant, given their potential to impair athletes performance
(Owens, Twist, Cobley, Howatson, & Close, 2019).
Potentially, EIMD may make an athlete work at a higher intensity to maintain their performance or cause unaccustomed strain on the compensating muscles, joints, ligaments and tendons, therefore increasing the risk of injury. EIMD may impair oxygen kinetics and glycogen repletion, both of which can impair performance, especially in endurance and intermittent sports that rely on these metabolic outputs (R. Davies et al.,
2008; O’Reilly et al., 1987). The perception of these functional impairments has further implications for subsequent skill acquisition and injury susceptibility (Saxton et al., 1995).
2.1.2 EIMD…so what?
Physiological stress elicits multisystem responses across the neuromuscular, endocrine and nervous systems. Recovery strategies attempt to address the various psychological and physiological factors affected by stress.
Athletes train repeatedly on a weekly and sometimes daily basis; recovering effectively is one of the most critical determinants of sport success. Athletes may only experience
EIMD during the early season – manifesting as delayed onset muscle soreness (DOMS)
– but increasingly athletes train year round and while the merits of this can be debated, the large variations in load that happened historically don’t typically exist outside the pre- season phase (Murray et al., 2018). Though it should be remembered that it is highly
29 unlikely, even within this phase, that the goal is to emphasise muscle damage rather than minimizing injury or maximising performance. The subtleties within sport are not reflected in studies that induce maximal EIMD in novice subjects.
Measuring and testing recovery in sport is challenging as the measurement itself can affect the recovery. For example, the formal assessment of sleep can increase anxiety and reduce the sleep quality experienced by the athlete or an assessment of recovery status via a performance test (i.e. a jump or submaximal assessment at a fixed work rate) can place an additional load on the athlete and cause further fatigue. The only true measure of readiness in sport is the next performance, which is a challenging outcome as it is influenced by a myriad of factors, yet the role of recovery is vital in optimizing adaptation from training sessions.
2.2 Recovery Methods
Research into recovery strategies began in the 1960’s (Devries, 1961; Gisolfi, Robinson,
& Turrell, 1966) with evidence that stretching and active recovery were effective post exercise. Massage, cold water immersion and contrast water therapy began appearing in the literature in the 1990’s (Cafarelli, Sim, Carolan, & Liebesman, 1990; Eston & Peters,
1999; Kuligowski, Lephart, Giannantonio, & Blanc, 1998) and in the 2000’s investigations into compression garments and electrostimulation (Duffield et al., 2008; Lattier, Millet,
Martin, & Martin, 2004) started the advent of athletes and support staff exploring novel, alternative strategies for an advantage over opponents.
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2.2.1 Sleep & Recovery: why is it needed?
It may be considered part of an athlete’s daily life rather than a bespoke recovery tool, but sleep is the most accessible means of recovery for restoring performance following training and competition. Two competing theories for the purpose of sleep are (1) a neurometabolic theory that waking imposes a cost; neural and metabolic, that is ’paid’ for via subsequent sleep. This includes both detoxifying and restoring the nervous system, through an increase of cerebrospinal fluid that removes interstitial proteins (Xie et al., 2013). Alternatively, (2) a cognitive theory that sleep plays a vital role in synaptic plasticity, memory and learning, (i.e. sleep is primarily for the brain rather than the body).
2.2.1.1 Sleep deprivation
The prevalence of inadequate or deprived sleep has been reported to be high in athletic populations. A measure of global sleep quality was assessed in a systematic review and showed that between 38-57% of athletes indicated a clinical sleep disturbance with around a fifth of athletes reporting highly disturbed sleep (Gupta, Morgan, & Gilchrist,
2016). While these were assessed by formal questionnaire, across a range of sports, the relationship between sleep deprivation, physical fatigue and performance have been investigated with objective markers. Within the literature a range of protocols and designs have been utilised, making comparisons difficult, particularly as a control group is generally absent, though it is possible to typically distinguish between designs utilising prolonged efforts and those using high intensity exercises. In general, 1 or 2 sleepless nights do not affect high intensity exercises (Blumert et al., 2007; B. Martin, 1986;
Symons, VanHelder, & Myles, 1988), but longer periods of deprivation (64 hours) do affect
31 explosive and dynamic strength negatively (Takeuchi, Davis, Plyley, Goode, & Shephard,
1985), though this level of deprivation is not normal for athletes.
The same pattern applies to cycling sprints; a single sleepless night does not affect lab performances (D. Hill, Borden, Darnaby, & Hendricks, 1994; Mougin et al., 1996; Souissi et al., 2007); though they do decline if duration and intensity are combined (Skein,
Duffield, Edge, Short, & Mündel, 2011) or the period of disruption extends (Mougin et al.,
2001). Sleep duration’s importance is confirmed by the interaction with principally aerobic activities (B. Martin, 1981; B. Martin & Chen, 1984; Mougin et al., 2001; Oliver,
Costa, Walsh, Laing, & Bilzon, 2009).
In a particular study an 11% drop in performance levels (measured by distance covered) during walking at 80% of aerobic capacity has been shown following 36 hours sleep deprivation (B. Martin, 1981). This varied markedly between subjects with some more resistant to the effects of sleep loss than others; indeed 50% of the subjects showed a negligible change in performance. This level of sleep deprivation is uncommon in athletes who are more likely to have disturbed or short sleep rather than be deprived over multiple days. A recent systematic review highlighted the prevalence of insomnia symptoms in elite athletes but highlights the need to treat the individual rather than the group response (Gupta et al., 2016).
2.2.1.2 Causes of performance decrement
Potential reasons for the decrease in performance caused by sleep deprivation have been proposed in both the physiological and psychological areas. Physiologically, sleep
32 deprivation reduces anabolic hormone and increases catabolic hormone concentrations
(Obal & Krueger, 2004). It also inhibits restoration by increasing inflammation (Cohen,
Doyle, Alper, Janicki-Deverts, & Turner, 2009; Mullington, Simpson, Meier-Ewert, & Haack,
2010) and sympathetic nervous system activity. It alters the function of the cardiorespiratory system, reduces respiratory muscle endurance (H. I. Chen & Tang,
1989), causes later sweating (Dewasmes, Bothorel, Hoeft, & Candas, 1993), increased metabolic acidosis (H. I. Chen, 1991) and reduced effectiveness of aerobic metabolism enzymes (Vondra et al., 1981). These changes can lead to impaired cognitive function, limited glycogen repletion and reduced strength and power production.
Psychologically disturbed sleep has been linked with deviations in wellbeing measures
(Lastella, Lovell, & Sargent, 2014; Oginska & Pokorski, 2006) and a reduction in overall exercise tolerance and a perception of increased effort that is more noticeable at sub- maximal aerobic intensities (Marcora, Staiano, & Manning, 2009; Millet, 2011). This may be a consequence of physiological inflammatory changes caused by acute or chronic sleep loss (Haack, Sanchez, & Mullington, 2007; Vgontzas et al., 1999); subsequently exacerbating pain and causing a performance reduction. Sleep deprivation can decrease voluntarily chosen workload and also result in a loss of high-level sport skill, both of which may be viewed as important recovery features (Cook, Beaven, Kilduff, & Drawer, 2012;
Cook, Crewther, Kilduff, Drawer, & Gaviglio, 2011).
2.2.1.3 Benefits of sleep
An individual’s recent sleep history manifests in the quality of their daytime functioning and overall mood (Belenky et al., 2003; Bessesen, 2006; Davis & Krueger, 2012; Kahn-
33
Greene, Killgore, Kamimori, Balkin, & Killgore, 2007; Spiegel, Leproult, & Van Cauter, 1999).
Sleep coincides with the bulk of daily release of growth hormone (Finkelstein, Roffwarg,
Boyar, Kream, & Hellman, 1972); the majority happening about an hour after you fall asleep, this major anabolic stimulus if not released from the pituitary can affect muscle growth.
Cognitive functions and the central nervous system also replenish through sleep (K.
Jones & Harrison, 2001). Chronic sleep loss can impair the ability to sustain attention and response speed in a cumulative manner directly related to the amount of restricted sleep (Belenky et al., 2003; Van Dongen, Maislin, Mullington, & Dinges, 2003). Conversely the process of learning novel tasks can be enhanced by sleep as much as 30% (Fischer,
Hallschmid, Elsner, & Born, 2002). Other areas that benefit are, metabolic control of energy balance, appetite, weight (Knutson & Van Cauter, 2008; Sharma & Kavuru, 2010) and tissue repair (Maquet, 2001). These are crucial physiological processes that underwrite overall recovery, training capacity and ultimately performance.
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2.2.1.4 Consequences of not getting enough sleep
A systematic review across 12 different sports found that sports requiring high skill, tactical inputs or speed, were most sensitive to sleep manipulation. Chronic deprivation increased the likelihood of an effect on performance highlighting that the particular sport and the length of deprivation are important considerations for the impact of sleep loss on a particular athlete (Kirschen, Jones, & Hale, 2018). These equivocal findings across sports may reflect an over representation of male subjects and the typical absence of a control group in studies looking at sleep deprivation.
A lack of sleep can evoke feelings of fatigue and negatively affect cognitive development
(Gruber, 2013); it alters the post-exercise endocrine response (VanHelder & Radomski,
1989). Acute sleep loss around exercise extends the post-exercise recovery window
(Skein et al., 2011) by increasing the psychophysiological stress associated with exercise. Increased cortisol levels and decreased growth hormone release (i.e. a catabolic state) are related to a reduction in sleep duration (Obal & Krueger, 2004) where there is reduced muscle protein re-synthesis and increased muscle degradation. If this continues over a period of time the athlete’s ability to recover after EIMD will be compromised relative to obtaining consistent sleep (VanHelder & Radomski, 1989).
2.2.1.5 Constraints for athletes
High-performance athletes are involved in regular cycles of intense training that manipulate volume and intensity to create psychophysiological adaptations and subsequently increase their performance capacity (figure 2.2; section 2.0). Getting enough sleep may be the single most efficacious recovery strategy after exercise in elite
35 athletes (Halson, 2008). Indeed, practitioners believe this also and look to monitor it.
Within professional soccer 26 out of 51 teams surveyed, monitor qualitative sleep variables daily (i.e. self-reported sleep quality), with practitioners believing it is a time efficient and effective monitor of recovery status and prevention of NFOR or OT (L. D.
Harper et al., 2019; Meeusen et al., 2006)
Despite this, achieving appropriate sleep quality can be difficult for logistical reasons
(Lastella, Roach, et al., 2014); for example, competition schedules may dictate evening fixtures leading to hyperarousal and reduced sleep quality; training times may see compromised sleep with an early start; short- and long-haul travel demands may see early starts and late finishes to the day and associated time-zone changes may all contribute.
Partly due to these factors athletes often suffer from sleep disorders (D. Bishop, 2004;
Richmond et al., 2007) or experience sleep deprivation (Leeder, Glaister, Pizzoferro,
Dawson, & Pedlar, 2012), especially after evening competition. The range of impacts for individual athletes differ across a scale; from limited insomnia (<4 hours) to excessive disturbance of the entire night’s sleep.
A study of potential Olympic athletes from multiple sports (canoeing, diving, rowing, speed skating) compared sleep quality and quantity measured by wrist worn actigraphy to 20 sedentary subjects over 4 nights (Leeder et al., 2012). Overall, the athletes spent 30 minutes more in bed (8.5 hours per night on average). Despite spending more time in bed, the athletic group took longer to fall asleep; (18.2 vs 5.0 minutes), had lower sleep quality than controls (80.6 vs 88.7%), resulting in a similar time asleep (~7 hours). While
36 they had a comparable volume, athletes sleep was of a significantly lower quality than the control group.
While the values reported for sleep quality are not in the range of subjects suffering from deep sleep disorders (Sadeh, Hauri, Kripke, & Lavie, 1995) some specific contexts appear to heighten these effects. A study in Australian swimmers (Sargent, Halson, & Roach,
2014) characterised their sleep parameters in a 2-week intensified training block pre-
Olympics. The swimmers trained on 12 days of the block, with a 6 am start time. On training days, swimmers had an average bedtime of ~10 pm and woke up close to the sessions (~12 minutes before it began). Their time asleep was 5.4 hours from the period in bed. During recovery days, the athletes went to bed later (~12:30am) and slept in
(~9.47am wake time). This increased their effective sleep duration around 2 hours (7.1 hours). These results show swimmers slept significantly less prior to training and it is possible that starting the training sessions too early could impair positive training adaptations.
While the previous data was obtained during training only, athletes may experience sleep disturbances prior to competitive sessions. One study in German athletes assessed sleep loss pre-competition via questionnaire. Only one-third of the group reported no disruption to sleep pre-competition. Of those who experienced a disruption, 80% reported problems getting to sleep, 43% reported waking early and 32% reported waking up at night. The major factors that contributed to these were dwelling on the upcoming competition or simply feeling nervous (Erlacher, Ehrlenspiel, Adegbesan, & Galal El-Din, 2011). These findings were confirmed in a sample of Australian athletes; 64% reported poor sleep prior
37 to a competition with 82% reporting problems falling asleep. Poor sleep is common in athletes prior to competition though the perceptions of this differed between team sport and individual athletes (Juliff, Halson, & Peiffer, 2015).
2.2.1.6 Student athlete subset
While simply lengthening the total hours slept each night had a marked effect on athletic performance in student basketball players (Mah, Mah, Kezirian, & Dement, 2011) this outcome came from a study with no control group that was subject to training and motivational effects for the subjects and so may be treated with caution. The dual demands of academics and athletics on student athletes do see an increased demand on athletes time at a young age suggesting student athletes particularly may be highly susceptible to sleep disruption (Govus, Coutts, Duffield, Murray, & Fullagar, 2018); 60% of college students suffer from poor sleep quality (Lund, Reider, Whiting, & Prichard, 2010).
When sleep was deprived during a 60-hour period, students had increased levels of subjective fatigue, reduced mood state and poorer performance in cognitive and auditory tasks, however these changes were not exacerbated by additional exercise (Angus,
Heslegrave, & Myles, 1985). While this level of deprivation does not reflect the reality for most student athletes the decline over the first 24 hours was marked in all cases; going one full day without sleep can happen for time challenged students particularly around periods of intense academic workload. For athletes who train rather than the lay-person who exercises, there is a need to couple effort with technical proficiency; this was compromised in young tennis players after 5 hours sleep (Reyner & Horne, 2013).
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For student athletes particularly, the additional challenges to training are their academic loads. It has been shown that academic demands can in themselves increase the injury risk in student athletes (J. B. Mann, Bryant, Johnstone, Ivey, & Sayers, 2015). Student athletes training is often scheduled prior to school to try and fit within their academic schedule; early morning training is traditional in swimming and while it may in fact encourage a certain type of athlete (Lastella, Roach, Halson, & Sargent, 2016), these early morning starts may also occur due to completing academic work before practice in the afternoon. For example, adolescent student athletes in the UK self-reported less than the recommended 8 hours sleep a night (7.7±1.5 hours; Sawczuk, Jones, Scantlebury, & Till,
2018b). The phase of the season may also be a factor as with student athletes at an
NCAA College it has been shown that during a pre-season camp total sleep time was reduced by ~40 minutes, ~1 hour in season (when there are no classes) and ~30 minutes when classes begin (Fullagar, Sampson, Delaney, McKay, & Murray, 2019).
As mentioned previously (section 2.2.1.3) poor sleep can increase discomfort, the ability to cope with stressors and overall mood and feelings of wellbeing. Recent research within adolescent student athletes showed that sleep quality was a better predictor of wellbeing than overall sleep duration (Sawczuk, Jones, Scantlebury, & Till, 2018a). This highlights the importance of sleep, not only in aiding physical performance, but ensuring athletes are not suffering from a lack of enjoyment and application resulting from poor mood states, tiredness and/or a lack of proficiency. Indeed, self-reported sleep duration has been shown to have a small positive influence on fatigue and daily wellbeing in adolescent student athletes (Sawczuk et al., 2018b).
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2.2.1.7 Sleep Monitoring
The modalities used to characterise sleep include subjective monitoring of duration and quality, through to definitive measures of sleep stages and brain and muscle activity via polysomnography. There must be consideration of any measurement tool used as orthosomnia - a “perfectionist quest to achieve perfect sleep” (Baron, Abbott, Jao,
Manalo, & Mullen, 2017) - is increasingly common among athletes and affects sleep itself. Athletes worry more about getting enough sleep to show a good ‘score’ and create a vicious cycle of worry and lack of sleep.
Polysomnography is considered the gold standard monitoring tool for sleep and usually includes monitoring of eye movement, brain activity, heart rate, muscle activity, oxygen saturation and body movement (Roomkham, Lovell, Cheung, & Perrin, 2018). Given the cost and intrusive nature it is usually reserved for research applications even though it requires the participant to sleep in an unfamiliar environment (i.e. the lab) and so is not reflective of real-world conditions (i.e. the field). In the field the typical measure utilised is activity monitoring.
Activity monitoring uses measures of movement to infer sleep or wakeful states from an algorithm (Kolla, Mansukhani, & Mansukhani, 2016). While research grade monitors are validated in athletic populations (K. L. Fuller, Juliff, Gore, Peiffer, & Halson, 2017; Sargent,
Lastella, Halson, & Roach, 2015) many devices utilised to measure sleep are not validated
(Peake, Kerr, & Sullivan, 2018) and almost all are less accurate than the gold standard polysomnography. Using sleep monitors alone whilst giving a measure, is unlikely to change behaviour of an athlete (Baron et al., 2018); if the athlete reports a poor night’s
40 sleep and the total duration, until there is an intervention strategy to remedy the issue practitioners should consider the value of additional monitoring than subjective reporting. Indeed, actigraphy and self-reported sleep duration in athletic populations have strong agreement, particularly when estimating time in bed (Caia et al., 2018; Knufinke,
Nieuwenhuys, Geurts, Coenen, & Kompier, 2018). Intervention strategies, be that modifying practice times or removing life stressors, will not be affected by the measurement technique itself.
The connection between sleep and regeneration can be assessed in terms of three factors; length (total sleep requirement); quality (disturbances due to the environment or sleep fragmentation) and phase (circadian timing of sleep). They affect an athletes training capacity and intensity and their recovery; ultimately affecting their performance
(Walker, 2010). Ensuring enough sleep can mitigate the risk of developing NFOR/OT and help ensure a robust and resilient athlete (Jürimäe, Mäestu, Purge, & Jürimäe, 2004;
Samuels, 2009).
2.2.2 Hydration
Maintaining hydration may also be key in ensuring athletes are able to recover from and tolerate the physical demands of their sport and avoid NFOR. Short recovery between same-day competitions in sport can increase perceptual discomfort and physiological strain, which can be alleviated by adequate hydration. Athlete education is a key aspect of attenuating the deleterious effects of dehydration. Student athletes have a lack of knowledge around sport nutrition (Andrews, Wojcik, Boyd, & Bowers, 2016) and particularly hydration behaviours (Judge et al., 2016). Indeed, this education already
41 occurs; 69% of coaches recommended hydration to athletes during recovery in a survey of Brazilian coaches, but this is in contrast to 95% of the sample recommending it during training sessions (Juzwiak & Ancona-Lopez, 2004).
Whilst flavouring and composition may affect uptake (Wilk, Timmons, & Bar-Or, 2010) there is conjecture whether voluntary intake is sufficient to avoid dehydration. One study showed that regardless of whether the beverage is water, flavoured water or a 6% carbohydrate solution, losses matched intake (Wilk et al., 2010). Another suggested that moderate hypo-hydration occurred as adolescent American footballers only replaced two-thirds of sweat losses during practice and failed to address the deficit between training sessions (Yeargin et al., 2010).
2.2.3 Active v Passive recovery strategies
Anaerobic exercise depletes adenosine triphosphate (ATP) and other substrates and increases the accumulation of metabolic by-products within the working muscle, leading to a reduction in force or power output and subsequently fatigue. Therefore, the ability to remove these by-products, such as magnesium, adenosine diphosphate (ADP), inorganic phosphate, lactate and hydrogen ions from the muscle after exercise is important where time between sessions is limited and an improved oxygen supply may enhance clearance.
Logistical constraints often drive recovery options amongst athletes; for example, academic commitments, the time events are scheduled, and travel plans all play a part.
Teams competing on the road may have a scheduled flight that hampers post-game
42 recovery or the duration between events may vary if there are multiple games in one day
(for example in rugby sevens on day 1 of the tournament there are variable times between typically 3 games (2-4 hours) and on day 2 the tournament is a knockout format after game 1 and the sequence of play for the remaining games is determined by results on the day). Where time and/or space are limited active recovery may be a useful addition to the post-competition and training schedule of athletes. Active recovery is commonly used when less than 8 hours separate exercise sessions in a multi-game situation.
Active recovery often involves performing a variation of the fatiguing exercise, but at a lower intensity, performed during or following training sessions and competition. Doing something active after intense sessions induces a 'muscle pump'. This recovery modality is likely effective as it cycles contraction and relaxation of muscles to facilitate blood flow and return ROM to the pre-exercise 'normal', in turn this causes metabolites and inflammatory markers to leave the muscle (Dotan, Falk, & Raz, 2000; Dotan, Ohana, Bediz,
& Falk, 2003; Gill et al., 2006; Kinugasa & Kilding, 2009).
When compared to other recovery modalities active recovery has received a greater degree of attention in the literature, albeit without an optimal intensity being identified
(Van Hooren & Peake, 2018). Active recovery (of at least 15 min duration and at a moderate intensity) enhances clearance for blood lactate and accelerates pH recovery in comparison to doing nothing (Nédélec et al., 2013), alternating high and low intensity is no more beneficial than a recovery of low intensity only (Dodd, Powers, Callender, &
Brooks, 1984) but notably this study did not include an outcome performance measure.
A period of active recovery of just 20 minutes at a low intensity can produce favourable
43 outcomes when assessed several days later (Gill et al., 2006). Positive effects of active recovery have also been shown on the restoration of heart rate (Buchheit et al., 2010) and neuromuscular function (Buchheit et al., 2010; Zarrouk et al., 2011) following exercise.
Active recovery for 15-min at 30% VO2 peak was shown to be as efficacious as cold water immersion and massage in maintaining performance 24 hours following an intermittent cycling task (Lane & Wenger, 2004). An absolute active recovery intensity of 90-110W, combined with one minute cold water immersion, yielded a higher degree of perceived recovery than cold water immersion alone following three matches in seven days amongst youth soccer players (Kinugasa & Kilding, 2009). Active recovery, especially when performed on a cycle ergometer may represent an effective method, when combined with nutritional intake to maximize the acute phase following exercise
(Nedelec, Wisloff, McCall, Berthoin, & Dupont, 2013).
In comparison with passive recovery, to the authors knowledge, no detrimental performance effects have been reported following active recovery between training sessions, along with some cases of enhanced performance (Bogdanis, 1996; Dorado,
Sanchis-Moysi, & Calbet, 2004; Losnegard, Andersen, Spencer, & Hallén, 2015). Typically, studies have focused on intra-session recovery and practitioners have attempted to extrapolate this to inter-session application. Studies have employed absolute intensities based on power output (Kinugasa & Kilding, 2009), percentages of VO2 peak (Thevenet,
Tardieu-Berger, Berthoin, & Prioux, 2007) and maximal aerobic running velocities (Wahl et al., 2013) as well as self-selected recovery intensities (Menzies et al., 2010).
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For athletes, active recovery is important to reduce lactate concentrations and muscle soreness after exercise (Barnett, 2006). Indeed, many researchers and practitioners also use the lactate removal rate as the primary recovery indicator; within swimming frequent measures of lactate are taken as a recovery marker of if enough of a ‘swim-down’ has been completed post competition. Despite this, absolute levels of lactate and/or its removal may not be a true indication of the capacity for repeat performances. Though, giving athletes a target to achieve may encourage them to complete active recovery, rather than a finite prescription, which have additional benefits on future performance.
The ease of application likely explains the dominance of active recovery use amongst athletes.
2.2.4 Electrical
Electromyographic stimulation (ES) can preserve neuromuscular function, however a high degree of heterogeneity exists in how it is administered. The most commonly used method with recovery from exercise is transcutaneous electrical nerve stimulation
(TENS) which involves high frequency electrical impulses (50-100Hz). Increased blood flow, vessel permeability and a heightened analgesic effect are all associated with the use of TENS (Babault, Cometti, Maffiuletti, & Deley, 2011; Denegar & Perrin, 1992;
Hultman, Sjöholm, Jäderholm-Ek, & Krynicki, 1983). As with other recovery modalities, there is no clear link between the reported physiological effects, such as an increased blood flow and enhanced recovery or performance in athletic tasks (Millet, Perrey, &
Foissac, 2006). ES has been shown to be have more value for perceptual recovery in comparison to other modalities (active recovery, alternating hot & cold water immersion
45 and compression; Cook & Beaven, 2013; Ferguson, Dodd, & Paley, 2014; Finberg et al.,
2013; Martyn Beaven et al., 2013; Tessitore et al., 2008).
2.2.4.1 Markers of recovery
Recovery studies have found ES to be better than doing nothing (i.e. passive rest) in reducing post-exercise blood lactate concentration, but not as good as doing something
(i.e. active recovery; Argus, Driller, Ebert, Martin, & Halson, 2013; Bieuzen, Borne,
Toussaint, & Hausswirth, 2014; Cortis, Tessitore, Dartibale, Meeusen, & Capranica, 2010;
J. K. Malone, Coughlan, Crowe, Gissane, & Caulfield, 2012; Neric, Beam, Brown, &
Wiersma, 2009). It is of course worth remembering that despite their convenience, absolute blood lactate levels should not be considered an absolute gauge of metabolite removal.
An additional objective marker of muscle damage is serum creatine kinase (CK). Serum
CK has been proposed as an indirect marker of muscle damage, mainly because it can be determined with point of care testing and inexpensive assays. It should be noted though that CK is highly individual in its variability and its blood concentration can be affected by body weight (Webber, Byrnes, Rowland, & Foster, 1989), training level (Koch,
Pereira, & Machado, 2014) and muscle groups involved in exercise to a greater extent than differences in exercise volume or intensity (Koch et al., 2014).
Four studies evaluated the changes in its concentration, after exhaustive exercise and demonstrated that ES was again better than doing nothing and just as good as doing something (Bieuzen, Pournot, Roulland, & Hausswirth, 2012; Lambert, Marcus, Burgess,
46
& Noakes, 2002; Neric et al., 2009; Vanderthommen & Soltani, 2007). Though as with lactate the suitability of creatine kinase as a recovery marker can be questioned as CK levels cannot be used to ascertain the recovery from a bout of exercise, as it doesn’t necessarily relate to performance measures.
Within contact sport a meta-analysis has shown that immediately and at 48 hours post- match there are moderate increases (95% CI for standardised mean difference 0.50-1.31) whilst at 24 hours the increase is large (95% CI 1.12-1.88; Hagstrom & Shorter, 2018). It may be contact itself which raises CK (Roe et al., 2017), though modifying training to remove contact can cause undesirable increases in intensity or distance and may equally remove an adaptive stimulus for collision sport athletes. The consensus around the physiological meaning of increased serum CK following various forms of exercise is that it may not always be indicative of muscle damage (Baird, Graham, Baker, & Bickerstaff,
2012). In fact, the levels of CK induced by sporting activities may induce myositis but are typically nowhere near the values recorded in case of rhabdomyolysis and up to 300% lower than the increases observed following myocardial infarct (Fournier et al., 2015) or in burn patients (Kopp, Loos, Spilker, & Horch, 2004). In summary, whilst athletes may be
‘sore’ they can still get up and perform the next day with their relatively low levels of CK.
In a clinical setting using neuromuscular electrical stimulation on the peroneal nerve showed significantly increased blood flow in the lower leg without an increase in heart rate (A. T. Tucker et al., 2010). Following an acute ankle injury pilot work using neuromuscular electrical stimulation has been shown to reduce oedema compared to a control group but did not affect performance or pain measures (Wainwright, Burgess, &
47
Middleton, 2019). The lack of conscious innervation required during electrostimulation may contribute to its apparent effectiveness when compared to active recovery which requires a degree of ‘motivation’ to perform and differentiation of central and peripheral fatigue has been well documented (Ross, Middleton, Shave, George, & Nowicky, 2007).
Collectively these data suggest that electrostimulation can be useful under circumstances where active recovery is not possible, and the reduction of blood lactate is the primary goal. Identifying the intended aim of the recovery modality is critical in ascertaining the most effective approach.
2.2.5 Stretching
Repetitive actions in training and competition often occur in limited joint ranges. These can have the effect of shortening muscle length and reducing range of motion.
Consequently, acute recovery interventions are often aimed at restoring mobility and flexibility (Sands et al., 2013). Stretching, and particularly static stretching per se, (placing the body in an extreme end-range of motion position and holding this through gravity or assistance), has been the preferred method employed.
Static stretching, whilst beneficial in the restoration of muscle length, has been associated with acute reductions in force and power production (Young & Behm, 2003;
Young & Elliott, 2001). The mechanisms responsible for this reduction are three-fold – biomechanical (can move through greater range with more pliable muscles), physiological (greater blood flow and higher muscle temperature) and/or psychological
(more relaxed and less anxious; Weerapong, Hume, & Kolt, 2005). As such it’s use may
48 be more appropriate at the end of exercise rather than during and/or before commencement.
Reviews focused on stretching and massage have resulted in varying conclusions. Some report no practical effect upon post recovery performance and perceptual responses
(Barnett, 2006; Herbert, de Noronha, & Kamper, 2011; Nédélec et al., 2013) while others show that there is an improved perception of muscle soreness (Howatson & Van
Someren, 2008; Torres, Ribeiro, Alberto Duarte, & Cabri, 2012). Therefore, whilst practitioners and athletes may continue to consider the use of stretching it is not a panacea and its place in any programme should be considered relative to alternatives with careful monitoring of effects and outcomes (Nuzzo, 2019).
2.2.6 Massage
Massage is a commonly used recovery strategy among athletes. Massage, foam rolling, acupuncture and other manipulations (e.g., osteopath, chiropractic) may all have recovery benefits, with the premise that an increased blood flow is the primary mechanism. Numerous review articles on massage and its outcomes have concluded that it is efficacious for perceived and psychological recovery but does not augment functional performance (Barnett, 2006; Weerapong et al., 2005). However, athletes likely keep massage in their training programmes not just for recovery per se but for injury management/prevention also. Individuality in recovery is an important feature, and people do respond differently when it comes to ‘healing-hand’ modalities. There may be a psychosomatic component to these modalities that should not be discounted for purely physiological outcomes.
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2.2.7 Foam rolling
Foam rolling is a contemporary form of self-massage prescribed to invoke myofascial release and involves utilizing a solid foam cylinder varying in hardness and size. The pressure exerted between foam roller and muscle stimulates the Golgi tendon unit, decreasing muscle tension which in turn improves force generating capacity (Junker &
Stöggl, 2015). Self-myofascial release is advocated in practice to enable athletes to self- manage their recovery, a key skill when training in a large squad and/or when travelling alone to competitions. Foam rolling is popular likely due to its accessibility and is proposed to mimic traditional massage by reducing pain and inflammation through increased blood flow. Its use has outpaced the scientific literature, the ease in which foam rollers can be transported and the relative ease of administration aiding long term adherence (Schroeder & Best, 2015).
Static stretching, foam rolling and a combination of both (both conditions 3 x 30 s with
15 s rest) was compared in swimmers (Škarabot, Beardsley, & Štirn, 2015). Unsurprisingly the additive condition had the greatest effect, however all interventions improved flexibility for a limited period (< 10 minutes). No volume-matched comparison was employed, so it is unclear how reducing the volume of stretching (and any potentially negative affects) and/or foam rolling would affect the outcome. Foam rolling for one 60 s bout had no acute effect on flexibility, muscle temperature or functional flexibility in adolescent squash players (Murray, Jones, Horobeanu, Turner, & Sproule, 2016).
A recent review (Cheatham, Kolber, Cain, & Lee, 2015) observed a reduction in soreness when self-delivered myofascial release was employed following exercise. These findings
50 may have an indirect effect on performance, if the athlete perceives greater recovery because of reduced soreness. The effects of foam rolling on performance and recovery are minor and partly negligible when changes up to 30 minutes are included, but can be relevant in some cases (Wiewelhove et al., 2019). These cases are to (1) increase sprint performance; though caution should be applied here as only 4-studies were included in the meta-analysis and 2 of them significantly affected the outcome; (2) increasing flexibility which incorporates more studies but includes variables from immediately post up to 30 minutes; or to (3) reduce muscle pain sensation from 24-72 hours after activity.
The variance of the protocols within the meta-analysis and the small sample sizes make any consensus difficult. A more recent meta-analysis incorporating additional papers since 2017 concludes that stratifying by the control group (stretching or no exercise) and potential moderators shows foam rolling having large effects on flexibility relative to no exercise but similar effects to stretching (Wilke et al., 2019). This may explain why athletes will continue to use the intervention and not wait for the literature to confirm their opinions that it can enhance flexibility.
2.2.8 Water based
Water based recovery is designed to reduce the negative symptoms of inflammation by changing muscle temperature, altering the perception of muscle soreness and restoring force producing capacity (Bleakley et al., 2014; Eston & Peters, 1999; Gulick & Kimura,
1996; Sellwood et al., 2007; Swenson et al., 1996; Yanagisawa et al., 2003). Cold water immersion (CWI) combines increased hydrostatic pressure, which compresses the muscle and triggers a physiological cascade that can reduce pain perception and facilitate recovery, with cold temperatures which can affect nerve conduction and
51 sensation. Contrast bathing utilizes a similar approach to improve recovery using water with differing temperatures to facilitate this.
Both approaches are common place as recovery tools within team sports (Buchheit,
Horobeanu, Mendez-Villanueva, Simpson, & Bourdon, 2011). Amongst team sport athletes immersion periods were 1-3 minutes hot and the same or less in the cold environment (Peiffer et al., 2014) at temperatures from 23-40 oC for hot and 10-15 oC for cold (Higgins, Cameron, & Climstein, 2012; Manshuri, Rezaee, Esfarjani, & Marandi, 2014;
Peiffer et al., 2014; Rowsell, Coutts, Reaburn, & Hill-Haas, 2011). More recently the recommendation in the literature for a protocol has been stated as 11-15 minutes in 11-
15 oC for CWI (Machado et al., 2016).
No difference has been found between showers and baths as an immersion medium
(Peiffer et al., 2014) and indeed not in comparison to active recovery, though the psychological benefits (i.e. improvements in mood state or perceived pain) are frequently remarked upon in the literature (Dupuy, Douzi, Theurot, Bosquet, & Dugué, 2018). Within a group of rugby players CWI, contrast and passive recovery were compared for their effect post-game on CMJ, sprints (10 & 40m) and delayed onset muscle soreness
(DOMS) up to 144 hours post (Higgins, Climstein, & Cameron, 2012). Findings suggested that a single session of CWI positively affected DOMS and RPE. Apparently, a single exposure to contrast baths after a game had little benefit in enhancing recovery during the subsequent week. Similar studies using repeated exposures found that CWI in tournament play was superior to thermoneutral immersion (Rowsell et al., 2011) in soccer players due to perceptual differences (Rowsell, Coutts, Reaburn, & Hill-Haas,
52
2009); in contrast there was no effect of CWI on repeated training exposures (De Nardi,
La Torre, Barassi, Ricci, & Banfi, 2011). CWI was superior to contrast bathing after both repeated training exposures (Elias et al., 2012) and single matches in Australian footballers (Elias, Wyckelsma, Varley, McKenna, & Aughey, 2013).
On an acute scale, many sports are contested over multiple heats, which can occur with short intermissions ( 60 min). In these instances, the acute effects of water based recovery are valid, notwithstanding any logistical constraints that may exist in prescribing their use, though the possible short-term detrimental aspects of reduced strength and flexibility need to be considered if a re-warm up is not possible (Bleakley, Costello, &
Glasgow, 2012). While these acute interventions are of interest, the longitudinal effects and time course of recovery are more relevant in sports where training is repeated in a cyclical pattern within defined meso-cycles.
Cumulatively, whilst there are positive findings for water based recovery the research is equivocal with no additional effect of CWI on performance over and above passive rest
(Rupp et al., 2012) and differences in its effect being apparent on groups of differing maturation status (Buchheit et al., 2011). Using CWI for recovery outcomes should be balanced against desired training outcomes as research has shown that CWI can attenuate resistance training induced muscle hypertrophy compared to passive recovery
(Fyfe et al., 2019). Speaking to the consideration of how to periodise recovery strategies across the training year to maximise adaptations and at other times reduce inflammation and muscle damage.
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2.2.9 Compression
Compression is used medically to decrease inflammation. Typically, compression garments are synthetically made, designed with a compression gradient and can be worn as garments on individual limbs, the upper- or lower- or full-body. Compression garments are designed to apply graded compression to the limbs from the extremities to the midline of the body to enhance venous return (Bochmann et al., 2005), enhancing recovery via mechanisms similar to hydrostatic pressure during water immersion
(Kraemer et al., 2001).
The pressure created by compression garments is suggested to reduce the intramuscular space. Hence reducing muscle soreness and the capacity for swelling, though it is unclear if it occurs through an attenuation of the inflammatory response or simply from stabilising and aligning the muscle fibres. It has been questioned whether garments can cater for the differences in body dimensions and tissue structure through commercial sizing (V. Davies, Thompson, & Cooper, 2009) though now 3D scans to create bespoke fit garments with a standardized compression pressure are commercially viable.
Despite disagreement in the literature, with some studies showing benefits (i.e. reducing
EIMD and perceived muscular soreness whilst restoring exercise performance; Jakeman,
Byrne, & Eston, 2010; Kovacs & Baker, 2014), and others none (Nédélec et al., 2013) the use of compression garments is common in applied practice. Their use (singularly or alongside other recovery strategies) may represent a placebo effect and there is certainly no indication that they impede the recovery process (J. Hill, Howatson, van Someren,
54
Leeder, & Pedlar, 2014; Pérez-Soriano, García-Roig, Sanchis-Sanchis, & Aparicio, 2019).
From reviews the effects of their use appear mixed, with positive influences on power but only maintenance of sprint or agility measures (MacRae, Cotter, & Laing, 2011). More recently the largest benefits resulting from compression garments were seen for recovery from resistance exercise and specifically recovery of strength from up to 8 hours post activity and >24 hours (Brown et al., 2017).
2.2.10 Psychological relaxation
Recovery is multidisciplinary in nature; therefore, it requires multiple inputs across disciplines to manage stress, to result in positive training adaptations and ultimately enhanced performances. Fatigue after training and competition may be associated with changes in behaviour (Gonzalez-Bono, Salvador, Serrano, & Ricarte, 1999), reduced motivation or general apathy, disturbed mood, impaired performance and/or sleep and increased perceived soreness (West et al., 2014). If insufficient recovery occurs, symptoms may be observable and detected before the athlete reaches OT. To detect the onset of these symptoms, monitoring psychometric variables as an efficient means of monitoring both OT and recovery is now commonplace in sport (Foster, 1998; Saw, Main,
& Gastin, 2015b). In addition, life stress should also be considered as the accumulation of stress does not only come from the training environment (Moreira, Arsati, de Oliveira
Lima-Arsati, Simões, & de Araújo, 2011).
2.2.10.1 Psychological and physiological interaction
Physiological functions may be somewhat determined by the psychological response to a stressor. The psychological perception of benefit appears both additive and subtractive
55 to any measured physiological change (Cook & Beaven, 2013). For example, watching video clips can change male testosterone concentration and influence subsequent exercise performance (Cook & Crewther, 2012; Hellhammer, Hubert, & Schurmeyer,
1985). Perceptions of, and the emotions associated with stress; including feelings of depression and anger, negatively affected wound healing through psycho-neuro- immunological pathways (Kiecolt-Glaser, Page, Marucha, MacCallum, & Glaser, 1998).
Athlete testosterone levels increased after athletes viewed aggressive training clips; correlating to an improvement in subsequent 3 rep-max squat when compared to a control condition when no video was shown (Cook & Crewther, 2012). Research has shown that viewing footage showing player success with positive coaching feedback increased testosterone and lowered cortisol responses compared with viewing footage of player failure and negative coaching feedback in Rugby Union (Crewther & Cook, 2012).
Furthermore, these changes in hormone response were associated with better key performance indicators during competition (Crewther & Cook, 2012).
Psychological perception and physiological change compound (or, more importantly, can act in opposition) in terms of recovery outcome (Cook & Beaven, 2013). A study using elite rugby players found that the degree of body temperature normalisation after exercise and a cool immersion predicted recovery outcomes but was confounded by the psychological perception of the treatment; if temperature declined, but players reported not enjoying the procedure, recovery was less than predicted by the temperature change itself (Cook & Beaven, 2013). Psychological and physiological components interweave, and this quality is an essential point to consider for recovery.
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2.2.11 Longitudinal monitoring
Longitudinal monitoring of athletes and their recovery state can be practically achieved by simple, valid questionnaires. Subjective measures can be more nuanced and consistent than objective measures in reflecting external loads. Subjective well-being was shown to be sensitive to changes in both acute and chronic load (Saw, Main, &
Gastin, 2015a). However, the measure or methods used in the literature are not always reflective of practice, or indeed data primarily obtained from practice can be accused of lacking empirical quality (Saw, Kellmann, Main, & Gastin, 2017). Whilst there are now recommendations for steps that can be taken in order to minimise sources of error when implementing these short custom-designed psychometric scales in practise, future research is still required to assess the efficacy of using single item scales compared to the validated but longer multidimensional tools (Saw et al., 2017).
While longer questionnaires that include multidimensional assessment of factors surrounding fatigue (and recovery) status of athletes have strong empirical support, in the practical setting, specific single item questionnaires may be attractive as they are time efficient and offer ease of interpretation (Bowling, 2005). Other domains are also offering alternative methods such as iterative decision making amongst service providers and coaches to gather consensus in an ecologically valid way (Becker,
Brackbill, & Centola, 2017).
2.3 Monitoring stress & recovery
The importance of monitoring stress and recovery in order to detect early signs of OT and allow small course corrections of the training stimulus and/or recovery activities has
57 been established (Rushall, 1990). Typically, this takes the format of subjective self- reporting of psychological factors but may be supplemented by physiological markers for the most effective means to monitor stress and recovery (Alves, Oliveira, Costa, &
Samulski, 2006; Moreira et al., 2011). However, it is not always feasible to regularly collect biochemical and/or physiological parameters.
2.3.1 Challenges
The challenges of recovery monitoring are varied from the differences between sports holistically and the nuances of individuals and their preferences within sports. The logistical constraints also differ and some of these considerations are discussed here.
2.3.1.1 Individual variation
Practitioners should be wary of individualised responses to a stressor (Hecksteden et al.,
2015). Research shows an individualised nature of recovery after contact sports, suggesting that recovery and subsequent training should be individually prescribed
(Ward, Coutts, Pruna, & McCall, 2018; West et al., 2014). Effective monitoring procedures should be established to determine expected recovery patterns—and how these may vary according to performance—and to identify athletes who may require altered recovery or training provision. The variable response to post-exercise strategies should also be considered. The interactions between psychological and physiological recovery as described previously would suggest a rationale for the application of recovery strategies aimed at enhancing physiological functions that are perceived to reduce pain, soreness and fatigue. It would also be pertinent for practitioners to monitor individual responses to the recovery strategies that seek to enhance psychological recovery.
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2.3.1.2 Markers
A range of markers can be used to assess psychological and physiological recovery and the associated time course including, but not limited to: haematological measures; self- report questionnaires; technology (including mobile apps); force production; and actual performance. Within adult and adolescent populations increases in IL-6 have been shown after many types of exercise (Croisier et al., 1999; Eliakim et al., 2009; Hellsten, Frandsen,
Orthenblad, Sjødin, & Richter, 1997; Ostrowski, Rohde, Zacho, Asp, & Pedersen, 1998; L.
Smith et al., 2000). Cytokines have been shown to increase with cycling, tennis and wrestling (Nemet, Mills, & Cooper, 2004; Nieman, Kernodle, Henson, Sonnenfeld, &
Morton, 2000; Timmons, Tarnopolsky, Snider, & Bar-OrR, 2006). Serum CK has been used as an indirect marker of muscle damage, but this needs consideration of other factors as discussed in section 2.2.4.
All the above are more directly indicative of muscle damage and the degree of recovery is inferred from them. The commonality amongst them and others is that they are invasive in nature and the repeated drawing of blood needs to be considered. Other objective markers of recovery that are non-invasive may be based around heart rate variability (Flatt, Esco, & Nakamura, 2018; Flatt et al., 2017; Michael, Jay, Graham, & Davis,
2018), heart rate response via a submaximal assessment (Buchheit, 2014; Vesterinen et al., 2016; Veugelers, Naughton, Duncan, Burgess, & Graham, 2016), muscle response in reaction to a stimulus (García-Manso et al., 2011; Hunter et al., 2012; Rey, Lago-Peñas,
Lago-Ballesteros, & Casáis, 2012) or thermal imaging (Al-Nakhli, Petrofsky, Laymon, &
Berk, 2012). While in some cases subjective measures can be effective (Saw et al.,
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2015a), this may not be the case across athlete groups at different educational stages or in different cultures.
From a scientific perspective there is value in understanding the mechanisms responsible for changes associated with different recovery methods. From an applied perspective, practitioners must consider many factors when assessing recovery markers. The first is likely to be expense, especially if multiple sample points are necessary (i.e. haematology). Secondly, which marker has the strongest relationship with performance, itself a difficult construct to assess in many sports. In addition, thought should be given to the reliability of the measures and the time taken to record, analyse and interpret resultant data. Non-invasive markers that are easily administered or indeed utilising data that is already being collected but could be analysed in a supplementary way are perhaps the best option for practitioners working with athletes, especially when the number of participants is large.
2.3.1.3 Time course
Fatigue is multifaceted and so any intervention designed to reduce the impact that it has on performance must detect multiple stressors over a variety of time points relevant to training and competition schedules. When considering the time-course of recovery one may postulate that the process be complete when the individual is able to achieve, or exceed, his or her benchmark baseline performance (P. Bishop, Jones, & Woods, 2008).
While group mean values reflect statistically significant changes, the effectiveness of any recovery modality in sport, especially at the highest level, must be measured against the individual response (Ward et al., 2018). Research in a variety of populations has
60 demonstrated large individual variability in responses to a similar intervention (T. N.
Mann, Lamberts, & Lambert, 2014) and as such athletes should experiment with different modalities to identify what works best for them from an objective and subjective standpoint (Howatson, Leeder, & Someren, 2016).
At various points during competition and training, fatigue will be present, a necessary and desired by-product of athletic performance – acute recovery may not be complete by the definition of achieving the baseline performance. For example, it has been shown that performance levels in ice hockey players (Lau, Berg, Latin, & Noble, 2001) and the biochemistry of rugby players (Suzuki et al., 2004) were unaffected by active recovery practices despite improvement in the athletes’ perception of their physiological state.
The job of the coach and practitioner is to limit the magnitude of the fatigue response through careful programming so that injury and illness do not manifest. Complete elimination of the homeostatic disruption would likely diminish the adaptation but there needs to be a balance between positive adaption and developing NFOR or OT (figure 2.2).
2.4 Invisible monitoring
There are of course different facets of recovery. As mentioned previously there is recovery: from injury which can take days to months depending on the injury; between sessions, which can be as short as hours but as long as days; between games which is usually in the order of days; and there is recovery within sessions which can be judged in minutes.
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Within this thesis the focus will be on the typical recovery in sport between sessions and games from day to day. How does what an athlete did yesterday, or in the recent past, affect what the athlete is doing today or tomorrow? The rationale for this decision is that such fatigue and recovery cycles are dealt with by practitioners embedded within sport on a daily basis.
Whilst it is tempting to treat athletes as “lab rats” and collect as much information as possible from every session, as mentioned previously it is not always feasible to regularly collect biochemical markers. The athlete themselves will very quickly push back – especially if the feedback loop is not closed with information or interaction with themselves. The ideal is to gather as much information about the athlete, their performance and their current training status, without them even knowing and thus minimising any Hawthorne effect. Finding an ecologically valid method to monitor recovery without drain on resources (including the athlete’s time and energy) is the ultimate goal. Field assessments may have lower reliability but higher ecological validity than taking athletes to the lab.
Analysing dose-response relationships is a central component of managing athletes for performance and while some measures are highly variable in game play, others have used submaximal assessments within sessions (Buchheit, 2014; Thorpe, Atkinson, Drust,
& Gregson, 2017), typically based around aerobic exercises (Bradley et al., 2010; Buchheit et al., 2013) which lack validity for overall physical performance. The development of wearable devices has allowed the integration of external and internal loads during sport specific training modalities (Lacome, Simpson, Broad, & Buchheit, 2018; Lacome,
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Simpson, & Buchheit, 2018) but still requires a focus on the quality assurance of the obtained data (Halson et al., 2019).
The challenge is the management and application of this information caused by increased ease of collection. Within the last few years the management of load has been facilitated through increased use of technology to collect external load measures.
Typically, these are distilled down to ‘acute’ (average or total of chosen variable over last
~3-7 days) and ‘chronic’ (average or total of chosen variable over last ~21-36 days) loads and their relationship; a simplification of Bannisters original training load model (figure
2.4; Banister, Calvert, Savage, & Al., 1975). Now there are almost as many editorials and opinion pieces as there are original studies and the arbitrary cut-offs to discretize risk for practitioners are not defined but are more of a heuristic.
Within the literature there are debates around the validity of the acute-chronic workload ratio (ACWR); whether it is analysed as a ratio of the variables or based on an exponentially weighted moving average (EWMA) that values more recent training with greater importance (Menaspà, 2017; S. Williams, West, Cross, & Stokes, 2017); or if the data is calculated on a coupled or uncoupled ratio (Lolli et al., 2019; Windt & Gabbett,
2019).
Indeed, employing a cut-off may not be statistically valid as treating a continuous data set as a categorical variable means that each part of the category is equal. For example splitting data into tertiles means that the 67th and 99th percentile are treated the same
(Carey et al., 2018). This approach causes a loss of statistical power and increases the
63 chance of a false negative outcome – practically speaking it may mean an unnecessary modification of training or a missed opportunity for improvement. There has been recent criticism that even using the ACWR as a heuristic is a problem: as it may be a symptom of the athlete’s sport and associated schedule (Bornn, Ward, & Norman, 2019); in the first
4 weeks of returning to practice how can training be modified without a ‘chronic’ period
(Buchheit, 2017a); the evidence base is not sufficient (Impellizzeri, 2018); and some of the systematic reviews it is based upon don’t have a sufficient level of evidence (Ardern,
2016; A. Weir, Rabia, & Ardern, 2016).
This is not to say that athletes should not train between events simply to avoid injury. If nothing else the controversy in the literature has created an increased awareness of load management and an increased interest in the area. Whilst many of the arguments are based on the use of the ACWR to reduce injury others use it to maximise performance, associations with injury and predicting injury are different terms (Bahr, 2016) and are analysed for different outcomes (i.e., explanation and classification). There is though a need to establish the correct contextual values for each sport and situation to allow appropriate interpretation (Carey et al., 2017; Sampson et al., 2018) and if practitioners choose to utilise the ACWR they should not do so in isolation (Windt & Gabbett, 2019).
They should consider potential confounding variables such as previous injury, multiple injuries in the same player, fitness or age.
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푃(푡) = 푝(0) + 푘1𝑔(푡) – 푘2ℎ(푡)
Performance = Fitness – Fatigue
k1 > k2 is indicative of a long recovery; k2 > k1 is indicative of a shorter recovery
1 𝑔′(푡) = 푤(푡) − 𝑔(푡) 휏1
Fitness Today = Training Load Today – Constant * Training Load Yesterday
휏1 is the time decay for fitness in days (time it takes for fitness to return to baseline)
1 ℎ′(푡) = 푤(푡) − ℎ(푡) 휏2
Fatigue Today = Training Load Today – Constant * Training Load Yesterday
휏2 is the time decay for fatigue in days (time it takes for fatigue to return to baseline)
Figure 2.4: Banister Training Load equations. From Clarke & Skiba (2013)
2.5 Summary
Monitoring training load is essential for determining if athletes are adapting positively or negatively to their training program. Such collective information allows discussions to be had with coaches about what is important for them to know to modify training but also what is important for them to develop for success in their sport. Regardless of the system or measures, if there is not effective communication among staff then the cumulative loads can be too high and may lead to injury.
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Regardless of the load variable measured, the interpretation needs context to evaluate the success of its implementation relative to the plan. Within any sport the efficacy of training is often judged by the scoreboard rather than the performance or fitness level.
For the moment, accumulating chronic loads in a safe manner (i.e., avoiding large spikes in training load; < 10% increase) seems to be the primary factor to consider in training design to avoid injury; to most coaches this is just sensible training design. The correct amount of load for an athlete is that which is “just right” (M. J. Cross et al., 2016); however, determining this level without specific information about training loads and the benefit of experience is the challenge. The goal should be to establish parsimonious monitoring systems that consider training loads holistically, are both cost- and time- effective (Coutts, 2014) and allow informed decisions to be made around athlete management.
This management includes recovery practices which set the athlete up for positive adaptations to the stress or training stimulus. While no single recovery method dominates in its efficacy they likely appeal to different athletes at different times.
Therefore, whilst practitioners need an understanding and knowledge of the range of available modalities treating the athlete as an individual with preferences should be considered.
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Executive Summary
This chapter compares differing populations, investigated for their attitudes and beliefs around recovery, both to specific identified methods and recovery in general. The perceived effectiveness relative to the athletes’ use is unpacked against the situational reasons for their actions, and conclusions are drawn to direct next steps in the research approach.
3.0 Place of recovery within training programmes
The principles of overload and supercompensation are the basis of any periodized training programme (section 2.0.1). Suitable recovery periods need to be programmed in combination with practices that are proportional to both the training load and fatigue level
(Hausswirth & Mujika, 2013). The practical definition of recovery covers the ‘processes that result in the ability to meet or exceed previous performances’ (Hausswirth & Mujika,
2013). While the use of recovery practices are commonplace in diverse athletic populations, recovery remains an under-researched area relative to training studies, with many practices not fully supported by under-pinning evidence (Simjanovic, Hooper,
Leveritt, Kellmann, & Rynne, 2009).
3.1 How recovery methods are chosen: evidence or experience?
Many coaches/practitioners implement recovery strategies without truly assessing the cost-benefit of such an approach. As discussed in Chapter 2, prescribed recovery
67 strategies may interfere with the planned training adaptations in an acute or chronic context and so caution should surround their use. Coaches may implement strategies based on personal experience rather than research evidence (Simjanovic et al., 2009).
This contrasts with many practitioners who focus on research outcomes into the multi- dimensional components of recovery with the aim of gaining a competitive advantage
(Coutts, 2016b). Practitioners in the US use research evidence in practice as it relates to recovery and value this as more than twice as important as their personal experience
(Fullagar, Harper, et al., 2019). Many practitioners are technophiles, (early adopters of new technology) and opinion leaders (Valente & Davis, 1999) in training methods. The chosen course of action within teams in regards recovery can at times come down to the dominant personality or the key decision maker rather than an objective evidence-based decision. This is not helped as there is no definition of the most ‘appropriate’ modality, protocol and timing according to the level of the athlete and their training goals (Barnett,
2006; Kellmann et al., 2018) despite numerous post-exercise recovery options currently available for athletes (Crowther, Sealey, Crowe, Edwards, & Halson, 2017).
3.1.1 Short term or long term?
Very few data are available on prolonged implementations of recovery strategies as compared to the amount of studies conducted on the acute physiological responses of most modalities. Methodologically the investigation of terms longer than 96 hours is challenging with an increasing number of factors to control. Indeed a study looking at a weekly micro cycle in elite sport identified a number of logistical hurdles that needed to be overcome due to the multifarious professional team sports environment restricting
68 practitioners’ capacity to prescribe recovery systematically and schedule subsequent training within evidence-based recommendations (R. Cross et al., 2019).
Recent work in Australian Football players over the course of a season approached the issue of longer term recovery, assessing perceptual measures and physiological tests conducted after games (Bahnert, Norton, & Lock, 2013). Typically, players who chose cold immersion, stretching and compression garments as their strategy reported a greater perceived recovery. However, no relationship was evident between recovery outcomes measured via objective physiological measures and the choice of recovery methods used
(Bahnert et al., 2013). This may be due to the measure of recovery being a repeated vertical jump which may not have received 100% effort from the players despite being repeated later in the week if more than a standard deviation below their mean score. It may also be as the recovery was not entirely optional or free to be completed at the players discretion. It was dictated in that it was compulsory and had to include at least one form of stretching and if available one active and hydrotherapy modality; this may not have been to the players preference. It is therefore clear that research should include perceptual measures of recovery as part of a comprehensive approach to athlete monitoring (Saw et al., 2015a), but also that research findings are somewhat limited and need to consider the importance of belief and attitudes.
3.1.2 Physiological or psychological?
There has been little investigation into the attitudes and beliefs associated with the choice and use of recovery practices in diverse socio-cultural environments across the world. How athletes choose to recover and the advice they receive about recovery may
69 be affected by the immediate environment and climatic conditions, which in turn affects their beliefs (Institute of Medicine & National Research Council, 2011). Such information could potentially affect the effectiveness of recovery and should be investigated to highlight biases that practitioners or athletes hold due to their interactions with the surrounding culture and/or environment.
Previous work surveying 890 South African team sport athletes of mixed gender (57% men), showed that the top 3 recovery preferences, regardless of playing level or gender, were, in order of preference: sleep, fluid replacement and prayer (Venter, 2012). What is not clear is how the athletes made the choices and preferences for these modalities. In particular, whether individual preference or coaching staff’s advice was the basis for the choice. While one of the top modalities was prayer, it has been suggested that, with regards to recovery modalities, focus is often placed on physical therapies and not so much on psychosocial aspects of recovery (Kellmann, 2009).
It is known that psychosocial and mental stress can impact physiological processes
(Mehta & Agnew, 2012) and that a subject’s belief of the efficacy of an intervention can influence subsequent responses (Beedie & Foad, 2009). Perceptual recovery has been demonstrated in participants after running to exhaustion on a treadmill (V. Coffey,
Leveritt, & Gill, 2004), undergoing cycling activity (Stanley, Buchheit, & Peake, 2012) and within a team setting (Cook & Beaven, 2013) utilising contrast and cold water immersion techniques. Somatic reasons integrate with psychological and sociological factors in athletes' minds (Van Wilgen & Verhagen, 2012) and can influence their behaviours.
Similarly, negative subjective impressions of a recovery intervention have been shown to
70 impact negatively on its effectiveness (Higgins, Heazlewood, & Climstein, 2011). Spanish basketball players were shown to have varying perception of recovery strategies and so individual approaches were recommended (Moreno, Ramos-Castro, Rodas, Tarragó, &
Capdevila, 2015). Therefore, while enforced uniform recovery strategies may help adherence, they are unlikely to be equally effective across all athletes in a team setting.
3.1.3 Real or placebo?
The effectiveness and importance of placebo as a therapeutic intervention in medicine has been well reviewed elsewhere (Beedie & Foad, 2009; Hurst et al., 2019) and should be also considered positively in a sporting environment (Bérdi, Köteles, Hevesi, Bárdos, &
Szabo, 2014). A practitioner could implement an intervention with an athlete regardless of its scientific basis; they may be successful despite their intervention. Conversely, if an athlete does not believe in an intervention, regardless of the underpinning research, it is likely to be unsuccessful. As an example the commonly-hypothesised physiological benefits surrounding cold water immersion were challenged using a placebo-based approach that found improved ratings of readiness for exercise, pain and vigour after a high-intensity interval session (Broatch, Petersen, & Bishop, 2014).
3.2 Incorporating beliefs into recovery
Integrating athletes’ belief systems into their recovery, or developing education programmes around a chosen method, may contribute to planning more adequate interventions with greater compliance and aid selection strategies for implementation
(Van Wilgen & Verhagen, 2012). This may challenge tradition or the ‘status-quo’ in a sport.
Anecdotally in the author’s experience it is typical for student-athletes to follow the
71 direction of their technical and/or strength coach, rather than display autonomous thought, around the choice of recovery practice, which may reflect the coach-athlete relationship. Recent work has shown that athletes may not be aware of the intended effects of a specific recovery modality on their physical status, despite around two-thirds performing some type of recovery after sport (Crowther et al., 2017).
3.3 Student athlete challenges
As an athletic subset, student athletes compete in unique circumstances; with a requirement to perform at both a high academic level to maintain their eligibility (Aquilina,
2013) and a high sporting level (Singer, 2008) to potentially further their professional career upon leaving college or university. Student athletes may be on scholarships, identifying as more athlete than student (Huml, Bergman, Newell, & Hancock, 2019), and hence whilst meeting the minimum academic standards, they are not admitted for their scholastic ability. Student athletes must balance the effects of training and the subsequent adaptation or recovery periods to optimize physical condition, alongside the associated mental pressures of academic studies (Romo, 2017).
Athletes feel they must develop the ability to cope with stress, prioritise, time manage and find time for rest and recuperation to be successful in their ‘dual-career’ (Linnér,
Stambulova, Lindahl, & Wylleman, 2019). Requisite time management skills are developed to meet with tutors independently (McCullough, Gibb, Pennington, & Heath,
2019) to allow them to maintain their academic eligibility (monitored by compliance offices at NCAA institutions) as well as working with the athletics department to complete sufficient training (intensity and type) to induce positive (e.g. muscular)
72 adaption and avoid burnout (Huml et al., 2019). Finding adequate recovery between these sessions to both allow physical supercompensation process to occur and minimize the potential for injury can be the challenge.
3.4 Aims
Overall this thesis seeks to examine practical issues faced by practitioners around recovery. This initial study was primarily descriptive in nature, including exploration of some associations and differences to ensure that the research applies in the field and includes both on and off-field inputs (figure 1.3).
Given the limitations on current knowledge around the interaction of beliefs and personal position as it relates to recovery, the purpose of this study was to establish current practice and attitudes towards recovery in some diverse student athlete groups; adolescent athletes in Asia and the UK and collegiate athletes in North America.
3.5 Methods
The survey was administered to a convenience sample of athletes in Asia who were included if they: trained in the Aspire Academy facility regularly or visited for a training camp; were coaches of adolescents or were by the UN definition an adolescent athlete
(aged 10-19 years2). The Asian group comprised 51 athletes & 6 coaches from athletics and squash regularly training at the Aspire Academy, 48 athletes & 45 coaches on an
2 https://data.unicef.org/topic/adolescents/overview/
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Asian Olympic Youth Athletics Camp held at the academy and 13 national level squash athletes and 2 coaches from Malaysia. Response rate was 100%.
A comparative sample from the UK was recruited via governing bodies and coaches within the UK sporting system who work with adolescent athletes; an age-matched cohort of Olympic sport athletes of similar ages (n=53 athletes & 8 coaches). Similar to the Asian athletes all those from the UK had represented their country at youth level. The participants were recruited over a 9-month period from July 2014 to March 2015.
Response rate was 100%.
A convenience sample of 152 athletes from a D1 college across 3 sports (Men’s
Basketball n=10, Men’s Football n=116, Women’s Soccer n=26) participated in this study.
A total of 161 athletes were invited across the 3 team rosters (9 declined to complete the survey; response rate of 94%). There was no penalty for not completing the survey.
Participants were invited to complete the study over a 2-month period (September &
October 2016) with a requirement for it to be completed only once. The support staff for each team differed and so there were no common influencers on the student athletes across sports. The age range of the participants was between 18 and 24 years (20.5±1.5 years).
The survey was discussed amongst two practitioners with over 30 years of combined experience in the fields of sport science and coaching and piloted with a group of 10 boys from Aspire Academy and 5 practitioners who worked with them before use. Following review of pilot testing data minor changes were made to the question wording to ease
74 understanding and to ensure the emphasis was correct to native and non-native English speakers.
The principal researcher administered the survey using a distributed printed-paper and pencil version for the Asian cohorts to complete and an email invitation to an online questionnaire (Google Docs) for the UK cohort whilst the USA cohort completed the same questionnaire on a tablet directly. They were offered the chance to complete the survey voluntarily but at no time was it compulsory. The survey was split into four sections; subject demographics, current practice, recovery beliefs and evidence for recovery (table
3.1). Typically, the survey took less than 15 minutes to complete. Any questions arising from culture or understanding were typically answered at completion as the principal investigator administered each questionnaire in person for Asian and USA groups. The ability to clarify for the online group (UK) was available via email but was never taken.
This was likely as the questions were administered in English. If needed the principal investigator followed up in person or via telephone as appropriate if there was a need for clarity from any answers provided (i.e. differing perception around recovery modalities or unclear answers).
Additional questions not present in the original instrument were added for the US cohort within the beliefs section prior to data collection on the effectiveness of foam rolling and compressive massage as these are routinely used in the D1 NCAA population (Behara &
Jacobson, 2017; Zwerling, 2014). A combination of open (n=6) and closed (n=11) questions was used to maximize the response rate, yet enable more detail from the answers (Thomas, Nelson, & Silverman, 2011). The open questions enabled athletes and
75 coaches to express opinions and elaborate on beliefs (Portney & Watkins, 2009).
Participants could return to prior questions until the survey was completed.
The study had ethical approval from the Moray House School of Education, University of
Edinburgh, Ethics Committee (appendices A-E) and the rights of the participants were protected.
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Table 3.1: Questionnaire detail
Demographics 1 Name Open 2 Gender Male | Female Closed 3 Experience in current position (i.e. years as an athlete)? <18 mths | 18mths - 3 years | 3-5 years | 5-10 years | >10 years Closed Current practice 4 Which sport & discipline do you primarily compete in? Open 5 What do you currently do to recover from training? Open 6 What do you currently do to recover from competition? Open 7 Why do you do this? Evidence | Experience | Both Closed 8 Please expand on the answer above… Open Beliefs 9 How would you rate the effectiveness of sleep on recovery? No Effect | Minor Effect | Neutral | Moderate Effect | Major Effect Closed 10 How would you rate the effectiveness of nutrition on recovery? No Effect | Minor Effect | Neutral | Moderate Effect | Major Effect Closed 11 How would you rate the effectiveness of compression on recovery? No Effect | Minor Effect | Neutral | Moderate Effect | Major Effect Closed 12 How would you rate the effectiveness of active recovery on recovery? No Effect | Minor Effect | Neutral | Moderate Effect | Major Effect Closed 13 How would you rate the effectiveness of contrast baths on recovery? No Effect | Minor Effect | Neutral | Moderate Effect | Major Effect Closed 14 How would you rate the effectiveness of ice baths on recovery? No Effect | Minor Effect | Neutral | Moderate Effect | Major Effect Closed 15 How would you rate the effectiveness of Normatec on recovery? No Effect | Minor Effect | Neutral | Moderate Effect | Major Effect Closed 16 How would you rate the effectiveness of Foam Rolling on recovery? No Effect | Minor Effect | Neutral | Moderate Effect | Major Effect Closed Evidence
How do you know you or your athletes have recovered? 17 Open Please list markers you use, performance, physiological, psychological etc
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3.5.1 Demographics
In the first three questions the participant's name, gender and experience level within their chosen sport were assessed. In terms of experience, the participants chose the appropriate option from less than 18 months to more than 10 years.
3.5.2 Open questions
The first of the open-ended questions asked the participant which sport they competed in (question 4). The next concerned the participant's current practice of recovery post-training and competition (questions 5 & 6). The fourth was an optional expansion on the limited response of experience, evidence or both outlining why the participant undertook the specified recovery strategy (question 8). In the final evidence section, participants were asked to state how they knew they had recovered
(question 17).
3.5.3 Closed questions
The first closed question asked participants why they currently undertook the specified recovery strategy, from a choice of evidence, experience or both (question
7). Subsequently they were asked to rate their opinion on a range of common recovery methods’ effectiveness (questions 9 – 16). Belief of effectiveness was assessed via closed questions assessing the athlete's perceived benefit of a technique. A 5-point scale of no effect, minor, neutral, moderate or major was used to reflect the participants’ beliefs. The answers were assigned a numerical value (5 = most benefit,
1 = least). If the athlete rated the effectiveness as 4 or 5 then this was coded as a benefit, if the athletes rated the effectiveness as 1 or 2 then this was coded as no benefit. Answers coded as 3 remained neutral. This reduction to nominal levels
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(Lavrakas & Battaglia, 2008) was taken to avoid any bias from central tendency, acquiescence or social desirability.
3.6 Statistical Analyses
The absolute values of responses were calculated from the information contained in the returned questionnaires. For the open questions, the answers were subsequently coded on completion of all questionnaires by the lead author into subcategories for subsequent analysis of the frequency of occurrence. Coding accounted for all answers given across the sample groups. Closed questions were assigned a numerical value based on their response and assessed as continuous data. No difference was found between the three Asian subgroups and so these were combined for comparison to the other cohorts. All analyses were carried out using R v3.5 (R Core Team (2018). R: A language and environment for statistical computing.
R Foundation for Statistical Computing, Vienna, Austria). Differences between groups were assessed between frequency of responses using the chi-square test (χ2) and appropriate post hoc tests. A k-means analysis was made to cluster the type of recovery groups. Alpha was set at p<0.05. Word clouds were generated from text answers based on the frequency of responses.
3.7 Results
3.7.1 Demographics
3.7.1.1 Coaches
There were no significant differences between the levels of experience in the coaches between cohorts (p=0.63, X2=6.14). Across the cohorts, the coaches’ experience was predominantly in the more than 10-year category. Of the individual cohorts surveyed,
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17%, 26% & 38% had 5-10 years’ experience in USA, UK and Asia respectively. For more than 10-years’ experience the split was 83%, 55% and 25%.
3.7.1.2 Athletes
Despite the athletes being of a similar age range there was a significant difference in training experience reported in the cohorts (p<0.001, X2=37.32). Within Asia 34% had
3-5 years’ training experience with 11% citing 5-10 years in their sport. Within the UK the majority of athletes reported having more than 10 years within their sport (45%) with 34% having 5-10 years’ experience. Across the US cohort 35% had more than 10 years’ experience in their chosen sport. Overall there were significant differences in the <18 months category versus 5-10 years and >10 years (p=0.027 & p=0.0009 respectively) and 18 months – 3 years, 3-5 years and 5-10 years versus >10 years
(p=0.0247, p=0.0005. p=0.0004). Figure 3.1 shows the distribution of experience levels for both athletes and coaches.
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Asia UK USA
6% 11% 14% 14%
15%
35%
12% 10% 45%
Athletes 34%
22% 34% 29% 19%
12% 2% 4% 25% 17%
13%
25%
55% Coaches
26%
38% 83%
<18 mths 18 mths - 3 years 3-5 years 5-10 years >10 yrs
Figure 3.1: Experience levels of athletes and coaches by region
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Table 3.2: Belief in efficacy of treatments for athletes. Overall rating is a numerical value out of 5 based on 5=most benefit, 1=least). For belief groups the % of the overall sample and response count is given. Groups of recovery interventions that do not share a letter are significantly different (i.e. sleep alone has the greatest belief in its efficacy (A), Normatec, foam rolling and compression have similar belief levels (D)). CWI=Cold Water Immersion.
Ave/5 Benefit Neutral No Benefit Ave/5 Benefit Neutral No Benefit Ave/5 Benefit Neutral No Benefit Groups
Sleep 4.55 133|88% 14|9% 5|3% 4.68 51|96% 2|4% 0|0% 3.90 70|69% 21|21% 11|11% A
Nutrition 4.19 121|80% 25|16% 6|4% 4.42 49|92% 4|8% 0|0% 3.75 59|58% 30|29% 13|13% B
Active 3.75 99|65% 43|28% 10|7% 3.77 37|70% 11|21% 5|9% 3.51 53|52% 31|30% 18|18% C
Contrast 3.99 114|75% 29|19% 9|6% 3.30 23|43% 23|43% 7|13% 3.57 55|54% 34|33% 13|13% C
CWI 4.23 127|84% 16|11% 9|6% 3.71 31|58% 17|32% 5|9% 3.21 44|43% 33|32% 25|25% C
Normatec 3.78 100|66% 36|24% 16|11% C,D
Foam Roll 3.85 108|71% 34|22% 10|7% C,D
Compression 3.61 82|54% 59|39% 11|7% 3.30 23|43% 19|36% 11|21% 2.94 31|30% 37|36% 34|33% D
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3.7.2 Effectiveness
There were significant differences between the beliefs of athletes as to the effectiveness of particular recovery techniques across the different populations
(table 3.2). Belief of effectiveness was assessed via closed questions assessing the athlete's perceived benefit of a technique. The answers were assigned a numerical value (5 = most benefit, 1 = least).
Athletes from the UK have significantly greater belief than those in Asia that sleep can benefit their recovery (i.e. a higher frequency of positive beliefs; p<0.001, X2=24.15).
Within Asia there is limited belief in the benefit of nutrition for recovery and this is significantly different to the perception of the athletes outside of Asia (p<0.001,
X2=28.09). There were differences in active recovery (p=0.03, X2=10.70) between Asia and the USA. The belief in the effects of contrast differed across populations
(p<0.001, X2=21.90) with USA being different to the UK and Asia. Compression differed between the USA and Asia in terms of belief (p<0.001, X2=30.82). Cold differed across populations with USA significantly different to the UK and Asia
(p<0.001, X2=49.67). There were no differences between coaches’ beliefs in modalities across the population groups in the majority of cases. There was a difference within the USA with most coaches favouring foam rolling.
3.7.3 Use
There was one significant difference between training and competition for the use of the recovery modalities in terms of frequency; rest was more frequently used after training sessions (p<0.001; table 3.3). Across all athletes 11 (3.6%) reported that they did not undertake a recovery strategy at all.
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Table 3.3: Use of treatments. For each modality, the response count and the % of the overall sample who used the treatment are given. Psych Relaxn=Psychological Relaxation.
Training Competition Stretch 128|41.7% 120|39.1% CWI 110|35.8% 134|43.6% Sleep 91|29.6% 76|24.8% Nutrition 85|27.7% 68|22.1% Foam Roll 71|23.1% 56|18.2% Active 44|14.3% 58|18.9% Heat 38|12.4% 53|17.3% Hydration 37|12.1% 30|9.8% Rest 32|10.4% 3|1.0% Massage 24|7.8% 18|5.9% Normatec 21|6.8% 26|8.5% Compression 19|6.2% 22|7.2% Professional 19|6.2% 16|5.2% Swim 16|5.2% 10|3.3% Psych Relaxn 4|1.3% 2|0.7% NMES 2|0.7% 3|1.0% Training n/a 2|0.7%
3.7.4 Belief
Looking at more than simply the use of modalities, this study assessed if the athlete
‘believed’ in a recovery technique, (i.e. they rated its effectiveness as moderate or major in previous questions), in combination with if they stated that they used this technique to recover from training or competition (table 3.4). This gave four possible combinations of the athlete believing in the method and using it (+/+), the athlete not believing in the method and not using it (-/-), believing in it and not using it (+/-) or finally, not believing in it but using it anyway (-/+).
For athletes there was a clear disconnect between the belief and the stated use of sleep. While all athletes sleep, those that rated it highly as a recovery strategy did not
84 tend to adopt sleep as a recovery modality (55.0%; figure 3.2). Around a quarter of the athletes (27.7%) believed in and used sleep as a recovery strategy. Conversely, the belief and use of CWI aligned better with around two-fifths of the sample (41.7%) using and believing in CWI. Nutrition practices did not mirror beliefs as 50.5% didn’t list it as a recovery practice despite believing in it. Belief in, and use of, contrast therapy and nutrition did match with just over half the group (51.1% & 50.5%) neither using nor believing in it.
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Figure 3.2: Belief and use of modalities by athletes (-/- = No belief or use by athlete; -/+ = No belief but use by athlete; +/- = Belief but no use by athlete; +/+ = Belief and use by athlete)
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Table 3.4: Belief of treatments relative to use. For each modality, the response count and the % of the overall sample who used the treatment are given. CWI = Cold Water Immersion.
Belief & use by No belief but Belief but no No belief or use athlete use by athlete use by athlete by athlete (+/+) (-/+) (+/-) (-/-) Active 3|3% 0|0% 96|88% 10|9% Compression 3|3% 0|0% 79|85% 11|12% Contrast 20|16% 0|0% 94|76% 9|7% CWI 99|73% 4|3% 28|21% 5|4%
USA Foam Roll 41|35% 4|3% 67|57% 6|5% Normatec 29|25% 2|2% 71|61% 14|12% Nutrition 22|17% 0|0% 99|78% 6|5% Sleep 37|27% 1|1% 96|70% 4|3% Active 19|45% 1|2% 18|43% 4|10% Compression 11|32% 3|9% 12|35% 8|24%
Contrast 12|40% 0|0% 11|37% 7|23%
UK CWI 21|58% 1|3% 10|28% 4|11% Nutrition 23|47% 0|0% 26|53% 0|0% Sleep 7|14% 0|0% 44|86% 0|0% Active 32|45% 3|4% 21|30% 15|21% Compression 1|2% 0|0% 30|46% 34|52% Contrast 3|4% 0|0% 52|76% 13|19%
Asia CWI 8|12% 4|6% 36|52% 21|30% Nutrition 29|40% 5|7% 30|42% 8|11% Sleep 41|51% 9|11% 29|36% 2|2%
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3.7.5 Assessment of recovery
There was no difference in the number of recovery modalities used after training or competition (Training: 2.4±1.5 v Competition: 2.4±1.6). The majority of athletes relied on subjective feel to determine if they had recovered (48%), the subsequent performance (35%) was second in determining how athletes judged their recovery.
3.7.6 Reasons
Most athletes indicated that they chose their method of recovery based on both evidence and experience (60%); a third of athletes cited their own experience as the main reason with only 7% using an evidence base. Cluster analysis for post-training recovery responses showed 5 main groups in terms of their responses: a traditional group who stretch & eat (cluster 3); an active group who sleep and take active recovery (cluster 2); a stretch group who stretch and foam roll (cluster 4); a cold group who eat and take ice baths (cluster 5), and a mixed-modality group who do ice baths, stretch and foam roll (cluster 1; figure 3.3).
These groups were slightly different in post-competition strategies with one group choosing cold & stretch; a second group favouring these modalities and active recovery; a third group who favoured sleep; a group who used stretch, hydration and nutrition and a group who used almost all of the intervention modalities (figure 3.4).
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Figure 3.3: Clusters of recovery modalities within training. NMES=Neuromuscular electrical stimulation. CWI=Cold Water immersion
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Figure 3.4: Clusters of recovery modalities within competition. NMES=Neuromuscular electrical stimulation. CWI=Cold Water immersion
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3.7.7 Ways / Means
Analysis of the frequency of words used in open answers suggests in general, behaviour in Asia is ruled by what the coach says and in the UK by educated self-decision. There was a difference in the proportion of the use of the word ‘coach’ or its derivatives between the populations. In Asia the use was proportionally higher by 2.6% (p<0.001, 95% CI 1.2 –
4.0%). Words that indicated taking responsibility for self (i.e. I, Me, My) did not differ between populations (p=0.33). There are exceptions to this with individual recovery practices but looking at recovery globally shows this difference. Overall athletes relied heavily on feel to know they had recovered mentioning it as 5.9% of all words in the open text answers (figure 3.5). The reasons why athletes recover the way they do is shown in figure 3.6.
Figure 3.5: How athletes know they have recovered
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Figure 3.6: Why athletes recover the way they do
3.8 Discussion
This study aimed to establish current practice and attitudes towards recovery in diverse groups of athletes and to provide a first descriptive step into establishing the current knowledge and practice of both athletes and coaches. As reported in previous research with athlete populations (Crowther et al., 2017; Tavares, Healey, Smith, & Driller, 2017), there are a wide range of recovery modalities used by athletes. The use of some of the recovery modalities is not fully supported by the current evidence base, for example CWI was used widely despite mixed support in the literature (Tipton, Collier, Massey, Corbett,
& Harper, 2017). In contrast, active recovery was utilised by less than a fifth of the population reflecting the lack of evidence that active recovery enhances recovery between training sessions (Barnett, 2006). There was no difference in the number of recovery approaches used post-training and post-competition.
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Importantly, this study has identified some clear discrepancies between the beliefs and practices of the athletes in terms of recovery, especially in relation to sleep and nutrition.
This data presents several interesting challenges and opportunities for researchers and practitioners. In this cohort of student-athletes the highest rated recovery intervention by participants was sleep; however, in contrast the most used intervention was stretching.
Furthermore, although sleep was rated the most important, it was only the third most used modality by athletes. Just over half of the sample believed in sleep but didn’t mention it as a modality that they used to recover, whereas just over a quarter of athletes believed in, and used, sleep.
3.8.1 Sleep
Within the literature there are suggestions that athletes need around 10 hours each night to recover, exceeding the recommendation for young adults (18-25 yrs) to get 7 to 9 hours of sleep per night (Fullagar et al., 2014; Hirshkowitz et al., 2015). Previous research has shown that adult athletes value sleep (Venter, 2012) as one of the main methods of recovery (Tavares et al., 2017). Recent work has suggested that due to training schedules and life constraints, some athletes sleep far less than recommended (Sargent et al.,
2014). This could be partly due to stress around competitions (Erlacher et al., 2011; Juliff et al., 2015; Roberts, Teo, & Warmington, 2019), or simply the increased training volume providing a competing time demand (Milewski, McCracken, Meehan, & Stracciolini,
2019).
In this population the belief that sleep is critical to optimal performance seemed to hold true. In stark contrast to this, however, only a quarter of athletes both believed in, and
93 used, sleep as a recovery modality. It is possible that extraneous factors exist which may compromise athletes’ ability to obtain sleep.
3.8.1.1 Student athletes & possible factors
Student-athletes are possibly the most at-risk (healthy) population for sleep disruption
(Carney, Edinger, Meyer, Lindman, & Istre, 2006). More than 70% of college students have been reported to obtain less than 8 hours of sleep per night during the week (Lund et al.,
2010). The commencement of university classes (Hershner & Chervin, 2014) within the sporting season could pose a risk to sleep quality with early morning training (Fullagar,
Govus, Hanisch, & Murray, 2017).
In adolescent athletes there is limited information available in the literature. Though it has been shown that less than 7 hours of sleep can nearly triple the risk of developing an upper respiratory tract infection in healthy individuals relative to those with more than 8 hours of sleep (Cohen et al., 2009). Less than 8 hours of sleep has been reported in adolescent student athletes (Sawczuk et al., 2018b) but has also been associated with almost double the injury risk (Milewski et al., 2014). This threat may be accentuated at times of high stress and anxiety (for example exams or end of school year) (J. B. Mann et al., 2015).
Other possibilities for poor or reduced quantity of sleep could include the increase in technology use and blue light providing general brain activation later in the evening
(Cajochen et al., 2011). Blue light creates a perceived difficulty in falling asleep (M. J.
Jones, Dawson, Gucciardi, et al., 2019) and there is a negative relationship between
94 technology use and sleep in adolescents (Hysing et al., 2015), though it should be noted that removing devices in the short term does not improve adolescent sleep quality or cognitive performance (Dunican et al., 2017; M. J. Jones, Dawson, Eastwood, et al., 2019).
These theories remain speculative and further research is required to assess the mechanisms behind the discrepancy between the belief and usage of sleep in student athletes.
There may also exist a possibility in which student-athletes misinterpreted the language surrounding timing of sleep as a recovery strategy. The language used in definition of activities has been shown to be important in education of athletes (Banna, Richards, &
Brown, 2016). For instance, whilst participants reported they were less likely to use sleep compared to its perceived importance, they may have been referring to purely sleep at night rather than the combination of naps (for instance in the afternoon following an early training session), or vice versa. Future analyses which depicts sleep in greater detail with regards to recovery use and perceived importance would aid such understanding.
Athletes should understand their sleep needs and should be educated regarding aspects such as sleep hygiene and potential positive effects of sleep extension (Fullagar et al.,
2014).
3.8.2 Cold water immersion
The modality that aligned best between belief and use was CWI with around two fifths being consistent in these categories. It was the second most used modality in this population in both training and competition. Athletes may utilise this modality due to their coach but this interaction may differ between populations; in Asia coaches are seen as a
95 respected elder or a teacher (Nangalia & Nangalia, 2010); in the UK and USA they may still be influencers on the choice of recovery modality, but it seems equally likely to be support staff or peers influencing athletes than just the coach.
This pattern of use is similar to international team sport athletes in previous studies
(Crowther et al., 2017; Venter, 2012). The reported reason for using CWI in other populations was to reduce swelling and inflammation (Crowther et al., 2017), although previous research studies have shown that this is not the case (Ingram, Dawson,
Goodman, Wallman, & Beilby, 2009) and any positive effects of CWI are small and more applicable to single sprints rather than endurance or team sport performance
(Poppendieck et al., 2013). Hence the choice to use CWI as an intervention may be more influenced by the perceived outcome; for example being perceived in a positive light as has been shown in track athletes (Omoniyi et al., 2017), rather than the actual physiological effect (Murray & Cardinale, 2015).
3.8.3 Foam rolling
A third of athletes based in the USA (35%) believed in, and used, foam rolling. Other questionnaire based studies did not assess this modality specifically, but soccer athletes have mentioned massage (Venter, 2012) to be important for recovery, as did a high percentage of international team sport athletes (Crowther et al., 2017). In contrast 57% of athletes believed in the modality but did not use it. Foam rolling is believed to have similar effects to massage which include relief of muscle tension, increased flexibility and range of motion (ROM) (section 2.2.7; Cheatham, Kolber, Cain, & Lee, 2015). Interestingly, there is limited evidence on the physiological benefits of foam rolling, however some
96 studies have shown that ROM is improved by foam rolling (Macdonald, Button, Drinkwater,
& Behm, 2014; MacDonald et al., 2013). Longer application of foam rolling has been shown to positively affect both range of motion and perceived soreness in the short-term (Jay et al., 2014), although this was not in trained participants. In contrast, it has been shown that a single bout of foam rolling had no statistically significant effect on muscle contractility markers or temperature in adolescent athletes (Murray et al., 2016). The associated discomfort with the modality may contribute to why it was not more widely used
(Behara & Jacobson, 2017). Changing the perception of this discomfort may help with the implementation (Leknes et al., 2013), although clearly more chronic intervention studies are needed in adolescent athletes.
3.8.4 Compression
Most athletes in the current sample did not use compression as a recovery method (<8%); despite 44% believing in its efficacy. Previous research into the efficacy of compression garments used post-exercise has produced equivocal results on performance when tested on well-trained athletes (Ali, Caine, & Snow, 2007; V. Davies et al., 2009). This though may be affected by belief status as it was found that ‘believers’ found a positive effect on performance when wearing compression compared to ‘non-believers’, despite no significant difference in muscle soreness or fatigue (Brophy-Williams, Driller, Kitic, Fell,
& Halson, 2016). As previously mentioned, the placebo effect in sport may be present with the use of any recovery modality and strongly influences perception of recovery
(Halson & Martin, 2013). Over 80% of athletes surveyed believe that placebos could affect sporting performance (Beedie & Foad, 2009; Bérdi et al., 2014). This placebo effect is likely as expectancy plays a major role in the success of interventions in the field of high-
97 performance sport (McClung & Collins, 2007). It has been postulated that a conditioning effect strengthens the attitude towards placebos (Bérdi et al., 2014). If this is true, it may be that despite any limited evidence for an intervention used for recovery if the athletes believe it to be of benefit this may be the case. This is also supported in Chapter 2 when contrasting the performance vs perceived benefits of recovery modalities (section
2.2.10.1).
3.8.5 Contextual factors
It is also possible that cultural barriers exist to prevent the adoption of some recovery practices despite a belief in its efficacy (i.e. nutrition may be compromised as food preparation opportunities for athletes in Asia are limited as there is a reliance on parents or maids to prepare meals, or poor nutritional choices may be more convenient). Equally, availability or access to certain equipment may be the issue, as in the case of foam rolling, or it may be that education of athletes and coaches may affect their choices. This is supported by individualised education approaches in elite cricketers that improved sleep efficiency (Driller, Lastella, & Sharp, 2019). Finally, environmental conditions may affect the perception of a particular modality – for example CWI use in Qatar after an outdoor endurance run in summer is likely not perceived the same as its use after an outdoor football session in Scotland in the winter. This suggests there is a need to understand the evidence-base for current recovery practices in a student athlete population.
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3.8.5.1 Accessibility
While sleep is one of the few modalities that is free of cost, the provision of recovery modalities across these populations means that almost all the mentioned interventions were available, so feasibility is likely less of an explanation. Within this study nutrition and hydration were not noted as high use modalities, indeed 50.5% of athletes believed in nutrition but did not utilise it in recovery. This may well be as athletes viewed nutrition and hydration as part of their routine, rather than a specific recovery component (for example there was no conscious choice made around nutritional intake to reflect that they were recovering or refuelling). This may have also been due to the terminology employed in the survey failing to differentiate the multiple benefits for both performance and recovery (as previously noted around sleep). Alternatively this could simply be a lack of understanding as it has been shown previously that student athletes’ knowledge around sport-nutrition is less than adequate (Andrews et al., 2016).
3.8.5.2 Training age
The Asian cohort of adolescent athletes in this study came mainly from a sports academy. These academy-aged athletes are typically chosen as they were the best athletes in their country for their sport. They still have a young training age, so have not been involved in a performance programme for a concerted period. Athletes within the
UK tended to have more training and competition experience, which may reflect early specialisation in one sport or a lifetime of sport involvement across differing activities that may reflect the differing socio-cultural conditions.
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3.8.5.3 Influence of Coaches
The opposite was true of coaches, most of whom had high levels of experience; 83% of the coaches in USA had more than 10 years’ experience and so met one of the criteria of being an ‘expert coach’ (Nash & Sproule, 2009). In Asia, only 25% of coaches had more than 10 years’ experience but 38% had 5-10 years’ experience. These highly experienced coaches likely impart their beliefs upon their athletes as they work with and educate them.
Previous work has shown that the choice of recovery modality in team sport players is influenced by coaches and support staff (Van Wyk & Lambert, 2009). Indeed the reason for performing recovery may be due to the coach as around one fifth of athletes completed recovery at instruction of their coach in a group of adolescent rugby players
(Nash & Sproule, 2018). The majority of athletes and coaches in the current study came from team sports (54%). Within Asia the majority of athletes stated that they knew they had recovered due to their coach telling them this was the case, in stark contrast to current recommendations regarding adult athlete monitoring, where the athlete's subjective perception is potentially most reliable (Saw et al., 2015a). Recent work has shown that social learning within networks (i.e. teams) can amplify accurate individuals influence, leading to higher accuracy in individual and group judgements than those that could be obtained if operating solo (Becker et al., 2017). This could manifest in sports by coaches rating the perceived exertion or recovery of individual athletes but could reinforce the coaches influence over recovery.
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This influence over decision making may reflect the coach-athlete relationship. This dyad is important to facilitate technical and physical competencies (Jackson, Beauchamp, &
Knapp, 2007). At some levels (e.g., elite competitors), athletes may not occupy such a subordinate position and may therefore have greater autonomy to decide what is right for them in terms of training and/or recovery (Jackson, Grove, & Beauchamp, 2010).
Among young athletes, perceptions of high quality interactions with coaches are related to a greater sport enjoyment (Fraser-Thomas, Côté, & Deakin, 2008). Controlling coaches disrupt athletes experiencing fulfilment of their basic psychological needs (Balaguer et al., 2012); this may be as they are coercive, and authoritarian in imposing a preconceived way of thinking upon athletes (Bartholomew, Ntoumanis, & Thøgersen-Ntoumani, 2010).
It has been shown that a high degree of confidence in the other person’s capabilities predicted enhanced commitment for the other member of the coach or athlete (Rey et al.,
2012). Therefore, the athlete may take what the coach says as the truth i.e. telling them they have recovered so the athlete believes it, as a lower perception of muscle soreness could have a positive effect on the player’s work attitude during subsequent training sessions (Rey et al., 2012). This is demonstrated by the answer of one athlete to question
8, how do you know you have recovered; “Coach told me”. Alternatively, coaches may champion that the athlete is recovered as they used a particular modality and so reinforce its use by the athlete. One athlete stated, “I have a paper from my coach with a list of things to do i.e. stretch, jog etc. If I do them I feel good”.
This self-perpetuating phenomenon whereby some coaches ‘do what they have always done’ is highlighted by the majority of self-directed learning among coaches occurring with other coaches and colleagues (Brink, Kuyvenhoven, Toering, Jordet, & Frencken,
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2018; Fullagar, McCall, et al., 2019); perhaps as they typically have a negative experience from formal learning (~98%) (Stoszkowski & Collins, 2015). The perception of recovery changes with experience (Nash & Sproule, 2018). Thus, our finding that over half of athletes believe in sleep, and nutrition and over 40% active recovery but do not utilise it, could potentially impact practice of coaches and support staff if they are open to utilising this information to change their practice.
3.8.6 Environmental factors
Choices around recovery strategy may also be influenced by what athletes have observed at higher (professional) levels, as previous work has shown that athletes replicate the behaviours of the elite (Crowther et al., 2017). In the Asian and UK cohorts’ athletes only utilised cold as a recovery modality 6% and 8% of the time respectively in training, while
24% of USA athletes utilized it. While speculative, this may reflect some cultural difference as Asian athletes do not see this practice at a more senior level and hence don’t replicate it. Though this could also be perceptual; amateur and professional rugby players both believe recovery is important but there was a difference in the number of modalities used in a week (24 v 6) (Tavares et al., 2017).
3.8.7 Perception
Perceptual recovery after games has been shown to take longer than 96 hours to return to pre-competition levels within collegiate athletes (Fullagar, Govus, et al., 2017). It has also been shown that individuals are able to closely predict full recovery without the need for external validation (Glaister, Pattison, Dancy, & McInnes, 2012) utilising internal cues
(McEwan, Arthur, Phillips, Gibson, & Easton, 2018) which may be affected by maturation
102 status (Brownstein, Ball, Micklewright, & Gibson, 2018). This raises important questions around monitoring of recovery as an extension of the Hawthorne effect (Wickstrom &
Bendix, 2000), as this process may affect the variable itself and its efficacy (for example a push to monitor sleep may affect the actual quality and quantity achieved; section 2.1.2;
(Van-den-Bulck, 2015)). Within this population the majority (48%) relied on how they felt to know they had recovered, supporting further exploration of subjectivity within recovery as has shown to be effective in athlete monitoring (Saw et al., 2015a). Indeed, with a blinded caffeine intervention if a participant believes that they are having a positive treatment (caffeine), they display a decreased RPE with exercise. If they believe themselves to be under a harmful treatment (lactate acid) then they increase their RPE, regardless of the actual intervention (Azevedo et al., 2019). Future research should establish if these self-perceptions are accurate in the educated athlete as regards recovery and remove the need for continual objective monitoring and intervention.
The differences between belief and practice highlight that the education of athletes across their sporting life cycle is important. Developing a curriculum of knowledge ensures that senior athletes set the social norms and impact positively on the younger athletes. Education around these topics may not be needed whereas emphasis on other chosen modalities may provide a better return on investment of time. However, further work is required to demonstrate a similar pattern in other populations to highlight potential differences between sub-cultures, sports, investment in facilities and teaching/coaching practices. Further research should focus on replicating these findings following an educational intervention for both athletes and support staff that focuses on developing knowledge around recovery practice. Effective approaches to enhance coach
103 education and continued professional development, may increase the use of evidence- based, or at least evidence-informed, approaches through enhanced belief of coaches transferring to increased belief and use in athletes.
3.8.8 Limitations
It is difficult to provide a measure of honesty or accuracy of the subject responses. This would improve future research in this area – perhaps by observation of behaviours as a follow-up to the questionnaire. Given the responses of this study were subjective in nature, further studies which investigate objective use of recovery modalities, and the subsequent effect of these modalities on either upcoming exercise sessions or cognitive performance, would strengthen future applied practice. Indeed, investigating the combination of perceived and objective effectiveness of recovery in combination would be the most robust approach and may allow a minimal clinically important difference (Atkinson, 2003) to be established for modalities for both perceptual and objective measures.
This study focused on a subset of recovery techniques while others are available and used by athletes. Indeed, future investigations could investigate other, less popular, recovery techniques such as photobiomodulation (de Oliveira et al., 2017), sensory deprivation
(Morgan, Salacinski, & Stults-Kolehmainen, 2013) or blood flow restriction (Borne,
Hausswirth, & Bieuzen, 2016). Taking the participants’ age into further account may help stratify effects directly related to age and stage of development. Indeed, an extension of this would be not just chronological age but maturity level and years of training also. Future research from a large sample across differing schools and sports may benefit from insights into the differing responses – this study did not find differences in beliefs across sports, but
104 a bigger sample size is needed to individualise sports. This approach may also lend itself to a more structured interview style of collection to avoid any potential misunderstandings around the questions posed and this may also allow exploration in more detail as who the key influencers are of practice (for example individual, captain or coach). This approach though would need to consider both potential sport and culture differences and may need a prohibitively large sample size across cohorts and levels.
3.9 Conclusion
This study describes athletes’ recovery practices and highlights the discrepancies between their beliefs and their implementation. Collectively, there is a discrepancy between perception and use of recovery modalities in athletes. It appears that the primary variances are around the belief and use of sleep and CWI for recovery. The results of this study suggest that there is a need to educate athletes on the benefits of different facets of recovery and to examine the coaches’ role in dictating recovery behaviours.
3.9.1 Next Steps
The examination of recovery practices for a practitioner can be crystallised by assessing if the practice of recovery itself matters if the athlete is ‘ready’ to compete at the time of competition (i.e. does the ‘how’ matter or is it simply the ‘outcome’ that does?). The differing stages of physical development within pathways and specifically within seasons for different athletes will dictate that recovery varies in its level of importance like any other training stimulus. Within a competitive period, the demand will be higher than within a training or preparation period. If the athlete and practitioner can find the balance to
105 ensure there is not undertraining and over recovery this study highlights that the practitioner needs a way to assess the athlete’s internal ‘feel’.
While the ‘science of feel’ is a burgeoning industry in sports technology (Thiessen, 2019) the ability to quantify and objectively measure ‘feel’ for the practitioner and move it from being an internal and unquantifiable measure to a measurable and impactful one can only help athlete performance. This likely begins with some education of the athlete around
‘feel’ for their own learning – for example ratings of perceived exertion in the practical environment can frequently reach the maximum from athletes regardless of the session content due to poor education of the process or purpose of the data collection (Burgess,
2017). The other method athletes utilized here was to wait until the next competition to know they are ready – be that training or competition. Many coaches would not want to wait until the biggest competition to find out if their athletes are prepared though this speaks to the dichotomy of how much of the monitoring and information collection around preparation and recovery is linked to making the coach and/or practitioner feel prepared rather than the athlete.
As previously mentioned ‘feel’ alone may not be enough to rely upon within elite sport, but this needs to be balanced with constantly interacting with athletes to get this information. The holy grail would be the ability to measure recovery without any intervention to the athlete whatsoever – invisible monitoring (section 2.4). Subjective information can be the first stage of this but how this is collected and by whom can affect the outcome. The response to an intern is likely different to that of the head coach when asked how an athlete is feeling. Indeed, with the increased number of ‘touchpoints’
106 and/or attempts at behaviour modification that support the performance process, athletes may become disengaged (Drust, 2019) or suffer decision fatigue which can in turn affect their performance (Coutts, 2016a).
The increase in wearable technology has made this monitoring more accessible and increasingly individual in nature and uses the subjective data to triangulate the objective information collected. What is needed with objective monitoring is a robust system that can reliably measure metrics that change with intensity and/or fatigue across and within a session.
While some of these approaches have been implemented in individual sports (McGregor,
Busa, Yaggie, & Bollt, 2009; Schütte et al., 2015) there is a need to validate this particular objective approach within team sports from a practical perspective to ensure the feedback is timely and could potentially be linked with additional subjective or objective markers.
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Executive Summary
This chapter examines the relevant research of movement variability and how its measurement can be related to the assessment and determination of recovery in athletic populations. This examines how variability can be examined with sufficient control of the data and what possible measures can be used to determine variability in the athletic population. Finally, it makes a recommendation for an appropriate measure, coefficient of mean determination, and sets up its use in future studies in this thesis.
4.0 Why movement variability?
Monitoring the external training dose and the internal training response helps determine when to apply the next stimulus (Halson, 2014). Having an objective measure of performance helps coach and practitioners prescribe precisely the next ‘dose’.
Commonly the response to a training dose is quantified via neuromuscular fatigue
(Cormack, Newton, McGuigan, & Cormie, 2008; Gathercole, Sporer, Stellingwerff, &
Sleivert, 2015; Johnston et al., 2012). Within a field setting for team sports this tends to favour explosive movements such as maximal effort jumps, sprints or push-ups rather than the typical gold standard lab based approach of nerve stimulation (Tofari, Opar,
Kemp, Billaut, & Cormack, 2016).
While these approaches may allow immediate alteration of the training session in question if a robust process is in place to identify non-responders or deviation from an athlete’s ‘normal’ in a timely manner all the tests are subject to motivation, the athletes
109 capacity which may be compromised due to injury and they place an additional load on athletes.
The use of the accelerometery data collected within the session may provide an avenue to examine the impact of training sessions without additional load being placed upon the athlete through an analysis of the gait variability.
4.1 Movement variability
Human gait is a complex phenomenon incorporating inputs from multiple systems in the body. The motor cortex, cerebellum, and the basal ganglia incorporates feedback from visual, vestibular and proprioceptive sensors to produce carefully controlled motor and muscle firings resulting in limb movements. In a healthy, non-fatigued state this system produces controlled motor commands and when working ‘correctly’ motor skills are extremely stable and consistent (Newell & Ranganathan, 2009).
The concept of ‘healthy’ variability in movement and sport in particular, whilst in its relative infancy, has reached a consensus that there is much we still don’t know (Bartlett,
2008; Bartlett, Wheat, & Robins, 2007). Movement variability exists even in highly trained skills performed by elite athletes, (Bartlett et al., 2007); it may be viewed positively or negatively. Negatively as an unwanted error that impedes the task outcome or variability in movement (Davids, Glazier, Araújo, & Bartlett, 2003; Stergiou & Decker, 2011a).
Alternatively movement variability may be positive in that it avoids exact repetition of the same action and hence overuse and subsequent injury (Hamill, Van Emmerik,
Heiderscheit, & Li, 1999). Theoretically, movement variability has been proposed to
110 represent an optimal bandwidth of variability for an individual within the overall movement universe (Stergiou, Harbourne, & Cavanaugh, 2006). Changes from the normal range are associated with pathology; either through being stiffer (i.e. reduced variability) or more unstable (i.e. increased variability). It has various sources that can be categorized as internal or external (figure 4.1).
Variability in gait
Internal External
Methodological Natural variation (i.e. run at a particular speed)
Environmental Aging (i.e weather, surface)
Situational Pathology (i.e. game conditions, opponents)
Figure 4.1: Sources of variability in gait measurements – adapted from Chau, Young, & Redekop (2005)
The internal sources of variability are typically inherent to health (be that neurological, metabolic or musculoskeletal) but could reflect acute physiological conditions such as fatigue (Komar, Seifert, & Thouvarecq, 2015). Fatigue is an inevitable and essential component of the training process, functions are limited by the fatigability of performance and perception interacting (section 2.0.2). In most high performance environments athletes are required to train or compete before they are fully free from fatigue (i.e. fully recovered from previous training or competition; Taylor, 2012). The
111 external stimuli relate more to variables or constraints in the sporting environment such as the opponents, teammates and their interactions, weather, surface or footwear.
4.1.1 So what…using variability in a meaningful way
Within a clinical biomechanics context, the tendency is to compare individuals to the
“healthy” average or the normal physiological range. Within sports, however, we typically deal with outliers in performance relative to such norms. We should look to enhance their individual capabilities, be that in terms of injury reduction, wellness, performance, technique or consistency in results (Preatoni et al., 2013). Variability exhibited by an individual under the same task conditions is inherently normal – no movement will be completely identical if performed twice. The differences can be both within and between trials as well as between participants. The band-width of these differences reduces as expertise increases and operating within this band is inherent with increased motor control.
For example, Preatoni and colleagues found more variability in the hip angle of skilled race walkers compared to lower abilities and hypothesized that a less regular gait could reveal an adaptive ability to cope with the imposed yet unnatural knee position (Preatoni,
Ferrario, Donà, Hamill, & Rodano, 2010). Conversely, another study found the opposite and suggested that higher skilled athletes may be more at risk of a repetitive mechanical stress as they have a more defined technique that remains within tighter limits of variability (Cazzola, Pavei, & Preatoni, 2016). In basketball free-throw shooting was examined as a function of skill but decreased variability was not found with increased skill – though it was more consistent in nature (Button, 2003). This may reflect a U-
112 shaped relationship of variability in coordination and skill. Novices and experts have similar magnitudes of variability which is greater than intermediate participants, but experts have increased compensatory control which is more functional for performance.
The variability in novices reflects exploring the best option for performance (Robins,
Wheat, Irwin, & Bartlett, 2006).
Using motor control to evaluate recovery status, or undesirable training adaptations, is something that has only occurred sparingly in the literature thus far (J. T. Fuller et al.,
2017); with coaches tending to rely on their eye to detect deviations from a ‘smooth gait’
(Kiely, Pickering, & Collins, 2019). As discussed in 3.5.1, the ability to measure recovery without any intervention to the athlete whatsoever would be the ideal scenario and would separate the influence of their belief on recovery stages. Movement variability may well be a suitable tool for that process.
4.2 Wearables: accelerometers
A ‘wearable’ can generally be defined as an object that interacts with the human body
(Gemperle, Kasabach, Stivoric, Bauer, & Martin, 1998) by detecting and transmitting information concerning internal/external variables (Düking, Hotho, Holmberg, Fuss, &
Sperlich, 2016). In the literature the number of publications on wearable technology or accelerometry have grown exponentially (figure 4.1). This likely reflects the use of wearable technology in sport as a means to enhance performance and reduce injury
(Clermont, Benson, Osis, Kobsar, & Ferber, 2019). They can provide sophisticated and nuanced objective quantification and feedback on the actions within a sport. These can supplement the coaches ‘eyeometer’ (Cardinale, 2016) which can traditionally be used to
113 assess training progression but relies on domain expertise. A significant advantage of these devices is the ability to monitor athletes on-field. Laboratory testing comes with more control but associated limitations, while natural sporting environments are more ecologically valid for biomechanical measurements (Adesida, Papi, & McGregor, 2019), providing they meet reliability criteria.
2004: Exponential rise in publications
Figure 4.2: Publications in wearable technology & accelerometry over the years. Data from Pubmed search (October 2019) using terms Wearable Technology or accelerometer.
4.2.1 Accelerometers: movement & recovery
Accelerometers have now become valuable tools in analysing movement in the field in many situations (i.e. gait analysis and/or sport specific technical assessments). In recent years, various approaches have been implemented. Research has made reference to the detection of gait events and spatiotemporal characteristics (Gabbett, 2015; Sabatini,
Martelloni, Scapellato, & Cavallo, 2005) in both healthy (S. W. Lee, Mase, & Kogure, 2005)
114 and impaired (Trojaniello et al., 2014) groups. As well as distinguishing between different movement strategies (Y. S. Lee et al., 2015) and different age groups (Kobsar, Olson,
Paranjape, Hadjistavropoulos, & Barden, 2014). Increasingly, studies focus more on practical approaches with fewer or even single tri-axial accelerometers – typically mounted on the torso. This single unit has been shown to be reliable for temporal stride characteristics and asymmetries (J. B. Lee, Mellifont, & Burkett, 2010; J. B. Lee, Sutter,
Askew, & Burkett, 2010) and as valid as more complex infrared camera methods. Indeed, there was a strong agreement between both methods (r=0.91-0.96). Fixation of accelerometers at other locations can cause discomfort, come loose or are simply not practical in team sport settings when the MEMS device worn between the shoulder blades typically already contains one. Additionally, the attenuation of accelerations at the trunk mean there is lower chance of signal saturation.
4.2.2 Running economy
Running economy has been the subject of many studies (Frost, Bar-Or, Dowling, & Dyson,
2002; Krahenbuhl & Williams, 1992). While the metabolic aspects are well studied, little research has investigated the relationship between kinematic and kinetic parameters and running economy. Running induced fatigue can cause changes in the spatio-temporal and upper extremity kinematics of runners, and this has been recently shown to not correlate with the increase in oxygen cost during fatigued running (Sackey, Schütte, &
Venter, 2019). As is traditional this analysis occurred in a lab-based setting rather than in the field and utilized external systems rather than wearables. The same group have utilized wearables with a single trunk mounted accelerometer that has potential to transfer to more typical outdoor settings (Schütte, Sackey, Venter, & Vanwanseele, 2018).
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4.2.3 Gait
Conventional approaches to the analysis of gait parameters are now evolving to consider regularity statistics (measurements conducted to assess the variability of a measure) as a possible alternative to the detection of gait events and spatiotemporal characteristics that may improve our understanding of the regularity and complexity of running
(McGregor, Busa, Skufca, Yaggie, & Bollt, 2009; Parshad, Skufca, Bollt, McGregor, & Busa,
2011).
Typically, the accelerometers used in sports research are a secondary component within inertial measurement units that are primarily worn for load quantification via GPS and hence worn on the upper torso to maximize the quality of the GPS signal during training and competition. The amplitude of peak accelerations have been validated – demonstrating that when worn in accordance with the manufacturer’s instructions, 10Hz filtered data can assess peak accelerations (CV: 8.9%; Wundersitz, Gastin, Richter,
Robertson, & Netto, 2015). These units can also accurately identify temporal stride characteristics (contact time, r=0.96; flight time, r=0.68) and distinguish between strides on each side of the body as the single tri-axial accelerometer can look at all three axes relative to the anatomical body axes compared to a much more expensive and lab-based instrumented treadmill (Buchheit, Gray, & Morin, 2015).
Artificially constraining movement on one side by taping an ankle showed similar magnitudes of difference identified by both accelerometry and instrumented treadmill
(contact time, 3.7% difference with accelerometer v 4.5% on treadmill). This capability of
116 accelerometers to detect contextual factors has been shown in a controlled laboratory setting. Extrapolating this to field-based assessments to provide stride characteristic information over the course of a season would potentially provide valuable insight for coaches and practitioners into their athletes’ physical state. The athlete will not generate kinematic/kinetic stride patterns that overlap perfectly but rather there will be a family of similar cyclical waveforms differing in magnitude and timing. For example, in the presence of fatigue a difference in the movement profile has been identified using a field based approach to an individual axes accelerometer load in a submaximal running test
(Garrett, Graham, et al., 2019).
4.2.4 Gait: best practice monitoring
Collecting gait data via inertial measurement units requires guidelines to establish a
‘typical’ gait pattern (Benson, Ahamed, Kobsar, & Ferber, 2019). Ensuring that a new trial altered existing ‘norms’ less than 5% researchers examined running in various conditions to establish the volume of runs needed from a waist mounted IMU. They found that 3-4 runs were necessary to define a stable running pattern looking at a univariate outcome.
Where possible multiple trials should be recorded for the subject (Preatoni, Ferrario, et al., 2010; Preatoni et al., 2013) to avoid erroneous outputs from a single trial. Within a team sport context this would suggest that any trend analysis needs a period to establish a baseline before assessing change. Within professional sports that train daily this is promising as variables of interest can be assessed quickly across the season and provide higher levels of ecological validity than collection outside of the specific sport, as well as limit potential Hawthorne effects when players know their gait is being monitored.
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Within team sports that are predominantly running based (i.e. the football codes), the ability to assess a number of athletes for fatigue or differences in the running pattern is becoming more commonplace (Cormack, Mooney, Morgan, & McGuigan, 2013; Garrett,
Graham, et al., 2019; Marrier, Le Meur, et al., 2017; Mooney, Cormack, OʼBrien, Morgan, &
McGuigan, 2013; Nagahara, Morin, & Koido, 2016). Analysing the cyclical waveform can be done by identifying distinct key points within waveforms (Buchheit et al., 2015) over an entire session to derive contact time and flight time. Alternatively, the variance of the entire waveform for an action (Dadashi, Millet, & Aminian, 2016) can be evaluated from the global mean, and a single variance variable can be compiled across the session.
Using one or both of these methods to analyse gait potentially provides a method of analysis that can be used in the field that could identify imperceptible waveform variations, whilst athletes go about their normal sporting activities.
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4.3 Gait-related variability & pathology
Figure 4.3: Average stride waveforms for an individual during high speed linear running, measured at the trunk by axis
Runners naturally self-optimize their running pattern to an optimum level of efficiency
(Moore, 2016). Deviations away from that pattern and outside the normal variability of the athlete can be used to indicate the relative ‘health’ of an athlete. This has previously been shown with the occurrence of patella-femoral pain (Hamill et al., 1999), though this study compared individual subjects only, running on a treadmill rather than over ground.
Research into ACL patients with or without a reconstruction showed stride variability differences between the injured and uninjured side (Moraiti, Stergiou, Ristanis, &
Georgoulis, 2007; Moraiti, Stergiou, Vasiliadis, Motsis, & Georgoulis, 2010). In the case of
ACL deficient patients there was less variability in the injured leg whereas in ACL reconstructions there was more variability on the injured side. In both cases the assessments happened on treadmill rather than over ground.
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For an athlete this reduced deficiency in the case of an ACL injury is important as it was measured on average at 33 months post injury. This reflects a decrease in the overall system’s complexity, which indicates narrowed functional responsiveness, which is to be expected in the presence of injury but perhaps not also on the healthy side. This may have implications for functional exercise choices particularly if the athletes do not pursue a surgical option as ACL deficient individuals compensate for the loss of ligament. The increased variability in the presence of repair suggests there has been an adaption of the system, not unexpected given the graft coming from the patient themselves, but the increase in variability is again a deviation away from normal and without sufficient gait retraining may reflect the large number of reinjuries that occur post ACL surgery.
While these assessments happened post injury it has been shown that ACL injury may manifest due to aggravation of antecedent tissue damage, likely caused by activities that induced excessive but sub catastrophic loads (J. Chen et al., 2019); such as pivot landings with forces in excess of three times body weight (Lipps, Wojtys, & Ashton-Miller,
2013). While it is tempting to speculate on whether the changes in variability are pre- cursors for injury this requires further investigation. Though if a rapid increase in jumping activities without sufficient recovery could cause ACL tissue damage to accumulate this would likely facilitate an adaptation at the system level which could lead to injury.
One perspective on this is that the ‘path to frailty’ is marked by a loss of variability (Lipsitz,
2002). A shift away from an athlete’s normal ‘healthy’ pattern of movement variability has been associated with a pathological state (Cortes, Onate, & Morrison, 2014; Hamill,
Palmer, & Van Emmerik, 2012; Heiderscheit, Hamill, & Van Emmerik, 2002; Stergiou &
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Decker, 2011a; Stergiou et al., 2006). This path is identified as a ‘loss of complexity’, characterized by reductions in effective degrees of freedom (i.e., interacting components in the control of the biological system) that become more regular in action and dissipate forces across smaller and potentially different areas until a threshold is crossed and injury occurs (Hamill et al., 2012), but this is highly likely to be influenced by training status, familiarity of the task and current physiological context such as the recent training background (see section 2.4). It may be that passing a critical threshold results in injury.
Indeed it is likely that extreme levels of variability (high & low) see the system functioning at a less than optimal level (Ducharme & Van Emmerik, 2018). To understand variability and compensations caused by fatigue in line with this hypothesis of ‘loss of complexity’ may help to assess recovery status in the presence of FOR or NFOR. This could then allow management of load and prevent progression towards overuse injury or OT as has been shown in distance runners (Schütte et al., 2015).
However, various confounders can affect variability of movement (figure 4.1). Variability can stabilize within certain ranges (James, 2004) and this may be dependent on the subject themselves and the task at hand. One example of a confounder is the speed of movement regardless of the natural gait (Jordan, Challis, Cusumano, & Newell, 2009).
The lowest values for variability of stride interval are found when moving at a self- selected pace demonstrating that variability (particularly for temporal stride characteristics) alters with speed. Similarly when discussing running economy the most commonly used reference velocity is 16 km.hr−1 (4.47 m.s−1), which represents a six minute mile, (3:44/km) – this is despite velocities from 12 to 21 km.hr−1 appearing in the literature (Barnes & Kilding, 2015). Interpolating the VO2 to a common value to compare
121 individuals is common however measuring at different speeds is problematic across populations (Daniels & Daniels, 1992). Running economy data for speeds slower than typical race paces, has shown that marathon runners are the most economical, but this reflects a speed used closest to their race pace. To be confident that there has been a change in variability across a period of time within an individual the features of the action being investigated must be tightly controlled for accurate longitudinal assessments and correct interpretation (i.e. the speed of locomotion must be similar).
4.4 Controlling constraints: identifying corresponding sections within sessions
Stride characteristics in sprinting (Bergamini et al., 2012), and distance running (Wixted,
Billing, & James, 2010) have been assessed outside the laboratory, although these data were not extracted from actual sessions or game situations. They have also been examined in team sport athletes (Jaskowak, Williams, & Tegarden, 2019; J. H. Williams,
Jaskowak, & Tegarden, 2019), but again this did not encompass data from the actual session, rather an additional task after the warm-up which adds to the volume of work and again brings in observation effects. A recent meta-analysis within team sports looking at changes in running, gait or stride characteristics found that most studies that did not involve in-game analysis included runs over 20-40m (Garrett, Gunn, et al., 2019).
The limitations of assessing outside the game are increased load and time demands on the player as well as a possible conscious change in technique. If field assessments are to be conducted within existing team sport sessions, periods of consistent high-speed locomotion without direct interference from opponents need to be identified to allow comparison of like for like periods. While this has previously been done with video (Faude,
Koch, & Meyer, 2012), GPS devices can accurately identify high-speed running periods.
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Utilizing this method to increase the ecological validity of any assessment in team sport athletes is compelling and possible given the miniaturization of recording equipment that has previously been used in discrete running tasks (Giandolini, Pavailler, Samozino,
Morin, & Horvais, 2015).
4.5 Measuring movement variability
Within gait analysis stride characteristics are an example of highly complex data that is analysed in a reductionist manner (Colyer, Evans, Cosker, & Salo, 2018). This approach offers less sensitivity than the variability of joint characteristics (Van Emmerik,
Wagenaar, Winogrodzka, & Wolters, 1999). Hence, the use of specific gait characteristics
(such as stance duration) requires a different variability analysis compared to metrics that interrogate the full waveform. Assessing variability across the entire waveform does require some data reduction to take place before an assessment of the variability can take place. Several indices exist to assess the similarity in gait analysis and the correlations between curves. Simple correlations and distances between points do not extend to curves.
Variability of stride characteristics have commonly been assessed by a number of measures (table 4.1), examples are standard deviations (Balasubramanian, Neptune, &
Kautz, 2009) and coefficient of variation – expressed directly or as a percentage,
(Heiderscheit et al., 2002; Hollman et al., 2010; Kadaba et al., 1989); similarly to coefficient of variation methods continuous relative phase (Hamill et al., 1999) and relative motion plots have also been utilised (Heiderscheit et al., 2002). A number of these similarity indices such as standard deviation, coefficient of variation, ICC, SEM or
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TEM have been used to quantify the data dispersion around a reference value at specific instants of the gait cycle (Di Marcoa et al., 2018).
Extracting a single measurement from this continuous variable discards a lot of potentially useful information (Queen, Gross, & Liu, 2006). Waveform variability has also been characterized as variability around a mean. Other indices exist to describe the whole within-stride variability to quantify the curve similarity across a gait cycle. These include root mean square deviation (RMSD), mean absolute variability (MAV), the linear fit method (LFM), and coefficient of multiple correlation (CMC) and the associated coefficient of multiple determination (CMD) (table 4.1). Additionally, measures defining the whole system (stance duration, step length or progression speed) in race walkers had a low coefficient of variation (CV; <3%), but technique parameters of movement execution (i.e. kinematic and kinetic variables) had a higher CV (>10%; Preatoni, 2007;
Preatoni, Santambrocio, Rodano, & Torre, 2010).
Within swimming the intra-stroke velocity variability around a mean stroke velocity has been examined (Dadashi et al., 2016). This method treats each waveform independently and averages the variability of each isolated waveform to describe the overall trial variability. Though this is an effective method to determine overall variability it does not discriminate between different mechanisms resulting in the same outcome. Two differing technical models could be employed that would not show as a difference in variability. For example, if the peak velocity is the same between strokes but occurs at different points this would not manifest as a difference in variability but likely would in terms of performance. Combining examinations of both waveform amplitude and
124 temporal characteristics will offer greater understanding of the overall movement consistency. Table 4.1 outlines possible metrics of measurement and their high-level advantages and disadvantages.
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Table 4.1: Linear and non-linear methods. Linear methods only investigate the magnitude of variability rather than the structure, hence ignoring the deterministic nature of the data Method Definition Pros Cons Used to calculate other measures Influenced by extreme scores Relevance of deviation from mean Average of deviations around mean (‘goal’) Standard deviation; dispersion of a Assumes random & independent SD group of values (square root of Majority of data within 1SD of mean variations between repetitions of a variance) task Can compare across individuals Needs reported relevant to mean Same units as original measure Don't consider temporal order Absolute variability measure
Normalizing to mean can be Coefficient of variation: statistical Quotient of SD and mean misleading if across groups means CV measure of data dispersion around differ greatly the mean. (SD/mean) Relative variability measure Don't consider temporal order
Greatest value minus the smallest in a Simple to understand – large range = Global measure – not nuanced data set. IQR is the central grouping large variability enough to be useful Range that contains the middle 50% of the IQR not affected by outliers Don't consider temporal order data.
Intraclass correlation coefficient; Dimensionless No agreement on thresholds proportion of variance due to the ICC time-to-time variability in the total Calculate from single session Don't consider temporal order variance
Standard error of mean; standard Descriptive of random sampling Usually don’t compare to population SEM deviation divided by square root of process for individual athletes number of observations Error from population mean Don't consider temporal order
Not directly relatable to measured Root Mean Square Deviation; square Larger errors have disproportionately values RMSD root of mean square error large effect Can't compare across data sets Single measure only
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Method Definition Pros Cons
Compare segments of body Only applicable to sinusoidal data Comprehensive indication of Only use on certain frequencies of Continuous Relative Phase; difference coordination and variability at each segment (i.e. 1:1) CRP between respective phase angles of point in cycle segments being analysed Needs normalized displacements and velocities of segments before calculating
Assumes linear relationship between Need to normalize if not a linear segments/joints (not synchronous relationship movement) Evaluate waveform similarity through Uses time lag to find high May be unsuitable if normalization Autocorrelation sum of variables multiplied by next correlations doesn’t produce linear relationship stride sum of variables Need to determine phase lag or phase lead for lag Only one measure per movement cycle
Approximate Entropy; probability that the configuration of one segment of Dimensionless Sensitive to time series length ApEn data in a time series will allow the Deterministic & Stochastic System prediction of another a certain Self-matches distance apart application
Useful but doesn’t characterize Complexity Index Sample Entropy; refinement of variability alone SampEn Approximate Entropy - quantifies the Inverted-U-relationship with chaotic irregularity of a sequence of numbers temporal variations in steady state Sensitive to choice of lag time
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Method Definition Pros Cons Control Entropy; derived from sample entropy - entropy of the differences Calculates entropy of differences CE between neighbourhood values, as Handles non-stationary data between signals rather than signals opposed to the absolute values themselves themselves.
Detrended Fluctuation Analysis; Relies on line fitting - value does not scaling analysis method used to Can detect correlation in seemingly DFA necessarily imply a similar time quantify long-range power-law non-stationary data series correlations in signals
Needs a validation method because Detection of chaos in signal the presence of determinism within a Lyapunov Exponent; divergence of time series LyE movement trajectories Outcome needs interpretation and can contradict due to analysing short trajectories only
Principal Components Analysis; Aid in the interpretation of the time Little benefit if variables not multivariate statistical technique used series dynamic variability correlated (i.e. waveforms) to know to what extent the variability Reduce the large number of non- PCA within a group of variables is related linear tools in factors according to between them and, therefore, allows the characteristics of the complexity to reduce the dimensionality of the and to establish the relationship dataset between them
Across rather than within athletes Functional Principal Components Truncates time series to adjust for fPCA Not for longitudinal monitoring Analysis undesirable variation in data Computationally expensive
Describe whole within stride Struggles with small ROM Coefficient of multiple determination; variability variance around mean at each time Doesn’t account for between CMD Dimensionless point in a waveform divided by all individuals. points around the ground mean Global measure
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Method Definition Pros Cons
Direct comparison to individual Can over estimate curve similarity if Coefficient of multiple correlations; longitudinally in similar action sample rate is not considered CMC square root of CMD
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4.5.1 Entropy
The approach taken to calculate movement variability is dependent on the underlying metrics. Entropy has been recently suggested as an analytical technique that provides information regarding the degree of complexity of the system’s behaviour by indexing the regularity of patterns present in the dynamics of running movements (T. R. T. Lindsay,
2012).
This non-linear method looks at repetitions as repetitive cycles rather than similar but independent events. They examine the continuous pseudo-periodic time-series to evaluate the dynamics of changes between cycles (Preatoni et al., 2013). Entropy is particularly appropriate for sports movements where variability is likely to have an externally, randomly determined origin and may be affected by outliers.
Several approaches to entropy analysis exist. In 1983 an algorithm to estimate the
Kolmogorov–Sinai entropy from time series generated by chaotic systems was proposed
(Grassberger & Procaccia, 1983). To extend the formula to time series generated by stochastic processes approximate entropy (ApEn) was introduced in 1991 (Pincus,
1991). It can be applied to both deterministic and stochastic systems (Ramdani, Seigle,
Lagarde, Bouchara, & Bernard, 2009). In 2000, Richman and Moorman modified the ApEn method and proposed sample entropy (SampEn) to study time series of varying lengths
(Richman & Moorman, 2000). SampEn quantifies the irregularity of a sequence of numbers and hence naturally provides a complexity index.
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Despite the widespread use of ApEn in the biomechanics literature (Yentes et al., 2013) the algorithm is inconsistent as it is sensitive to the time series length. It is also inherently bias as it can compare vectors to themselves and so self-match (Costa, Goldberger, &
Peng, 2005). SampEn corrects for self-matching; again a large SampEn is indicative of complexity (Van Emmerik, Ducharme, Amado, & Hamill, 2016); though both ApEn and
SampEn are sensitive to over-sampling. Govindan and colleagues showed that the choice of the time delay (or lag time) has an effect on the SampEn value and that its use improved the characterization of system complexity (Govindan, Wilson, Eswaran, Lowery,
& Preißl, 2007).
SampEn has been able to distinguish skill level between athletes (Preatoni, 2007). A reduction in variability may indicate a loss of system complexity and has been associated with pathology. SampEn has also been used to examine postural control (Cortes et al.,
2014) and resistance training manipulations (Moras et al., 2018). Within resistance training it was shown that entropy could assess adaptation to exercises to determine when it is time to increase the complexity of the challenge or in the short term if fatigue is evident.
Another serious limitation to many regularity statistics is the requirement of stationarity
(Bollt, Skufca, & McGregor, 2008). ApEn and SampEn are not suitable for non-stationary data commonly analysed in the literature (i.e. gait). There is a desire to use non-linear approaches in non-stationary conditions; to meet this need researchers developed control entropy (CE; Bollt et al., 2008). CE allows the inference of how much ‘effort’ a dynamic system is making to control its signal. Bollt and colleagues argued that CE is
131 generally applicable to any signal, but it is well suited to continuous monitoring; in contrast to other commonly used regularity statistics that are typically applied to discrete samples.
Work from McGregor and colleagues reported for the first time, the regularity values of well-trained runners, suggesting this approach is a valid way to ascertain the control constraints during running in such a population (McGregor, Busa, Skufca, et al., 2009).
They utilized a treadmill at a fixed 1% gradient but the use of two-minute stages at each speed may mean that a steady state was not achieved compromising the interpretation of the CE values. In the same group of athletes the vertical axis was the only axis to show a significant effect for speed when expressed as an economy measure of accelerations
(g/km/h; McGregor, Busa, Yaggie, & Bollt, 2009). The trained runners showed a greater distribution of accelerations in the vertical axis perhaps displaying an increased stiffness than an age-matched untrained control group, though this remains speculative.
4.5.2 Entropy – running economy
Running economy has been the subject of many studies indicating that this parameter improves with age (Frost et al., 2002; Krahenbuhl & Williams, 1992). While the metabolic aspects are well studied, (Krahenbuhl & Williams, 1992) little research has investigated the relationship between kinematic and kinetic parameters and running economy.
One pilot study undertaken, examined adolescent endurance runners during an incremental sub maximal lab test up to the lactate threshold (4mmol/L). Two tri-axial accelerometers on the shank and lower back measured vertical, medio-lateral and
132 anterio-posterior axes to allow the calculation of sample entropy (Murray et al., 2017).
They found that vertical acceleration of the trunk had a strong relationship with oxygen uptake (r2=0.76, P<0.01). Schutte and colleagues reported that fatigue from running on a treadmill may result in a greater variability of horizontal trunk accelerations (Schütte et al., 2015). As a potential predictor of fatigue, entropy has value as any physiological change, acute across stages in this case, or chronic as in NFOR (Meur et al., 2013), can alter the magnitude and/or structure of a movement through changes in the acceleration pattern and hence alter the entropy.
One of the six participants in the adolescent endurance runners showed a decrease in
SampEn of mediolateral trunk accelerations as a determinant of higher submaximal oxygen uptake with increasing velocity. Though with the small sample size it is possible that both oxygen consumption and SampEn were similarly detecting correlations with intra-individual changes in running speed. Another study also showed lower sample entropy medio-lateral values were related with an increased cost of running gait (Schütte et al., 2018). This association is supported by dynamical systems perspective in that the interaction of person, task and environment results in poorer quality movement
(Bernstein, 1967); specifically reduced degrees of freedom for mediolateral trunk control.
4.6 Correlations
Autocorrelation procedures have been used previously to discriminate between populations (Moe-Nilssen & Helbostad, 2005) and describe variability within gait (Moe-
Nilssen & Helbostad, 2004). The unbiased autocorrelation method can evaluate waveform similarity through calculating the sum of variables in a time series (stride) and
133 multiplying these by variables from the next (i.e. shifted one phase or stride). The autocorrelation process is effective in appraising the repeatability of both stride characteristics and whole waveforms of sequential steps and has been demonstrated to be capable of correcting for different locomotive speeds (Moe-Nilssen & Helbostad,
2004).
Within running activities long range correlations in stride interval have been shown to be sensitive to acute and long term fatigue (J. T. Fuller et al., 2017; Meardon, Hamill, &
Derrick, 2011; Nakayama, Kudo, & Ohtsuki, 2010). The time it takes to complete each stride is known to be very stable at fixed running speeds with only small fluctuations about the mean (Jordan, Challis, & Newell, 2006). The minimum correlation comes at the preferred movement speed – showing that each stride is minimally influenced by those preceding it and hence gait at the preferred speed is more adaptable to perturbations
(Srinivasan & Mathiassen, 2012).
4.7 Detrended fluctuation analysis
Detrended fluctuation analysis analyses steady state activity, for the structure of gait variability, for a spatiotemporal variable. This permits detection of long-range correlations in seemingly non-stationary time series data (i.e. detection of stride interval variations at a given time and those occurring several hundred strides earlier that are statistically correlated). This process determines ‘boxes’ of equal length within the time series and determined a least-squares line of fit for each box. Then root-mean square fluctuation of the integrated and detrended timeseries is calculated (Peng, Havlin,
Stanley, & Goldberger, 1995). By using multiple spatiotemporal parameters derived using
134 continuous wavelet transformation, a comprehensive examination of gait variability can be carried out (J. H. Williams, Jaskowak, & Tegarden, 2018). Investigators can examine gait complexity as a function of speed (Jordan, Challis, & Newell, 2007b) and/or use speed as a covariate when comparing gait variability adjustments due to OT, injury or fatigue.
4.8 Lyapunov exponent
Non-linear approaches, based on research into postural control (Harbourne & Stergiou,
2003) assess gait waveforms via the Lyapunov Exponent (Stergiou et al., 2006). This represents the slope of the average logarithmic divergence of the trajectories from sequential trials in a three-dimensional space. This is especially effective for evaluating sequential cyclic waveforms in gait data as the position of the previous waveform influences subsequent waveform at the same point within the movement cycle. One example of this is a study of cross-country skiers who exhausted themselves by skiing at a constant speed and slope at 90% maximal oxygen uptake, showing there was a larger variability and randomness observed with fatigue as measured by the Lyapunov exponent (Cignetti, Schena, & Rouard, 2009). It can be argued that this is not representative of the task of cross-country (i.e. skiers do not encounter a fixed gradient and speed for the duration of their event). To relate this to performance would be better to assess the changes in variability across the event to reflect the gradual onset of fatigue encountered in competition rather than pre and post.
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4.9 Multivariate statistics
Principal components analysis (PCA) attempts to find a more parsimonious representation of the data using only a few variables. When transforming the data with
PCA the first few PCs generally capture most of the variance (i.e. information) in the observed data set, with the first PC accounting for most of the variance. PCA performs better when there is considerable redundancy in the data, whereas little benefit is derived if the variables are weakly correlated or uncorrelated with each other. With waveforms the drawback of this technique is that points in a continuous time series are not independent of each other as the analysis assumes. This makes it challenging to relate waveforms described by each PC to a specific subject in the population.
Functional data analysis (FDA) represents the entire sequence of a time series as a single entity and then applies multivariate data analysis techniques. A comprehensive review identified this as a growth technique (Ullah & Finch, 2013) with applications in rowing
(Warmenhoven et al., 2017), swimming (Sacilotto, Warmenhoven, Mason, Ball, & Clothier,
2015), running (N. Coffey, Harrison, Donoghue, & Hayes, 2011) and weightlifting (Kipp &
Harris, 2015) to date. Functional principal components analysis (fPCA) is the most common FDA technique and typically truncates the time series to adjust for undesirable variation in the data (Kipp & Harris, 2015; Warmenhoven et al., 2015). This approach is more applicable to compare across rather than within individuals and so is not suitable for monitoring athletes longitudinally. In addition it is computationally expensive with a need to fit data with a temporal normalization prior to analysis which can create artefacts if landmark registration is not employed (Warmenhoven et al., 2019).
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4.10 Co-efficient of multiple determination
The coefficient of multiple determination (CMD; Kadaba et al., 1989) and related coefficient of multiple correlation (CMC) have previously been used to analyse many forms of cyclic kinematic and kinetic data (Buckeridge, LeVangie, Stetter, Nigg, & Nigg,
2015; Farana, Irwin, Jandacka, Uchytil, & Mullineaux, 2015). They give a method of assessing a waveform’s repeatability, both within and between testing days. The CMD calculates the variance around the mean at each time point in a waveform divided by the variance of all points around the grand mean.
4.10.1 Benefits
Within gymnastics different trials of a skill were assessed for repeatability (Farana et al.,
2015). Within ice-hockey the variability of individual strides representing acceleration and constant speed phases within a continuous skate were analysed (Buckeridge et al.,
2015). Both studies used non-adjacent waveforms, either as there were different trials
(gymnastics) or they were comparing different phases (ice-hockey). These different approaches highlight the adaptability of this measure of repeatability.
It also considers the concurrent effects of differences in offset, correlation and gain. A benefit of this approach over the detrended fluctuation analysis, Lyapunov Exponent and autocorrelation procedures is that it does not require successive waveforms to be to determine the overall variability, and the similarity of a group of waveforms from different time points within, or even between, sessions can be evaluated (Buttfield, 2016). This is important when examining waveform variability in the field, where data and waveforms are extracted from sessions, rather than much of the previous research using artificially
137 constrained scenarios. In these circumstances, the adjusted co-efficient of multiple determination (and related adjusted co-efficient of multiple correlation, which is the square root of the co-efficient of multiple determination) appears to be the most appropriate method of analysing waveform variability (regarding both magnitude and temporal aspects of a waveform; Buttfield, 2016).
Another study demonstrated the value of using this statistical tool to evaluate accelerometry generated waveform repeatability. The authors compared angular acceleration of the shank and linear acceleration of the knee (measured via accelerometers) to motion capture data (Mayagoitia, Nene, & Veltink, 2002). Results showed high agreement and similarity between accelerometery and motion capture waveforms. What is not comparable are the equivalent costs of the methods; financial outlay to secure the equipment; computational time to process the measures; human capital to process the data are all costlier with the lab-based motion capture than accelerometry. There are also practical advantages of the accelerometry approach in that it can measure outside the lab in context specific situations with a high ecological validity.
4.10.2 Drawbacks
CMC or CMD were used in 35% of studies in a meta-analysis of gait studies with a focus on between-session repeatability and reliability (McGinley, Baker, Wolfe, & Morris, 2009).
However, concerns were raised about the aptness of the measure in assessing repeatability in gait kinematics and echoed in a recent paper comparing test-retest reliability of curves which favoured an integrated pointwise index (Pini, Markström, &
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Schelin, 2019). These concerns were principally due to the influence of joint range of motion on the CMC magnitude and the adoption of arbitrary values for determining acceptability. It was recommended that steps be taken to quantify the confidence in the measure – both by reporting measurement error in combination with the CMD and developing minimum levels of detectable change (MDC) or the smallest worthwhile change.
An individual study that simulated variability around gait data and investigated the effects on CMC took a similarly prudent view (Røislien, Skare, Opheim, & Rennie, 2012). They demonstrated some limitations in the use of CMC for curve similarity in kinematic gait data; different ranges of motion between compared joints, data reduction techniques that resulted in overestimations of waveform similarity due to the adjacent points within a gait cycle being highly correlated. They concluded that it is not an objective statistical measure for kinematic gait data and advise against its use as is. Others have sited situations with a small ROM which can give a negative value preventing calculation of
CMC (Chia & Sangeux, 2017; Sangeux, Passmore, Graham, & Tirosh, 2016); imputation may then falsely create a low variability measure.
4.10.3 Use case
Despite the outlined concerns there are several reasons why the CMD can still be an appropriate statistical tool. If comparisons are limited to longitudinal within-subject analyses of the same action the range of motion error argument is reduced. This can be reduced further still by focusing comparisons on only one of the x, y and z axes. In addition, if an individual’s longitudinal CMD is combined with an individual minimum
139 detectable change (Haley & Fragala-Pinkham, 2006), and hence analysed against him- or her-self rather than the group, the confidence in the measure can be further increased.
Regarding the data reduction techniques, the typical sample rate of accelerometers contained with GPS devices in the team sport environment is 100 Hz, which is low compared to sampling rates used in gait assessments (100 Hz - 500 Hz). The relatively low sample rate reduces the risk of overestimating curve similarity.
Previous research has shown extracted waveforms from normal training sessions could identify strides (Buchheit et al., 2015; Buttfield, 2016). In addition movement patterns can be identified to limit the analysis to common activities (namely straight line high speed running) and then extract entire waveforms of each stride (as opposed to reductionist metrics describing stride aspects) to examine their consistency across sessions
(Buttfield, 2016). Furthermore, as the data used is collected during normal sessions the athlete is less focused on maintaining correct technique and more focused on the tactical and technical implications of their actions, thereby maximizing the ecological validity. This also minimizes the impact on the athlete as no additional task is required for data acquisition.
4.11 Summary
Despite the benefits of implementation and ease of use of wearable devices, more studies monitoring gait and movement variability over extended periods, among large populations, and in natural environments are needed to analyse real-world gait patterns, and would facilitate stratified prospective, participant-specific investigations (Benson,
Clermont, Bosnjak, & Ferber, 2018).
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Having presented the different means of assessing variability, these are summarized in table 4.1. While none of the approaches are perfect either for their statistical method or reductionist techniques it appears that over constrained lab-based testing, as long as the following steps are taken utilizing the CMD approach from data already obtained on athletes in sessions for load management purposes could be a valid approach for measuring their recovery by looking at groups of associated waveforms.
To ensure sufficient data quality the following criteria should be met:
(1) athletes should be compared only to themselves overtime;
(2) this comparison should occur separately for each axis of movement and
consider;
(3) the smallest worthwhile change for that individual doing;
(4) a similar task (i.e. uninterrupted high-speed running) within;
(5) an everyday training session where an athlete is less focused on maintenance of
form and more focused on the performance implications of their actions.
4.12 Aims & objectives
Throughout the following chapters an investigation of CMD will be undertaken with the following aims;
(1) Quality assuring the method: establishing the validity of CMD as a tool to measure recovery in situ
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(2) Investigating gait-related variability across differing team sports in relation to subjective and objective measures across their competitive seasons: can movement variability complement current recovery markers?
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Executive Summary
Building on the selection of an appropriate variable in chapter 4 this chapter examines the robustness of the measure in an applied context in a manner that will be reproduced throughout the remainder of the thesis. It examines the validity and reliability of the coefficient of mean determination in each axis in both treadmill and over ground running and establishes the confidence in the measure for the remaining studies.
5.0 Data collection/load monitoring in sports using MEMS
An increase in research-quality data collection in the field alongside the benefits of ecological research have blurred the lines separating ‘servicing’ of sports in the applied environment and lab-based research projects. Within team sport it is common to collect training load data via a MEMS device worn between the scapulae so as not to impede player movements. Around a third of all published research with similar devices utilizes a harness to attach the device between the scapulae (Camomilla, Bergamini, Fantozzi, &
Vannozzi, 2018). It is generally accepted that the centre of mass is the optimal anatomical location for MEMS devices, (Halsey, Shepard, & Wilson, 2011; McGregor,
Busa, Yaggie, et al., 2009) though there are often exceptions in the literature (Boyd, Ball,
& Aughey, 2013; Cormack et al., 2013; Cormack, Smith, Mooney, Young, & O’Brien, 2014;
Fudge et al., 2007; B. R. Scott, Lockie, Knight, Clark, & Janse de Jonge, 2013; T. J. Scott,
Black, Quinn, & Coutts, 2013).
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MEMS devices typically house GPS and tri-axial accelerometers alongside a magnetometer and gyroscope (Chambers, Gabbett, Cole, & Beard, 2015). As GPS data is collected frequently (typically 10 Hz) and inertial sensor data an order of magnitude greater (100 Hz), each session produces a significant amount of data. The use of manufacturer software can allow for efficient processing and analysis of workload data to manage player loads (Fox, Scanlan, & Stanton, 2017). The daily collection of athlete data from sessions allows management of variables, assuming the data collection is controlled and precise, for both individuals and groups to maximize performance and minimize the risk of negative adaptations (section 4.1; Halson, Hahn, & Coutts, 2019).
However, there can be issues of reliability and/or validity if going beyond the simple metrics such as total distance, average speed or overall player load to obtain information that can affect training programs. While there is potential to combine information from one or more sensors (Buchheit & Simpson, 2017), different manufacturers use their own proprietary algorithms to smooth data before calculating any metrics and some utilize different definitions for actions or use different thresholds across technologies in the same device. Also, the different collection frequencies may cause problems if combining accelerometry and GPS.
In an attempt to make informed decisions on readiness to train, practitioners seek to demonstrate or quantify fatigue (McCall et al., 2014). Whilst the reliability and validity of
GPS units have been extensively established in the literature for velocity (CV: 3.1-8.3%, r=0.94-0.97) and distance (CV: 1.3-11.5%, no significant difference to criterion; Boyd, Ball,
& Aughey, 2011; Jennings, Cormack, Coutts, Boyd, & Aughey, 2010; Varley, Fairweather,
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& Aughey, 2012) there have been limited investigations of the torso worn accelerometer reliability (Rantalainen, Gastin, Spangler, & Wundersitz, 2018; Wundersitz et al., 2015).
5.1 Data collection/load monitoring in sports using accelerometers
Accelerometers have readily been employed in a number of team sports; Australian Rules
Football (Boyd et al., 2013; Cormack et al., 2013; Mooney et al., 2013; T. J. Scott et al.,
2013), netball (Cormack et al., 2014), basketball (Montgomery, Pyne, & Minahan, 2010), and soccer (Barrett, Midgley, Reeves, et al., 2016; Barrett, Midgley, Towlson, et al., 2016).
The conclusions are that accelerometer data is valid for assessing peak accelerations around gait and the use of triaxial accelerometers, has demonstrated an ability to detect fatigue post exercise (Cormack et al., 2013) but only at a gross level, noting a reduced contribution to the vertical loading vector in the presence of fatigue.
Fatigue has been shown to result in an altered gait strategy during running (Patterson,
McGrath, & Caulfield, 2011; Schütte et al., 2015), which in turn has been associated with increased injury risk (De Ste Croix, Priestley, Lloyd, & Oliver, 2015). A meta-analysis highlighted changes in running strategy from fatigue as indicated by how accelerometer load was accumulated (Garrett, Gunn, et al., 2019). Cormack and colleagues (2013) studied fatigue in AFL athletes and attributed the reduced vertical load to changes in leg extensors force dampening and generating capacity, which could potentially modify injury risk (Barrett, Midgley, Reeves, et al., 2016). The result of an altered running strategy is typically determined by a reduction in the vertical acceleration of the centre of mass
(CoM) during running (C. R. Lee & Farley, 1998), which has previously been detected by an integrated accelerometer worn between the scapulae (Barrett, Midgley, & Lovell, 2014)
145 during competitive soccer matches and in a repeated sprint protocol (Akenhead,
Marques, & Paul, 2017).
5.2 Field based monitoring
Previously the incorporated accelerometers in MEMS units have been shown to identify temporal stride characteristics accurately (section 4.1 & 4.2; Buchheit, Gray, & Morin,
2015), thus confirming the ability of these types of MEMS units to identify small but practically important differences in stride characteristics. Examining the subsequent waveforms of trunk accelerations and their variability within gait has been shown to be valid as a measure of stride characteristics within the lab setting.
This is an important avenue to consider as monitoring of fatigue in the field from associated performance tests such as sprint time showed marginal differences that did not reflect substantial differences in the underlying mechanics (Garrett, Gunn, et al.,
2019). Moving the analysis to the field needs to be cognizant of some factors that can imply a loss of experimental control. For an athlete in motion the magnitude of ground reaction forces multiplies as a function of speed (Brughelli, Cronin, & Chaouachi, 2011), so it is expected that moving at higher velocities (i.e. running) will exacerbate any between-stride variability effects. Furthermore, as speed increases beyond an individual’s preferred running speed, variability in several gait variables have been shown to increase (Jordan et al., 2009) this implies a need to limit the range for comparison.
Therefore, any analytical approach needs to account for this and not compromise the practical application by creating overly restrictive rules that reduce the length and total number of sections included. One extreme is to compare case study examples from data
146 collected in situ (J. H. Williams, Rizzuto, Jaskowak, & Eaton, 2019) but longitudinal monitoring of comparable data sets is of course more powerful for a practitioner and/or researcher.
5.3 Aim
A myriad of outcome measures have been used by both practitioners and researchers to try and monitor fatigue on a regular basis, with varying sensitivity. Reliability refers to an acceptable level of consistency between repeated tests within a practically relevant timeframe (Atkinson & Nevill, 1998). Poor reliability would suggest an inability to detect a true change in the measure (Hopkins, 2000). Therefore, the aims of this initial methodological study were to establish the reproducibility of the waveform analysis tool and then to detect any differences when moving at a regulated speed on a treadmill or over ground.
5.4 Methods
This study consisted of a repeated measure, randomized counter-balanced design over
3 experimental sessions, over 3 consecutive days, in which subjects completed 9 straight line runs and 3 bouts of running on a treadmill. Participants (n=6; 30.2±4.8 yrs, 93.8±8.3 kg, 1.79±0.08 m) had a high current level of training (at least 4 training sessions per week) and were current or former team sports athletes (soccer & rugby) from national to international levels. The speeds (12, 14 & 16 km/h) were balanced across subjects and randomized between days. These absolute speeds were chosen as they surround thresholds for high speed running in various team sports (Dubois et al., 2017; Dwyer &
Gabbett, 2012). The straight line running occurred on a grass rugby field (100m) with
147 subjects guided by an audible beep to regulate their pacing at fixed points on the field.
Each run started from the dead-ball line with athletes accelerating to the goal line and maintaining a regulated speed to the opposite goal line. Three runs were completed at the initial speed before completing three at the second and subsequently three at the final pace. Runs were separated by 2 minutes passive recovery. The treadmill runs occurred in the same order as the outdoor runs that day with bouts of 4 minutes duration separated by 8 minutes recovery. Prior to the sessions a 10Hz GPS & 100Hz accelerometer device (Catapult S5, Optimeye, Australia) was positioned between the scapulae of the subjects in a manufacturers harness. The units had been switched on in the open for 15 minutes to obtain a signal lock and sufficient signal strength. Participants wore the same unit and harness for every session (Nicolella, Torres-Ronda, Saylor, &
Schelling, 2018). The study had ethical approval from the Moray House School of
Education, University of Edinburgh, Ethics Committee (appendices F & G) and the rights of the participants were protected.
For a full description of the analysis tool and its methodology refer to Buttfield, (2016).
The methods will be explained briefly here. The 100 Hz accelerometer data were exported from the manufacturers software and processed with a novel analysis tool developed specifically for identifying instances of high speed running and determining the variability of the remaining waveforms via a within-section and between-section CMD. The algorithm for calculating CMD can be applied in two ways, one where the variance about the mean within test days is evaluated, the other where variance about the mean over all test days is evaluated (Kadaba et al., 1989). In the current study, each identified straight line high-speed (SLHS) running section is treated as a separate ‘test day’. This effectively
148 means that there are two CMD measures calculated, one where variance around the mean within SLHS sections is evaluated (referred to in this thesis as within section CMD) and one where variance around the mean between SLHS sections is evaluated (referred to as between-section CMD).
For the accelerometry data where it is concerned with stride variability specifically; firstly, the analysis should reduce the data examined by establishing an algorithm to identify matched sections of running. This is also necessary as using closely matched sections will minimize the stride variability due to contextual activity demands. The criteria used to identify matched sections utilized GPS data, by examining direction and velocity.
Once matched sections have been identified, individual steps are extracted from the accelerometer data of these sections. This procedure includes step identification, classification (by side), filtering (outlier removal), normalization and matching (equal number of steps per side per section). After extracting the step by step waveforms from the accelerometer data these are quantified statistically via CMD. It was chosen because it examines the entire waveform rather than specific points such as at foot strike or toe- off (see section 4.9.1).
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Figure 5.1: Subsection of treadmill running highlighting extracted wave forms as vector sum and individual accelerometry components
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Throughout all analyses:
• x axis accelerations were anterior/posterior, with positive accelerations in the
anterior direction (which corresponds to the “forwards” accelerations in the
manufacturers software).
• y axis accelerations were medial/lateral, with positive accelerations being
towards the athletes left (which corresponds to the “sideways” accelerations in
the manufacturers software).
• z axis accelerations were vertical with positive accelerations being towards the
athlete’s head (which corresponds to the “up” accelerations in the manufacturers
software).
5.4.1 Dealing with outliers
Stride characteristics naturally vary within gameplay. To identify segments where an individual’s stride characteristics have varied from ‘normal’, the selection of sections to be analysed must represent similar activities. Strides should be taken from similar situations to minimize the natural variability and maximize variability due to factors such as fatigue and injury. By identifying straight-line high-speed (SLHS) running sections, the influence of the natural variation in stride characteristics due to contextual factors can be minimized.
Within outdoor runs and utilizing GPS information, latitude and longitude positional data were examined point by point to identify periods of straight line running using the procedure outlined in figure 5.2.
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Figure 5.2: Calculation of straight-line sections. As a player moves in a game he is continuously monitored via GPS. Briefly, the instantaneous bearing was calculated using the latitude and longitude between the ‘start’ and ‘end’ (1.5s apart). Δbearing was calculated by determining the difference between ‘start’ and ‘end’ points (i.e. each 1.5s). If the average Δbearing during the subsequent 5 s was between ±0.05 rad then that point was a valid straight-line running point.
To be considered a valid SLHS section, both direction and speed rules need to be satisfied for at least 5 s. These variables were selected to determine straight line running because they allow some flexibility; permitting small deviations from a perfectly straight line to incorporate GPS measurement error and minimal deviations in direction during
SLHS running.
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Within the accelerometry analysis (for all treadmill sections and select occasions for outdoor runs), a step frequency of 2.75 steps per second for at least five seconds was chosen as the lower limit for high speed running. This step frequency was chosen after pilot testing (with the aim of achieving a similar number of step waveforms available for further analysis as was achieved in previous applications of the analysis tool; Buttfield,
2016) and is in general agreement with previous research (Neville, Rowlands, Lee, &
James, 2016).
Consequently, in both cases, the step waveforms likely to have been influenced by
‘gameplay’ demands were identified as steps where the mean vertical acceleration in the first 20% of the step was at least 2 standard deviations greater or less than the mean vertical acceleration for the first 20% of all steps in that session – these were eliminated from the analysis. The two standard deviation threshold was adopted to limit the chance of discarding waveforms generated from ‘normal’ running as these generally do not show much variation in the first 20% of the waveform, and a two standard deviation rule will only remove the most extreme cases.
Post completion of variability analysis the sessions were analysed in multiple ways to assess the validity and reliability of the tool. Despite there being no criterion measure it was assumed that constant speed running on the treadmill would fulfil the ‘criterion’ role whilst over ground running at a ‘constant’ pace would be the reference. As such the conditions were:
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A. Validity: Treadmill v Outdoor Run – blocks of activity at each pace and same
temporal location were analysed to assess the validity of activity with the
assumption that strides would be similar based on pace despite a different bout
of running (Figure 5.3 – red squares)
B. Reliability: Within modality across speeds – discrete runs (1-9) and discrete
minutes of activity on the treadmill were analysed (3 minutes, from 30s after
onset) to assess if the efforts were reproducible. (Figure 5.3– yellow, green and
purple squares highlight individual outdoor runs and minutes of activity)
C. Reliability: Within modality across bouts and speeds – discrete blocks of runs (3
total) at each speed and discrete blocks of activity on the treadmill were analysed
to assess if the efforts were reproducible. (Figure 5.3– purple boxes)
D. Reliability: Analysis tool methods – outdoor run data was assessed in blocks of
three to see if the GPS method gives similar results as the accelerometer method.
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Figure 5.3: Schematic of analysis of acceleration data. Data from the nine outdoor runs and three treadmill runs is shown in its entirety to represent the analysis. Subjects followed a randomized order of allocation over three consecutive days. The order was the same for both treadmill and outdoor. A: Compares treadmill to blocks of outdoor runs for validity. B: Compares within modalities across speeds (i.e. treadmill v treadmill or outdoor v outdoor) for reliability. C: Compares within modalities across bouts of activities and speeds for reliability. D: (not shown on diagram) Outdoor run data was analysed in blocks of 3 using both accelerometry and GPS data for reliability.
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5.4.2 Statistical analysis
The agreement between treadmill (criterion measure) and accelerometer-derived data was examined using a specifically-designed spreadsheet (Hopkins, 2000), which calculated the mean bias (90% confidence limits, CL) and the typical error of the estimate
(TEE, 90% CL) both in percentage and standardized units, and Pearson correlation coefficients (r, 90% CL) and Bland Altman limits of agreement (LoA). If the 90% CI overlapped small positive and negative values, the magnitude was deemed unclear; otherwise that magnitude was deemed to be the observed magnitude (Hopkins, Marshall,
Batterham, & Hanin, 2009). Threshold values for standardized differences of biases were
>0.2 (small), >0.6 (moderate), >1.2 (large) and >2 (very large) (Hopkins et al., 2009).
Threshold values for standardized differences of TEE were >0.1 (small), >0.3 (moderate),
>0.6 (large), >1 (very large) and >2 (extra-large) as standard deviations need doubled before the magnitudes are interpreted (T. B. Smith & Hopkins, 2011). Finally, the following criteria were adopted to interpret the magnitude of the correlation: ≤0.1 (trivial); >0.1-0.3
(small); >0.3-0.5 (moderate); >0.5-0.7 (large); >0.7-0.9 (very large); and >0.9-1.0 (almost perfect).
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5.5 Results
5.5.1 A: Validity: Treadmill v Outdoor Run
Table 5.1 shows the TEEraw, CV, Pearson Correlation, Bland-Altman limits of agreement and the mean bias between the treadmill and outdoor run data. For both vertical conditions (‘up’) the biases were small-moderate with low CV (1.4-2.4%). Within the
‘sideways’ condition the biases were small or trivial but had much wider confidence intervals – reflected by the large CV’s (37.9-71.4%). In the ‘forwards’ axis the bias was moderate in all cases with CV’s ranging from 5.2-8.9%.
While the raw TEE was measured as the values range from 0-1 these can be equated to
% by multiplying by 100. Hence most values were around or under a 5% error with the exception of the sideways axis.
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Table 5.1: Treadmill v outdoor run variability for each speed and condition. TEEraw=Typical Error of estimate. CV=TEE as a CV%. r=Pearson correlation. LOA=95% Bland-Altman limits of agreement. Effect sizes for standardized variables $Trivial, *Small, ^Large, !Very large, !!Extremely large
Condition Speed TEEraw CV r LOA Bias 12 0.02 (0.01, 0.02)! 1.7 (1.4, 2.3) 0.46 ± 0.26 0.03 -0.9% ± 0.6%*
14 0.01 (0.01, 0.02)^ 1.4 (1.1, 1.8) 0.73 ± 0.17! 0.03 -1.3% ± 0.5% CMDwthn-vert Up 16 0.02 (0.01, 0.02)!! 1.9 (1.6, 2.5) 0.34 ± 0.28 0.05 -1.8% ± 0.9% All 0.02 (0.01, 0.02)! 1.8 (1.6, 2.0) 0.51 ± 0.14^ 0.04 -1.3% ± 0.4% 12 0.14 (0.12, 0.18)! 42.3 (33.5, 58.2) 0.78 ± 0.13! 0.28 -9.4% ± 15.8%* 14 0.14 (0.11, 0.18)^ 37.9 (29.8, 52.7) 0.82 ± 0.12! 0.26 -0.1% ± 12.8%$ CMDwthn-side 16 0.16 (0.13, 0.21)^ 45.7 (35.9, 63.6) 0.75 ± 0.15! 0.31 -1.2% ± 15.7%$
Sideways All 0.14 (0.13, 0.17)^ 44.1 (38.2, 52.3) 0.78 ± 0.07! 0.28 -3.9% ± 8.4%$
12 0.04 (0.04, 0.06)^ 5.6 (4.6, 7.4) 0.78 ± 0.13! 0.12 -6.6% ± 2.5% 14 0.04 (0.03, 0.05)^ 5.2 (4.2, 7.0) 0.81 ± 0.12! 0.12 -6.7% ± 3.0% CMDwthn-fwd 16 0.05 (0.04, 0.06)^ 6.3 (5.2, 8.3) 0.75 ± 0.15! 0.13 -6.3% ± 2.8%
Forwards All 0.04 (0.04, 0.05)^ 5.7 (5.0, 6.5) 0.78 ± 0.07! 0.12 -6.5% ± 1.5% 12 0.01 (0.01, 0.02)! 1.4 (1.1, 1.8) 0.48 ± 0.26 0.03 -1.7% ± 0.5%*
14 0.02 (0.02, 0.02)! 2.1 (1.7, 2.7) 0.69 ± 0.18^ 0.04 -0.6% ± 0.7%* CMDbtwn-vert Up 16 0.02 (0.02, 0.03)! 2.4 (2.0, 3.2) 0.60 ± 0.21^ 0.06 -1.7% ± 1.1%* All 0.02 (0.02, 0.02)! 2.1 (1.8, 2.4) 0.66 ± 0.11^ 0.04 -1.0% ± 0.5%* 12 0.16 (0.13, 0.22)! 60.3 (46.2, 87.8) 0.74 ± 0.17! 0.32 -5.2% ± 21.1%$ 14 0.15 (0.12, 0.20)^ 50.3 (39.2, 71.0) 0.80 ± 0.13! 0.29 -2.6% ± 21.9%$ CMDbtwn-side 16 0.20 (0.17, 0.27)! 71.4 (55.1, 102.3) 0.60 ± 0.22^ 0.40 13.9% ± 26.2%*
Sideways All 0.17 (0.15, 0.20)! 60.3 (51.7, 72.7) 0.70 ± 0.10! 0.34 1.7% ± 11.9%$ 12 0.04 (0.03, 0.05)^ 4.6 (3.7, 6.1) 0.81 ± 0.12! 0.14 -8.6% ± 3.2% 14 0.05 (0.04, 0.07)! 7.1 (5.7, 9.4) 0.69 ± 0.18^ 0.14 -5.4% ± 3.4% CMDbtwn-fwd 16 0.06 (0.05, 0.08)! 8.9 (7.2, 11.7) 0.54 ± 0.23^ 0.19 -7.4% ± 4.5%
Forwards All 0.05 (0.05, 0.06)! 7.2 (6.3, 8.3) 0.66 ± 0.11^ 0.16 -7.2% ± 2.1%
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5.5.2 B: Reliability: Within modality across speeds – individual outdoor run & minutes
The measures of reliability are shown for outdoor runs in table 5.2 and the treadmill section in table 5.3. The vertical axis consistently showed the lowest CV and the medial- lateral axis showed the highest. TEE is moderate to large in all cases. The SWC ranges from 0.69% in the vertical axis for outdoor runs to 14.26% in the between metric. For the treadmill the range is 0.43% in the CMDwthn-vert to 29% in the CMDbtwn-side. The correlations between outdoor runs and between minutes on the treadmill are large to very large.
Table 5.2: Reliability metrics across all individual outdoor runs and all speeds. Effect size = $Trivial, *Small, ^Large, !Very large, !!Extremely large
CV ICC r TEEstd
CMDwthn-vert 2.5 (2.3, 2.8) 0.45 (0.32, 0.60) 0.58^ 0.8 (0.69, 0.83)^ ! CMDwthn-side 26.3 (23.8, 29.4) 0.75 (0.65, 0.83) 0.73 0.5 (0.47, 0.57) (10.1, 12.3) (0.55, 0.77) ! (0.54, 0.66) CMDwthn-fwd 11.1 0.66 0.74 0.6
CMDbtwn-vert 3.9 (3.5, 4.3) 0.43 (0.30, 0.58) 0.62^ 0.8 (0.70, 0.84)^
CMDbtwn-side 48.4 (43.5, 55.1) 0.48 (0.34, 0.63) 0.59^ 0.7 (0.67, 0.82)^
CMDbtwn-fwd 14 (12.7, 15.6) 0.63 (0.51, 0.75) 0.69^ 0.6 (0.57, 0.68)^
Table 5.3: Reliability metrics across all minutes on treadmill. Effect size = $Trivial, *Small, ^Large, !Very large, !!Extremely large
CV ICC r TEEstd
! CMDwthn-vert 1.0 (0.9, 1.3) 0.78 (0.65, 0.87) 0.83 0.5 (0.41, 0.60) ! CMDwthn-side 33 (27.6, 42.7) 0.84 (0.74, 0.91) 0.89 0.4 (0.35, 0.51) (3.3, 4.7) (0.76, 0.91) ! (0.34, 0.49) CMDwthn-fwd 3.8 0.85 0.88 0.4
! CMDbtwn-vert 1.1 (0.9, 1.3) 0.87 (0.78, 0.92) 0.88 0.4 (0.32, 0.47) ^ CMDbtwn-side 36.6 (30.2, 47.1) 0.89 (0.81, 0.94) 0.90 0.4 (0.29, 0.43) ! CMDbtwn-fwd 3.8 (3.3, 4.8) 0.87 (0.78, 0.93) 0.87 0.4 (0.32, 0.46)
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5.5.3 C: Reliability: Within modality across bouts and speeds – blocks of outdoor runs v treadmill
The measures of reliability are shown for outdoor runs in table 5.4 and the treadmill section in table 5.5. The vertical axis consistently showed the lowest CV and the medial lateral axis showed the highest. There was a lower TEEstd and CV for the vertical axis than for looking at individual trials for outdoor runs. This was not consistent in the other axes.
TEE was moderate to large in all cases.
Table 5.4: Reliability metrics across all outdoor running bouts and all speeds. Effect size = $Trivial, *Small, ^Large, !Very large, !!Extremely large
CV ICC r TEEstd
CMDwthn-vert 1.5 (1.3, 1.9) 0.86 (0.78, 0.92) 0.62^ 0.4 (0.33, 0.47) ! CMDwthn-side 45.9 (37.8, 58.9) 0.64 (0.46, 0.78) 0.77 0.6 (0.52, 0.75)^ (7.8, 11.4) (0.52, 0.81) (0.49, 0.71) CMDwthn-fwd 9.1 0.68 0.53^ 0.6
! CMDbtwn-vert 1.7 (1.5, 2.1) 0.71 (0.55, 0.83) 0.71 0.6 (0.47, 0.68) ! CMDbtwn-side 22.1 (18.5, 28.5) 0.85 (0.74, 0.91) 0.71 0.4 (0.34, 0.51)
CMDbtwn-fwd 12.5 (10.6, 15.8) 0.39 (0.15, 0.60) 0.30 0.8 (0.68, 0.99)^
Table 5.5: Reliability metrics across all treadmill bouts and all speeds. Effect size = $Trivial, *Small, ^Large, !Very large, !!Extremely large
CV ICC r TEEstd
CMDwthn-vert 1.2 (1.0, 1.5) 0.64 (0.45, 0.77) 0.69^ 0.6 (0.52, 0.76)^ ! CMDwthn-side 29.7 (24.9, 38.0) 0.91 (0.86, 0.95) 0.74 0.3 (0.26, 0.37) (4.9, 7.2) (0.37, 0.72) (0.57, 0.82)^ CMDwthn-fwd 5.8 0.57 0.67^ 0.7
! CMDbtwn-vert 1.4 (1.2, 1.7) 0.76 (0.63, 0.86) 0.79 0.5 (0.42, 0.62)
CMDbtwn-side 38.9 (37.3, 50.3) 0.72 (0.56, 0.83) 0.63^ 0.5 (0.46, 0.67)
CMDbtwn-fwd 7.1 (6.0, 8.9) 0.45 (0.22, 0.64) 0.16 0.8 (0.64, 0.93)^
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5.5.4 D: Reliability: Analysis tool methods – GPS v accelerometer
The lowest CV was again in the vertical axis but there was high agreement between methods in all measures (r=0.74-0.94) despite identifying a different number of strides.
There were small TEE across the board for CMD measures.
Table 5.6: Analysis of outdoor running bouts with GPS and accelerometry data approaches. Effect size = $Trivial, *Small, ^Large, !Very Large, %Almost Perfect
CV ICC r TEEstd ! CMDwthn-vert 0.9 (0.8, 1.0) 0.88 (0.83, 0.92) 0.88 0.4 (0.31, 0.40) ! CMDwthn-side 23.5 (20.6, 27.6) 0.74 (0.64, 0.81) 0.74 0.5 (0.46, 0.60) (2.6, 3.4) (0.91, 0.96) % (0.22, 0.29) CMDwthn-fwd 3.0 0.94 0.95 0.3
% CMDbtwn-vert 0.9 (0.8, 1.0) 0.92 (0.89, 0.95) 0.92 0.3 (0.25, 0.33) % CMDbtwn-side 13.1 (11.4, 15.3) 0.93 (0.91, 0.95) 0.94 0.3 (0.23, 0.30) (5.4, 7.0) (0.76, 0.88) ! (0.37, 0.48) CMDbtwn-fwd 6.1 0.83 0.83 0.4
Number of Strides 91.9 (78.0, 112.2) 0.36 (0.19, 0.52) 0.36 0.8 (0.71, 0.92)^
5.6 Discussion
The aim of the present study was to establish the reproducibility of the waveform analysis tool and to detect any differences when moving at a regulated speed on a treadmill or over ground as well as define if different analysis methods were equivalent.
5.6.1 Validity & reliability
For the validity the biases were trivial or small in the majority of cases with large or very large correlations between the two conditions. Considering the practical benefits (field
161 based, outdoor use, no additional testing on a treadmill) and the fact that the assessed conditions were not from the same occurrence of running gives a new perspective for field-based monitoring within team sport athletes. That CMDs are not significantly different between conditions at similar speeds reinforces our conclusion that the errors associated with the changing conditions were minimal and suggests that a consistent method of assessment across sport and season would be valid provided the sport lends itself to the analysis. One consideration may be if the constraints of the sport lend themselves to periods of linear high speed running within the constraints of the analysis
(i.e. high speed and minimum time-based thresholds). For example, indoor team sports such as basketball or handball may struggle to have sufficient strides for analysis due to the dimensions of the court. Other team sports may see positional constraints (i.e. goalkeepers in soccer).
5.6.2 Conditions & method
It was shown that across repeated outdoor runs and within bouts of treadmill running there were large or very large correlations which show more variation whilst running outdoors than within bouts on the treadmill – this reflects partly the pacing aspect of the outdoor running and perhaps some variability in the overall pacing relative to achieving a steady state on the treadmill where the pace is externally controlled. Looking at bouts as a whole (i.e. 3 outdoor runs rather than 1), as would be the case in analysing gameplay improves the reliability of the tool as more discrete bouts of running and strides are identified. Finally, the ability of the tool to give similar results when analysing bouts of running activity, either utilizing the GPS data or solely the accelerometry data provides comparable results from sessions but identifies different numbers of strides.
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Conducting research in typical team sport settings requires the use of technology and accounts for self-selected running speed and enables analysis of a sufficient number of identified steps. While there are competing elements of compromise between ecologically validity and biomechanical accuracy assessing an athlete within an unsupervised and uncontrolled setting is the ideal (Schütte, 2017).
5.6.3 Treadmill v over ground
Treadmill running has limiting elements that are absent from over ground running. The imposed constant progression speed may require less flexibility in movement execution by its very nature; as it is more consistent the conditions are more constant and perturbations reduced (Alton, Baldey, Caplan, & Morrissey, 1998; Dingwell, Cusumano,
Cavanagh, & Sternad, 2001; Wank, Frick, & Schmidtbleicher, 1998); the tightly controlled speed, constrained dimensions, and straight path of a treadmill represent physical constraints that reduce the available degrees of freedom afforded the individual (Terrier
& Dériaz, 2011). It has been observed that stride time and stride length were greater under an over ground walking condition than when walking at the same speed on a treadmill
(Watt et al., 2010); treadmill running can exhibit more regular dynamics than over ground running (T. R. Lindsay, Noakes, & McGregor, 2014) which is more random in nature. The variability is thought to depend on the level of voluntary control and the available degrees of freedom (Van Orden, Kloos, & Wallot, 2011).
It may be that under such circumstances smaller bandwidth for joint coupling variation is most effective, whereas moving at a self-imposed speed in an open skill scenario
163 requires a wider variety of coordinative patterns (Preatoni, Ferrario, et al., 2010). It has been shown that under walking conditions the treadmill significantly increases the stability of the upper trunk reflecting an increased avoidance of falling, which can in turn change some spatiotemporal parameters. People tend to walk more cautiously, but ignore the coordination of their locomotor system on the treadmill; this will lead to an increase of stability for gait (Shi, Duan, Yang, & Sun, 2019). Differences in degrees of freedom and conscious control is something that this tool is intentionally trying to avoid through a non-invasive application of monitoring in regular tasks.
It has also been found that treadmill walking can cause the decrease of long-range correlation in stride intervals. Although stride time and fractal dynamics of stride intervals under the two conditions are different, CV of stride time is very similar (Terrier
& Dériaz, 2011). A treadmill may also draw the attention and consciousness to the task of locomotion (Frenkel-Toledo et al., 2005) – which may be reflected in the finding that walking on the treadmill can reduce centre of pressure path efficiency, (i.e. pressure transitions through the foot during the stance phase in a less direct way; Shi et al., 2019).
Taken together the findings for walking suggest that the environmental constraint contributes to changes in dynamics. The treadmill itself (environmental constraint) rather than the physiological stress (organismic constraint) is the major contribution to any changes in dynamics.
5.6.4 Speed
Stride dynamics may depend on the speed of movement, which is the most extensively studied task characteristic (Hausdorff et al., 2001; Jordan, Challis, & Newell, 2007a;
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Jordan & Newell, 2008). In this example, we analysed data from three speeds separately and as a whole. High-intensity treadmill running (at 80-120% preferred pace) has previously demonstrated increased regularity for stride time series (T. R. Lindsay et al.,
2014) and continuous acceleration data (McGregor, Busa, Skufca, et al., 2009).
Using absolute speeds may result in subjects running at different relative intensities.
Future investigations may assess and then use a percentage of peak treadmill velocity in both conditions, though within a sporting context it is the absolute speed of an individual that matters relative to their opponent rather than their own relative maximum.
5.6.5 Axis
The low CV and high correlations in the vertical axis regardless of the modality or the measure suggests this is the most sensitive axis to use for long-term tracking of athletes.
There may also be value in the anterior-posterior axis to identify changes. The sideways axis is too variable to provide reliable measures with large CV’s throughout. Kottner and colleagues made the case that reliability may be defined as variability between participants normalized to the total sample variability (i.e. judged as the ability of a measurement to separate between individuals; Kottner et al., 2011). Here we seek to examine individuals longitudinally and so should be able to distinguish between ‘states’ and it appears that the vertical and anterior-posterior axis give sufficient insight to do this.
The small nature of the absolute values may artificially inflate the consequence of any bias and TEE values expressed as a percentage. In the main, the vertical axis of the
165 accelerometer followed by the forward axis shows the lowest variability. The greater repeatability of accelerations in the vertical direction is at least partially due to the constant effect of gravitational acceleration and thus less subjected to system “noise”
(Slawinski & Billat, 2004) and is consistent with other research (Byun, Han, Kim, & Kim,
2016). The medial axis shows the highest, though this may be due to running being constrained on the treadmill and showing less deviation in the torso from side-to-side to allow the analyser to identify steps on each side – this may lead to less steps being selected for analysis.
5.6.6 CMD
Previous investigation of repeatability and reproducibility indices in gait analysis looked at root mean square deviation, mean absolute variability, coefficient of multiple correlation (CMC) and linear fit in relation to changes in range of motion, amplitude, offset, time shift and curve shape via a Fourier decomposition of lower limb kinematics
(Di Marcoa et al., 2018). CMC was insensitive to ROM but displayed small changes in response to amplitude. Within subject variations showed very good to excellent similarity for CMC; thus, CMC is sensitive to curve shape (i.e. in relation to this study – different planes should be analysed separately). The CMC was affected by large offset and time shifts and so the exclusion criteria in this study to look at only straight-line high-speed running are justified. In this case landmark registration was utilized as a method for domain alignment (Moudy, Richter, & Strike, 2018), using the foot strike, before assessing the reliability. The more flexible the alignment technique is, the less interpretable the results tend to be (Pini, Markström, & Schelin, 2019) but in this case we can be confident that curves were aligned.
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5.6.7 Statistical approaches
The underlying movement variability does not equate to measuring if an athlete has strayed from their 'normal' variability bandwidth. Both are needed for context and explanation; movement variability measures kinematics on a day whereas the latter measures variance from the mean measured over time.
There are arguments that for measures of reliability studies should assess relative reliability (the relation between two or more sets of repeated measures; based on correlation coefficients: ICC and Pearson’s r). ICCs rather than Pearson’s correlation coefficient assess the proximity of the data points to a straight line (with a gradient of 1 and passing through the origin), Pearson’s r assesses the proximity to any straight line, which is not the same as agreements between two measures (Holmbäck, Porter,
Downham, & Lexell, 1999). Thus, Pearson’s r could suggest high reliability in the presence of systematic bias.
Both ICCs and Pearson’s r are greatly affected by the range of the data set since both methods evaluate consistency of an individual within a group. Thus, if the group is homogenous on the measure of interest there is little mathematical basis for determining relativity and hence any correlation coefficients may be low. Therefore, measures of absolute reliability must also be included.
Absolute reliability measures can distinguish low retest reliability coefficients (ICC’s) caused by variability within subjects from low coefficients caused by narrow ranges of
167 values within the study sample (Domholdt, 2000). The ICC is dependent of sample size and largely affected by heterogeneity of the between-subject measures (J. P. Weir, 2005).
Moreover, ICC reflects the ability of a test to differentiate between individuals (i.e., relative reliability, (J. P. Weir, 2005). Typical error has been proposed as the most appropriate measure of absolute reliability in the applied field setting. Typical error represents the noise from trial-to-trial, which might confound the assessment of real changes in repeated measures (i.e., when monitoring changes in athletes) (Hopkins,
2000). Both absolute and relative reliability measures are presented here.
5.7 Summary
The validity and reliability of the analysis tool was assessed. It showed it can identify
SLHS sections and subsequently analyse these for variability across individual axes. The vertical and anterior axes show promising results for the long-term analysis of athlete across a season and if fatigue manifests as a reduced recovery in their gait-related stride variability across a season. The medial axis is too variable to monitor trends. The ability to assess variability from the embedded accelerometer in a regularly worn MEMS device creates minimal disruption for the athlete and potentially greater insight for the practitioner. Invisibly monitoring the athlete in an ecologically valid setting will be explored in the following chapters after an explanation of the common approach taken for each.
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Executive Summary
Given the establishment of the reliability and validity of the coefficient of mean determination, measured via trunk mounted accelerometers in two axes in Chapter 5, this
Chapter looks to establish the common methods and statistical approaches taken in each subsequent chapter of the thesis.
6.0 Variability in practice
Running is a motor skill pattern that has a ’healthy’ bandwidth of variability and is repeated almost daily in team sports practice and games. Indeed, individuals with patellofemoral knee pain have been shown to exhibit reduced movement variability compared to a healthy group (Hamill et al., 1999). Although subsequent studies have produced contradictory findings, (Heiderscheit et al., 2002) movement variability has been shown to increase in subjects affected by patellofemoral pain when their pain is reduced through a therapeutic intervention (Heiderscheit et al., 2002), suggesting there is an individual level of movement variability in gait and that variability is decreased when pain is present. Increased fatigue has been shown to lead to increased variability in knee kinematics during a cutting manoeuvre, which in turn will lead to a reduced ability to produce a controlled movement (Cortes et al., 2014).
In addition to health or injury status the greater explained variance of more skilled performance is considered to reflect a high level of coordination across a complex array
169 of degrees of freedom (Verrel, Pologe, Manselle, Lindenberger, & Woollacott, 2013). Less skilled performers may reduce movement complexity by freezing degrees of freedom, thus moving multiple segments as one rigid unit. For example expert cello players used a larger range of motion at the wrist and elbow joints but also exhibited more coordinated movement between these joints than novices (Verrel et al., 2013).
Consequently, the use of movement variability as a practical tool to identify when an individual has a non-optimal movement pattern, is entirely possible as long as the measurement tool has sufficient resolution to identify significant changes in an individual’s movement variability.
6.1 Monitoring variability in sport
Wireless accelerometry is a popular approach to continuously assess both proximal (e.g. trunk) and distal (e.g. tibial) mechanics in human locomotion unobtrusively. This approach is common in inertial measurement units that are used with athletes and are worn on the torso – typically incorporating accelerometers, global positioning systems
(GPS), magnetometers, and gyroscopes. Using this approach the magnitude of peak accelerations have been validated (Wundersitz et al., 2015) which demonstrates that data collected by a MinimaxX S4 (Catapult Sports, Australia) provides a reliable means of assessing peak accelerations (CV=8.9%). An alternate unit (SPI HPU, GPSports,
Canberra, Australia) has been shown to accurately identify temporal stride characteristics (contact time r=0.98; flight time r=0.68) when compared to an instrumented treadmill (Buchheit et al., 2015). Ankle movement was constrained through
170 taping and two of the three variables examined (contact time and vertical stiffness) correctly identified side-to-side differences in stride characteristics.
A study of trained runners found that with the onset of blood lactate accumulation there was an increase in vertical stiffness due to a decrease in their vertical oscillation whilst displaying minimal changes in maximal force production (Bitchell, McCarthy-Ryan,
Goom, & Moore, 2019). This may be tied to the increase in speed associated with the different phases which interacts with the stiffness and the adaptation of the individual to maintain the task. This may reflect more ‘time on task’ and hence an ability to modulate the activity within a small bandwidth. The findings correlate with those in adolescent endurance runners where an increase in the stiffness of the lower body, specifically in higher hopping frequency appears to improve longer distance running performance by improving running economy (Ryu & Murray, 2016). These findings confirm the ability of units incorporating GPS and accelerometers, to identify small but practically important differences in stride characteristics due to physical constraints within a laboratory setting. This is particularly useful to applied practitioners given the practical and economic aspects of accelerometer technology.
6.2 Fatigue changes
In the presence of fatigue during repetitive tasks, such as training, people may compensate by changing their movement patterns (McDonald, Tse, & Keir, 2016), typically by using fewer degrees of freedom to achieve the outcome (Côté, Mathieu,
Levin, & Feldman, 2002; Côté, Raymond, Mathieu, Feldman, & Levin, 2005). Changes in movement patterns suggest that subjects adopted a more rigid movement strategy in
171 the presence of fatigue in a timed ratcheting task (Cowley & Gates, 2017). Similar effects have been observed during throwing (Forestier & Nougier, 1998) and reaching (J. R.
Fuller, Fung, & Côté, 2011) in the presence of fatigue and are consistent with the idea of fewer degrees of freedom reducing task complexity (Verrel et al., 2013).
The modification method for inter-joint coordination reorganization, is difficult to address with traditional biomechanics analysis of an individual, that looks at variables at discrete time points (such as movement screening). Many studies have analysed differences only between trials performed before and after fatigue. This approach reduces fatigue to binary states (unfatigued or fatigued) and therefore cannot explain how people transition between movement patterns as fatigue moves along a continuum.
During simulated work, kinematic changes show cyclic variations associated with rest- work cycles (Qin, Lin, Faber, Buchholz, & Xu, 2014). It is clear from these studies that people continuously modify their movement patterns against the backdrop of fatigue.
Across a competitive season there are periods of fatigue and freshness in team sports in the rhythm of the season. Within the season the priority is recovery between games in preparation for the next fixture. Across the season the wear and tear can create degenerative changes in physical capacity. Sport science practitioners should ideally employ methods and technologies to identify and manage important risk factors for injury occurrence, as well as monitor fatigue and recovery.
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6.3 CMD measure
The coefficient of multiple determination (CMD) and related coefficient of multiple correlation (CMC) have previously been used to analyse many forms of cyclic kinematic and kinetic data that have ranged from an analysis of kinematic variability in gymnastics
(Farana, Jandacka, Uchytil, Zahradnik, & Irwin, 2013) to electromyographic, kinematic and kinetic measures of ice hockey skating (Buckeridge et al., 2015). Assessing the variability of waveforms has previously been done with gait data and been shown to be valid as a measure of stride characteristics via a single tri-axial accelerometer mounted on the upper torso (Buttfield, 2016). Such analysis examines the entire waveform rather than at specific points (i.e. foot strike or toe-off; section 4.9.1); therefore, identification of specific points within the gait cycle will be less influential on the analysis result. In addition, using CMD to determine waveform variability does not require the waveforms to be from a continuous time period. This is a crucial consideration when analysing data collected in gameplay and training rather than controlled laboratory settings.
As defined by Kadaba and colleagues (1989), the CMD within a test day is given by;
푀 푁 푇 2 ∑푖=1 ∑푗=1 ∑푡=1( 푌푖푗푡 − 푌̅푖푡) /푀푇(푁 − 1) 푅2 = 1 − 훼 푀 푁 푇 ̅ 2 ∑푖=1 ∑푗=1 ∑푡=1( 푌푖푗푡 − 푌푖) /푀(푁푇 − 1)
(1)
M refers to the total number of sections, N refers to the total waveform number in each section and T refers to the total duration of each waveform (a constant of 50 as data were normalised). 푌푖푗푡is the tth time point of the jth waveform in the ith section.
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푌̅푖푡 is the average at time point t in the ith section, where;
푁 1 푌̅ = ∑ 푌 푖푡 푁 푖푗푡 푗=1
(2)
푌̅푖is the grand mean in the ith section and is given by;
푁 푇 1 푌̅ = ∑ ∑ 푌 푖 푁푇 푖푗푡 푗=1 푡=1
(3)
In this approach for a section of straight-line high-speed running, T will be equal to the number of sections identified in the session (within-section CMD; the variability of waveforms within each section i.e. compare the shape of the waveforms to the other waveforms in the immediate area, within the same section). These numbers are then averaged to provide a single number describing waveform variability.
Between section CMD
푀 푁 푇 2 ∑푖=1 ∑푗=1 ∑푡=1( 푌푖푗푡 − 푌̅푡) /푇(푀푁 − 1) 푅2 = 1 − 훼 푀 푁 푇 ̅ 2 ∑푖=1 ∑푗=1 ∑푡=1( 푌푖푗푡 − 푌) /(푀푁푇 − 1)
(4)
푌̅푡is the average at time point t over NM waveforms;
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푀 푁 1 푌̅ = ∑ ∑ 푌 푡 푀푁 푖푗푡 푖=1 푗=1
(5)
푌̅ is the grand mean in the ith section and is given by;
푀 푁 푇 1 푌̅ = ∑ ∑ ∑ 푌 푀푁푇 푖푗푡 푖=1 푗=1 푡=1
(6)
The between-section CMD determines the difference in the mean shape of the waveforms between sections within a session. This gives information on how stride characteristics alter over the course of a session.
CMD returns values between 0 and 1. These can be stratified as (i) ‘poor association’ when 0≤CMD<0.42, (ii) ‘moderate association’ when 0.42≤CMD<0.56, (iii) ‘good association’ when 0.56≤CMD<0.72, (iv) ‘very good association’ when 0.72≤CMD<0.90; and (v) ‘excellent association’ when 0.90≤CMD≤1 (Garofalo et al., 2009).
6.4 Proposed Approach
It is common place to take accelerometer derived running loads into consideration for the management of athletes (Casals & Finch, 2017; Wellman, Coad, Flynn, Siam, &
McLellan, 2017). Such data are used alongside additional measures such as wellness questionnaires (Govus et al., 2018), movement screens (McCall et al., 2014) and other objective measures (Esmaeili et al., 2018) and athletes self-reporting muscle symptoms.
The following chapters aim to combine screening measures of subjective and objective
175 markers that have previously been tied to perception of recovery by athletes or practitioners in the field with a measure of gait-related variability obtained from the actual task. This approach will attempt to combine both physiological and biomechanical loads and their interaction (Vanrenterghem, Nedergaard, Robinson, &
Drust, 2017) by assessing the relationship between measures.
6.5 Standard methods
The research approach taken within the thesis will use the same MEMS device to collect stride information, but within particular chapters the sport of the participants differs.
These different sports necessitate a different approach based on their primary training location (i.e. indoors v outdoors); between utilizing the accelerometer or the GPS data to identify the strides.
6.5.1 Accelerometry (indoor sports)
The 100Hz accelerometer data were processed as outlined in section 5.1.1. Briefly the raw files were exported via the manufacturer’s software (Catapult Sports, Openfield software, version 1.11.1). A step frequency of 2.75 steps per second for at least five seconds was chosen as the lower limit for high speed running. Those more than 2 standard deviations away from the mean vertical acceleration for the first 20% of all steps in that session were eliminated from the analysis. Step waveforms were separated into left and right-side steps by examining the lateral accelerations, with steps displaying a negative to positive acceleration around foot strike being designated right side steps and vice versa. Foot strike events were identified to match strides after temporal normalization.
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The CMD was then calculated on the set of vertical (z-axis) and forward (x-axis) step waveforms to determine the variability of those waveforms (Kadaba et al., 1989). CMD values were calculated for each session for each player. They were combined over sections of high speed running during each session and CMD values were calculated from the within and between-stride variability, and then averaged over all sections of high speed running and turned into percentage of variation to improve interpretability. The data were therefore hierarchical in nature, with strides nested within sections within games within players. However, section-level data were unavailable for analysis. In all calculations, higher CMD scores indicate less waveform variability.
6.5.2 GPS (outdoors)
The data collected outdoors and used in these studies were GPS (measured at 10 Hz) and tri-axial accelerometer (measured at 100 Hz). The GPS identified specific sections of high-speed running based on the speed. It matched these with linear movements based on the latitude and longitude. All other processing steps were the same.
6.6 Statistical Analysis
All analyses were carried out using R v3.5 (R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna,
Austria URL https://www.R-project.org). Since repeated measures per player were available, linear mixed models were used to account separately for within-player and between-player variability in CMD values, while investigating their association with the additional markers on that day. A random intercept term for player was used to allow for
177 different average CMD values between athletes, while random slope terms allowed for different changes over time in CMD between players. A random effect for side of measurement was tested but led to convergence issues, hence it was included only as a fixed effect. To account for nonlinear changes in CMD over time, quadratic and cubic time terms were included as fixed and random effects. An AR1 process was included for within-subject variability to account for auto-regressive aspect of CMD during the period of measurement. In each model, we also controlled for the number of strides, number of sections and side of measurement (left/right leg) to account for confounding. The association between each marker of recovery with CMD are presented as coefficients with 95% confidence intervals and p-values. In this case a one-unit increase in the variable relates to an equivalent percentage change in CMD. Model residuals were checked to validate the assumptions underlying the linear mixed model. In order to compare between the markers, we took the z-score of each of these and repeated the analysis, with the resulting coefficients plotted showing the effect of a 1 standard deviation change in exposure (i.e. a one standard deviation change in the variable relates to an equivalent percentage change in CMD).
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Executive Summary
Using the approach outlined in Chapter 6 and the metrics validated in Chapter 5 this study examines a group of collegiate American football athletes over a domestic season.
The measures of wellness and external load are established and evaluated in relation to movement variability for athletes overall and in instances of compromised training.
7.0 Introduction
As referenced in chapters 5 & 6, the use of movement variability as a practical tool to identify when an individual has a less than optimal movement pattern, is entirely possible, as long as the chosen measurement tool has sufficient resolution to identify changes in an individual’s movement variability. Using wearable accelerometers housed in MEMS devices and CMD as the outcome measure of gait-related variability this study aimed to investigate if a deeper analysis of the accelerometry data can be used to explore relationships between gait-related variability and both objective (load) and subjective (wellness and soreness) markers to provide actionable insight into training and game induced disruptions.
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7.1 Methods
7.1.1 Participants
Data from 63 American Football athletes (age = 20.6±1.5 yrs; body mass = 102.4±20.1 kg; height = 186±7.7 cm) operating at the Division 1 level in the NCAA were collected across a regular season. Athletes provided informed consent to participate in data collection throughout the season as part of the athlete support process and the institutional ethics committee provided ethical approval for the research.
7.1.2 Design
This was a prospective cohort design using a convenience sample of American Football athletes. MEMS devices containing GPS and accelerometers (Optimeye S5, Catapult
Sports, Australia) were worn for every field session. The data collected and used in these studies were from the tri-axial accelerometer (measured at 100 Hz). For the purposes of this observational study, with repeated measures on the participants, only data from the main training sessions (Tuesday and Wednesday) and the match (Saturday) were recorded. This means light walk-through sessions on Sunday and Thursday were excluded, as were Friday sessions that were short and light in comparison to other sessions.
7.1.3 Methodology
7.1.3.1 Accelerometry
The approach taken to the analysis was that outlined for the accelerometry in chapter 6.
Briefly, matched sections of high-speed running were identified and then the variability of these waveforms was assessed using CMD. They were combined over sections of
180 high speed running during each game and CMD values were calculated from the within and between-stride variability, and then averaged over all sections of high speed running and turned into percentage of variation to improve ease of interpretation.
7.1.3.2 Wellness
Over the course of the season the athletes completed a wellness questionnaire on training days, as used previously in the literature (Govus et al., 2018). This recorded any areas of soreness as well as noting their fatigue, sleep quality and overall muscle soreness (1=poor, 5=good). As part of the wellness questionnaire athletes noted any specific locations of soreness and then rated these in term of severity (1-10). Any area greater than a 5 out of 10 for pain triggered a ‘flag’ to the practitioners working with the athletes. These flags are considered compromised training days in this study.
7.1.3.3 Load
Accelerometry determined daily workloads (PlayerloadTM) were calculated and expressed as arbitrary units (AU) via the manufacturer’s software (Catapult Sports,
Openfield software, version 1.11.1) for every session. Participants wore the same device during every training session and match (Nicolella et al., 2018). Rolling mean loads for acute and chronic periods were calculated before sub setting the data to the main training sessions and games. The acute period was defined as 7 days and the chronic as 21 in line with previous American Football research (Sampson et al., 2018) .
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7.2 Aim
This study aimed to investigate if the session accelerometery data can be used to establish relationships between gait-related variability measured at the trunk, objective player load, and subjective markers of wellness and soreness.
7.3 Statistical analysis
All analyses were carried out as outlined in chapter 6. The association with CMD, wellness (fatigue, sleep, soreness) and load (acute (7-day average), chronic (21-day average) and acute-chronic workload ratio) on that day were examined and presented as the effect of a one unit change in the variable on the CMD percentage.
In order to compare between the load and wellness markers, the z-score of each of these
(fatigue, sleep, soreness, ACWR, acute load, chronic load) was calculated and analysis repeated, with the resulting coefficients plotted showing the effect of a 1 standard deviation change in exposure.
A secondary analysis focused on compromised training. Here a generalized linear mixed model was used to model flagged injury status against non-flagged status for hamstring, ankle and foot injuries separately. The key variables under examination were within
(CMDwthn) and between stride CMD (CMDbtwn) measured during the flagged and non- flagged strides. In each model, the analysis included day of measurement as a fixed effect and used a random effect for athlete to allow each to have their own intercept. A logit link was used to model the three (hamstring, ankle and foot) binary outcomes
(injured v not injured), and odds ratios are reported alongside 95% confidence intervals
182 and p-values. Model residuals were again checked to validate the assumptions underlying the mixed model.
7.4 Results
Descriptive statistics for the key variables in the study are given in Table 7.1. All wellness variables had a mean of ~3, while acute (7-day) load was slightly higher than chronic (21- day) load. There were 4.9±5.8 (±SD) sections of high speed running per session across players on average, with a mean of 47.7±69.7 strides within a section. Figure 7.1 shows the nonlinear changes in CMD over the period of measurement, with similar patterns of change for CMDwthn and CMDbtwn.
Table 7.1: Descriptive statistics for the 63 American football athletes. *denotes median and interquartile range rather than mean and standard deviation (SD). AU=arbitrary units.
Variable Mean SD
CMDwthn-vert 0.824 0.085
CMDbtwn-vert 0.837 0.091
CMDwthn-fwd 0.620 0.236
CMDbtwn-fwd 0.677 0.182 Fatigue (AU) 3.22 0.75 Soreness (AU) 3.04 0.78 Sleep (AU) 3.06 0.78 Acute player load (7-day mean; AU) 418.81 112.33 Chronic player load (21-day mean; AU) 405.09 97.19 ACWR 1.03 0.15 Average games per player 9.23 2.93 Average sessions per player 21.80 5.64 Number of sections 3* 1-7* Number of strides within section 20* 5-59*
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Figure 7.1: Within- and between-stride CMD over the season for individuals, with group mean in red bold for vertical (A & B) and blue bold for forward (C & D) axes
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7.4.1 Wellness
There was some evidence for an inverse relationship between fatigue and between- stride CMD. A one-point increase in fatigue score (i.e. feeling better) being related to a
0.508% decrease in CMDbtwn-vert (increased variability; table 7.2; 95% CI -0.953, -0.063%, p=0.025) and 1.029% decrease in CMDbtwn-fwd (95% CI -0.137, -1.922%, p=0.024). There was no evidence for a relationship between sleep score and either CMDwthn-vert or CMDbtwn- vert (p=0.174 & 0.193 respectively). There was though an association with sleep and
CMDfwd. A one-point increase in sleep was related with a 1.126% (95% CI 0.400, 1.853%, p=0.002) & 1.002% (95% CI 0.172, 1.832%, p=0.018) increase in CMDwthn-fwd and CMDbtwn- fwd. Finally, there was evidence for a negative association between soreness and CMDvert.
A one-point increase in soreness score (i.e. less sore) was related to a 0.337% decrease in mean CMDwthn-vert (increased variability; table 2; 95% CI -0.670, -0.005%, p=0.047). The opposite was true for CMDwthn-fwd with a one-point increase in soreness score related to a 0.749% (95% CI 0.047, 1.451%, p=0.037) increase in CMDwthn-fwd.
ACWR had a negative effect on both CMDwthn-vert and CMDbtwn-vert, with a 1 unit increase in
ACWR associated with a 6.849% decrease in mean CMDwthn-vert (increased variability; 95%
CI -8.580, -5.117%, p<0.001) and a 7.257% decrease in mean CMDbtwn-vert (increased variability; 95% CI -9.355, -5.160%, p<0.001). The opposite was true with CMDwthn-fwd
(34.973%; 95% CI 31.412, 38.535%, p<0.001) and CMDbtwn-fwd (10.57%; 95% CI 6.284,
14.855%, p<0.001) increasing with ACWR. Acute load (7-day average) was also associated with within- and between stride variability. Inversely with vertical CMD and positively with forward CMD. A one unit increase in acute load was related to a 0.012%
185 decrease in mean CMDwthn-vert (increased variability; 95% CI -0.016, -0.009%, p<0.001) and a 0.013% decrease in mean CMDbtwn-vert (increased variability; 95% CI -0.017, -0.010%, p<0.001). A one-unit increase in acute load was related to a 0.068% (95% CI 0.060,
0.077%, p<0.001) and 0.008% (95% CI 0.000, 0.016%, p=0.037) increase in CMDwthn-fwd and CMDbtwn-fwd. Finally, an increase in chronic load (21-day average) was also inversely related to CMDwthn-vert and CMDbtwn-vert. A one unit increase in chronic was associated with a 0.007% decrease in mean CMDwthn-vert (increased variability; 95% CI -0.011, -0.002%, p=0.002) and a 0.005% decrease in mean CMDbtwn-vert (increased variability; 95% CI -
0.011, 0.000%, p=0.034). In the forward axis CMDwthn-fwd increased with chronic load
(0.019%; 95% CI 0.007, 0.031%, p=0.002) and CMDbtwn-fwd decreased between stride (-
0.015%; 95% CI -0.005, -0.025%, p=0.004).
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Table 7.2: Linear Mixed Model Outputs. Significant relationships marked as bold. Coefficients represent percentage change in CMD from a one unit change in the variable. +/- = 95% confidence interval of coefficient.
Raw Within Stride CMD Raw Between Stride CMD Raw Within Stride CMD Raw Between Stride CMD Outcome (vertical) (vertical) (forward) (forward) Exposure Coefficient +/- p-value Coefficient +/- p-value Coefficient +/- p-value Coefficient +/- p-value
Fatigue -0.039 0.377 0.837 -0.508 0.445 0.025 0.216 0.787 0.591 -1.029 0.455 0.024 Soreness -0.337 0.332 0.047 -0.356 0.395 0.078 0.749 0.702 0.037 0.748 0.407 0.066 Sleep -0.242 0.349 0.174 -0.274 0.412 0.193 1.126 0.727 0.002 1.002 0.424 0.018 ACWR -6.849 1.732 <0.001 -7.257 2.098 <0.001 34.973 3.561 <0.001 10.570 2.187 <0.001 Acute player load (7-day mean) -0.012 0.003 <0.001 -0.013 0.004 <0.001 0.068 0.008 <0.001 0.008 0.004 0.037 Chronic player load (21-day mean) -0.007 0.004 0.002 -0.005 0.005 0.034 0.019 0.012 0.002 -0.015 0.005 0.004
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Figure 7.2: Standardized (z-scored) effects of wellness and load on CMD. Coefficients show the effect of a 1 standard deviation change in exposure and allow comparisons across variables.
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7.4.2 Comparing load and wellness
In order to compare the standardized coefficients across load and wellness where each exposure variable has been z-scored with the resulting coefficients showing the effect of a 1 standard deviation change in exposure (Figure 7.2). In this plot, the coefficients can be better compared. From Figure 7.2, acute load appears to have the strongest effect on CMDwthn-vert (-1.400%, 95% CI -1.751, -1.048%; p<0.001), followed by ACWR (-1.055%,
95% CI -1.322, -0.788%; p<0.001) and chronic load (-0.659%, 95% CI -1.079, -0.239%; p=0.002), with wellness measures having a weaker (per-SD) effect. Similarly, for CMDbtwn- vert, load had a stronger effect in the same order, with acute being strongest (-1.493%,
95% CI -1.914, -1.073%; p<0.001) followed by ACWR (-1.118%, 95% CI -1.441, -0.795%; p<0.001) and chronic load (-0.532%, 95% CI -1.022, -0.041%; p=0.034).
For the forward axis the relationships differ. The load-based variables have the biggest effect, but they are in the opposite direction. CMDwthn-fwd is affected by acute load
(7.692%, 95% CI 6.744, 8.640%; p<0.001), ACWR (5.389%, 95% CI 4.840, 5.937%; p<0.001) and chronic load (1.858%, 95% CI 0.587, 3.010%; p=0.002) and CMDbtwn-fwd is mostly affected by ACWR (1.629%, 95% CI 0.968, 2.289%; p<0.001) and acute (0.916%, 95% CI
0.055, 1.776%; p=0.037).
7.4.3 Compromised training
Table 7.3 summarizes the models of compromised training and the effect of CMDwthn-vert and CMDbtwn-vert on these episodes. Relationships in the forward axis were not examined
189 due to the higher variability of this measure. There were 9, 22 and 26 flagged hamstring, ankle, and foot injuries respectively. There was no strong evidence for an association between either CMDwthn-vert or CMDbtwn-vert on any of the injury sites. However, given the small number of episodes, this analysis is underpowered. Within the sample, a one unit increase in CMDbtwn-vert was related to 3 times the odds of compromised training (odds ratio 3.111), but the interval estimate here is extremely wide (95% CI 0.297, 32.553) due to so few (n=9) hamstring episodes.
Table 7.3: Results from a generalized linear mixed model of flagged events
Outcome Variable Odds ratio CI p-value
Hamstring CMDwthn-vert 0.595 0.060, 5.932 0.658
n=9 CMDbtwn-vert 3.111 0.297, 32.553 0.343
Ankle CMDwthn-vert 1.872 0.432, 8.118 0.402
n=22 CMDbtwn-vert 0.643 0.187, 2.208 0.483
Foot CMDwthn-vert 1.156 0.163, 8.212 0.885
n=26 CMDbtwn-vert 1.118 0.179, 6.986 0.905
7.5 Discussion
The purpose of this study was to determine if analysis of the accelerometry data can provide actionable insight into training or match induced disruptions with no further testing on the athlete. This study has presented novel data showing that variability in stride detected by commonly used accelerometers is associated with fatigue, soreness and training load (table 7.4). Such an ability to identify times when an athlete has high injury risk or may require a training modification to maximize their performance in subsequent activities (whether that be a reduction or increase to their training load), is crucial in the preparation of athletes for competition.
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Table 7.4: Summary of variability changes from variables. wv=within stride-vertical axis. wf=within stride-forward axis. bv=between stride-vertical axis. bf=between stride- forward axis.
More Sleep wv|bv Less Fatigue bv|bf Less Soreness wf Less Soreness wv Higher ACWR wf|bf Higher ACWR wv|bv Higher Acute Load wf|bf Higher Acute Load wv|bv Higher Chronic Load wf Higher Chronic Load wv|bv|bf
7.5.1 Load & wellness
The more fatigued athletes reported being the lower their gait-related variability.
Previously with fatigue it has been shown that along with increased leg stiffness, the
CoM moves less in the vertical direction with prolonged exhaustive running (Morin,
Samozino, & Millet, 2011). However, few studies have previously used trunk accelerometry to assess running related fatigue (Le Bris et al., 2006; McGregor, Busa,
Yaggie, et al., 2009; Schütte et al., 2015). In contrast to the current study, one study found increased variability of vertical CoM accelerations, when sub-elite distance runners underwent a short intensive track run to exhaustion (Le Bris et al., 2006). Similarly, another showed that treadmill running-induced fatigue results in anteroposterior trunk accelerations that are more variable from step-to-step and hence more unpredictable
(Schütte et al., 2015). The final study showed that CoM movement could accurately estimate increases in metabolic work during an incremental running protocol to exhaustion (McGregor, Busa, Yaggie, et al., 2009). Though a more recent study has shown fatigue induced changes in the spatio-temporal and upper extremity kinematics
191 of runners without a correlated increase in oxygen cost (Sackey et al., 2019). It may be that the increased variability seen with these American Football players may signal a re- organization of motor strategies for the purpose of preserving performance (i.e. this increased stride variability may manifest as decreased variability in the upper body).
Previous research has demonstrated that fatigue alters the way player load is accumulated in Australian Rules Football matches (Cormack et al., 2013). Other authors found that a one unit decrease in wellness z-score resulted in a 4.9% (standard error
3.1%) and 8.6% (standard error 3.9%) decrease in player load and player load slow
(running activity < 2 m.s-1), respectively (Gallo et al., 2015). Players with reduced wellness may maintain the running variables that they deem critical to performance but modify other aspects of activity profile such as change of speed, low speed running and/or body contact that were not measured in this study (Coutts, Quinn, Hocking, Castagna, &
Rampinini, 2010).
Within American Football specifically it has been shown that a one unit increase in wellness z-score and energy were associated with a trivial 2.3% and 2.6% increase in player load (Govus et al., 2018). A one unit increase in muscle soreness (players felt less sore) corresponded to a trivial 4.4% decrease in sessional RPE training load. In addition, significant (p<0.05) differences in movement variables were demonstrated for individuals who responded more or less favourably on their rating of perceived wellness
(Wellman, Coad, Flynn, Climstein, & McLellan, 2017). In the current study while, there were no associations with sleep and vertical CMD, a decreased soreness was associated with an increase in variability – further investigations may look to establish the cause
192 and effect between soreness and variability and examine at the relationship between variability and sRPE directly.
An increase in load (both acute (7d) and chronic (21d)) saw an increased variability in the vertical axis of these team sport athletes. Although the mechanism underlying this increase in variability is currently unclear, it is roughly in agreement with previous theories (Hamill et al., 2012; Stergiou et al., 2006), that suggest that a shift away from
‘optimal’ trends towards a pathological state for an individual. A shift to higher variability could be a sign of an irregular system, which has been demonstrated to be a characteristic of individuals who had undergone knee reconstructions to repair a damaged anterior cruciate ligament (Moraiti et al., 2010; Stergiou & Decker, 2011a)
(possibly due to not being able to restore the proprioceptive pathways found in a healthy knee).
There is a high practical value to these findings as while current metrics are argued to have the ability to predict injury risk, especially when examining cumulative load measures, (Colby, Dawson, Heasman, Rogalski, & Gabbett, 2014) they require a full training history. Whereas if there is data missing or unavailable (such as when athletes are recruited into a squad on an intermittent basis or miss days through modified training) then the methods outlined here will still be able to identify individual athletes who have an elevated period of load compared to their normal training load (provided a baseline level of healthy movement variability has already been established).
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7.5.2 Compromised training
While there was an increased odds ratio of decreased variability in the presence of a flagged hamstring the analysis was too underpowered to draw a conclusion. Reduced variability would be expected for an acute injury. It has been observed that ACL deficient patients (Moraiti et al., 2007, 2010; Stergiou & Decker, 2011a) have less step-to-step variability in walking gait, inferring that they are being more “careful” when they were walking, trying to eliminate extraneous movements. The authors speculate that participants may be attempting to constrain movements and reduce step-to-step variability within the current results. The hamstring conditions likely indicate a compromised system. Further study may reveal if these flags are more indicative of chronic rather than acute conditions and if so athletes have developed strategies to cope in these circumstances.
7.6 Limitations
The current investigation was limited to a single team over a single season, but still includes a total of 127,715 strides collected across 1177 sessions and 443 matches. A wider group would allow comparisons of differing training styles and approaches.
Analysing the occurrence of self-reported flags set at an arbitrary level (5/10) can be criticized as not everyone views discomfort in the same way and so potentially looking at an individual comparison may improve this metric.
Also, there were limited flags compared to the number of injuries that occur in collegiate football. The typical injury rates would suggest that 20% of injuries are in the knee (Krill,
Borchers, Hoffman, Krill, & Hewett, 2018) but these may be catastrophic one-off issues
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(i.e. ACL) rather than a degenerative issue that can be detected by flagging in a routine questionnaire. Football commonly has the highest injury rate in collegiate sports (9.2 per
1000 athlete exposures; Kerr et al., 2015) and at high schools (3.3 per 1000 exposures;
Kerr, Roos, Djoko, Dompier, & Marshall, 2016). This is likely reflective of the contact nature of the sport but doesn’t account for the experience level of the athletes involved and how their training is modified based on experience (McCunn et al., 2017). So, while early detection of issues as this study has shown possible is key, the differing positional demands and subsequent injury rates may need future studies to delineate the effects for particular positions in American Football in the context of injury history and experience (Sampson et al., 2018).
7.6.1 Practical applications
The difference in the measures outlined is that predictions can be made from physical symptoms, but these track well with at least some of the subjective markers that athletes are giving. What is not known is how many athletes are not accurately flagging symptoms of soreness and so are going undetected in this analysis. It has been shown that athletes can fail to engage and accurately report symptoms if they don’t receive timely feedback on their data or if they perceive the modification of training to not fit with the magnitude of their response (Neupert, Cotterill, & Jobson, 2019). In the absence of
100% disclosure from athletes the assessment of variability therefore has the potential to identify athletes who are displaying physical symptoms that would indicate the need to modify training. Conversely, it may be able to identify athletes who do satisfy flagging criteria but are showing no physical symptoms and who therefore may not need training modifications.
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The significant associations with load suggest sensible training design may in itself be a management and monitoring tool for athletes. While we accounted for individual relationships and responses through the statistical analysis looking at a different team in a different circumstance may produce different results. It would seem that the strongest associations are with load-based metrics and while these are most remarkable in the more variable forward axis they persist in the more consistent vertical axis. This suggests a significant relationship that would endure across populations and individuals.
7.7 Conclusions
This study has shown that gait-related variability is associated with fatigue and 7-day training load in American Football athletes. Combining both objective and subjective methods is likely to enhance the predictive ability and become a very powerful tool within elite sport environments, and while further investigations into this are warranted, the assessment of variability has the potential to identify athletes who are displaying physical symptoms that would indicate the need to modify training.
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Executive Summary
Given the established relationship with acute load and subjective wellness in Chapter 7, an examination of additional objective markers in an alternative population was undertaken. The purpose was to combine flexibility screening measures with a measure of movement variability obtained from within the sport. In addition, the external load relationship was investigated as previously in Chapter 7.
8.0 Why soccer
Within soccer it has been shown that the positional differences across outfield players in terms of activity profile are reduced (Curtis et al., 2018) compared to American Football where there are marked differences between positional groups (Fullagar, McCunn, &
Murray, 2017). Within the peculiarities of the NCAA where soccer games can occur within
42-hours the second game still sees around 150% more distance covered than in a
American Football game (McCormack et al., 2015; ~8k v ~5k). This should give both longer and more sequences of high speed running across a greater number of athletes to see if there are similar outcomes in the approach when assessing movement variability against additional objective markers.
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There are though differences in training approach between the sports, with Football favouring practices that replicate the game through position specific drills or team practice (Ward, 2018), while soccer sees wide spread use of small sided games on reduced pitch sizes with lower player numbers (Hill-Haas, Dawson, Impellizzeri, & Coutts,
2011). These size constraints may balance the increased running demands seen in games by reducing the data available for analysis from training.
8.1 Introduction
To reduce the early onset of degenerative changes, sports science and medicine practitioners should ideally identify important exposures of influence for injury and provide evidence-based preventative suggestions. It was shown in chapter 7 that gait- related variability is associated with fatigue and 7-day training load in American footballers. Combining such objective methods with subjective methods is likely to further enhance the predictive ability of any data set. The assessment of variability has the potential to identify athletes who are displaying physical symptoms that would indicate the need to modify training.
Within soccer specifically congested fixtures are common place. In one professional team competing over two seasons (116 games) a study identified occurrences of two games in a week (i.e. two games within 4 days), playing in the domestic league and
European competition (Dupont et al., 2010). They concluded that the recovery time between matches, (72 to 96 hours), was sufficient to maintain physical performance
(total and high intensity distance) but saw an associated increase in injury rate. This may be reflected at the highest level within technical performance as leading into the 2002
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World Cup physicians at the top European clubs monitored match exposure and found that the players who underperformed (as judged subjectively by an ‘expert’ panel) had played more matches during the ten weeks before the World Cup than those who performed better than expected (Ekstrand, Waldén, & Hägglund, 2004). While the performance rating was subjective it was conducted by a panel of three international experts who gave ratings across all players – thus minimizing the intra-rater reliability.
The study itself acknowledges the subjective rating as a limitation but notes that assessments were ‘almost identical’ for most players. Quantifying effective performance remains one of the hardest holistic measures to define in sport.
At the youth level the focus is less on winning games and more on player development.
Unfortunately, games with short turnarounds are also common at this level with multiple studies examining intensified competition through tournament structures. One study in under-15 international level age groups showed that accelerations decreased across five sixty-minute games in 3 days (Arruda et al., 2015). At the club level a comparative age group showed no effect playing six games of similar length in five days on movement characteristics (Gibson, McCunn, MacNay, Mullen, & Twist, 2018). This may be a function of age and stage as older players (U23) playing three successive matches with 72 hours of recovery showed that sprint performance was unaffected regardless of playing position (Varley, Di Salvo, Modonutti, Gregson, & Mendez-Villanueva, 2018). Though the outcomes were position and individual specific, suggesting that players should be monitored on an individual basis. In contrast, a period of intensified load has been shown to alter the complexity of gait in a running task performed separately to the content of a
199 training session, likely representing fatigue manifested by a change in motor control (J.
H. Williams, Jaskowak, et al., 2019).
Whilst it has been shown that comparative periods from within team sports training and competition can be assessed (Buttfield, 2016; Murray, Buttfield, Simpkin, Sproule, &
Turner, 2019) most previous analyses have focused on average or maximum joint positions during movement. This can be judged similarly as repeated screening of muscle flexibility and or range of motion across a season. This approach does not quantify how multiple joints are coordinated to produce a movement.
8.1.1 Movement screens
The majority of movement screens identified lack a substantial evidence base in relation to both their reliability and their ability to predict injury (Bahr, 2016; McCall, Davison, et al., 2015; McCunn, aus der Fünten, Fullagar, McKeown, & Meyer, 2016; Whiteley, 2016) but they can be useful to build relationships, detect current issues and establish baseline measures (Bakken et al., 2016). Within elite sporting contexts it is commonplace to routinely screen athletes in a series of dynamic movements, even by those who recognize the limitations of injury prediction (Bahr, 2016). This approach ensures they maintain a normal range of motion and detect issues before they affect performance, however, the underlying evidence to support this approach is also very limited (Esmaeili et al., 2018).
Within domestic football the most common test, or series of movements, to identify risk is a functional movement screen (McCall et al., 2014), again despite there not being sufficient evidence to support this (McCall, Carling, et al., 2015). Flexibility and joint
200 mobility are among the five most common measures (McCall, Davison, et al., 2015) in movement screens used during tournaments.
An ideal daily preparticipation examination system applicable to all sports does not exist
(Maffey & Emery, 2006) but objectifying the screening process can help to standardize the process. Removing the objective element in the scoring as far as possible (i.e. having the same scorer each time) can help this. The use of commercially available and popular marker-less motion capture systems to assess movement has been previously suggested (Giblin, Collins, & Button, 2014). Within a professional sporting context it is more ecologically feasible than the use of digital motion capture systems on a daily basis due to shorter set up time and lower financial cost (Kotsifaki, Whiteley, & Hansen, 2018).
One such product utilizing Microsoft Kinect (Capture, Kitman Labs, Dublin) was developed to record, display, and analyse skeletal information about athletes for specific pre-defined movements. The Kinect uses infrared emissions and a multiple sensing camera to scan people/objects. The Kinect provides both depth data and RGB image data from which a simple human skeletal model is inferred. In contrast to the subtle, dimensional motions tracked with marker-based systems, 6 degrees of freedom about a joint are not tracked using the marker-less approach. However, 3 degrees of freedom (x, y, z coordinates) are provided to derive relative planar motions. The process presents an appealing alternative to the resource intensive marker-based systems.
Using MRI as a reference it was previously shown that the deviation of the actual joint location was less than 4 cm using this system (Giblin, Smith, et al., 2015). Other studies
201 have shown that when correction algorithms were applied, knee flexion can be measured with high accuracy, deviating less than 4° from the angle measured using a gold standard marker based reference system (Vicon; McGroarty, Giblin, Meldrum, & Wetterling, 2016).
Likewise the shoulder rotation accuracy deviates 7.9° and 4.3°, respectively for external and internal rotation (Giblin, Meldrum, O’Brien, & Wetterling, 2015), despite the Kinect having a sampling rate ten times lower. However, this noted limitation must be weighed against the many benefits of the ecological validity of the Kinect for assessing movement in a practical setting (i.e. professional sports facilities) in time sensitive situations.
8.2 Aim
The purpose of this investigation was to combine the screening measures with a measure of movement variability obtained from within the actual performance task (i.e. playing the sport) to attempt to combine both physiological and biomechanical loads and their interaction (Vanrenterghem et al., 2017) by assessing the relationship between measures of the musculoskeletal system (movement variability & screening measures) and external measures of training load (Player Load).
This builds on Chapter 7, where subjective markers were associated with movement variability in American Football, by examining additional objective markers commonly used to assess recovery and readiness for the next session. Assessing this in a different sport looks to confirm the previous findings whilst investigating additional associations.
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8.3 Methods
Data from 17 female D1 NCAA Collegiate Soccer players (age = 20.1 ± 1.2 years; body mass = 66.9 ± 6.6 kg; height = 169.4 ± 6.8 cm) were monitored during the regular playing season from August to November. For the purposes of this observational study, with repeated measures on the participants, only data from the main soccer training sessions and matches were recorded. Athletes were monitored across 20.5±0.6 games and
73±4.8 training sessions across the season. Athletes provided informed consent to participate in data collection throughout the season as part of the athlete support process and the institutional ethics committee provided ethical approval for the research.
MEMS devices containing GPS and accelerometers were worn for every field session.
The data collected and used in these studies were GPS (measured at 10 Hz) and tri-axial accelerometer (measured at 100 Hz).
The raw GPS data were processed as outlined in chapter 6. Over the course of the season the athletes completed a wellness questionnaire daily on awakening on their smartphone which entered the data into the athlete management system. This recorded any areas of soreness as well as noting their resting heart rate, hours of sleep, stress and overall muscle soreness (0=poor, 10=good). Players had been using the questionnaire as part of their daily routine since joining the team.
On a weekly basis each player also completed a movement screen consisting of 3 key movements – knee-to-wall to measure ankle dorsiflexion, a single leg anterior reach or
Y-balance measure and an overhead squat (Figure 8.1). Athletes completed the screening in trainers, shorts and t-shirt prior to a strength & conditioning session. All
203 screens occurred between 9 and 10am and were the first session of the day. Within each movement several data points were collected by the marker-less system and fed into the athlete management system alongside the subjective wellness. The whole process took
90-180s for each athlete. The information in-season directed athletes to self-directed stretching and intervention if they were flagged as being outside of their own normal range and so led them to develop a self-managed approach to managing their readiness before they sought out a practitioner. Typically, the screening occurred 2 days after the final competitive fixture of the week. Usually in NCAA season, athletes completed a fixture on both Thursday and Sunday and so screened each Tuesday.
As discussed in section 7.1.3.3, accelerometry determined daily workloads
(PlayerloadTM) were calculated and expressed as arbitrary units (AU) via the manufacturer’s software (Catapult Sports, Openfield software, version 1.11.1) for every session. Participants again wore the same device and harness during every training session and match (Nicolella et al., 2018).
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Figure 8.1: Movements and measures from the marker-less motion capture system
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8.4 Statistical analysis
CMD values were investigated for their association with subjective, objective markers and load (acute (7-day average), chronic (21-day average) and acute-chronic workload ratio) on that day as outlined in chapter 6 (both as coefficients showing the effect of a one unit change in the variable on CMD percentage and a standardized z-score to allow comparison of variables for the effect of a one standard deviation change). Data are presented for all valid models within the analysis (i.e. those that converge to a solution).
8.5 Results
Descriptive statistics for the key variables in the study are given in Table 8.1. The average subjective markers of soreness and stress are relatively low throughout (~3.5). The acute and chronic loads were essentially equivalent at ~235 arbitrary units. Most measures of flexibility were comparative across sides except for knee valgus which was moderately greater on the left side. Figures 8.2 – 8.4 show the non-linear changes in CMD over the period of measurement with similar patterns across most variables, with a decrease across the season, apart from hip angle which increased towards the end of the year and ankle range-of-motion shows a clear increase over the first few months of the season.
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Table 8.1: Descriptive Statistics of 17 female soccer athletes. *denotes mean and interquartile range rather than median and standard deviation. o = degrees; AU=arbitrary units; bpm=beats per minute; h= hours.
Variable Mean SD Ankle Dorsiflexion Left (o) 23.74 6.03 Ankle Dorsiflexion Right (o) 24.01 6.38 Y-balance ankle angle left (o) 62.97 6.34 Y-balance ankle angle right (o) 61.91 6.18 Y-balance knee valgus left (o) 0.69 7.73 Y-balance knee valgus right (o) 0.01 6.42 Y-balance max reach left (m) 0.50 0.07 Y-balance max reach right (m) 0.50 0.07 Y-balance hip angle left (o) 56.89 13.43 Y-balance hip angle right (o) 58.16 14.43 Overhead Squat knee valgus left (o) -84.62 23.25 Overhead Squat knee valgus right (o) -85.40 23.58 Resting Heartrate (bpm) 57.81 9.21 Hours of Sleep (h) 7.59 1.36 Soreness (AU) 3.67 1.78 Stress (AU) 3.62 2.35 Acute player load (7-day mean) (AU) 235.81 91.92 Chronic player load (21-day mean) (AU) 233.10 68.82 ACWR 1.01 0.23
CMDwthn-vert 0.826 0.069
CMDbtwn-vert 0.842 0.074
CMDwthn-fwd 0.712 0.109
CMDbtwn-fwd 0.726 0.121 Number of sections 9* 5-114* Number of strides within section 66* 1-941*
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Figure 8.2: Ankle Dorsiflexion during ‘knee-to-wall’ test by participant and group average across season. Mean response in red for left and blue for right with associated confidence intervals.
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Figure 8.3: Y-balance measures by participant and group average across season. Mean response in red for left and blue for right with associated confidence intervals.
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Figure 8.4: Overhead squat by participant and group average across season. Mean response in red for left and blue for right with associated confidence intervals.
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Table 8.2: Linear Mixed Model Outputs. Significant relationships marked in bold. Coefficients represent percentage change in CMD from a one unit change in the variable. +/- = 95% confidence interval of coefficient.
Outcome Raw Within Stride CMD (vertical) Raw Between Stride CMD (vertical) Raw Within Stride CMD (forward) Raw Between Stride CMD (forward)
Exposure Coefficient +/- p-value Coefficient +/- p-value Coefficient +/- p-value Coefficient +/- p-value
Ankle Dorsiflexion Left -0.001 0.157 0.988 0.007 0.179 0.940 -0.306 0.290 0.040 -0.295 0.327 0.078
Ankle Dorsiflexion Right 0.007 0.146 0.923 0.071 0.163 0.395 -0.133 0.263 0.320 -0.186 0.292 0.213
Y-balance ankle angle left 0.039 0.131 0.565 0.002 0.148 0.982 -0.293 0.230 0.014 -0.312 0.282 0.032
Y-balance ankle angle right 0.009 0.117 0.881 0.066 0.137 0.348 -0.022 0.216 0.840 0.171 0.250 0.181
Y-balance knee valgus left -0.121 0.107 0.028 -0.054 0.128 0.410 -0.062 0.204 0.549 -0.085 0.239 0.488
Y-balance knee valgus right -0.008 0.136 0.913 -0.053 0.158 0.517 0.045 0.249 0.726 -0.262 0.285 0.074
Y-balance max reach left -0.520 15.310 0.947 4.671 16.686 0.584 14.025 27.360 0.316 -2.083 32.444 0.900
Y-balance max reach right -1.519 11.712 0.800 -5.533 13.913 0.437 -24.726 21.666 0.027 -34.216 26.418 0.012
Y-balance hip angle left 0.025 0.065 0.450 -0.105 0.072 0.005 -0.018 0.113 0.761 -0.158 0.131 0.019
Y-balance hip angle right 0.012 0.062 0.717 -0.106 0.069 0.003 -0.004 0.108 0.935 -0.147 0.127 0.025
Overhead Squat knee valgus left 0.013 0.040 0.525 -0.034 0.045 0.141 0.026 0.068 0.452 -0.010 0.082 0.809
Overhead Squat knee valgus right -0.065 0.041 0.002 -0.063 0.050 0.015 -0.026 0.074 0.495 -0.113 0.091 0.016
Resting Heartrate -0.038 0.056 0.188 -0.035 0.061 0.264 0.049 0.100 0.333 -0.018 0.112 0.755
Hours of Sleep -0.143 0.246 0.255 0.247 0.279 0.083 -0.296 0.435 0.182 0.391 0.495 0.122
Soreness 0.006 0.201 0.953 0.164 0.224 0.152 0.043 0.352 0.812 0.407 0.393 0.042
Stress 0.177 0.154 0.024 0.143 0.174 0.108 0.107 0.275 0.446 0.054 0.310 0.731
Acute player load (7-day mean) 0.000 0.004 0.917 -0.005 0.005 0.065 -0.007 0.008 0.098 -0.008 0.009 0.106
Chronic player load (21-day mean) 0.006 0.008 0.135 -0.003 0.010 0.605 -0.007 0.016 0.416 -0.011 0.018 0.225
ACWR 0.855 1.212 0.167 -0.061 1.445 0.934 -2.114 2.290 0.071 -2.140 2.581 0.104
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8.5.1 CMDwthn-vert
There was some evidence for an inverse relationship between knee-valgus and CMDwthn- vert (increased variability; -0.121%; 95% CI -0.014, -0.229%, p=0.028) though the small impact of this was not likely practically relevant. An increase in the subjective stress marker was related to a 0.18% increase in CMDwthn-vert (decreased variability; Table 8.2;
95% CI 0.024, 0.331%, p=0.024).
8.5.2 CMDbtwn-vert
The Y-balance measure showed an inverse relationship between hip angle and CMDbtwn- vert. A one-degree increase was related to a 0.11% decrease on both sides of the body
(increased variability; p=0.003 & 0.005 for right and left respectively).
8.5.3 CMDwthn-fwd
The ankle measures had an inverse influence on CMDwthn-fwd with ankle dorsiflexion and ankle angle on the Y-balance test CMDwthn-fwd (increasing variability) by 0.31 (95% CI -
0.016, -0.596%, p=0.04) and 0.29% (95% CI -0.063, -0.523%, p=0.014) respectively with a one-degree increase. On the right side the maximum distance reached in the Y-balance measure showed an effect that was highly variable (increased variability; -24.726%; 95%
CI -3.060, -46.393%; p=0.027).
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8.5.4 CMDbtwn-fwd
Soreness had the largest influence on CMDbtwn-fwd with one-unit increase relating to a
0.41% (95% CI 0.014, 0.800%, p=0.042) increase in CMD (decreased variability). On the right side the maximum distance reached showed an effect that was highly variable
(decreased variability; -34.2%; 95% CI -7.798, -60.634%; p=0.012). Hip angle on both sides had a significant effect with a one-unit increase relating to a 0.15-0.16% decrease on both sides (increased variability; left -0.158%; 95% CI -0.027, -0.289; p=0.019, right -0.147%;
95% CI -0.020, -0.274%; p=0.025).
8.6 Standardized scores
To compare the standardized coefficients across measures each exposure variable has been z-scored with the resulting coefficients showing the effect of a 1 standard deviation change in exposure (Fig. 8.5). In this plot, the coefficients can be better compared.
From figure 8.5 knee valgus appears to be the biggest influence on CMDwthn-vert (-1.54% for overhead squat, 95% CI -0.567, -2.518%; p=0.002 and for the Y-balance (-0.93%; 95%
CI -0.107, -1.766%; p=0.028). Stress also has a significant relationship (p=0.023) with an increase in stress resulting in decreased variability (0.42%; 95% CI 0.056, 0.776%). For
CMDbtwn-vert hip angle in the Y-balance (-1.59, 95% CI -0.533, -2.526; p=0.003 & -1.41%, 95%
CI -0.443, -2.371; p=0.005 for right and left) and knee valgus in the overhead squat (-
1.48%, 95% CI -0.298, -2.653; p=0.015 & -0.79%, 95% CI 0.258, -1.839; p=0.141, right and left respectively).
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For the forward axis ankle angle on the y-balance and dorsiflexion tests seem to have the clearest association with CMDwthn-fwd (-1.86%, 95% CI -0.398, -3.314%; p=0.013 and -1.84%,
95% CI -0.096, -3.591%; p=0.040). CMDbtwn-fwd was most affected by Y-balance measures; max reach & hip angle left and right (-2.36%, 95% CI -0.539, -4.188%; p=0.012, -2.12%, 95%
CI -0.369, -3.886; p=0.019, -2.12%, 95% CI -0.287, -3.958%, p=0.025 respectively)
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Figure 8.5: Standardized scores for outcome variables by axis. Coefficients show the effect of a 1 standard deviation change in exposure and allow comparisons across variables.
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8.7 Discussion
The purpose of this study was to determine if analysis of the accelerometry data can provide actionable insight into training induced disruptions to gait-related variability with no further testing on the athlete and if there was a demonstrated change in objective markers of flexibility. There was no single test that was clearly associated with gait- related variability in either axis, although the subjective markers of soreness and stress did show an association in the forward and vertical axes respectively. There were large inter- and intra-individual variations in response which contribute to the inconsistent findings across the group (table 8.3).
Table 8.3: Summary of variability changes from variables. wv=within stride-vertical axis. wf=within stride-forward axis. bv=between stride-vertical axis. bf=between stride- forward axis.
More stress wv Greater hip angle on Y-balance bv|bf More soreness bf Greater ankle ROM wf Greater reach on Y-balance bf Greater reach on Y-balance wf
The ankle flexibility measures showed inconsistent relationships with some measures showing an increase in range of motion associated with increased variability for within stride CMD in the forward axis. It has been shown in the presence of patellofemoral pain that there is reduced ankle flexibility (Wyndow, Collins, Vicenzino, Tucker, & Crossley,
2018). It has also been shown previously that with the occurrence of patellofemoral pain there is a reduced movement variability (Hamill et al., 1999). While authors speculated that lower variability was tied to the presence of an injury it may be that changes in flexibility within the normal range also affect variability as shown here. The cause and
216 effect could be investigated further to determine which manifests primarily and where interventions could be targeted.
When standardizing the markers, knee valgus appeared to be the biggest influence on gait-related variability, though there was variability between the various tests used to measure this and between sides of the body. It has been shown that reduced ankle dorsiflexion is associated with knee valgus (Lima et al., 2018) as well as other injuries further up the kinetic chain such as patellar tendinopathy (Backman & Danielson, 2011).
A change in movement pattern in the lower extremity is a risk factor for injury whether this manifests in the ankle, knee or hip. Although the mechanism underlying this change in variability is currently unclear, it is in some agreement with previous theories that suggest that a shift away from an individual’s optimal level of variability is indicative of a pathological state. A shift to an increased level of variability could be a sign of a noisy and irregular system, which has been demonstrated to be a characteristic in ACL knee reconstructions (possibly due interrupted/modified proprioceptive pathways found in a healthy knee; Stergiou & Decker, 2011b).
In the present study, while hip angle in the Y-balance standard measures shows a relationship with CMD in the vertical axis that is not significant, this may reflect a lack of control in a unilateral pose which can be associated and manifested with a change in movement control while running. Back extensors control the forward momentum at impact while the obliques decelerate the torso during the stance phase (Preece, Mason,
& Bramah, 2016). Fatigue in these areas may contribute to instability which when induced by restricting arm movement or modifying step width has been associated with increased
217 energy costs (Arellano & Kram, 2012; Schütte et al., 2018). In the presence of fatigue changes can also occur in upper body kinematics without increasing the oxygen cost
(Sackey et al., 2019) suggesting fatigue manifesting in the postural muscles may be most impactful on gait.
8.8 Limitations
The reason that no single test of flexibility is clearly associated with gait-related variability may be that the accuracy of the measurement tool was not sufficient to measure subtle changes in flexibility. While range of motion differences are under 10o
(Fernández-Baena, Susín, & Lligadas, 2012) and location differences are in the order of 5 cm (Mortazavi & Nadian-Ghomsheh, 2018; Suma, Lange, Rizzo, Krum, & Bolas, 2011;
Yang, Zhang, Dong, Alelaiwi, & Saddik, 2015), these may still be too large to detect real change in this population. Another possible source of error is the effort and engagement of the participants. If they were late for training and so rushed the measures or simply did not give 100% on each test their range of motion may appear reduced between tests but would simply reflect a reduced level of engagement. In relation to screening it has been shown that athletes display stability in screening test reliability despite physical challenges of a long competitive season (Esmaeili et al., 2018). This suggests that substantial changes in weekly scores (if they can occur) cannot be simply attributed to training-induced test changes, various sources (technique variation, the presence of injury, equipment error, true change in athletes’ test performance) contribute to the week to week variation in screening scores (Hopkins, 2000). Thus, it is important for practitioners to interpret the findings of weekly screening considering possible contributing factors toward the change in the scores.
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Using a different analysis of variability (detrended fluctuation analysis) in a similar population (D1 female soccer players) recently showed a relationship between increased load and decreased gait complexity (variability) (J. H. Williams, Jaskowak, et al., 2019).
Though this was measured during a specific task (400 m run) rather than within game.
The drawbacks of assessing out-with the game are that the attentional focus of athletes is directed purely towards the task (running) and so some modification of gait may occur in these submaximal measures. The authors maintain that a link with fatigue or muscle soreness is speculative but reasonable. Here we did not find a strong association with load per se, but we did not specifically assess pre and post session measures, rather repeated measures across a longitudinal season. Perhaps if we had looked in the short term at a congested period of fixtures we would have found similar outcomes.
Alternatively, it may simply be that in this population relationships between gait-related variability and the measured variables are not present or that the training stimulus was not of sufficient volume or intensity to create a change in the movement variability
8.9 Practical applications
The additional factor here to chapter 7 was to consider changes in gait-related variability that may be linked to indicators that are more readily available to practitioners (i.e. musculoskeletal screening) and routinely used to monitor athletes. In this case it was found that there were limited changes in variability to be linked to screening measures across sessions and no one test consistently indicated itself as the best measure.
Regular musculoskeletal movement screening cannot detect induced changes in movement patterns that may indicate risk of injury based on the results of this study.
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This may be as the normal variability of the test scores throughout the season masks any associated changes in the movement pattern.
The absence of the link of variability to load in this study, contrary to previous findings may reflect that the mix of sessions (training and competition) clouded the issue.
Looking at games alone may be more representative in this population as a truly representative session with complex and unpredictable demands. While the screening results within a given week were not linked to a change in movement pattern this may be as there were implemented interventions in response to the screening scores (e.g., additional treatment sessions) which could have affected the subsequent movements.
8.10 Conclusions
Whereas it has been previously shown that load is related to changes in variability it does not appear that there is a similar association with measures of flexibility. This suggests that the objective flexibility measures and the methods to obtain them in this study cannot be used to infer changes in variability that may be related to fatigue and subsequent injury in collegiate soccer players.
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Executive Summary
While there was no relationship with objective flexibility measures and movement variability in Chapter 8, given the established relationship with acute load and subjective wellness in Chapter 7, an examination of additional objective markers of recovery in an additional contact sport was undertaken. The purpose was to combine objective measures already used by practitioners with a measure of movement variability obtained from within the sport. In addition, the external load relationship was investigated as previously in Chapters 7 & 8.
9.0 Why Rugby?
The adequacy of movement variability as an outcome variable requires consideration of the validity (does it measure what is supposed to); reliability (measure of biological and technical variation) and sensitivity (definition of the smallest worthwhile effect) (Currell
& Jeukendrup, 2008). Within chapter 5 we addressed all these measures to show that the method was satisfactory for use in the field and could be considered valid and reliable.
The differing outcomes in the previous chapters across contrasting sports (Chapter 7;
American Football v Chapter 8; Soccer) could question the sensitivity of the measure between different populations. Alternatively, it may simply reflect differences in training
221 approach, intensity and/or volume between these sports that have arisen as a consequence of the differing competitive demands. The training ‘dose’ in soccer may not have been sufficient to perturbate the movement variability outside the typical homeostatic bandwidth of these athletes.
This differing ecological dynamic between sports (Woods, McKeown, Rothwell, Davids, &
Robertson, 2020) may be reflected by these outcomes. For example, it has been shown consistently that there are differences in movement variability between skill levels with tighter bandwidths and reduced variability as skill increases. Given that American football is a revenue generating sport within the NCAA and takes the bulk of the athletic department scholarships (Bergman & Logan, 2016), it may be that the athletes in this program are more competent movers or relatively higher skilled at movment than the soccer players and so are more prone to perturbation from training.
Using a population of professional athletes who can be considered high skill through virtue of being professional, train in a similar pattern to American Football and train with an intensity to induce changes in the outcome variable of movement variability across a season will allow direct comparison of the populations to see if the validity, reliability and sensitivity is adequate.
9.1 Introduction
Skill “consists in the ability to bring about some end result with maximum certainty and minimum outlay of energy, or of time and energy” (Guthrie, 1952). Developed motor skills represent the ability to obtain a predetermined outcome with a high degree of certainty.
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Running is a motor skill pattern that has a ’healthy’ bandwidth of variability and is repeated almost daily in team sports practice and games. Variability of movement is inherently present but has been linked to a reduction in performance due to a lack of consistency in motor skills. It was shown in Chapter 8 to be an indicator of system health within strides of American Football athletes. An increase in acute (7d) load saw an increased gait-related variability. Feeling less fatigued and/or lower muscle soreness was associated with higher gait-related variability. This result and others support the use of movement variability, as a clinical tool to identify when an individual has a less than optimal movement pattern, is entirely possible within stride waveforms measured using a single tri-axial accelerometer mounted on the upper torso.
9.1.1 Monitoring fatigue
Measuring the context has become the key to interpreting performance outcomes, as the specific motor demands of the sport differ based on the contextual factors surrounding training and competition. Within team sports sprint performance is regularly assessed, typically as a measure of workload within sessions (i.e. if the athlete reached max velocity and if not, what percentage of it did they hit?). Some sports concentrate on measuring sprint performance to assess maintenance of this ability within season and others have utilized it as a fatigue measure (Gathercole et al., 2015). Decreased sprint ability following fatiguing exercise is a common observation within the literature (Girard,
Brocherie, Tomazin, Farooq, & Morin, 2016; Girard, Micallef, & Millet, 2011; Morin, Jeannin,
Chevallier, & Belli, 2006). Though typically single sprint performance recovers within a short window (Gathercole et al., 2015); recovery of sprint ability therefore appears relatively quick compared to other fatigable components which have a greater residual
223 from a given stimulus. Over a chronic period repeat testing at the same point within a micro cycle (i.e. week) can allow comparison of fatigue state from week-to-week and a subsequent training volume adjustment.
9.1.2 Monitoring readiness
Specifically, within rugby additional stress testing to elicit hormonal responses has been utilized as a determinant of performance. Completing a modified sub-maximal Yo-Yo test in the middle of a preparation week, as the warm-up for an afternoon session, over seven minutes with increasing speeds from 10 – 15 km/h and a saliva sample pre and post was used as a model to associate match outcome with the hormonal state (Crewther, Potts,
Kilduff, Drawer, & Cook, 2018). However, the response to the test itself was not as predictive as the difference in cortisol response from the morning to prior to the test. This would suggest that the test itself is just supplying additional load to the athletes with little return. A further investigation in international rugby athletes showed links between subjective markers of stress and mood, sleep duration and performance indicators
(Crewther, Potts, Kilduff, Drawer, & Cook, 2019). This suggests that controlling the recovery (sleep; section 2.2.1.3) and managing the emotional regulation of athletes can affect the match outcome and in turn could be used as markers of readiness and/or recovery (section 2.2.10).
9.1.3 Injury risk/monitoring
Monitoring fatigue is one component of the complex challenges of managing injury risk within team sports (section 2.0.2). The lower limb is commonly injured in rugby (S.
Williams, Trewartha, Kemp, & Stokes, 2013) and so assessments of calf and hamstring
224 flexibility (de la Motte, Lisman, Gribbin, Murphy, & Deuster, 2019; Malliaras, Cook, & Kent,
2006) and pelvic girdle strength (Whittaker, Small, Maffey, & Emery, 2015) are routine and can be used as a reflection of short term decrements in performance as well as indicators of injury risk. This approach is common in sports teams with frequent test batteries across a number of sports (Esmaeili et al., 2018; Henderson et al., 2019). This approach has also been replicated with subjective markers of ‘wellness’ which have been shown to align to variability in a squad of collegiate American football players (Murray, Buttfield,
Simpkin, Sproule, & Turner, 2019; Chapter 7), although measuring some flexibility markers in a digital platform (i.e. no practitioner or manual testing) did not show any association in a squad of female college soccer players (Chapter 8).
9.2 Aim
Therefore, given the mixed results reported in differing team sport athletes, this study looked to examine if these relationships with load and subjective measures could be demonstrated in a different team sport at the professional level and whether there was an association with additional objective measures of sprint speed and manually measured measures of flexibility.
9.3 Methods
Data from 64 professional rugby athletes (25.9 ± 3.26 years; 103.6 ± 12.0 kg; 1.87 ± 0.07 cm) operating at the highest level of European Competition were collected across a regular season from August - April. Athletes provided informed consent to participate in data collection throughout the season as part of the athlete support process and the institutional ethics committee provided ethical approval for the research.
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For all sessions, players were fitted with a 10 Hz Catapult T6 Local Positioning System
(LPS) tag (indoor sessions), or a 10 Hz Catapult S5 Global Navigation System (GNSS) device (outdoor sessions). Both LPS and GNSS devices were worn in manufacturers’ recommended harnesses. Both LPS and GPS systems have established acceptable validity and reliability in measuring the physical output of team-sport athletes (Coutts &
Duffield, 2010; Luteberget, Spencer, & Gilgien, 2018). The data collected and used in these studies were GPS (measured at 10 Hz) and tri-axial accelerometer (measured at 100 Hz).
For the purposes of this observational study, with repeated measures on the participants, only data from the rugby training sessions and matches were recorded. This means skill and unit sessions that did not incorporate the full aspects of the game in a tactical context were omitted. Athletes were monitored across a maximum of 28 games and 54 training sessions.
The raw GPS data were processed with a novel analysis tool as outlined in chapter 6. At regular speed sessions within the training week and as part of the pre-match potentiation session on game day athletes ran through a set of maximal sprints over 10m and the time was recorded electronically with dual photocell gates set at hip height (Microgate,
Witty, Bolzano, Italy). Athletes started 1 m behind the start line and sprinted maximally in their own time through the 10m gate.
As part of their daily monitoring athletes completed screening tests (sit & reach, groin squeeze & knee-to wall) and completed subjective markers of wellness (hours of sleep,
226 soreness, energy (1-10 scale). In the case of soreness and pain a lower score was better and for energy a higher score was better.
9.4 Statistics
Athletes with less than 20 sessions over the season were not analysed leaving 47 athletes in total. These athletes on average were involved in 46.98±13.1 sessions. All analyses were carried out using R v3.5 (R Core Team (2018) as described in chapter 6.
The outputs relate to an analysis of each variable showing the effect of a one unit change on CMD percentage and to allow comparison across variables a secondary analysis showing the effect of a one standard deviation change in the variable.
9.5 Results
Descriptive statistics for the key variables in the study are given in Table 9.1. Throughout the year players averaged ~7.5 hours of sleep a night; had low soreness (~3.5/10) and high levels of energy (~7/10). They displayed low levels of discomfort on the groin squeeze (~1) and maintained a similar level of pressure throughout the year (SD ~ 35 mmHg).
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Table 9.1: Descriptive statistics for the 47 elite male rugby athletes. *denotes Median rather than mean and interquartile range rather than standard deviation. h = hours; AU = arbitrary units; cm = centimetres; s = seconds; mmHg = millimetres of mercury; m = meters; m/s2 = meters per second per second.
Variable Mean SD Hours of Sleep (h) 7.42 0.99 Soreness (AU) 3.39 1.54 Energy (AU) 7.09 1.39 Groin Squeeze Pain (45o) (AU) 1.17 1.01 Groin Squeeze Pain (0o) (AU) 1.17 1.03 SOS (AU) 2.31 1.97 Sit & Reach (cm) 28.62 14.80 10m Sprint (s) 1.66 0.08 Groin Squeeze Pressure (45o) (mmHg) 261.12 35.97 Groin Squeeze Pressure (0o) (mmHg) 252.54 36.22 Knee-to-wall left (cm) 11.00 3.33 Knee-to-wall right (cm) 10.97 3.60 Acute distance (7-day) (m) 4436.91 984.69 Chronic distance (21-day) (m) 4431.92 799.84 ACWR (Distance) 1.00 0.16 Acute Ave ACC (7-day) (m/s2) 0.32 0.05 Chronic Ave ACC (21-day) (m/s2) 0.32 0.04 ACWR (Ave ACC) 1.00 0.12 Acute PL (7-day) (AU) 234.85 177.36 Chronic PL (21-day) (AU) 235.71 137.58 ACWR (PL) 1.00 0.20 CMDwthn-vert 0.832 0.073 CMDbtwn-vert 0.860 0.077 CMDwthn-fwd 0.666 0.153 CMDbtwn-fwd 0.684 0.173 Number of sections 5* 3-47* Number of strides within section 25* 3-310*
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There was no relation between any exposure markers and the CMDwthn-vert (table 9.2).
However, for CMDbtwn-vert there was some evidence for positive relationships with SOS
(sum of squeeze pain), sit and reach, 10m sprint and from GPS the average acceleration marker in the last week and the overall ratio. Increases in these markers were related to
0.17% (95% CI 0.028, 0.318; p=0.019), 0.02% (95% CI 0.007, 0.032; p=0.002), 9.45% (95%
CI 1.746, 17.158; p=0.017), 7.26% (95% CI 3.584, 10.929; p<0.001) & 2.91% (95% CI 1.440,
4.378; p<0.001) increases in CMD respectively (decreased variability; table 9.2).
CMDwthn-fwd was positively associated with hours of sleep (decreased variability; 1.36%;
95% CI 0.783, 1.935; p<0.001; table 9.2) and inversely associated with knee to wall on both sides (increased variability; -0.68%; 95% CI -0.319, -1.030; p<0.001 & -0.54%; 95% CI
-0.202, -0.868; p=0.002 on left and right respectively). All the GPS markers were negatively related to CMDwthn-fwd (increased variability) with larger associations with the acceleration markers (-29.9 to -35.8%; p<0.001) than distance measures.
CMDbtwn-fwd had positive relationships with hours of sleep (decreased variability; 0.96%;
95% CI 0.299, 1.615; p=0.004) and inverse relationships with knee to wall (increased variability; -0.83%; 95% CI -0.449, -1.200; p<0.001 & -0.50%; 95% CI -0.144, -0.845; p=0.006) and all GPS measures except for chronic average acceleration. Average acceleration in the last 7 days had the largest association (increased variability; -15.2%; 95% CI -5.414, -
24.972; p=0.002), greater than distance measures (Table 9.2).
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To compare the standardized coefficients across measures each exposure variable has been z-scored with the resulting coefficients showing the effect of a 1 standard deviation change in exposure (figure 9.1). In this plot, the coefficients can be better compared.
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Table 9.2: Linear mixed model outputs. Significant relationships marked as bold. Coefficients represent percentage change in CMD from a one unit change in the variable. +/- = 95% confidence interval of coefficient.
Raw Within Stride CMD Raw Between Stride CMD Raw Within Stride CMD Raw Between Stride CMD Outcome (vertical) (vertical) (forward) (forward) Exposure Coefficient +/- p-value Coefficient +/- p-value Coefficient +/- p-value Coefficient +/- p-value Hours of Sleep 0.032 0.206 0.759 -0.213 0.227 0.067 1.359 0.576 0.000 0.957 0.658 0.004
Soreness -0.014 0.130 0.835 0.117 0.143 0.109 -0.322 0.364 0.083 -0.318 0.415 0.133 Energy -0.099 0.155 0.212 -0.061 0.170 0.483 0.204 0.423 0.344 -0.067 0.493 0.791
Groin Squeeze Pain (45o) -0.031 0.249 0.806 0.165 0.262 0.216 -0.227 0.670 0.507 0.463 0.756 0.230 Subjective Groin Squeeze Pain (0o) -0.082 0.267 0.548 0.233 0.277 0.100 -0.484 0.706 0.179 0.364 0.795 0.370 SOS -0.015 0.141 0.829 0.173 0.145 0.019 -0.037 0.371 0.844 0.402 0.418 0.059 Sit & Reach 0.003 0.011 0.550 0.020 0.013 0.002 -0.025 0.029 0.103 -0.031 0.037 0.097
10m Sprint 7.019 8.196 0.094 9.452 7.706 0.017 6.191 15.602 0.437 7.189 17.108 0.411
Groin Squeeze Pressure (45o) -0.003 0.009 0.570 -0.004 0.009 0.350 0.004 0.023 0.698 -0.011 0.025 0.408
o
Objective Groin Squeeze Pressure (0 ) -0.002 0.009 0.714 -0.001 0.009 0.813 0.009 0.022 0.423 -0.004 0.025 0.781 Knee-to-wall left -0.002 0.180 0.979 -0.041 0.151 0.596 -0.675 0.356 <0.001 -0.825 0.375 <0.001 Knee-to-wall right -0.033 0.168 0.704 -0.033 0.139 0.645 -0.535 0.333 0.002 -0.495 0.350 0.006 Acute distance (7-day) 0.000 0.000 0.644 0.000 0.000 0.791 -0.002 0.000 <0.001 -0.001 0.001 <0.001 Chronic distance (21-day) 0.000 0.000 0.741 0.000 0.000 0.553 -0.002 0.001 <0.001 -0.001 0.001 0.002 ACWR (Distance) -0.247 1.002 0.629 0.286 1.142 0.624 -6.603 2.613 <0.001 -5.682 3.012 <0.001
Acute Ave ACC (7-day) -1.724 3.271 0.302 7.257 3.673 <0.001 -29.879 8.472 <0.001 -15.193 9.779 0.002
Chronic Ave ACC (21-day) -0.328 4.668 0.890 5.403 5.131 0.039 -35.789 12.145 <0.001 -9.674 13.853 0.171 GPS ACWR (Ave ACC) -0.250 1.290 0.704 2.909 1.469 <0.001 -8.398 3.364 <0.001 -5.608 3.878 0.005 Acute PL (7-day) 0.000 0.001 0.740 0.001 0.001 0.168 -0.002 0.002 0.123 0.000 0.003 0.748 Chronic PL (21-day) 0.000 0.001 0.680 0.001 0.002 0.092 -0.002 0.004 0.242 -0.003 0.004 0.258 ACWR (PL) 0.009 0.806 0.982 0.492 0.912 0.290 -2.163 2.117 0.045 -0.071 2.386 0.953
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Figure 9.1: Standardized (z-scored) effects of variables on outputs. Coefficients show the effect of a 1 standard deviation change in exposure and allow comparisons across variables.
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From figure 9.1 no variable has a clear association with CMDwthn-vert. CMDbtwn-vert has positive associations, illustrated by the confidence interval not containing zero, with sit and reach (0.29%, 95% CI 0.102, 0.479; p=0.002), average acceleration ACWR (0.34%, 95%
CI 0.167, 0.507; p<0.001), SOS (0.34%, 95% CI 0.055, 0.627; p=0.019), acute average ACC
(0.37%, 95% CI 0.184, 0.561, p<0.001), chronic average ACC (0.22%, 95% CI 0.011, 0.439, p=0.039) and 10m sprint (0.76%, 95% CI 0.139, 1.386, p=0.017).
CMDwthn-fwd showed a positive relationship with hours of sleep (figure 9.1; 1.34%, 95% CI
0.772, 1.907, p<0.001) and negative relationships with knee to wall (-2.24%, 95 CI -1.061,
-3.427, p<0.001 and -1.93%, 95% CI -0.729, -3.129, p=0.002 left and right respectively).
Distance measures; acute (-1.97%, 95% CI -1.504, -2.427, p<0.001) and chronic (-1.92%,
95% CI -1.389, -2.459, p<0.001) and average acceleration; acute (-1.53%, 95% CI -1.098, -
1.968, p<0.001) and chronic (-1.49%, 95% CI -0.986, -2.000, p<0.001).
For CMDbtwn-fwd sleep is positively associated (figure 9.1; 0.94%, 95% CI 0.295, 1.592, p=0.004). There are negative associations with knee to wall (left -2.74%, 95% CI -1.495, -
3.992, p<0.001 & right, -1.78%, 95% CI -0.520, -3.046, p=0.006), acute distance, (-1.38%,
95% CI -0.836, -1.917, p<0.001), chronic distance, (-0.98%, 95% CI -0.365, -1.595, p=0.002), acute average acceleration (-0.78%, 95% CI -0.278, -1.282, p=0.002) and the acute:chronic ratio for average acceleration (-0.65%, 95% CI -0.200, -1.098, p=0.005).
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9.6 Discussion
This study looked to examine relationships between gait-related variability and objective markers of flexibility collected in a full-time sports environment as part of the athletic development processes. It appears that ankle flexibility as measured manually by the knee to wall test has some relationships with movement variability as measured by CMD as does the average magnitude of accelerations performed. A summary of the variables with interactions with variability are given in table 9.3.
Table 9.3: Summary of variability changes from variables. wv=within stride-vertical axis. wf=within stride-forward axis. bv=between stride-vertical axis. bf=between stride- forward axis.
Higher SOS bv Greater ankle dorsiflexion wf|bf Greater sit and reach bv Higher distances wf|bf Slower sprint time bv Higher average acceleration wf|bf Higher acute average acceleration bv Higher loads wf|bf Higher ACWR for acceleration bv More sleep wf|bf
9.6.1 Player Load
Player Load itself was not shown to have any relationships with variability in the rugby population presented in this chapter. It would be expected that there would be more of a relationship with player load given the previous findings in American football (chapter 7) and the association of the forward axis and distance, given the correlation of distance covered and player load (Aughey, 2011; Polglaze, Dawson, Hiscock, & Peeling, 2015).
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A possible explanation may be player load in the current chapter did not come directly from the manufacturer’s software but was calculated from the raw data using the published formula for player load (7).
2 2 2 (푎푐푐푒푙푦(푡)− 푎푐푐푒푙푦(푡−1)) − (푎푐푐푒푙푥(푡)− 푎푐푐푒푙푥(푡−1)) − (푎푐푐푒푙푧(푡)− 푎푐푐푒푙푧(푡−1)) Player Load = √ 100
(7)
Previously within the literature it has been shown that manufacturer generated player load was consistently lower (~15%) than calculated (Nicolella et al., 2018). This is likely due to unpublished data filtration techniques. Due to the inherent noise in the raw data, manufacturers routinely apply different filtering techniques to smooth velocity and acceleration data (Varley, Jaspers, Helsen, & Malone, 2017). However, this alone should not change the relationship other than perhaps change the magnitude of the player load value.
Within rugby league it has been shown that increased load is the best marker of increased risk of illness (Thornton et al., 2016). This has also been augmented by a reduction in overall wellbeing. In other studies a direct relationship between wellbeing and player load has been shown (Gallo et al., 2015; Govus et al., 2018). Therefore, the assessment of load may still have value in a contact sport population as shown in Chapter 7 for American
Footballers despite there being no association with variability in this population.
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9.6.2 Sleep
In the forward axes a relationship was demonstrated with sleep which suggests recovery is an important factor related to variability, adding further support to this observation in
American Football athletes shown in chapter 7.
In this case the volume of sleep (hours) was assessed while previously the same associations have been shown in American Football but with quality of sleep (chapter 7).
In public health, quality may be more important for predicting outcomes as the risk of developing metabolic diseases is 25-200% higher for reduced sleep quality rather than volume (Bin, 2016). Within college students sleep quality was also more closely related to sleepiness and health than sleep quantity (Pilcher, Ginter, & Sadowsky, 1997).
Choosing one over the other to prioritize is likely unwise as both are related to recovery and health outcomes, and in this case variability. Practitioners should encourage athletes to get as much high quality sleep as they can within the constraints of their environment by following sleep hygiene advice (Kölling, Duffield, Erlacher, Venter, & Halson, 2019;
Nédélec et al., 2015). Educating coaches and support staff to advise athletes may remove some of the biggest barriers to achieving this (Miles, Clark, Fowler, Miller, &
Pumpa, 2019). They should also be cautious with the amount of time spent monitoring sleep due to the potential associated anxiety it can cause, hence affecting the sleep itself
(Sawczuk et al., 2018a). A meta-analysis found that subjective reports can provide unique and relevant information in the absence of more objective sleep measures via actigraphy
(Claudino et al., 2019) and there may be value in assessing athletes chronotypes for individual interventions (Bender, Van Dongen, & Samuels, 2018).
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9.6.3 Sprints
When standardizing the outcomes in the vertical axis, CMDbtwn-vert showed an association with a decreased sprint time over 10m (improved performance). If the athlete is slower this is associated with an increase in CMD and hence lower variability.
Monitoring of sprint speed is simple to administer in a team sport environment. It has been applied in an examination of short term fatigue in female team sport athletes and was validated as a recovery measure (Wiewelhove et al., 2015). Indeed over the course of the season soccer players who started games showed variations in their 20 yard sprint time (Kraemer et al., 2004). Quantifying the decline can be done statistically as a smallest worthwhile change relative to the population or individual, but recently a study looked to integrate expert opinions into the decision (Kyprianou et al., 2019). While they examined validity and agreement between a criterion measure of speed (laser) and a GPS system the same principle can be applied to quantifying change. Using an analytical method informed by expert opinion on the significant amount of change for practical significance
(incorporating measurement error) could potentially enhance validity. Either way it may be that a decline in sprint speed that is sufficient to be of note to a practitioner happens too late in the cycle and the changes that built to the decline may be detected by a more sensitive measure such as reduced variability.
While short term fatigue during a repeated sprint protocol can be seen by the practitioners’ eye, and their chosen timing system, there are associated decrements in player load, alongside sprint time increasing. These could potentially be used to monitor individual fatigue responses to repeated sprint training (Akenhead et al., 2017). Within
237 the cadence of a season, and training and recovery cycles, it may be useful to measure athlete readiness within the season. If though this creates the need for an additional test and the associated load monitoring within session using the movement variability measure from the completed sessions would be favourable (i.e. invisible monitoring; section 2.4).
Sprints are the most sensitive fatigue marker in the short-term but typically recover within
24-hours making them a relatively stable marker across the season for repeated use
(Gathercole et al., 2015). Within rugby sevens it was shown that a decrease of 30-m sprint performance is related to the repetition of runs at speeds above MAS in an acute session
(Marrier, Meur, et al., 2017) though they also highlighted how the main sprint acceleration mechanical outputs (F0, V0 and Pmax), and their fatigue related changes differed in relation to performance. This is an additional level of insight to this study and may be a potential future avenue for investigation.
9.6.4 Accelerations
The frequency of high-intensity accelerations both positive and negative, during match play is commonly associated with reduced neuromuscular performance capacity and muscle damage. Rugby union as a sport was shown to favour high and very high intensity negative accelerations over positive ones (D. J. Harper, Carling, & Kiely, 2019). This is likely associated with increased spatial constraints, which restrict opportunities for high- speed running. Thereby demanding players to control their forward momentum to be in a position to tackle or gather the ball by performing more rapid short negative acceleration movements (Kempton, Sirotic, Rampinini, & Coutts, 2015). Within a soccer
238 group decelerations were identified by a machine learning process as an important external load indicator (Jaspers et al., 2018). The authors also suggest that similary to utilsiing expert opinion around setting validity paramters and smallest worthwhile changes, a paradigm shift in the selection of the key external load metrics to monitor may be achieved by comparing objective measures to the subjective as a criterion for construct validity.
It has been proposed that the average acceleration measure may be more appropriate than threshold based methods due to increased reliability (CV ~7% v >11%) and maintained sensitivity (Delaney, Cummins, Thornton, & Duthie, 2017). This is as it represents the overall acceleration load imposed on the athlete, rather than estimated energy consumption. In this study an association with average acceleration and variability was large but opposite depending on the plane (increased acceleration led to decreased variability in the vertical axis but had mixed effects in the forward axis). The current chapter showed an increase in average acceleration metric (i.e. more changes in movement and stops and starts) was associated with an increase in CMD (i.e. reduced variability) which is concurrent with accelerations causing muscle damage and being fatiguing in nature.
There is also an association with the average acceleration metric which is large, though being an average, this marker is relatively stable within sports and so this may not be applicable practically as a variable to influence. For example, in AFL athletes the average acceleration ranges from 1.05 – 0.69 m.s-2 across positions and a performance window of 1-10 minutes. Within soccer players it ranges from 0.8 - 0.5 m.s-2 and in rugby league
239 athletes from 1.27 - 0.72 m.s-2 across similar windows of time (Delaney et al., 2016;
Delaney, Thornton, Burgess, Dascombe, & Duthie, 2017; Delaney, Thornton, Rowell, et al.,
2017). Here averaging across sessions as a whole, athletes averaged 0.32±0.06 m.s-2 across 78 minutes comparative to these windows. These values seem consistent with the power law relationship exhibited across sports and further investigation may look at intense discrete periods of work as characterized by acceleration metrics.
9.6.5 Fatigue changes
Even though there were changes in variability across the season the athletes still performed high-speed running actions within the context of the game and training sessions. These actions to the eye would have appeared regular and similar in action.
What may have been noticeable were tactical outcomes due to fatigue or decision making that prevented an episode of high-speed running. As we detected a change in variability this may reflect degeneracy (Edelman & Gally, 2001) in the system (section
6.0); the ability of elements within the system to perform the same function for the same output (i.e. compensations that allow the body to adapt the motor behaviour structurally to the environment and still run at a high speed). This concept suggests that we can change behaviours without compromising function and raises the question of what level of change in variability is significant or ‘unhealthy’. While degeneracy has been demonstrated to be the basis for diverse actions in complex environments (Seifert,
Wattebled, et al., 2014) to give stability against disruptions, this adaptability may be what we see here manifesting as short term fatigue – i.e. in response to the conditions the athlete is solving the task demands in a slightly different way by altering the degrees of freedom of a task. For example in swimming, constraining the glide phase (as may
240 happen with fatigue) changes the overall stroke by affecting the number of kicks taken
(Seifert, Komar, Crettenand, & Millet, 2014).
Here the interaction of stride length and frequency may operate at a different point on the athlete’s continuum and move away from their preferred or optimal state. It may be that future studies should consider years within the sport or some measure of ‘expert status’ for the task assessed, as it has been shown that neurobiological degeneracy is a precursor for stable coordination patterns and adaptive behaviours that can distinguish between different skill levels (Seifert, Wattebled, et al., 2014). Therefore more expert performers with a different set of moderators (Windt, Zumbo, Sporer, MacDonald, &
Gabbett, 2017) may react differently to fatigue or variability changes as has been shown previously with physical fitness and recovery (Johnston, Gabbett, Jenkins, & Hulin, 2015) and within runners of differing levels of experience (Hafer, Peacock, Zernicke, & Agresta,
2019); highlighting a future avenue for research.
9.6.6 Flexibility
A decreased maximal speed (i.e. slower sprint time) was associated with less variability as was an increase in average accelerations. Practically this makes sense as athletes who have more changes in velocity (i.e. accelerations and decelerations) are more fatigued and are therefore potentially unable to produce a maximal sprint due to muscle damage. Also, there was an association with increased sit and reach (i.e. increased flexibility) being related to less variability in the vertical axis within stride. This may be related to what the athletes did acutely as previously in team sports it has been shown that acute static stretching exerts a negative effect on sprint performance (Sayers, Farley,
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Fuller, Jubenville, & Caputo, 2008). Also, stiffness has been associated with better performance in runners (Kubo, Miyazaki, Shimoju, & Tsunoda, 2015; Kubo, Miyazaki,
Yamada, et al., 2015; Yamaguchi, Takizawa, & Shibata, 2015) and so an increase in compliance may also negatively affect gait-related variability. This may also explain the presence of increased ankle flexibility in the forward axis both within and between stride being associated with a decrease in variability.
Within Australian Rules football the normal variability for similar tests of sit and reach, groin squeeze and dorsiflexion were shown to be 1.01 cm, 7.4% and 0.48 cm similar to previous papers on the topic (Esmaeili et al., 2018). These authors found that the tests do not seem to be affected by regular training and competition across a season and so are suitable for use to flag individual changes outside the normal range. Within a rugby population similar representative values were found for knee-to-wall, but in the current population the participants were much stronger in the pelvic girdle, as reflected by an average groin squeeze around 150% of that previously reported (Yeomans et al., 2019); likely reflecting the professional level of these players compared to the amateur players in the comparative study.
While the groin squeeze is time efficient, low-cost and easily implemented in a training schedule (Roe et al., 2016) no relation to gait-related variability was shown in the current chapter. While it has been hypothesized to be a marker of additional recovery needs
(Tiernan, Lyon, Comyns, Nevill, & Warrington, 2019), when conducted with a frequency of more than once a week the correlations and associations with load are very weak
(R2<8%). In contrast, there were larger associations with perceived fatigue which begs
242 the question if the two are related via a Hawthorne effect – i.e. “I’ve told you I feel tired so I won’t push as hard” or vice versa “I can’t push as hard so I must be tired” (Marcora,
2009).
9.7 Limitations
As before, examining one team over one season could be improved by having a wider group of athletes and exposure to differing training methodologies of coaches. As mentioned in section 8.6, similarly to the soccer population it has been previously shown that athletes display stability in screening tests across a season (Esmaeili et al., 2018).
Thus, it is important for practitioners to consider the context when interpreting findings of weekly screening measures (i.e. is this a period of intensified fixtures relative to a bye week).
9.8 Practical applications
Practitioners should strive for the most parsimonious monitoring system possible
(Coutts, 2014). The use of, and interpretation of, accelerometry data for the assessment of movement variability does not require extra time from the athlete due to the data being used to monitor the physical output in sessions via GPS also. The additional monitoring measures of sprint speed, flexibility and subjective markers are included in the monitoring system for different reasons, justified by the performance staff. Given this it should be asked if this measure gives additional insight into maximizing performance or minimizing injury risk. There are clear relationships in a few areas that provide value: recovery (hours of sleep); flexibility (sit and reach and knee to wall), volume (distance) and intensity (average acceleration). Conversely to the football population (chapter 7)
243 where the variability measures act as an early detector to wellness measures; here they may provide confirmation of changes manifested from changes in any one of these four areas. This confirmation may give practitioners the confidence to intervene and make changes to a periodized plan.
9.9 Conclusions
This study has shown that gait-related variability is associated with objective markers of recovery such as sprint time and ankle flexibility, as measured manually. Combining the inputs to give confidence to practitioners on the manifestation of physical changes from the imposed training. The objective measures of flexibility do show an association in this group which may indicate a preference for manual testing over the attempted use of technology in this space as in chapter 8.
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Executive Summary
This chapter synthesises, examines and discusses the research findings in Chapters 5,
7, 8 & 9. It starts by restating the aims and objectives and summarizing the key findings within each sporting group. A general discussion around perceptions and beliefs follows in the context of periodized training programmes and measuring recovery in the athlete’s natural environment. The limitations of the approach are acknowledged and recommendations for future research are made before the overall conclusions.
10.1 Introduction
The purpose of this chapter is to firstly review main findings of the research relative to the stated aims and objectives associated with this series of investigations. This is followed by a general discussion section, orienting the research outcomes to broader theoretical and methodological frameworks in this area. Practical considerations for the support staff practitioner are also presented. Finally, suggestions for future research based on the insights gained are made.
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Figure 10.1: Schematic showing progression throughout thesis. A = Aim; W&W = What & Whom; M = Measures; O = Outcomes; ROM = range of motion; CMD = coefficient of multiple determination; acc = average acceleration.
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10.2 Aims and objectives
The primary aim of this thesis was to look at recovery practices in athletes, from how they are perceived to practically how they are monitored in athletes. The process to fulfilling this aim included 3 phases: (1) Review of perceptions and beliefs around recovery in athletes (chapter 3); (2) Establishing the validity of a tool to measure recovery in situ (chapter 5); (3) investigation of gait-related variability across differing team sport seasons (chapters 7-9).
10.2.1 Summary of outcomes
10.2.1.1 Stating the problem
Sport science is about using the best available evidence, at the right time, in the right environment, with the right individual, to improve performance (D. Bishop, 2008). For the applied practitioner this involves accessing on and off-field brains (figure 1.3) to influence daily practice. The first series of studies of the thesis were developed to try and develop a line of enquiry in recovery. This involved establishing and defining the problem
(figure 1.2).
10.2.1.2 What athletes do for recovery does not match what they believe in
There has been little previous investigation into the attitudes and beliefs associated with the choice and use of these practices in diverse socio-cultural environments across the world. Integrating athletes’ belief systems into their recovery, or developing education programs around a chosen method, may contribute to planning more adequate interventions and aid selection strategies for implementation (Van Wilgen & Verhagen,
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2012). Chapter 2 showed that athletes use a diverse range of recovery modalities, not all of which are supported by the current evidence in the literature.
10.2.1.3 Best to monitor athletes within session and educate them out with
There are also discrepancies in what athletes believe and what they do in practice. For example, whilst they rate sleep highly for recovery they do not demonstrate this belief in their adoption of sleep for recovery. The differences between belief and practice highlight that the education of athletes across their sporting life cycle is important.
The main method athletes utilized to assess recovery was to wait until the next session to know they are ready – be that training or competition. Many coaches and practitioners would not want to wait until the biggest competition to find out if their athletes are prepared. The increase in wearable technology has made monitoring within session more accessible and increasingly individual in nature, then using the subjective data to triangulate the objective information collected. The ideal is to gather as much information about the athlete, their performance and their current training status, without them even knowing and thus minimising any Hawthorne effect (section 2.4). Finding an ecologically valid and reliable method to monitor recovery without drain on resources
(including the athlete’s time and energy) is the ultimate goal.
10.2.2 Determining the utility of wearables to quantify gait-related variability in different settings
Within individual sports it is clear that there is a relationship between non-traditional gait analysis and physiology (Schütte et al., 2015). This is more difficult to define in team
248 sports, though during the course of this PhD study the research has utilised a tool that identifies straight line high speed running within the context of a team sports session and can then analyse waveforms for variability, to potentially characterise recovery states between sessions. This outcome may be an indicator of whether recovery is achieving its desired outcome, or if additional rest is needed. This works best when combined alongside the athlete’s perception at the next session, something that the athletes believe in (Chapter 3).
A quality assurance study (chapter 5) was designed to evaluate the reliability and validity of the proposed analysis tool in measuring gait-related variability from integrated micro technology units (wearables) worn by team sport athletes. This built upon examples in the literature where units can distinguish strides and ground contact on each side of the body in situ though gait related trunk accelerations (Buchheit et al., 2015; J. H. Williams,
Rizzuto, et al., 2019). The results demonstrated that in the vertical and forward axes the reliability was sufficiently sensitive to measure athletes in situ using this technology.
Therefore, it was deemed that trunk worn wearable GPS units with embedded accelerometers could be used to monitor gait-related variability within sessions for team sport athletes in this thesis.
10.3 Sport Specific Outcomes
10.3.1 American football
Variability in stride detected by commonly used accelerometers was associated with fatigue, soreness and training load. The more fatigued that athletes reported being, the lower their gait-related variability. Chapter 7 has shown that gait-related variability was
249 associated with fatigue and 7-day training load in the context of Collegiate American
Football. Combining such objective methods with subjective methods is likely to enhance the predictive ability and become a very powerful tool within elite sport environments.
Practically, subjective measures and feedback from athletes could be utilised as a guide to check if recovery activities are successful in making the athlete feel better.
In the absence of 100% disclosure from athletes the assessment of variability therefore has the potential to identify those athletes who are displaying physical symptoms that would indicate the need to modify training. Conversely, it may be able to identify athletes who satisfy flagging criteria but are showing no physical symptoms and therefore may not need training modifications. This can also reflect if the chosen recovery strategies are having a positive effect on the athlete, through measuring both wellness and movement variability.
“Look good, feel good. Feel good, play good.”
Deion Sanders
10.3.2 Soccer
Within a different sport and gender, objective markers of flexibility, collected without practitioner intervention in a time efficient manner, were assessed for association with variability. There was no single physical test that was clearly related to gait-related variability in either axis, although subjective markers of soreness and stress did show an association in the forward and vertical axes respectively. Given that separate measures
(i.e. hip angle and knee valgus) had relationships within movements, future research
250 could assess if particular global exercises (i.e. overhead squat) or individual components of them (i.e. knee valgus) are suitable objective markers to monitor recovery with greater control exhibited over the movement. In this context athletes self-corrected if outside their normal range and the act of assessment triggered an intervention and potentially changed the outcome; practitioners need to be careful about defining these as measures or targets for athletes; otherwise, this may reflect the effort or application of the participants.
Whereas it has been previously shown that load is related to changes in gait related variability (chapter 7) it does not appear that there is a similar association with measures of flexibility. This suggests that the objective flexibility measures used in this study cannot be used to infer changes in gait related variability that may be related to fatigue and subsequent injury for female collegiate soccer players.
10.3.3 Rugby
In a collision-based team sport, and again a male population, additional objective markers and gait-related variability were examined. The association with ankle flexibility suggested that it was worth observing some of the markers in relation to gait-related variability, but it may require a large dataset and machine learning approaches to accurately determine which ones (Jaspers et al., 2018) in a larger or different population.
There is a relationship between gait-related variability and load in both the American
Football and rugby populations. The lack of collisions in soccer may influence why load does not seem to impact variability as much. It has been shown that EIMD can affect
251 motor control in a novel task (Leite et al., 2019) and also that the addition of contacts can reduce performance in: a simple repeated sprint task (Johnston & Gabbett, 2011); a simulated team sport activity circuit (Singh, Guelfi, Landers, Dawson, & Bishop, 2011); or a small sided game (Johnston, Gabbett, Seibold, & Jenkins, 2014).
It has been shown that sensible management of load allows the management of recovery through modifying the training stress. This monitored ‘softly, softly’ approach is not necessarily overly cautious but allows the data driven coach to be progressive and measure the impact through feedback from athletes – potentially a combination of subjective (‘how do you feel?’) and objective measures of the response to the training dose. Where the measurement of moment variability may be useful is potentially for younger coaches, those working within new sports or as an early warning sign in all athletes.
10.3.4 Comparisons of standardized metrics
Figures 10.2 – 10.4 show the relative effects across sports as a whole (figure 10.2) and then subdivided as load (figure 10.3) and non-load-based metrics (figure 10.4). Figure
10.2 shows comparatively what metrics were investigated in each cohort and sport and how these reactions compared.
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Figure 10.2: All standardized metrics across sports
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Figure 10.3: Load based standardized metrics across sports
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Figure 10.4: Subjective and objective measures not related to load across sports
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Within figure 10.3 we can break down the differences in responses to load. The football athletes react in a different way (positive association) in the forward axis – except for chronic load. While this axis showed greater variability from the validation study (higher
CV; chapter 5) the large effect in the standardized CMDwthn-fwd response for American
Football is extremely different to the other sports. This may be a manifestation of collisions as discussed below, but may also reflect poor training design as has been discussed in the thesis, whereby the highest loads come in the first few weeks of the season and progressively taper from there (Murray et al., 2018). They also experienced the highest loads throughout – none of this though explains why the response is in the opposite direction.
In the CMDbtwn-vert axis it appears that the pattern is more homogeneous but of a different magnitude. This may reflect the professional nature of the rugby population; this may mean they have developed a smaller degree of variability as well as having fewer competing time demands outside sport compared to student athletes (i.e. typically they have no academic load (Wilson & Pritchard, 2005)). They may though have additional life stressors and high psychological loads from social media or pressures they feel placed on them by themselves or their family and friends that can lead to mental health issues
(Beable, Fulcher, Lee, & Hamilton, 2017; Moesch et al., 2018). Also, they do not play with protective padding as American football do and so technically they are less inclined to put themselves in compromising positions on the field (i.e. tackling with the head across the oncoming opponents’ knees in American football is common as they wear helmets).
Again, in the within stride condition we see football respond to load in an opposing
256 manner to the other sports without a good explanation, suggesting that it’s less likely to be a sex-based difference.
Figure 10.4 shows the non-load-based variables. The pragmatic approach to the research utilised existing variables that athletes were familiar with and comfortable reporting as the athletes had utilised them for a number of seasons. While few of these were common across sports due to their individual monitoring systems there was some commonality.
Sleep was assessed across all sports (duration of sleep was assessed in both rugby and soccer and quality in American Football). Sleep or hours of sleep show the same relationship with CMDfwd.
It has been proposed that the high neural activity during REM sleep is associated with memory consolidation and learning of motor skills (O’Donnell, Beaven, & Driller, 2018); this may manifest in motor control patterns. Sleep has shown performance improvements in basketball players as measured by sprint speed and crucially an improvement in free throws and 3-point shots (Mah et al., 2011) and poorer sleep has been associated with injury in a case study of a professional soccer player and in adolescent athletes (Milewski et al., 2014; Nédélec, Leduc, Dawson, Guilhem, & Dupont,
2019); again all reflective of different motor control strategies.
Within the more reliable vertical axis the directional changes and associations are relatively common across sports and greatest in the collision-based sports. The measure of ankle dorsiflexion was completed digitally for soccer players and physically by measuring knee-to-wall with a practitioner in the rugby population. In this case all the
257 changes and associations occurred in the same direction which suggests that despite the different methods this is a reliable measure that has merit in being included in a monitoring regime. There were relatively few variables that give a clear indication of their relationship with variability by not crossing the 0 axis, unlike the load measures (figure
10.4). Of those that did, it appears that the Y-balance anterior reach test is a good indicator due to changes in the hip angle; suggestive of motor control strategies in running as it employs a single leg stance and subsequent control to reach the foot as far forward as possible. General flexibility would also appear important given the relationship of sit-and-reach to variability. Groin squeeze was not effective but likely as it is a global measure of pain and so is not granular enough to use in this decision making, but satisfies as a screening measure (Falvey, King, Kinsella, & Franklyn-Miller, 2016; Light et al., 2019).
10.4 General discussion
10.4.1 Perceptions and beliefs around recovery in athletes
The Pygmalion effect (whereby others’ expectations affect performance; such as the sculptor who fell in love with his own carved statue) has shown that expectations can affect behaviour, which can in turn affect performance outcomes. Inducing high expectation in teachers can lead to high levels of IQ test performance in students
(Brookover, Rosenthal, & Jacobson, 1969; Timmermans, Rubie-Davies, & Rjosk, 2018).
Similarly, differences in ‘mindset’ or outlook have also been researched (Dweck, 2006).
Cultivating a growth mindset, people believe that they can develop their abilities through dedication and hard work. In contrast, a fixed mindset is portrayed by a belief that talent alone, without effort, creates success. In part these examples could be considered self-
258 fulfilling prophecies and likely explain how many recovery practices are perceived by athletes due to their relationship with their coach. If the coach believes in it, it probably works for the athlete (section 3.8.5.3). Indeed this social placebo effect has been shown using pain perception; whereby the patient’s experience, in a simulated clinical interaction, was modulated by the doctor’s beliefs about its effectiveness and transmitted via subtle facial cues (P. A. Chen et al., 2019).
Within sport the emotional environment of an individual has been associated with performance in international rugby (Crewther et al., 2019). The interplay between sleep and mood modify their reaction to a training week similarly to how facial cues affect patients. As belief effects are powerful, and can result in positive outcomes, hence the effect of placebo, a persuasive practitioner could implement an intervention with an athlete regardless of its scientific basis (Hurst et al., 2019). They may be successful despite their intervention. Conversely, if an athlete does not believe in an intervention, regardless of the research in its favour, it is likely its implementation will be unsuccessful and so may represent a missed opportunity for improvement. There may also be cases where traditions in sport (i.e. we have always done it this way) reflect poorly supported practices with low belief from athletes (D. Martin, 2019). These cases reflect why the trust between practitioner and athlete is important to work together for a true outcome because of and not in spite of, their training and interventions (Neupert et al., 2019).
10.4.2 Measuring recovery in situ
It has been speculatively hypothesized that asymmetries in stride kinetics may increase injury risk due to asymmetrical limb loading (Tenforde, Ruder, Jamison, Singh, & Davis,
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2018). However, asymmetries appear to occur naturally at running speeds, where uninjured runners have shown differences of ~3% for vertical ground reaction force
(Zifchock, Davis, & Hamill, 2006). This may also reflect different training regimens for particular sports. For example, in soccer, footballers perform repetitive kicking actions, which place different demands on the stance and kicking leg muscles which drives differential development (Brophy et al., 2007). In theory, these training adaptations could also affect the magnitude of running asymmetry kinetics. Ideally these different training backdrops would be accounted for in any analysis (Hughes, Jones, Starbuck, Sergeant, &
Callaghan, 2019). While to a degree they are by analysing one team’s training and applying a random subject factor in the analysis, these different training regimens may reflect differences in load found here and studies across mixed cohorts in the future could account for this. Equally within sports there is the opportunity to account for training history by looking at the year in school or years spent within the sport.
Running kinetics have been traditionally quantified via motion capture, measured external
GRFs (Lafortune & Hennig, 1991; Raper et al., 2018) or accelerometers that measure internal tibial shock (Kavanagh & Menz, 2008). However, these technologies are expensive, invasive, and typically limited to a laboratory setting, which may prohibit natural running patterns (Charry, Hu, Umer, Ronchi, & Taylor, 2013) and restrict the ecological validity. Wearables have been shown to detect differences in gait in the field and in this thesis have demonstrated their validity and potential use to assess athletes in their natural environments via ‘invisible’ monitoring (section 2.4).
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10.4.3 Team sport differences
American football is characterized by frequent high intensity actions and collisions
(Wellman, Coad, Goulet, & McLellan, 2016). While there are inherent risks of injury that are influenced by position and injury (McCunn et al., 2017), historically it has been shown that the injury rate is higher in preseason (Steiner, Berkstresser, Richardson, Elia, & Wang,
2016). A high player load in the first weeks of pre-season has been consistently shown in studies of American Football (Murray et al., 2018; Wellman, Coad, Flynn, Siam, et al.,
2017). This contrasts advice on optimal design to improve performance and mitigate injury risk. This training design (long sessions with minimal recovery days) may contribute to the relationship of variability and load found in this thesis. Previously it has been speculated that American football employs a ‘survival of the fittest’ approach to loading and recovery (Murray et al., 2018), though this may reflect external constraints on the pre-season length and session number at the collegiate level (NCAA, 2016); while a similar pattern of high load declining across the season is evident at the professional level also (Ward, 2018), it has been shown that athletes recover at different rates within this collegiate population (Fullagar, Govus, et al., 2017) and needs considered as it may also affect the variability. It is not known if a reduced load reflects decreased fitness and conditioning, but the other sports seem stable in loads (similar acute and chronic periods) and may reflect a more sensible training design (figure 10.3; section 10.3.4).
While we controlled for the effect of individuals using a mixed model design it may be that a more traditional training design may negate any detriment in variability associated with load. Alternatively, it may be that this decline reflects a change in conditioning levels across the season that are induced by a declining load across the season.
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Any loading pattern has short- and long-term effects. In the short term a stressor can negatively affect performance, but in the long term can promote regeneration and resilience and so positively influence performance. The differing temporal aspects of these adaptive responses reflect a lack of cooperation among biological levels which can reduce the synergy of the musculoskeletal system (Vázquez, Hristovski, & Balagué,
2016). This needs to be considered in training program design. Further perturbation from soft-tissue or collision injuries may also contribute to the differences in American
Football athletes.
This backdrop of differing circumstances in terms of environmental and personal constraints may explain how some micro-injuries can combine to become more significant while at other times they dissipate (Pol, Hristovski, Medina, & Balague, 2019).
This may reflect fluctuations in perceived readiness or fatigue which manifest as coordinative reconfigurations (Aragonés, Balagué, Hristovski, Pol, & Tenenbaum, 2013) or degeneracy (section 9.5.5).
10.4.3.1 Periodization differences
Training periodization discussions are consistent in that they reference the need for training to vary, to drive adaptation through the principle of overload, though it can very quickly become more complex from there. Typically, phases are built in sequence to manipulate volume and intensity to vary the training demands and adaptations. Over the course of the year this may be referred to as a macro cycle. Smaller meso-cycles common within the year are the off-season, pre-season and in-season. These can be
262 subdivided into smaller phases with a specific focus (J. J. Malone et al., 2015). In turn meso-cycles are comprised of micro-cycles which represent the shortest period within a plan and typically represent a week to fit with the calendar. Micro-cycles typically reflect the preparation leading into the next match (J. J. Malone et al., 2015).
Periodization has recently been unpacked in the literature and the stress-based model may not be sufficient to explain a stress response in humans due to the number of areas that input into the overall stress, both within and away from sport, from the physical to the psychological (Coyne, Gregory Haff, Coutts, Newton, & Nimphius, 2018; Kiely, 2018).
Mechanical training stress as measured by external training load is not an independent signal for adaptation. Indeed there are a number of factors that can be periodized within the overall training from the exposure to heat and altitude, tactical models and nutrient timing through to recovery strategies (Mujika, Halson, Burke, Balagué, & Farrow, 2018).
This is what was called for in the recent debate on ACWR; that the heuristic overcomplicates the model of training design and being sensible in the approach is what is needed, rather than overly complex models and restrictive guidelines (section 2.4). An overly complex and prescriptive training plan is likely to be too rigid and will quickly become redundant as an athlete responds and adapts to the training stimulus. As has been shown in a review of the merits of ‘block periodization’ (concentrating training on the development of a specific ability (Issurin, 2019)), that compared varied training with non-varied training (Kiely, Pickering, & Halperin, 2019), variation itself can improve the training stimulus rather than block periodization as an approach.
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Looking at meta-analyses comparing periodized to non-periodized programs finds only two papers (Rhea & Alderman, 2004; T. D. Williams, Tolusso, Fedewa, & Esco, 2017). A recent paper examined these meta-analyses and found that no papers within the analyses had compared periodized with varied but non-periodized programs (Afonso et al., 2019); rather they looked at comparing to a consistently loaded program. Little evidence supports the use of a completely structured periodization model in team sport athletes (Morgans, Orme, Anderson, & Drust, 2014), though variability in training load patterns across phases has been identified (Ritchie, Hopkins, Buchheit, Cordy, & Bartlett,
2016). This may reflect coaches’ intuition in modifying training systematically regardless of whether they term it periodization or not. Indeed, there may be parallels in the findings of this thesis. Specifically, the large effect of load in the football population where the modification of training load is historically poor in relation to ‘best practice’ models.
Comparing soccer to American football the 7-d acute load was almost double in football
(235±92 v 419±112) whilst the standard deviation was similar. Compared to rugby the load was similar to football but with almost double the standard deviation (235±177).
These higher loads against a backdrop of similar variation may be why there was more impact. Where the load was most variable (i.e. highest SD) there was almost no impact on the variability measure (i.e. rugby). This suggests that monotony is a key metric to monitor in training as recent papers have done (Delecroix, McCall, Dawson, & Berthoin,
2018) revisiting earlier work proposing this by Foster and colleagues (2001). This may create an avenue for future research in how transformational coaching styles can give autonomy back to athletes at professional (Hodge, Henry, & Smith, 2014) and collegiate
264 levels (Bourne et al., 2015) to drive their own training to achieve agreed outcomes with minimal risk if empowered (i.e. athletes know what they need; let them do it).
Generally, pre-season loads and volumes are higher than in-season (Moreira,
Bilsborough, Sullivan, Ciancosi, et al., 2015; Ritchie et al., 2016). In-season much of the intensity component is contained within the competitive team performance (Anderson et al., 2016; Manzi et al., 2010; Moreira, Bilsborough, Sullivan, Cianciosi, et al., 2015) and there is little teams can do about the placement of these fixtures. They can though modify loads in the days preceding and immediately after the fixture. For example, in the English
Premier League it has been shown that the days leading into the game have significantly lower loads than those furthest from the match (Anderson et al., 2016; J. J. Malone et al.,
2015).
A novel approach to examine the variation in training load may be to abandon comparative statistics but look at rate of change in training load. One such investigation at the professional level of American Football examined loads across a 17-week season
– typically the team practiced 3 times every 7 days (Ward, 2018). Across the season player load decreased at the team level, though the quarterback position had a similar load each week and there was of course variance within each position group at the individual level.
In section 2.4 I discussed that the seasonal structure may be a constraint too far for coaches as this affects the relationship of injury and load (Bornn et al., 2019). Having a variable load within the constraints of the schedule is a sensible approach to
265 progressively overload athletes within and across micro, meso and macro cycles. This can incorporate elements of peaking for a particular opponent (Robertson & Joyce, 2015) or being cognisant of the upcoming travel schedule to manage the load (Robertson &
Joyce, 2018). Equally it can be used to drive skill acquisition (Farrow & Robertson, 2017) and so we are reminded that variability within a bandwidth is a good thing as we have found in our investigation.
10.5 Limitations
10.5.1 Challenges of wearable sensors
The advancements in micro sensor technology in recent years have led to an explosion in use and awareness by professional athletes, but also increased scrutiny and investigation from professional sports leagues. FIFA have approved in-game use in soccer and the NBA have investigated a number of wearables alongside the NBAPA to assess their suitability for use. As mentioned previously, there is an issue with compliance amongst athletes who view the process as intrusive and with suspicion over how it is handled and who for, but the data obtained is rich in nature and gives information beyond the simple metrics of distance and speed that are routinely reported.
Traditionally, the sensors can give information on the physiological (Barrett et al., 2014;
Cormack et al., 2013) or biomechanical load (Vanrenterghem et al., 2017). It has been shown that raw trunk GPS integrated accelerometers overestimate ground reaction forces during team sports movements (Nedergaard et al., 2017; Tran, Netto, Aisbett, &
Gastin, 2012; Wundersitz, Netto, Aisbett, & Gastin, 2013) and so should be used with
266 caution to monitor biomechanical loading, but it is possible to look at differences in movement variability as shown here (Chapter 5).
The approach taken in chapters 7-9, where analysis of gait-related waveform variability from a GPS device can drive training load decision making, demonstrates how researchers can transform the signal from wearable sensors into meaningful information. The biggest strength of wearable sensors is undoubtedly the ability to easily collect large non-invasive datasets in the natural environment (Aughey, 2011). At times the commercial companies are overly reductionist in their software to give a manageable number of metrics to practitioners (i.e. player load or body load being the accumulated accelerometer vector). Though they still produce in the order of hundreds of variables, this is undoubtedly effective in summarizing large data sets on a daily basis, but it makes it difficult to relate observed changes to the context of the situation without appreciating how it is constructed.
The appeal is high for capturing data on a number of participants simultaneously, regardless of location (accelerometers can be used indoors or out, unlike GPS) and without additional demands on athletes’ time. Examples from AFL (Cormack et al., 2013;
Garrett, Graham, et al., 2019; Mooney et al., 2013) and soccer (Barrett, Midgley, Reeves, et al., 2016) have shown differences in the movement profile due to fatigue but these studies examined contributions of individual axes to overall accelerometer load – not the gait-related variability per se.
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10.6 Future Research Directions
In learning a skill we look to ‘master the redundant degrees of freedom’ (Bernstein, 1967) to perform the task optimally. We may freeze out certain degrees of freedom to simplify the task (Vereijken, Emmerik, Whiting, & Newell, 1992). In fatigue we are attempting to learn new skills ‘on-the-fly’ as the body is encountering new challenges. To this end we may do the same and reduce the degrees of freedom to simplify the task. This may be why we see changes in variability closely linked with load that are indicative of fatigue and under recovery.
More motor variability may imply a slower development of fatigue (Cignetti et al., 2009) and/or reflect strategies of adaptation that can relieve the load on fatiguing tissues – this relationship is likely bi-directional in nature. There may be a reorganization of motor strategies to preserve performance (Sparto, Parnianpour, Reinsel, & Simon, 1997). This rapid search for a new movement strategy to preserve task performance is reflected with increased variability (J. R. Fuller et al., 2011). Depending on the task this can be skill related; it has been shown that table tennis players can compensate for fatigue at the expert level, by manipulating their movement pattern to maintain performance, distinguishing them from the recreational player (Aune, Ingvaldsen, & Ettema, 2008).
10.6.1 Recommendations for future research
While this thesis examined the application of movement variability from a perspective of recovery and its relationship with both subjective and objective markers, there are recommendations for future research based on these findings.
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10.6.1.1 Efficacy of a recovery intervention
This PhD started as the investigation of a ‘specific factor, in a specific location’ (Section
1.1.2.1); establishing the efficacy of individual recovery techniques within an adolescent population. Contextualising the investigations in an applied setting was always the challenge to take an applied approach to research. Now it has been shown that the measure of movement variability is sensitive and reliable and can show associations to several subjective and objective measures, further investigation may return to evaluating the efficacy of recovery interventions using movement variability as the outcome of interest.
This more research driven, academic approach would allow for more precise control of the stimulus to induce fatigue in a reliable and consistent fashion. This could target a number of differing variables with measurement of movement variability, either on a treadmill or in outdoor running tasks, pre and post the intervention. This would allow manipulation of the exercise mode, changing the fatigue target from a physical (e.g. aerobic, anaerobic or strength based) to a mental perspective (e.g. cognition or decision making) and the particular recovery intervention applied.
10.6.1.2 Biomechanical loading
As research and technological developments increase the miniaturization of sensors and the integration into garments there is potential in team sports for full body inertial measurement unit technologies as the case in the clinical gait space (e.g. Xsens 3D bodysuit). The technologies that also integrate electromyography into their garments allow the monitoring of muscle activity (Düking et al., 2016; Finni, Hu, Kettunen, Vilavuo,
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& Cheng, 2007) and relating the muscle activation to changes in variability may be interesting (Hug et al., 2019). However, there will be necessary work to establish the reliability, validity and generalizability of these new technologies before they are deployed in a team sport setting. Also, assuming these are scalable options, the introduction of further technologies will further increase the data processing time, unless there are suitable advances in application of techniques such as machine learning. Any values they provide should be insightful, meaningful and easy to access for the practitioner.
10.6.1.3 Intensified training
While the studies have looked longitudinally it may be of value to investigate periods of intensified training in sports – whether that is the pre-season training period or camp in soccer or football, or periods in the season with congested fixtures; it would be interesting to investigate short periods of high loading on the gait-related variability. An extension of this would be to establish a dose-response relationship with the gait-related variability changes and the application of load. How does the manipulation of volume, frequency and intensity in stress affect the gait-related variability? What is the minimum dose that causes a change in gait-related variability? What is the maximum dose of training load that can be tolerated before there is a negative change in gait-related variability? What is the recovery time in variability once these changes occur?
10.6.1.4 Relationship between variability changes and overuse training risk
Future research may have the possibility to explore if gait-related variability changes are a better predictor of overuse injury than other measures, for example the accumulated
270 vector magnitude (Player Load) currently obtained from the integrated accelerometer in
MEMS units (Bowen, Gross, Gimpel, & Li, 2017; Colby et al., 2014)
10.6.1.5 Application of movement variability assessment
This thesis focused on gait-related variability as a method to monitor fatigue and hence by extension recovery. It is not novel for coaches and practitioners to make daily attempts to control the external stressors players are exposed to. It is common place to modify loads in relation to the upcoming fixture (Anderson et al., 2016; J. J. Malone et al., 2015) or factors such as travel and turnaround time (Robertson & Joyce, 2015). It is common practice to manage loads in rehab through off-feet conditioning in the pool or anti-gravity treadmill. The benefit of this being that metabolic and neuromuscular stimuli are maintained with a reduction in mechanical loading.
Being able to utilize a collected data set in multiple ways to assess the reaction to the training load is imperative to practitioners being successful in their outcomes of maximizing health and performance of their athletes. The challenge currently for the application of the tool is the additional time required for analysis of the data. A recent study on the practices and perception of training load monitoring in high level soccer was conducted and identified that the human cost is the highest perceived barrier for effective monitoring of training load (Akenhead & Nassis, 2016). Finding a way to make this tool seamless, either through integrating into commercial companies’ software or building into an open source tool such as R-Studio would increase its effectiveness and use. It has already been shown that completing filtering and analysis on GPS data outside of the manufacturer’s software can reduce some of the variability and issues that come with
271 firmware and software updates (Buchheit et al., 2014; Thornton, Nelson, Delaney,
Serpiello, & Duthie, 2019) and adding an additional layer of analysis that combines accelerometry information may provide additional insight (Buchheit & Simpson, 2017).
While some teams have utilized standardized drills or submaximal tasks as the monitoring tool, this can take time from the schedule and interfere with the weekly schedule and see a change in movement strategy in practice from the athlete as they consciously complete the task differently. Postural sway has been used in rugby union
(Troester & Duffield, 2019), submaximal runs (J. H. Williams, Jaskowak, et al., 2019) and standardized drills or small sided games in soccer (Lacome, Simpson, Broad, et al., 2018;
Rowell, Aughey, Clubb, & Cormack, 2018). The drawback of these is the frequency of use
(i.e. weekly/monthly) as opposed to the method investigated in this thesis which examines athletes with a higher frequency and looks at changes relative to the individual to allow subsequent adjustments to be made to the training provision. This is similar to other approaches tracking adaptation through indices to measure the acute response to training (Delaney, Duthie, Thornton, & Pyne, 2018).
10.6.1.6 Real-time feedback
Directly targeted kinematic variables can be altered using visual biofeedback within just two sessions in a complex, whole limb skill such as the fencing lunge (Mulloy, Irwin,
Williams, & Mullineaux, 2019). External feedback has been shown to be effective for pacing activity in swimming (Thompson, MacLaren, Lees, & Atkinson, 2002), so future applications of the variability tool with an increase in feedback may see near or real-time feedback to athletes that flag that their technique has undergone a change from ‘normal’
272 and some intervention may be required. Indeed this has been an identified need in prior research papers (Pol et al., 2019). This may lend itself to a period of retraining on a treadmill (to facilitate the feedback). Gait modifications though field based interventions have been shown to be effective at reducing impact forces, peak hip adduction and eccentric knee joint work through focusing on step rate (Willy et al., 2016).
10.7 Conclusions
A key finding of this combined body of research was the applicability and practical use of wearables to analyse variability in a real world setting without additional interruption to athletes, confirming previous approaches in this area (Buchheit et al., 2015; Buttfield,
2016). Recent literature cites this emerging area as one to monitor training via movement outputs measured via wearable technology non-invasively (Kiely, Pickering, & Collins,
2019). As such practitioners should be able to have confidence in these measures and use them as early warning signs or confirmatory indicators when other subjective or objective markers are assessed, particularly if working with a young coach with less developed contextual domain-specific knowledge.
It should be noted that while achieving the overall aim, this is a preliminary investigation into the use of gait-related variability in monitoring athlete recovery status and should now serve as a springboard for ‘future research’, potentially in identified areas. One example of this application is work in Australian Football looking at modified training and variability relationships (Buttfield & Ball, 2019).
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Correspondence and coherence are both essential to our goals of better understanding the training environment and athlete. Both are essential for the iterative process of training plan design and execution that incorporates contextual factors in an ongoing feedback loop to create a more comprehensive and coherent design (Dawson & Gregory,
2009).
In summary, a combination of factors is still the best approach (figure 10.5); suggestive of utilizing a couple of subjective questions about sleep and fatigue levels and three potential objective measures; two of flexibility (y-balance and ankle dorsiflexion) and regular sprint monitoring, alongside a sensible training design that accounts for dose and response of load should in its own right create a parsimonious monitoring system that could be supplemented by insights of movement variability. Continual monitoring in the field will reduce the need to complete separate screening points and perhaps can reduce load and burden on athletes and practitioners; perhaps to only a single mid-week test for muscle screening and subjective questions backed up by daily load and gait-related variability monitoring.
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Figure 10.5: Parsimonious monitoring system in practice. An example of how teams may incorporate measures of external load on training days alongside subjective and objective markers early in the training week to assess recovery from and preparation for the next game (based on 1 game a week). Sensible training design is the most important element of the system. Elements are chosen based on the results from this thesis (left column) and placed on the most important days (centre column; i.e. MV from main training sessions and fatigue measures as markers of recovery from previous game). These surround the athlete with a protective ‘early warning system’ (right column). MV=movement variability measured in session, PL=player load from accelerometry, Flex=Flexibility measures, Sleep=measures of sleep quality/quantity, Sprint=measure of sprint speed/acceleration.
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Appendix A: Survey ethics approval
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Appendix B: Survey ethics application
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Appendix C: Survey ethics information sheet
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Appendix E: Consent Oregon University of Oregon Athletics Department Release of Liability
I understand that participating in any intercollegiate athletics sport (including but not limited to playing and practicing/training) can be a dangerous activity involving many risks of injury which may range from minor to catastrophic. I understand that the dangers and risks of participating may include, but are not limited to: death, serious neck injury, serious spinal cord injury, which may result in complete or partial paralysis, brain damage, serious injury to virtually all internal organs, serious injury to virtually all bones, joints, ligaments, muscles, tendons, and other aspects of the muscular-skeletal system, serious injury or eye impairment, and serious injury to other aspects of my body, general health and well-being. I understand that the dangers and risk of participating in a University of Oregon intercollegiate athletics sports program may result not only in serious impairment of my future abilities to earn a living, to engage in other business, social, and recreational activities, and generally to enjoy life.
I further understand that participants can and have the responsibility to help reduce the chance of injury. I know that I must obey all safety rules and report all athletic injuries to the athletic trainers or medical staff. I understand that the University of Oregon cannot be held responsible for any injuries of conditions which may be caused by the actions of other athletes or teams. I also understand that injuries may be caused by my own failure to follow playing techniques, training and team rules, safety procedures and techniques made know to me by coaching staff, athletics training staff, or are otherwise known to me from any other source including, but not limited to, the University’s medical personnel, and I agree to obey such instructions.
In consideration for the University of Oregon granting me the privilege of participating in intercollegiate athletics at the University of Oregon, subject to the obligations of University policies and the Student-Athlete Code of Conduct, I agree to save, hold harmless, discharge and release the University of Oregon, its officers, agents and employees from any and all liability, claims, causes of action, damages or demands of any kind and nature whatsoever that may arise from or in connection with my participation in any activities related to my aforementioned participation in intercollegiate athletics at the University of Oregon and from any treatment or training, medical or otherwise, provided to me by University of Oregon and its employees and agents, whether caused by the negligence or carelessness of the University, its employees or agents or otherwise. I further understand that this is a release of liability and indemnity agreement, and it is intended to be as broad and inclusive as permitted by law. If any portion herein is held invalid, it is agreed that the balance shall continue in full force and legal effect.
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In signing this Release of Liability, I hereby acknowledge and represent: (a) that I have read this document in its entirety, understand it, voluntarily agree to all of the statements contained in it, and sign it voluntarily; and (b) that this Release of Liability is the entire agreement between the parties hereto and its terms are contractual and not a mere recital.
Student-Athlete Signature: ______Date:______
Print Name: ______
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University of Oregon Athletics Department Consent for Treatment
I understand that I may be injured while participating (including but not limited to playing and practicing/training) in intercollegiate athletics at the University of Oregon (UO). I hereby authorize and give my consent to the UO team physicians and/or their consulting physicians to render any treatment or medical or surgical care that they deem medically necessary to my health and well-being, while participating in, or traveling under, the UO’s intercollegiate athletic program while enrolled as a student athlete. I further understand that it is my responsibility to report any injuries I have experienced—including but not limited to signs and symptoms of concussion (i.e. headache, nausea)—to UO team physicians and athletic training staff. This includes past conditions and injuries, as such reporting is part of my responsibility as a student athlete to assist in the evaluation of my ability to participate. Accurate information is very important in the management of my physical conditioning, care and prevention of injuries that may occur as a result of my participation in intercollegiate athletics at UO.
I also hereby authorize and give my consent to the UO athletic trainers, who are under the direction and guidance of the UO team physicians, to render any preventive, first- aid, rehabilitation, or emergency treatment they deem necessary to my health and well-being to my health and well-being, while participating in, or traveling under, the UO’s intercollegiate athletic program whilst enrolled as a student athlete.
I also authorize and give my consent to UO team physicians and trainers to obtain, through a physician of its own choice, any emergency care that may become necessary while participating in, or traveling under, the UO’s Intercollegiate Athletic Program.
Student-Athlete Signature: ______Date:______
Print Name: ______
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University of Oregon Athletics Department Consent for Disclosure of Medical and Performance-Related Information
I hereby authorize and give my consent to the UO Athletic Medical & Performance Staff (including but not limited to Team Physician(s), Assigned Trainers, X-ray technicians, Sports Nutritionists, Insurance Administrators, Sport Scientists, Biomechanists, Mental Health Care providers and Strength & Conditioning Personnel) to share confidential health/medical information such as lab findings and test results, and other tests relating to my medical, mental, and nutritional health, and my athletic performance, only among members of the UO Athletic Medical & Performance Staff or outside providers as deemed necessary by the practitioner in charge of my care.
In addition, I consent to release of my records as stated below; (1) Records that are related to and are being provided in response to public health and safety emergencies; (2) Records that are related to and are being provided in order to prevent or control disease, injury or disability; (3) Records that are related to your treatment and care being provided in order to further your treatment and care; and (4) Records relating to and being provided in order to: i. bill third parties for health care services or pharmaceutical drugs provided to me; ii. pay for health care services or pharmaceutical drugs provided to me.
In these instances, releases could be made to; (1) Public health authorities that are legally authorized to receive reports for the purpose of preventing or controlling public health emergencies, disease, injury or disability. (“Public health authorities” include agencies or authorities of the United States Government, a State, a Territory, a political subdivision of a State or Territory, as well as a person acting under a grant of authority from, or under a contract with a public health authority); (2) Persons who are at risk of contracting or spreading a disease or condition if other law authorizes the University to notify such individuals as necessary to carry out public health interventions or investigations; (3) HIPAA covered entities and their staff participating in the electronic medical exchange network; (4) Health Care Providers treating me and their staffs; (5) Insurance Companies that are obligated to pay for health care services and pharmaceutical drugs provided to me; and (6) Other third parties that process payment for health care services and pharmaceutical drugs provided to me.
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I understand by signing below confidential information can be shared between and amongst the above mentioned practitioners and UO Medical & Performance Staff. This consent will be valid for the length of my association with the UO Athletic Department, unless otherwise noted or revoked.
Student-Athlete Signature: ______Date:______
Print Name: ______
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University of Oregon Athletics Department Consent for Information Release for Research Purposes
I hereby authorize the UO Athletic Medical & Performance Staff and members of the Graduate Athletic Training Program at the UO to use my movement screen, injury, and performance data in research, presentations, and other means in further research and education. I understand that any and all information used will remain de-identified, anonymous, and confidential.
Student-Athlete Signature: ______Date:______
Print Name: ______
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Appendix F: Variability ethics approval
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Appendix G: Variability ethics application
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