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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: • This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated. • A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. • This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author. • The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author. • When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given. 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 i ii 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. v vi 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 vii 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 viii 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 ix 4.10.3 Use case 139 4.11 Summary 140 4.12 Aims & objectives 141 5.