Monitoring Training Load to Understand Fatigue in Athletes

Many athletes, coaches, and support staff are taking an increasingly scientific approach to both designing and monitoring training programs. Appropriate load monitoring can aid in determining whether an athlete is adapting to a training program and in minimizing the risk of developing non-functional overreaching, illness, and/or injury. In order to gain an understanding of the training load and its effect on the athlete, a number of potential markers are available for use. However, very few of these markers have strong scientific evidence supporting their use, and there is yet to be a single, definitive marker described in the literature. Research has investigated a number of external load quantifying and monitoring tools, such as power output measuring devices, time-motion analysis, as well as internal load unit measures, including perception of effort, heart rate, blood lactate, and training impulse. Dissociation between external and internal load units may reveal the state of fatigue of an athlete. Other monitoring tools used by high-performance programs include heart rate recovery, neuromuscular function, biochemical/hormonal/immunological assessments, questionnaires and diaries, psychomotor speed, and sleep quality and quantity. The monitoring approach taken with athletes may depend on whether the athlete is engaging in individual or team sport activity; however, the importance of individualization of load monitoring cannot be over emphasized. Detecting meaningful changes with scientific and statistical approaches can provide confidence and certainty when implementing change. Appropriate monitoring of training load can provide important information to athletes and coaches; however, monitoring systems should be intuitive, provide efficient data analysis and interpretation, and enable efficient reporting of simple, yet scientifically valid, feedback

Sleep, circadian biology and skeletal muscle interactions : Implications for metabolic health

There currently exists a modern epidemic of sleep loss, triggered by the changing demands of our 21st century lifestyle that embrace ‘round-the-clock’ remote working hours, access to energy-dense food, prolonged periods of inactivity, and on-line social activities. Disturbances to sleep patterns impart widespread and adverse effects on numerous cells, tissues, and organs. Insufficient sleep causes circadian misalignment in humans, including perturbed peripheral clocks, leading to disrupted skeletal muscle and liver metabolism, and whole-body energy homeostasis. Fragmented or insufficient sleep also perturbs the hormonal milieu, shifting it towards a catabolic state, resulting in reduced rates of skeletal muscle protein synthesis. The interaction between disrupted sleep and skeletal muscle metabolic health is complex, with the mechanisms underpinning sleep-related disturbances on this tissue often multifaceted. Strategies to promote sufficient sleep duration combined with the appropriate timing of meals and physical activity to maintain circadian rhythmicity are important to mitigate the adverse effects of inadequate sleep on whole-body and skeletal muscle metabolic health. This review summarises the complex relationship between sleep, circadian biology, and skeletal muscle, and discusses the effectiveness of several strategies to mitigate the negative effects of disturbed sleep or circadian rhythms on skeletal muscle health

Reliability of a 2-Bout exercise test on a Wattbike cycle ergometer

Purpose: To determine the intraday and interday reliability of a 2 × 4-min performance test on a cycle ergometer (Wattbike) separated by 30 min of passive recovery (2 × 4MMP). Methods: Twelve highly trained cyclists (mean ± SD; age = 20 ± 2 y, predicted VO2max = 59.0 ± 3.6 mL · kg–1 · min–1) completed six 2 × 4MMP cycling tests on a Wattbike ergometer separated by 7 d. Mean power was measured to determine intraday (test 1 [T1] to test 2 [T2]) and interday reliability (weeks 1–6) over the repeated trials. Results: The mean intraday reliabilities of the 2 × 4MMP test, as expressed by the typical error of measurement (TEM, W) and coefficient of variation (CV, %) over the 6 wk, were 10.0 W (95% confidence limits [CL] 8.2–11.8), and 2.6% (95%CL 2.1–3.1), respectively. The mean interday reliability TEM and CV for T1 over the 6 wk were 10.4 W (95%CL 8.7–13.3) and 2.7% (95%CL 2.3–3.5), respectively, and 11.7 W (95%CL 9.8–15.1) and 3.0% (95%CL 2.5–3.9) for T2. Conclusion: The testing protocol performed on a Wattbike cycle ergometer in the current study is reproducible in highly trained cyclists. The high intraday and interday reliability make it a reliable method for monitoring cycling performance and for investigating factors that affect performance in cycling events

Development of the athlete sleep behavior questionnaire: A tool for identifying maladaptive sleep practices in elite athletes

INTRODUCTION: Existing sleep questionnaires to assess sleep behaviors may not be sensitive in determining the unique sleep challenges faced by elite athletes. The purpose of the current study was to develop and validate the Athlete Sleep Behavior Questionnaire (ASBQ) to be used as a practical tool for support staff working with elite athletes. METHODS: 564 participants (242 athletes, 322 non-athletes) completed the 18-item ASBQ and three previously validated questionnaires; the Sleep Hygiene Index (SHI), the Epworth Sleepiness Scale (ESS) and the Pittsburgh Sleep Quality Index (PSQI). A cohort of the studied population performed the ASBQ twice in one week to assess test-retest reliability, and also performed sleep monitoring via wrist-actigraphy. RESULTS: Comparison of the ASBQ with existing sleep questionnaires resulted in moderate to large correlations (r=0.32 - 0.69). There was a significant difference between athletes and non-athletes for the ASBQ global score (44±6 vs. 41±6, respectively, p<0.01) and for the PSQI, but not for the SHI or the ESS. The reliability of the ASBQ was acceptable (ICC=0.87) when re-tested within 7 days. There was a moderate relationship between ASBQ and total sleep time (r=-0.42). CONCLUSION: The ASBQ is a valid and reliable tool that can differentiate the sleep practices between athletes and non-athletes, and offers a practical instrument for practitioners and/or researchers wanting to evaluate the sleep behaviors of elite athletes. The ASBQ may provide information on areas where improvements to individual athletes' sleep habits could be made

Compression-induced improvements in post-exercise recovery are associated with enhanced blood flow, and are not due to the placebo effect

The aim of this study was to investigate the physiological effects of compression tights on blood flow following exercise and to assess if the placebo effect is responsible for any acute performance or psychological benefits. Twenty-two resistance-trained participants completed a lower-body resistance exercise session followed by a 4 h recovery period. Participants were assigned a post-exercise recovery intervention of either compression tights applied for 4 h (COMP), placebo tablet consumed every hour for 4 h (PLA) or control (CON). Physiological (markers of venous return, muscle blood flow, blood metabolites, thigh girth), performance (countermovement jump, isometric mid-thigh pull), and psychological measures (perceived muscle soreness, total quality of recovery) were collected pre-exercise, immediately post-exercise, at 30 (markers of venous return and muscle blood flow) and 60 min (blood metabolites, thigh girth and psychological measures) intervals during 4 h of recovery, and at 4 h, 24 h and 48 h post-exercise. No significant (P > 0.05) differences were observed between interventions. However, effect size analysis revealed COMP enhanced markers of venous return, muscle blood flow, recovery of performance measures, psychological measures and reduced thigh girth compared to PLA and CON. There were no group differences in blood metabolites. These findings suggest compression tights worn after resistance exercise enhance blood flow and indices of exercise recovery, and that these benefits were not due to a placebo effect

Evaluating the typical day-to-day variability of WHOOP-derived heart rate variability in Olympic water polo athletes

Heart rate (HR) and HR variability (HRV) can be used to infer readiness to perform exercise in athletic populations. Advancements in the photoplethysmography technology of wearable devices such as WHOOP allow for the frequent and convenient measurement of HR and HRV, and therefore enhanced application in athletes. However, it is important that the reliability of such technology is acceptable prior to its application in practical settings. Eleven elite male water polo players (age 28.8 ± 5.3 years [mean ± standard deviation]; height 190.3 ± 3.8 cm; body mass 95.0 ± 6.9 kg; international matches 117.9 ± 92.1) collected their HR and HRV daily via a WHOOP strap (WHOOP 3.0, CB Rank, Boston, MA, USA) over 16 weeks ahead of the 2021 Tokyo Olympic Games. The WHOOP strap quantified HR and HRV via wrist-based photoplethysmography during overnight sleep periods. The weekly (i.e., 7-day) coefficient of variation in lnRMSSD (lnRMSSDCV) and HR (HRCV) was calculated as a measure of day-to-day variability in lnRMSSD and HR, and presented as a mean of the entire recording period. The mean weekly lnRMSSDCV and HRCV over the 16-week period was 5.4 ± 0.7% (mean ± 95% confidence intervals) and 7.6 ± 1.3%, respectively. The day-to-day variability in WHOOP-derived lnRMSSD and HR is within or below the range of day-to-day variability in alternative lnRMSSD (~3–13%) and HR (~10–11%) assessment protocols, indicating that the assessment of HR and HRV by WHOOP does not introduce any more variability than that which is naturally present in these variables

Sleep regularity and predictors of sleep efficiency and sleep duration in elite team sport athletes

Background Many elite athletes have suboptimal sleep duration and efficiency, potentially due to factors that may impact sleep onset and offset times. Variability in sleep onset and offset may negatively influence sleep. The sleep regularity index (SRI) is a novel metric for sleep regularity, however there are no published descriptions of SRI in elite athletes. Further, contributors to sleep efficiency and duration in elite athletes using objective measures have not been explored. Methods Sleep was monitored over a minimum of seven consecutive days (7 to 43)—in 203 elite team sport athletes (age range = 19–36 years; female, n = 79; male, n = 124, total sleep nights = 1975) using activity monitoring and sleep diaries. The sleep regularity index (SRI) was calculated to reflect the night-to-night shifts in sleep by accounting for changes in sleep onset and sleep offset. Sleep characteristics were compared between regular and irregular sleepers and important contributors to sleep efficiency and total sleep time were assessed using multiple linear regression models. Results The median sleep regularity index and interquartile range were 85.1 (81.4 to 88.8). When compared to irregular sleepers, regular sleepers demonstrated (1) significantly greater sleep efficiency (p = 0.006; 0.31 medium effect size [ES]), (2) significantly less variability in total sleep time (− p ≤ 0.001; − 0.69, large ES) and sleep efficiency (− 0.34, small ES), (3) similar total sleep time and (4) significantly less variation in sleep onset (p ≤ 0.001; − 0.73, large ES) and offset (p ≤ 0.001; − 0.74, large ES) times. Sleep characteristics explained 73% and 22% of the variance in total sleep time and sleep efficiency, respectively. The most important contributor to total sleep time was a later sleep offset time, while the most important contributors to sleep efficiency were an earlier bedtime and less variable sleep onset times. Conclusions Bedtime and a consistent sleep onset time are important factors associated with sleep efficiency in athletes, while sleep offset is an important factor for total sleep time. Coaches and staff can assist their athletes by providing training schedules that allow for both regularity and sufficiency of time in bed where possible

A validation study of a commercial wearable device to automatically detect and estimate sleep

The aims of this study were to: (1) compare actigraphy (ACTICAL) and a commercially available sleep wearable (i.e., WHOOP) under two functionalities (i.e., sleep auto-detection (WHOOP-AUTO) and manual adjustment of sleep (WHOOP-MANUAL)) for two-stage categorisation of sleep (sleep or wake) against polysomnography, and; (2) compare WHOOP-AUTO and WHOOP-MANUAL for four-stage categorisation of sleep (wake, light sleep, slow wave sleep (SWS), or rapid eye movement sleep (REM)) against polysomnography. Six healthy adults (male: n = 3; female: n = 3; age: 23.0 ± 2.2 yr) participated in the nine-night protocol. Fifty-four sleeps assessed by ACTICAL, WHOOP-AUTO and WHOOP-MANUAL were compared to polysomnography using difference testing, Bland–Altman comparisons, and 30-s epoch-by-epoch comparisons. Compared to polysomnography, ACTICAL overestimated total sleep time (37.6 min) and underestimated wake (−37.6 min); WHOOP-AUTO underestimated SWS (−15.5 min); and WHOOP-MANUAL underestimated wake (−16.7 min). For ACTICAL, sensitivity for sleep, specificity for wake and overall agreement were 98%, 60% and 89%, respectively. For WHOOP-AUTO, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 90%, 60%, 86% and 63%, respectively. For WHOOP-MANUAL, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 97%, 45%, 90% and 62%, respectively. WHOOP-AUTO and WHOOP-MANUAL have a similar sensitivity and specificity to actigraphy for two-stage categorisation of sleep and can be used as a practical alternative to polysomnography for two-stage categorisation of sleep and four-stage categorisation of sleep

Putting the squeeze on compression garments: current evidence and recommendations for future research: a systematic scoping review

Background: Compression garments are regularly worn during exercise to improve physical performance, mitigate fatigue responses, and enhance recovery. However, evidence for their efficacy is varied and the methodological approaches and outcome measures used within the scientific literature are diverse. Objectives: The aim of this scoping review is to provide a comprehensive overview of the effects of compression garments on commonly assessed outcome measures in response to exercise, including: performance, biomechanical, neuromuscular, cardiovascular, cardiorespiratory, muscle damage, thermoregulatory, and perceptual responses. Methods: A systematic search of electronic databases (PubMed, SPORTDiscus, Web of Science and CINAHL Complete) was performed from the earliest record to 27 December, 2020. Results: In total, 183 studies were identified for qualitative analysis with the following breakdown: performance and muscle function outcomes: 115 studies (63%), biomechanical and neuromuscular: 59 (32%), blood and saliva markers: 85 (46%), cardiovascular: 76 (42%), cardiorespiratory: 39 (21%), thermoregulatory: 19 (10%) and perceptual: 98 (54%). Approximately 85% (n = 156) of studies were published between 2010 and 2020. Conclusions: Evidence is equivocal as to whether garments improve physical performance, with little evidence supporting improvements in kinetic or kinematic outcomes. Compression likely reduces muscle oscillatory properties and has a positive effect on sensorimotor systems. Findings suggest potential increases in arterial blood flow; however, it is unlikely that compression garments meaningfully change metabolic responses, blood pressure, heart rate, and cardiorespiratory measures. Compression garments increase localised skin temperature and may reduce perceptions of muscle soreness and pain following exercise; however, rating of perceived exertion during exercise is likely unchanged. It is unlikely that compression garments negatively influence exercise-related outcomes. Future research should assess wearer belief in compression garments, report pressure ranges at multiple sites as well as garment material, and finally examine individual responses and varying compression coverage areas