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 has numerous important physiological and cognitive functions that may be particularly important to elite athletes. Recent evidence, as well as anecdotal information, suggests that athletes may ...experience a reduced quality and/or quantity of sleep. Sleep deprivation can have significant effects on athletic performance, especially submaximal, prolonged exercise. Compromised sleep may also influence learning, memory, cognition, pain perception, immunity and inflammation. Furthermore, changes in glucose metabolism and neuroendocrine function as a result of chronic, partial sleep deprivation may result in alterations in carbohydrate metabolism, appetite, food intake and protein synthesis. These factors can ultimately have a negative influence on an athlete’s nutritional, metabolic and endocrine status and hence potentially reduce athletic performance. Research has identified a number of neurotransmitters associated with the sleep–wake cycle. These include serotonin, gamma-aminobutyric acid, orexin, melanin-concentrating hormone, cholinergic, galanin, noradrenaline, and histamine. Therefore, nutritional interventions that may act on these neurotransmitters in the brain may also influence sleep. Carbohydrate, tryptophan, valerian, melatonin and other nutritional interventions have been investigated as possible sleep inducers and represent promising potential interventions. In this review, the factors influencing sleep quality and quantity in athletic populations are examined and the potential impact of nutritional interventions is considered. While there is some research investigating the effects of nutritional interventions on sleep, future research may highlight the importance of nutritional and dietary interventions to enhance sleep.
In any sport, successful performance requires a planned approach to training and recovery. While sleep is recognized as an essential component of this approach, the amount and quality of sleep ...routinely obtained by elite athletes has not been systematically evaluated. Data were collected from 70 nationally ranked athletes from seven different sports. Athletes wore wrist activity monitors and completed self-report sleep/training diaries for 2 weeks during normal training. The athletes also recorded their fatigue level prior to each training session using a 7-point scale. On average, the athletes spent 08:18 ± 01:12 h in bed, fell asleep at 23:06 ± 01:12 h, woke at 6:48 ± 01:30 h and obtained 06:30 ± 01:24 h of sleep per night. There was a marked difference in the athletes' sleep/wake behaviour on training days and rest days. Linear mixed model analyses revealed that on nights prior to training days, time spent in bed was significantly shorter (p = 0.001), sleep onset and offset times were significantly earlier (p < 0.001) and the amount of sleep obtained was significantly less (p = 0.001), than on nights prior to rest days. Moreover, there was a significant effect of sleep duration on pre-training fatigue levels (p 0.01). Specifically, shorter sleep durations were associated with higher levels of pre-training fatigue. Taken together, these findings suggest that the amount of sleep an elite athlete obtains is dictated by their training schedule. In particular, early morning starts reduce sleep duration and increase pre-training fatigue levels. When designing schedules, coaches should be aware of the implications of the timing of training sessions for sleep and fatigue. In cases where early morning starts are unavoidable, countermeasures for minimizing sleep loss - such as strategic napping during the day and correct sleep hygiene practices at night - should be considered.
The ongoing global pandemic brought on by the spread of the novel coronavirus SARS-CoV-2 is having profound effects on human health and well-being. With no viable vaccine presently available and the ...virus being rapidly transmitted, governments and national health authorities have acted swiftly, recommending ‘lockdown’ policies and/or various levels of social restriction/isolation to attenuate the rate of infection. An immediate consequence of these strategies is reduced exposure to daylight, which can result in marked changes in patterns of daily living such as the timing of meals, and sleep. These disruptions to circadian biology have severe cardiometabolic health consequences for susceptible individuals. We discuss the consequences of reductions in patterns of daily physical activity and the resulting energy imbalance induced by periods of isolation, along with several home-based strategies to maintain cardiometabolic health in the forthcoming months.
Water immersion is increasingly being used by elite athletes seeking to minimize fatigue and accelerate post-exercise recovery. Accelerated short-term (hours to days) recovery may improve competition ...performance, allow greater training loads or enhance the effect of a given training load. However, the optimal water immersion protocols to assist short-term recovery of performance still remain unclear. This article will review the water immersion recovery protocols investigated in the literature, their effects on performance recovery, briefly outline the potential mechanisms involved and provide practical recommendations for their use by athletes. For the purposes of this review, water immersion has been divided into four techniques according to water temperature: cold water immersion (CWI; ≤20 °C), hot water immersion (HWI; ≥36 °C), contrast water therapy (CWT; alternating CWI and HWI) and thermoneutral water immersion (TWI; >20 to <36 °C). Numerous articles have reported that CWI can enhance recovery of performance in a variety of sports, with immersion in 10-15 °C water for 5-15 min duration appearing to be most effective at accelerating performance recovery. However, the optimal CWI duration may depend on the water temperature, and the time between CWI and the subsequent exercise bout appears to influence the effect on performance. The few studies examining the effect of post-exercise HWI on subsequent performance have reported conflicting findings; therefore the effect of HWI on performance recovery is unclear. CWT is most likely to enhance performance recovery when equal time is spent in hot and cold water, individual immersion durations are short (~1 min) and the total immersion duration is up to approximately 15 min. A dose-response relationship between CWT duration and recovery of exercise performance is unlikely to exist. Some articles that have reported CWT to not enhance performance recovery have had methodological issues, such as failing to detect a decrease in performance in control trials, not performing full-body immersion, or using hot showers instead of pools. TWI has been investigated as both a control to determine the effect of water temperature on performance recovery, and as an intervention itself. However, due to conflicting findings it is uncertain whether TWI improves recovery of subsequent exercise performance. Both CWI and CWT appear likely to assist recovery of exercise performance more than HWI and TWI; however, it is unclear which technique is most effective. While the literature on the use of water immersion for recovery of exercise performance is increasing, further research is required to obtain a more complete understanding of the effects on performance.
Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography ...(PPG). This study evaluated the validity of WHOOP's PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via WHOOP and ECG over 15 opportunities. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP's proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10-11%) and SWC (5-5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP's proprietary filter, which approached or exceeded the CV (3-13%) and SWC (1.5-6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.
Abstract Objectives There is a growing interest in monitoring the sleep of elite athletes. Polysomnography is considered the gold standard for measuring sleep, however this technique is impractical ...if the aim is to collect data simultaneously with multiple athletes over consecutive nights. Activity monitors may be a suitable alternative for monitoring sleep, but these devices have not been validated against polysomnography in a population of elite athletes. Design Participants ( n = 16) were endurance-trained cyclists participating in a 6-week training camp. Methods A total of 122 nights of sleep were recorded with polysomnography and activity monitors simultaneously. Agreement, sensitivity, and specificity were calculated from epoch-for-epoch comparisons of polysomnography and activity monitor data. Sleep variables derived from polysomnography and activity monitors were compared using paired t -tests. Activity monitor data were analysed using low, medium, and high sleep–wake thresholds. Results Epoch-for-epoch comparisons showed good agreement between activity monitors and polysomnography for each sleep–wake threshold (81–90%). Activity monitors were sensitive to sleep (81–92%), but specificity differed depending on the threshold applied (67–82%). Activity monitors underestimated sleep duration (18–90 min) and overestimated wake duration (4–77 min) depending on the threshold applied. Conclusions Applying the correct sleep–wake threshold is important when using activity monitors to measure the sleep of elite athletes. For example, the default sleep–wake threshold (>40 activity counts = wake) underestimates sleep duration by ∼50 min and overestimates wake duration by ∼40 min. In contrast, sleep–wake thresholds that have a high sensitivity to sleep (>80 activity counts = wake) yield the best combination of agreement, sensitivity, and specificity.
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.
Abstract Objectives Anecdotally many athletes report worse sleep in the nights prior to important competitions. Despite sleep being acknowledged as an important factor for optimal athletic ...performance and overall health, little is understood about athlete sleep around competition. The aims of this study were to identify sleep complaints of athletes prior to competitions and determine whether complaints were confined to competition periods. Design Cross-sectional study. Methods A sample of 283 elite Australian athletes (129 male, 157 female, age 24 ± 5 y) completed two questionnaires; Competitive Sport and Sleep questionnaire and the Pittsburgh Sleep Quality Index. Results 64.0% of athletes indicated worse sleep on at least one occasion in the nights prior to an important competition over the past 12 months. The main sleep problem specified by athletes was problems falling asleep (82.1%) with the main reasons responsible for poor sleep indicated as thoughts about the competition (83.5%) and nervousness (43.8%). Overall 59.1% of team sport athletes reported having no strategy to overcome poor sleep compared with individual athletes (32.7%, p = 0.002) who utilised relaxation and reading as strategies. Individual sport athletes had increased likelihood of poor sleep as they aged. The poor sleep reported by athletes prior to competition was situational rather than a global sleep problem. Conclusion Poor sleep is common prior to major competitions in Australian athletes, yet most athletes are unaware of strategies to overcome the poor sleep experienced. It is essential coaches and scientists monitor and educate both individual and team sport athletes to facilitate sleep prior to important competitions.
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