More than metrics: The role of socio-environmental factors in determining the success of athlete monitoring.

The perceived value of athlete monitoring systems (AMS) has recently been questioned. Poor perceptions of AMS are important, because where practitioners lack confidence in monitoring their ability to influence programming, and performance is likely diminished. To address this, researchers have primarily sought to improve factors related to monitoring metrics, e.g., validity rather than socio-environmental factors, e.g., buy-in. Seventy-five practitioners (response rate: n = 30) working with Olympic and Paralympic athletes were invited to take part in a survey about their perceptions of AMS value. Fifty-two per cent (n = 13) was confident in the sensitivity of their athlete self-report measures, but only 64% (n = 16), indicated their monitoring was underpinned by scientific evidence. A scientific base was associated with improved athlete feedback (rS (23) = 0.487, p =0.014*) and feedback correlated with athlete monitoring adherence (rS (22) = 0.675, p = <0.001**). If athletes did not complete their monitoring, 52% (n = 13) of respondents felt performance might be compromised. However, most respondents 56% (n = 14), had worked with internationally successful athlete(s) who did not complete their monitoring. While AMS can be a useful tool to aid performance optimisation, its potential value is not always realised. Addressing socio-environmental factors alongside metric-factors may improve AMS efficacy.

Athlete monitoring practices in elite sport in the United Kingdom.

Athlete monitoring systems (AMS) aid performance optimisation and support illness/injury prevention. Nonetheless, limited information exists on how AMS are employed across elite sports in the United Kingdom. This study explored how athlete monitoring (AM) data, in particular athlete self-report measures, were collected, analysed and disseminated within elite sports. Thirty elite sports practitioners representing 599 athletes responded to a survey on their AM methodologies. The majority, 83%, (n = 25) utilised an AMS, and a further 84% (n = 21) stated the collection of their AMS data was underpinned by a scientific rationale. Athlete self-report measures (ASRM) were the most commonly employed tool, with muscle soreness, sleep and energy levels amongst the most frequently collected measures. The ubiquitous use of custom single-item ASRM resulted in considerable variability in the questionnaires employed, thus potentially impacting questionnaire validity. Feedback processes were largely felt to be ineffective, with 44% (n = 11) respondents indicating that athletes did not receive sufficient feedback. Some respondents indicated that AMS data was never discussed with athletes and/or coaches. Overall, significant disparities exist in the use of athlete monitoring systems between research and elite sports practice, and the athlete, coach and practitioner experience of monitoring risks being poor if these disparities are not addressed.

Managing Travel Fatigue and Jet Lag in Athletes: A Review and Consensus Statement.

Athletes are increasingly required to travel domestically and internationally, often resulting in travel fatigue and jet lag. Despite considerable agreement that travel fatigue and jet lag can be a real and impactful issue for athletes regarding performance and risk of illness and injury, evidence on optimal assessment and management is lacking. Therefore 26 researchers and/or clinicians with knowledge in travel fatigue, jet lag and sleep in the sports setting, formed an expert panel to formalise a review and consensus document. This manuscript includes definitions of terminology commonly used in the field of circadian physiology, outlines basic information on the human circadian system and how it is affected by time-givers, discusses the causes and consequences of travel fatigue and jet lag, and provides consensus on recommendations for managing travel fatigue and jet lag in athletes. The lack of evidence restricts the strength of recommendations that are possible but the consensus group identified the fundamental principles and interventions to consider for both the assessment and management of travel fatigue and jet lag. These are summarised in travel toolboxes including strategies for pre-flight, during flight and post-flight. The consensus group also outlined specific steps to advance theory and practice in these areas.

Napping in high-performance athletes: Sleepiness or sleepability?

Daytime napping is a common practice in high-performance athletes, and is widely assumed to reflect sleepiness arising from sports-related sleep debt. The possibility that athlete naps may also be indicative of 'sleepability', a capacity to nap on demand that is only weakly related to homeostatic sleep pressure, has not previously been tested. The present study compared daytime sleep latencies in high-performance athletes and non-athlete controls using a single nap opportunity model. Elite (n = 10), and sub-elite (n = 10) athletes, and non-athlete controls (n = 10) attended the laboratory for a first adaption trial, and a subsequent experimental trial. Subjective sleepiness was assessed using the Karolinska Sleepiness Scale (KSS) at 14:00, 14:30 and immediately prior to a 20-minute nap opportunity at 15:00. Sleep latencies were measured using polysomnography, and defined as the time from lights out to the first epoch of any stage of sleep (N1, N2, N3, REM). In unadjusted comparisons with non-athlete controls, elite athletes showed significantly shorter sleep latencies in both the adaptation (p < 0.05) and experimental trials (p < 0.05). These significant differences were maintained in models controlling for pre-trial KSS scores and pre-trial total sleep time (all p < 0.05). Sleep latency scores for sub-elite athletes showed similar trends, but were more labile. These results are consistent with a conclusion that, among elite athletes, napping behaviour can reflect sleepability and may not necessarily result from nocturnal sleep disruption and daytime sleepiness.

Sleep and the athlete: narrative review and 2021 expert consensus recommendations.

Elite athletes are particularly susceptible to sleep inadequacies, characterised by habitual short sleep (<7 hours/night) and poor sleep quality (eg, sleep fragmentation). Athletic performance is reduced by a night or more without sleep, but the influence on performance of partial sleep restriction over 1-3 nights, a more real-world scenario, remains unclear. Studies investigating sleep in athletes often suffer from inadequate experimental control, a lack of females and questions concerning the validity of the chosen sleep assessment tools. Research only scratches the surface on how sleep influences athlete health. Studies in the wider population show that habitually sleeping <7 hours/night increases susceptibility to respiratory infection. Fortunately, much is known about the salient risk factors for sleep inadequacy in athletes, enabling targeted interventions. For example, athlete sleep is influenced by sport-specific factors (relating to training, travel and competition) and non-sport factors (eg, female gender, stress and anxiety). This expert consensus culminates with a sleep toolbox for practitioners (eg, covering sleep education and screening) to mitigate these risk factors and optimise athlete sleep. A one-size-fits-all approach to athlete sleep recommendations (eg, 7-9 hours/night) is unlikely ideal for health and performance. We recommend an individualised approach that should consider the athlete's perceived sleep needs. Research is needed into the benefits of napping and sleep extension (eg, banking sleep).

When research leads to learning, but not action in high performance sport.

Research from sports science and related clinical and scientific fields produces knowledge that is often highly relevant to high performance sport. However, there exists a gap between current science and applied coaching or sports science practice. Addressing, and bridging, this gap from both sides of the research-application divide is an important ambition. In this commentary we discuss the wonderful, yet often unforgiving challenge of improving athlete performance as a practitioner or coach in high performance sport. Blending existing knowledge (that which we know works), transferable knowledge (that which has worked elsewhere), anecdotal observations (that which we have seen work) and innovation (where the risk: reward ratio of it working at all is high), to find the right solution, at the right time, for the specific (and often unique) individual is the challenging "art" of application. Here we explore this challenge related to the high performance sports system and the people within it, then more specifically to coaching and learning, and finally to practitioners and athletes using a case study example of applying sleep science to the high performance environment.

Does Elite Sport Degrade Sleep Quality? A Systematic Review.

BACKGROUND: Information on sleep quality and insomnia symptomatology among elite athletes remains poorly systematised in the sports science and medicine literature. The extent to which performance in elite sport represents a risk for chronic insomnia is unknown. OBJECTIVES: The purpose of this systematic review was to profile the objective and experienced characteristics of sleep among elite athletes, and to consider relationships between elite sport and insomnia symptomatology. METHODS: Studies relating to sleep involving participants described on a pre-defined continuum of 'eliteness' were located through a systematic search of four research databases: SPORTDiscus, PubMed, Science Direct and Google Scholar, up to April 2016. Once extracted, studies were categorised as (1) those mainly describing sleep structure/patterns, (2) those mainly describing sleep quality and insomnia symptomatology and (3) those exploring associations between aspects of elite sport and sleep outcomes. RESULTS: The search returned 1676 records. Following screening against set criteria, a total of 37 studies were identified. The quality of evidence reviewed was generally low. Pooled sleep quality data revealed high levels of sleep complaints in elite athletes. Three risk factors for sleep disturbance were broadly identified: (1) training, (2) travel and (3) competition. CONCLUSION: While acknowledging the limited number of high-quality evidence reviewed, athletes show a high overall prevalence of insomnia symptoms characterised by longer sleep latencies, greater sleep fragmentation, non-restorative sleep, and excessive daytime fatigue. These symptoms show marked inter-sport differences. Two underlying mechanisms are implicated in the mediation of sport-related insomnia symptoms: pre-sleep cognitive arousal and sleep restriction.

Respiratory muscle specific warm-up and elite swimming performance.

BACKGROUND: Inspiratory muscle training has been shown to improve performance in elite swimmers, when used as part of routine training, but its use as a respiratory warm-up has yet to be investigated. AIM: To determine the influence of inspiratory muscle exercise (IME) as a respiratory muscle warm-up in a randomised controlled cross-over trial. METHODS: A total of 15 elite swimmers were assigned to four different warm-up protocols and the effects of IME on 100 m freestyle swimming times were assessed.Each swimmer completed four different IME warm-up protocols across four separate study visits: swimming-only warm-up; swimming warm-up plus IME warm-up (2 sets of 30 breaths with a 40% maximum inspiratory mouth pressure load using the Powerbreathe inspiratory muscle trainer); swimming warm-up plus sham IME warm-up (2 sets of 30 breaths with a 15% maximum inspiratory mouth pressure load using the Powerbreathe inspiratory muscle trainer); and IME-only warm-up. Swimmers performed a series of physiological tests and scales of perception (rate of perceived exertion and dyspnoea) at three time points (pre warm-up, post warm-up and post time trial). RESULTS: The combined standard swimming warm-up and IME warm-up were the fastest of the four protocols with a 100 m time of 57.05 s. This was significantly faster than the IME-only warm-up (mean difference=1.18 s, 95% CI 0.44 to 1.92, p<0.01) and the swim-only warm-up (mean difference=0.62 s, 95% CI 0.001 to 1.23, p=0.05). CONCLUSIONS: Using IME combined with a standard swimming warm-up significantly improves 100 m freestyle swimming performance in elite swimmers.