The importance of sleep for athletic performance and recovery is widely acknowledged (Rae et al., 2017). Despite this importance, however, it appears that particularly elite athletes are facing compromised sleep quantity and quality (Lastella et al., 2015b; Leeder et al., 2012). Studies on partial and total sleep deprivation have highlighted the adverse effects of sleep loss on athletic performance (see: Fullagar et al., 2015 for a review of this literature), while other studies show that sleep extension may actually benefit performance (Mah et al., 2011; Schwartz and Simon, 2015). Yet, the minimal magnitude of sleep deprivation or extension required to impact athletic performance is unknown. Routinely, extreme changes in sleep duration ([+ or -] 4 hours) are fairly rare among elite athletes. Minor variations, however, are more frequently encountered. For example, literature indicates that unfavourable training schedules (Sargent et al., 2014), competition times (Lastella et al., 2015a), and (inter-meridian) travel (Manfredini et al., 1998), may all cause small, but significant reductions in sleep quantity, especially when compared to sleep on rest days (Sargent et al., 2014). Against this background, the current study aimed to assess the extent to which natural variations in sleep are reflected in the performance of elite athletes.
Regarding athletic performance, a classical distinction is often made between "fine motor skills" and 'gross motor skills' (Davis et al., 2000; Fullagar et al., 2015). Fine motor skills are skills that incorporate intricate, precise movements, which use small muscle groups and involve high levels of hand-eye coordination (e.g., golf putting). In contrast, gross motor skills are skills that incorporate less precise, whole-body movement, which use large muscle groups and involve lower levels of hand-eye coordination (e.g., jumping, running, cycling; Davis et al., 2000). As such, an important aspect that distinguishes fine motor skills and gross motor skills is the extent to which they rely on cognitive functions to accurately coordinate the movement. Given the well-documented effect of sleep on cognitive functioning (e.g., reduced behavioral alertness and more cognitive errors after sleep loss; Jarraya et al., 2013; Van Dongen et al., 2003), it is often proposed that fine motor skills are more strongly affected by sleep loss than gross motor skills (Fullagar et al., 2015). Yet, empirical research is scarce, findings remain equivocal, and the effect of sleep on athletic performance is still poorly understood (Fullagar et al., 2015). To produce more insight in this matter, the current study tested the impact of sleep on elite athletes' psychomotor performance as well as their performance on sport-specific fine and gross motor skills.
Furthermore, while most studies have focused on implications of reduced sleep duration (i.e., sleeping fewer hours), to our knowledge, no studies have investigated how natural between-days variations in sleep staging (e.g., differences in the proportion of light, deep or REM sleep) may affect athletic performance. Still, specific recovery functions associated with different sleep stages make it likely that (natural) variation in the absolute time spent in a certain sleep stage might influence performance. For example, deep sleep (also referred to as Slow Wave Sleep or N3) has been associated with the release of growth hormone and, hence, is believed to contribute specifically to muscle restoration and physiological recovery (Shapiro et al., 1981). With regard to psychomotor performance, studies indicate that sheer reductions in sleep quantity rather than variations in the time spent in a certain sleep stage (e.g., more time spent in deep sleep) are responsible for the observed effects (e.g., Edinger et al., 2000; Tilley and Wilkinson, 1984). With regard to athletic performance, however, such information is currently lacking. Therefore, in assessing the impact of sleep on psychomotor and athletic performance, the current study assessed effects of (changes in) sleep quantity as well as sleep staging (i.e., absolute time spent in light sleep, deep sleep, and REM sleep).
In view of the considerations above, the aim of the current study was to investigate the effect of natural between-days variation in sleep quantity and the absolute time spent in different sleep stages on (sport-specific) performance in elite athletes. In particular, effects of sleep were assessed on a general measure of, i) psychomotor performance; and sport-specific measures of, ii) fine motor skill performance, and iii) gross motor skill performance. To provide a robust answer, objective measures of sleep and performance were taken on three non-consecutive occasions within a 7-day monitoring period, on uniform times, among a large cohort of elite athletes (i.e., within-subject, repeated measures design). The overarching hypothesis was that variations in sleep (i.e., increase or decrease in sleep from one day to the next) would impact psychomotor performance more strongly than sport-specific performance and that fine motor skill performance would be more impacted than gross motor skill performance (Fullagar et al., 2015). We had no a priori expectation with regard to which distinct sleep characteristic (i.e., sleep quantity or sleep staging) would impact performance most.
Athletes were recruited via the Netherlands Olympic Committee*Netherlands Sport Federation (NOC*NSF) or via the head coaches of the respective Dutch sport associations. In total 98 elite athletes (56 female) participated. All participants were part of the national (youth) selection in their respective sport and competed at the highest national and international (youth) level. Athletes were aged 18.8 [+ or -] 3.0 (range 15-32) years, had an average Body Mass Index of 21.3 [+ or -] 1.6 kg/m2, had practiced their sport on average for 10 [+ or -] 3.5 years, and spent on average 19.3 [+ or -] 5.1 hours per week on training and competition. Athletes competed in different individual and team sports (Table 1). Athletes were screened for overall sleep quality ((PSQI; Buysse et al., 1989; 4.61 [+ or -] 2.04, M [+ or -] SD)) and subjective sleep complaints (HSDQ; Kerkhof et al., 2013; 1.64 [+ or -] 0.35, M [+ or -] SD) 1.64 [+ or -] 0.35, M [+ or -] SD). A detailed description of subjective and objective sleep estimates of the current sample can be found elsewhere (Knufinke et al., 2018a; Knufinke et al., 2018b). No athletes were excluded based on their sleep history. Ethical approval was obtained from the faculty's ethical committee and all participants or responsible guardians signed informed consent [ECSW2013-1612-170].
Measures and procedures
As part of a larger project assessing sleep among Dutch elite athletes, sleep was assessed for seven consecutive nights. Within this period, measures of (sport-specific) performance were taken on three occasions, typically scheduled 48 hours apart (i.e., on day 1, 4, and 7). In specific cases, adjustments needed to be made in scheduling the performance tests to avoid interference with existing training schedules: female road cyclists (n = 9): performance tests on day 1, 6, 7; soccer players (n = 17): performance tests on day 1, 3, 6; volleyball players (n = 30): performance tests on day 1, 5, 8). Before starting the initial study protocol, athletes underwent three nights of habituation to sleep-wake assessment and one performance test practice session, to become familiar with the performance tests and to get used to the sleep monitoring. All athletes slept at home or in a (training) environment that was highly familiar to them and sleep-wake schedules were habitual (self-chosen). In all cases, the monitoring period was free from competition, with the exception of exhibition matches. Handball and volleyball players, triathletes and mountain bikers were monitored during a training period at their home-base. Road cyclists and soccer players were monitored during one of their annual training camps abroad. The female cyclists (n = 9) crossed six time-zones in a westward inter-meridian travel. For those athletes, data collection started after 6 days to allow for circadian adaptation to the new time-zone.
Sleep quantity and sleep stages were assessed by means of wrist-actigraphy and one-channel EEG sensors, respectively. In addition, a sleep diary (Expanded Consensus Sleep Diary; Carney et al., 2012) was kept to facilitate analysis of the actigraphy data and to monitor background variables related to the athletes' sleep hygiene (data reported elsewhere: Knufinke et al., 2018a). Performance assessment included tests of (1) psychomotor performance, (2) fine motor performance and (3) gross motor performance. Psychomotor performance was assessed by means of a standardized 10-minute psychomotor vigilance task (PVT; Dinges and Powell, 1985). In consultation with the athletes' coaches, fine and gross motor skill performance were assessed sport-specifically (Table 1). To allow for comparison across sports, test outcomes for fine and gross motor skill performance were transformed into norm scores (see 'Data Processing'). In case of multiple performance tests, the test sequence was standardized. All performance measures were taken in the morning on uniform time points, following a standardized (sport-specific) warm-up. Performance tests were conducted between 6 AM and 10 AM, depending on sport and team, but at standardized times within individuals.
Sleep quantity was assessed by means of an actigraph that was continuously worn around the non-dominant wrist and only detached during training or when being in contact with water (Actiwatch 2, Philips Respironics, Munysville, USA). The Actiwatch has been validated against polysomnography (e.g., Weiss et al., 2010). Motion was sampled at 32Hz, averaged and stored in 60 second bins. Parameters of...