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Alcohol is commonly consumed prior to bedtime with the belief that it facilitates sleep. This systematic review and meta-analysis investigated the impact of alcohol on the characteristics of night-time sleep, with the intent to identify the influence of the dose and timing of alcohol intake. A systematic search of the literature identified 27 studies for inclusion in the analysis. Changes in sleep architecture were observed, including a delay in the onset of rapid eye movement (REM) sleep and a reduction in the duration of REM sleep. A dose-response relationship was identified such that disruptions to REM sleep occurred following consumption of a low dose of alcohol (≤0.50 g∙kg -1 or approximately two standard drinks) and progressively worsened with increasing doses of alcohol. Reductions in sleep onset latency and latency to deep sleep (i.e., non-rapid eye movement stage three (N3)) were only observed following the consumption of a high dose of alcohol (≥0.85∙g kg -1 or approximately five standard drinks). The effect of alcohol on the remaining characteristics of sleep could not be determined, with large uncertainty observed in the effect on total sleep time, sleep efficiency, and wake after sleep onset. The results of the present study suggest that a low dose of alcohol will negatively impact (i.e., reduce) REM sleep. It appears that high doses of alcohol may shorten sleep onset latency, however this likely exacerbates subsequent REM sleep disruption. Future work on personal and environmental factors that affect alcohol metabolism, and any differential effects of alcohol due to sex is encouraged., Competing Interests: Conflicts of interest The authors do not have any conflicts of interest to disclose., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Sleep is a critical component of recovery, but it can be disrupted following prolonged endurance exercise. The objective of this study was to examine the capacity of male and female professional cyclists to recover between daily race stages while competing in the 2022 Tour de France and the 2022 Tour de France Femmes, respectively. The 17 participating cyclists (8 males from a single team and 9 females from two teams) wore a fitness tracker (WHOOP 4.0) to capture recovery metrics related to night-time sleep and autonomic activity for the entirety of the events and for 7 days of baseline before the events. The primary analyses tested for a main effect of 'stage classification'-i.e., rest, flat, hilly, mountain or time trial for males and flat, hilly or mountain for females-on the various recovery metrics., Results: During baseline, total sleep time was 7.2 ± 0.3 h for male cyclists (mean ± 95% confidence interval) and 7.7 ± 0.3 h for female cyclists, sleep efficiency was 87.0 ± 4.4% for males and 88.8 ± 2.6% for females, resting HR was 41.8 ± 4.5 beats·min -1 for males and 45.8 ± 4.9 beats·min -1 for females, and heart rate variability during sleep was 108.5 ± 17.0 ms for males and 119.8 ± 26.4 ms for females. During their respective events, total sleep time was 7.2 ± 0.1 h for males and 7.5 ± 0.3 h for females, sleep efficiency was 86.4 ± 1.2% for males and 89.6 ± 1.2% for females, resting HR was 44.5 ± 1.2 beats·min -1 for males and 50.2 ± 2.0 beats·min -1 for females, and heart rate variability during sleep was 99.1 ± 4.2 ms for males and 114.3 ± 11.2 ms for females. For male cyclists, there was a main effect of 'stage classification' on recovery, such that heart rate variability during sleep was lowest after mountain stages. For female cyclists, there was a main effect of 'stage classification' on recovery, such that the percentage of light sleep (i.e., lower-quality sleep) was highest after mountain stages., Conclusions: Some aspects of recovery were compromised after the most demanding days of racing, i.e., mountain stages. Overall however, the cyclists obtained a reasonable amount of good-quality sleep while competing in these physiologically demanding endurance events. This study demonstrates that it is now feasible to assess recovery in professional athletes during multiple-day endurance events using validated fitness trackers., (© 2024. The Author(s).)

Introduction: Recent sleep guidelines regarding evening exercise have shifted from a conservative (i.e., do not exercise in the evening) to a more nuanced approach (i.e., exercise may not be detrimental to sleep in circumstances). With the increasing popularity of wearable technology, information regarding exercise and sleep are readily available to the general public. There is potential for these data to aid sleep recommendations within and across different population cohorts. Therefore, the aim of this study was to examine if sleep, exercise, and individual characteristics can be used to predict whether evening exercise will compromise sleep. Methods: Data regarding evening exercise and the subsequent night's sleep were obtained from 5,250 participants (1,321F, 3,929M, aged 30.1 ± 5.2 yrs) using a wearable device (WHOOP 3.0). Data for females and males were analysed separately. The female and male datasets were both randomly split into subsets of training and testing data (training:testing = 75:25). Algorithms were trained to identify compromised sleep (i.e., sleep efficiency <90%) for females and males based on factors including the intensity, duration and timing of evening exercise. Results: When subsequently evaluated using the independent testing datasets, the algorithms had sensitivity for compromised sleep of 87% for females and 90% for males, specificity of 29% for females and 20% for males, positive predictive value of 32% for females and 36% for males, and negative predictive value of 85% for females and 79% for males. If these results generalise, applying the current algorithms would allow females to exercise on ~ 25% of evenings with ~ 15% of those sleeps being compromised and allow males to exercise on ~ 17% of evenings with ~ 21% of those sleeps being compromised. Discussion: The main finding of this study was that the models were able to predict a high percentage of nights with compromised sleep based on individual characteristics, exercise characteristics and habitual sleep characteristics. If the benefits of exercising in the evening outweigh the costs of compromising sleep on some of the nights when exercise is undertaken, then the application of the current algorithms could be considered a viable alternative to generalised sleep hygiene guidelines., Competing Interests: EC is a stakeholder and employee of WHOOP Inc. DM, GR, and CS are part of a research group in which WHOOP Inc. provides research funding. The remaining author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Miller, Roach, Lastella, Capodilupo and Sargent.)

The consumption of caffeine in response to insufficient sleep may impair the onset and maintenance of subsequent sleep. This systematic review and meta-analysis investigated the effect of caffeine on the characteristics of night-time sleep, with the intent to identify the time after which caffeine should not be consumed prior to bedtime. A systematic search of the literature was undertaken with 24 studies included in the analysis. Caffeine consumption reduced total sleep time by 45 min and sleep efficiency by 7%, with an increase in sleep onset latency of 9 min and wake after sleep onset of 12 min. Duration (+6.1 min) and proportion (+1.7%) of light sleep (N1) increased with caffeine intake and the duration (-11.4 min) and proportion (-1.4%) of deep sleep (N3 and N4) decreased with caffeine intake. To avoid reductions in total sleep time, coffee (107 mg per 250 mL) should be consumed at least 8.8 h prior to bedtime and a standard serve of pre-workout supplement (217.5 mg) should be consumed at least 13.2 h prior to bedtime. The results of the present study provide evidence-based guidance for the appropriate consumption of caffeine to mitigate the deleterious effects on sleep., Competing Interests: Declaration of competing interest The authors do not have any conflicts of interest to disclose., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Driver fatigue is a contributory factor in approximately 20% of vehicle crashes. While other causal factors (eg, drink-driving) have decreased in recent decades due to increased public education strategies and punitive measures, similar decreases have not been seen in fatigue-related crashes. Fatigued driving could be managed in a similar way to drink-driving, with an established point (ie, amount of prior sleep) after which drivers are "deemed impaired". This systematic review aimed to provide an evidence-base for the concept of deemed impairment and to identify how much prior sleep may be required to drive safely. Four online databases were searched (PubMed, Web of Science, Scopus, Embase). Eligibility requirements included a) measurement of prior sleep duration and b) driving performance indicators (eg, lane deviation) and/or outcomes (eg, crash likelihood). After screening 1940 unique records, a total of 61 studies were included. Included studies were categorised as having experimental/quasi-experimental (n = 21), naturalistic (n = 3), longitudinal (n = 1), case-control (n = 11), or cross-sectional (n = 25) designs. Findings suggest that after either 6 or 7 hours of prior sleep, a modest level of impairment is generally seen compared with after ≥ 8 hours of prior sleep (ie, well rested), depending on the test used. Crash likelihood appears to be ~30% greater after 6 or 7 hours of prior sleep, as compared to individuals who are well rested. After one night of either 4 or 5 hours of sleep, there are large decrements to driving performance and approximately double the likelihood of a crash when compared with well-rested individuals. When considering the scientific evidence, it appears that there is a notable decrease in driving performance (and associated increase in crash likelihood) when less than 5h prior sleep is obtained. This is a critical first step in establishing community standards regarding the amount of sleep required to drive safely., Competing Interests: Professor Gregory Roach reports grants from Department of Infrastructure, Transport, Regional Development Communications and the Arts, during the conduct of the study. The authors declare no other conflicts of interest in this work., (© 2023 Sprajcer et al.)

Transition to night shift may be improved by strategically delaying the main sleep preceding a first night shift. However, the effects of delayed timing on sleep may differ between chronotypes. Therefore, the study aim was to compare the impacts of chronotype on sleep quality and architecture during a normally timed sleep opportunity and a delayed sleep opportunity. Seventy-two (36 female, 36 male) healthy adults participated in a laboratory study. Participants were provided with a normally timed sleep opportunity (23:00-08:00) and a delayed sleep opportunity (03:00-12:00) over two consecutive nights in a sleep laboratory. Sleep was monitored by polysomnography (PSG), and chronotype was determined from dim light melatonin onset (DLMO). A tertile split of DLMO defined early (20:24 ± 0:42 h), intermediate (21:31 ± 0:12 h), and late chronotype (22:56 ± 0:54 h) categories. Although there was no main effect of chronotype on any sleep measure, early chronotypes obtained less total sleep with delayed sleep than with normally timed sleep (p = 0.044). Intermediate and late chronotypes obtained more rapid eye movement (REM) sleep with delayed sleep than with normally timed sleep (p = 0.013, p = 0.012 respectively). Wake was more elevated for all chronotypes in the later hours of the delayed sleep opportunity than at the start of the sleep opportunity. Strategically delaying the main sleep preceding a first night shift appears to benefit intermediate and late chronotypes (i.e., more REM sleep), but not early chronotypes (i.e., less total sleep). Circadian processes appear to elevate wakefulness for all chronotypes in the later stages of a delayed sleep opportunity., (© 2022 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)

There is good evidence to indicate severe sleep restriction increases subjective feelings of hunger, but the impact of mild to moderate sleep restriction (i.e., 5-7 h) on hunger has not been systematically evaluated. Healthy male participants (n = 116; 22.8 ± 2.1 years; 22.9 ± 3.7 kg⋅m -2 ) were recruited to a ten-day laboratory study. In a between groups design, participants were allocated to one of five time in bed conditions (5 h, 6 h, 7 h, 8 h or 9 h) for seven consecutive nights. Participants were provided a eucaloric diet and ratings of hunger, nausea and desire to eat certain foods were collected using visual analogue scales prior to meals (breakfast, lunch, afternoon snack, dinner and evening snack) on four days during the study. Data were analysed using linear mixed models with time in bed, time of day and study day as fixed effects and participant as a random effect. There was no main effect of time in bed, and no interaction between time in bed and study day, on hunger, nausea, prospective hunger or desire to eat certain foods. However, post-hoc analyses indicated that participants in the 5-h condition had an elevated desire to consume sweet foods and fruit on the final morning of the protocol. There was a main effect of time of day and study day on hunger; participants were hungriest prior to lunch time and hunger decreased over consecutive days of the protocol. When provided with a eucaloric diet, only 5-h time in bed increased desire to consume sweet foods and fruit in healthy young men., Competing Interests: Declaration of competing interest The authors have no conflict of interest to declare., (Copyright © 2022. Published by Elsevier Ltd.)

The 'first night effect' refers to individuals experiencing poorer sleep during their first night in a laboratory. The effect is attributed to sleeping in a new environment, as well as wearing electrodes on the head and face, and is often cited as a reason for including an adaptation night in sleep research protocols. However, in the time since the 'first night effect' was initially reported, the conditions and equipment used in modern sleep laboratories have changed considerably, which may reduce the 'first night effect.' The aim of this study was to examine the impact of the 'first night effect' on sleep in a sample of healthy adults. Participants (n = 124; 22.7 ± 3.6 years) were given a 9-hour sleep opportunity (23:00-08:00 h) on two consecutive nights in a time-isolated sleep laboratory with sleep measured via polysomnography. Differences in dependent sleep variables between Night 1 and Night 2 were examined using paired t-tests. There was no difference in sleep onset latency (p = .295), total sleep time (p = .343), wake after sleep onset (p = .410), or sleep efficiency (p = .342) between Nights 1 and 2. However, participants spent more time in stage one (p = .001), and less time in stages two (p = .029) and three (p = .013) on Night 1 compared with Night 2. This suggests that, where primary sleep variables are the focus and not sleep architecture or arousals (e.g., where sleep is used as an independent variable), including an adaptation night may not be necessary.

The cortisol awakening response (CAR) is a distinct rise in cortisol that occurs upon awakening that is thought to contribute to arousal, energy boosting, and anticipation. There is some evidence to suggest that inadequate sleep may alter the CAR, but the relationship between sleep duration and CAR has not been systematically examined. Healthy males ( n = 111; age: 23.0 ± 3.6 yrs) spent 10 consecutive days/nights in a sleep laboratory. After a baseline night (9 h time in bed), participants spent either 5 h ( n = 19), 6 h ( n = 23), 7 h ( n = 16), 8 h ( n = 27), or 9 h ( n = 26) in bed for seven nights, followed by a 9 h recovery sleep. The saliva samples for cortisol assay were collected at 08:00 h, 08:30 h and 08:45 h at baseline, on experimental days 2 and 5 and on the recovery day. The primary dependent variables were the cortisol concentration at awakening (08:00 h) and the cortisol area under the curve (AUC). There was no effect of time in bed on either the cortisol concentration at awakening or cortisol AUC. In all the time in bed conditions, the cortisol AUC tended to be higher at baseline and lower on experimental day 5. Five consecutive nights of mild to moderate sleep restriction does not appear to affect the CAR in healthy male adults.

Background: Sleep and wellness outcomes have been explored in elite netball athletes, but research examining these outcomes in sub-elite athletes is lacking despite high participation rates at the sub-elite level. The aim was to investigate the impact of the scheduling of games over consecutive days during a truncated four-round netball competition on the sleep and wellness outcomes of sub-elite netballers., Methods: A total of 12 female, sub-elite netball players were examined via sleep outcomes, stress, fatigue, mood, muscle soreness, and overall wellness on the night before, the night of, and for two nights after games during a four-round truncated competition. Linear mixed models examined changes for variables across days around game day., Results: Bedtime (P=0.038), wake time (P=0.001), fatigue (P=0.003), and muscle soreness (P<0.001) differed according to game status (i.e., pre-game days, game days, and post-game days). Bedtime was later on game days compared to pre-game days, and wake-up time was later on post-game days than pre-game days. Fatigue and muscle soreness were greater the day after the game, compared to days before the game. Over the four rounds, bedtime (P=0.027) and wake-up time (P<0.001) tended to be later, while wellness did not change., Conclusions: These data may help guide coaching staff to plan training and travel arrangements during truncated multi-week competitions.

Abstract: Fox, JL, Scanlan, AT, Stanton, R, and Sargent, C. The effect of game-related contextual factors on sleep in basketball players. J Strength Cond Res 36(10): 2942-2945, 2022-The purpose of this study was to determine whether contextual factors including game location, game outcome, and score-line margin influence subsequent nightly sleep duration and quality in basketball players. Seven semiprofessional, male basketball players were monitored across one competitive season using self-reported sleep diaries and activity monitors. Linear mixed models and effect sizes (ESs) were used to compare sleep duration and quality following games based on game location (played at home or away venues), game outcome (win or loss), and score-line margin (balanced [≤ 8-point margin] or unbalanced [≥ 9-point margin]). Sleep onset was later following away games (ES = 0.79 ± 0.86 [ moderate ], p = 0.12), losses (ES = 0.63 ± 0.79 [ moderate ], p = 0.18), and unbalanced games (ES = 1.36 ± 0.90 [ large ], p = 0.01). Wake time was later following losses (ES = 1.14 ± 0.85 [ moderate ], p = 0.02). Sleep fragmentation was higher following wins (ES = 0.65 ± 0.74 [ moderate ], p = 0.17). Sleep efficiency was higher following losses (ES = 0.70 ± 0.80 [ moderate ], p = 0.14). All other differences in sleep variables yielded trivial - small ESs ( p > 0.05). Because of the later sleep onset times of players, basketball practitioners should consider avoiding early morning travel or training sessions following away games, losses, and unbalanced games, to maintain similar nightly sleep durations in light of the contextual factors encountered. Basketball practitioners may also need to ensure player sleep is optimized following wins compared with losses given the lower quality observed., (Copyright © 2020 National Strength and Conditioning Association.)

The aim of this laboratory-based study was to examine the effect of sleep restriction on glucose regulation during nighttime sleep. Healthy males were randomly assigned to one of two conditions: 9 h in bed ( n = 23, age = 24.0 year) or 5 h in bed ( n = 18, age = 21.9 year). Participants had a baseline night with 9 h in bed (23:00-08:00 h), then seven nights of 9 h (23:00-08:00 h) or 5 h (03:00-08:00 h) in bed. Participants were mostly seated during the daytime but had three bouts of treadmill walking (4 km·h -1 for 10 min) at ~14:40 h, ~17:40 h, and ~20:40 h each day. On the baseline night and night seven, glucose concentration in interstitial fluid was assessed by using continuous glucose monitors, and sleep was assessed by using polysomnography. On night seven, compared to the 9 h group, the 5 h group obtained less total sleep (292 min vs. 465 min) and less REM sleep (81 min vs. 118 min), but their slow-wave sleep did not differ (119 min vs. 120 min), and their glucose concentration during sleep did not differ (5.1 mmol·L -1 vs. 5.1 mmol·L -1 ). These data indicate that sleep restriction does not cause elevated levels of circulating glucose during nighttime sleep when slow-wave sleep is maintained. In the future, it will be important to determine whether increased insulin is required to maintain circulating glucose at a normal level when sleep is restricted.

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 (lnRMSSD CV ) and HR (HR CV ) 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 lnRMSSD CV and HR CV 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.

The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura Ring Generation 2, WHOOP 3.0 and Somfit, for assessing sleep. The secondary aim was to examine the validity of the six devices for assessing heart rate and heart rate variability during, or just prior to, night-time sleep. Fifty-three adults (26 F, 27 M, aged 25.4 ± 5.9 years) spent a single night in a sleep laboratory with 9 h in bed (23:00-08:00 h). Participants were fitted with all six wearable devices-and with polysomnography and electrocardiography for gold-standard assessment of sleep and heart rate, respectively. Compared with polysomnography, agreement (and Cohen's kappa) for two-state categorisation of sleep periods (as sleep or wake) was 88% (κ = 0.30) for Apple Watch; 89% (κ = 0.35) for Garmin; 87% (κ = 0.44) for Polar; 89% (κ = 0.51) for Oura; 86% (κ = 0.44) for WHOOP and 87% (κ = 0.48) for Somfit. Compared with polysomnography, agreement (and Cohen's kappa) for multi-state categorisation of sleep periods (as a specific sleep stage or wake) was 53% (κ = 0.20) for Apple Watch; 50% (κ = 0.25) for Garmin; 51% (κ = 0.28) for Polar; 61% (κ = 0.43) for Oura; 60% (κ = 0.44) for WHOOP and 65% (κ = 0.52) for Somfit. Analyses regarding the two-state categorisation of sleep indicate that all six devices are valid for the field-based assessment of the timing and duration of sleep. However, analyses regarding the multi-state categorisation of sleep indicate that all six devices require improvement for the assessment of specific sleep stages. As the use of wearable devices that are valid for the assessment of sleep increases in the general community, so too does the potential to answer research questions that were previously impractical or impossible to address-in some way, we could consider that the whole world is becoming a sleep laboratory.

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., (© 2022. The Author(s).)

Purpose: This study examined the impact of sleep inertia on physical, cognitive, and subjective performance immediately after a 1- or 2-hour afternoon nap opportunity., Methods: Twelve well-trained male athletes completed 3 conditions in a randomized, counterbalanced order-9 hours in bed overnight without a nap opportunity the next day (9 + 0), 8 hours in bed overnight with a 1-hour nap opportunity the next day (8 + 1), and 7 hours in bed overnight with a 2-hour nap opportunity the next day (7 + 2). Nap opportunities ended at 4:00 PM. Sleep was assessed using polysomnography. Following each condition, participants completed four 30-minute test batteries beginning at 4:15, 4:45, 5:15, and 5:45 PM. Test batteries included a warm-up, self-ratings of readiness to perform, motivation to perform and expected performance, two 10-m sprints, 2 agility tests, a 90-second response-time task, and 5 minutes of seated rest., Results: Total sleep time was not different between conditions (P = .920). There was an effect of condition on readiness (P < .001), motivation (P = .001), and expected performance (P = .004)-all 3 were lower in the 8 + 1 and 7 + 2 conditions compared with the 9 + 0 condition. There was no effect of condition on response time (P = .958), sprint time (P = .204), or agility (P = .240), but a large effect size was observed for agility., Conclusions: After waking from a nap opportunity, agility may be reduced, and athletes may feel sleepy and not ready or motivated to perform. Athletes should schedule sufficient time (∼1 h) after waking from a nap opportunity to avoid the effects of sleep inertia on performance.

Night shift workers experience circadian misalignment and sleep disruption, which impact hunger and food consumption. The study aim was to assess the impact of chronotype on hunger and snack consumption during a night shift with acute sleep deprivation. Seventy-two (36f, 36m) healthy adults participated in a laboratory study. A sleep opportunity (03:00-12:00) was followed by a wake period (12:00-23:00) and a simulated night shift (23:00-07:00). Subjective measures of hunger, prospective consumption, desire to eat fruit, and desire to eat fast food were collected before (12:20, 21:50) and after (07:20) the night shift. Snack opportunities were provided before (15:10, 19:40) and during (23:50, 03:30) the night shift. A tertile split of the dim light melatonin onset (DLMO) distribution defined early (20:24 ± 0:42 h), intermediate (21:31 ± 0:12 h), and late chronotype (22:56 ± 0:54 h) categories. There were no main effects of chronotype on any subjective measure ( p = 0.172-0.975), or on snack consumption ( p = 0.420), and no interactions between chronotype and time of day on any subjective measure ( p = 0.325-0.927) or on snack consumption ( p = 0.511). Differences in circadian timing between chronotype categories were not associated with corresponding differences in hunger, prospective consumption, desire to eat fruit, desire to eat fast food, or snack consumption at any measurement timepoint.

Purpose: Professional road cycling races are physiologically demanding, involving successive days of racing over 1 to 3 weeks of competition. Anecdotal evidence indicates that cyclists' sleep duration either increases or deteriorates during these competitions. However, sleep duration in professional cyclists during stage races has not been assessed. This study examined the amount/quality of sleep obtained by 14 professional cyclists competing in the Australian Tour Down Under., Methods: Sleep was assessed using wrist activity monitors and self-report sleep diaries on the night prior to start of the race and on each night during the race. The impact of each day of the race on sleep onset, sleep offset, time in bed, sleep duration, and wake duration was assessed using separate linear mixed effects models., Results: During the race, cyclists obtained an average of 6.8 (0.9) hours of sleep between 23:30 and 07:27 hours and spent 13.9% (4.7%) of time in bed awake. Minor differences in sleep onset (P = .023) and offset times (P ≤.001) were observed during the week of racing, but these did not affect the amount of sleep obtained by cyclists. Interestingly, the 3 best finishers in the general classification obtained more sleep than the 3 worst finishers (7.2 [0.3] vs 6.7 [0.3] h; P = .049)., Conclusions: Contrary to anecdotal reports, the amount of sleep obtained by cyclists did not change over the course of the 1-week race and was just below the recommended target of 7 to 9 hours for adults.

Objective: The aim of this study was to examine whether the timing of sleep in the break between consecutive night-shifts affects the quantity and quality of sleep obtained during the daytime and/or neurobehavioural function and self-perceived capacity during the night-time., Methods: Participants (n = 12, all male, aged 22.9±5.2 y) completed three randomised, counterbalanced conditions in a sleep laboratory, consisting of two consecutive 12-hour night-shifts (18:00-06:00) with 7 hours in bed in the break between shifts. The three conditions differed only in the timing of the sleep opportunities - immediate (07:00-14:00), delayed (10:00-17:00), split (07:00-10:30 and 13:30-17:00). Neurobehavioural function (attention, memory, throughput) and self-perceived capacity (sleepiness, alertness, fatigue, mood) were assessed at 2-hour intervals during the night-shifts., Results: Condition did not affect total sleep time (p = 0.465), but it did affect sleep onset latency (p < 0.001; W = 0.780; large effect), wake after sleep onset (p = 0.018; W = 0.333; moderate effect) and the amount of Stage N3 sleep (p < 0.001; η 2 =0.510; small effect). Compared to the immediate and delayed sleep conditions, the split sleep condition had less wake after sleep onset and more Stage N3 sleep; and compared to the delayed condition, the split sleep condition had longer latency to sleep onset. There was no effect of condition on measures of neurobehavioural function or self-perceived capacity during the second night-shift., Conclusion: None of the three sleep strategies examined here - immediate, delayed or split - are clearly superior or inferior to the others in terms of the capacity to sleep during the daytime or to work at night. Therefore, those who work consecutive night-shifts should employ the strategy that best suits their personal preferences and/or circumstances., Competing Interests: The authors report no conflicts of interest in this work., (© 2022 Sargent et al.)

Background: The aims of this retrospective study were to (i) provide a description of sleep quality in elite athletes as measured by the Pittsburgh Sleep Quality Index (PSQI), (ii) provide normative PSQI data, (iii) identify differences across sex and sport, (iv) identify components that contribute to high PSQI scores and (v) assess PSQI test-retest reliability., Methods: The PSQI was completed by 479 athletes (371 female and 108 male) across 20 Olympic team and individual sports. For ordinal and categorical variables, the Wilcoxon rank sum test and Chi Squared tests were used, respectively. A random forest regression was built to determine the importance of each PSQI component. Test-retest reliability was assessed using two-way mixed effects intraclass correlation coefficients., Results: Fifty-two percent of athletes had a global PSQI score ≥ 5. Team sport athletes reported significantly longer sleep onset latency times but longer sleep durations compared with individual sport athletes. Sleep onset latency and sleep quality made the greatest contribution to the global PSQI scores. The PSQI demonstrated variability over periods of 2 months or more, with a minimal detectable change of 3 arbitrary units (AU)., Conclusion: Long sleep onset latency and poor perceived sleep quality made the greatest contribution to the high PSQI scores observed in approximately half of elite athletes investigated. The PSQI should be administered at regular intervals due to variability within individuals over periods of 2 months or more. Individual questionnaire items or component scores of the PSQI may be useful for practitioners in guiding decision-making regarding sleep interventions in athletes., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Objective: Fatigue Risk Management Systems (FRMS) are a data-driven set of management practices for identifying and managing fatigue-related safety risks. This approach also considers sleep and work time, and is based on ongoing risk assessment and monitoring. This narrative review addresses the effectiveness of FRMS, as well as barriers and enablers in the implementation of FRMS. Furthermore, this review draws on the literature to provide evidence-based policy guidance regarding FRMS implementation., Methods: Seven databases were drawn on to identify relevant peer-reviewed literature. Relevant grey literature was also reviewed based on the authors' experience in the area. In total, 2129 records were screened based on the search strategy, with 231 included in the final review., Results: Few studies provide an evidence-base for the effectiveness of FRMS as a whole. However, FRMS components (e.g., bio-mathematical models, self-report measures, performance monitoring) have improved key safety and fatigue metrics. This suggests FRMS as a whole are likely to have positive safety outcomes. Key enablers of successful implementation of FRMS include organisational and worker commitment, workplace culture, and training., Conclusions: While FRMS are likely to be effective, in organisations where safety cultures are insufficiently mature and resources are less available, these systems may be challenging to implement successfully. We propose regulatory bodies consider a hybrid model of FRMS, where organisations could choose to align with tight hours of work (compliance) controls. Alternatively, where organisational flexibility is desired, a risk-based approach to fatigue management could be implemented., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Most athletes sleep poorly around competition. The aim of this study was to examine sleep before/after games during an entire season in elite Australian Rules footballers (N = 37) from the same team., Methods: Sleep was monitored using activity monitors for 4 consecutive nights (beginning 2 nights before games) during 19 rounds of a season. Differences in sleep on the nights before/after games, and differences in sleep before/after games as a function of game time (day vs evening), location (local vs interstate), and outcome (win vs loss), were examined using linear mixed effects models., Results: Players fell asleep earlier (+1.9 h; P < .001), and woke up later (+1 h; P < .001) on the night before games compared with the night of games. Players obtained less sleep on the night of games than on the night before games (5.2 h vs 7.7 h; P < .001), and this reduction was exacerbated when games were played in the evening-after evening games, players obtained approximately 40 minutes less sleep than after day games (P < .001). Sleep duration on the nights before and after games was not affected by game location or game outcome, but players had later sleep onset (P < .001) and offset times (P < .001) on most nights when sleeping away from home., Conclusions: Elite footballers obtain good sleep on the night before games but obtain approximately 30% less sleep on the night of games. Given the role of sleep in recovery, it will be important to determine whether a reduction in sleep duration of this magnitude impairs recovery on the days following games.

To investigate associations between acute workload and in-game performance in basketball. Eight semi-professional, male basketball players were monitored during all training sessions (N = 28) and games (N = 18) across the season. External workload was determined using absolute (arbitrary units[AU]) and relative (AU·min -1 ) PlayerLoad TM (PL), and absolute (count) and relative (count·min -1 ) low-intensity, medium-intensity, high-intensity, and total Inertial Movement Analysis (IMA) events (accelerations, decelerations, changes-of-direction, and jumps). Internal workload was determined using absolute and relative Summated-Heart-Rate-Zones workload, session-rating of perceived exertion, rating of perceived exertion, and time (min) spent working > 90% of maximal heart rate. In-game performance was indicated by the player efficiency statistic. Repeated measures correlations were used to determine associations between acute workload variables (across the previous 7 days) and player efficiency. Relative PL (r = 0.13, small) and high-intensity IMA events (r = 0.13, small) possessed the strongest associations with player efficiency of the investigated workload variables (P > 0.05). All other associations were trivial in magnitude (P > 0.05). Given the trivial-small associations between all external and internal workload variables and player efficiency, basketball practitioners should not rely solely on monitoring acute workloads to determine performance potential in players., Competing Interests: All authors declare they have no conflicts of interest., (Copyright © Biology of Sport 2021.)

Continuous glucose monitoring devices measure glucose in interstitial fluid. The devices are effective when used by patients with type 1 and 2 diabetes but are increasingly being used by researchers who are interested in the effects of various behaviours of glucose concentrations in healthy participants. Despite their more frequent application in this setting, the devices have not yet been validated for use under such conditions. A total of 124 healthy participants were recruited to a ten-day laboratory study. Each participant underwent four oral glucose tolerance tests, and a total of 3315 out of a possible 4960 paired samples were included in the final analysis. Bland-Altman plots and mean absolute relative differences were used to determine the agreement between the two methods. Bland-Altman analyses revealed that the continuous glucose monitoring devices had proportional bias ( R = 0.028, p < 0.001) and a mean bias of -0.048 mmol/L, and device measurements were more variable as glucose concentrations increased. Ninety-nine per cent of paired values were in Zones A and B of the Parkes Error Grid plot, and there was an overall mean absolute relative difference of 16.2% (±15.8%). There was variability in the continuous glucose monitoring devices, and this variability was higher when glucose concentrations were higher. If researchers were to use continuous glucose monitoring devices to measure glucose concentrations during an oral glucose tolerance test in healthy participants, this variability would need to be considered.

Abstract: Sargent, C, Lastella, M, Schwerdt, S, and Roach, GD. An individualized intervention increases sleep duration in professional athletes. J Strength Cond Res 35(12): 3407-3413, 2021-Athletes typically obtain less sleep than is generally recommended for healthy adults. The aim of this study was to determine whether individualized feedback could increase sleep duration in professional cricket players in the 3 weeks before the start of the domestic season. Players were randomly assigned to a control group (i.e., no individual feedback; n = 8) or an intervention group (i.e., individual feedback about bedtime, wake time, and sleep duration; n = 7). Night-time sleep and daytime naps were monitored using wrist activity monitors in conjunction with self-report sleep diaries for 1 week before, and 1 week after, the feedback intervention. Cumulative sleep duration was calculated as the sum of the sleep duration for a night-time sleep episode and any naps that occurred on the following day. Differences in cumulative sleep duration before and after the intervention were examined using a mixed-effects analysis of variance. There was an interaction between group and week for cumulative sleep duration (p = 0.039; η2 = 0.6; large). The average cumulative sleep duration was longer (+36 minutes) in the intervention group in week 2 compared with week 1. Individualized feedback can be used to increase sleep duration in professional cricket players. In future, it will be important to determine whether improvements in sleep duration can be maintained throughout the season., (Copyright © 2021 National Strength and Conditioning Association.)

Purpose: Anecdotal reports indicate that many elite athletes are dissatisfied with their sleep, but little is known about their actual sleep requirements. Therefore, the aim of this study was to compare the self-assessed sleep need of elite athletes with an objective measure of their habitual sleep duration., Methods: Participants were 175 elite athletes (n = 30 females), age 22.2 (3.8) years (mean [SD]) from 12 individual and team sports. The athletes answered the question "how many hours of sleep do you need to feel rested?" and they kept a self-report sleep diary and wore a wrist activity monitor for ∼12 nights during a normal phase of training. For each athlete, a sleep deficit index was calculated by subtracting their average sleep duration from their self-assessed sleep need., Results: The athletes needed 8.3 (0.9) hours of sleep to feel rested, their average sleep duration was 6.7 (0.8) hours, and they had a sleep deficit index of 96.0 (60.6) minutes. Only 3% of athletes obtained enough sleep to satisfy their self-assessed sleep need, and 71% of athletes fell short by an hour or more. Specifically, habitual sleep duration was shorter in athletes from individual sports than in athletes from team sports (F1,173 = 13.1, P < .001; d = 0.6, medium), despite their similar sleep need (F1,173 = 1.40, P = .24; d = 0.2, small)., Conclusions: The majority of elite athletes obtain substantially less than their self-assessed sleep need. This is a critical finding, given that insufficient sleep may compromise an athlete's capacity to train effectively and/or compete optimally.

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., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Sleep loss causes mood disturbance in non-clinical populations under severe conditions, i.e., two days/nights of sleep deprivation or a week of sleep restriction with 4-5 h in bed each night. However, the effects of more-common types of sleep loss on mood disturbance are not yet known. Therefore, the aim of this study was to examine mood disturbance in healthy adults over a week with nightly time in bed controlled at 5, 6, 7, 8 or 9 h. Participants ( n = 115) spent nine nights in the laboratory and were given either 5, 6, 7, 8 or 9 h in bed over seven consecutive nights. Mood was assessed daily using the Profile of Mood States (POMS-2). Mixed-linear effects models examined the effect of time in bed on total mood disturbance and subscales of anger-hostility, confusion-bewilderment, depression-dejection, fatigue-inertia, tension-anxiety, vigour-activity and friendliness. There was no effect of time in bed on total mood disturbance ( F (4, 110.42) = 1.31, p = 0.271) or any of the subscales except fatigue-inertia. Fatigue-inertia was higher in the 5 h compared with the 9 h time in bed condition ( p = 0.012, d = 0.75). Consecutive nights of moderate sleep loss (i.e., 5-7 h) does not affect mood but does increase fatigue in healthy males.

Objective . Sleepiness-related motor vehicle crashes, caused by lack of sleep or driving during night-time hours, often result in serious injury or fatality. Sleepiness detection technology is rapidly emerging as a sleepiness risk mitigation strategy for drivers. Continuous monitoring technologies assess and alert to driver sleepiness in real-time, while fit for duty technologies provide a single assessment of sleepiness state. The aim of this rapid review was to evaluate and compare sleepiness detection technologies in relation to specifications, cost, target consumer group and validity. Approach . We evaluated a range of sleepiness detection technologies suitable for consumer groups ranging from regular drivers in private vehicles through to work-related drivers within large businesses. Main results . Continuous monitoring technologies typically ranged between $100 and $3000 AUD and had ongoing monthly costs for telematics functionality and manager alerts. Fit for duty technologies had either a one-off purchase cost or a monthly subscription cost. Of concern, the majority of commercial continuous monitoring technologies lacked scientific validation. While some technologies had promising findings in terms of their ability to detect and reduce driver sleepiness, further validation work is required. Field studies that evaluate the sensitivity and specificity of technology alerts under conditions that are regularly experienced by drivers are necessary. Additionally, there is a need for longitudinal naturalistic driving studies to determine whether sleepiness detection technologies actually reduce sleepiness-related crashes or near-crashes. Significance . There is an abundance of sleepiness detection technologies on the market, but a majority lacked validation. There is a need for these technologies and their validation to be regulated by a driver safety body. Otherwise, consumers will base their technology choices on cost and features, rather than the ability to save lives., (© 2021 Institute of Physics and Engineering in Medicine.)

The study's aim was to examine the effect of chronotype on cognitive performance during a single night shift. Data were collected from 72 (36f) young, healthy adults in a laboratory study. Participants had a 9 h sleep period (03:00-12:00) followed by an 8 h night shift (23:00-07:00). During the night shift, participants completed five test sessions, which included measures of subjective sleepiness, subjective alertness, and sustained attention (i.e., psychomotor vigilance task; PVT). Dim light melatonin onset (DLMO) was derived from saliva samples taken during the evening preceding the night shift. A tertile split of DLMO was used to determine three chronotype categories: earlier (DLMO = 20:22 ± 0:42), intermediate (DLMO = 21:31 ± 0:13), and later (DLMO = 22:54 ± 0:54). There were (a) significant main effects of test session (all p < 0.001); (b) no main effects of chronotype; and (c) no interaction effects between chronotype and test session on sleepiness, alertness, PVT response time, and PVT lapses. The results indicate that under controlled sleeping conditions, chronotype based on dim light melatonin onset did not affect nighttime performance. Differences in performance during night shift between chronotypes reported by field studies may be related to differences in the amount and/or timing of sleep before or between night shifts, rather than circadian timing.

Chronotype reflects circadian timing and can be determined from biological markers (e.g., dim light melatonin onset; DLMO), or questionnaires (e.g., Morningness-Eveningness Questionnaire; MEQ, or Munich Chronotype Questionnaire; MCTQ). The study's aim was to quantify concordance between chronotype categorisations based on these measures. A total of 72 (36f) young, healthy adults completed the MEQ and MCTQ and provided saliva samples hourly in dim light during the evening in a laboratory. The corrected midpoint of sleep on free days (MSF sc ) was derived from MCTQ, and tertile splits were used to define early, intermediate and late DLMO-CT, MEQ-CT and MSF sc -CT chronotype categories. DLMO correlated with MEQ score ( r = -0.25, p = 0.035) and MSF sc ( r = 0.32, p = 0.015). For early, intermediate and late DLMO-CT categories, mean(SD) DLMO were 20:25(0:46), 21:33(0:10) and 23:03(0:53). For early, intermediate and late MEQ-CT categories, mean(SD) MEQ scores were 60.5(5.3), 51.4(2.9) and 40.8 (5.0). For early, intermediate and late MSF sc -CT categories, mean(SD) MSF sc were 03:23(0:34), 04:37(0:12) and 05:55(0:48). Low concordance of categorisations between DLMO-CT and MEQ-CT (37%), and between DLMO-CT and MSF sc -CT (37%), suggests chronotype categorisations depend on the measure used. To enable valid comparisons with previous results and reduce the likelihood of misleading conclusions, researchers should select measures and statistical techniques appropriate to the construct of interest and research question.

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.

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.

Purpose: To assess the efficacy of a combined light exposure and sleep hygiene intervention to improve team-sport performance following eastward long-haul transmeridian travel., Methods: Twenty physically trained males underwent testing at 09:00 and 17:00 hours local time on 4 consecutive days at home (baseline) and the first 4 days following 21 hours of air travel east across 8 time zones. In a randomized, matched-pairs design, participants traveled with (INT; n = 10) or without (CON; n = 10) a light exposure and sleep hygiene intervention. Performance was assessed via countermovement jump, 20-m sprint, T test, and Yo-Yo Intermittent Recovery Level 1 tests, together with perceptual measures of jet lag, fatigue, mood, and motivation. Sleep was measured using wrist activity monitors in conjunction with self-report diaries., Results: Magnitude-based inference and standardized effect-size analysis indicated there was a very likely improvement in the mean change in countermovement jump peak power (effect size 1.10, ±0.55), and likely improvement in 5-m (0.54, ±0.67) and 20-m (0.74, ±0.71) sprint time in INT compared with CON across the 4 days posttravel. Sleep duration was most likely greater in INT both during travel (1.61, ±0.82) and across the 4 nights following travel (1.28, ±0.58) compared with CON. Finally, perceived mood and motivation were likely worse (0.73, ±0.88 and 0.63, ±0.87) across the 4 days posttravel in CON compared with INT., Conclusions: Combined light exposure and sleep hygiene improved speed and power but not intermittent-sprint performance up to 96 hours following long-haul transmeridian travel. The reduction of sleep disruption during and following travel is a likely contributor to improved performance.

Purpose: To investigate the associations between sleep and competitive performance in basketball., Methods: A total of 7 semiprofessional, male players were monitored across the in-season. On nights prior to competition, sleep duration and quality were assessed using actigraphs and sleep diaries. The data were accumulated over 1 (night 1), 2 (nights 1-2 combined), 3 (nights 1-3 combined), and 4 (nights 1-4 combined) nights prior to competition. Performance was reported as player statistics (field goal and free-throw accuracy, rebounds, assists, steals, blocks, and turnovers) and composite performance statistics (offensive rating, defensive rating, and player efficiency). Linear regression analyses with cluster-robust standard errors using bootstrapping (1000 replications) were performed to quantify the association between sleep and performance., Results: The night before competition, subjective sleep quality was positively associated with offensive rating and player efficiency (P < .05)., Conclusions: Strategies to increase subjective sleep quality the night before competition should be considered to increase the likelihood of successful in-game performance, given its association with composite performance metrics.

COVID-19, the disease caused by the SARS-CoV-2 virus, can cause shortness of breath, lung damage, and impaired respiratory function. Containing the virus has proven difficult, in large part due to its high transmissibility during the pre-symptomatic incubation. The study's aim was to determine if changes in respiratory rate could serve as a leading indicator of SARS-CoV-2 infections. A total of 271 individuals (age = 37.3 ± 9.5, 190 male, 81 female) who experienced symptoms consistent with COVID-19 were included- 81 tested positive for SARS-CoV-2 and 190 tested negative; these 271 individuals collectively contributed 2672 samples (days) of data (1856 healthy days, 231 while infected with COVID-19 and 585 while negative for COVID-19 but experiencing symptoms). To train a novel algorithm, individuals were segmented as follows; (1) a training dataset of individuals who tested positive for COVID-19 (n = 57 people, 537 samples); (2) a validation dataset of individuals who tested positive for COVID-19 (n = 24 people, 320 samples); (3) a validation dataset of individuals who tested negative for COVID-19 (n = 190 people, 1815 samples). All data was extracted from the WHOOP system, which uses data from a wrist-worn strap to produce validated estimates of respiratory rate and other physiological measures. Using the training dataset, a model was developed to estimate the probability of SARS-CoV-2 infection based on changes in respiratory rate during night-time sleep. The model's ability to identify COVID-positive individuals not used in training and robustness against COVID-negative individuals with similar symptoms were examined for a critical six-day period spanning the onset of symptoms. The model identified 20% of COVID-19 positive individuals in the validation dataset in the two days prior to symptom onset, and 80% of COVID-19 positive cases by the third day of symptoms., Competing Interests: The authors of this manuscript have read the journal's policy and have the following competing interests: ERC and JVC are shareholders and employees of Whoop, Inc; VHL is an employee of Whoop, Inc; DJM’s position at CQUniversity is sponsored by Whoop Inc. There are no other competing interests for the authors. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

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)., Competing Interests: Competing interests: AMB and CH have received grant funding from Own the Podium and Mitacs. MAG has received grant funding from Kemin Foods, Nexalin Technology and Jazz Pharmaceuticals; he has performed consulting activities for Fitbit, Natrol, Casper Sleep, Curaegis Technologies, Smartypants, Thrive Global, Pharmavite and Merck., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

The aim of the study was to compare the WHOOP strap - a wearable device that estimates sleep based on measures of movement and heart rate derived from actigraphy and photoplethysmography, respectively. Twelve healthy adults (6 females, 6 males, aged 22.9 ± 3.4 years) participated in a 10-day, laboratory-based protocol. A total of 86 sleeps were independently assessed in 30-s epochs using polysomnography and WHOOP. For WHOOP, bed times were entered by researchers and sleeps were scored by the company based on proprietary algorithms. WHOOP overestimated total sleep time by 8.2 ± 32.9 minutes compared to polysomnography, but this difference was non-significant. WHOOP was compared to polysomnography for 2-stage (i.e., wake, sleep) and 4-stage categorisation (i.e., wake, light sleep [N1 or N2], slow-wave sleep [N3], REM) of sleep periods. For 2-stage categorisation, the agreement, sensitivity to sleep, specificity for wake, and Cohen's kappa were 89%, 95%, 51%, and 0.49, respectively. For 4-stage categorisation, the agreement, sensitivity to light sleep, SWS, REM, and wake, and Cohen's kappa were 64%, 62%, 68%, 70%, 51%, and 0.47, respectively. In situations where polysomnography is impractical (e.g., field settings), WHOOP is a reasonable method for estimating sleep, particularly for 2-stage categorisation, if accurate bedtimes are manually entered.

The aim of this study was to assess the effect of working multiple, consecutive night shifts on crash risk during the morning commute. Participants (36 F, 36 M, aged 23.1 ± 3.6 y) completed a laboratory-based shiftwork protocol with seven consecutive night shifts (23:00-07:00 h) that each started and ended with a 20 min simulated commute. Compared to the corresponding pre-work commutes, the likelihood of crashing during the post-work commutes was 11.0-, 8.5-, and 5.6-fold higher at the start, middle, and end of the week, respectively. The results of this simulation study indicate that crash risk is relatively high during the morning commute but declines throughout a week of night work.

Determination of circadian phase is required to diagnose and treat circadian abnormalities, but the measurement of dim light melatonin onset (DLMO), the most common phase marker, is laborious. As sleep timing reflects circadian phase, measurement of sleep markers (e.g., sleep onset, sleep midpoint, sleep offset) provides a simple way to estimate DLMO. The study aim was to compare methods to estimate DLMO from markers derived from the Pittsburgh Sleep Quality Index (PSQI), Munich Chronotype Questionnaire (MCTQ), sleep diaries, and actigraphy. PSQI, MCTQ, and 1 week of diary and actigraphy data were collected from 72 (36 f, 36 m) healthy adults aged 23.1 (± 3.6) y prior to a laboratory sleep study. Saliva samples were collected hourly in dim light during the second evening of the study. The sleep markers most strongly associated with DLMO from each source were PSQI onset, MCTQ average midpoint, 7-d diary midpoint, and 7-d actigraphy midpoint. Estimates of DLMO as a fixed interval before the sleep marker exhibited proportional bias. DLMO estimated from regression models based on sleep midpoint from 7 d of diary or 7 d of actigraphy showed the narrowest limits of agreement with measured DLMO without proportional bias (±1.8 h and ±1.9 h, respectively). Our findings indicate none of the methods provided precise estimates of DLMO from sleep markers. The best estimates were from linear regressions on sleep midpoints from 7 d of diary or actigraphy, and these estimates of DLMO may be suitable for limited research purposes.

Background: Disturbed sleep may negatively influence physical health, cognitive performance, metabolism, and general wellbeing. Nutritional interventions represent a potential non-pharmacological means to increase sleep quality and quantity., Objective: (1) Identify an optimal suite of nutritional ingredients and (2) validate the effects of this suite utilising polysomnography, and cognitive and balance tests., Methods: The optimal and least optimal combinations of six ingredients were identified utilising 55 male participants and a Box-Behnken predictive model. To validate the model, 18 healthy, male, normal sleepers underwent three trials in a randomised, counterbalanced design: (1) optimal drink, (2) least optimal drink, or (3) placebo were provided before bed in a double-blinded manner. Polysomnography was utilised to measure sleep architecture. Cognitive performance, postural sway, and subjective sleep quality, were assessed 30 min after waking., Results: The optimal drink resulted in a significantly shorter sleep onset latency (9.9 ± 12.3 min) when compared to both the least optimal drink (26.1 ± 37.4 min) and the placebo drink (19.6 ± 32.0 min). No other measures of sleep, cognitive performance, postural sway, and subjective sleep quality were different between trials., Conclusion: A combination of ingredients, optimised to enhance sleep, significantly reduced sleep onset latency. No detrimental effects on sleep architecture, subjective sleep quality or next day performance were observed.

Purpose: To examine the impact of workload volume during training sessions and games on subsequent sleep duration and sleep quality in basketball players., Methods: Seven semiprofessional male basketball players were monitored across preseason and in-season phases to determine training session and game workloads, sleep duration, and sleep quality. Training and game data were collected via accelerometers, heart-rate monitors, and rating of perceived exertion (RPE) and reported as PlayerLoad™ (PL), summated heart-rate zones, and session RPE (sRPE). Sleep duration and sleep quality were measured using wrist-worn activity monitors in conjunction with self-report sleep diaries. For daily training sessions and games, all workload data were independently sorted into tertiles representing low, medium, and high workload volumes. Sleep measures following low, medium, and high workloads and control nights (no training/games) were compared using linear mixed models., Results: Sleep onset time was significantly later following medium and high PL and sRPE game workloads compared with control nights (P < .05). Sleep onset time was significantly later following low, medium, and high summated heart-rate-zones game workloads, compared with control nights (P < .05). Time in bed and sleep duration were significantly shorter following high PL and sRPE game workloads compared with control nights (P < .05). Following low, medium, and high training workloads, sleep duration and quality were similar to control nights (P > .05)., Conclusions: Following high PL and sRPE game workloads, basketball practitioners should consider strategies that facilitate longer time in bed, such as napping and/or adjusting travel or training schedules the following day.

Objectives: We investigated the management of travel fatigue and jet lag in athlete populations by evaluating studies that have applied non-pharmacological interventions (exercise, sleep, light and nutrition), and pharmacological interventions (melatonin, sedatives, stimulants, melatonin analogues, glucocorticoids and antihistamines) following long-haul transmeridian travel-based, or laboratory-based circadian system phase-shifts., Design: Systematic review Eligibility criteria Randomised controlled trials (RCTs), and non-RCTs including experimental studies and observational studies, exploring interventions to manage travel fatigue and jet lag involving actual travel-based or laboratory-based phase-shifts. Studies included participants who were athletes, except for interventions rendering no athlete studies, then the search was expanded to include studies on healthy populations., Data Sources: Electronic searches in PubMed, MEDLINE, CINAHL, Google Scholar and SPORTDiscus from inception to March 2019. We assessed included articles for risk of bias, methodological quality, level of evidence and quality of evidence., Results: Twenty-two articles were included: 8 non-RCTs and 14 RCTs. No relevant travel fatigue papers were found. For jet lag, only 12 athlete-specific studies were available (six non-RCTs, six RCTs). In total (athletes and healthy populations), 11 non-pharmacological studies (participants 600; intervention group 290; four non-RCTs, seven RCTs) and 11 pharmacological studies (participants 1202; intervention group 870; four non-RCTs, seven RCTs) were included. For non-pharmacological interventions, seven studies across interventions related to actual travel and four to simulated travel. For pharmacological interventions, eight studies were based on actual travel and three on simulated travel., Conclusions: We found no literature pertaining to the management of travel fatigue. Evidence for the successful management of jet lag in athletes was of low quality. More field-based studies specifically on athlete populations are required with a multifaceted approach, better design and implementation to draw valid conclusions. PROSPERO registration number The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO: CRD42019126852)., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Sleep inertia is the transitional state marked by impaired cognitive performance and reduced vigilance upon waking. Exercising before bed may increase the amount of slow-wave sleep within the sleep period, which has previously been associated with increased sleep inertia. Healthy males (n = 12) spent 3 nights in a sleep laboratory (1-night washout period between each night) and completed one of the three conditions on each visit - no exercise, aerobic exercise (30 min cycling at 75% heart rate), and resistance exercise (six resistance exercises, three sets of 10 repetitions). The exercise conditions were completed 90 min prior to bed. Sleep was measured using polysomnography. Upon waking, participants completed five test batteries every 15 min, including the Karolinska Sleepiness Scale, a Psychomotor Vigilance Task, and the Spatial Configuration Task. Two separate linear mixed-effects models were used to assess: (a) the impact of condition; and (b) the amount of slow-wave sleep, on sleep inertia. There were no significant differences in sleep inertia between conditions, likely as a result of the similar sleep amount, sleep structure and time of awakening between conditions. The amount of slow-wave sleep impacted fastest 10% reciprocal reaction time on the Psychomotor Vigilance Task only, whereby more slow-wave sleep improved performance; however, the magnitude of this relationship was small. Results from this study suggest that exercise performed 90 min before bed does not negatively impact on sleep inertia. Future studies should investigate the impact of exercise intensity, duration and timing on sleep and subsequent sleep inertia., (© 2019 European Sleep Research Society.)

Purpose: To quantify the sleep/wake behaviors of adolescent, female basketball players and to examine the impact of daily training load on sleep/wake behaviors during a 14-day training camp., Methods: Elite, adolescent, female basketball players (N = 11) had their sleep/wake behaviors monitored using self-report sleep diaries and wrist-worn activity monitors during a 14-day training camp. Each day, players completed 1 to 5 training sessions (session duration: 114 [54] min). Training load was determined using the session rating of perceived exertion model in arbitrary units. Daily training loads were summated across sessions on each day and split into tertiles corresponding to low, moderate, and high training load categories, with rest days included as a separate category. Separate linear mixed models and effect size analyses were conducted to assess differences in sleep/wake behaviors among daily training load categories., Results: Sleep onset and offset times were delayed (P < .05) on rest days compared with training days. Time in bed and total sleep time were longer (P < .05) on rest days compared with training days. Players did not obtain the recommended 8 to 10 hours of sleep per night on training days. A moderate increase in sleep efficiency was evident during days with high training loads compared with low., Conclusions: Elite, adolescent, female basketball players did not consistently meet the sleep duration recommendations of 8 to 10 hours per night during a 14-day training camp. Rest days delayed sleep onset and offset times, resulting in longer sleep durations compared with training days. Sleep/wake behaviors were not impacted by variations in the training load administered to players.

Sleep is essential in the preparation for, and the recovery from, training and competition. Despite being important for all individuals, young athletes are considered an at-risk group for reduced sleep duration and quality. The purpose of this review is to synthesise current literature relating to sleep duration and quality in young (14-25 years) athletes. Specifically, typical sleep and wake patterns, factors affecting sleep and wake patterns, and the consequences of altered sleep and wake patterns in young athletes are discussed. Scheduling training and competition in the afternoon or evening appears to result in reduced sleep duration due to less time in bed. Evidence suggests that young athletes who obtain less than 8 h of sleep per night are at a higher risk of musculoskeletal injury. An increase in sleep duration above habitual nightly sleep may be associated with favourable performance in young athletes; however, the associations between sleep quality and performance- and health-related outcomes remain unclear.

Introduction: Abnormal rapid eye movement (REM) sleep is often symptomatic of chronic disorders, however polysomnography, the gold standard method to measure REM sleep, is expensive and often impractical. Attempts to develop cost-effective ambulatory systems to measure REM sleep have had limited success. As elevated twitching is often observed during REM sleep in some distal muscles, the aim of this study was to assess the potential for a finger-mounted device to measure finger twitches, and thereby differentiate periods of REM and non-REM (NREM) sleep., Methods: One night of sleep data was collected by polysomnography from each of 18 (3f, 15m) healthy adults aged 23.2 ± 3.3 (mean ± SD) years. Finger movement was detected using a piezo-electric limb sensor taped to the index finger of each participant. Finger twitch densities were calculated for each stage of sleep., Results: Finger twitch density was greater in REM than in NREM sleep ( p < 0.001). Each sleep stage had a unique finger twitch density, except for REM and stage N1 sleep which were similar. Finger twitch density was greater in late REM than in early REM sleep ( p = 0.005), and there was a time-state interaction: the difference between finger twitch densities in REM and NREM sleep was greater in late sleep than in early sleep ( p = 0.007)., Conclusion: Finger twitching is more frequent in REM sleep than in NREM sleep and becomes more distinguishable as sleep progresses. Finger twitches appear to be too infrequent to make definitive 30-second epoch determinations of sleep stage. However, an algorithm informed by measures of finger twitch density has the potential to detect periods of REM sleep and provide estimates of total REM sleep time and percentage., Competing Interests: The authors report no conflicts of interest in this work., (© 2020 Reiter et al.)

Match activity demands of basketball players have been well reported but little exists in terms of quantifying the demands encountered by referees during match-play. Potential differences between referees and players may identify different fitness capacities and subsequent need for different training regimes. Purpose : The aims of this brief report were to: 1) document the activity demands of sub-elite basketball referees during a match; and 2) compare referee and player match activity demands. Method : Three referees and six players participating in the same sub-elite, basketball match were monitored for external activity (PlayerLoad™, PL) via microsensor technology. The proportion of each quarter and entire match time spent in pre-set PL bands was also examined to develop PL profiles. Differences in activity demands between referees and players were calculated using effect size (ES) analyses (± 95% confidence intervals). Results : Referees experienced an absolute PL of 310 ± 28 arbitrary units (4.2 ± 0.4 AU.min -1 ) during the match which was ~40% lower than that of the players (ES = 1.14; -0.45, 2.48). Referees exhibited a match PL profile dominated by the lowest PL band (~91%) with the players' PL profile shifted slightly towards higher bands. Conclusion : Sub-elite basketball referees experienced activity demands substantially lower than that of players during the same match. Limited movement patterns due to recommended court positioning may have contributed to the lower PL of referees. Differences in PL between referees and players highlight a need to develop specific training regimes to focus on key fitness capacities for each match role.

Objectives: To assess the validity of the Polar Team Pro Sensor for measuring speed and distance indoors during continuous locomotive and change-of-direction tasks at low, medium, and high intensities., Design: Descriptive validation study., Methods: 26 recreationally-active participants (age: 32.2 ± 11.0 yr; stature: 173.3 ± 9.9 cm; body mass: 74.2 ± 16.2 kg) completed three trials of low- (walking speed), medium- (jogging speed), and high-intensity (maximal sprinting speed) continuous locomotive and change-of-direction tasks. Participants wore back- and chest-mounted sensors to determine mean speed and total distance covered. One-way analysis of variance, t-tests, Pearson's Product moment correlation, and Bland-Altman plots were utilised to compare the speed and distance measured with the back- and chest-mounted sensors to reference measures (measured distance of the court via a trundle wheel and speed derived from measured distance and electronic timing lights)., Results: Speed and distance measured using the back- and chest-mounted sensors showed wide limits of agreement, which increased at high intensities for speed. The sensors typically underestimated speed and distance by as much as 2.76 km h -1 and 32.6 m, and overestimated speed and distance by as much as 4.52 km h -1 and 59.6 m across tasks and intensities compared to reference measures (168.45 and 40.00 m)., Conclusions: There was low agreement between both back- and chest-mounted sensors and the reference devices for measuring speed and distance indoors. Practitioners should understand the limitations and potential for error when using the Polar Team Pro Sensors indoors to measure speed and distance during continuous locomotive and change-of-direction tasks., (Copyright © 2019 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)