The Night-Time Sleep and Autonomic Activity of Male and Female Professional Road Cyclists Competing in the Tour de France and Tour de France Femmes.

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.

Hit the gym or hit the hay: can evening exercise characteristics predict compromised sleep in healthy adults?

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.

A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults.

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.

Running on Empty: Self-Reported Sleep/Wake Behaviour during Ultra-Marathon Events Exceeding 100 Miles.

The aim of this study was to examine sleep/wake behaviour and sleep strategies before, during and after ultra-marathon running events exceeding 100 miles (161 km). A total of 119 athletes completed a web-based questionnaire regarding their habitual sleep/wake behaviour before, during, and after ultra-marathon participation. Event-specific data were grouped by race distance categories; 100-149 miles (161-240 km), 150-199 miles (241-321 km), and ≥200 miles (322 km). Athletes commonly reported not sleeping throughout the duration of their races (74%). However, for events that were ≥200 miles, athletes reported more sleep opportunities, longer sleep duration, and more total sleep when compared to events that were 100-149 miles in distance (p ≤ 0.001). This suggests that for races of shorter distances, the benefit of continuous racing outweighs the negative impact of continuous wakefulness/sleep deprivation. However, for longer races (≥200 miles), there is an apparent tradeoff between sleep deprivation and race strategy, whereby athletes cannot sustain a desired level of performance without obtaining sleep. This is consistent with established sleep/wake behaviour models suggesting that sleep need increases as wakefulness increases, or in this case, as race duration increases. For athletes participating in ultra-marathons, sleep management education and/or consultation with a sleep scientist prior to racing may be beneficial. Future research should examine the optimal strategies concerning the frequency and duration of sleep during ultra-marathons and the subsequent impact on performance.

The Impact of Sleep Inertia on Physical, Cognitive, and Subjective Performance Following a 1- or 2-Hour Afternoon Nap in Semiprofessional Athletes.

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.

Correction: Bellenger et al. Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP. Sensors 2021, 21, 3571.

The authors wish to correct the following errors in the original paper [...].

Sleep-Wake Behaviour of 200-Mile Ultra-Marathon Competitors: A Case Study.

The aim of this study was to examine the sleep-wake behaviour of 200-mile ultra-marathon runners before, during, and after a competition. A longitudinal, observational study was conducted to collect the sleep data of four (two females; mean age: 45.5 ± 3.1 years) runners competing in a 200-mile ultra-marathon (N = 4). Wrist-worn activity monitors, in conjunction with self-report sleep diaries, were used to measure sleep, beginning seven days prior to the race and concluding seven days following the race (2-19 June 2021). Descriptive analysis of runners' subjective and objective sleep data was conducted. All runners completed the 200-mile event in an average of 82.5 ± 7.1 h. On average, runners obtained 4.7 ± 3.0 h of sleep from 4.8 ± 2.4 sleep episodes, averaging 59.9 ± 49.2 min of sleep per episode. Runners averaged 6.0 ± 1.3 h of sleep per night in the week before the competition and 6.3 ± 1.3 h per night in the week following the competition. Runners in the 200-mile (326 km) ultra-marathon drastically restricted their sleep. However, obtained sleep, the number of sleep episodes, and sleep episode length were greater than those previously reported with 100-mile (161 km) runners. In-race sleep data suggest an increased need for sleep as race duration increases. Interestingly, runners obtained less than the recommended ~8 h of sleep per night, in both pre-race and post-race phases of the competition.

The influence of training and competition on sleep behaviour of soccer referees.

OBJECTIVES: The aims of the present study were to (1) quantify sleep behaviours of soccer referees and (2) compare sleep behaviours between nights before training, before matches, and after matches. METHODS: Fourteen professional soccer referees from the A-League (mean±SD; age 34 ± 4 years; sex: 11 males, 3 females) participated in this observational study. Referees' sleep behaviours were examined using sleep diaries and wrist activity monitors for 31 consecutive nights during the 2018-2019 A-League season. Separate linear mixed models were conducted to assess differences in sleep behaviours between nights before training, before matches, and after matches. RESULTS: On average, referees did not obtain recommended sleep durations across the in-season (mean±SD sleep duration: 6.4 h ± 0.7 h). Referees went to bed later, spent less time in bed, and slept significantly less post-matches compared to pre-training and pre-match nights (p< 0.05). CONCLUSIONS: Referees were particularly susceptible to inadequate sleep on nights following training and matches. The findings related to poor sleep behaviours highlight the importance of implementing monitoring systems to understand the sleep behaviours of referees, with further research encouraged to ascertain the efficacy of various sleep hygiene practices to optimise sleep in this population.

The Impact of Chronotype on the Sleep and Training Responses of Elite Female Australian Footballers.

The primary aims of the present study were to examine the impact of chronotype on sleep/wake behaviour, perceived exertion, and training load among professional footballers. Thirty-six elite female professional football player's (mean ± SD: age, 25 ± 4 y; weight, 68 ± 7 kg) sleep and training behaviours were examined for 10 consecutive nights during a pre-season period using a self-report online player-management system and wrist activity monitors. All athletes completed the Morningness-Eveningness Questionnaire (rMEQ) on the first day of data collection. Eleven participants were morning types, seventeen participants were intermediate types, and three participants were evening types. Separate linear mixed models were conducted to assess differences in sleep, perceived exertion, and training behaviours between chronotype groups. Morning types woke up earlier (wake time: 07:19 ± 01:16 vs. 07:53 ± 01:01, p = 0.04) and reported higher ratings of perceived exertion compared to intermediate types (6.7 ± 1.1 vs. 5.9 ± 1.2, p = 0.01). No differences were observed between chronotype groups for bedtime, time in bed, total sleep time, sleep efficiency, training duration, or training load. In circumstances where professional female football players are required to train at a time opposing their natural circadian preference (e.g., morning type training in the evening), their perceived exertion during training may be higher than that of players that are training at a time that aligns with their natural circadian preference (e.g., evening type training in the evening). It is important for practitioners to monitor individual trends in training variables (e.g., rating of perceived exertion, training load) with relation to athlete chronotype and training time. Future research should examine the relationship between chronotype, training time, and rating of perceived exertion across different training durations.

Changes in health promoting behavior during COVID-19 physical distancing: Utilizing wearable technology to examine trends in sleep, activity, and cardiovascular indicators of health.

The COVID-19 pandemic incited unprecedented restrictions on the behavior of society. The aims of this study were to quantify changes to sleep/wake behavior and exercise behavior, as well as changes in physiological markers of health during COVID-19 physical distancing. A retrospective analysis of 5,436 US-based subscribers to the WHOOP platform (mean age = 40.25 ± 11.33; 1,536 females, 3,900 males) was conducted covering the period from January 1st, 2020 through May 15th, 2020. This time period was separated into a 68-day baseline period and a 67-day physical distancing period. To provide context and allow for potential confounders (e.g., change of season), data were also extracted from the corresponding time periods in 2019. As compared to baseline, during physical distancing, all subjects fell asleep earlier (-0.15 hours), woke up later (0.29 hours), obtained more sleep (+0.21 hours) and reduced social jet lag (-0.13 hours). Contrasting sleep behavior was seen in 2019, with subjects falling asleep and waking up at a similar time (-0.01 hours; -0.03 hours), obtaining less sleep (-0.14 hours) and maintaining social jet lag (+0.06 hours) in corresponding periods. Individuals exercised more intensely during physical distancing by increasing the time spent in high heart rate zones. In 2020, resting heart rate decreased (-0.90 beats per minute) and heart rate variability increased (+0.98 milliseconds) during physical distancing when compared to baseline. However, similar changes were seen in 2019 for RHR (-0.51 beats per minute) and HRV (+2.97 milliseconds), suggesting the variation may not be related to the introduction of physical distancing mandates. The findings suggest that individuals improved health related behavior (i.e., increased exercise intensity and longer sleep duration) during physical distancing restrictions. While positive changes were seen to cardiovascular indicators of health, it is unclear whether these changes were a direct consequence of behavior change.

A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep.

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

Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP.

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.

Analyzing changes in respiratory rate to predict the risk of COVID-19 infection.

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.

Optimisation and Validation of a Nutritional Intervention to Enhance Sleep Quality and Quantity.

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.

A validation study of the WHOOP strap against polysomnography to assess sleep.

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.

Exercise before bed does not impact sleep inertia in young healthy males.

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.

Daytime naps can be used to supplement night-time sleep in athletes.

This study examined the efficacy of daytime napping to supplement night-time sleep in athletes. Twelve well-trained male soccer players completed three conditions in a randomised, counterbalanced order: 9 h in bed overnight with no daytime nap (9 h + 0 h); 8 h in bed overnight with a 1-h daytime nap (8 h + 1 h); and 7 h in bed overnight with a 2-h daytime nap (7 h + 2 h). Sleep was assessed using polysomnography. The total amount of sleep obtained in the three conditions was similar, i.e. 8.1 h (9 h + 0 h), 8.2 h (8 h + 1 h), and 8.0 h (7 h + 2 h). Daytime napping may be an effective strategy to supplement athletes' night-time sleep.

Athletes underestimate sleep quantity during daytime nap opportunities.

This study examined the difference between athletes' self-reported and objective sleep durations during two nap opportunities. Twelve well-trained male soccer players' sleep durations were assessed using polysomnography and a self-report question during a 60- and 120-min nap opportunity. Participants underestimated sleep compared to objective sleep assessments for both the 60-min nap opportunity (p = 0.004) and 120-min nap opportunity (p = 0.001). Soccer players underestimated their sleep duration by approximately 10 min per hour of nap opportunity. It is yet to be determined if athletes are likely to underestimate sleep duration during their main nighttime sleep period.

How well does a commercially available wearable device measure sleep in young athletes?

The validity of a commercially available wearable device for measuring total sleep time was examined in a sample of well-trained young athletes during night-time sleep periods and daytime naps. Participants wore a FitBit HR Charge on their non-dominant wrist and had electrodes attached to their face and scalp to enable polysomnographic recordings of sleep in the laboratory. The FitBit automatically detected 24/30 night-time sleep periods but only 6/20 daytime naps. Compared with polysomnography, the FitBit overestimated total sleep time by an average of 52 ± 152 min for night-time sleep periods, and by 4 ± 8 min for daytime naps. It is important for athletes and practitioners to be aware of the limitations of wearable devices that automatically detect sleep duration.