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.

How Tired is Too Tired to Drive? A Systematic Review Assessing the Use of Prior Sleep Duration to Detect Driving Impairment.

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.

The effect of caffeine on subsequent sleep: A systematic review and meta-analysis.

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.

The effect of mild to moderate sleep restriction on subjective hunger in healthy young men.

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.

The night before night shift: Chronotype impacts total sleep and rapid eye movement sleep during a strategically delayed sleep.

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.

Mild to Moderate Sleep Restriction Does Not Affect the Cortisol Awakening Response in Healthy Adult Males.

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.

Evaluating the Typical Day-to-Day Variability of WHOOP-Derived Heart Rate Variability in Olympic Water Polo Athletes.

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 (lnRMSSDCV) and HR (HRCV) 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 lnRMSSDCV and HRCV 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.

A Week of Sleep Restriction Does Not Affect Nighttime Glucose Concentration in Healthy Adult Males When Slow-Wave Sleep Is Maintained.

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.

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.

Sleep Regularity and Predictors of Sleep Efficiency and Sleep Duration in Elite Team Sport Athletes.

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.

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 [...].

No Effect of Chronotype on Hunger or Snack Consumption during a Night Shift with Acute Sleep Deprivation.

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.

Timing of Sleep in the Break Between Two Consecutive Night-Shifts: The Effect of Different Strategies on Daytime Sleep and Night-Time Neurobehavioural Function.

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; η2=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.

The Sleep Behaviors of Elite Australian Rules Footballers Before and After Games During an Entire Season.

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.

Consecutive Days of Racing Does Not Affect Sleep in Professional Road Cyclists.

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.

Sleep Quality in Elite Athletes: Normative Values, Reliability and Understanding Contributors to Poor Sleep.

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.

Glucose Concentrations from Continuous Glucose Monitoring Devices Compared to Those from Blood Plasma during an Oral Glucose Tolerance Test in Healthy Young Adults.

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.

An Individualized Intervention Increases Sleep Duration in Professional Athletes.

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.