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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.

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.)

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.

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.)

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.

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.

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.

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.)

Purpose: To quantify and compare internal and external workloads in regular and overtime games and examine changes in relative workloads during overtime compared with other periods in overtime games in male basketball players., Methods: Starting players for a semiprofessional male basketball team were monitored during 2 overtime games and 2 regular games (nonovertime) with similar contextual factors. Internal (rating of perceived exertion and heart-rate variables) and external (PlayerLoad and inertial movement analysis variables) workloads were quantified across games. Separate linear mixed-models and effect-size analyses were used to quantify differences in variables between regular and overtime games and between game periods in overtime games., Results: Session rating-of-perceived-exertion workload (P = .002, effect size 2.36, very large), heart-rate workload (P = .12, 1.13, moderate), low-intensity change-of-direction events to the left (P = .19, 0.95, moderate), medium-intensity accelerations (P = .12, 1.01, moderate), and medium-intensity change-of-direction events to the left (P = .10, 1.06, moderate) were higher during overtime games than during regular games. Overtime periods also exhibited reductions in relative PlayerLoad (first quarter P = .03, -1.46, large), low-intensity accelerations (first quarter P = .01, -1.45, large; second quarter P = .15, -1.22, large), and medium-intensity accelerations (first quarter P = .09, -1.32, large) compared with earlier periods., Conclusions: Overtime games disproportionately elevate perceptual, physiological, and acceleration workloads compared with regular games in starting basketball players. Players also perform at lower external intensities during overtime periods than earlier quarters during basketball games.

Purpose: To quantify and compare external and internal game workloads according to contextual factors (game outcome, game location, and score-line)., Methods: Starting semiprofessional, male basketball players were monitored during 19 games. External (PlayerLoad™ and inertial movement analysis variables) and internal (summated-heart-rate-zones and rating of perceived exertion [RPE]) workload variables were collected for all games. Linear mixed-effect models and effect sizes were used to compare workload variables based on each of the contextual variables assessed., Results: The number of jumps, absolute and relative (in min-1) high-intensity accelerations and decelerations, and relative changes-of-direction were higher during losses, whereas session RPE was higher during wins. PlayerLoad™ the number of absolute and relative jumps, high-intensity accelerations, absolute and relative total decelerations, total changes-of-direction, summated-heart-rate-zones, session RPE, and RPE were higher during away games, whereas the number of relative high-intensity jumps was higher during home games. PlayerLoad™, the number of high-intensity accelerations, total accelerations, absolute and relative decelerations, absolute and relative changes-of-direction, summated-heart-rate-zones, sRPE, and RPE were higher during balanced games, whereas the relative number of total and high-intensity jumps were higher during unbalanced games., Conclusions: Due to increased intensity, starting players may need additional recovery following losses. Given the increased external and internal workload volumes encountered during away games and balanced games, practitioners should closely monitor playing times during games. Monitoring playing times may help identify when players require additional recovery or reduced training volumes to avoid maladaptive responses across the in-season.

Objective: The magnitude of travel completed by professional Australian soccer teams during domestic competition is substantial. The inclusion of Australian soccer teams into the Asian Champions league has seen additional stress placed on soccer players' training and competition schedules. For management staff, the complexity of organizing training and travel schedules during domestic competition and the Asian Champions league is challenging., Design: Case study., Participants: Seven male professional soccer players (mean ± SD: age 25.2 ± 3.2 years, height 182.8 ± 5.2 cm, body mass 84.6 ± 7.4 kg)., Measurements: This study examined the sleep and fatigue levels of Australian soccer players during an intensive home and away travel schedule during the Asian Champions league. Seven male professional soccer players' (mean ± SD: age 25.2 ± 3.2 years, height 182.8 ± 5.2 cm, body mass 84.6 ± 7.4 kg) sleep/wake behavior was assessed using sleep diaries and wrist activity monitors for 19 days, including 9 days before, 5 days during, and 4 days after a home and away group stage match of the Asian Champions league. Analyses examined differences in sleep/wake behavior and fatigue levels between day type (training day, rest day, pregame, and postgame) and between sleep location (Adelaide, during flight, and Hiroshima)., Results: Sleep/wake behavior and fatigue levels were poorest the night immediately after games compared to the night before games, training days, and rest days. Soccer players' sleep/wake behaviors were disrupted during flights such that they obtained 3.6 hours less sleep during flights compared to sleep in Adelaide (7.0 ± 1.6 hours) and Hiroshima (7.0 ± 2.1 hours)., Conclusion: The sleep/wake behaviors of professional soccer players are compromised when they are required to travel and compete in multiple matches within a short period of time., (Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.)

Purpose: This study aimed to determine the recovery timeline of sleep, subjective jet lag and fatigue, and team sport physical performance after east and west long-haul travel., Methods: Ten physically trained men underwent testing at 0900 h and 1700 h local time on four consecutive days 2 wk before outbound travel (BASE), and the first 4 d after 21 h of outbound (WEST) and return (EAST) air travel across eight time zones between Australia and Qatar. Data collection included performance (countermovement jump, 20-m sprint, and Yo-Yo intermittent recovery level 1 [YYIR1] test) and perceptual (jet lag, motivation, perceived exertion, and physical feeling) measures. In addition, sleep was measured via wrist activity monitors and self-report diaries throughout the aforementioned data collection periods., Results: Compared with the corresponding day at BASE, the reduction in YYIR1 distance after EAST was significantly different from the increase in WEST on day 1 after travel (P < 0.001). On day 2, significantly slower 20-m sprint times were detected in EAST compared with WEST (P = 0.03), with large effect sizes (ES) also indicating a greater reduction in YYIR1 distance in EAST compared with WEST (d = 1.06). Mean sleep onset and offset were significantly later and mean time in bed and sleep duration were significantly reduced across the 4 d in EAST compared with BASE and WEST (P < 0.05). Lastly, mean jet lag, fatigue, and motivation ratings across the 4 d were significantly worse in EAST compared with BASE and WEST (P < 0.05) and WEST compared with BASE (P < 0.05)., Conclusions: Long-haul transmeridian travel can impede team sport physical performance. Specifically, east travel has a greater detrimental effect on sleep, subjective jet lag, fatigue, and motivation. Consequently, maximal and intermittent sprint performance is also reduced after east travel, particularly within 72 h after arrival.

Objectives: To investigate the acute benefits of breaking up prolonged sitting with light-intensity physical activity on (i) glucose metabolism under conditions of sleep restriction, and (ii) cognitive deficits associated with sleep restriction., Methods: This counterbalanced, crossover trial consisted of two five-day (5 night) experimental conditions separated by a two-week washout period. On the first night, participants were given a 9-h sleep opportunity to allow the collection of steady-state baseline measures the following day. This was followed by three consecutive nights of sleep restriction (5-h sleep opportunity). In the sitting condition (SIT), participants remained seated between 1000 and 1800 h. In the physical activity condition (ACT), participants completed 3-min bouts of light-intensity walking every 30 min on a motorised treadmill between 1000 and 1800 h. At all other times, in both conditions, participants remained seated, except when walking to the dining room or to use the bathroom (max distance = 32 m). Six physically inactive, healthy males were randomised to one of two trial orders, 1) SIT then ACT, or 2) ACT then SIT. Continuous measures of interstitial glucose were measured at 5-min intervals. A cognitive and subjective test battery was administered every two hours during wake periods. Analyses were conducted using a series of linear mixed-effect ANOVAs., Results: No differences in interstitial glucose concentration or cognitive performance were observed between the SIT condition and the ACT condition. Participants reported higher levels of sleepiness, and felt less alert in the SIT condition compared with the ACT condition., Conclusions: There were no observable benefits of breaking up prolonged sitting on glucose metabolism under conditions of sleep restriction. These findings have implications for behaviour change interventions. Future studies will need to include larger, less homogenous study populations and appropriate control conditions (i.e., 8-9 h sleep opportunities).

Fatigue is a significant contributor to motor-vehicle accidents and fatalities. Shift workers are particularly susceptible to fatigue-related risks as they are often sleep-restricted and required to commute around the clock. Simple assays of performance could provide useful indications of risk in fatigue management, but their effectiveness may be influenced by changes in their sensitivity to sleep loss across the day. The aim of this study was to evaluate the sensitivity of several neurobehavioral and subjective tasks to sleep restriction (SR) at different circadian phases and their efficacy as predictors of performance during a simulated driving task. Thirty-two volunteers (M±SD; 22.8±2.9 years) were time-isolated for 13-days and participated in one of two 14-h forced desynchrony protocols with sleep opportunities equivalent to 8h/24h (control) or 4h/24h (SR). At regular intervals during wake periods, participants completed a simulated driving task, several neurobehavioral tasks, including the psychomotor vigilance task (PVT), and subjective ratings, including a self-assessment measure of ability to perform. Scores transformed into standardized units relative to baseline were folded into circadian phase bins based on core body temperature. Sleep dose and circadian phase effect sizes were derived via mixed models analyses. Predictors of driving were identified with regressions. Performance was most sensitive to sleep restriction around the circadian nadir. The effects of sleep restriction around the circadian nadir were larger for simulated driving and neurobehavioral tasks than for subjective ratings. Tasks did not significantly predict driving performance during the control condition or around the acrophase during the SR condition. The PVT and self-assessed ability were the best predictors of simulated driving across circadian phases during SR. These results show that simple performance measures and self-monitoring explain a large proportion of the variance in driving when fatigue-risk is high., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

This study compared the impact of split and consolidated sleep/wake schedules on subjective sleepiness during the biological day and biological night. This was achieved using a between-group design involving two forced desynchrony protocols: consolidated sleep/wake and split sleep/wake. Both protocols included 7×28-h days with 9.33h in bed and 18.67h of wake each day. While the consolidated sleep/wake protocol had 1×9.33-h sleep opportunity and 1×18.67-h wake period each day, the split sleep/wake protocol had 2×4.67-h sleep opportunities and 2×9.33-h wake periods each day. For both protocols, subjective sleepiness was measured using the Karolinska Sleepiness Scale every 2.5h during wake. A total of 29 healthy adult males participated, with 13 in the consolidated sleep/wake group (mean age=22.5 yrs) and 16 in the split sleep/wake group (mean age=22.6 yrs). On average, subjective sleepiness during wake periods of the split condition was significantly higher than that during the first half of wake periods of the consolidated condition, but was similar to the level during the second half. These findings were observed for wake periods that occurred during both the biological day and biological night. Previous data have shown that cognitive impairment at night is lower for split schedules than consolidated schedules, but the current data indicate that feelings of sleepiness are greater for split schedules than consolidated schedules for at least half of the time awake. Thus, it should be explained to people operating split sleep/wake schedules that although they may perform well, they are likely to feel sleepy., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

The objective of this study was to assess the validity of a sleep/wake activity monitor, an energy expenditure activity monitor, and a partial-polysomnography system at measuring sleep and wake under identical conditions. Secondary aims were to evaluate the sleep/wake thresholds for each activity monitor and to compare the three devices. To achieve these aims, two nights of sleep were recorded simultaneously with polysomnography (PSG), two activity monitors, and a partial-PSG system in a sleep laboratory. Agreement with PSG was evaluated epoch by epoch and with summary measures including total sleep time (TST) and wake after sleep onset (WASO). All of the devices had high agreement rates for identifying sleep and wake, but the partial-PSG system was the best, with an agreement of 91.6% ± 5.1%. At their best thresholds, the sleep/wake monitor (medium threshold, 87.7% ± 7.6%) and the energy expenditure monitor (very low threshold, 86.8% ± 8.6%) had similarly high rates of agreement. The summary measures were similar to those determined by PSG, but the partial-PSG system provided the most consistent estimates. Although the partial-PSG system was the most accurate device, both activity monitors were also valid for sleep estimation, provided that appropriate thresholds were selected. Each device has advantages, so the primary consideration for researchers will be to determine which best suits a given research design.

Human fatigue, caused by sleep loss, extended wakefulness, and/or circadian misalignment, is a major cause of workplace errors, incidents and accidents. In some industries, employees are required to undertake fitness for duty testing at the start of a shift to identify instances where their fatigue risk is elevated, so that minimisation and/or mitigation strategies can be implemented. Postural balance has been proposed as a fitness for duty test for fatigue, but it is largely untested. Therefore, the purpose of this study was to examine the impact of sleep loss, extended wakefulness and circadian phase on postural balance. Fourteen male participants spent 10 consecutive days in a sleep laboratory, including three adaptation days and eight simulated shiftwork days. To simulate a quickly rotating roster, shiftwork days were scheduled to begin 4h later each day, and consisted of a 23.3-h wake episode and a 4.7-h sleep opportunity. Every 2.5h during wake, balance was measured while standing as still as possible on a force platform with eyes open for one minute, and eyes closed for one minute. Subjective sleepiness was assessed using the Karolinska Sleepiness Scale. Core body temperature, continuously recorded with rectal thermistors, was used to determine circadian phase. For measures of postural balance and subjective sleepiness, data were analysed using three separate repeated measures ANOVA with two within-subjects factors: circadian phase (six phases) and prior wake (nine levels). For subjective sleepiness, there was a significant effect of prior wake and circadian phase. In particular, sleepiness increased as prior wake increased, and was higher during biological night-time than biological daytime. For the eyes open balance task, there was no effect of prior wake or circadian phase. For the eyes closed balance task, there was a significant effect of circadian phase such that balance was poorer during the biological night-time than biological daytime, but there was no effect of prior wake. These results indicate that postural balance may be a viable tool for assessing fatigue associated with time of day, but may not be useful for assessing fatigue associated with extended hours of wake., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Most of the research related to human fatigue in the aviation industry has focussed on long-haul pilots, but short-haul pilots also experience elevated levels of fatigue. The aim of this study was to examine the impact of early start times on the amount of sleep obtained prior to duty and on fatigue levels at the start of duty. Seventy short-haul pilots collected data regarding their duty schedule and sleep/wake behaviour for at least two weeks. Data were collected using self-report duty/sleep diaries and wrist activity monitors. Mixed-effects regression analyses were used to examine the effects of duty start time (04:00-10:00 h) on (i) the total amount of sleep obtained in the 12h prior to the start of duty and (ii) self-rated fatigue level at the start of duty. Both analyses indicated significant main effects of duty start time. In particular, the amount of sleep obtained in the 12h prior to duty was lowest for duty periods that commenced between 04:00 and 05:00 h (i.e. 5.4h), and greatest for duty periods that commenced between 09:00 and 10:00 h (i.e. 6.6h). These data indicate that approximately 15 min of sleep is lost for every hour that the start of duty is advanced prior to 09:00 h. In addition, self-rated fatigue at the start of duty was highest for duty periods that commenced between 04:00 and 05:00 h, and lowest for duty periods that commenced between 09:00 and 10:00 h. Airlines should implement a fatigue risk management system (FRMS) for short-haul pilots required to work early-morning shifts. One component of the FRMS should be focussed on the production of 'fatigue-friendly' rosters. A second component of the FRMS should be focussed on training pilots to optimise sleep opportunities, to identify circumstances where the likelihood of fatigue is elevated, and to manage the risks associated with fatigue-related impairment., (Copyright © 2011 Elsevier Ltd. All rights reserved.)