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BackgroundDisturbed 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.MethodsThe 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.ResultsThe 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.ConclusionA 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.

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

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]