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Purpose: To examine the effect of match schedule on self-reported wellness and sleep in rugby union referees during the 2019 Rugby World Cup., Methods: Following an observational design, 18 international-level male referees participating in the 2019 Rugby World Cup completed a daily questionnaire to quantify wellness status (sleep quality, mood, stress, fatigue, muscle soreness, and total wellness) and sleep characteristics (bedtime, wake-up time, and time in bed) from the previous night across the tournament. Linear mixed models and effect sizes (Hedges gav) assessed differences in wellness and sleep characteristics between prematch and postmatch days surrounding single-game and 2-game congested match schedules (prematch1, postmatch1, prematch2, and postmatch2 days)., Results: During regular schedules, all self-reported wellness variables except stress were reduced (gav = 0.33-1.05, mean difference -0.32 to -1.21 arbitrary units [AU]) and referees went to bed later (1.08, 1:07 h:min) and spent less time in bed (-0.78, 00:55 h:min) postmatch compared with prematch days. During congested schedules, only wellness variables differed across days, with total wellness reduced on postmatch1 (-0.88, -3.56 AU) and postmatch2 (-0.67, -2.70 AU) days, as well as mood (-1.01, -0.56 AU) and fatigue (-0.90, -1.11 AU) reduced on postmatch1 days compared with prematch days., Conclusion: Referees were susceptible to acute reductions in wellness on days following matches regardless of schedule. However, only single-game regular match schedules negatively impacted the sleep characteristics of referees. Targeted strategies to maximize wellness status and sleep opportunities in referees considering the match schedule faced should be explored during future Rugby World Cup competitions.

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.

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.

Purpose: Australian football has elite men's (Australian Football League; AFL) and women's (Australian Football League Women's; AFLW) competitions. This study compared AFL and AFLW players' sleep and characterized players' sleep in the context of current sleep recommendations., Methods: A total of 70 players (36 AFL, 34 AFLW) had their sleep monitored via actigraphy over a 10-day preseason period. Sleep outcomes and their intraindividual variation, were compared between AFL and AFLW players using linear mixed models. Proportions of players sleeping ≥7 and ≥8 hours per night, and achieving ≥85% sleep efficiency, were compared using chi-square analyses., Results: Compared with AFL players, AFLW players slept less (7.9 [0.5] vs 7.1 [0.6] h, P = .000), had lower sleep efficiency (89.5% [2.8%] vs 84.0% [4.4%], P = .000), and greater intraindividual variation in sleep efficiency (3.1% [0.9%] vs 5.1% [2.1%], P = .000). A total of 47% of AFLW versus 3% of AFL players averaged <7 hours sleep (χ2 = 18.6, P = .000). A total of 88% of AFLW versus 50% of AFL players averaged <8 hours sleep (χ2 = 11.9, P = .001). A total of 53% of AFLW versus 14% of AFL players averaged <85% sleep efficiency (χ2 = 12.1, P = .001)., Conclusions: AFLW players slept less and had poorer sleep quality than AFL players. Many AFLW players do not meet current sleep duration or sleep quality recommendations. Research should test strategies to improve sleep among Australian rules footballers, particularly among elite women.

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.

Purpose: To examine correlations between peak force and impulse measures attained during the isometric midthigh pull (IMTP) and basketball-specific sprint and jump tests., Methods: Male, adolescent basketball players (N = 24) completed a battery of basketball-specific performance tests. Testing consisted of the IMTP (absolute and normalized peak force and impulse at 100 and 250 ms); 20-m sprint (time across 5, 10, and 20 m); countermovement jump (CMJ; absolute and normalized peak force and jump height); standing long jump (distance); and repeated lateral bound (distance). Correlation and regression analyses were conducted between IMTP measures and other attributes., Results: An almost perfect correlation was evident between absolute peak force attained during the IMTP and CMJ (r = .94, R2 = 56%, P < .05). Moderate to very large correlations (P < .05) were observed between IMTP normalized peak force and 5-m sprint time (r = -.44, R2 = 19%), 10-m sprint time (r = -.45, R2 = 20%), absolute (r = .57, R2 = 33%), normalized (r = .86, R2 = 73%) CMJ peak force, and standing long-jump distance (r = .51, R2 = 26%). Moderate to very large correlations were evident between impulse measures during the IMTP and 5-m sprint time (100 ms, r = -.40, R2 = 16%, P > .05) and CMJ absolute peak force (100 ms, r = .73, R2 = 54%; 250 ms, r = .68, R2 = 47%; P < .05)., Conclusions: The IMTP may be used to assess maximal and rapid force expression important across a range of basketball-specific movements.

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.