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Purpose: Few data are available on sleep characteristics of elite track-and-field athletes. Our study aimed to assess (1) differences in sleep between sexes and among different track-and-field disciplines, (2) the effect of individualized sleep-hygiene strategies on athletes' sleep parameters, and (3) daytime nap characteristics in track-and-field athletes. Methods: Sleep characteristics of 16 elite Olympic-level track-and-field athletes (male: n = 8; female: n = 8) were assessed during the preseason period, at baseline (T0), and during the in-season period, after the adoption of individualized sleep-hygiene strategies (T1). Sleep parameters were objectively monitored by actigraphy for a minimum of 10 days, for each athlete, at both T0 and T1. A total of 702 nights were analyzed (T0 = 425; T1 = 277). Results: Female athletes displayed better sleep efficiency (88.69 [87.69–89.68] vs 91.72 [90.99–92.45]; P =.003, effect size [ES]: 0.44), lower sleep latency (18.99 [15.97–22.00] vs 6.99 [5.65–8.32]; P <.001, ES: 0.65), higher total sleep time (07:03 [06:56–07:11] vs 07:18 [07:10–07:26]; P =.030, ES: 0.26), earlier bedtime (00:24 [00:16–00:32] vs 00:13 [00:04–00:22]; P =.027, ES: 0.18), and lower nap frequency (P <.001) than male athletes. Long-distance runners had earlier bedtime (00:10 [00:03–00:38] vs 00:36 [00:26–00:46]; P <.001, ES: 0.41) and wake-up time (07:41 [07:36–07:46] vs 08:18 [08:07–08:30]; P <.001, ES: 0.61), higher nap frequency, but lower sleep efficiency (88.79 [87.80–89.77] vs 91.67 [90.95–92.38]; P =.013, ES: 0.44), and longer sleep latency (18.89 [15.94–21.84] vs 6.69 [5.33–8.06]; P <.001, ES: 0.67) than athletes of short-term disciplines. Furthermore, sleep-hygiene strategies had a positive impact on athletes' total sleep time (429.2 [423.5–434.8] vs 451.4 [444.2–458.6]; P <.001, ES: 0.37) and sleep latency (14.33 [12.34–16.32] vs 10.67 [8.66–12.68]; P =.017, ES: 0.19). Conclusions: Sleep quality and quantity were suboptimal at baseline in Olympic-level track-and-field athletes. Large differences were observed in sleep characteristics between sexes and among different track-and-field disciplines. Given the positive effect of individualized sleep-hygiene strategies on athlete's sleep, coaches should implement sleep education sessions in the daily routine of top-level athletes. [ABSTRACT FROM AUTHOR]

The aims of this study were 1) to investigate Global Positioning System (GPS)-based match physical performance according to players' playing position in three different playing formations (4-4-2, 3-5-2, 4-3-3) and 2) to analyse the differences in match performance between 1st and 2nd half. Twenty-three U19 elite male soccer players (age: 18 ± 1 year, height: 1.80 ± 0.04 m, body mass: 70.65 ± 6.02 kg), categorized as Central Back (CB, n = 5), Full Back (FB, n = 4), Central Midfielders (CM, n = 4), Wingers (W, n = 3), Strikers (S, n = 7), were monitored using 10 Hz GPS during 31 competitive matches. The results showed that FB and W always had the highest very high-speed running distance and number of sprints in all playing formations. Significant decrease in all GPS variables was observed in the 2nd half of the match for all playing positions. Strength coaches should adopt specific training regimes in accordance with players' playing position. [ABSTRACT FROM AUTHOR]

Objective: To investigate the effects of mental fatigue on soccer-specific physical and technical performance in young players. Methods: Twelve under-14 (U14), twelve under-16 (U16) and twelve under-18 (U18) soccer players completed the two parts of the investigation. Part one assessed the soccer-specific physical performance using the Yo-Yo Intermittent Recovery Test, Level 1 (Yo-Yo IR1). Part two assessed the soccer-specific technical performance using the Loughborough Soccer Passing and Shooting Tests (LSPT, LSST). Each part was preceded by 30 min of Stroop task or 15 min of control task performed in a randomised and counterbalanced order. Results: Subjective ratings of mental fatigue were higher after the Stroop task compared to the control in U14, U16 and U18 in both parts. Mental fatigue significantly reduced Yo-Yo IR1 distance, alongside an increase in heart rate and ratings of perceived exertion, and this effect was moderated by age as older players showed greater declines in performance. Soccer-specific technical performance was negatively affected by mental fatigue condition only in U18 in the LSPT.. [ABSTRACT FROM AUTHOR]

Background: The aims of this cross-sectional study were to (a) assess actigraphy-based sleep parameters (total sample and gender differences), (b) assess differences in morphological parameters and physical function between short- versus normal-sleepers and poor- versus good-sleepers, and (c) assess the possible correlations between sleep variables and morphological and physical function parameters in older subjects., Methods: This study enrolled 42 healthy older participants (60-80 years). Participants completed the following clinical evaluations: (1) whole-body dual-energy X-ray absorptiometry to assess the appendicular skeletal muscle mass index; (2) magnetic resonance imaging acquisition to determine the cross-sectional muscle area of thigh muscles and intermuscular adipose tissue; (3) risk of fall assessment through the mini-Balance Evaluation Systems Test; (4) strength assessment: (a) chair stand test and (b) handgrip strength test; (5) sleep monitoring by actigraphy to assess total sleep time, sleep efficiency, wake after sleep onset, sleep latency, fragmentation index, mobile time, and subjective sleep quality., Results: 31.0% of subjects were short-sleepers (total sleep time < 6 hr), 19.1% were poor-sleepers (sleep efficiency < 85%), and gender differences were detected in mobile time (males: 15.8 ± 6.0 and females: 13.4 ± 6.8; p < .001) and fragmentation index (males: 35.3 ± 14.3 and females: 29.6 ± 14.6; p < .001); no significant differences were observed between groups (short- vs. normal-sleepers and poor- vs. good-sleepers) in morphological and physical function variables; correlation analysis showed that sleep latency negatively correlated with Mini-Balance Evaluation Systems Test (r = -.352; p = .022) and a positive correlation was detected between cross-sectional muscle area and mobile time (r = .349, p = .023)., Conclusion: No differences were observed in morphological and function parameters between good- versus poor-sleepers, those subjects with worse sleep onset latency (i.e., longer time to fall asleep) registered higher for risk of fall. The potential role of sleep in the physiological mechanisms of muscular aging must be explored through cross-sectional cohort studies with a larger population.