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Objective: In a convenience sample of athletes, we conducted a survey of COVID-19-mediated lockdown (termed 'lockdown' from this point forward) effects on: (i) circadian rhythms; (ii) sleep; (iii) eating; and (iv) training behaviors. Methods: In total, 3911 athletes [mean age: 25.1 (range 18–61) years, 1764 female (45%), 2427 team-sport (63%) and 1442 elite (37%) athletes] from 49 countries completed a multilingual cross-sectional survey including the Pittsburgh Sleep Quality Index and Insomnia Severity Index questionnaires, alongside bespoke questions about napping, training, and nutrition behaviors. Results: Pittsburgh Sleep Quality Index (4.3 ± 2.4 to 5.8 ± 3.1) and Insomnia Severity Index (4.8 ± 4.7 to 7.2 ± 6.4) scores increased from pre- to during lockdown (p < 0.001). Pittsburgh Sleep Quality Index was predominantly influenced by sleep-onset latency (p < 0.001; + 29.8%), sleep efficiency (p < 0.001; − 21.1%), and total sleep time (p < 0.001; − 20.1%), whilst Insomnia Severity Index was affected by sleep-onset latency (p < 0.001; + 21.4%), bedtime (p < 0.001; + 9.4%), and eating after midnight (p < 0.001; + 9.1%). During lockdown, athletes reported fewer training sessions per week (− 29.1%; d = 0.99). Athletes went to bed (+ 75 min; 5.4%; d = 1.14) and woke up (+ 150 min; 34.5%; d = 1.71) later during lockdown with an increased total sleep time (+ 48 min; 10.6%; d = 0.83). Lockdown-mediated circadian disruption had more deleterious effects on the sleep quality of individual-sport athletes compared with team-sport athletes (p < 0.001; d = 0.41), elite compared with non-elite athletes (p = 0.028; d = 0.44) and older compared with younger (p = 0.008; d = 0.46) athletes. Conclusions: These lockdown-induced behavioral changes reduced sleep quality and increased insomnia in athletes. Data-driven and evidence-based recommendations to counter these include, but are not limited to: (i) early outdoor training; (ii) regular meal scheduling (whilst avoiding meals prior to bedtime and caffeine in the evening) with appropriate composition; (iii) regular bedtimes and wake-up times; and (iv) avoidance of long and/or late naps. [ABSTRACT FROM AUTHOR]

Football academy settings may pose risks to adolescent athletes achieving sufficient sleep because of the contextual challenges these players face (e.g., psychosocial pressure, changes in training, competition, and academic stress). Given the importance of sleep to overall health as well as physical athletic development and injury risk, this study aimed to investigate whether differences in training schedules (morning vs. evening training sessions) affected objective measures of sleep in adolescent academy football (soccer) players. Twelve academy players (mean age 14.18 ± 1.36 years) wore an ActiGraph accelerometer on nights before, and nights of, training days in 2 separate weeks where morning (09:00-11:00 hours) and evening (18:00-20:00 hours) training occurred. Objective sleep parameters and training load data were collected. Night-time sleep periods were categorized as sleep preceding morning training, preceding evening training, or after evening training. One-way univariate and multivariate analyses of variance for repeated measures were performed to determine the impact of the training schedule on sleep. Significance levels were set at p < 0.05. The total sleep time was below the recommended guidelines (<8 hours) across conditions. A large significant effect of the training schedule on time attempted to fall asleep (p = 0.004, effect size [ES] = 0.40) and time of sleep (p = 0.003, ES = 0.41) was present, with post-evening sessions resulting in the latest times. Overall, the players' sleep behavior was resilient to changes in training schedules. However, the low sleep durations (and potential risks to physical performance/injury) suggest that sleep education coupled with practical interventions are required in this cohort. [ABSTRACT FROM AUTHOR]

Despite the perceived importance of sleep for elite footballers, descriptions of the duration and quality of sleep, especially following match play, are limited. Moreover, recovery responses following sleep loss remain unclear. Accordingly, the present study examined the subjective sleep and recovery responses of elite footballers across training days (TD) and both day and night matches (DM and NM). Sixteen top division European players from three clubs completed a subjective online questionnaire twice a day for 21 days during the season. Subjective recall of sleep variables (duration, onset latency, time of wake/sleep, wake episode duration), a range of perceptual variables related to recovery, mood, performance and internal training loads and non-exercise stressors were collected. Players reported significantly reduced sleep durations for NM compared to DM (−157 min) and TD (−181 min). In addition, sleep restfulness (SR; arbitrary scale 1 = very restful, 5 = not at all restful) and perceived recovery (PR; acute recovery and stress scale 0 = not recovered at all, 6 = fully recovered) were significantly poorer following NM than both TD (SR: +2.0, PR: −2.6), and DM (SR: +1.5; PR: −1.5). These results suggest that reduced sleep quantity and quality and reduced PR are mainly evident following NM in elite players. [ABSTRACT FROM PUBLISHER]

Objective: The influence of variations in exercise mode, thermal state, and load carriage on cardiac and ventilatory predictors of metabolic rate were investigated. Methods: Fifteen males were studied at rest and during whole-, upper-, and lower-body exercise (unloaded and loaded) under thermoneutral and hot conditions. Results: Ventilatory predictions were superior in thermoneutral (residual mean square error range: 0.04 to 0.17 L•min vs 0.21 to 0.36 L•min) and hot conditions (0.03 to 0.07 L•min vs 0.21 to 0.24 L•min). Predictions derived from whole- or lower-body exercise, and unloaded or loaded exercise could be interchanged without significant error. Nevertheless, a mode-specific prediction was required for upper-body work, and mild hyperthermia significantly reduced the precision of cardiac predictions. Conclusion: Ventilatory predictions were more precise, but errors from heart-rate predictions could be minimized by using thermal-state and exercise mode-specific predictions. [ABSTRACT FROM AUTHOR]