Esports is becoming increasingly professionalized, yet research on performance management is remarkably lacking. The present study aimed to investigate the sleep and mood of professional esports athletes. Participants were 17 professional esports athletes from South Korea (N = 8), Australia (N = 4), and the United States (N = 5) who played first person shooter games (mean age 20 ± 3.5 years, 100% male). All participants wore a wrist-activity monitor for 7–14 days and completed subjective sleep and mood questionnaires. Participants had a median total sleep time of 6.8 h and a sleep efficiency of 86.4% per night. All participants had significantly delayed sleep patterns (median sleep onset 3:43 a.m. and wake time 11:24 a.m.). Participants had a median sleep onset latency of 20.4 min and prolonged wake after sleep onset of 47.9 min. Korean players had significantly higher depression scores compared to the other groups (p < 0.01) and trained longer per day than the Australian or United States teams (13.4 vs. 4.8 vs. 6.1 h, respectively). Depression scores were strongly correlated with number of awakenings, wake after sleep onset, and daily training time (p < 0.05). As the first pilot sleep study in the esports field, this study indicates that esports athletes show delayed sleep patterns and have prolonged wake after sleep onset. These sleep patterns may be associated with mood (depression) and training time. Sleep interventions designed specifically for esports athletes appear warranted.

Open water swimming ultra-marathon events ≥10 km have become increasingly popular amongst master athletes. However, very little is known about the timing of training sessions and the impact on sleep. This study aimed to examine sleep behaviours, sleep problems and disorders and the relationship with training timings. This study used a longitudinal observational design for 42 nights with 24 masters' swimmers (n = 13 females), aged 39 ± 11 years, body mass index of 26 ± 3 kg/m2 during a training squad for an ocean ultra-swim (19.7 km) in Western Australia. Objective measures of sleep were obtained from a wrist-activity monitor, the Readiband™ (Fatigue Science Inc., Canada). Swimmers completed a survey instrument related to sleep problems, disorders, chronotype, anthropometric and demographic information. Generalised linear mixed models were fitted to examine relationships between predictor variables and sleep responses. Body mass index was associated with a decline in Total Sleep Time (TST), each one-unit increase in BMI was associated with 5 min less TST (p = 0.04). Swimmers with a “high risk” of sleep apnea had 21 min more wake time (p = 0.04) and 5% lower Sleep Efficiency (p = 0.04). Sleep Offset on the morning of a morning training session was earlier by 48 min (p < 0.001) resulting in less TST by 39 min (p < 0.001). This study provides evidence that coaches need to consider sleep behaviours and problems before designing training schedules. Swimmers need to plan and allocate an adequate sleep opportunity and those who have a suspected sleep disorder or problem should seek the support of a sleep physician.

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Sleep for recovery is an essential factor for performance in athletes. One such group is recreational ultra-marathon swimmers (>10km). We aimed to compare measures of sleep before and after a sleep hygiene education intervention during a 16-week training programme. Using a prospective study design, the experiment was conducted in two phases (pre- and post-intervention), whereby pre- and post-intervention data were collected for 42 nights after the sleep hygiene education. This study had 24 masters’ swimmers (n = 13 females), aged 39 ± 11 years, and body mass index (BMI) of 26 ± 3 kg/m2 during a training squad for an ocean ultra-marathon swimmer (19.7 km) in Perth, Western Australia. Objective measures of sleep were obtained from a wrist activity monitor, the Readiband™ (Fatigue Science Inc., Canada). Participants underwent a 2-hour sleep hygiene education session. Generalised linear mixed models were fitted to examine relationships between predictor variables and sleep responses. Sleep onset and offset increased by 12 minutes post-intervention ( p < 0.001). For nights before morning training, sleep onset increased by 12 minutes and offset by 24  minutes post-intervention. Females increased sleep onset by 18 minutes and delayed sleep offset by 12 minutes sleep ( p < 0.05) post-intervention. The sleep hygiene education was insufficient in making meaningful improvements to measures of sleep. Individual sleep hygiene education and continuous reinforcement of sleep for recovery during a training programme may be required to observe improvements. Coaches should aim to design training schedules to minimise the impact on swimmer’s sleep opportunity and swimmers need to involve family in the planning of rest periods during a training programme.

Shiftwork may adversely impact an individual’s sleep-wake patterns and result in sleep loss (&lt;6 h. following night shift), due to the circadian misalignment and the design of rosters and shifts. Within a mining operation, this sleep loss may have significant consequences due to fatigue, including an increased risk of accidents and chronic health conditions. This study aims to (i) determine the efficacy of an intervention that comprises a sleep education program and biofeedback through a smartphone app on sleep quality, quantity, and alertness (ii) determine the prevalence of risk for a potential sleep disorder, and (iii) quantify and describe the sleep habits and behaviors of shift workers in a remote mining operation. This study consists of a randomized controlled trial whereby eighty-eight shift workers within a remote mining operation are randomized to a control group or one of three different treatment groups that are: (i) a sleep education program, (ii) biofeedback on sleep through a smartphone app, or (iii) a sleep education program and biofeedback on sleep through a smartphone app. This study utilizes wrist-activity monitors, biomathematical modeling, and a survey instrument to obtain data on sleep quantity, quality, and alertness. A variety of statistical methods will determine the prevalence of risk for a potential sleep disorder and associations with body mass index, alcohol, and caffeine consumption. A generalized linear mixed model will examine the dependent sleep variables assessed at baseline and post-intervention for the control group and intervention groups, as well as within and between groups to determine changes. The findings from this study will contribute to the current understanding of sleep and alertness behaviors, and sleep problems and disorders amongst shift workers. Importantly, the results may inform fatigue policy and practice on interventions to manage fatigue risk within the mining industry. This study protocol may have a broader application in other shiftwork industries, including oil and gas, aviation, rail, and healthcare.