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.

Kim SJ, et al. J Korean Med Sci. 2021 Jun;36(25):e184. https://doi.org/10.3346/jkms.2021.36.e184

This study evaluated a brief sleep intervention designed to improve the sleep, mood, and cognitive performance of professional electronic sports (esports) athletes from three major esports regions (i.e., Asia, North America, and Oceania). Fifty-six esports athletes from South Korea (N = 34), the United States (N = 7), and Australia (N = 15) completed the study. Participants completed an initial 2-week pre-intervention phase to establish a baseline, followed by a 2-week intervention phase that involved a group sleep education class, 1:1 session with a trained clinical psychologist, and daily biofeedback. A wrist activity monitor and daily sleep diary were used to monitor sleep during both phases, while at pre- and post-intervention, participants completed a battery of sleep and mood questionnaires and underwent cognitive performance testing. Sleep knowledge increased from pre- to post-intervention (d = 0.83 [95% CI −1.21, −0.43], p =< 0.001), while there were modest improvements in sleep diary estimates (i.e., sleep onset latency (M_diff = −2.9 min, p = 0.02), sleep onset time (M_diff = −12 min, p = 0.03), and sleep efficiency (M_diff = 1.1%, p = 0.004)) and wrist activity monitor estimates (i.e., sleep onset time (M_diff = −18 min, p = 0.01)). Insomnia severity scores decreased significantly (d = 0.47 [95% CI 0.08, 0.84], p = 0.001), while sleepiness scores increased but not meaningfully (d = 0.23 [95% CI −0.61, 0.14], p = 0.025). However, there was no significant change in mood (i.e., depression and anxiety) or cognitive performance scores (i.e., mean reaction time or lapses). Sleep interventions for esports athletes require further investigation. Future research should examine whether a stepped-care model, whereby increasing therapeutic input is provided as needed, can optimize sleep, mood, and cognitive performance outcomes.

Background: Firefighters are vulnerable to irregular sleep patterns and sleep disturbance due to work characteristics such as shift work and frequent dispatch. However, there are few studies investigating intervention targeting sleep for firefighters. This preliminary study aimed to develop and test a sleep intervention, namely FIT-IN (Firefighter’s Therapy for Insomnia and Nightmares), which was based on existing evidence-based treatment tailored to firefighters in consideration of their occupational characteristics. Methods: This study implemented a single-group pre-post study design, utilizing an intervention developed based on brief behavior therapy for insomnia with imagery rehearsal therapy components. FIT-IN consisted of a total of three sessions (two face-to-face group sessions and one telephone session). Participants were recruited from Korean fire stations, and a total of 39 firefighters participated. Participants completed a sleep diary for two weeks, as well as the following questionnaires to assess their sleep and psychological factors: insomnia severity index (ISI), disturbing dream and nightmare severity index (DDNSI), Epworth sleepiness scale (ESS), depressive symptom inventory-suicidality subscale (DSI), and Patient Health Questionnaire-9 (PHQ-9). These questionnaires were administered before the first session and at the end of the second session. Results: The FIT-IN program produced improvements in sleep indices. There was a significant increase in sleep efficiency (p < 0.01), and a decrease in sleep onset latency, number of awakenings, and time in bed (p < 0.05), as derived from weekly sleep diaries. In addition, significant decreases were shown for insomnia (p < 0.001) and nightmare severity (p < 0.01). Conclusion: There were significant improvements in sleep and other clinical indices (depression, PTSD scores) when comparing pre-and post-intervention scores. FIT-IN may be a feasible and practical option in alleviating sleep disturbance in this population. Further studies will be needed to ascertain FIT-IN’s effectiveness.

Screen-based activity refers to the use of screened devices, which are changing from stationary devices such as televisions and desktop computers to newer portable devices such as smartphones and electronic tablets. The exposure of younger children to all types of screened devices has increased. This review aimed to provide an overview of previous studies and identify the correlations associated with screen-based activities in preschoolers. We conducted a systematic literature review of databases including PubMed, Embase, and PsycInfo targeting quantitative studies published between 2010 and 2019. We then analyzed the associations between screen time and various individual, familial, and environmental factors in healthy children aged 0–6 years. We specifically included studies that provided a subgroup analysis for this age category to systematically identify the factors influencing screen time patterns among preschoolers. The search yielded 36 articles that met the inclusion criteria. We investigated age, sex, and social, family, and personal factors as well as media parenting. The results suggested that media parenting for screen-based activities was an important protective factor. Limited research has been conducted on preschoolers' screen-based activities with both stationary and portable screened devices. Most factors have been studied using stationary devices and are too infrequent for solid conclusions to be reached. Parental screen time and the setting of appropriate media limits were crucial protective factors against excessive media exposure. However, information about family and personal factors remain scarce, and further research is needed.

Yeonkyu Choi, Dong Yun Lee, Sangha Lee, Eun-Jin Park, Hee Jeong Yoo, and Yunmi Shin. J Korean Acad Child Adolesc Psychiatry 2021;32:154-60. https://doi.org/10.5765/jkacap.210015