Power Cody J, Fox Jordan L, Teramoto Masaru, Scanlan Aaron T
School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD 4701, Australia.
Rural Clinical School, The University of Queensland, Rockhampton, QLD 4700, Australia.
Brain Sci. 2023 Jan 31;13(2):238. doi: 10.3390/brainsci13020238.
Quantifying athlete sleep patterns may inform development of optimal training schedules and sleep strategies, considering the competitive challenges faced across the season. Therefore, this study comprehensively quantified the sleep patterns of a female basketball team and examined variations in sleep between nights. Seven semi-professional, female basketball players had their sleep monitored using wrist-worn activity monitors and perceptual ratings during a 13-week in-season. Sleep variables were compared between different nights (control nights, training nights, training nights before games, nights before games, non-congested game nights, and congested game nights), using generalized linear mixed models, as well as Cohen's and odds ratios as effect sizes. Players experienced less sleep on training nights before games compared to control nights, training nights, nights before games, and congested game nights ( < 0.05, = 0.43-0.69). Players also exhibited later sleep onset times on non-congested game nights compared to control nights ( = 0.01, = 0.68), and earlier sleep offset times following training nights before games compared to all other nights ( < 0.01, = 0.74-0.79). Moreover, the odds of players attaining better perceived sleep quality was 88% lower on congested game nights than on nights before games ( < 0.001). While players in this study attained an adequate sleep duration (7.3 ± 0.3 h) and efficiency (85 ± 2%) on average across the in-season, they were susceptible to poor sleep on training nights before games and following games. Although limited to a team-based case series design, these findings suggest basketball coaches may need to reconsider scheduling team-based, on-court training sessions on nights prior to games and consider implementing suitable psychological and recovery strategies around games to optimize player sleep.
考虑到整个赛季面临的竞争挑战,量化运动员的睡眠模式可能有助于制定最佳训练计划和睡眠策略。因此,本研究全面量化了一支女子篮球队的睡眠模式,并考察了夜间睡眠的差异。七名半职业女子篮球运动员在为期13周的赛季中,使用腕部活动监测器和感知评分来监测她们的睡眠。使用广义线性混合模型以及作为效应量的科恩d值和优势比,比较了不同夜晚(对照夜、训练夜、比赛前训练夜、比赛前夜、非密集比赛夜和密集比赛夜)的睡眠变量。与对照夜、训练夜、比赛前夜和密集比赛夜相比,运动员在比赛前训练夜的睡眠时间更少(P<0.05,d=0.43-0.69)。与对照夜相比,运动员在非密集比赛夜的入睡时间也更晚(P=0.01,d=0.68),与所有其他夜晚相比,比赛前训练夜后的起床时间更早(P<0.01,d=0.74-0.79)。此外,在密集比赛夜,运动员获得更好感知睡眠质量的几率比比赛前夜低88%(P<0.001)。虽然本研究中的运动员在赛季期间平均睡眠时间充足(7.3±0.3小时)且睡眠效率较高(85±2%),但她们在比赛前训练夜和比赛后容易出现睡眠不佳的情况。尽管本研究限于基于团队的病例系列设计,但这些发现表明篮球教练可能需要重新考虑在比赛前一晚安排基于团队的场内训练课程,并考虑在比赛前后实施合适的心理和恢复策略,以优化运动员的睡眠。