Faculty of Medicine and Health Sciences, Macquarie University, Australia.
Athletic Performance Unit, Greater Western Sydney Giants Football Club, Australia.
J Sci Med Sport. 2019 Feb;22(2):130-134. doi: 10.1016/j.jsams.2018.06.011. Epub 2018 Jun 19.
To determine the incidence of illness, and identify the relationship between sleep, training load and illness in nationally competitive Australian football athletes. Second, to assess multivariate effect between training load and/or sleep variables.
Cohort study.
Retrospective analyses of prospectively collected cohort data were conducted on forty-four male athletes over a 46-week season. The primary outcome was illness incidence, recorded daily by medical doctors. Independent variables were acute, chronic and acute:chronic ratios of: sleep quality, sleep quantity, internal training load and external training load defined as: total running distance, high speed running distance and sprint distance. Generalised estimating equations using Poisson (count) models were fit to examine both univariate and multivariate associations between independent variables and illness incidence.
67 incidences of illness were recorded, with an incidence rate of 11 illnesses per 1000 running hours. Univariate analysis showed acute and chronic sleep hours and quality, as well as acute sprint and total running distance to be significantly associated with illness. Multivariate analysis identified that only acute sleep quantity was significantly, negatively associated with illness incidence (OR 0.49, CI 0.25-0.94) once all univariate significant variables were controlled for. There was no relationship between external training load and illness when sleep metrics were controlled for.
In a cohort of Australian football athletes, whose load was well monitored, reduced sleep quantity was associated with increased incidence of illness within the next 7 days. Monitoring sleep parameters may assist in identifying individuals at risk of illness.
确定疾病发病率，并确定澳大利亚足球运动员的睡眠、训练负荷与疾病之间的关系。其次，评估训练负荷和/或睡眠变量之间的多变量影响。
队列研究。
对 44 名男性运动员在 46 周赛季中前瞻性收集的队列数据进行回顾性分析。主要结果是由医生每天记录的疾病发病率。自变量为睡眠质量、睡眠量、急性和慢性比、内部训练负荷和外部训练负荷，分别定义为：总跑动距离、高速跑动距离和冲刺距离。使用泊松（计数）模型广义估计方程来检查独立变量与疾病发病率之间的单变量和多变量关联。
记录了 67 例疾病发作，发病率为每 1000 跑动小时 11 例。单变量分析表明，急性和慢性睡眠时间和质量，以及急性冲刺和总跑动距离与疾病显著相关。多变量分析表明，仅急性睡眠时间与疾病发病率显著负相关（OR 0.49，CI 0.25-0.94），一旦控制了所有单变量显著变量。当控制睡眠指标时，外部训练负荷与疾病之间没有关系。
在一组澳大利亚足球运动员中，其负荷得到了很好的监测，睡眠时间减少与接下来 7 天内疾病发病率增加有关。监测睡眠参数可能有助于识别有患病风险的个体。
