Institute for Social Science Research, University of Queensland, Brisbane, Australia; Centre for Rural and Remote Health, James Cook University, Mount Isa, Australia.
School of Psychological Science, University of Western Australia, Perth, Australia.
Sleep Med. 2020 Dec;76:120-127. doi: 10.1016/j.sleep.2020.10.020. Epub 2020 Oct 20.
To explore sleep trajectories and identify the risk factors and mediators of poor sleep in middle-aged adults.
Group-based multi-trajectory modelling was applied to the three waves of sleep data the from UK Biobank cohort to identify latent trajectories of sleep and group characteristics. Self-reported sleep duration, sleep problems (based on insomnia symptoms, snoring and trouble waking up) and daytime sleepiness (based on daytime tiredness and sleepiness) were included in the trajectory analyses. Multinomial logistic regression and mediation analysis were used to identify the main factors associated with poor sleep.
Analysis of sleep data from 41,094 participants (51.9% females) with a median age of 57 years (interquartile range 50-62 years) identified three distinct trajectories of sleep: healthy sleepers (40.8%); borderline poor sleepers (31.6%); and poor sleepers (27.6%). Socio-economic disadvantage, ethnic minority background, shift work, unhealthy lifestyle, poor health, depressive symptoms and obesity were the main risk factors associated with poor sleep. Around a third of the total effect of socio-economic deprivation on poor sleep was mediated through depressive symptoms.
The distinct groups with differential risk for developing sleep issues and stable sleep trajectories highlight the non-transient nature of sleep issues. Early management of depressive symptoms can help in reducing the future burden of poor sleep. Due to the increased risk of poor sleep, people from socio-economically deprived groups, particularly females from ethnic minorities, should be the highest priority for interventions aiming to improve population sleep health.
探讨中年人群的睡眠轨迹,确定睡眠质量差的风险因素和中介因素。
采用基于群组的多轨迹建模方法对英国生物银行队列的三波睡眠数据进行分析,以确定睡眠的潜在轨迹和群组特征。轨迹分析中包括自我报告的睡眠时间、睡眠问题(基于失眠症状、打鼾和难以醒来)和白天嗜睡(基于白天疲劳和嗜睡)。采用多项逻辑回归和中介分析来确定与睡眠质量差相关的主要因素。
对来自 41094 名参与者(51.9%为女性)的睡眠数据进行分析,这些参与者的中位年龄为 57 岁(四分位距为 50-62 岁),研究确定了三种不同的睡眠轨迹:健康睡眠者(40.8%);边缘性睡眠不佳者(31.6%);和睡眠不佳者(27.6%)。社会经济劣势、少数民族背景、轮班工作、不健康的生活方式、较差的健康状况、抑郁症状和肥胖是与睡眠质量差相关的主要风险因素。社会经济剥夺对睡眠质量差的总影响约有三分之一是通过抑郁症状来介导的。
具有不同睡眠问题风险的不同群体和稳定的睡眠轨迹突出了睡眠问题的非暂态性质。早期管理抑郁症状有助于减轻未来睡眠质量差的负担。由于睡眠质量差的风险增加,来自社会经济贫困群体的人群,特别是少数民族女性,应成为旨在改善人群睡眠健康的干预措施的重中之重。