School of Medicine & Public Health, Faculty of Health and Medicine, The University of Newcastle, University Drive, Callaghan, Australia; Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, University Drive, Callaghan, Australia.
School of Medicine & Public Health, Faculty of Health and Medicine, The University of Newcastle, University Drive, Callaghan, Australia; Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, University Drive, Callaghan, Australia.
Sleep Health. 2020 Dec;6(6):828-834. doi: 10.1016/j.sleh.2020.04.006. Epub 2020 Aug 18.
To identify the patterns of activity, sitting and sleep that adults engage in, the demographic and biological correlates of activity-sleep patterns and the relationship between identified patterns and self-rated health.
Online panel of randomly selected Australian adults (n = 2034) completing a cross-sectional survey in October-November 2013.
Panel members who provided complete data on all variables were included (n = 1532).
Participants self-reported their demographic characteristics, height, weight, self-rated health, duration of physical activity, frequency of resistance training, sitting time, sleep duration, sleep quality, and variability in bed and wake times. Activity-sleep patterns were determined using latent class analysis. Latent class regression was used to examine the relationships between identified patterns, demographic and biological characteristics, and self-rated health.
A 4-class model fit the data best, characterized by very active good sleepers, inactive good sleepers, inactive poor sleepers, moderately active good sleepers, representing 38.2%, 22.2%, 21.2%, and 18.4% of the sample, respectively. Relative to the very active good sleepers, the inactive poor sleepers, and inactive good sleepers were more likely to report being female, lower education, higher body mass index, and lower self-rated health, the moderately active good sleepers were more likely to be older, report lower education, higher body mass index and lower self-rated health. Associations between activity-sleep pattern and self-rated health were the largest in the inactive poor sleepers.
The 4 activity-sleep patterns identified had distinct behavioral profiles, sociodemographic correlates, and relationships with self-rated health. Many adults could benefit from behavioral interventions targeting improvements in physical activity and sleep.
确定成年人的活动、久坐和睡眠模式,活动-睡眠模式的人口统计学和生物学相关性,以及所确定的模式与自我评估健康之间的关系。
2013 年 10 月至 11 月期间,通过在线随机选择澳大利亚成年人的小组(n=2034)进行横断面调查。
参与小组并提供所有变量完整数据的成员(n=1532)。
参与者自我报告其人口统计学特征、身高、体重、自我评估健康、体力活动时间、抗阻训练频率、久坐时间、睡眠时间、睡眠质量以及睡眠时间和起床时间的可变性。使用潜在类别分析确定活动-睡眠模式。使用潜在类别回归检查所确定的模式、人口统计学和生物学特征以及自我评估健康之间的关系。
一个 4 类模型最适合数据,分别由非常活跃的良好睡眠者、不活跃的良好睡眠者、不活跃的较差睡眠者和适度活跃的良好睡眠者代表,分别占样本的 38.2%、22.2%、21.2%和 18.4%。与非常活跃的良好睡眠者相比,不活跃的较差睡眠者和不活跃的良好睡眠者更有可能是女性、受教育程度较低、身体质量指数较高、自我评估健康状况较差,而适度活跃的良好睡眠者更有可能是年龄较大、受教育程度较低、身体质量指数较高、自我评估健康状况较差。活动-睡眠模式与自我评估健康之间的关联在不活跃的较差睡眠者中最大。
所确定的 4 种活动-睡眠模式具有不同的行为特征、社会人口统计学相关性以及与自我评估健康的关系。许多成年人可能受益于以改善体力活动和睡眠为目标的行为干预措施。