Crosley-Lyons Rachel, Li Jixin, Wang Wei-Lin, Wang Shirlene D, Huh Jimi, Bae Dayoung, Intille Stephen S, Dunton Genevieve F
Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, USA.
J Sleep Res. 2025 Feb 5:e14471. doi: 10.1111/jsr.14471.
Sleep and circadian characteristics are associated with health outcomes, but are often examined cross-sectionally or using variable-centred analyses. Person-centred longitudinal research is needed to identify combined effects of sleep and circadian characteristics while allowing for change over time. We aimed to classify individuals into sleep-circadian statuses (aim 1), determine whether they transitioned between statuses over time (aim 2), and explore associated covariates and health outcomes (aim 3). Young adults (N = 151) wore smartwatches continuously for 6 months. Sleep (total sleep time, wake after sleep onset) and circadian rest-activity cycle indicators (interdaily stability, intradaily variability, relative amplitude) were derived from acceleration data and aggregated into person-means for months 1, 3, and 6. These values were entered into a latent transition model for aims 1 and 2. Multinomial logistic regressions, ANOVA, and ANCOVA addressed aim 3. Four statuses were extracted (entropy = 0.88): optimal sleepers, restless sleepers, short sleepers, and nappers. 10%-13% of optimal sleepers and 21% of restless sleepers became nappers, 7%-18% of nappers transitioned to other statuses, and 94%-100% of short sleepers remained unchanged. Males were more likely than females to be short versus optimal sleepers (p < 0.001). Restless sleepers had more physical dysfunction than nappers and short sleepers (p = 0.014, 0.022), while short sleepers reported more excessive sleepiness than optimal sleepers and nappers (p = 0.006, 0.060). This study identified four sleep-circadian statuses and found evidence for change over time. Our longitudinal person-centred approach could help inform the development of tailored diagnostic guidelines for sleep and circadian-related disorders that fluctuate within-individuals.
睡眠和昼夜节律特征与健康结果相关,但通常采用横断面研究或基于变量的分析方法进行考察。需要开展以个体为中心的纵向研究,以确定睡眠和昼夜节律特征的综合影响,并考虑到随时间的变化。我们旨在将个体分类为睡眠-昼夜节律状态(目标1),确定他们是否随时间在不同状态之间转换(目标2),并探索相关的协变量和健康结果(目标3)。年轻成年人(N = 151)连续佩戴智能手表6个月。睡眠指标(总睡眠时间、睡眠开始后觉醒时间)和昼夜休息-活动周期指标(日间稳定性、日内变异性、相对振幅)从加速度数据中得出,并汇总为第1、3和6个月的个体均值。这些值被输入到一个潜在转换模型中以实现目标1和目标2。多项逻辑回归、方差分析和协方差分析用于实现目标3。提取出四种状态(熵 = 0.88):最佳睡眠者、不安睡眠者、短睡眠者和打盹者。10%-13%的最佳睡眠者和21%的不安睡眠者变成了打盹者,7%-18%的打盹者转换为其他状态,94%-100%的短睡眠者保持不变。与最佳睡眠者相比,男性更有可能是短睡眠者(p < 0.001)。不安睡眠者比打盹者和短睡眠者有更多的身体功能障碍(p = 0.014, 0.022),而短睡眠者比最佳睡眠者和打盹者报告有更多的过度嗜睡(p = 0.006, 0.060)。本研究确定了四种睡眠-昼夜节律状态,并发现了随时间变化的证据。我们以个体为中心的纵向研究方法有助于为制定针对个体内部波动的睡眠和昼夜节律相关疾病的定制诊断指南提供信息。