STUDY OBJECTIVES Studies examining time-use activity behaviors (sleep, sedentary behavior and physical activity) on school days compared to non-school days have examined these behaviors independently, ignoring their interrelated nature, limiting our ability to optimize the health benefits of these behaviors. This study examines the associations of school-day (versus non-school day) with time-use activity behaviors. METHODS Time series data (6,642 days) from Fitbits (Charge-2) were collected (n=196, 53% female, 5-10yrs). We used a variable-centered dynamic structural equation modeling (DSEM) approach to estimate day-to-day associations of time-use activity behaviors on school days for each child. We then used person-centered cluster analyses to group individuals based on these estimates. RESULTS Within-participant analysis showed that on school days (vs. non-school days), children (1) slept less (β=-0.17, 95%CI=-0.21, -0.13), (2) were less sedentary (β=-0.05, 95%CI=-0.09, -0.02), and (3) had comparable MVPA (β=-0.05, 95%CI=-0.11, 0.00). Between-participant analysis showed that, on school days, children with higher sleep carryover experienced greater decreases in sleep (β=0.44 95%CI=0.08, 0.71), children with higher zBMI decreased sedentary behavior more (β=-0.41, 95%CI=-0.64, -0.13), and children with lower MVPA increased MVPA more (β=-0.41, 95%CI -0.64, -0.13). Cluster analysis demonstrated four distinct patterns of connections between time-use activity behaviors and school (High Activity, Sleep Resilient, High Sedentary and Dysregulated Sleep). CONCLUSIONS Using a combination of person-centered and more traditional variable-centered approaches, we identified patterns of interrelated behaviors that differed on school, and non-school days. Findings can inform targeted intervention strategies tailored to children's specific behavior patterns.

中文翻译：

---

学校与非学校日的睡眠动态、久坐行为和 MVPA

研究目标 与非上学日相比，在校期间检查时间使用活动行为（睡眠、久坐行为和身体活动）的研究独立审查了这些行为，忽略了它们相互关联的性质，限制了我们优化这些行为的健康益处的能力。本研究考察了上课日（相对于非上课日）与时间使用活动行为的关联。方法 收集了来自 Fitbits（Charge-2）的时间序列数据（6,642 天）（n=196，53% 的女性，5-10 岁）。我们使用以变量为中心的动态结构方程模型 (DSEM) 方法来估计每个孩子在上学日的时间使用活动行为的日常关联。然后，我们使用以人为中心的聚类分析，根据这些估计对个人进行分组。结果 参与者内部分析显示，在上学日（与非上学日相比），儿童 (1) 睡眠较少 (β=-0.17, 95%CI=-0.21, -0.13)，(2) 久坐不动 (β =-0.05, 95%CI=-0.09, -0.02) 和 (3) 具有可比的 MVPA (β=-0.05, 95%CI=-0.11, 0.00)。参与者间分析表明，在上学期间，睡眠结转率较高的儿童的睡眠减少幅度较大（β=0.44 95%CI=0.08, 0.71），zBMI 较高的儿童久坐行为的减少幅度更大（β=-0.41, 95% CI=-0.64, -0.13)，MVPA 较低的儿童 MVPA 增加更多 (β=-0.41, 95%CI -0.64, -0.13)。聚类分析表明时间使用活动行为与学校之间存在四种不同的联系模式（高活动、睡眠弹性、久坐和睡眠失调）。结论 结合以人为本和更传统的以变量为中心的方法，我们确定了在学校和非学校​​日不同的相关行为模式。调查结果可以为针对儿童特定行为模式量身定制的有针对性的干预策略提供信息。