Longitudinal behavioral, sociodemographic and contextual predictors of young adult sleep health and well-being

项目总结 睡眠健康是公共健康的优先事项。睡眠中的社会经济和民族差异(“社会差异 儿童和青春期的睡眠)可能有助于健康的出现和持续 青年时期的差异，这是一个充满许多关键人生事件的过渡期。利用脆弱的人 家庭和儿童福祉研究(FFCWS)的数据收集，拟议的研究将调查 青少年睡眠健康的纵向行为、社会人口学和背景预测因素，以及程度 青少年睡眠健康轨迹和其他健康行为导致了年轻人的差异 幸福。作为FFCWS的22岁浪潮的子研究，这项更新建议将收集睡眠和健康 目标1中的行为数据。首先，我们将在22岁的青少年普查中添加与睡眠有关的问题 FFCWS全队列(n~3600)。然后，对大约(n~900)名参与调查的青少年进行了亚样本调查 15岁的活动数据收集，我们将收集14天的睡眠和体力活动的活动数据， 同时使用基于智能手机的每日两次日记应用程序收集屏幕使用和自我报告的活动数据 (例如，物质使用、饮食)。这项研究使首次基于动作图的睡眠轨迹分析成为可能 青春期进入青春期。在目标2中，我们试图确定睡眠健康差距的大小 青壮年，以及环境和行为因素在多大程度上解释了社会差异 睡吧。我们假设，少数族裔和那些早年生活在较低SES的人将继续表现出 年轻人的睡眠健康状况较差，这些差异部分是由家庭和 背景因素，睡眠和健康行为，以及青壮年的自主性和制约因素。在《目标3》中， 我们将评估儿童和青春期的睡眠健康轨迹如何与儿童的幸福感相关 青壮年，包括身体健康、社会情绪健康和社会经济健康。在目标4中，我们 将对健康和健康危险行为(即，体力活动， 利用两周的活动记录和每日日记数据，研究睡前屏幕时间、物质使用、饮食)和睡眠健康状况。 这里我们假设，在一个人内，从事健康危险行为(例如，睡前屏幕时间， 久坐活动、物质使用)将对当晚的睡眠产生不利影响，而积极的健康行为(例如， 体力活动)将有益于当晚的睡眠。我们进一步假设睡眠对人与人之间的影响 第二天的活动(例如，睡眠时间越长，第二天的体力活动就越多)。建议数 对从童年到22岁的睡眠轨迹的严格的基于动作图的研究推动了我们的 了解青壮年睡眠健康和幸福感方面的社会差异。机械论的见解 关于可修改行为的作用(例如，就寝时间习惯、屏幕使用行为、体力活动、 物质使用、饮食)和环境因素可以影响对 旨在减少睡眠和健康差距并改善年轻人福祉的政策和干预措施。

Project summary Sleep health is a public health priority. Socioeconomic and ethnoracial differences in sleep (“social disparities in sleep”) across childhood and adolescence may contribute to the emergence and persistence of health disparities into young adulthood, a transitional period dense with many key life events. Leveraging the Fragile Family and Child Wellbeing Study (FFCWS) data collection, the proposed research will investigate the longitudinal behavioral, sociodemographic, and contextual predictors of young adult sleep health, and the extent to which youth sleep health trajectories and other health behaviors contribute to disparities in young adult wellbeing. As a sub-study of the age 22 wave of the FFCWS, this renewal proposal will collect sleep and health behavior data in Aim 1. First, we will add sleep-related questions to the age 22 young adult general survey in the full FFCWS cohort (n~3600). Then, on a subsample of approximately (n~900) adolescents who participated in the age 15 actigraphy data collection, we will collect 14 days of actigraphy data for sleep and physical activity, with a concurrent smartphone-based, twice-daily diary app collecting screen use and self-reported activity data (e.g., substance use, diet). This study enables the first actigraphy-based analyses of sleep trajectories from adolescence into young adulthood. In Aim 2, we seek to identify the magnitude of sleep health disparities in young adulthood, and the extent to which contextual and behavioral factors account for social disparities in sleep. We hypothesize that ethnoracial minorities and those with early life lower SES will continue to exhibit poorer sleep health in young adulthood, and that these differences are partially accounted for by family and contextual factors, sleep and health behaviors, and the autonomy and constraints of young adulthood. In Aim 3, we will assess how sleep health trajectories across childhood and adolescence are associated with wellbeing in young adulthood, including physical health, social-emotional health, and socioeconomic wellbeing. In Aim 4, we will model within-person temporal dynamics between health and health risk behaviors (i.e., physical activity, pre-bed screen time, substance use, diet) and sleep health using two weeks of actigraphy and daily diary data. Here we hypothesize that, within a person, engaging in health risk behaviors (e.g., pre-bed screen time, sedentary activity, substance use) will adversely affect that night’s sleep, and that positive health behaviors (e.g., physical activity) will benefit that night’s sleep. We further hypothesize that sleep has within-person effects on next-day activities (e.g., more sleep duration is associated with more next-day physical activity). The proposed rigorous actigraphy-based investigation of sleep trajectories from childhood through age 22 advances our understanding of social disparities in sleep health and wellbeing in young adulthood. Mechanistic insights regarding the role of modifiable behaviors (e.g., bedtime routines, screen use behaviors, physical activity, substance use, diet) and contextual factors can influence the development, implementation, and evaluation of policies and interventions to reduce sleep and health disparities and improve wellbeing of young adults.