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

项目摘要 睡眠健康是公共卫生的优先事项。睡眠中的社会经济和种族差异（“睡眠中的社会差异”） 睡眠”）可能有助于健康的出现和持续 青年期是一个充满许多关键生活事件的过渡时期。利用脆弱 家庭和儿童福利研究（FFCWS）数据收集，拟议的研究将调查 青年人睡眠健康的纵向行为、社会人口统计学和背景预测因素，以及 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，这一更新提案将收集睡眠和健康 目标1中的行为数据。首先，我们将在22岁年轻人普查中增加与睡眠有关的问题， 全FFCWS队列（n~3600）。然后，在一个大约（n~900）的青少年参加了一个子样本， 15岁的体动记录数据收集，我们将收集14天的睡眠和身体活动的体动记录数据， 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 (e.g.,物质使用、饮食）。这项研究使第一个基于活动记录的睡眠轨迹分析成为可能， 从青春期到青年期。在目标2中，我们试图确定睡眠健康差异的大小， 青年期，以及环境和行为因素在多大程度上解释了社会差异， 睡吧我们假设少数民族和那些早期社会经济地位较低的人将继续表现出 年轻人的睡眠健康状况较差，这些差异部分是由家庭和 环境因素，睡眠和健康行为，以及年轻人的自主性和约束。在目标3中， 我们将评估儿童和青少年的睡眠健康轨迹如何与健康相关， 青年期，包括身体健康，社会情感健康和社会经济福祉。在目标4中， 将对健康和健康风险行为之间的人内时间动态进行建模（即，身体活动， 睡前屏幕时间，物质使用，饮食）和睡眠健康，使用两周的活动记录和每日日记数据。 在这里，我们假设，在一个人，从事健康风险行为（例如，床前屏幕时间， 久坐不动的活动，物质使用）将不利地影响晚上的睡眠，并且积极的健康行为（例如， 体力活动）将有利于晚上的睡眠。我们进一步假设，睡眠对人的内在影响， 第二天的活动（例如，更多的睡眠时间与更多的第二天身体活动有关）。拟议 对儿童期至22岁的睡眠轨迹进行严格的基于活动记录的调查， 了解年轻人睡眠健康和幸福的社会差异。机械的见解 关于可修改行为的角色（例如，睡前习惯，屏幕使用行为，身体活动， 物质使用，饮食）和环境因素可以影响的发展，实施和评价， 政策和干预措施，以减少睡眠和健康差距，改善年轻人的福祉。

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.