Microtemporal Processes Underlying Health Behavior Adoption and Maintenance

总结 成年期（18-24岁）的特点是体重大幅增加，导致体重增加。 癌症和其他慢性疾病的终生风险。参与足够的体力活动， 睡眠和限制久坐时间是预防体重增加的重要因素。然而，在这方面， 从事这些健康行为的高峰期在儿童和青少年时期， 在成年期逐渐恶化促进身体活动的干预措施，减少久坐时间， 充足的睡眠通常集中在这些行为的采用上。然而，当这些干预措施 成功的、新的行为模式得不到维持，通常会退回到基线水平。 传统的健康行为理论对行为背后的因素提供了有限的指导 上维护为了解决这一差距，我们的工作表明，决策和 行为可以揭示收养与维护机制的差异。 反射过程（例如，效能，审议，自我控制）可能会在更大程度上被激活， 行为采纳相反，反应过程（例如，上下文线索，自动性，习惯）可能会发挥作用， 在行为维护方面发挥更大作用。然而，反应性流程很难使用回顾性方法进行测量 方法，因为它们可以在微时间尺度上展开（即，在几分钟或几小时内变化）。解决这个 问题，我们建议使用实时移动的技术来收集密集的纵向数据 研究微时间过程的差异，这些微时间过程是采用和维持 身体活动、久坐时间少、睡眠时间充足。我们会在- 受试者病例交叉观察性研究，为期12个月。种族多样的新兴成年人 (ages 18-24，N=300）将从幸福与健康队列（R 01 DA 033296）中招募。我们会进行 间歇性自我报告（即，生态瞬时评估）的反射变量;和连续的，传感器- 基于反应变量的被动监视（例如，位置、社交接近度、语音/文本通信）以及 行为（即，身体活动、久坐时间、睡眠）。这些数据将 用于预测行为“发作”可能性的受试者内变化（天内、天间） (e.g.,≥10分钟的体力活动，≥120分钟的久坐时间，≥7小时的睡眠）和“失误”（即，未能实现 推荐水平≥7天）。具体目标是（1）具体地使用机器学习来识别 预测行为事件的时变反射和反应因素的个人特定组合， 失效;（2）按规则确定是否有一般的，组级模式的时间变化 预测因素，以及这些模式是否预测成功的行为维持结果。的数据和 该项目的方法将有助于U 01/U24强化纵向行为倡议的集体 目标是建立更多的预测性健康行为理论，为个性化干预指定目标。

SUMMARY Emerging adulthood (ages 18-24 years) is marked by substantial weight gain, leading to increased lifetime risks of cancer and other chronic diseases. Engaging in sufficient levels of physical activity and sleep, and limiting sedentary time are important contributors to the prevention of weight gain. However, engaging in these healthy behaviors peaks during the childhood and adolescent years, and steeply deteriorates into emerging adulthood. Interventions promoting physical activity, reduced sedentary time, and sufficient sleep typically focus on the adoption of these behaviors. Yet, when these interventions are successful, new patterns of behavior are not maintained and typically regress back to baseline levels. Traditional health behavior theories provide limited guidance regarding factors underlying behavior maintenance. To address this gap, our work suggests that dual-process models of decision-making and behavior can shed light on differences in the mechanisms underlying adoption versus maintenance. Reflective processes (e.g., efficacy, deliberations, self-control) may be activated to a greater extent during behavior adoption. In contrast, reactive processes (e.g., contextual cues, automaticity, habits) may play a greater role in behavior maintenance. However, reactive processes are difficult to measure using retrospective methods because they can unfold on a micro-timescale (i.e., change across minutes or hours). To solve this problem, we propose to use real-time mobile technologies to collect intensive longitudinal data examining differences in the micro-temporal processes underlying the adoption and maintenance of physical activity, low sedentary time, and sufficient sleep duration. We will conduct a prospective within- subject case-crossover observational study across a 12-month period. Ethnically-diverse, emerging adults (ages 18-24, N=300) will be recruited from the Happiness & Health Cohort (R01DA033296). We will conduct intermittent self-report (i.e., ecological momentary assessment) of reflective variables; and continuous, sensor- based passive monitoring of reactive variables (e.g., location, social proximity, voice/text communication) and behaviors (i.e., physical activity, sedentary time, sleep) using smartwatches and smartphones. These data will be used to predict within-subject variation (within-days, between-days) in the likelihood of behavior “episodes” (e.g., ≥10 min of physical activity, ≥120 min sedentary time, ≥7 hr sleep) and “lapses” (i.e., failure to achieve recommended levels ≥7 days). The specific aims are to (1) idiographically use machine learning to identify person-specific combinations of time-varying reflective and reactive factors that predict behavior episodes and lapse; and (2) nomothetically determine whether there are general, group-level patterns of time-varying predictors, and whether those patterns predict successful behavior maintenance outcomes. The data and methods from this project will contribute to the U01/U24 Intensive Longitudinal Behavior Initiative’s collective goal to build more predictive health behavior theories that specify targets for personalized interventions.