Microtemporal Processes Underlying Health Behavior Adoption and Maintenance

摘要-家长U01 成年初期(18-29岁)的特点是体重大幅增加，导致体重增加 癌症和其他慢性病的终生风险。从事足够水平的体力活动和 睡眠和限制久坐时间是预防体重增加的重要因素。传统养生 行为理论对行为维持的潜在因素提供了有限的指导；因此，对于广告-- 弥补这一差距，我们的工作表明，决策和行为的双重过程模型可以摆脱 阐明采用与维护的基础机制的差异。反思性过程 (例如，效能、自我控制)在行为采用过程中可能在更大程度上被激活。相比之下，被动的 过程(例如，上下文线索、自动性)可能在行为维护中扮演更重要的角色。然而，请重新- 活动过程很难衡量，因为它们可以在微时间尺度上展开(即，变化 分钟或小时)。为了解决这个问题，我们建议使用实时移动技术来收集信息。 检验采用基础的微时间过程中的差异的随意性纵向数据 并保持体力活动、少坐时间和充足的睡眠时间。我们将进行 对反射变量的间歇性自我报告(即生态瞬时评估)；以及连续的传感器- 基于被动监测反应变量(例如，位置)和行为(例如，身体活动、久坐不动 时间、睡眠)使用智能手表和智能手机。数据将用于预测受试者内部的变化(在- 天、天之间)发生行为的可能性(例如，≥10分钟的体力活动)和“失误”(即， 未能达到建议的水平≥7天)。具体目标是(1)具体地使用机器学习- ING确定预测行为的时变反射和反应因素的特定人的组合 事件和失误；以及(2)名义上决定是否存在群体水平的时变模式 预测因素，以及这些模式是否预测成功的行为维持结果。数据和冰毒- 正式文件系统将有助于U01/U24集约型纵向健康行为网络的集体目标，即建立更多 预测性健康行为理论，为个性化干预指定目标。

SUMMARY – Parent U01 Emerging adulthood (ages 18-29 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. Traditional health behavior theories provide limited guidance regarding factors underlying behavior maintenance; therefore, to ad- dress 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, self-control) may be activated to a greater extent during behavior adoption. In contrast, reactive processes (e.g., contextual cues, automaticity) may play a greater role in behavior maintenance. However, re- active processes are difficult to measure 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 inten- sive 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 intermittent self-report (i.e., ecological momentary assessment) of reflective variables; and continuous, sensor- based passive monitoring of reactive variables (e.g., location) and behaviors (i.e., physical activity, sedentary time, sleep) using smartwatches and smartphones. 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) and “lapses” (i.e., failure to achieve recommended levels ≥7 days). The specific aims are to (1) idiographically use machine learn- ing 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 group-level patterns of time-varying predictors, and whether those patterns predict successful behavior maintenance outcomes. The data and meth- ods will contribute to the U01/U24 Intensive Longitudinal Health Behavior Network’s collective goal to build more predictive health behavior theories that specify targets for personalized interventions.