Microtemporal Processes Underlying Health Behavior Adoption and Maintenance

健康行为采用和维持的微时间过程

• 批准号: 10336709
• 负责人: Genevieve Fridlund Dunton
• 金额: $ 2.13万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10336709
• 关键词: Acute; Address; Adopted; Adoption; Affect; Affective; Age; Behavior; Behavior Therapy; Buffers; Cellular Phone; Chronic; Chronic Disease; Cues; Data; Decision Making; Ecological momentary assessment; Emotional; Enrollment; Exercise; Failure; Goals; Health behavior; Hour; Individual; Influentials; Intervention; Intervention Studies; Life; Light; Location; Machine Learning; Maintenance; Malignant Neoplasms; Measures; Mediating; Methods; Modeling; Monitor; Moods; Movement; Obesity; Outcome; Parents; Patient Self-Report; Pattern; Persons; Physical activity; Play; Predictive Factor; Problem Solving; Process; Psyche structure; Public Health; Reporting; Role; Self-control as a personality trait; Sleep; Specific qualifier value; Time; Variant; Weight; Weight Gain; Work; attentional control; base; cancer risk; cardiovascular disorder risk; emerging adult; emerging adulthood; emotion regulation; experience; improved; insight; lifetime risk; mobile computing; moderate-to-vigorous physical activity; negative affect; novel; obesity treatment; parent grant; personalized intervention; prevent; sedentary; sedentary lifestyle; sensor; smart watch; successful intervention; theories; weight gain prevention; young adult

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.

总结-母公司U 01 成年期（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。传统保健 行为理论提供有限的指导，有关因素的行为维持;因此，广告- 填补这一空白，我们的工作表明，决策和行为的双过程模型可以摆脱 光的差异机制的基础上采用与维护。反思过程 (e.g.,效能感、自我控制）在行为采纳过程中可能会被更大程度地激活。相反，Reactive 过程（例如，情境线索、自动性）可能在行为维持中发挥更大的作用。然而，再- 活动过程难以测量是因为它们可以在微时间尺度上展开（即，的变革 分钟或小时）。为了解决这个问题，我们建议使用实时移动的技术来收集国际上的信息， 纵向数据研究微观时间过程的差异， 保持体力活动，减少久坐时间和充足的睡眠时间。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）和行为（即，体力活动，久坐 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变异（ 天，天之间）的行为“发作”的可能性（例如，≥10分钟的体力活动）和“失误”（即， 未能达到推荐水平≥7天）。具体目标是（1）具体地使用机器学习- 识别预测行为的随时间变化的反射和反应因素的个人特定组合 事件和失效;（2）规则性地确定是否存在随时间变化的组水平模式 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- ods将有助于U 01/U24密集纵向健康行为网络的集体目标，建立更多的 预测健康行为理论，为个性化干预指定目标。