Optimizing Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: A Micro-randomized Trial

优化及时适应性干预以提高行为肥胖治疗中的饮食依从性：一项微观随机试验

• 批准号: 10029156
• 负责人: Stephanie Paige Goldstein
• 金额: $ 72.79万
• 依托单位: MIRIAM HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10029156
• 关键词: Adherence; Adult; Algorithms; Awareness; Behavior; Behavioral; Body Weight Changes; Body Weight decreased; Cardiovascular Diseases; Cellular Phone; Clinical; Data; Development; Diet; Dietary Intervention; Eating Behavior; Ecological momentary assessment; Education; Energy Intake; Ensure; Environmental Risk Factor; Food; Frequencies; Funding; Future; Goals; Gold; Health behavior; Hour; Individual; Informal Social Control; Intake; Intervention; Location; Machine Learning; Measures; Methodology; Monitor; Moods; Motivation; Obesity; Outcome; Overweight; Palate; Participant; Patient Self-Report; Patients; Persons; Pharmaceutical Preparations; Psychological Factors; Randomized; Research; Research Methodology; Risk; Sampling; Science; Self Efficacy; Severities; Suggestion; Testing; Text; Theoretical model; Time; United States National Institutes of Health; Update; Weight; Work; Wrist; adaptive intervention; base; behavioral adherence; brief intervention; clinically significant; dietary adherence; disorder risk; experience; falls; follow-up; handheld mobile device; high risk; improved; innovation; interest; machine learning algorithm; obesity treatment; pilot trial; pragmatic trial; prediction algorithm; psychologic; randomized trial; skills; skills training; theories; treatment program

PROJECT SUMMARY/ABSTRACT Behavioral obesity treatment produces clinically significant weight loss and reduced disease risk/severity for many individuals with overweight/obesity and cardiovascular disease. Yet, about half of patients fall short of expected outcomes, which can be largely attributed to lapses from the recommended diet. Our work has shown that dietary lapses (specific instances of nonadherence to dietary goals) are frequent during weight loss attempts (~3-4 times per week), associated with poorer weight losses, and triggered by momentary changing states (e.g., changes in mood or availability of palatable food). Thus, there is a clear need for innovative solutions that can provide dynamic in-the-moment interventions to improve adherence to the prescribed diet in obesity treatment. Our research team was the first to develop a smartphone-based just-in-time adaptive intervention (JITAI) that includes: 1) daily ecological momentary assessment (EMA; repeated sampling via mobile device) of relevant behavioral, psychological, and environmental triggers for lapse; 2) a machine learning algorithm that uses information gathered via EMA to determine real-time lapse risk; & 3) delivery of brief intervention during high-risk moments. Our pilot work revealed that the JITAI was feasible, acceptable, and produced reductions in average lapse frequency. However, we have not yet shown a direct effect of the JITAI on eating behavior in the moment of heightened lapse risk and know little about the types of interventions that are most effective for reducing lapse. We therefore propose to extend our research via a micro- randomized trial (MRT), a methodology that involves random assignment to intervention (or control) at a specific decision point, i.e., when our algorithm predicts heightened risk for a lapse. The MRT will determine whether a specific intervention in a specific moment had its intended effect. We will therefore port our JITAI to a more scalable online platform and conduct a MRT to evaluate the effects of a generic lapse risk alert message and theory-driven just-in-time interventions on dietary lapses. After refinement testing with n=15 to ensure proper technical functioning of our updated JITAI, adults with overweight/obesity (n=159) will participate in a well-established 12-week online obesity treatment program + JITAI, with 12 weeks of JITAI-only follow-up. When an individual is at risk for lapsing s/he will be randomized to no intervention, a generic risk alert, or one of 4 theory-driven interventions with interactive skills training. The outcome of interest will be the occurrence (or lack thereof) of dietary lapse, as measured both subjectively (i.e., via EMA) and objectively (i.e., via wrist- based intake monitoring), in the hours following randomization. Results of the MRT will inform an optimized algorithm for intervention delivery that will drive the finalized JITAI. A future RCT will compare weight loss in obesity treatment with and without the optimized JITAI. This highly innovative approach will advance the science of adherence by supporting the development of sophisticated theoretical models of adherence behavior and informing JITAIs that target adherence to other health behaviors (e.g., medication, activity goals).

项目摘要/摘要 行为肥胖治疗可导致临床上显着的体重减轻，疾病风险/严重程度降低 许多患有超重/肥胖和心血管疾病的人。但是，大约一半的患者没有 预期的结果很大程度上归因于推荐饮食中的失误。我们的工作有 表明减肥期间饮食失误（对饮食目标的特定实例）很频繁 尝试（每周约3-4次），与减肥较差有关，并通过瞬间改变而触发 国家（例如，情绪的变化或可口食品的可用性）。因此，显然需要创新 可以提供动态内部干预措施以提高对规定饮食的依从性的解决方案 肥胖治疗。我们的研究团队是第一个开发基于智能手机的即时自适应的人 干预（Jitai），其中包括：1）每日生态瞬时评估（EMA；重复采样 相关行为，心理和环境触发器的移动设备）； 2）一台机器 学习使用通过EMA收集的信息来确定实时失效风险的算法； ＆3）交付 在高风险时刻的短暂干预。我们的飞行员工作表明，Jitai是可行的，可以接受的， 并在平均失误频率中产生降低。但是，我们尚未显示 吉泰（Jitai 最有效地减少失误。因此，我们建议通过微观扩展我们的研究 随机试验（MRT），这种方法涉及在A处随机分配干预（或对照） 具体的决策点，即当我们的算法预测衰退的风险增加时。地铁将确定 特定时刻的特定干预是否具有预期效果。因此，我们将向我们的Jitai移植 一个更可扩展的在线平台，并进行MRT来评估通用失误风险警报的效果 信息和理论驱动的饮食失误的即时干预措施。细化测试后n = 15至 确保我们更新的Jitai的适当技术功能，超重/肥胖的成年人（n = 159）将参加 在一项良好的12周在线肥胖治疗计划 + Jitai中，仅需进行12周的Jitai的随访。 当一个人处于逃亡的风险时，他/他将被随机分配给没有干预，通用风险警报或一个 通过互动技能培训进行4种理论驱动的干预措施。感兴趣的结果将是发生 （或缺乏）饮食浮肿，如主观（即通过EMA）和客观（即通过腕带）测量的那样 基于随机分组后的小时内基于进气口监测）。 MRT的结果将告知优化 用于干预交付的算法将推动最终确定的Jitai。未来的RCT将比较体重减轻 有或没有优化的Jitai的肥胖治疗。这种高度创新的方法将推动 通过支持建立复杂理论模型的依从性科学 行为并告知Jitais，以遵守其他健康行为（例如，药物，活动目标）。