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: 10223435
• 负责人: Stephanie Paige Goldstein
• 金额: $ 60.78万
• 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/10223435
• 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; dietary adherence; disorder risk; experience; falls; follow-up; handheld mobile device; high risk; improved; innovation; interest; machine learning algorithm; obesity treatment; patient engagement; 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).

项目概要/摘要 行为肥胖治疗可产生临床上显着的体重减轻并降低疾病风险/严重程度 许多患有超重/肥胖和心血管疾病的人。然而，大约一半的患者达不到 预期结果，这在很大程度上可归因于推荐饮食的失误。我们的工作有 研究表明，减肥过程中经常出现饮食失误（不遵守饮食目标的具体情况） 尝试（每周约 3-4 次），与较差的减肥效果相关，并由瞬时变化触发 状态（例如情绪的变化或可口食物的可用性）。因此，显然需要创新 可以提供动态即时干预措施的解决方案，以提高对规定饮食的依从性 肥胖症治疗。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 干预（JITAI），包括：1）每日生态瞬时评估（EMA；通过 移动设备）相关的行为、心理和环境触发因素； 2) 一台机器 使用通过 EMA 收集的信息来确定实时失误风险的学习算法； & 3) 交付 在高风险时刻进行短暂干预。我们的试点工作表明，JITAI 是可行的、可接受的、 并降低了平均失效频率。然而，我们尚未显示出该措施的直接影响。 JITAI对失范风险升高时的饮食行为的看法，但对干预措施的类型知之甚少 对于减少失误最有效。因此，我们建议通过微观扩展我们的研究 随机试验（MRT），一种涉及随机分配干预（或对照）的方法 具体的决策点，即当我们的算法预测失误风险较高时。 MRT将决定 特定时刻的特定干预是否达到了预期效果。因此，我们将把 JITAI 移植到 一个更具可扩展性的在线平台并进行 MRT 来评估通用失效风险警报的效果 信息和理论驱动的对饮食失误的及时干预。经过 n=15 的细化测试后 确保我们更新的 JITAI 的技术正常运行，超重/肥胖的成年人 (n=159) 将参加 在完善的 12 周在线肥胖治疗计划 + JITAI 中，仅进行 12 周的 JITAI 随访。 当一个人有衰退风险时，他/她将被随机分配到不干预、一般风险警报或一项 4 种理论驱动的干预措施和互动技能培训。感兴趣的结果将是发生 （或缺乏）饮食失误，通过主观（即通过 EMA）和客观（即通过腕带）测量 基于摄入量监测），在随机化后的几个小时内进行。 MRT 的结果将告知优化 干预实施的算法将推动最终的 JITAI。未来的随机对照试验将比较体重减轻情况 使用和不使用优化的 JITAI 进行肥胖治疗。这种高度创新的方法将推动 通过支持复杂的依从性理论模型的发展来实现依从性科学 行为并告知 JITAI，以遵守其他健康行为（例如药物、活动目标）。