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

项目摘要/摘要 行为性肥胖治疗在临床上显著减轻体重并降低疾病风险/严重程度 许多有超重/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，这在很大程度上可以归因于偏离推荐饮食。我们的工作已经完成 研究表明，在减肥期间经常出现饮食失误(不遵守饮食目标的具体情况) 尝试(每周约3-4次)，与较差的体重减轻相关，并由瞬间变化触发 状态(例如，情绪的变化或可口食物的供应)。因此，显然有必要进行创新 可以提供动态即时干预的解决方案，以改善对处方饮食的遵守 肥胖症治疗。我们的研究团队率先开发了基于智能手机的即时自适应 干预(吉泰)，包括：1)每日生态瞬时评估(EMA；通过以下方式重复采样 移动设备)相关的行为、心理和环境触发因素；2)机器 学习算法，使用通过EMA收集的信息来确定实时失误风险；和3)交付 在高风险时刻进行短暂干预。我们的试点工作表明，吉泰是可行的，可以接受的， 并减少了平均失误频率。然而，我们还没有表现出直接的影响 吉泰对失误风险增加时刻的饮食行为和对干预类型知之甚少 对减少失误最有效的方法。因此，我们建议通过一种微观的 随机试验(MRT)，一种涉及随机分配干预(或对照)的方法学 特定决策点，即我们的算法预测失误的高风险时。捷运将决定 在特定时刻进行的特定干预是否产生了预期效果。因此，我们将把我们的吉台转移到 更具伸缩性的在线平台，并进行MRT以评估通用失误风险警报的影响 信息和理论驱动的及时干预饮食失误。在n=15的细化测试之后 确保我们最新的吉泰的技术功能正常，有超重/肥胖的成年人(n=159)将参加 在一个成熟的为期12周的在线肥胖症治疗计划中，只有12周的跟踪调查。 当个人面临失误的风险时，S/他将被随机分为不干预、一般风险警报或 4项以理论为导向的干预措施，并进行互动技能培训。感兴趣的结果将是发生 (或缺乏)主观(即通过EMA)和客观(即通过手腕)衡量的饮食失误- 基于摄入量监测)，在随机化后的几个小时内。捷运的结果将通知优化的 干预交付的算法，将推动最终的吉泰。未来的随机对照试验将比较 使用和不使用优化的吉泰治疗肥胖症。这种高度创新的方法将推动 通过支持复杂的坚持理论模型的发展来支持坚持科学 行为和通知JITAI，目标是遵守其他健康行为(例如，药物、活动目标)。

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).