A Micro-Randomized Trial to Optimize Just-in-Time Adaptive Intervention for Binge Eating & Weight-related Behaviors

五分之二的美国成年人患有肥胖症，寻求治疗的肥胖症成年人中高达30%的人会暴饮暴食 进食，一种进食障碍行为，其特征是吃了大量的食物，并经历了 边吃边控制。一线干预是面对面的治疗，但目前的方法通常失败 以解决这两种情况，并不能接触到所有有需要的人。为了填补这一空白，我们设计了食物步骤， 第一个针对肥胖和暴饮暴食的干预，由移动设备提供，以提高可扩展性。我们 整合行为和心理治疗的关键机制，提供个性化的药物 干预五个循证治疗目标的方法。每周，用户都会选择一个目标并 为他们将如何实践这个目标来改变他们的行为制定一个计划。我们的试点数据显示FoodSteps是 参与高完成率和合规率，并对目标进行干预，可以改善每周的狂欢 平均饮食和体重，但价格不是最理想的。我们的数据表明，需要更精确的干预， 但有三个挑战阻碍了这一目标的实现。目前还不清楚1)哪些循证目标是最多的 对哪些人有影响；以及2)以什么顺序；以及3)如何最好地交付目标以推动用户 改变他们的行为。我们将通过微随机试验来解决这些挑战，方法学上 理想的设计，因为它使用重复的随机化来告知如何根据个体进行精确干预 需要。肥胖和经常暴饮暴食的成年人将接受为期16周的食品步骤。每周，有一位 5个目标将随机分发给每个用户，以确定哪些目标为谁工作(目标1)，以及在什么领域 序列(目标2)。每周目标也将随机交付，作为推荐目标用户可以 选择或作为分配的目标，以确定如何交付目标以推动行为改变(目标3)。我们会 评估时变的用户特征作为主持人，为个性化算法的开发提供信息 随着时间的推移，根据用户需求定制干预措施。我们感兴趣的结果是暴饮暴食的每周变化， 因为这是一种将整体减肥置于危险之中的行为，并长期改变体重(目标4)。我们的数据将 提供基础设施以构建即时自适应干预(JITAI) 暴饮暴食与行为临界点体重相关行为的个性化干预 改变流程；我们将在未来的试验中测试吉泰。这项试验进一步推动了NIH和NIDDK推进 通过指定哪些治疗目标驱动行为改变，通过更精确的方法进行治疗，由一个 团队专家，在优化数字干预，干预肥胖和暴饮暴食，并进行微 随机试验。考虑到这些目标和过程的作用，结果具有超越食品步骤的影响 用于广泛的行为改变，并将通过提供复杂的模型来推动个性化医疗 实现数字化和非数字化干预措施的个性化交付的最终目标。

Two in five U.S. adults have obesity, and up to 30% of treatment-seeking adults with obesity engage in binge eating, an eating disorder behavior characterized by eating a large amount of food and experiencing a loss of control while eating. First-line interventions are face-to-face treatments, but current approaches commonly fail to address both conditions and cannot reach all people in need. To fill this gap, we designed FoodSteps, the first intervention for both obesity and binge eating, delivered by mobile device to increase scalability. We integrated key mechanisms of behavioral and psychological treatments to provide a personalized medicine approach that intervenes on five evidence-based treatment targets. Each week, users select a target and create a plan for how they will practice that target to change their behavior. Our pilot data show FoodSteps is engaging with high rates of completion and compliance, and intervening on the targets improves weekly binge eating and weight on average, but rates are suboptimal. Our data indicate more precise intervention is needed, but three challenges impede achieving this goal. It is unknown 1) which evidence-based targets are most impactful for which people; and 2) in what sequence; as well as 3) how best to deliver targets to propel users to change their behavior. We will resolve these challenges with a micro-randomized trial, the methodologically ideal design because it uses repeated randomization to inform how to precisely intervene based on individual needs. Adults with obesity and recurrent binge eating will receive FoodSteps for 16 weeks. Each week, 1 of the 5 targets will be randomly delivered to each user, to identify which targets work for whom (Aim 1) and in what sequence (Aim 2). Weekly targets also will be randomly delivered either as a recommended target users can select or as an assigned target, to identify how to deliver targets to propel behavior change (Aim 3). We will assess time-varying user characteristics as moderators to inform the development of personalized algorithms to tailor interventions to user needs over time. Our outcomes of interest are weekly changes in binge eating, since it is a behavior that puts overall weight loss at risk, and change in weight long-term (Aim 4). Our data will provide the infrastructure to build a just-in-time adaptive intervention (JITAI) capable of delivering highly personalized intervention for binge eating and weight-related behaviors at a critical point in the behavior change process; we will test the JITAI in a future trial. This trial furthers NIH and NIDDK’s mission to advance treatment via more precise approaches by specifying which treatment targets drive behavior change, led by a team expert in optimizing digital interventions, intervening on obesity and binge eating, and conducting micro- randomized trials. Results have implications beyond FoodSteps given the role of these targets and processes for behavior change broadly, and will propel personalized medicine by informing sophisticated models for achieving the ultimate goal of personalizing the delivery of digital and non-digital interventions.