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

优化暴饮暴食即时适应性干预的微随机试验

• 批准号: 10501064
• 负责人: Andrea Kass Graham
• 金额: $ 72.94万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10501064
• 关键词: Address; Adult; Age; Algorithms; Behavior; Behavior Disorders; Behavioral; Behavioral Mechanisms; Binge Eating; Body Weight Changes; Body Weight decreased; Caring; Characteristics; Clinical; Data; Development; Diet; Drops; Eating; Eating Disorders; Ethnic Origin; Evidence based treatment; Failure; Food; Future; Goals; Health Care Costs; Health Services Accessibility; Individual; Infrastructure; Intervention; Learning; Metabolic syndrome; Methodology; Mission; Modeling; Moods; Morbidity - disease rate; Motivation; National Institute of Diabetes and Digestive and Kidney Diseases; Obesity; Outcome; Persons; Physical activity; Process; Race; Randomized; Recommendation; Recurrence; Risk; Role; Shapes; Specific qualifier value; Testing; Theoretical model; Time; Treatment outcome; United States National Institutes of Health; Weight; Weight maintenance regimen; Work; adaptive intervention; adult obesity; base; behavior change; behavior influence; behavioral economics; comorbidity; design; digital; digital delivery; digital intervention; evidence base; excessive weight gain; experience; follow-up; handheld mobile device; improved; improved outcome; interest; mortality; multi-component intervention; negative affect; personalized intervention; personalized medicine; post intervention; psychologic; randomized trial; self help; sex; targeted delivery; therapy design

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.

五分之二的美国成年人患有肥胖症，高达 30% 寻求治疗的肥胖成年人有暴食行为 饮食，一种饮食失调行为，其特征是吃大量食物并丧失 一线干预措施是面对面治疗，但目前的方法通常会失败。 为了解决这两个问题，但无法惠及所有有需要的人。为了填补这一空白，我们设计了 FoodSteps，即 FoodSteps。 我们首次通过移动设备对肥胖和暴饮暴食进行干预，以提高可扩展性。 整合行为和心理治疗的关键机制，提供个性化医疗 用户每周选择一个目标并干预五个基于证据的治疗目标。 制定一个计划，指导他们如何实践该目标来改变他们的行为。我们的试点数据显示，FoodSteps 是。 提高完成率和遵守率，并对目标进行干预可以改善每周的狂欢 平均饮食和体重，但我们的数据表明需要更精确的干预， 但尚不清楚以下三个挑战阻碍了这一目标的实现：1) 哪些基于证据的目标最重要。 对哪些人有影响；2) 以什么顺序以及 3) 如何最好地实现目标以推动用户实现 我们将通过微观随机试验（方法论）来解决这些挑战。 理想的设计，因为它使用重复随机化来告知如何根据个人情况进行精确干预 患有肥胖症和经常暴饮暴食的成年人将每周接受 1 周的 FoodSteps。 5 个目标将随机交付给每个用户，以确定哪些目标适用于谁（目标 1）以及什么用途 顺序（目标 2）也将随机交付给用户推荐的目标。 选择 或 作为指定目标，以确定如何实现目标以推动行为改变（目标 3）。 评估随时间变化的用户特征作为调节者，为个性化算法的开发提供信息 随着时间的推移，根据用户的需求定制干预措施，我们感兴趣的结果是每周暴饮暴食的变化， 因为这是一种将整体体重减轻和体重长期变化置于危险之中的行为（我们的数据会如此）。 提供基础设施来构建及时适应性干预（JITAI），能够提供高度 在行为的关键点对暴饮暴食和体重相关行为进行个性化干预 我们将在未来的试验中测试 JITAI，进一步推进 NIH 和 NIDDK 的使命。 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 优化数字干预、干预肥胖和暴饮暴食以及进行微观干预的团队专家 考虑到这些目标和流程的作用，随机试验的结果具有超出 FoodSteps 范围的影响。 广泛地改变行为，并将通过为复杂的模型提供信息来推动个性化医疗 实现个性化提供数字和非数字干预措施的最终目标。