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%的肥胖寻求治疗的成年人参与狂欢 进食，一种饮食失调行为，其特征是吃大量食物， 控制吃的时候。一线干预是面对面的治疗，但目前的方法通常失败 这两种情况都无法解决，无法惠及所有需要帮助的人。为了填补这一空白，我们设计了FoodSteps， 通过移动终端对肥胖和暴饮暴食进行首次干预，以提高可扩展性。我们 整合行为和心理治疗的关键机制，提供个性化药物 该方法干预五个循证治疗目标。每周，用户选择一个目标， 制定一个计划，告诉他们如何实践这个目标来改变他们的行为。我们的试点数据显示， 参与高完成率和遵守率，并对目标进行干预，改善了每周的狂欢 饮食和体重的平均水平，但利率是次优。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。目前尚不清楚1）哪些基于证据的目标是最重要的 对哪些人有影响; 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。我们将通过一项微观随机试验来解决这些挑战， 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 需求患有肥胖症和反复暴食的成年人将接受为期16周的FoodSteps治疗。每周，其中1名 5个目标将随机发送给每个用户，以确定哪些目标对谁有效（目标1）以及在什么情况下有效 序列（目标2）。每周目标也将随机交付，无论是作为一个推荐的目标用户可以 选择或作为指定的目标，以确定如何提供目标来推动行为改变（目标3）。我们将 评估随时间变化的用户特征作为调节器，以告知个性化算法的开发 随着时间的推移，使干预措施适应用户的需求。我们感兴趣的结果是暴饮暴食的每周变化， 因为这是一种使整体体重减轻处于危险之中的行为，并且长期改变体重（目标4）。我们的数据将 提供基础设施，以建立一个及时的适应性干预（JITAI），能够提供高度 在行为的关键点对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。这项试验进一步推进了NIH和NIDDK的使命， 通过更精确的方法进行治疗，具体说明哪些治疗目标驱动行为改变， 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，结果的影响超出了FoodSteps 行为改变广泛，并将推动个性化医疗告知复杂的模型， 实现个性化数字和非数字干预措施交付的最终目标。