Optimization of a mHealth Behavioral Weight Loss Intervention for Young Adults

优化年轻人的移动健康行为减肥干预措施

• 批准号: 10213025
• 负责人: Deborah F. Tate
• 金额: $ 65.89万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-10 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10213025
• 关键词: Address; Adult; Age; Behavior; Behavior Therapy; Behavioral; Body Weight Changes; Body Weight decreased; Body mass index; Characteristics; Clinical; Collection; Competence; Coupled; Decision Making; Development; Diet Monitoring; Dietary intake; Distal; Electronic Mail; Engineering; Epidemic; Ethnic Origin; Experimental Designs; Feedback; Frequencies; Gender; Goals; Individual; Informal Social Control; Internet; Intervention; Life Style; Mediation; Mediator of activation protein; Modeling; Monitor; Morbidity - disease rate; Obesity; Outcome; Overweight; Participant; Persons; Physical activity; Population; Population Heterogeneity; Problem Solving; Public Health; Race; Randomized; Schedule; Science; Specific qualifier value; Structure; Testing; Text Messaging; Time; Treatment Protocols; United States; Update; Weight; Weight Gain; Work; adaptive intervention; alternative treatment; clinically significant; cost; design; digital; digital health; digital intervention; evidence base; experimental study; follow up assessment; follow-up; health data; high risk; improved; individual variation; mHealth; middle age; mortality; multiphase optimization strategy; obesity treatment; public health intervention; randomized trial; remote delivery; skills training; tool; web-based intervention; weight loss intervention; young adult

PROJECT ABSTRACT Obesity has reached epidemic proportions in the United States and is recognized as a major cause of morbidity and mortality. Young adults (18-35 years) are at particularly high risk for weight gain and obesity. In- person behavioral interventions generally produce clinically significant weight losses; however, cost and access limit their potential to reduce obesity at a population level. Although web-based interventions that mimic the structure of weekly face-to-face treatment have proven a viable alternative treatment, weight losses are generally smaller than in-person treatment. Exclusively mobile treatments have been less effective, producing 1-3 kgs over 6 months. Newer digital intervention approaches called “Just-in-Time Adaptive Interventions” (JITAIs) promise to improve upon outcomes by offering adaptive, personalized feedback on behavior “when needed” in “real time,” rather than on a fixed schedule. This “just-in-time,” or JIT, approach is made possible by the emergence of low-cost and widely available digital health tools that allow for the collection of continually updated health data. However, few studies have used JIT approaches in remotely delivered, fully scalable weight loss interventions. Although JITAIs are a potentially transformative approach to delivering obesity interventions, a major obstacle in their development is efficient selection of components and systematic design of an optimized intervention package that produces clinically meaningful weight losses with a population-level strategy. To solve this problem, we will use the Multiphase Optimization Strategy (MOST), an engineering- inspired framework, and a highly efficient experimental design to identify which levels of 5 intervention components contribute meaningfully to change in weight over 6 months among young adults with overweight and obesity. All participants (n=608) will receive a core 6-month weight loss intervention that includes evidence-based lessons, behavioral skills training, and daily weighing. With the goal of determining if greater adaptation will lead to greater weight loss, we will randomize participants to standard versus more adaptive options of 5 additional intervention components: 1) diet monitoring approach (standard vs. simplified), 2) adaptive physical activity goals (weekly vs. daily), 3) decision points for message timing (fixed vs. adaptive), 4) decision rules for message content (standard vs. adaptive), and 5) message choice (no vs. yes). Candidate components have been carefully selected from empirical evidence, tested in our prior studies, or in our pilot micro-randomized trial. Assessments will occur at 0, 3 and 6 months to accomplish the following specific aims: 1) Build an optimized JITAI consisting of the set of intervention components that yield the greatest improvement in weight change among young adults at 6 months; 2) Conduct mediation analyses to test the relationships between the intervention components and hypothesized proximal mediators (self-regulation, competence, relatedness, relevance, autonomy) and more distal behavioral mediators (dietary intake, physical activity, and daily self-weighting); and 3) Conduct exploratory

项目摘要 肥胖在美国已经达到流行病的程度，并被认为是 发病率和死亡率。年轻人(18-35岁)体重增加和肥胖的风险特别高。在- 个人行为干预通常会在临床上产生显著的体重减轻；然而，成本和 可获得性限制了他们在人口水平上减少肥胖症的潜力。尽管基于网络的干预模仿了 每周面对面治疗的结构已被证明是一种可行的替代治疗方法，减肥是 一般比面对面治疗要小。专门的移动治疗效果较差，产生了 6个月内1-3公斤。称为“即时自适应干预”的新型数字干预方法 (JITAI)承诺通过提供适应性的、个性化的行为反馈来改进结果 需要“实时”，而不是按照固定的时间表。这种“准时制”或JIT方法是通过 低成本和广泛可用的数字健康工具的出现，允许收集持续的 更新了健康数据。然而，很少有研究将JIT方法用于远程交付、完全可扩展 减肥干预。尽管JITAI是传递肥胖的一种潜在的变革性方法 干预措施，其发展的一个主要障碍是有效地选择组成部分和系统设计 优化的干预方案，在人群水平上产生临床上有意义的减肥效果 策略。为了解决这个问题，我们将使用多阶段优化策略(MOST)，这是一种工程-- 受启发的框架和高效的实验设计，以确定哪些级别的5种干预 在超重的年轻人中，成分对体重在6个月内的变化有重要意义 还有肥胖症。所有参与者(n=608)将接受为期6个月的核心减肥干预，包括 循证课程、行为技能培训和日常称重。其目标是确定更大的 适应将导致更大的体重减轻，我们将随机选择标准参与者与更适应的参与者 5个额外干预组件的选项：1)饮食监测方法(标准与简化)，2) 适应性体力活动目标(每周与每日)，3)消息计时决策点(固定与自适应)，4) 消息内容的决策规则(标准与自适应)，以及5)消息选择(否与是)。侯选人 组件是从经验证据中精心挑选出来的，在我们之前的研究中或在我们的试点中进行了测试 微随机试验。将在0个月、3个月和6个月进行评估，以实现以下具体目标： 1)构建由产生最大收益的干预组件集组成的优化JITAI 6个月后年轻人体重变化的改善；2)进行中介分析，以测试 干预成分和假想的近端介体之间的关系(自我调节， 能力、关联性、相关性、自主性)和更远端的行为中介(饮食摄入量、身体状况 活动和每日自我称重)；以及3)进行探索性