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: 10034950
• 负责人: Deborah F. Tate
• 金额: $ 67.44万
• 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/10034950
• 关键词: Address; Adult; Age; Behavior; Behavior Therapy; Behavioral; Body Weight Changes; Body Weight decreased; 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; Health; Individual; Informal Social Control; Internet; Intervention; Life Style; Mediation; Mediator of activation protein; Modeling; Monitor; Morbidity - disease rate; Obesity; Online Systems; 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; evidence base; experimental study; follow up assessment; follow-up; health data; high risk; improved; individual variation; mHealth; middle age; mortality; multiphase optimization strategy; public health intervention; randomized trial; skills training; tool; 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，并且被认为是导致肥胖的主要原因。 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- 人的行为干预通常产生临床上显著的体重减轻;然而，成本和 限制了它们在人口水平上减少肥胖的潜力。虽然基于网络的干预措施， 每周面对面治疗结构已被证明是一种可行的替代治疗， 一般来说，小于个人治疗。完全移动的治疗效果较差， 1-3公斤超过6个月。新的数字干预方法称为“及时适应性干预” （JITAIs）承诺通过提供自适应的，个性化的行为反馈来改善结果， 在“真实的时间”内，而不是在固定的时间表上。这种“准时制”或JIT方法是通过以下方式实现的： 低成本和广泛可用的数字健康工具的出现，允许不断收集 更新健康数据。然而，很少有研究在远程交付、完全可扩展的 减肥干预。尽管JITAIs是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选择组成部分和系统设计 优化的干预方案，可在人群水平上产生具有临床意义的体重减轻 战略为了解决这个问题，我们将使用多阶段优化策略（MOST），一个工程- 启发性的框架，以及一个高效的实验设计，以确定哪些水平的5干预 在超重的年轻人中， 和肥胖。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、行为技能培训和每日称重。目的是确定 适应性将导致更大的体重减轻，我们将随机将参与者分为标准组和更适应性组 5个额外干预组成部分的选项：1）饮食监测方法（标准与简化），2） 自适应体力活动目标（每周与每日），3）消息定时的决策点（固定与自适应），4） 消息内容的判定规则（标准对自适应），以及5）消息选择（否对是）。候选 我们从经验证据中仔细挑选了一些成分，这些成分在我们以前的研究或试点中进行了测试。 微随机试验将在0、3和6个月时进行评估，以实现以下具体目标： 1)构建一个优化的JITAI，由一组干预组件组成， 6个月时年轻成年人体重变化的改善; 2）进行中介分析， 干预成分和假设的近端介质之间的关系（自我调节， 能力，相关性，相关性，自主性）和更远的行为介质（饮食摄入，身体 活动和每日自我加权）;和3）进行探索性