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 肥胖症已达到美国的流行比例，被认为是 发病率和死亡率。年轻人（18-35岁）的体重增加和肥胖症的风险特别高。在- 人行为干预通常会产生临床上显着的体重减轻；但是，成本和 访问限制了他们在人群水平上降低对象的潜力。尽管基于网络的干预措施模仿 每周面对面治疗的结构已证明是一种可行的替代治疗，体重减轻是 通常小于面对面治疗。独家移动治疗的效果较低，产生 1-3公斤在6个月内。较新的数字干预方法称为“即时自适应干预措施” （Jitais）承诺通过提供对行为的自适应，个性化的反馈来改善结果。 需要”“实时”，而不是按固定的时间表进行。这种“即时”或JIT的方法是由 低成本和广泛可用的数字健康工具的出现，可以连续收集 更新的健康数据。但是，很少有研究在远程交付，完全可扩展的情况下使用JIT方法 减肥干预措施。尽管吉塔斯是一种赋予肥胖症的潜在变革方法 干预措施，开发的主要障碍是有效选择组件和系统设计 通过人群级别产生临床有意义的减肥措施的优化干预套件 战略。为了解决这个问题，我们将使用多相优化策略（大多数），工程 - 灵感框架和高效的实验设计，以确定5个干预措施的水平 组件在超重的年轻人中有意义地改变了6个月内体重的变化 和肥胖。所有参与者（n = 608）将获得6个月的核心减肥干预措施，其中包括 基于证据的课程，行为技能培训和日常体重。目的是确定是否更大 适应将导致更大的体重减轻，我们将随机将参与者与标准和更适应性 其他5个其他干预组件的选项：1）饮食监测方法（标准与简化），2） 自适应身体活动目标（每周与每日），3）消息时机的决策点（固定与自适应），4） 消息内容的决策规则（标准与自适应）和5）消息选择（否vs.是）。候选人 已经从经验证据中仔细选择了组件，在我们先前的研究中或我们的飞行员中进行了测试 微型试验。评估将在0、3和6个月进行，以实现以下具体目标： 1）建立一个优化的Jitai，该jitai由一组最大的干预组件组成 6个月的年轻人体重变化的改善； 2）进行调解分析以测试 干预成分与假设的近端介体之间的关系（自我调节， 能力，相关性，相关性，自主权）和更多张开的行为介体（饮食摄入量，身体 活动和日常自我加权）； 3）进行探索