Optimizing self-monitoring in a digital health intervention for weight loss

优化减肥数字健康干预中的自我监控

• 批准号: 10609075
• 负责人: Michele Lanpher Patel
• 金额: $ 19.41万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10609075
• 关键词: Accelerometer; Address; Adult; Behavior Therapy; Behavior monitoring; Behavioral; Body Weight; Body Weight Changes; Body Weight decreased; Cellular Phone; Chronic Disease; Clinical; Clinical Trials; Coupled; Data; Dedications; Demographic Factors; Diet; Dietary intake; Eating; Energy Intake; Enrollment; Evaluation; Feedback; Goals; Grant; Interdisciplinary Study; Intervention; Interview; K-Series Research Career Programs; Measures; Mentors; Mentorship; Methodology; Methods; Monitor; Obesity; Outcome; Overweight; Paper; Participant; Patients; Persons; Phase; Physical activity; Population; Positioning Attribute; Preparation; Process; Psychosocial Factor; Public Health; Qualifying; Randomized; Research; Research Methodology; Research Personnel; Resources; Risk; Structure; Testing; Training; Universities; Weight; Work; behavior change; career; clinically significant; collaborative environment; combat; cost; design; digital; digital health; digital intervention; digital tool; digital treatment; ethnic minority; innovation; insight; mobile application; multiphase optimization strategy; novel; obesity treatment; portability; post intervention; programs; racial minority; randomized trial; recruit; remote delivery; retention rate; secondary outcome; skills training; success; theories; weight loss intervention; wireless electronic

PROJECT SUMMARY/ABSTRACT Behavioral obesity treatments can produce clinically significant weight loss but are often too costly or intensive to be implemented on a large scale. Standalone digital health interventions offer greater scalability than traditional in-person approaches, but produce only modest weight loss. To maximize efficacy, it is vital to determine the “active ingredients” of an intervention and eliminate the ineffective, or even detrimental, ones. Self-monitoring is a core component of behavioral obesity treatment that can be delivered via digital tools, yet little is known about the unique and combined impact of different self-monitoring strategies. The K23 candidate, Dr. Michele Patel, will address this gap by applying an innovative framework – the Multiphase Optimization Strategy (MOST) – to identify the most potent combination of digital self-monitoring strategies for weight loss. As the first part of this programmatic line of research, Dr. Patel will conduct a 6-month optimization trial that randomizes 176 adults with overweight/obesity to 0-3 self-monitoring components (tracking dietary intake, physical activity, and/or body weight) using a full factorial design. This study will leverage existing commercial platforms for self-monitoring, including a mobile app, wearable activity monitor, and wireless electronic scale. All participants will also receive an empirically- and theory-informed core weight loss intervention that includes goal setting, weekly tailored feedback, action plans, and behavioral skills training – components that enhance engagement and are well-supported by prior research. Aim 1a will examine the optimal combination of self-monitoring strategies that maximizes 6-month weight loss while Aim 1b will examine self-monitoring engagement and its association with weight loss. Aim 2 will evaluate barriers to and facilitators of engaging in these self-monitoring strategies, which will be assessed via semi-structured qualitative interviews with 40 trial participants. Aim 3 will assess a novel, interactive recruitment strategy via an embedded trial. Together, results will inform an R01 grant that evaluates the newly optimized intervention in an RCT. Building on Dr. Patel’s background in clinical trial methodology and behavioral obesity treatment, the proposed career development award will provide substantive training in 1) MOST and factorial designs; 2) qualitative and mixed methods research; 3) innovative recruitment and retention strategies; and 4) preparation for the transition into independent research. To facilitate successful completion of these goals, Stanford University’s outstanding environment for interdisciplinary research will be coupled with a highly-qualified, well- rounded mentorship team comprised of Primary Mentor Dr. Abby King, Co-mentors Dr. Gary Bennett and Dr. Lisa Rosas, and Consultants Mr. John Gallis (biostatistician) and Dr. Linda Collins (developer of MOST). This K23 will position Dr. Patel to become a leader in optimizing digital interventions for weight loss and will launch her career as an independent investigator dedicated to treating obesity through innovative solutions.

项目总结/摘要 行为肥胖治疗可以产生临床显着的体重减轻，但通常过于昂贵或密集 to be implemented实施on a large大scale规模.独立的数字健康干预措施提供了比 传统的面对面的方法，但只产生适度的减肥。为了最大限度地发挥功效， 确定干预措施的“有效成分”，消除无效甚至有害的成分。 自我监测是行为肥胖治疗的核心组成部分，可以通过数字化工具提供，但 人们对不同自我监测战略的独特和综合影响知之甚少。K23 候选人米歇尔·帕特尔博士将通过应用创新框架--多阶段 优化策略（MOST）-确定数字自我监控策略的最有效组合， 减肥.作为这一纲领性研究路线的第一部分，帕特尔博士将进行为期6个月的优化 将176名超重/肥胖成年人随机分为0-3个自我监测部分（跟踪饮食） 摄入量、体力活动和/或体重）。这项研究将利用现有的 用于自我监测的商业平台，包括移动的应用程序、可穿戴活动监测器和无线 电子秤所有参与者还将接受经验和理论指导的核心减肥 干预，包括目标设定，每周量身定制的反馈，行动计划和行为技能培训- 提高参与度的组成部分，并得到先前研究的充分支持。目标1a将审查 自我监控策略的最佳组合，使6个月的体重减轻最大化，而Aim 1b将 检查自我监控的参与度及其与减肥的关系。目标2将评估障碍， 参与这些自我监控策略的促进者，将通过半结构化的 对40名试验参与者进行定性访谈。目标3将评估一种新的，互动的招聘战略， 嵌入式试验总之，结果将告知R 01赠款，该赠款将评估新优化的干预措施， RCT。基于Patel博士在临床试验方法学和行为肥胖治疗方面的背景， 拟议的职业发展奖将提供以下方面的实质性培训：1）社会变革管理计划和因子设计; 2） 定性和混合方法研究; 3）创新的招聘和保留战略;以及4）准备 过渡到独立研究。为了促进这些目标的顺利完成，斯坦福大学 大学优秀的跨学科研究环境将与一个高素质的，良好的， 由初级导师Abby King博士、共同导师加里班尼特博士和 丽莎罗萨斯和顾问约翰·加利斯先生（生物统计学家）和琳达柯林斯博士（MOST的开发者）。这 K23将使Patel博士成为优化数字干预减肥的领导者，并将推出 她的职业生涯是一个独立的调查员，致力于通过创新的解决方案治疗肥胖。