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: 10448186
• 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/10448186
• 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; 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; 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 treatment; ethnic minority; innovation; insight; mobile application; multiphase optimization strategy; novel; obesity treatment; portability; post intervention; programs; racial and ethnic; randomized trial; recruit; remote delivery; retention rate; secondary outcome; skills training; success; theories; tool; 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.

项目摘要/摘要 行为性肥胖治疗可以在临床上产生显著的减肥效果，但往往过于昂贵或过于密集 要大规模实施。独立的数字健康干预提供了比 传统的面对面减肥方法，但只能产生适度的减肥效果。为了最大限度地发挥功效，至关重要的是 确定干预措施的“有效成分”，排除无效的，甚至有害的成分。 自我监控是行为肥胖治疗的核心组成部分，可以通过数字工具提供，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一个创新的框架--多阶段--来解决这一差距 优化策略(MOST)-确定数字自我监控策略的最有效组合 减肥。作为这一计划研究路线的第一部分，Patel博士将进行为期6个月的优化 这项试验将176名超重/肥胖的成年人随机分为0-3个自我监测组件(跟踪饮食 摄入量、体力活动和/或体重)使用完全析因设计。这项研究将利用现有的 用于自我监控的商业平台，包括移动应用、可穿戴活动监控器和无线 电子秤。所有参与者还将获得基于经验和理论的核心减肥 干预措施，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- 增强参与度并得到先前研究的良好支持的组件。目标1a将审查 自我监测战略的最佳组合，最大限度地实现6个月的减肥，而目标1b将 检查自我监控参与度及其与减肥的关系。目标2将评估障碍和 参与这些自我监测战略的促进者，将通过半结构进行评估 对40名试验参与者进行了定性访谈。AIM 3将通过以下方式评估一种新颖的互动招聘策略 内嵌审判。总而言之，结果将通知R01拨款，以评估在 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1)MOST和析因设计方面的实质性培训；2) 定性和混合方法研究；3)创新的招聘和保留战略；4)准备 向独立研究过渡。为了促进这些目标的顺利完成，斯坦福大学 大学卓越的跨学科研究环境将与高素质、良好的 完善的导师团队由初级导师Abby King博士、共同导师Gary Bennett博士和Dr. Lisa Rosas，以及顾问John Gallis先生(生物统计学家)和Linda Collins博士(MOST的开发者)。这 K23将使Patel博士成为优化数字减肥干预措施的领导者，并将推出 她是一名独立调查员，致力于通过创新的解决方案治疗肥胖症。