# Optimizing self-monitoring in a digital health intervention for weight loss > **NIH NIH K23** · STANFORD UNIVERSITY · 2023 · $194,076 ## Abstract 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 t... ## Key facts - **NIH application ID:** 10609075 - **Project number:** 5K23DK129805-02 - **Recipient organization:** STANFORD UNIVERSITY - **Principal Investigator:** Michele Lanpher Patel - **Activity code:** K23 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $194,076 - **Award type:** 5 - **Project period:** 2022-04-15 → 2027-02-28 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10609075 ## Citation > US National Institutes of Health, RePORTER application 10609075, Optimizing self-monitoring in a digital health intervention for weight loss (5K23DK129805-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10609075. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*