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Michele L. Patel, PhD is an Instructor at the Stanford University School of Medicine. Her research focuses on optimizing digital health interventions for treating obesity. She is particularly interested in improving engagement in these interventions and examining the impact of psychosocial factors on treatment success. Dr. Patel received a K23 career development award from NIH (2022-2027). This work investigates the most potent combination of self-monitoring strategies in a behavioral weight loss intervention for adults with overweight or obesity. Dr. Patel is interested in using digital tools such as commercial apps, wearables, text-messaging, and telehealth to improve access to and engagement in treatment. Dr. Patel received her BA in psychology from Duke University in 2010 and her PhD in clinical psychology from Duke in 2018. She completed her clinical internship at the VA Palo Alto, specializing in behavioral medicine, and her postdoctoral fellowship at the Stanford Prevention Research Center.Primary Research Interests:-- Conducting clinical trials to evaluate digital health interventions for obesity-- Improving engagement in self-monitoring and other behavioral intervention strategies-- Examining the impact of psychosocial factors (e.g., health literacy, stress) on treatment success-- Applying the Multiphase Optimization Strategy (MOST) framework to efficiently construct behavioral interventions
Obesity disproportionately affects racial/ethnic minority populations, yet these groups are under-represented in behavioral weight loss treatment. Remotely-delivered digital interventions have the potential to broaden reach and minimize some of the traditional barriers to enrolling in these treatments. Self-monitoring, such as tracking dietary intake, is an effective behavioral approach that can be delivered remotely; however, it is unknown whether a detailed versus simplified diet tracking approach is most acceptable and feasible among racial/ethnic minority groups. Using mixed methods, this project will first compare these two strategies in the context of a 3-month digital weight loss intervention among 40 racial/ethnic minority adults with overweight/obesity to determine feasibility and acceptability of study procedures; then, qualitative interviews with 10 participants will gather feedback on acceptability of each self-monitoring approach as well as barriers to and facilitators of intervention engagement.
Palo Alto, California
NIH Career Development Award (K23DK129805): 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. 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. 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: examine the optimal combination of self-monitoring strategies that maximizes 6-month weight lossAim 1b: examine self-monitoring engagement and its association with weight lossAim 2: evaluate barriers to and facilitators of engaging in these self-monitoring strategies, which will be assessed via semi-structured qualitative interviewsAim 3: assess a novel, interactive recruitment strategy via an embedded trial
Remote (anywhere in the U.S.)
Using a mixed methods approach, this project will first consist of a pilot factorial trial of four types of goals that vary in how challenging they are to attain. We will evaluate the feasibility and acceptability of these different types of goals among 32 adults with overweight/obesity in a 3-month digital weight loss intervention. Next, we will evaluate barriers to and facilitators of intervention engagement through semi-structured qualitative interviews with a subset of participants. Integrating the trial’s quantitative findings with the qualitative feedback will allow our study team to make any needed modifications prior to conducting a fully-powered trial to evaluate efficacy of these goals.