Medicine
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Michele Lanpher Patel
Instructor, Medicine - Stanford Prevention Research Center
BioMichele L. Patel, PhD is an Instructor in the Stanford Prevention Research Center at the Stanford University School of Medicine. Trained as a clinical health psychologist, Dr. Patel conducts research to optimize scalable digital health interventions for adults with obesity. She leads clinical trials that test innovative strategies to address the critical challenge of suboptimal engagement in digital interventions.
Dr. Patel's research is supported by an NIH career development award (K23; 2022-2027). Her recent Spark trial investigated the most potent combination of self-monitoring strategies in a behavioral weight loss intervention among 176 adults with overweight or obesity. Results are expected to be published in 2026.
She is also testing other strategies to promote engagement, weight loss, and health. This includes the impact of easier vs. harder goals, and the effect of high-frequency, low-friction behavioral strategies. Her work leverages the Multiphase Optimization Strategy (MOST) to systematically identify intervention strategies that maximize effectiveness while minimizing patient burden.
Dr. Patel received her PhD in clinical psychology from Duke University. She completed her clinical internship at the VA Palo Alto with a specialization in behavioral medicine, followed by a postdoctoral fellowship (T32) at the Stanford Prevention Research Center. She currently serves as the Chair of the Optimization of Behavioral and Biobehavioral Interventions special interest group in the Society of Behavioral Medicine.
Primary Research Interests:
-- Optimizing digital interventions for obesity treatment and prevention
-- Leveraging behavioral science to create lower-burden self-monitoring strategies to improve engagement and outcomes
-- Identifying psychosocial factors (including health literacy and self-efficacy) that influence treatment success
Methodological Expertise:
-- Randomized clinical trials, including factorial designs and embedded trials
-- Systematic reviews
-- Signal detection analysis
