Spark: Finding the Optimal Tracking Strategy for Weight Loss in a Digital Health Intervention


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Trial ID: NCT05249465


This optimization trial will examine three tracking (or "self-monitoring") strategies for weight loss -- tracking dietary intake, steps, and/or body weight -- all delivered through digital health tools. The purpose of the study is to evaluate the combination of these strategies that maximizes 6-month weight loss in the context of a standalone digital health intervention for adults with overweight or obesity. The investigators will recruit 176 total participants to the trial. Recruitment will occur through remote channels. Interested individuals will be directed to an online screening questionnaire; those who are eligible will then be invited to attend an initial remote session with study personnel to ensure interest and eligibility in the study. The weight loss intervention will last 6 months, and all participants will receive a "core" treatment consisting of goal setting, behavioral lessons, action plans, and tailored feedback - all of which will be delivered remotely. Depending on which group participants are assigned to in the study, some individuals will be asked to track their dietary intake, their steps, and/or their body weight via digital tools. All study tasks will occur remotely, thus, participants never need to come in-person for any intervention or assessment tasks. The investigators will use the Multiphase Optimization Strategy (MOST) framework to identify the most effective combination of self-monitoring strategies. The factorial design will allow the research team to determine the unique and combined impact of each self-monitoring component on weight change. The primary outcome is weight change from baseline to 6 months. The research team will also assess self-monitoring engagement over 6 months and its association with weight change. To complement the main trial, the research team will also randomize half of participants to receive an interactive orientation video, in order to assess its impact on trial retention at 6 months. Overall, the information gathered from this trial will enable the construction of an optimized digital health intervention for weight loss that can be delivered remotely, which, if found to be effective, could have high potential for scalability.

Official Title

Optimizing Self-monitoring in a Digital Health Intervention for Weight Loss


Inclusion Criteria:

   - adults (ages 18+ years)

   - body mass index (BMI) 25.0 to 45.0 kg/m^2

   - smartphone ownership

   - willingness to install a mobile app on their phone

   - access to a personal email account

   - English language proficiency

   - interest in losing weight through behavioral strategies

Exclusion Criteria:

   - concurrent enrollment in another weight management intervention

   - loss of ≥10 lbs. in the past 6 months

   - current use of a weight loss medication

   - prior or planned bariatric surgery

   - current or planned pregnancy in the trial period

   - currently breastfeeding

   - lives with someone else participating in the study

   - hospitalization for a mental health condition in the past 12 months

   - inability to engage in moderate forms of physical activity akin to brisk walking
   (assessed by the Physical Activity Readiness Questionnaire)

   - if weight loss is contraindicated or might be impacted by a condition or medication
   (e.g., end stage renal disease, cancer, schizophrenia, dementia, steroids,

   - if an individual would be better suited for a more intensive or different type of
   intervention based on a health condition (e.g., individuals with history of an eating
   disorder or cardiovascular event, uncontrolled hypertension, or uncontrolled diabetes

   - investigator discretion for safety reasons


behavioral: Core behavioral weight loss intervention

behavioral: Self-monitoring diet

behavioral: Self-monitoring weight

behavioral: Self-monitoring steps


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Contact Information

Stanford University
School of Medicine
300 Pasteur Drive
Stanford, CA 94305
Michele L Patel, PhD