4.1 Article

SMART: Study protocol for a sequential multiple assignment randomized controlled trial to optimize weight loss management

Journal

CONTEMPORARY CLINICAL TRIALS
Volume 82, Issue -, Pages 36-45

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.cct.2019.05.007

Keywords

Weight loss; Optimization; mHealth; Stepped care; Obesity; Resource allocation

Funding

  1. National Institutes of Health [R01DK108678, R01DK097364, R01DA039901, UL1TR001422, P30CA60553]

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Background: Stepped care is a rational resource allocation approach to reduce population obesity. Evidence is lacking to guide decisions on use of low cost treatment components such as mobile health (mHealth) tools without compromising weight loss of those needing more expensive traditional treatment components (e.g., coaching, meal replacement). A sequential multiple assignment randomization trial (SMART) will be conducted to inform the development of an empirically based stepped care intervention that incorporates mHealth and traditional treatment components. Objective: The primary aim tests the non-inferiority of app alone, compared to app plus coaching, as first line obesity treatment, measured by weight change from baseline to 6 months. Secondary aims are to identify the best tactic to address early treatment non-response and the optimal treatment sequence for resource efficient weight loss. Study design: Four hundred participants, 18-60 years old with Body Mass Index between 27 and 45 kg/m(2) will be randomized to receive a weight loss smartphone app (APP) or the app plus weekly coaching (APP + C) for a 12 week period. Those achieving < 0.5 Ib. weight loss on average per week, assessed by wireless scale at 2, 4, and 8 weeks, will be classified as non-responders and re-randomized once to step-up modestly (adding another mHealth component) or vigorously (adding mHealth and traditional treatment components) for the remaining treatment period. Weight will be assessed in person at baseline, 3, 6, and 12 months. Significance: Results will inform construction of an obesity treatment algorithm that balances weight loss outcomes with resource consumption.

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