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

Angela Fidler Pfammatter1, Inbal Nahum-Shani2, Margaret DeZelar1

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Summary
This summary is machine-generated.

This study compares mobile health (mHealth) apps versus apps with coaching for obesity treatment. It aims to find the most effective and resource-efficient stepped care approach for weight loss.

Keywords:
ObesityOptimizationResource allocationStepped careWeight lossmHealth

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Area of Science:

  • Obesity treatment
  • Digital health interventions
  • Behavioral medicine

Background:

  • Stepped care models offer a rational approach to resource allocation for population-level obesity reduction.
  • Evidence is needed to guide the integration of low-cost mobile health (mHealth) tools within stepped care strategies without compromising weight loss outcomes.
  • A sequential multiple assignment randomization trial (SMART) is proposed to develop an evidence-based stepped care intervention combining mHealth and traditional components.

Purpose of the Study:

  • To test the non-inferiority of a weight loss smartphone app (APP) alone versus APP plus coaching (APP + C) as a first-line obesity treatment.
  • To identify optimal strategies for addressing early treatment non-response.
  • To determine the most resource-efficient treatment sequence for achieving significant weight loss.

Main Methods:

  • A sequential multiple assignment randomization trial (SMART) involving 400 participants (18-60 years, BMI 27-45 kg/m²).
  • Randomization to either APP or APP + C for 12 weeks.
  • Non-responders (<0.5 lb/week weight loss) at weeks 2, 4, or 8 are re-randomized to a stepped-up intervention (modest or vigorous).
  • Weight assessed in-person at baseline, 3, 6, and 12 months; wireless scale used for interim assessments.

Main Results:

  • The study is designed to provide data on weight change at 6 months to compare APP vs. APP + C.
  • It will identify effective re-randomization strategies for non-responders.
  • Outcomes will quantify weight loss achieved through different stepped care sequences.

Conclusions:

  • Findings will inform the development of an obesity treatment algorithm.
  • The algorithm will aim to balance effective weight loss with efficient resource utilization.
  • This research contributes to optimizing digital and traditional interventions in obesity management.