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Accepted for/Published in: JMIR Research Protocols

Date Submitted: Apr 22, 2020
Date Accepted: Dec 1, 2020

The final, peer-reviewed published version of this preprint can be found here:

Optimizing an Obesity Treatment Using the Multiphase Optimization Strategy Framework: Protocol for a Randomized Factorial Trial

Bennett GG, Steinberg D, Bolton J, Gallis JA, Treadway C, Askew S, Kay MC, Pollak KI, Turner EL

Optimizing an Obesity Treatment Using the Multiphase Optimization Strategy Framework: Protocol for a Randomized Factorial Trial

JMIR Res Protoc 2021;10(1):e19506

DOI: 10.2196/19506

PMID: 33459600

PMCID: 7850907

Optimizing an Obesity Treatment Using the MOST Framework: Protocol for a Standalone Text Messaging Intervention (The Charge Study)

  • Gary G Bennett; 
  • Dori Steinberg; 
  • Jamiyla Bolton; 
  • John A Gallis; 
  • Cayla Treadway; 
  • Sandy Askew; 
  • Melissa C Kay; 
  • Kathryn I Pollak; 
  • Elizabeth L Turner

ABSTRACT

Background:

Effective weight loss interventions exist, yet few innovative approaches exist to deliver them to scale. Further, none has been fully delivered via text message. The multiphase optimization strategy (MOST) can assist us in developing multicomponent interventions that consist only of active components, those that have been experimentally determined to impact the chosen outcome.

Objective:

To optimize a standalone text messaging obesity intervention, “Charge,” using the MOST framework.

Methods:

Using a 25 factorial design, participants (n=534) were randomized to one of 32 experimental conditions, consisting of a combination of five two-level text message-based intervention components: message frequency (weekly vs. daily), motivational messaging (self- vs. expert-generated), reminders to track goals (one vs. multiple), feedback type (summary score vs. individual score), and comparison unit (self vs. group). All study participants received a core 6-month weight loss texting intervention that included tailored behavior change goals, interactive self-monitoring, automated feedback, and skills training videos. We used a mixed effects model to assess the main effects and interactions of all five components. To define a significant main effect or interaction, we used a priori effect size of 0.7 kg difference between levels of a component.

Results:

At baseline, the mean (SD) participant weight was 97.1 (20.3) kg. Participants had a mean (SD) of BMI of 33.9 (6.1) kg/m2 and the mean (SD) age was 42.1 (11.9) years. The sample was 73% non-Hispanic white, and 74% worked full-time.

Conclusions:

Full factorial trials are particularly efficient in terms of cost and logistics when leveraged for standalone digital treatments. Accordingly, MOST has potential to promote the rapid advancement of digital health treatments. Subject to positive findings here and in a future efficacy trial, the intervention will be low cost, immediately scalable, and ready for dissemination. This will be of great potential use to the millions of Americans with obesity and the providers who treat them. Clinical Trial: ClinicalTrials.gov NCT03254940


 Citation

Please cite as:

Bennett GG, Steinberg D, Bolton J, Gallis JA, Treadway C, Askew S, Kay MC, Pollak KI, Turner EL

Optimizing an Obesity Treatment Using the Multiphase Optimization Strategy Framework: Protocol for a Randomized Factorial Trial

JMIR Res Protoc 2021;10(1):e19506

DOI: 10.2196/19506

PMID: 33459600

PMCID: 7850907

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