A Factorial Experiment to Optimize Remotely Delivered Behavioral Treatment for Obesity: Results of the Opt‐IN Study. Issue 9 (12th July 2020)

• Record Type: Journal Article
• Authors: Spring, Bonnie; Pfammatter, Angela F.; Marchese, Sara H.; Stump, Tammy; Pellegrini, Christine; McFadden, H. Gene; Hedeker, Donald; Siddique, Juned; Jordan, Neil; Collins, Linda M.
• Is Part Of: Obesity. Volume 28:Issue 9(2020)
• Page Start: 1652
• Page End: 1662
• Publication Date: 2020-07-12
• Subjects: Obesity -- Periodicals; 616.398005
• DOI: 10.1002/oby.22915
• Languages: English
• ISSNs: 1930-7381

Abstract

Abstract : Objective: Intensive behavioral obesity treatments face scalability challenges, but evidence is lacking about which treatment components could be cut back without reducing weight loss. The Optimization of Remotely Delivered Intensive Lifestyle Treatment for Obesity (Opt‐IN) study applied the Multiphase Optimization Strategy to develop an entirely remotely delivered, technology‐supported weight‐loss package to maximize the amount of weight loss attainable for ≤$500. Methods: Six‐month weight loss was examined among adults ( N = 562) with BMI ≥ 25 who were randomly assigned to conditions in a factorial experiment crossing five dichotomous treatment components set to either low/high (12 vs. 24 coaching calls) or off/on (primary care provider reports, text messaging, meal replacements, and buddy training). Results: About 84.3% of participants completed the final assessment. The treatment package yielding maximum weight loss for ≤$500 included 12 coaching calls, buddy training, and primary care provider progress reports; produced average weight loss of 6.1 kg, with 57.1% losing ≥5% and 51.8% losing ≥7%; and cost $427 per person. The most expensive candidate‐treatment component (24 vs. 12 coaching calls) was screened out of the optimized treatment package because it did not increase weight loss. Conclusions: Systematically testing each treatment component's effect on weight loss made it possible to eliminate more expensive but less impactful components, yielding an optimized, resource‐efficient obesity treatment for evaluation in a randomized controlled trial.