Design of Project STAR: A Randomized Controlled Trial Evaluating the Impact of an Adaptive Intervention on Long-term Weight-loss Maintenance

Overview

Journal Contemp Clin Trials

Publisher Elsevier

Date 2024 Oct 9

PMID 39384066

Authors

Kathryn M Ross

Meena N Shankar

Peihua Qiu

Zibo Tian

Taylor N Swanson

Armaan Shetty

Jaime Ruiz

Lisa Anthony

Michael G Perri

Affiliations

Soon will be listed here.

Abstract

Background: Without provision of additional intervention, most individuals regain weight after the end of weight-loss programs. Extended-care programs have been demonstrated to improve long-term weight-loss maintenance, but effects are modest.

Methods: We proposed to evaluate whether delivering extended-care telephone sessions on an ADAPTIVE (provided when individuals are deemed to be at high-risk for weight regain) versus STATIC (the once-per-month schedule typically used in extended-care programs) schedule improves weight regain after initial weight loss. Adults with obesity were initially recruited for a 16-week lifestyle weight-loss program, and those who lost ≥5 % of their initial weight were eligible for enrollment in the Project STAR maintenance trial.

Results: A total of 449 individuals (mean ± SD age = 49.5 ± 11.4 years, BMI = 35.7 ± 4.0 kg/m, 83.5 % female, 23.4 % Black or African American, 9.8 % Hispanic) were recruited for the initial weight-loss program and lost an average of 6.4 ± 4.9 % of their initial body weight; 255 were randomized to the maintenance trial. There were no significant differences between participants randomized to the trial versus those who were not in terms of baseline weight, gender, race/ethnicity, education, or marital status, all ps > 0.05; however, participants who were randomized to the trial were older, p = .014, and reported higher incomes, p < .001.

Conclusion: Results from Project STAR will demonstrate whether providing extended-care intervention on an individually adaptable schedule improves long-term weight-loss maintenance. Moreover, the rich longitudinal dataset collected during the trial will serve as a foundation for building future predictive algorithms of weight regain and novel weight-maintenance interventions.

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