Background: The Weight Loss Maintenance Trial (WLM) is a multi-center, randomized, controlled trial that compares the effects of two 30-month maintenance interventions, i.e., Personal Contact (PC) and Interactive Technology (IT) to a self-directed usual care control group (SD), in overweight or obese individuals who are at high risk for cardiovascular disease. Purpose: This paper provides an overview of the design and methods, and design considerations and lessons learned from this trial. Methods: All participants received a 6-month behavioral weight loss program consisting of weekly group sessions. Participants who lost 4 kg were randomized to one of three conditions (PC, IT, or SD). The PC condition provided monthly contacts with an interventionist primarily via telephone and quarterly face-to-face visits. The IT condition provided frequent, individualized contact through a tailored, website system. Both the PC and IT maintenance programs encouraged the DASH dietary pattern and employed theory-based behavioral techniques to promote maintenance. Results: Design considerations included choice of study population, frequency and type of intervention visits, and choice of primary outcome. Overweight or obese persons with CVD risk factors were studied. The pros and cons of studying this population while excluding others are presented. We studied intervention contact strategies that made fewer demands on participant time and travel, while providing frequent opportunities for interaction. The primary outcome variable for the trial was change in weight from randomization to end of follow-up (30 months). Limitations: Limits to generalizability are discussed. Individuals in need of weight loss strategies may have been excluded due to barriers associated with internet use. Other participants may have been excluded secondary to a comorbid condition. Conclusions: This paper highlights the design and methods of WLM and informs readers of discussions of critical issues and lessons learned from the trial.
ASJC Scopus subject areas