Angela Fidler Pfammatter1, Samuel L Battalio2, Charlie Olvera2, Margaret DeZelar2, Dominique Moore2, Laura Scanlan2, Juned Siddique2, Bonnie Spring2, Su-Hsin Chang3. 1. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States. Electronic address: angela@northwestern.edu. 2. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States. 3. Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, United States.
Abstract
BACKGROUND: Obesity is a substantial public health concern; however, gold-standard behavioral treatments for obesity are costly and burdensome. Existing adaptations to the efficacious Diabetes Prevention Program (DPP) demonstrate mixed results. Our prior research applying the Multiphase Optimization Strategy (MOST) to DPP identifies a more parsimonious, less costly intervention (EVO) resulting in significant weight loss. OBJECTIVE: The aim of the remotely conducted EVO trial is to test the non-inferiority of EVO against DPP. We will conduct economic evaluations alongside the trial to estimate delivery and patient costs, cost-effectiveness, and lifetime healthcare costs of EVO as compared to DPP. Exploratory analyses will examine maintenance, moderators, and mediators of the treatment effect. STUDY DESIGN: The EVO trial will recruit nationally to randomize 524 participants with obesity. Participants will receive either EVO or DPP over a 6 month period. EVO participants will be provided online lessons, a smartphone application to self-monitor diet, physical activity, and weight, and attend 12 brief calls with a Health Promotionist. DPP participants will receive the first 6 months of the Center for Disease Control's T2D materials and attend 16 one-hour video call sessions with staff certified in DPP delivery. Weight will be measured at baseline, 3-, 6-, and 12-months. Itemized delivery cost will be collected. Staff and participants will also provide information to estimate costs for intervention-related activities. SIGNIFICANCE: The EVO trial could establish evidence supporting dissemination of a scalable, cost-effective behavioral treatment with potential to shift clinical practice guidelines, inform policy, and reduce the prevalence of obesity.
BACKGROUND: Obesity is a substantial public health concern; however, gold-standard behavioral treatments for obesity are costly and burdensome. Existing adaptations to the efficacious Diabetes Prevention Program (DPP) demonstrate mixed results. Our prior research applying the Multiphase Optimization Strategy (MOST) to DPP identifies a more parsimonious, less costly intervention (EVO) resulting in significant weight loss. OBJECTIVE: The aim of the remotely conducted EVO trial is to test the non-inferiority of EVO against DPP. We will conduct economic evaluations alongside the trial to estimate delivery and patient costs, cost-effectiveness, and lifetime healthcare costs of EVO as compared to DPP. Exploratory analyses will examine maintenance, moderators, and mediators of the treatment effect. STUDY DESIGN: The EVO trial will recruit nationally to randomize 524 participants with obesity. Participants will receive either EVO or DPP over a 6 month period. EVO participants will be provided online lessons, a smartphone application to self-monitor diet, physical activity, and weight, and attend 12 brief calls with a Health Promotionist. DPP participants will receive the first 6 months of the Center for Disease Control's T2D materials and attend 16 one-hour video call sessions with staff certified in DPP delivery. Weight will be measured at baseline, 3-, 6-, and 12-months. Itemized delivery cost will be collected. Staff and participants will also provide information to estimate costs for intervention-related activities. SIGNIFICANCE: The EVO trial could establish evidence supporting dissemination of a scalable, cost-effective behavioral treatment with potential to shift clinical practice guidelines, inform policy, and reduce the prevalence of obesity.
Authors: Ian W Eisenberg; Patrick G Bissett; Jessica R Canning; Jesse Dallery; A Zeynep Enkavi; Susan Whitfield-Gabrieli; Oscar Gonzalez; Alan I Green; Mary Ann Greene; Michaela Kiernan; Sunny Jung Kim; Jamie Li; Michael R Lowe; Gina L Mazza; Stephen A Metcalf; Lisa Onken; Sadev S Parikh; Ellen Peters; Judith J Prochaska; Emily A Scherer; Luke E Stoeckel; Matthew J Valente; Jialing Wu; Haiyi Xie; David P MacKinnon; Lisa A Marsch; Russell A Poldrack Journal: Behav Res Ther Date: 2017-10-05
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