Multi-sensor ecological momentary assessment of behavioral and psychosocial predictors of weight loss following bariatric surgery: study protocol for a multicenter prospective longitudinal evaluation

Stephanie P Goldstein, J Graham Thomas, Sivamainthan Vithiananthan, George A Blackburn, Daniel B Jones, Jennifer Webster, Richard Jones, E Whitney Evans, Jody Dushay, Jon Moon, Dale S Bond, Stephanie P Goldstein, J Graham Thomas, Sivamainthan Vithiananthan, George A Blackburn, Daniel B Jones, Jennifer Webster, Richard Jones, E Whitney Evans, Jody Dushay, Jon Moon, Dale S Bond

Abstract

Background: Bariatric surgery is currently the most effective strategy for producing significant and durable weight loss. Yet, not all patients achieve initial weight loss success and some degree of weight regain is very common, sometimes as early as 1-2 years post-surgery. Suboptimal weight loss not fully explained by surgical, demographic, and medical factors has led to greater emphasis on patient behaviors evidenced by clinical guidelines for appropriate eating and physical activity. However, research to inform such guidelines has often relied on imprecise measures or not been specific to bariatric surgery. There is also little understanding of what psychosocial factors and environmental contexts impact outcomes. To address research gaps and measurement limitations, we designed a protocol that innovatively integrates multiple measurement tools to determine which behaviors, environmental contexts, and psychosocial factors are related to outcomes and explore how psychosocial factors/environmental contexts influence weight. This paper provides a detailed description of our study protocol with a focus on developing and deploying a multi-sensor assessment tool to meet our study aims.

Methods: This NIH-funded prospective cohort study evaluates behavioral, psychosocial, and environmental predictors of weight loss after bariatric surgery using a multi-sensor platform that integrates objective sensors and self-report information collected via smartphone in real-time in patients' natural environment. A target sample of 100 adult, bariatric surgery patients (ages 21-70) use this multi-sensor platform at preoperative baseline, as well as 3, 6, and 12 months postoperatively, to assess recommended behaviors (e.g., meal frequency, physical activity), psychosocial indicators with prior evidence of an association with surgical outcomes (e.g., mood/depression), and key environmental factors (e.g., type/quality of food environment). Weight also is measured at each assessment point.

Discussion: This project has the potential to build a more sophisticated and valid understanding of behavioral and psychosocial factors contributing to success and risk after bariatric surgery. This new understanding could directly contribute to improved (i.e., specific, consistent, and validated) guidelines for recommended pre- and postoperative behaviors, which could lead to improved surgical outcomes. These data will also inform behavioral, psychosocial, and environmental targets for adjunctive interventions to improve surgical outcomes.

Trial registration: Registered trial NCT02777177 on 5/19/2016.

Keywords: Bariatric surgery; Diet; Ecological momentary assessment; Obesity; Physical activity; Technology; Weight loss.

Conflict of interest statement

All participants provided written consent to participate in this study. Study procedures and consent were approved by The Miriam Hospital Institutional Review Board in Providence, Rhode Island (board reference number: 211215 45CFR 46.110(4)(7)) and Beth Israel Deaconess Medical Center in Boston, Massachusetts (IRB #2015P000407​).Not applicable.JM is President of MEI Research, Ltd., which is the company that developed the PiLR Health system described in this manuscript. The remainder of the authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Study Timeline
Fig. 2
Fig. 2
EMA System and Components

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