The ENGAGE study: Integrating neuroimaging, virtual reality and smartphone sensing to understand self-regulation for managing depression and obesity in a precision medicine model. (February 2018) - Williams, Leanne M.; Pines, Adam; Goldstein-Piekarski, Andrea N.; Rosas, Lisa G.; Kullar, Monica; Sacchet, Matthew D.; Gevaert, Olivier; Bailenson, Jeremy; Lavori, Philip W.; Dagum, Paul; Wandell, Brian; Correa, Carlos; et al.
Abstract: Precision medicine models for personalizing achieving sustained behavior change are largely outside of current clinical practice. Yet, changing self-regulatory behaviors is fundamental to the self-management of complex lifestyle-related chronic conditions such as depression and obesity - two top contributors to the global burden of disease and disability. To optimize treatments and address these burdens, behavior change and self-regulation must be better understood in relation to their neurobiological underpinnings. Here, we present the conceptual framework and protocol for a novel study, "Engaging self-regulation targets to understand the mechanisms of behavior change and improve mood and weight outcomes (ENGAGE)". The ENGAGE study integrates neuroscience with behavioral science to better understand the self-regulation related mechanisms of behavior change for improving mood and weight outcomes among adults with comorbid depression and obesity. We collect assays of three self-regulation targets (emotion, cognition, and self-reflection) in multiple settings: neuroimaging and behavioral lab-based measures, virtual reality, and passive smartphone sampling. By connecting human neuroscience and behavioral science in this manner within the ENGAGE study, we develop a prototype for elucidating the underlying self-regulation mechanisms of behavior change outcomes and their application in optimizing intervention strategies for multiple chronic diseases. Highlights: Uses a novel approach integrating neuroscience with behavioral medicine. Includes self-regulation assays across lab, virtual reality, and natural settings. Utilizes a brain-based experimental medicine approach to behavioral intervention. Aims to identify self-regulation profiles to tailor intervention strategies. Identifies a new taxonomy based on emotion, cognition, and self-reflection domains.