The ENGAGE study: Integrating neuroimaging, virtual reality and smartphone sensing to understand self-regulation for managing depression and obesity in a precision medicine model

  • Leanne M. Williams
  • , Adam Pines
  • , Andrea N. Goldstein-Piekarski
  • , Lisa G. Rosas
  • , Monica Kullar
  • , Matthew D. Sacchet
  • , Olivier Gevaert
  • , Jeremy Bailenson
  • , Philip W. Lavori
  • , Paul Dagum
  • , Brian Wandell
  • , Carlos Correa
  • , Walter Greenleaf
  • , Trisha Suppes
  • , L. Michael Perry
  • , Joshua M. Smyth
  • , Megan A. Lewis
  • , Elizabeth M. Venditti
  • , Mark Snowden
  • , Janine M. Simmons
  • Jun Ma

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)58-70
Number of pages13
JournalBehaviour Research and Therapy
Volume101
DOIs
StatePublished - Feb 2018

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Clinical Psychology
  • Psychiatry and Mental health

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