Walter Greenleaf's presentation to the Virtual Medicine 2019 Conference at Cedars-Sinai Medical Center
Immersive Environments, Machine Learning, Neuroimaging, And Wearable Sensing Technology - Treating Depression, Addictions, and Facilitating Behavior Change Using A Precision Medicine Model
Based on the methods used in the ENGAGE Study
Walter Greenleaf, PhD
Virtual Human Interaction Lab | Stanford University
Precision medicine models for treating depression, managing addictions and achieving sustained behavior change are largely outside of current clinical practice. Yet, changing self-regulatory behavior is fundamental to the self-management of complex lifestyle-related chronic conditions such as depression and substance use disorder - two top contributors to the global burden of disease and disability.
To optimize treatments and address these burdens, methods to facilitate behavior change and self-regulation must be better understood in relation to their neurobiological underpinnings. Treatment strategies can then be developed that leverage the recent advances in immersive environments, machine learning, and wearable sensing technology and apply them to treat depression, manage addictions, and facilitate behavior change using a precision medicine model that is personalized to the individual.
This presentation will review the conceptual framework and protocol for a large multi-subject longitudinal study named Project 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 can 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.
Behav Res Ther. 2018 Feb;101:58-70. doi: 10.1016/j.brat.2017.09.012. Epub 2017 Oct 7.
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