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

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 con...

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Bibliographic Details
Published inBehaviour research and therapy Vol. 101; pp. 58 - 70
Main Authors 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, Greenleaf, Walter, Suppes, Trisha, Perry, L. Michael, Smyth, Joshua M., Lewis, Megan A., Venditti, Elizabeth M., Snowden, Mark, Simmons, Janine M., Ma, Jun
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.02.2018
Elsevier Science Ltd
Subjects
Behavior change
Behavior Control - methods
Behavior modification
Behavioral sciences
Body Weight
Chronic illnesses
Clinical medicine
Clinical Protocols
Cognition
Comorbidity
Computer applications
Depression
Depression - epidemiology
Depression - therapy
Disability
Emotional regulation
Humans
Lifestyles
Magnetic Resonance Imaging
Medical imaging
Mental depression
Mood
Nervous system
Neuroimaging
Obesity
Obesity - epidemiology
Obesity - therapy
Precision medicine
Precision Medicine - methods
Sampling
Self regulation
Self-Control - psychology
Selfmanagement
Selfreflection
Smartphone
Virtual Reality
Neuroimaging
Behavior change
Obesity
Depression
Virtual reality
Self-regulation
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Summary: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. •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.
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Author contribution
LMW and JM are co-PIs on the ENGAGE project and JM is the original PI of the RAINBOW project, and both have contributed the study conception, design, and implementation. AP was the first research coordinator on the project (now a graduate student), who helped develop and implement the study protocols and undertook data collection and participant testing on the ENGAGE measures. MK is a current research coordinator on the ENGAGE project who has also helped implement the study protocols and has undertaken data collection and participant testing on ENGAGE measures. AGP commenced as a postdoctoral fellow on the project who implemented the ENGAGE data analysis pipelines for the imaging data, and is a now a faculty instructor. MDS is a postdoctoral fellow on the project who has contributed to developing the imaging analysis protocols. LGR is a co-PI with JM on the RAINBOW study following JM’s change of institution in August 2015, and contributed to the conception and design of the ENGAGE project. OG is a co-I on the ENGAGE project who shares responsibility with PWL for data analytic models. JB is a co-I who has contributed to study design, with emphasis on virtual reality environments and assays. PWL is a co-I who developed the original data analysis plan, and shares responsibility with OG for data analytic models. PD is a consultant who has contributed to study design and data analysis, with emphasis on passive smartphone sampling. BW is a consultant who has contributed to integrated data management, with emphasis on neuroimaging and integration with other data assays. CC is a research software engineer who has had designed and developed the data pipeline for integrating sources of data and for enabling computation of the data. WG is a consultant who has contributed to study design, with emphasis on virtual reality environments. TS is a consultant who has contributed to trial design and clinical protocols. LMP is a key personnel contributor to establishing the data pipeline. JMSm is a consultant who has contributed expertise in ambulatory assessment, emotion regulation, and naturalistic behavior. MAL is a co-I for RAINBOW and a consultant for ENGAGE who has contributed to mixed methods evaluations of behavioral interventions in primary care settings. EMV is a co-I for RAINBOW and a consultant for ENGAGE who has contributed to behavioral weight loss intervention design and data interpretation. MS is a co-I for RAINBOW and ENGAGE who has contributed to study design, with emphasis on clinical depression management. JMSi is the NIH scientific officer for the project who has contributed to study design and implementation. All authors have contributed to the writing of the manuscript and have approved the final manuscript.
ISSN:0005-7967
1873-622X
DOI:10.1016/j.brat.2017.09.012