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Harnessing Digital Health to Understand Clinical Trajectories of Opioid Use Disorder (D-TECT)

Funding Source

NIDA Clinical Trials Network (NIH HEAL Initiative), CTN-0084-A2

Project Period

2/15/2019 – 5/31/2022

Principal Investigator

Lisa Marsch, PhD (Northeast Node, Geisel School of Medicine at Dartmouth College) and Cynthia Campbell, PhD (Health Systems Node, Kaiser Permanente Division of Research)

Other Project Staff

David Kotz, PhD (Northeast Node), Saeed Hassanpour, PhD (Northeast Node), Catherine Stanger, PhD (Northeast Node), Varun Mishra, BTech (Northeast Node), Chantal Lambert-Harris, MA (Northeast Node), Bethany McLeman, BA (Northeast Node), Emily Hichborn, BS (Northeast Node), Craig Ganoe, MS (Northeast Node), Weiyi Wu, BS (Northeast Node); Ching-Hua Chen, PhD , RN (IBM); Elise Blaese, MS, MBA (IBM); Tian Hao, PhD (IBM); Harry Stavropolous, MS (IBM), Zhiguo Li (IBM); Monique Does, MPH (Health Systems Node), Heather Jones, MPH (Health Systems Node), Sara Adams, MPH (Health Systems Node); Geetha Subramaniam, MD (CCTN); Kathleen Carroll, PhD (Yale)

Project Summary

Given the ubiquity of access to digital technologies worldwide, digital tools allow for the examination of health behavior and clinical trajectories within-individuals through intensive collection of individual-level, real-time data collected via surveys on mobile device (referred to as Ecological Momentary Assessment [EMA]), wearable sensors (on smartphones and/or smartwatches), and mapping digital footprints. Digitally-derived data allow for the development of dynamic models of health behavior to understand behavior in real-time and in response to changing environmental, social, physiological, and intrapersonal factors.

As applied to persons with opioid use disorder (OUD), digital data that offers ongoing assessment of behavior as individuals live their daily lives can help us better understand the trajectory of clinically important behaviors (e.g., treatment retention; medication adherence over time; opioid use) and identify fluctuating contextual factors that greatly influence such behaviors, (e.g., patterns leading to relapse or treatment dropout).

The primary objective of this study is to evaluate the feasibility of utilizing digital health technology with OUD patients as measured by a 12-week period of continuous assessment using EMA and digital sensing.

The secondary objective of this study is to examine the utility of EMA, digital sensing, and social media data (separately and compared to one another) in predicting OUD treatment retention and buprenorphine medication adherence.

This study is the first to employ passive mobile sensing, social media data, and active responses to queries on mobile devices using EMA to obtain moment-by-moment quantification of individual-level data that may predict retention and other treatment outcomes in a population of persons in buprenorphine medication treatment with OUD (MOUD). This project brings together experts in OUD, digital health, mobile sensing, digital health data analytics, and EHR research and reflects a novel collaboration between academic institutions, a healthcare system, and an industry partner.

Public Health Relevance

The opioid overdose rate has increased significantly in recent years and is now the leading cause of death in individuals under 50 years old. Better characterization of individuals with OUD is needed to improve the identification of novel targets, treatment retention/outcomes, and advancement of personalized medicine by identifying biological and behavioral markers of severity, treatment response, as well as new conceptualizations of symptom clusters. This study has the potential to inform the advancement of personalized OUD treatment and significantly impact the response to the opioid crisis.