Patient Engagement in a Multimodal Digital Phenotyping Study of Opioid Use Disorder

Picture of Cynthia I. Campbell
Cynthia I. Campbell
Picture of Ching-Hua Chen
Ching-Hua Chen
Picture of Sara R. Adams
Sara R. Adams
Picture of Asma Asyyed
Asma Asyyed
Picture of Ninad R. Athale
Ninad R. Athale
Picture of Monique B. Does
Monique B. Does
Picture of Saeed Hassanpour
Saeed Hassanpour
Picture of Emily Hichborn
Emily Hichborn
Picture of Melanie Jackson-Morris
Melanie Jackson-Morris
Picture of Nicholas C. Jacobson
Nicholas C. Jacobson
Picture of Heather K. Jones
Heather K. Jones
Picture of David Kotz
David Kotz
Picture of Chantal A. Lambert-Harris
Chantal A. Lambert-Harris
Picture of Zhiguo Li
Zhiguo Li
Picture of Bethany McLeman
Bethany McLeman
Picture of Catherine Stanger
Catherine Stanger
Picture of Geetha Subramaniam
Geetha Subramaniam
Picture of Weiyi Wu
Weiyi Wu
Picture of Christopher Zegers
Christopher Zegers
Picture of Lisa A. Marsch
Lisa A. Marsch
Published at Journal of Medical Internet Research (JMIR) 2023


Background: Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD.
Objective: The aim is to examine patient engagement with multiple digital phenotyping methods among patients receiving buprenorphine medication for OUD.
Methods: The study enrolled 65 patients receiving buprenorphine for OUD between June 2020 and January 2021 from 4 addiction medicine programs in an integrated health care delivery system in Northern California. Ecological momentary assessment (EMA), sensor data, and social media data were collected by smartphone, smartwatch, and social media platforms over a 12-week period. Primary engagement outcomes were meeting measures of minimum phone carry (≥8 hours per day) and watch wear (≥18 hours per day) criteria, EMA response rates, social media consent rate, and data sparsity. Descriptive analyses, bivariate, and trend tests were performed.
Results: The participants’ average age was 37 years, 47% of them were female, and 71% of them were White. On average, participants met phone carrying criteria on 94% of study days, met watch wearing criteria on 74% of days, and wore the watch to sleep on 77% of days. The mean EMA response rate was 70%, declining from 83% to 56% from week 1 to week 12. Among participants with social media accounts, 88% of them consented to providing data; of them, 55% of Facebook, 54% of Instagram, and 57% of Twitter participants provided data. The amount of social media data available varied widely across participants. No differences by age, sex, race, or ethnicity were observed for any outcomes.
Conclusions: To our knowledge, this is the first study to capture these 3 digital data sources in this clinical population. Our findings demonstrate that patients receiving buprenorphine treatment for OUD had generally high engagement with multiple digital phenotyping data sources, but this was more limited for the social media data.