Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Jan 10, 2023
Date Accepted: Apr 10, 2023

The final, peer-reviewed published version of this preprint can be found here:

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

Campbell CI, Chen CH, Adams SR, Asyyed A, Athale NR, Does MB, Hassanpour S, Hichborn E, Jackson-Morris M, Jacobson NC, Jones HK, Kotz D, Lambert-Harris CA, Li Z, McLeman B, Mishra V, Stanger C, Subramaniam G, Wu W, Zegers C, Marsch LA

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

J Med Internet Res 2023;25:e45556

DOI: 10.2196/45556

PMID: 37310787

PMCID: 10337375

Patient Engagement in a Multi-Modal Digital Phenotyping Study of Opioid Use Disorder

  • Cynthia I Campbell; 
  • Ching-Hua Chen; 
  • Sara R Adams; 
  • Asma Asyyed; 
  • Ninad R Athale; 
  • Monique B Does; 
  • Saaed Hassanpour; 
  • Emily Hichborn; 
  • Melanie Jackson-Morris; 
  • Nicholas C Jacobson; 
  • Heather K Jones; 
  • David Kotz; 
  • Chantal A Lambert-Harris; 
  • Zhiguo Li; 
  • Bethany McLeman; 
  • Varun Mishra; 
  • Catherine Stanger; 
  • Geetha Subramaniam; 
  • Weiyi Wu; 
  • Christopher Zegers; 
  • Lisa A. Marsch

ABSTRACT

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:

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 four addiction medicine programs in an integrated healthcare delivery system in Northern California. Ecological momentary assessment (EMA), sensor data, and social media data were collected by smartphone, smartwatch, and from social media platforms over a 12-week period. Primary engagement outcomes were meeting measures of minimum phone carry (≥8 hours/day) and watch wear (≥18 hours/day) criteria, EMA response rates, and social media consent rate and data sparsity. Descriptive analyses and bivariate associations were performed.

Results:

The participants’ average age was 37 years, 48% were female, and 71% were White. On average, participants met phone carry criteria on 94% of study days, met watch worn criteria on 74% of days, and wore the watch to sleep on 77% of days. Mean EMA response rate was 70%, declining from 83% to 56% from Week 1 to 12. Among participants with social media accounts, 88% consented to provide data; of those, 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, or race or ethnicity were observed for any outcomes.

Conclusions:

To our knowledge, this was the first study to capture these three digital data sources in this clinical population. Findings demonstrate that patients in buprenorphine treatment for OUD had generally high engagement with multiple digital phenotyping data sources, but with limited intersection for social media data. Clinical Trial: NCT04535583


 Citation

Please cite as:

Campbell CI, Chen CH, Adams SR, Asyyed A, Athale NR, Does MB, Hassanpour S, Hichborn E, Jackson-Morris M, Jacobson NC, Jones HK, Kotz D, Lambert-Harris CA, Li Z, McLeman B, Mishra V, Stanger C, Subramaniam G, Wu W, Zegers C, Marsch LA

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

J Med Internet Res 2023;25:e45556

DOI: 10.2196/45556

PMID: 37310787

PMCID: 10337375

Per the author's request the PDF is not available.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.