A longitudinal observational study with ecological momentary assessment and deep learning to predict non-prescribed opioid use, treatment retention, and medication nonadherence among persons receiving medication treatment for opioid use disorder.

Journal: Journal of substance use and addiction treatment
PMID: 40127869

Abstract

BACKGROUND: Despite effective treatments for opioid use disorder (OUD), relapse and treatment drop-out diminish their efficacy, increasing the risks of adverse outcomes, including death. Predicting important outcomes, including non-prescribed opioid use (NPOU) and treatment discontinuation among persons receiving medications for OUD (MOUD) can provide a proactive approach to these challenges. Our study uses ecological momentary assessment (EMA) and deep learning to predict momentary NPOU, medication nonadherence, and treatment retention in MOUD patients.

Authors

  • Michael V Heinz
    Center for Technology and Behavioral Health, Dartmouth College, Lebanon, New Hampshire.
  • George D Price
    Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
  • Avijit Singh
    Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, United States.
  • Sukanya Bhattacharya
    Dartmouth College, 46 Centerra Parkway; Suite 300, Office # 333S, Lebanon, NH, 03766, USA.
  • Ching-Hua Chen
    Center for Computational Health, IBM T.J. Watson Research Center, Yorktown Heights, NY.
  • Asma Asyyed
    The Permanente Medical Group, Northern California, Addiction Medicine and Recovery Services, Oakland, CA, United States.
  • Monique B Does
    Division of Research, Kaiser Permanente Northern California, Oakland, CA, United States.
  • Saeed Hassanpour
    Lia Harrington, Todd MacKenzie, and Saeed Hassanpour, Geisel School of Medicine at Dartmouth College, Hanover; Roberta diFlorio-Alexander, Katherine Trinh, and Arief Suriawinata, Dartmouth-Hitchcock Medical Center, Lebanon, NH.
  • Emily Hichborn
    Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
  • David Kotz
    Computer Science, Dartmouth, Hanover, NH, United States.
  • Chantal A Lambert-Harris
    Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
  • Zhiguo Li
    Key Laboratory of Clean Energy Materials Chemistry of Guangdong Higher Education Institutes, School of Chemistry and Chemical Engineering, Lingnan Normal University, Zhanjiang 524048, China.
  • Bethany McLeman
    Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
  • Varun Mishra
    Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States; Department of Health Sciences, Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States.
  • Catherine Stanger
    Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
  • Geetha Subramaniam
    Center for Clinical Trials Network, National Institute on Drug Abuse, Bethesda, MD, United States.
  • Weiyi Wu
    Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
  • Cynthia I Campbell
    Kaiser Permanente Northern California Division of Research, Oakland, CA, USA.
  • Lisa A Marsch
    Center for Technology and Behavioral Health, Dartmouth College, Hanover, NH, 03755, USA.
  • Nicholas C Jacobson
    Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.

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