# Contextualized daily prediction of lapse risk in opioid use disorder by digital phenotyping

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2021 · $683,287

## Abstract

PROJECT SUMMARY
Opioid use disorder is increasingly widespread, leading to devastating consequences and costs for patients and their
families, friends, and communities. Available treatments for opioid and other substance use disorders (SUD) are not
successful at sustaining sobriety. The vast majority of people with SUD relapse within a year. Critically, they often fail to
detect dynamic, day-by-day changes in their risk for relapse and do not adequately employ skills they developed or take advantage of support available through continuing care. The broad goals of this project are to develop and deliver a highly contextualized, lapse risk prediction models for forecasting day-by-day probability of opioid and other drug use lapse among people pursuing drug abstinence. This lapse risk prediction model will be delivered within the Addiction-Comprehensive Health Enhancement Support System (A-CHESS) mobile app, which has been established by RCT as a state-of-the-art mHealth system for providing continuing care services for alcohol and substance use disorders.
To accomplish these broad goals, a diverse sample of 480 participants with opioid use disorder who are pursing
abstinence will be recruited. These participants will be followed for 12 months of their recovery, with observations
occurring as early as one week post-abstinence and as late as 18 months post-abstinence across participants in the
sample. Well-established distal, static relapse risk signals (e.g., addiction severity, comorbid psychopathology) will be
measured on intake. A range of more proximal, time-varying opioid (and other drug use) lapse risk signals will also be
collected via participants’ smartphones. These signals include self-report surveys every two months, daily ecological
momentary assessments, daily video recovery “check-ins”, voice phone call and text message logs, text message
content, moment-by-moment location (via smartphone GPS and location services), physical activity (via smartphone
sensors), and usage of the mobile A-CHESS Recovery Support app. The predictive power of these risk signals will be
further increased by anchoring them within an inter-personal context of known people, locations, dates, and times that
support or detract from participants’ abstinence efforts. Machine learning methods will be used to train, validate, and test opioid (and other drug) lapse risk prediction models based on these contextualized static and dynamic risk signals.
These lapse risk prediction models will provide participant specific, day-by-day probabilistic forecast of a lapse to opioid (or other drug) use among opioid abstinent individuals. These lapse risk prediction models will be formally added to the A-CHESS continuing care mobile app at the completion of the project for use in clinical care. These project goals position A-CHESS to make relapse prevention and recovery support, information, and risk monitoring available to patients continuously. Compared to conventional continuing...

## Key facts

- **NIH application ID:** 10172881
- **Project number:** 5R01DA047315-03
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** John J. Curtin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $683,287
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10172881

## Citation

> US National Institutes of Health, RePORTER application 10172881, Contextualized daily prediction of lapse risk in opioid use disorder by digital phenotyping (5R01DA047315-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10172881. Licensed CC0.

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