# MINDER: Wearable sensor-based detection of digital biomarkers of adherence to medications for opioid use disorder

> **NIH NIH R01** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2024 · $637,756

## Abstract

PROJECT SUMMARY/ABSRACT
Medications for opioid use disorder (MOUD), including the partial opioid agonist buprenorphine, provide a
treatment option for opioid use disorder (OUD) that significantly reduces morbidity and mortality. Even with
successful buprenorphine initiation, however, adherence is paramount to prevent return to non-medical opioid
use and its associated risks. Current methods of determining buprenorphine adherence are limited by their
retrospective nature and recall bias. We propose to develop a novel artificial intelligence-assisted wearable
sensor system, MINDER, which will continuously monitor physiologic changes, and will use machine learning
algorithms to accurately identify buprenorphine use. The MINDER system will be comprised of a custom
wearable sensor (MINDER-band), a companion mobile app and a clinician facing portal. The MINDER-band,
which is a low profile, upper arm band with a user-driven design, continuously records physiologic data. We will
use the band to curate a high-quality dataset of MOUD ingestions and subsequently use machine learning to
evaluate the ability of the sensor to detect MOUD (specifically buprenorphine) ingestion events. Finally, we will
deploy the MINDER system in real-world MOUD treatment settings to understand usability factors. The
investigative team brings together complementary expertise in toxicology/addiction medicine, mobile health
(Carreiro, Smelson), machine learning, human computer interaction (Venkatasubramanian), novel on-body
wearable sensors, and medical device development (Mankodiya, Solanki). The specific aims of the project are
to: 1) Understand the requirements, barriers, and facilitators for an ML driven buprenorphine adherence support
system, 2) Develop and test a novel wearable sensing system, MINDER, designed for individuals in
buprenorphine treatment, 3) Curate a high quality annotated dataset for machine learning-based modeling of
buprenorphine adherence, 4) Model the buprenorphine ingestion data collected from the MINDER-band to
build the ML algorithms infrastructure for the MINDER system. Upon completion, the MINDER system will be
ready for clinical deployment. This study will lay the groundwork for novel just-in-time adaptive behavioral
interventions to personalize OUD treatment, improve buprenorphine adherence and its success, and ultimately
reduce morbidity and mortality from OUD.

## Key facts

- **NIH application ID:** 10860998
- **Project number:** 5R01EB033581-02
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** STEPHANIE P CARREIRO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $637,756
- **Award type:** 5
- **Project period:** 2023-06-06 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10860998

## Citation

> US National Institutes of Health, RePORTER application 10860998, MINDER: Wearable sensor-based detection of digital biomarkers of adherence to medications for opioid use disorder (5R01EB033581-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10860998. Licensed CC0.

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