A Study to Train a Machine Learning Algorithm for an Evaluation of the Use of Biometric Data Captured at the Wrist for the Identification of Acute Opioid Use Events and the Quantification of Opioid Withdrawal in Opioid Dependent Individuals
NCT07405398 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 420
Last updated 2026-02-20
Summary
To train a machine learning model/algorithm for an evaluation of the use of biometric data captured at the wrist for the identification of acute opioid use events and the quantification of opioid withdrawal in opioid dependent individuals.
Conditions
- Treatment for Opioid Use Disorder
Interventions
- DEVICE
-
Train and evaluate the accuracy and reliability of the Strength Band Platform in identifying acute opioid dosing events from time-stamped biometric data collected from wrist-worn devices.
Subjects will be fitted with the wearable device (Samsung Galaxy Watch) for the purpose of data communication and will be instructed to wear the device continuously, except when charging the watch, showering or any activity in which submersion in water is required. Participants will wear the device for 14 days. Study subjects will be responsible for: * Wearing the Samsung Galaxy watch daily except when charging the watch, showering or any activity in which submersion in water is required * Charging the Samsung Galaxy watch daily * Answering prompts on the Samsung Galaxy watch * Answering the daily SOWS questionnaire(s)
Sponsors & Collaborators
-
National Institute on Drug Abuse (NIDA)
collaborator NIH -
OpiAID
lead INDUSTRY
Principal Investigators
-
David MacQueen, PhD · OpiAID
Study Design
- Allocation
- NA
- Purpose
- SUPPORTIVE_CARE
- Masking
- NONE
- Model
- SINGLE_GROUP
Eligibility
- Min Age
- 22 Years
- Sex
- ALL
- Healthy Volunteers
- Yes
Timeline & Regulatory
- Start
- 2025-05-01
- Primary Completion
- 2026-12-31
- Completion
- 2027-03-31
- FDA Device
- Yes
Countries
- United States
Study Locations
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