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

Conditions

• Treatment for Opioid Use Disorder

Eligibility Criteria

Sex

ALL

Age

22 Years - N/A

Healthy Volunteers

Accepted

Interventions

• 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. — DEVICE
  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)

Study Details

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.

Key Dates

Start date

May 1, 2025

Status verified

Feb 2026

Primary completion

Dec 31, 2026

Completion

Mar 31, 2027

Study Design

Enrollment

420 participants (estimated)

Allocation

NA

Intervention model

SINGLE_GROUP

Primary purpose

SUPPORTIVE_CARE

Arms

• Experimental: Single arm 14 day monitoring period
  The goal of this real-world, multi-center, outpatient study is to train a machine learning model/algorithm utilizing patient-specific physiological parameters from the OpiAID Strength Band Platform™ can accurately detect MOUD events during the induction phase with a predefined classification success when comparing the True Positive Rate against the False Positive Rate as plotted on a Receiver Operator Curve. In addition to MOUD detection, machine learning will be used to quantify participant withdrawal level from physiological parameters. To demonstrate that withdrawal quantification performs as well or better than current measures used for this purpose the correlation between quantified withdrawal and time since last opioid dose (TSLD) will be computed and compared against the association between SOWS and TSLD in a non-inferiority analysis. Prescribing physician must determine appropriate starting dose (titration expected over 2-6 weeks)

Primary Outcome Measure

Classification [ Time Frame: 14 days ]

Central Contacts

• Trace Brookins
  919.355.8221
  [email protected]
• David Reeser
  484.824.2248
  [email protected]

Locations (4)

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