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

OpiAID420 enrolled

Overview

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.

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 an 80% 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.

Study Type

INTERVENTIONAL

Allocation

Purpose

SUPPORTIVE_CARE

Masking

NONE

Enrollment

420

Conditions

Treatment for Opioid Use Disorder

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)

Eligibility

Sex: ALLMin age: 22 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: * Male or female * Age ≥22 years at signing of informed consent * Patients with a DSM-5 diagnosis of OUD who are eligible for MOUD induction with methadone or buprenorphine Exclusion Criteria: * Sleeve tattoo covering the wrist * Subject unable to independently navigate and operate smartwatch applications * Subject not proficient with written and spoken English * Subject determined likely to be non-compliant by physician/HCP * Subject likely to not be available to complete all protocol-required study visits or procedures, and/or to comply with all required study procedures to the best of the subject and investigator's knowledge. * History or evidence of any other clinically significant disorder, condition, or disease that, in the opinion of the investigator, would pose a risk to subject safety or interfere with the study evaluation, procedures or completion. * Subject has diminished decision making capability

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

Overview

Conditions

Interventions

Eligibility

Locations (1)

Outcomes

Primary Outcomes

Central Contacts