Treatment for Opioid Use Disorder
Conditions
Brief 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.
Detailed description
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.
Interventions
DEVICETrain 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
OpiAID
National Institute on Drug Abuse (NIDA)
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SUPPORTIVE_CARE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
22 Years to No maximum
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Classification | 14 days | Accurate algorithm-based classification of acute opioid dosing events in patients receiving treatment for opioid use disorder. |
Countries
United States
Contacts
CONTACTTrace Brookins
CONTACTDavid Reeser
STUDY_CHAIRDavid MacQueen, PhD
OpiAID
Outcome results
None listed