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.

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