Treatment for Opioid Use DisorderNCT07405398
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
A study testing 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. in people with Treatment for Opioid Use Disorder, run by OpiAID at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 22 years and olderHealthy volunteers can take part
- Conditions the study is for
- Treatment for Opioid Use Disorder
- What is being tested?
- 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.
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 420 participants
- Where
- 1 siteUnited States
- Study dates
- 1 May 2025to 31 Mar 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 22 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Coastal Horizon
Wilmington, North Carolina, 28409
David MacQueen, Ph.D, 910.262.6661, macqueend@uncw.edu
Rebecca Wlasiuj, rebecca@opiaid.tech
About this study
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.
Read the 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.
- Conditions
- Treatment for Opioid Use Disorder
- Being tested
- 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.
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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 group | Role | What they receive |
|---|---|---|
| Single arm 14 day monitoring period | Experimental | 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) Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Classification
Accurate algorithm-based classification of acute opioid dosing events in patients receiving treatment for opioid use disorder.
Measured: 14 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Feb 2026
- Study start1 May 2025
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Treatment for Opioid Use DisorderStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 420 participants (estimated)
Sponsor and investigators
- Lead sponsor
- OpiAID
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- National Institute on Drug Abuse (NIDA)
- Responsible party
- Sponsor
- Investigators
- David MacQueen, PhD, Study chair, OpiAID
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07405398
- Sponsor's protocol ID
- OA-SBIR-25-01
- Other IDs
- 4R44DA058474-02
- Status last verified
- Sep 2026
- First submitted
- 5 Feb 2026
- Last updated
- 15 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 15 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07405398
- Acronym / sponsor ID
- OA-SBIR-25-01
- Conditions
- Treatment for Opioid Use Disorder
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 420 participants (estimated)
- Lead sponsor
- OpiAIDIndustry (pharma, biotech, devices)
- Collaborators
- National Institute on Drug Abuse (NIDA)
- Study start
- 1 May 2025 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Mar 2027 (estimated)
- First posted
- 12 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Supportive care
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Single arm 14 day monitoring period | Experimental | 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.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) |
Who may qualify
- Ages
- 22 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (7 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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) | Device | Single arm 14 day monitoring period |
Primary outcome
Classification
Time frame: 14 days
Accurate algorithm-based classification of acute opioid dosing events in patients receiving treatment for opioid use disorder.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Sep 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
David MacQueen, PhD study chair
OpiAID
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing 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. in people with Treatment for Opioid Use Disorder, run by OpiAID at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 15 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Trace Brookins (919.355.8221, trace@opiaid.tech), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Classification (time frame: 14 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Sep 2026.