Machine Learning for Opioid Addiction
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial aims to enhance how technology assists individuals with opioid addiction. It employs a special wristband, the Strength Band Platform, to monitor body signals and detect opioid use or withdrawal. The goal is to determine if this technology can track these events as accurately as, or better than, traditional methods. Individuals diagnosed with opioid use disorder (OUD) and beginning treatment with medications like methadone or buprenorphine may be suitable candidates. As an unphased trial, this study provides a unique opportunity to contribute to innovative technology that could transform addiction treatment.
Will I have to stop taking my current medications?
The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.
What prior data suggests that the Strength Band Platform is safe for use in monitoring opioid use and withdrawal?
Research shows that the Strength Band Platform monitors and detects opioid use by tracking changes in the body. Early results suggest it uses sensors to collect data safely and effectively. No direct evidence indicates that these wearable devices cause harm. Similar technology has tracked other health conditions and is generally well-tolerated. Since this trial does not involve a new drug, the risks mainly relate to wearing the device, which are usually minimal.
For those considering participation, available data indicates that the platform is safe to use. It's non-invasive, meaning it doesn't enter the body, so serious side effects are unlikely. However, discussing any concerns with the trial team or a healthcare professional is always advisable.12345Why are researchers excited about this trial?
Researchers are excited about the Strength Band Platform for opioid addiction because it uses machine learning to offer a more personalized approach to treatment. Unlike traditional methods that often rely on self-reported symptoms, this platform can detect medication use and quantify withdrawal symptoms through real-time physiological data. This could lead to more accurate and timely interventions, potentially improving outcomes for patients by tailoring care to their specific needs.
What evidence suggests that the Strength Band Platform is effective for identifying opioid use events and quantifying withdrawal?
In this trial, participants will use the Strength Band Platform, which previous studies have shown to be promising for identifying opioid use and withdrawal through wrist-collected data. This technology uses digital markers, such as heart rate, to detect opioid use and assess withdrawal severity. Research suggests it accurately identifies these events by analyzing data patterns. The platform aims to match or exceed the effectiveness of current methods for monitoring opioid withdrawal, potentially enhancing how healthcare providers support individuals with opioid addiction.678910
Who Is on the Research Team?
David MacQueen, PhD
Principal Investigator
OpiAID
Are You a Good Fit for This Trial?
This trial is for adults over 22 with opioid use disorder (OUD) who can consent and are starting treatment with methadone or buprenorphine. It's not for those with decision-making impairments, non-English speakers, people unlikely to follow the study plan, those with wrist tattoos that could interfere with data collection, or anyone with conditions that might risk their safety in the study.Inclusion Criteria
Exclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Induction and Monitoring
Participants are monitored using the OpiAID Strength Band Platform™ to detect MOUD events and quantify withdrawal levels
Titration
Prescribing physician determines appropriate starting dose with expected titration over 2-6 weeks
Follow-up
Participants are monitored for safety and effectiveness after the monitoring period
What Are the Treatments Tested in This Trial?
Interventions
- Strength Band Platform
Trial Overview
The trial aims to train a machine learning algorithm using biometric data from wrist-worn devices to detect when someone has taken opioids and measure withdrawal symptoms in individuals dependent on opioids.
How Is the Trial Designed?
1
Treatment groups
Experimental Treatment
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)
Find a Clinic Near You
Who Is Running the Clinical Trial?
OpiAID
Lead Sponsor
National Institute on Drug Abuse (NIDA)
Collaborator
Citations
A Study to Train a Machine Learning Algorithm for an ...
Device : Train and evaluate the accuracy and reliability of the Strength Band Platform in identifying acute opioid dosing events from time- ...
Digital biomarker applications across the spectrum of opioid ...
This review explores the current evidence (and potential) for digital biomarkers monitoring across the spectrum of opioid use.
Development of the OpiAID strength band platform
Successful completion of this project will confirm the utility of OpiAid's Strength Band Platform for detecting relapse and withdrawal, and for enhancing ...
Users' Acceptability and Perceived Efficacy of mHealth for ...
This study aims to synthesize qualitative insights into opioid users' acceptability and perceived efficacy of mHealth and wearable technologies for opioid use ...
Using Data Science to Improve Outcomes for Persons ... - PMC
MOUD reduces illicit opioid use and associated mortality. Even if patients receive MOUD, 40%-55% of persons discontinue MOUD within a year after initiation, and ...
RePORT ⟩ RePORTER
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OpiTrack: A Wearable-based Clinical Opioid Use Tracker with ...
A mobile technology system that can identify high-risk opioid-related events (ie, development of tolerance in the setting of prescription opioid use)
AI-powered arm band to detect opioid use disorder ...
The upper-arm device will use an artificial intelligence-assisted sensor system that will continuously monitor physiologic changes and data to accurately ...
Digital biomarker applications across the spectrum of ...
This review explores the current evidence (and potential) for digital biomarkers monitoring across the spectrum of opioid use.
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