Opioid overdose rates have risen six-fold since the late 1990s and presently claim the lives of over 130 Americans each day. Amongst treatment options for opioid dependence, medication-assisted treatment (MAT) with opioid substitutes, such as methadone or buprenorphine maintenance, are the only treatments for which effectiveness has been confirmed by empirical study. While methadone maintenance is effective for reducing mortality over the course of treatment, individuals remain at significant risk of overdose death during the initial phase of treatment (methadone induction). On the other hand, although buprenorphine has lower risk of mortality, it is associated with greater risk of withdrawal and relapse. Therefore, patient monitoring is critical for effective MAT, improved treatment retention and reduced likelihood of overdose death. Yet, MAT is most commonly administered on an outpatient basis, limiting the time periods during which the patient can be actively assessed. OpiAID is developing the Strength Band Platform, a wearable device featuring a multi-sensor array and artificial intelligence to facilitate patient monitoring during MAT for opioid use disorder. This device will provide an expanded range of biometric variables enabling prediction of opioid use and withdrawal in real world settings. Real-time monitoring of patients will allow appropriate and timely interventions, directly improving the outcomes for the patients undergoing the treatment. There are currently no FDA approved, commercially available, wearable products for monitoring opioid use in real world settings. This Phase I SBIR program consists of two Specific Aims designed to develop the Strength Band Platform using a commercially available clinically validated armband. The first Specific Aim is to collect biometric variables from patients and develop prediction algorithms through supervised learning. In the second Specific Aim, barriers to end-user adoption, compliance and social stigma will be identified through focus groups and solutions based on the feedback will be incorporated into development frameworks. Completion of this Phase I project will produce a proof-of-concept platform which will undergo further development and evaluation for commercialization. Phase II research will focus on developing fully functional user and practitioner interfaces and completing multi-region clinical trials to demonstrate efficacy and effectiveness of the platform for improving patient outcomes. OpiAID will leverage extensive partnerships in the opioid dependence treatment community in Wilmington, NC to evaluate performance of the device and complete the clinical trials. Ultimately, identifying periods of increased risk of relapse will allow provision of just-in-time interventions.
Public Health Relevance Statement: PROJECT NARRATIVE To address the opioid crisis, OpiAID is developing a wearable biometric device platform that will predict use and withdrawal in patients undergoing medication-assisted therapy for opioid use disorder. Using a wearable device for real-time monitoring with predictive capability will allow timely interventions resulting in fewer deaths and relapses.
Project Terms: Abstinence; Accelerometer; Acute; Address; Adoption; Algorithms; Ambulatory Care; American; arm; Artificial Intelligence; base; Biometry; Buprenorphine; Cessation of life; Clinic; Clinical; Clinical Trials; Collection; commercialization; Communities; Computer software; contingency management; craving; Custom; Data; design; Development; Devices; Dose; Drops; Drug usage; Effectiveness; Evaluation; experience; Face; FDA approved; Feedback; flexibility; Focus Groups; Frequencies; Guns; high risk; Hour; illicit opioid; improved; improved outcome; Individual; Intervention; Machine Learning; Maintenance; medication-assisted treatment; Methadone; methadone treatment; Monitor; monitoring device; mortality; mortality risk; Opiate Addiction; Opioid; opioid abuse; opioid epidemic; opioid overdose; Opioid replacement therapy; Opioid Rotation; opioid therapy; opioid use; opioid use disorder; opioid withdrawal; Outpatients; overdose death; overdose risk; Patient Monitoring; Patient Self-Report; Patient-Focused Outcomes; Patients; Performance; Pharmaceutical Preparations; Phase; Photoplethysmography; Physicians; Physiology; prediction algorithm; prescription opioid; Process; programs; real time monitoring; Regimen; Relapse; relapse risk; Research; response; Risk; Safety; Sampling; sensor; Signal Transduction; Small Business Innovation Research Grant; social stigma; supervised learning; Technology; Testing; Time; Training; wearable device; Withdrawal
