NCT05976672

Machine Learning Technology in Predicting Relapse and Implementing Peer Recovery Intervention Before Drug Use Occurs

The Utilization of Machine Learning Technologies in Predicting Relapse: Identifying Risk Factors and Implementing Intervention Before Drug Use Occurs

• Status: Active Not Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 500 (estimated)
• Sponsor: West Virginia University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

The goal of this clinical trial is to study the relationship between substance cravings, cognitive performance, behaviors, and physiological markers in individuals with substance use disorder, as well as the effects of peer recovery intervention in response to abnormal biomarker data detected by wearable technology (e.g., Oura ring, smart watch) and participant responses to questionnaires and cognitive tasks completed on the RNI Health application.

Detailed description

The purpose of this study is to examine the relationship between cravings, cognitive performance, behaviors, and physiological markers in individuals with substance use disorder as well as the effects of peer-recovery intervention in response to biomarker data anomalies via wearable technology (e.g., Oura ring), and participant responses to questionnaires and cognitive tasks via the RNI Health application. All participants will initially be monitored for 3 months before being randomized to one of the following arms: 1) Treatment as usual; 2) PRSS (Peer Recovery Support) intervention. Participants will be randomized in a 1:1 ratio to receive either standard-of-care treatment (treatment as usual), or PRSS intervention. Participants will be asked to continuously wear a wearable device that measures heart rate, sleep, and physical activity for up to 5 years. Participants will also be asked to complete questionnaires about health, thinking and emotions, past experiences, and social background, as well as completing cognitive and physiological tasks when indicated. Questionnaires will be completed via the WVU RNI Health app on a smart device. Participant data will be analyzed through machine learning algorithms and standard statistical analyses. The researchers plan to identify abnormalities in participant data such as physiological biomarkers, cognitive performance, behaviors, and level of cravings associated with the increased risk for relapse and related mood conditions, in which participants may be contacted by a Peer Recovery Support Specialist (PRSS) to assess any participant needs, such as linkage or referral to resources (treatment options, housing resources, etc.) based on their standard of care. The objectives to the research are to develop machine-learning algorithms to predict risk for drug use recurrence, to develop a predictive model that may help determine prognosis and improve treatment planning based on physiological, cognitive, and behavioral response patterns, evaluate the efficacy of Peer Recovery Support Specialist interventions in preventing drug use recurrence, and use the data to better understand how wearable technology can help improve treatment plans.

Conditions

• Substance Abuse Disorder

Interventions

Type	Name	Description
OTHER	PRSS (Peer Recovery Support Specialist)	Upon receiving an alert through a study dashboard, the PRSS (who is blinded in not knowing whether the alert was caused by data anomaly or a random generation), the PRSS will be able to access identifiable contact details and contact the participant by phone and provide the necessary support assistance (e.g., locations of AA/NA meetings, sleep and/or relaxation techniques, etc).

Timeline

• Start date: 2023-04-27
• Primary completion: 2028-04-26
• Completion: 2028-04-26
• First posted: 2023-08-04
• Last updated: 2025-03-24

Locations

1 site across 1 country: United States