Evaluation of a Digital Tool for Detecting Stress and Craving in SUD Recovery: an Observational Trial of Accuracy and Engagement

BackgroundDigital health interventions offer opportunities to expand access to substance use disorder (SUD) treatment, collect objective real-time data, and deliver just-in-time interventions: however implementation has been limited. RAE (Realize, Analyze, Engage) Health is a digital tool which uses continuous physiologic data to detect high risk behavioral states (stress and craving) during SUD recovery.MethodsThis was an observational study to evaluate the digital stress and craving detection during outpatient SUD treatment. Participants were asked to use the RAE Health app, wear a commercial-grade wrist sensor over a 30-day period. They were asked to self-report stress and craving, at which time were offered brief in-app de-escalation tools. Supervised machine learning algorithms were applied retrospectively to wearable sensor data obtained to develop group-based digital biomarkers for stress and craving. Engagement was assessed by number of days of utilization, and number of hours in a given day of connection.ResultsSixty percent of participants (N=30) completed the 30-day protocol. The model detected stress and craving correctly 76% and 69% of the time, respectively, but with false positive rates of 33% and 28% respectively. All models performed close to previously validated models from a research grade sensor. Participants used the app for a mean of 14.2 days (SD 10.1) and 11.7 hours per day (SD 8.2). Anxiety disorders were associated with higher mean hours per day connected, and return to drug use events were associated with lower mean hours per day connected.ConclusionsFuture work should explore the effect of similar digital health systems on treatment outcomes and the optimal dose of digital interventions needed to make a clinically significant impact.