Predicting Craving-Related Emotions among Opioid Use Disorder Patients: Preliminary Results

Zachary King, Zoe Setiadi, Liana Hamdan, Hajar Ahmed, Bishal Lamichhane, Ashutosh Sabharwal, Ramiro Salas, Nidal Moukaddam, Akane Sano

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Keywords: Opioid Use Disorder, Machine Learning, Digital Health

TL;DR: Predicting emotions associated with drug cravings 90 minutes in advance using sensor data from patients with Opioid Use Disorder.

Abstract: Individuals with Opioid Use Disorder (OUD) often struggle to maintain sobriety, with many experiencing relapse within the first year. While medication-assisted treatment (MAT) is among the most effective approaches, access to intensive care is often limited by financial barriers. Mobile health (mHealth) technologies offer a promising, cost-effective alternative by enabling continuous monitoring and timely intervention through tools such as ecological momentary assessments (EMAs), wearable sensors, and smartphone data. In this study, we explore the feasibility of using mHealth data to predict emotions that align with cravings in OUD patients undergoing MAT. Using data collected from EMAs, wearables, smartphone tracking, and surveys, we demonstrate that machine learning models can accurately predict emotional states associated with cravings. These findings highlight the potential of mHealth systems to support individuals with OUD through timely and scalable interventions.

Track: 1. Digital Health Solutions (i.e. sensors and algorithms) for diagnosis, progress, and self-management

NominateReviewer: Zachary King ([e-mail])

Submission Number: 140