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Predicting Craving-Related Emotions among Opioid Use Disorder Patients: Preliminary Results
Zachary King1, Zoe Setiadi2, Liana Hamdan3
1Department of Electrical and Computer Engineering, Rice University, Houston, TX.
... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks
|June 8, 2026
Summary
Mobile health (mHealth) can predict emotional states linked to opioid cravings in patients undergoing medication-assisted treatment (MAT). This technology offers scalable support for maintaining sobriety in Opioid Use Disorder (OUD).
Area of Science:
- Digital Health
- Addiction Medicine
- Machine Learning in Healthcare
Background:
- Opioid Use Disorder (OUD) presents significant challenges to sustained sobriety, with high relapse rates in the first year.
- Medication-assisted treatment (MAT) is effective but faces accessibility issues due to financial barriers.
- Mobile health (mHealth) offers a potential solution for continuous monitoring and timely intervention.
Purpose of the Study:
- To investigate the feasibility of using mHealth data to predict emotions associated with cravings in OUD patients.
- To assess the utility of ecological momentary assessments (EMAs), wearable sensors, and smartphone data for this prediction.
Main Methods:
- Collected data from EMAs, wearable sensors, smartphone tracking, and patient surveys.
- Utilized machine learning models to analyze the collected mHealth data.
- Focused on predicting emotional states that correlate with opioid cravings.
Main Results:
- Machine learning models accurately predicted emotional states linked to cravings in OUD patients.
- mHealth data proved effective in identifying patterns associated with relapse risk.
Conclusions:
- mHealth systems show significant potential for supporting individuals with Opioid Use Disorder.
- This approach enables timely, scalable interventions to aid in maintaining sobriety.
- Predictive modeling of emotional states can enhance OUD treatment adherence and outcomes.
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