TY - GEN
T1 - Predicting Craving-Related Emotions among Opioid Use Disorder Patients
T2 - 2025 IEEE 21st International Conference on Body Sensor Networks, IEEE BSN 2025
AU - King, Zachary
AU - Setiadi, Zoe
AU - Hamdan, Liana
AU - Ahmed, Hajar
AU - Lamichhane, Bishal
AU - Sabharwal, Ashutosh
AU - Salas, Ramiro
AU - Moukaddam, Nidal
AU - Sano, Akane
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105033325033
UR - https://www.scopus.com/inward/citedby.url?scp=105033325033&partnerID=8YFLogxK
U2 - 10.1109/BSN66969.2025.11337847
DO - 10.1109/BSN66969.2025.11337847
M3 - Conference contribution
AN - SCOPUS:105033325033
T3 - 2025 IEEE 21st International Conference on Body Sensor Networks, IEEE BSN 2025
BT - 2025 IEEE 21st International Conference on Body Sensor Networks, IEEE BSN 2025
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 November 2025 through 5 November 2025
ER -