Barriers to Designing Inclusive Ecological Momentary Assessment and Wearable Data Collection Protocols for AI-Driven Substance Use Monitoring in Hawai'i.

Sun Y(1), Jaiswal A(2), Kargarandehkordi A(3), Slade C(4), Benzo RM(5), Phillips KT(6), Washington P(7).

Author information:
(1)The Information Systems and Technology (IST) Department, College of
Engineering and Computing, George Mason University, Fairfax, VA 22030, United
States2Department of Information and Computer Science, University of Hawaii,
Honolulu, HI 96822, United States, sunyinan@hawaii.edu.
(2)Department of Information and Computer Science, University of Hawaii,
Honolulu, HI 96822, United States, ajaiswal@hawaii.edu.
(3)Department of Information and Computer Science, University of Hawaii,
Honolulu, HI 96822, United States, kargaran@hawaii.edu.
(4)Department of Information and Computer Science, University of Hawaii,
Honolulu, HI 96822, United States, cslade@hawaii.edu.
(5)Division of Cancer Prevention and Control, Department of Internal Medicine,
College of Medicine, The Ohio State University Comprehensive Cancer Center, The
Ohio State University Wexner Medical Center, Columbus, OH 43210, United States,
roberto.benzo@osumc.edu.
(6)Center for Integrated Health Care Research, Kaiser Permanente Hawaii,
Honolulu, HI, United States, kristina.t.phillips@kp.org.
(7)Division of Clinical Informatics and Digital Transformation, Department of
Medicine, University of California, San Francisco, CA, United States,
peter.washington@ucsf.edu.

Pac Symp Biocomput. 2026;31:566-579

© 2026 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.

Abstract

Ecological momentary assessment (EMA) and wearable sensors offer unprecedented opportunities to capture the dynamics of substance use through real-time, high-resolution behavioral and physiological data. These data streams are increasingly used to train AI/ML models for digital phenotyping and predictive intervention, raising critical questions about fairness, bias, and inclusivity in model development. However, the adoption of these technologies, or the lack thereof, among diverse and historically marginalized groups raises questions and challenges of equity, cultural relevance, and participant trust. In this study, we conducted a four-week observational study with adults in Hawai.i where we combined continuous Fitbit monitoring and daily EMA surveys to document substance use patterns and cravings. Through semi-structured interviews and grounded theory analysis, we identified six primary barriers to study participation and adherence: (1) disruptions to daily routines, (2) physical and psychosocial discomfort associated with wearing the Fitbit device, (3) concerns about aesthetic compatibility and professional appearance, (4) phonerelated issues, (5) challenges related to substance use and cravings, and (6) socially sensitive contexts. We also highlight participant-identified facilitators, such as the value of participant-driven scheduling, motivational feedback, and contextually adaptive protocols. Drawing on these collective findings, we propose a set of design guidelines aimed at advancing the inclusivity, engagement, and fairness of wearable-based EMA research.