Barriers to Designing Inclusive Ecological Momentary Assessment and Wearable Data Collection Protocols for AI-Driven Substance Use Monitoring in Hawai‘i
Year: 2025
Type: conference-paper
Study design: Observational Study
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.
Institutions: University of Hawaiʻi at Mānoa, University of Hawaii System, George Mason University, The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, The Ohio State University Wexner Medical Center, The Ohio State University, Kaiser Permanente, University of California, San Francisco
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4417312699
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