Seamful Design Considerations for Human-in-the-Loop Digital Phenotyping of Mental Health
Abstract
Digital Phenotyping of Mental Health (DPMH) through passive sensing is a promising approach for personal health informatics and digital wellbeing. Its appeal lies in unobtrusiveness, making it appear seamless. However, this very quality leads users to find it impersonal, untrustworthy, and disengaging. To counteract challenges of seamlessness, researchers propose seamful design to deliberately engage users. Yet, it remains unclear how this principle can be incorporated into digital phenotyping. To address this, we conducted a formative study by developing DYMOND. It is a technology probe that estimates depression, explains estimates, reveals discrepancies, and provides user control over the underlying model. In a 6-week deployment, 22 individuals with moderate-severe depression monitored their state with DYMOND. They interviewed every two weeks with researchers to collaboratively reconfigure the model and co-design new interfaces. Our analysis of 57 sessions revealed (i) seams—friction points—across data, modeling, and output, and (ii) design requirements helping users evaluate and mitigate seams. These findings inform the design requirements for human-in-the-loop DPMH to support agency, transparency, and collaborative reflection. This study provides insight into theoretical re-conceptualization for passive sensing, opportunities to integrate large language models and human-AI interaction for better interfaces for digital mental health, and pathways to involve expert stakeholders in DPMH.
