Uichin Lee

Digital technologies now mediate much of health and care work, but their benefits are unevenly distributed, and research documenting this reaches practice slowly. One reason is that HCI and health evaluate work differently, so papers at their intersection are judged by whichever criteria a reviewer brings from their home community. In this introduction to the ACM HEALTH Continuous Special Section on Human-Centered Computing in Healthcare, we state the criteria we used and where they came from: which people a paper accounts for, whether the health context changes what the paper contributes to computing, whether its methods and any theory suit its question, and what its findings change for design, adoption, or governance. We introduce the 18 papers in this first installment.

This issue includes 121 articles, continuing the growing number of outstanding contributions to PACM IMWUT. As in previous issues, the published articles span several key areas of interest to the IMWUT community.

Mobile sensing systems for mental health leverage ubiquitous behavioral data to monitor states like stress and anxiety, enabling just-in-time interventions and context-aware care. However, the real-world scalability of these systems is severely limited by poor cross-user generalization—a challenge often attributed to interpersonal differences but rarely quantified systematically. We present CrossShift, a structured empirical framework for decomposing and measuring user-level covariate, label, conditional, and concept shifts in mobile mental-health sensing. CrossShift unifies shift testing, shift quantification, model evaluation, and shift-to-generalization attribution within a common analytical framework tailored to this domain. We evaluate our framework across eight diverse datasets, comprising three newly collected and five public mobile sensing repositories. Our analysis reveals that phone usage, sleep and social-interaction features are the most consistent sources of covariate shift across users. Crucially, we find that concept shift—the variation in the mapping from behavior to mental state—is the primary driver of performance degradation. Our findings suggest that scalable mobile mental health sensing requires moving beyond one-size-fits-all models toward shift-aware system design. We further discuss how CrossShift can support out-of-distribution user detection and selective personalization in real-world deployments.

The rise of IoT devices in domestic and shared home environments produces interpersonal data that implicates multiple family members, giving rise to privacy concerns that individual control alone cannot adequately address. We conducted vignette-based interviews with 10 families (N = 34) to examine how household members experience and negotiate sensing technologies in everyday family life. Participants understood family data not merely as aggregated personal data, but as a socially embedded and relationally meaningful form shaped by shared routines, roles, and caregiving practices. We present OurData as an analytical lens that emerged from these findings to capture how sensing data in family contexts is collectively produced, interpreted through relationships, and ethically implicated across household members. We further offer design guidance for ubiquitous sensing systems that support family privacy through relational accountability, interpersonal awareness, and context-sensitive negotiation of shared data.

This issue includes an outstanding collection of manuscripts spanning several key areas of interest to the IMWUT community.

Sensing-based stress management systems integrate self-report, physiological, and contextual data to assess users' perceived stress and physiological indicators related to stress responses and deliver just-in-time interventions. Prior work has largely focused on improving detection accuracy or evaluating intervention effectiveness, with relatively limited attention to how users interpret and make sense of these data in everyday work contexts. Our study addresses this gap by examining how emotional labor workers construct meaning around alignment and divergence between perceived stress and physiological responses to stress. We present a four-week in-the-wild study with 19 call center workers, combining mobile and wearable sensing, a reflection dashboard, and in-depth interviews. Our findings show that participants interpreted stress indicators through work context, bodily conditions, and prior experiences, and treated divergence as an informative cue rather than a simple error. They also recognized that perceived and physiological responses to stress could change differently before and after micro-interventions. Based on these findings, we discuss design implications for sensing-based stress management systems that support stress data literacy and flexible, context-grounded stress sensemaking in practice.

The initial psychiatric interview centers on patients’ chief complaints, symptoms, and functional impairments, forming the basis of diagnostic impressions. In real clinical practice, however, interviews are constrained by limited time and the unpredictability of patient responses, making it difficult to secure essential information efficiently. While prior conversational agents have focused on conversationalizing validated instruments or advancing interview systems in general medical domains, little research has addressed the distinctive challenges of initial psychiatric history-taking from clinicians’ perspective. We present a flexible psychiatric interviewer that dynamically adapts question flow and prioritizes clinically essential information within time constraints, with a clinical dashboard for efficient review. We evaluated the system through 1,440 simulated patient dialogues and follow-up interviews with 19 clinicians. Results show that it captures essential information within a limited time while preserving conversational flexibility and empathy, highlighting design implications for coachable and responsible AI interviewers that align with clinical practice.