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Kefan Xu

Publications and source records attributed to Kefan Xu.

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Human-centered Perspectives on a Clinical Decision Support System for Intensive Outpatient Veteran PTSD Care

Psychotherapy delivery relies on a negotiation between patient self-reports and clinical intuition. Growing evidence for technological support of psychotherapy suggests opportunities to aid the mediation of this tension. To explore this prospect, we designed a prototype of a clinical decision support system (CDSS) for treating veterans with post-traumatic stress disorder in a Prolonged Exposure (PE) therapy intensive outpatient program. We conducted a two-phase interview study to collect perspectives from practicing PE clinicians and former PE patients who are United States veterans. Our analysis distills opportunities for a CDSS (e.g., offering homework review at a glance, aiding patient conceptualization) and larger challenges related to context and deployment (e.g., navigating Veterans Affairs). By reframing our findings through three human-centered perspectives (distributed cognition, situated learning, infrastructural inversion), we highlight the complexities of designing a CDSS for psychotherapists in this context and offer theory-aligned design considerations.

cs.HC

Facilitating Individuals' Sensemaking about Sedentary Behavior via Contextualized Data

The sedentary lifestyle increases individuals' risks of developing chronic diseases. To support individuals to be more physically active, we propose a mobile system, MotionShift, that presents users with step count data alongside contextual information (e.g., location, weather, calendar events, etc.) and self-reported records. By implementing and deploying this system, we aim to understand how contextual information impacts individuals' sense-making on sensor-captured data and how individuals leverage contextualized data to identify and reduce sedentary activities. The findings will advance the design of context-aware personal informatics systems, empowering users to derive actionable insights from sensor data while minimizing interpretation biases, ultimately promoting opportunities to be more physically active.

cs.HC