LLMs exhibit authority inversion by prioritizing natural-language user claims over numerical sensor data in conflicts, diagnosed with new geometric metrics and mitigated via layer-level calibration.
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5 Pith papers cite this work. Polarity classification is still indexing.
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PULSE demonstrates that agentic LLM-based investigation of passive smartphone sensing data achieves balanced accuracies of 0.743 (with diary) and 0.713 (sensing-only) for predicting emotion regulation desire and intervention availability in 50 cancer survivors.
EmBot combines wearable-triggered stress detection with LLM conversational support and was probed via expert interviews to surface design considerations for daily stress management.
Mixed-methods research shows collective care practices are constrained by personal, relational, technological, and structural factors in existing PHI systems, leading to the CC-Proact operational map with three design levers and ten recommendations for collective health informatics.
PSI uses a shared personal-context bus to publish state and write-back affordances, turning isolated AI-generated modules into synchronized, chat-accessible instruments.
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Unpacking "Personal" Health Informatics for Proactive Collective Care
Mixed-methods research shows collective care practices are constrained by personal, relational, technological, and structural factors in existing PHI systems, leading to the CC-Proact operational map with three design levers and ten recommendations for collective health informatics.