Event-Aligned Analysis of Multi-Rater Pain Assessments Using Continuous Wearable Physiology
For researchers in pain assessment and wearable health, this work highlights the problem of ignoring rater identity in pain analysis, but the findings are exploratory and incremental.
The paper introduces a rater-aware, event-aligned framework for analyzing pain assessments from continuous wearable physiology, revealing substantial disagreement across patient, nurse, and clinician raters and preliminary evidence of rater-dependent physiological differences before pain increases.
Pain is assessed differently by patients, nurses, and clinicians, yet most computational approaches assume a single ground-truth label - effectively ignoring who is doing the rating. We introduce a rater-aware, event-aligned framework that converts sparse, rater-specific pain ratings into discrete pain-change events and aligns continuous wearable physiological signals to these events, preserving rater identity throughout. Applied to multimodal wearable data collected during spine-related pain procedures, the framework identifies substantial disagreement across rater groups and provides preliminary, exploratory evidence of rater-dependent physiological differences preceding reported pain increases. These findings suggest that pain-physiology relationships may not be rater-invariant, and that aggregating assessments across raters may mask meaningful physiological patterns. A rater-aware, event-aligned perspective is therefore a promising direction for interpreting wearable data in real-world clinical pain assessment.