Betrayal!: Contending with Misalignments in Temporal Health Status Representations with Self-Tracked Data

Endometriosis is a complex and poorly understood chronic condition with variable symptoms. While many personal informatics tools support self-tracking, fewer leverage artificial intelligence (AI) to generate actionable insights for care. This research applied a digital phenotyping approach to characterize weekly health statuses using self-tracking data from the Phendo app — a research platform co-designed with endometriosis patients. Applying an unsupervised probabilistic mixed-membership model to week-level data, we generated temporal health status phenotypes that captured severity-based illness dynamics. We also created a rule-based phenotype to explore an alternate approach. We evaluated these phenotypes with a mixed-methods user study, which revealed complexities with the ability to align computational representations of health status with lived illness experiences as perceived by individuals. At the same time, we document the value of these representations, even when imperfect. We found significant mismatches between participants’ self-assessments and computational health status phenotypes, driven by personalized interpretations of health indicators, symptom under-reporting shaped by stigma, and the inherent complexity of characterizing subjective health experiences, which result in fluctuating perceptions of symptom severity. Future work will be needed to create mechanisms to better align computational representations of health status with human perceptions and values and individualized notions of health.