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

Adrienne Pichon, Lena Mamykina, Noemie Elhadad
In: ACM Transactions on Computing for Healthcare · 2026 · doi:10.1145/3802542 · W7148651773
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This study characterized temporal health status phenotypes for endometriosis using self-tracking data, finding significant mismatches between computational representations and patient perceptions due to personalized interpretations, stigma, and subjective experiences.

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This paper explores the concept of "betrayal" in the context of self-tracked health data, focusing on misalignments between temporal health status representations and actual patient experiences. It examines how discrepancies arise when digital health metrics fail to accurately reflect the nuanced, evolving nature of an individual's well-being over time. The study highlights the challenges patients face when reconciling their subjective health narratives with objective, quantified data points collected through wearable devices or apps. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

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.
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Index Terms - Betrayal!: Contending with Misalignments in Temporal Health Status Representations with Self-Tracked Data Recommendations Aggregate Health Status: A Benchmark Index for Community Health A qualitative review of population health assessment models used throughout the United States and Canada indicate both individual and community-level domains of health. Individual-level domains of health include health habits, education, public safety, ... Quality and Cost Improvement of Healthcare via Complementary Measurement and Diagnosis of Patient General Health Outcome Using Electronic Health Record Data: Research Rationale and Design In this evolving `third era of health', one of the US Health Care Reform Act's goals is to effectively facilitate the primary care physician's ability to better diagnose and manage the health outcome of the outpatient. That goal must include research on ... Supporting Collaborative Health Tracking in the Hospital: Patients' Perspectives CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing SystemsThe hospital setting creates a high-stakes environment where patients' lives depend on accurate tracking of health data. Despite recent work emphasizing the importance of patients' engagement in their own health care, less is known about how patients ... Comments Information & Contributors Information Published In Publisher Association for Computing Machinery New York, NY, United States Publication History Check for updates Author Tags Qualifiers - Research-article Contributors Other Metrics Bibliometrics & Citations Bibliometrics Article Metrics - 0Total Citations - 75Total Downloads - Downloads (Last 12 months)... - Downloads (Last 6 weeks) ...

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endometriosis

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