Charting the COVID Long Haul Experience -- A Longitudinal Exploration of Symptoms, Activity, and Clinical Adherence

February 07, 2024 Β· Declared Dead Β· πŸ› International Conference on Human Factors in Computing Systems

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Authors Jessica Pater, Shaan Chopra, Juliette Zaccour, Jeanne Carroll, Fayika Farhat Nova, Tammy Toscos, Shion Guha, Fen Lei Chang arXiv ID 2402.04937 Category cs.HC: Human-Computer Interaction Cross-listed cs.CY Citations 6 Venue International Conference on Human Factors in Computing Systems Last Checked 4 months ago
Abstract
COVID Long Haul (CLH) is an emerging chronic illness with varied patient experiences. Our understanding of CLH is often limited to data from electronic health records (EHRs), such as diagnoses or problem lists, which do not capture the volatility and severity of symptoms or their impact. To better understand the unique presentation of CLH, we conducted a 3-month long cohort study with 14 CLH patients, collecting objective (EHR, daily Fitbit logs) and subjective (weekly surveys, interviews) data. Our findings reveal a complex presentation of symptoms, associated uncertainty, and the ensuing impact CLH has on patients' personal and professional lives. We identify patient needs, practices, and challenges around adhering to clinical recommendations, engaging with health data, and establishing "new normals" post COVID. We reflect on the potential found at the intersection of these various data streams and the persuasive heuristics possible when designing for this new population and their specific needs.
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