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

要旨

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.

著者
Jessica Pater
Parkview Health, Fort Wayne, Indiana, United States
Shaan Chopra
University of Washington, Seattle, Washington, United States
Jeanne Carroll
Parkview Research Center, Fort Wayne, Indiana, United States
Juliette Zaccour
University of Toronto, Toronto, Ontario, Canada
Taha Liaqat
Simon Fraser University, Burnaby, British Columbia, Canada
Fayika Farhat Nova
Parkview Health, Fort Wayne, Indiana, United States
Tammy Toscos
Parkview Health, Fort Wayne, Indiana, United States
Shion Guha
University of Toronto, Toronto, Ontario, Canada
Fen Lei Chang
Parkview Health, Fort Wayne, Indiana, United States
論文URL

doi.org/10.1145/3613904.3642827

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Chronic Conditions B

316B
4 件の発表
2024-05-16 20:00:00
2024-05-16 21:20:00