Charting the COVID Long Haul Experience -- A Longitudinal Exploration of Symptoms, Activity, and Clinical Adherence
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914670427766784 |
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| author | Pater, Jessica Chopra, Shaan Zaccour, Juliette Carroll, Jeanne Nova, Fayika Farhat Toscos, Tammy Guha, Shion Chang, Fen Lei |
| author_facet | Pater, Jessica Chopra, Shaan Zaccour, Juliette Carroll, Jeanne Nova, Fayika Farhat Toscos, Tammy Guha, Shion Chang, Fen Lei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04937 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Charting the COVID Long Haul Experience -- A Longitudinal Exploration of Symptoms, Activity, and Clinical Adherence Pater, Jessica Chopra, Shaan Zaccour, Juliette Carroll, Jeanne Nova, Fayika Farhat Toscos, Tammy Guha, Shion Chang, Fen Lei Human-Computer Interaction Computers and Society K.4 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. |
| title | Charting the COVID Long Haul Experience -- A Longitudinal Exploration of Symptoms, Activity, and Clinical Adherence |
| topic | Human-Computer Interaction Computers and Society K.4 |
| url | https://arxiv.org/abs/2402.04937 |