Physiological presentation and risk factors of long COVID in the UK using smartphones and wearable devices: a longitudinal, citizen science, case–control study
{"title":"Physiological presentation and risk factors of long COVID in the UK using smartphones and wearable devices: a longitudinal, citizen science, case–control study","authors":"","doi":"10.1016/S2589-7500(24)00140-7","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>The emergence of long COVID as a COVID-19 sequela was largely syndromic in characterisation. Digital health technologies such as wearable devices open the possibility to study this condition with passive, objective data in addition to self-reported symptoms. We aimed to quantify the prevalence and severity of symptoms across collected mobile health metrics over 12 weeks following COVID-19 diagnosis and to identify risk factors for the development of post-COVID-19 condition (also known as long COVID).</p></div><div><h3>Methods</h3><p>The Covid Collab study was a longitudinal, self-enrolled, community, case–control study. We recruited participants from the UK through a smartphone app, media publications, and promotion within the Fitbit app between Aug 28, 2020, and May 31, 2021. Adults (aged ≥18 years) who reported a COVID-19 diagnosis with a positive antigen or PCR test before Feb 1, 2022, were eligible for inclusion. We compared a cohort of 1200 patients who tested positive for COVID-19 with a cohort of 3600 sex-matched and age-matched controls without a COVID-19 diagnosis. Participants could provide information on COVID-19 symptoms and mental health through self-reported questionnaires (active data) and commercial wearable fitness devices (passive data). Data were compared between cohorts at three periods following diagnosis: acute COVID-19 (0–4 weeks), ongoing COVID-19 (4–12 weeks), and post-COVID-19 (12–16 weeks). We assessed sociodemographic and mobile health risk factors for the development of long COVID (defined as either a persistent change in a physiological signal or self-reported symptoms for ≥12 weeks after COVID-19 diagnosis).</p></div><div><h3>Findings</h3><p>By Aug 1, 2022, 17 667 participants had enrolled into the study, of whom 1200 (6·8%) cases and 3600 (20·4%) controls were included in the analyses. Compared with baseline (65 beats per min), resting heart rate increased significantly during the acute (0·47 beats per min; odds ratio [OR] 1·06 [95% CI 1·03–1·09]; p<0·0001), ongoing (0·99 beats per min; 1·11 [1·08–1·14]; p<0·0001), and post-COVID-19 (0·52 beats per min; 1·04 [1·02–1·07]; p=0·0017) phases. An increased level of historical activity in the period from 24 months to 6 months preceding COVID-19 diagnosis was protective against long COVID (coefficient –0·017 [95% CI –0·030 to –0·003]; p=0·015). Depressive symptoms were persistently elevated following COVID-19 (OR 1·03 [95% CI 1·01–1·06]; p=0·0033) and were a potential risk factor for developing long COVID (1·14 [1·07–1·22]; p<0·0001).</p></div><div><h3>Interpretation</h3><p>Mobile health technologies and commercial wearable devices might prove to be a useful resource for tracking recovery from COVID-19 and the prevalence of its long-term sequelae, as well as representing an abundant source of historical data. Mental wellbeing can be impacted negatively for an extended period following COVID-19.</p></div><div><h3>Funding</h3><p>National Institute for Health and Care Research (NIHR), NIHR Maudsley Biomedical Research Centre, UK Research and Innovation, and Medical Research Council.</p></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001407/pdfft?md5=41fde3576005e91fa40bb70d2e644041&pid=1-s2.0-S2589750024001407-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lancet Digital Health","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589750024001407","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
Background
The emergence of long COVID as a COVID-19 sequela was largely syndromic in characterisation. Digital health technologies such as wearable devices open the possibility to study this condition with passive, objective data in addition to self-reported symptoms. We aimed to quantify the prevalence and severity of symptoms across collected mobile health metrics over 12 weeks following COVID-19 diagnosis and to identify risk factors for the development of post-COVID-19 condition (also known as long COVID).
Methods
The Covid Collab study was a longitudinal, self-enrolled, community, case–control study. We recruited participants from the UK through a smartphone app, media publications, and promotion within the Fitbit app between Aug 28, 2020, and May 31, 2021. Adults (aged ≥18 years) who reported a COVID-19 diagnosis with a positive antigen or PCR test before Feb 1, 2022, were eligible for inclusion. We compared a cohort of 1200 patients who tested positive for COVID-19 with a cohort of 3600 sex-matched and age-matched controls without a COVID-19 diagnosis. Participants could provide information on COVID-19 symptoms and mental health through self-reported questionnaires (active data) and commercial wearable fitness devices (passive data). Data were compared between cohorts at three periods following diagnosis: acute COVID-19 (0–4 weeks), ongoing COVID-19 (4–12 weeks), and post-COVID-19 (12–16 weeks). We assessed sociodemographic and mobile health risk factors for the development of long COVID (defined as either a persistent change in a physiological signal or self-reported symptoms for ≥12 weeks after COVID-19 diagnosis).
Findings
By Aug 1, 2022, 17 667 participants had enrolled into the study, of whom 1200 (6·8%) cases and 3600 (20·4%) controls were included in the analyses. Compared with baseline (65 beats per min), resting heart rate increased significantly during the acute (0·47 beats per min; odds ratio [OR] 1·06 [95% CI 1·03–1·09]; p<0·0001), ongoing (0·99 beats per min; 1·11 [1·08–1·14]; p<0·0001), and post-COVID-19 (0·52 beats per min; 1·04 [1·02–1·07]; p=0·0017) phases. An increased level of historical activity in the period from 24 months to 6 months preceding COVID-19 diagnosis was protective against long COVID (coefficient –0·017 [95% CI –0·030 to –0·003]; p=0·015). Depressive symptoms were persistently elevated following COVID-19 (OR 1·03 [95% CI 1·01–1·06]; p=0·0033) and were a potential risk factor for developing long COVID (1·14 [1·07–1·22]; p<0·0001).
Interpretation
Mobile health technologies and commercial wearable devices might prove to be a useful resource for tracking recovery from COVID-19 and the prevalence of its long-term sequelae, as well as representing an abundant source of historical data. Mental wellbeing can be impacted negatively for an extended period following COVID-19.
Funding
National Institute for Health and Care Research (NIHR), NIHR Maudsley Biomedical Research Centre, UK Research and Innovation, and Medical Research Council.
期刊介绍:
The Lancet Digital Health publishes important, innovative, and practice-changing research on any topic connected with digital technology in clinical medicine, public health, and global health.
The journal’s open access content crosses subject boundaries, building bridges between health professionals and researchers.By bringing together the most important advances in this multidisciplinary field,The Lancet Digital Health is the most prominent publishing venue in digital health.
We publish a range of content types including Articles,Review, Comment, and Correspondence, contributing to promoting digital technologies in health practice worldwide.