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Gupta V, Kariotis S, Rajab MD, Errington N, Alhathli E, Jammeh E, Brook M, Meardon N, Collini P, Cole J, Wild JM, Hershman S, Javed A, Thompson AAR, de Silva T, Ashley EA, Wang D, Lawrie A. Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices. NPJ Digit Med 2023; 6:239. [PMID: 38135699 PMCID: PMC10746711 DOI: 10.1038/s41746-023-00974-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters. Unsupervised classification analysis of symptoms identified two trajectory patterns of long and short symptom duration. The prevalence for longitudinal persistence of any COVID-19 symptom was 36% with fatigue and loss of smell being the two most prevalent individual symptom trajectories (24.8% and 21.5%, respectively). 8 physical activity features obtained via the MyHeart Counts App identified two groups of trajectories for high and low activity. Of these 8 parameters only 'distance moved walking or running' was associated with COVID-19 symptom trajectories. We report a high prevalence of long-term symptoms of COVID-19 in a non-hospitalised cohort of HCWs, a method to identify physical activity trends, and investigate their association. These data highlight the importance of tracking symptoms from onset to recovery even in non-hospitalised COVID-19 individuals. The increasing ease in collecting real-world physical activity data non-invasively from wearable devices provides opportunity to investigate the association of physical activity to symptoms of COVID-19 and other cardio-respiratory diseases.
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Affiliation(s)
- Varsha Gupta
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Bioinformatics Institute, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Sokratis Kariotis
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Department of Neuroscience, University of Sheffield, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Mohammed D Rajab
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Niamh Errington
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Elham Alhathli
- Department of Neuroscience, University of Sheffield, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Department of Nursing, Faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Emmanuel Jammeh
- Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Martin Brook
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, UK
| | - Naomi Meardon
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Paul Collini
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Joby Cole
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Jim M Wild
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, UK
| | - Steven Hershman
- Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - Ali Javed
- Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - A A Roger Thompson
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Thushan de Silva
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Euan A Ashley
- Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - Dennis Wang
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Bioinformatics Institute, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Department of Computer Science, University of Sheffield, Sheffield, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Allan Lawrie
- National Heart and Lung Institute, Imperial College London, London, UK.
- Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, UK.
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