Bibliographic Details
| Title: |
Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices |
| Authors: |
Varsha Gupta, Sokratis Kariotis, Mohammed D. Rajab, Niamh Errington, Elham Alhathli, Emmanuel Jammeh, Martin Brook, Naomi Meardon, Paul Collini, Joby Cole, Jim M. Wild, Steven Hershman, Ali Javed, A. A. Roger Thompson, Thushan de Silva, Euan A. Ashley, Dennis Wang, Allan Lawrie |
| Source: |
npj Digital Medicine, Vol 6, Iss 1, Pp 1-11 (2023) |
| Publisher Information: |
Nature Portfolio, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Computer applications to medicine. Medical informatics |
| Subject Terms: |
Computer applications to medicine. Medical informatics, R858-859.7 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2398-6352 |
| Relation: |
https://doaj.org/toc/2398-6352 |
| DOI: |
10.1038/s41746-023-00974-w |
| Access URL: |
https://doaj.org/article/b4cfdc189a5a47bda93eae8d4dfd53a6 |
| Accession Number: |
edsdoj.b4cfdc189a5a47bda93eae8d4dfd53a6 |
| Database: |
Directory of Open Access Journals |
