Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices

Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices

npj Digital Medicine

10.1038/s41746-023-00974-w

http://dx.doi.org/10.1038/s41746-023-00974-w

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

Computer Science Applications, Health Informatics, Medicine (miscellaneous), Health Information Management

22 December 2023

Gupta, V., Kariotis, S., Rajab, M. D., Errington, N., Alhathli, E., Jammeh, E., Brook, M., Meardon, N., Collini, P., Cole, J., Wild, J. M., Hershman, S., Javed, A., Thompson, A. A. R., de Silva, T., Ashley, E. A., Wang, D., & Lawrie, A. (2023). Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices. Npj Digital Medicine, 6(1). https://doi.org/10.1038/s41746-023-00974-w

AbstractPrevious 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.

Attribution 4.0 International (CC BY 4.0)

There was no specific funding for the research done

https://oar.a-star.edu.sg/communities-collections/articles/19576

2398-6352

Singapore Institute for Clinical Sciences

https://doi.org/10.1038/s41746-023-00974-w