Medicine and Health

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

V. Gupta, S. Kariotis, et al.

Explore the intriguing findings of a study by Varsha Gupta and colleagues that delves into the ongoing impact of COVID-19 on healthcare workers. The research highlights the persistent symptoms like fatigue and loss of smell, alongside the role of physical activity in understanding these symptoms over time, leveraging innovative wearable technology. Discover how these insights could transform monitoring health outcomes in HCWs!

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Playback language: English

Index

Abstract

This study investigated longitudinal trajectories of COVID-19 symptoms and physical activity in healthcare workers (HCWs) using the MyHeart Counts App and smart devices. Unsupervised classification analysis identified two symptom duration patterns (long and short). Fatigue and loss of smell were the most prevalent long-term symptoms (24.8% and 21.5%, respectively). Physical activity analysis revealed high and low activity groups, with only 'distance moved walking or running' associated with symptom trajectories. The study highlights the prevalence of long-term COVID-19 symptoms and the potential of wearable devices for monitoring symptom-activity associations.

Publisher

npj Digital Medicine

Published On

Dec 22, 2023

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

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