Tracking Persistent Symptoms in Scotland (TraPSS): A Longitudinal Prospective Cohort Study of COVID-19 Recovery After Mild Acute Infection (preprint)

Background: COVID-19 disease results in disparate responses between individuals and has led to the emergence of Long-COVID, characterized by persistent and cyclical symptomology. To understand the complexity of Long-COVID, the importance of symptom surveillance and prospective longitudinal studies is evident. Methods: A 9-month longitudinal prospective cohort study was conducted within Scotland (n=287), using a mobile app to determine the proportion of recovered individuals, those with persistent symptoms, common symptoms, and associations with gender and age. Results: 3.1% of participants experienced symptoms at month 9, meeting the criteria for Long-COVID, as defined by the NICE terminology. Fatigue, cough, and muscle pain were the most common symptoms at baseline, with fatigue persisting the longest, while symptoms like cough improved rapidly. Older age increased the likelihood of reporting pain and cognitive impairment. Female gender increased the likelihood of headaches and post-exertional malaise (PEM), and increased recovery time from fatigue and PEM. Conclusions: The majority of people fully recover from acute COVID-19, albeit often slowly. Age and gender play a role in symptom burden and recovery rates, emphasizing the need for tailored approaches to Long-COVID management. Further analysis is required to determine the characteristics of the individuals still reporting ongoing symptoms months after initial infection to identify risk factors and potential predictors for the development of Long-COVID.

The Power of Wearables: Is it Time to Use Commercially Available Activity Tracker Data Retrospectively to Understand Population Physical Activity Patterns? (preprint)

Researchers from around the world have been rapidly mobilised to study the effects of the coronavirus pandemic on health behaviours, including physical activity. However, due to the speed at which physical and social distancing restrictions have been imposed, it has been impossible to obtain large-scale, device-based, data in order to understand the effect of the pandemic on physical activity levels. This situation highlights the pressing need for accurate, remote, and scalable methods to not only monitor, but support PA behaviour in the wider population.Existing data from wearable activity trackers could be the answer. These data can be obtained retrospectively to provide a clearer picture of the impact of the pandemic and could also be used more generally as a method to understand population-level physical activity levels. In this commentary we aim to highlight the potential benefits, as well as the limitations, of using existing data from wearable activity trackers to understand large-scale physical activity patterns and stimulate discussion amongst the scientific community on what the future holds with respect to physical activity measurement and surveillance.