Tracking Persistent Symptoms in Scotland (TraPSS): a longitudinal prospective cohort study of COVID-19 recovery after mild acute infection

Sculthorpe, Nicholas F; McLaughlin, Marie; Cerexhe, Luke; Macdonald, Eilidh; Dello Iacono, Antonio; Sanal-Hayes, Nilihan E M; Ingram, Joanne; Meach, Rachel; Carless, David; Ormerod, Jane; Hayes, Lawrence D

Abstract

Background COVID-19 disease results in disparate responses between individuals and has led to the emergence of long coronavirus disease (Long-COVID), characterised 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 and those with persistent symptoms and 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 National Institute for Health and Care Excellence terminology. The random effects model revealed a significant time (month) effect for infection recovery (p<0.001, estimate=0.07). 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 (p=0.028, estimate=0.07) and cognitive impairment (p<0.001, estimate=0.93). Female gender increased the likelihood of headaches (p=0.024, estimate=0.53) and post-exertional malaise (PEM) frequency (p=0.05, estimate=137.68), and increased time x gender effect for PEM frequency (p=0.033, estimate=18.96).

Conclusions The majority of people fully recover from acute COVID-19, although often slowly. Age and gender play a role in symptom burden and recovery rates, emphasising 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.

• Journal Article Type: Article
• Acceptance Date: Dec 9, 2024
• Online Publication Date: Jan 15, 2025
• Publication Date: 2025-01
• Deposit Date: Apr 22, 2025
• Publicly Available Date: Apr 23, 2025
• Journal: BMJ Open
• Electronic ISSN: 2044-6055
• Publisher: BMJ Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Issue: 1
• Pages: e086646
• DOI: https://doi.org/10.1136/bmjopen-2024-086646