Symptom number and reduced pre-infection training predict prolonged return to training after SARS-CoV-2 in athletes : AWARE IV

Abstract

PURPOSE : This study aimed to determine factors predictive of prolonged return to training (RTT) in athletes with recent SARS-CoV-2 infection. METHODS : This is a cross-sectional descriptive study. Athletes not vaccinated against COVID-19 (n = 207) with confirmed SARS-CoV-2 infection (predominantly ancestral virus and beta-variant) completed an online survey detailing the following factors: demographics (age and sex), level of sport participation, type of sport, comorbidity history and preinfection training (training hours 7 d preinfection), SARS-CoV-2 symptoms (26 in 3 categories; “nose and throat,” “chest and neck,” and “whole body”), and days to RTT. Main outcomes were hazard ratios (HR, 95% confidence interval) for athletes with versus without a factor, explored in univariate and multiple models. HR < 1 was predictive of prolonged RTT (reduced % chance of RTT after symptom onset). Significance was P < 0.05. RESULTS : Age, level of sport participation, type of sport, and history of comorbidities were not predictors of prolonged RTT. Significant predictors of prolonged RTT (univariate model) were as follows (HR, 95% confidence interval): female (0.6, 0.4–0.9; P = 0.01), reduced training in the 7 d preinfection (1.03, 1.01–1.06; P = 0.003), presence of symptoms by anatomical region (any “chest and neck” [0.6, 0.4–0.8; P = 0.004] and any “whole body” [0.6, 0.4–0.9; P = 0.025]), and several specific symptoms. Multiple models show that the greater number of symptoms in each anatomical region (adjusted for training hours in the 7 d preinfection) was associated with prolonged RTT (P < 0.05). CONCLUSIONS : Reduced preinfection training hours and the number of acute infection symptoms may predict prolonged RTT in athletes with recent SARS-CoV-2. These data can assist physicians as well as athletes/coaches in planning and guiding RTT. Future studies can explore whether these variables can be used to predict time to return to full performance and classify severity of acute respiratory infection in athletes.