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1.
Br J Sports Med ; 2023 Jun 06.
Article in English | MEDLINE | ID: covidwho-20234262
3.
Heart Lung Circ ; 2022 Nov 30.
Article in English | MEDLINE | ID: covidwho-2229115

ABSTRACT

BACKGROUND AND AIM: COVID-19 can be transmitted through aerosolised respiratory particles. The degree to which exercise enhances aerosol production has not been previously assessed. We aimed to quantify the size and concentration of aerosol particles and evaluate the impact of physical distance and surgical mask wearing during high intensity exercise (HIE). METHODS: Using a prospective observational crossover study, three healthy volunteers performed high intensity cardiopulmonary exercise testing at 80% of peak capacity in repeated 5-minute bouts on a cycle ergometer. Aerosol size and concentration was measured at 35, 150 and 300 cm from the participants in an anterior and lateral direction, with and without a surgical face mask, using an Aerodynamic Particle Sizer (APS) and a Mini Wide Range Aerosol Spectrometer (MiniWRAS), with over 10,000 sample points. RESULTS: High intensity exercise generates aerosol in the 0.2-1 micrometre range. Increasing distance from the rider reduces aerosol concentrations measured by both MiniWRAS (p=0.003 for interaction) and APS (p=0.041). However, aerosol concentrations remained significantly increased above baseline measures at 300 cm from the rider. A surgical face mask reduced submicron aerosol concentrations measured anteriorly to the rider (p=0.031 for interaction) but not when measured laterally (p=0.64 for interaction). CONCLUSIONS: High intensity exercise is an aerosol generating activity. Significant concentrations of aerosol particles are measurable well beyond the commonly recommended 150 cm of physical distancing. A surgical face mask reduces aerosol concentration anteriorly but not laterally to an exercising individual. Measures for safer exercise should emphasise distance and airflow and not rely solely on mask wearing.

6.
JACC Cardiovasc Imaging ; 13(12): 2635-2652, 2020 12.
Article in English | MEDLINE | ID: covidwho-1019240

ABSTRACT

As our understanding of the complications of coronavirus disease-2019 (COVID-19) evolve, subclinical cardiac pathology such as myocarditis, pericarditis, and right ventricular dysfunction in the absence of significant clinical symptoms represents a concern. The potential implications of these findings in athletes are significant given the concern that exercise, during the acute phase of viral myocarditis, may exacerbate myocardial injury and precipitate malignant ventricular arrhythmias. Such concerns have led to the development and publication of expert consensus documents aimed at providing guidance for the evaluation of athletes after contracting COVID-19 in order to permit safe return to play. Cardiac imaging is at the center of these evaluations. This review seeks to evaluate the current evidence regarding COVID-19-associated cardiovascular disease and how multimodality imaging may be useful in the screening and clinical evaluation of athletes with suspected cardiovascular complications of infection. Guidance is provided with diagnostic "red flags" that raise the suspicion of pathology. Specific emphasis is placed on the unique challenges posed in distinguishing athletic cardiac remodeling from subclinical cardiac disease. The strengths and limitations of different imaging modalities are discussed and an approach to return to play decision making for athletes post-COVID-19, as informed by multimodality imaging, is provided.


Subject(s)
Athletes , COVID-19/complications , Competitive Behavior , Heart Diseases/diagnostic imaging , Multimodal Imaging/standards , Return to Sport , COVID-19/diagnosis , COVID-19/therapy , Cardiorespiratory Fitness , Computed Tomography Angiography/standards , Consensus , Coronary Angiography/standards , Echocardiography/standards , Heart Diseases/etiology , Heart Diseases/physiopathology , Humans , Magnetic Resonance Imaging/standards , Predictive Value of Tests , Prognosis , Risk Assessment , Risk Factors , Tomography, Emission-Computed/standards
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