Face Masks and the Cardiorespiratory Response to Physical Activity in Health and Disease
Bibliographic record
Abstract
To minimize transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the novel coronavirus responsible for coronavirus disease (COVID-19), the U.S. Centers for Disease Control and Prevention and the World Health Organization recommend wearing face masks in public. Some have expressed concern that these may affect the cardiopulmonary system by increasing the work of breathing, altering pulmonary gas exchange and increasing dyspnea, especially during physical activity. These concerns have been derived largely from studies evaluating devices intentionally designed to severely affect respiratory mechanics and gas exchange. We review the literature on the effects of various face masks and respirators on the respiratory system during physical activity using data from several models: cloth face coverings and surgical masks, N95 respirators, industrial respirators, and applied highly resistive or high-dead space respiratory loads. Overall, the available data suggest that although dyspnea may be increased and alter perceived effort with activity, the effects on work of breathing, blood gases, and other physiological parameters imposed by face masks during physical activity are small, often too small to be detected, even during very heavy exercise. There is no current evidence to support sex-based or age-based differences in the physiological responses to exercise while wearing a face mask. Although the available data suggest that negative effects of using cloth or surgical face masks during physical activity in healthy individuals are negligible and unlikely to impact exercise tolerance significantly, for some individuals with severe cardiopulmonary disease, any added resistance and/or minor changes in blood gases may evoke considerably more dyspnea and, thus, affect exercise capacity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".