The impact of face masks on performance and physiological outcomes during exercise: a systematic review and meta-analysis
Bibliographic record
Abstract
Face masks are promoted for preventing spread of viruses; however, wearing a mask during exercise might increase CO 2 rebreathing, decrease arterial oxygenation, and decrease exercise performance. A systematic review and meta-analysis was conducted on the impact of wearing a mask during exercise. Data sources included SPORTDiscus, PubMed, and Medline. Eligibility criteria included all study designs comparing surgical, N95, or cloth masks to a no mask condition during any type of exercise where exercise performance and/or physiological parameters were evaluated. Healthy and clinical participants were included. Mean differences (MD) or standardized mean differences (SMD) with 95% confidence intervals were calculated and pooled effects assessed. Twenty-two studies involving 1573 participants (620 females, 953 males) were included. Surgical, or N95 masks did not impact exercise performance (SMD −0.05 [−0.16, 0.07] and −0.16 [−0.54, 0.22], respectively) but increased ratings of perceived exertion (SMD 0.33 [0.09, 0.58] and 0.61 [0.23, 0.99]) and dyspnea (SMD 0.6 [0.3, 0.9] for all masks). End-tidal CO 2 (MD 3.3 [1.0, 5.6] and 3.7 [3.0, 4.4] mm Hg), and heart rate (MD 2 [0,4] beats/min with N95 masks) slightly increased. Face masks can be worn during exercise with no influences on performance and minimal impacts on physiological variables. PROSPERO registration: CRD42020224988. Novelty: Face masks can be worn during exercise with no impacts on performance and minimal impacts on physiological variables.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".