Wearing a Surgical Face Mask Has Minimal Effect on Performance and Physiological Measures during High-Intensity Exercise in Youth Ice-Hockey Players: A Randomized Cross-Over Trial
Bibliographic record
Abstract
COVID-19 transmission is prevalent during ice-hockey; however, it is unknown whether wearing face masks as a mitigation strategy affects hockey players’ performance. We used a randomized cross-over study to compare wearing a surgical mask to a sham mask (control) in youth hockey players (21 males, 5 females, 11.7 ± 1.6 y) during a simulated hockey period (cycle ergometry; six shifts of 20 s of “easy” pedaling (40% peak power), 10 s of “hard” pedaling (95% peak power), 20 s of “easy” pedaling, with shifts separated by 5 min rests). A seventh shift involved two 20 s Wingate tests separated by 40 s rest. Heart rate, arterial oxygen saturation and vastus lateralis tissue oxygenation index (hemoglobin saturation/desaturation) was assessed each shift. On-ice testing was conducted with the maximal Yo-Yo intermittent recovery test. No differences between mask and control conditions for performance were found (Wingate average power: 245 ± 93 vs. 237 ± 93 W, Peak power: 314 ± 116 vs. 304 ± 115 W, on-ice distance: 274 ± 116 vs. 274 ± 110 m) and for heart rate or arterial oxygen saturation during simulated hockey shifts. Tissue oxygenation index was lower from shifts one to six for males (p < 0.05) and shift seven for females (p < 0.01) while wearing a mask. Wearing a face mask had no effect on performance in hockey players with only minor effects on muscle oxygenation. ClinicalTrials.gov (NCT04874766) (accessed on 6 May 2021).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".