Effects of wearing different facial masks on respiratory symptoms, oxygen saturation, and functional capacity during six-minute walk test in healthy subjects
Bibliographic record
Abstract
Background During the current COVID-19 pandemic and increased air pollution levels, wearing a facial mask has been recommended. This study aimed to compare the impact of wearing different masks when performing a submaximal functional activity (six-minute walk test; 6MWT) on respiratory symptoms, oxygen saturation, and functional capacity. Methods Twenty-nine subjects (10 men, 19 women; age 22 ± 1 yr.; FEV~1~/FVC 0.90 ± 0.01) performed four rounds of 6MWT wearing different masks (surgical (Medima SK, Thailand), handmade cloth, and N95 (3M AuraTM 1870^+^, USA)) and while not wearing a mask. Respiratory symptoms (dyspnea and breathing effort), oxygen saturation, and other physiological parameters were assessed before and after each walking trial. Results Six-minute walking distances were comparable between walking trials (P = 0.59). At the end of minute 6, a significant difference between groups was found on dyspnea (P = 0.02) and breathing effort (P \< 0.001). Post hoc tests showed that wearing a cloth mask significantly increased dyspnea (P = 0.004) compared to wearing a surgical mask. Wearing a cloth mask also significantly increased breathing effort compared to wearing a surgical mask (P \< 0.001) and not wearing a mask (P \< 0.001). Likewise, while wearing an N95 mask, breathing effort significantly increased compared to wearing a surgical mask (P = 0.007) and not wearing a mask (P = 0.002). Conclusions Wearing different masks while performing submaximal functional activity results in no differences in functional performance, oxygen saturation, heart rate, or blood pressure. However, wearing cloth masks and N95 masks results in higher respiratory symptoms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".