Effect of Wearing the Elevation Training Mask on Physiological Performance in Elite Kayaking Girls
Bibliographic record
Abstract
Introduction: The Elevation Training Mask 2.0 (ETM) has been introduced as a novel tool to allow for respiratory muscle training and altitude exposure during exercise that can improve performance and hematological markers in elite and well-trained athletes. Thus, the aim of the present study was to assess the effect of wearing ETM while training on erythropoietin (EPO), vascular endothelial growth factor (VEGF), peak oxygen consumption (VO2max), lactate, and 1000-m kayaking performance in elite Kayaking girls. Methods: Thirty elite Kayaking girls (14 to 18-year-old) were equally and randomly assigned into mask, no mask, and control groups. The mask and no-mask groups completed 8 weeks of repeated sprint training, while the control group performed their routine kayaking training programs. Pre- and post-training tests included EPO, VEGF, VO2max, lactate, and 1000-m kayaking performance. Results: 1000-m time trial significantly decreased for mask (p < 0.001) and control (p = 0.035) groups, and was significantly lower in mask group than control (p = 0.014) and no mask (p = 0.009) groups. EPO did not show any significant changes for all groups. VEGF was increased significantly for mask (p = 0.04) and no mask (p = 0.014) groups. Lactate was decreased significantly for mask group (p = 0.025). VO2max increased significantly for no mask group (p = 0.021). Conclusion: Wearing the ETM while participating in 8 weeks of repeated sprint training might improve specific blood markers and endurance performance, especially anaerobic pathways. Performing repeated sprint training while wearing ETM has the potential to enhance performance in Kayak racing.
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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) | 0.002 | 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".