Effects of high-intensity interval training while using a breathing-restrictive mask compared to intermittent hypobaric hypoxia
Bibliographic record
Abstract
Background: Previous studies of the Elevation Training Mask (ETM) describe comparisons between groups using the ETM and controls for effects on aerobic performance. However, comparisons have not been made to intermittent hypoxic training (IHT). Further, how the ETM impacts exercise economy is unknown. Therefore, we sought to determine the effects of training with the ETM compared to IHT on aerobic performance and cycling economy. Methods: Thirty participants were randomized into an ETM, IHT, or control group (n = 10 each). Pre-and post-testing occurred using a ramp VO2max test on a cycle ergometer allowing submaximal power output (PO) measures of economy. Economy was measured using POs of 100, 125, and 150W. High-intensity cycling interval training (HIIT) occurred 2x/week for 30 min/session for six weeks. Sessions were 20 min of HIIT (30s at 100% peak power output (PPO) of pre VO2max, 90s active recovery at 25W, 10 bouts) with a 5-minute warm-up and cool-down. Repeated measures ANOVA was used for statistical analyses. RESULTS: All participants improved VO2max, PPO, and PO at ventilatory threshold 2 pre-to post-training (p < 0.05). Interactions between groups showed that the RER for the IHT group increased at 100W and 125W, and decreased at RERmax pre-to post-training while the ETM group showed the opposite response (p < 0.05). Conclusion: The ETM and IHT groups performed similarly to the control at maximal and submaximal effort following six weeks of training. The IHT group, but not the ETM group, experienced an increased glycolytic energy shift during submaximal exercise.
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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) | 0.003 | 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".