Repeated Short-Term Bouts of Hyperoxia Improve Aerobic Performance in Acute Hypoxia
Bibliographic record
Abstract
ABSTRACT: Faulhaber, M, Schneider, S, Rausch, LK, Dünnwald, T, Menz, V, Gatterer, H, Kennedy, MD, and Schobersberger, W. Repeated short-term bouts of hyperoxia improve aerobic performance in acute hypoxia. J Strength Cond Res 37(10): 2016-2022, 2023-This study aimed to test the effects of repeated short-term bouts of hyperoxia on maximal 5-minute cycling performance under acute hypoxic conditions (3,200 m). Seventeen healthy and recreationally trained individuals (7 women and 10 men) participated in this randomized placebo-controlled cross-over trial. The procedures included a maximal cycle ergometer test and 3 maximal 5-minute cycling time trials (TTs). TT1 took place in normoxia and served for habituation and reference. TT2 and TT3 were conducted in normobaric hypoxia (15.0% inspiratory fraction of oxygen). During TT2 and TT3, the subjects were breathing through a face mask during five 15-second periods. The face mask was connected through a nonrebreathing T valve to a 300-L bag filled with 100% oxygen (intermittent hyperoxia) or ambient hypoxic air (placebo). The main outcome was the mean power output during the TT. Statistical significance level was set at p < 0.05. The mean power output was higher in the intermittent hyperoxia compared with the placebo condition (255.5 ± 49.6 W vs. 247.4 ± 48.2 W, p = 0.001). Blood lactate concentration and ratings of perceived exertion were significantly lower by about 9.7 and 7.3%, respectively, in the intermittent hyperoxia compared with the placebo condition, whereas heart rate values were unchanged. IH application increased arterial oxygen saturation (82.9 ± 2.6% to 92.4 ± 3.3%, p < 0.001). Repeated 15-second bouts of hyperoxia, applied during high-intensity exercise in hypoxia, are sufficient to increase power output. Future studies should focus on potential dose-response effects and the involved mechanisms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".