Effects of Hyperoxic Training on Performance and Cardiorespiratory Response to Exercise
Bibliographic record
Abstract
PURPOSE: To determine whether training in a hyperoxic environment would result in greater increases in VO2max and performance at 90% VO2max as compared with training in normoxia. METHODS: In a single blind design nine athletes trained for 6 wk on a cycle ergometer 3 d.wk(-1), 1 h.d(-1) (10 x 4-min intervals, with 2 min of rest between intervals) at 90% HR(max). Training HR range was maintained by adjusting the power output. Subjects were randomly assigned to H (60% O2) or N (21% O2) breathing conditions for training. After 12 wk of detraining, a second 6-wk training protocol was completed with the breathing conditions reversed. VO2max, performance time at 90% VO2max and cardiorespiratory response to a steady-state exercise at 80% VO2max were measured pre- and posttraining. All pre- and posttraining tests were conducted under normoxic conditions. RESULTS: There were no significant differences between pretraining results for any of the parameters. Power output was 8.1% higher while training in H compared with N, to maintain training HR. Both H and N training resulted in increased performance time, with H being greater than N. Although there was a trend for a greater increase in VO2max after H versus N training, this difference was not significant. HR(max) did not change for H or N. HR VE at 80% VO2max decreased posttraining with no differences between H and N. CONCLUSION: The data showed that a higher power output was required to maintain HR during H training. This increased training intensity during H resulted in improved exercise performance whereas cycling at 90% VO2max in room air and may be due to peripheral factors because cardiorespiratory responses were similar.
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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".