EFFECTS OF HIGH INTENSITY INTERVAL TRAINING IN HYPEROXIA COMPARED TO NORMOXIA
Bibliographic record
Abstract
PURPOSE This study was designed to determine if the increased intensity required to maintain training heart rate(HR) during interval workloads for hyperoxic breathing (H), as compared to normoxia (N), would result in increased training response. METHODS Seven athletes (19–28 yrs, mean VO2max 52 ml/kg) completed a training program consisting of 6 weeks of bike ergo work 3 days/week, 1hr/day, at 80–85% max HR, 4min work interval and 2 min rest. During training the HR intensity range was maintained by increasing the load. Using a single blind design, subjects were randomly assigned to H or N breathing conditions for the work intervals. For H condition 100% O2 was humidified and delivered through a mixing system with room air producing 57–60% O2, while for N the identical system was used without the O2 mix. After a 12 week training break, included to allow return to baseline values, a second identical 6 week training protocol was completed with the other breathing condition. Pre and post each training program measurements of VO2max, performance time at 90% VO2max and cardiorespiratory response to a steady state workload at 80% VO2max were completed. RESULTS There were no significant differences in any of these parameters between the pre-test results for each training period. Both H and N training programs showed significant increases in VO2max and performance time and although the increases were greater for H they were not significantly different. HR was reduced 10% more for the H program than N at the 80% VO2max load. The load increase throughout the 6 weeks of training needed to maintain the required HR was 15% greater for H. CONCLUSION Overall, these responses for a relatively modest training volume, suggest that the increased training intensity required to maintain target HR for H resulted in improved cardiorespiratory response to work. Whether performance variables would be significantly elevated by H training with an increased training volume requires further study.
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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 | 0.000 |
| 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".