Hyperoxia Improves Repeated-Sprint Ability and the Associated Training Load in Athletes
Bibliographic record
Abstract
This study investigated the impact of hyperoxic gas breathing (HYP) on repeated-sprint ability (RSA) and on the associated training load (TL). Thirteen team- and racquet-sport athletes performed 6-s all-out sprints with 24-s recovery until exhaustion (power decrement ≥ 15% for two consecutive sprints) under normoxic (NOR: F I O 2 0.21) and hyperoxic (HYP: F I O 2 0.40) conditions in a randomized, single-blind and crossover design. The following variables were recorded throughout the tests: mechanical indices, arterial O 2 saturation (S p O 2 ), oxygenation of the vastus lateralis muscle with near-infrared spectroscopy, and electromyographic activity of the vastus lateralis, rectus femoris, and gastrocnemius lateralis muscles. Session TL (work × rate of perceived exertion) and neuromuscular efficiency (work/EMG [Electromyography]) were calculated. Compared with NOR, HYP increased S p O 2 (2.7 ± 0.8%, Cohen's effect size ES 0.55), the number of sprints (14.5 ± 8.6%, ES 0.28), the total mechanical work (13.6 ± 6.8%, ES 0.30), and the session TL (19.4 ± 7.0%, ES 0.33). Concomitantly, HYP increased the amplitude of muscle oxygenation changes during sprints (25.2 ± 11.7%, ES 0.36) and recovery periods (26.1 ± 11.4%, ES 0.37), as well as muscle recruitment (9.9 ± 12.1%, ES 0.74), and neuromuscular efficiency (6.9 ± 9.0%, ES 0.24). It was concluded that breathing a hyperoxic mixture enriched to 40% O 2 improves the total work performed and the associated training load during an open-loop RSA session in trained athletes. This ergogenic impact may be mediated by metabolic and neuromuscular alterations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".