Repeated‐sprint training in heat and hypoxia: Acute responses to manipulating exercise‐to‐rest ratio
Bibliographic record
Abstract
ABSTRACT The aim of this study was to investigate acute performance and physiological responses to the manipulation of exercise‐to‐rest ratio (E:R) during repeated‐sprint hypoxic training (RSH) in hot conditions. Twelve male team‐sport players completed two experimental sessions at a simulated altitude of ∼3000 m (FIO2 0.144), air temperature of 40°C and relative humidity of 50%. Exercise involved either 3 × 5 × 10‐s (E:R1:2) or 3 × 10 × 5‐s (E:R1:4) maximal cycling sprints interspersed with active recoveries at 120W (20‐s between sprints, 2.5 and 5‐min between sets for E:R1:2 and E:R1:4 respectively). Sessions were matched for overall sprint and total session duration (47.5‐min). Peak and mean power output, and total work were greater in E:R1:4 than E:R1:2 (p < 0.05). Peak core temperature was significantly higher in E:R1:4 than E:R1:2 (38.44 ± 0.33 vs. 38.20 ± 0.35°C, p = 0.028). Muscle deoxygenation magnitude during sprints was greater in E:R1:2 (28.2 ± 1.6 vs. 22.4 ± 4.6%, p < 0.001), while muscle reoxygenation did not differ between conditions (p > 0.05). These results indicate E:R1:4 increased mechanical power output and core temperature compared to E:R1:2. Both protocols had different effects on measures of muscle oxygenation, with E:R1:2 generating greater muscle oxygen extraction and E:R1:4 producing more muscle oxygenation flux, which are both important signals for peripheral adaptation. We conclude that the E:R manipulation during RSH in the heat might be used to target different physiological and performance outcomes, with these findings forming a strong base for future mechanistic investigation.
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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) | 0.001 | 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".