Extremely short duration high-intensity training substantially improves endurance performance in triathletes
Bibliographic record
Abstract
High-intensity training (HIT) involving 30-s sprints is an effective training regimen to improve aerobic performance. We tested whether 6-s HITs can improve aerobic performance in triathletes. Six subelite triathletes (age, 40 ± 9 years; weight, 86 ± 11 kg; body mass index, 26 ± 3 kg·m⁻²) took part in cycle HIT and 6 endurance-trained subelite athletes (age, 36 ± 9 years; weight, 82 ± 11 kg; BMI, 26 ± 3 kg·m⁻²) maintained their normal training routine. Before and after 2 weeks of HIT, involving 10 × 6-s sprints or normal activity, participants performed a self-paced 10-km time trial and a time to exhaustion test on a cycle ergometer. Finger prick blood samples were taken throughout the time to exhaustion test to determine blood lactate concentration. Two weeks of HIT resulted in a 10% decrease in self-paced 10-km time trial (p = 0.03) but no significant change in time to exhaustion. The time taken to reach onset of blood lactate accumulation (OBLA, defined as the point where blood lactate reaches 4 mmol·L⁻¹) was significantly increased following 2 weeks of HIT (p = 0.003). The change in time trial performance was correlated to the change in time taken to reach OBLA (R² = 0.63; p = 0.001). We concluded that a very short duration HIT is a very effective training regimen to improve aerobic performance in subelite triathletes and this is associated with a delay in blood lactate build-up.
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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) | 0.001 | 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 | 0.000 | 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".