Effects of submaximal and supramaximal interval training on determinants of endurance performance in endurance athletes
Bibliographic record
Abstract
We compared the effects of submaximal and supramaximal cycling interval training on determinants of exercise performance in moderately endurance‐trained men. Maximal oxygen consumption (VO2max), peak power output (Ppeak), and peak and mean anaerobic power were measured before and after 6 weeks (3 sessions/week) of submaximal (85% maximal aerobic power [MP], HIIT85, n = 8) or supramaximal (115% MP, HIIT115, n = 9) interval training to exhaustion in moderately endurance‐trained men. High‐intensity training volume was 47% lower in HIIT115 vs HIIT85 (304 ± 77 vs 571 ± 200 min; P < 0.01). Exercise training was generally associated with increased VO2max (HIIT85: +3.3 ± 3.1 mL/kg/min; HIIT115: +3.3 ± 3.6 ml/kg/min; Time effect P = 0.002; Group effect: P = 0.95), Ppeak (HIIT85: +18 ± 9 W; HIIT115: +16 ± 27 W; Time effect P = 0.045; Group effect: P = 0.49), and mean anaerobic power (HIIT85: +0.42 ± 0.69 W/kg; HIIT115: +0.55 ± 0.65 W/kg; Time effect P = 0.01; Group effect: P = 0.18). Six weeks of submaximal and supramaximal interval training performed to exhaustion seems to equally improve VO2max and anaerobic power in endurance‐trained men, despite half the accumulated time spent at the target intensity.
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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".