The Effect of Duration on Performance and Perceived Fatigability During Acute High‐Intensity Interval Exercise in Young, Healthy Males and Females
Bibliographic record
Abstract
) completed eight, 4-min intervals at 105% of critical power with 3 min of rest. Maximal voluntary knee extension contractions (MVCs) coupled with electrical nerve stimulation were performed at baseline and after the first, fourth, and eighth intervals. MVC, potentiated twitch force (Pt), and Db10:100 ratio all declined throughout HIIT (p < 0.05). MVC sharply declined after interval 1 (-15 ± 9% relative to baseline; p < 0.05) and had only further declined after interval 8 (-26 ± 11%; p < 0.05), but not interval 4 (-19 ± 13%; p > 0.05). Pt and Db10:100 also sharply declined after interval 1 (Pt: -18 ± 13%, Db10:100: -14 ± 20%; p < 0.05) and further declined after interval 4 (Pt: -35 ± 19%, Db10:100: -30 ± 20%; p < 0.05) but not interval 8 (Pt: -41 ± 19%; Db10:100: -32 ± 18%; p > 0.05). Voluntary activation did not significantly change across the HIIT protocol (p > 0.05). Evoked force recovery was significantly blunted as more intervals were completed: after interval 1, Pt recovered by 7 ± 11% compared to -6 ± 7% recovery after interval 8 (p < 0.05). Ratings of perceived effort, fatigue, and leg pain rose throughout the session (p < 0.05 for each) and were greater (effort and fatigue) for females (p < 0.05). Otherwise, males and females exhibited similar performance fatigability kinetics, with contractile function declines blunted in response to additional intervals.
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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 | 0.001 |
| 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".