Prior exercise impairs subsequent performance in an intensity- and duration-dependent manner
Bibliographic record
Abstract
Prior constant-load exercise performed for 30-min at or above maximal lactate steady state (MLSSp) significantly impairs subsequent time-to-task failure (TTF) compared with TTF performed without prior exercise. We tested the hypothesis that TTF would decrease in relation to the intensity and the duration of prior exercise compared with a baseline TTF trial. Eleven individuals (6 males, 5 females, aged 28 ± 8 yrs) completed the following tests on a cycle ergometer (randomly assigned after MLSSp was determined): (i) a ramp-incremental test; (ii) a baseline TTF trial performed at 80% of peak power (TTFb); (iii) five 30-min constant-PO rides at 5% below lactate threshold (LT−5%), halfway between LT and MLSSp (Delta50), 5% below MLSSp (MLSS−5%), MLSSp, and 5% above MLSSp (MLSS+5%); and (iv) 15- and 45-min rides at MLSSp (MLSS15 and MLSS45, respectively). Each condition was immediately followed by a TTF trial at 80% of peak power. Compared with TTFb (330 ± 52 s), there was 8.0 ± 24.1, 23.6 ± 20.2, 41.0 ± 14.8, 52.2 ± 18.9, and 75.4 ± 7.4% reduction in TTF following LT−5%, Delta50, MLSS−5%, MLSSp, and MLSS+5%, respectively. Following MLSS15 and MLSS45 there were 29.0 ± 20.1 and 69.4 ± 19.6% reductions in TTF, respectively (P < 0.05). It is concluded that TTF is reduced following prior exercise of varying duration at MLSSp and at submaximal intensities below MLSS. Novelty: Prior constant-PO exercise, performed at intensities below MLSSp, reduces subsequent TTF performance. Subsequent TTF performance is reduced in a linear fashion following an increase in the duration of constant-PO exercise at MLSSp.
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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) | 0.002 | 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".