Post‐activation potentiation (PAP) in endurance sports: A review
Bibliographic record
Abstract
While there is strong support of the usefulness of post-activation potentiation (PAP) phenomenon in power demanding sports, the role that PAP could play in endurance sports has received less attention. The aim of this review is to present evidence for a better understanding of PAP in endurance athletes; and to discuss the physiological basis and methodological aspects necessary for better practices and designing further studies. A search for relevant articles on PAP and endurance trained athletes was carried out using Medline and ISI Web of Knowledge databases. Twenty-two studies were included in the review. The current evidence suggests the possible influence of PAP for performance enhancement after appropriate conditioning activities during warm up. Evaluation of PAP responses during testing, training and competition may be also important for athletes monitoring. There are many unresolved questions about the optimum load parameters for benefiting from PAP in both training and competition; and the role that PAP may exert for optimal performance while interacting with central and peripheral factors associated with muscle fatigue. Further studies should elucidate the association between PAP responses and long-term adaptations in endurance athletes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".