Influence of Intensity on Post-Running Jump Potentiation in Recreational Runners vs. Physically Active Individuals
Bibliographic record
Abstract
The aim of this study was to verify post-activation performance enhancement (PAPE) in jumping and sprinting after two endurance volume-equated running protocols with different intensities, in runners vs. active individuals. Nine recreational runners (age: 34.5 ± 9.3 years, body mass: 73.1 ± 11.9 kg, body height: 1.76 ± 0.06 m, 17.4 ± 4.4 %body fat; maximum aerobic speed [MAS]: 16.4 ± 1.0 km·h−1), and 9 active individuals (age: 34.1 ± 9.4 years; body mass: 83.2 ± 7.7 kg; body height: 1.79 ± 0.06 m; 25.6 ± 5.4 %body fat; MAS: 13.3 ± 1.2 km·h−1) volunteered for participation. The evaluations were performed over three days as follows: 1) anthropometric measures, physical fitness tests, and the University of Montreal Track Test (UMTT) to determine MAS and the distance to be covered in the running protocols; 2 and 3) the countermovement jump (CMJ) and the flying 20-m sprint (SPRINT) were assessed pre- and post-running at 70% of MAS or a time trial race (TTR), equated by volume and completed in random order. A three-way ANOVA (time*group*running) was performed to analyze the PAPE effects. The results showed a time effect (F = 10 .716; p < 0.01) and a group*running interaction (F = 12.094; p < 0.01) for the CMJ, indicating that active individuals demonstrated PAPE after running at 70% of MAS, while for runners both running interventions (70% of MAS and TTR) induced PAPE in CMJ performances. For the SPRINT, a time*group interaction (F = 4.790; p = 0.044) and a group effect were observed, with runners showing greater SPRINT performances than active individuals. From the current results, it can be suggested that training background and intensity can modulate PAPE responses in jumping and sprinting after volume-equated running protocols at different intensities.
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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".