Effect of maximum voluntary isometric contraction of the triceps surae muscle on a subsequent drop jump
Bibliographic record
Abstract
ABSTRACT Purpose Pre-muscle contraction improves sports performance because post-activation potentiation (PAP), induced by a previous intense voluntary contraction (conditioning contraction), amplifies the subsequent target muscle contraction. This study aims to examine the influence of conditioning contraction on the performance of ballistic motion, particularly on the stretch-shortening cycle (SSC). Methods Fourteen male university students specializing in athletic jumping events performed a drop jump from a height of 0.3 m. Maximal voluntary isometric plantarflexion for 6 seconds was considered a conditioning contraction. We set two conditions: in the PAP condition, participants performed a conditioning contraction 10 s before the drop jump, and in the control condition, they simply performed the drop jump. After 10 minutes of rest from the reference drop jump, both conditions were performed. A 3D motion analysis system, force plates, and surface electromyography were used to record the jump data. Results In the PAP conditions, the jump height and velocity of the center of gravity increased by 4 cm and 14 cm/s, respectively. Lower limb torque increment was observed only in the ankle joint between the PAP and control conditions (0.31 Nm/kg). However, there was no change in the magnitude of muscle activity based on the electromyogram. Conclusion Our study showed that PAP could potentiate the contraction mechanism under excitation-contraction coupling, irrespective of the effect on the central nervous system. Therefore, PAP enhances jump performance by improving SSC.
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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) | 0.003 | 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".