Effects of In-Season Short-term Plyometric Training Program on Sprint and Jump Performance of Young Male Track Athletes
Bibliographic record
Abstract
We studied the effect of supplementing normal in-season training by a 10-week lower limb plyometric training program (hurdle and depth jumping), examining measures of competitive potential (peak power output [PP], sprint running velocity, squat jump [SJ], countermovement jump [CMJ], drop jump [DJ], and lower limb muscle volume). The subjects (27 male track athletes, aged 11.9 ± 1.0 years; body mass: 39.1 ± 6.1 kg; height: 1.56 ± 0.02 m; body fat: 12.8 ± 4.4%) were randomly assigned between a control (normal training) group (C; n = 13) and an experimental group (E; n = 14) who also performed plyometric training 3 times per week. A force-velocity ergometer test determined PP and SJ, and an Optojump apparatus evaluated CMJ height and DJ (height and power). A multiple-5-bound test assessed horizontal jumping, and video-camera analyses over a 40-m sprint yielded velocities for the first step (VS), the first 5 m (V5m), and between 35 and 40 m (Vmax). Leg muscle volume was estimated anthropometrically. Experimental group showed gains relative to C in SJ height (p < 0.001); CMJ height (p < 0.01); DJ height and power relative to body mass (p < 0.01 for both); and all sprint velocities (p < 0.01 for VS and V(5m, p) ≤ 0.05 for Vmax). There was also a significant increase (p < 0.01) in thigh muscle volume, but leg muscle volume, thigh cross-sectional area, and PP remained unchanged. We conclude that adding plyometric training improved important components of athletic performance relative to standard in-season training in young runners.
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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) | 0.001 | 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".