Effects of a Combined Upper- and Lower-Limb Plyometric Training Program on High-Intensity Actions in Female U14 Handball Players
Bibliographic record
Abstract
PURPOSE: To analyze the effects of a 9-week plyometric training program on the sprint times (5, 10, 20, and 30 m), change-of-direction speed (modified T test and modified Illinois test), jumping (squat jump, countermovement jump, countermovement jump with arms, and horizontal 5-jump test), upper-body strength (right and left handgrip, back extensor strength, and medicine ball throw), and balance (Y and stork balance tests) of female handball players. METHODS: Athletes were randomly divided into experimental (n = 21; age = 13.5 [0.3] y) and control (n = 20; age = 13.3 [0.3] y) groups. Training exercises and matches were performed together, but the experimental group replaced a part of their normal regimen by biweekly upper- and lower-limb plyometric training. RESULTS: Both groups improved performance, but to a greater extent in the experimental group compared with controls for 20- and 30-m sprint times (Δ% = 9.6, P < .05, d = 0.557 and Δ% = 20.9, P < .001, d = 1.07, respectively), change of direction (T test: P < .01, Δ% = 14.5, d = 0.993 and Illinois test: P < .01, Δ% = 7.9, d = 0.769), vertical and horizontal jumping (P < .05), all measures of upper-limb strength (P < .001), and left-leg stork balance (P < .001, Δ% = 49.9, d = 1.07). CONCLUSIONS: A plyometric training program allows female junior handball players to improve important components of their physical performance.
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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) | 0.001 | 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 | 0.001 |
| 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".