Effects of 8-Week In-season Plyometric Training on Upper and Lower Limb Performance of Elite Adolescent Handball Players
Bibliographic record
Abstract
We hypothesized that replacement of a part of the normal in-season regimen of top-level adolescent handball players by an 8-week biweekly course of lower and upper limb plyometric training would enhance characteristics important to competition, including peak power output (Wpeak), jump performance, muscle volume, and ball throwing velocity. Study participants (23 men, age: 17.4 ± 0.5 years, body mass: 79.9 ± 11.5 kg, height: 1.79 ± 6.19 m, body fat: 13.8 ± 2.1%) were randomly assigned between controls (C; n = 11) and an experimental group (E, n = 12). Measures preintervention and postintervention included force-velocity ergometer tests for upper (Wupper peak) and lower limbs (Wlower peak), force platform determinations of squat jump (SJ) and countermovement jump (CMJ) characteristics (jump height, maximal force, initial velocity, and average power), video filming of sprint velocities (first step [V1S], first 5 m [V5m], and 25-30 m [Vmax]), and anthropometric estimates of leg muscle volume. E showed gains relative to C in Wupper peak and Wlower peak (p < 0.01 and p < 0.001), SJ (height p < 0.01; force p ≤ 0.05), CMJ (height p < 0.01; force p < 0.01 and relative power p ≤ 0.05), and sprint velocities (p < 0.001 for V1S, V5m, and Vmax). E also showed increases in leg and thigh muscle volumes (p < 0.001), but arm muscle volumes did not differ from control. We conclude that introduction of biweekly plyometric training into the standard regimen improved components important to handball performance, particularly explosive actions, such as sprinting, jumping, and ball throwing velocity.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".