Effects of Upper and Lower Limb Plyometric Training Program on Components of Physical Performance in Young Female Handball Players
Bibliographic record
Abstract
Purpose: This study examined the effects of 10-week combined upper and lower plyometric training (ULPT) programs on components of physical performance in young female handball players. Methods: Participants aged 15.8 ± 0.2 years were randomly assigned between the experimental (EG; n=17) and control (CG; n=17) groups. Two-way analyses of performance (group x time) assessed changes in handgrip force, back extensor strength; medicine ball throwing, 30-m sprint times, change of direction (CoD) (Modified Illinois Modified test (Illinois-MT)), four jumping tests (squat jump (SJ), countermovement jump (CMJ), CMJ with aimed arms (CMJA) and five jump test (5JT), static and dynamic balance, and repeated sprint T-test scores (RSTT). Results: After 10-week training (2 sessions per week), the EG showed significant changes relative to CG in right and left handgrip force, back extensor strength and medicine ball throwing (p<0.001, d=1.51 (large) ; p<0.0001, d=0.85 (large) ; p<0.001, d=0.90 (large) ; p<0.0001, d=0.52 (meduim)) respectively). The EG also showed improvements in sprint times [5 m (p=0.02, d=0.80 (large)) ; 10 m (p<0.0001, d=1.00 (large); 20 m (p=0.02, d=1.41 (large)) and 30 m (p=0.02, d=2.60 (large))], CoD [Illinois-MT (p<0.001, d=1.58 (large))] and jumping [(SJ, CMJ, CMJA and 5JT, (p=0.001, d=0.87 (large); p<0.001, d=1.17 (large); p<0.001, d=1.15 (large); and p=0.006, d=0.71 (medium)) respectively]. Further, all RSTT scores (best time, mean time, total time and fatigue index) increased significantly, with group×time interactions varying between p<0.001 and p=0.049 (d value large to medium). However, balance did not differ significantly between EG and CG. Conclusion: We conclude that 10-week of ULPT improved many measures of physical performance in young female handball players relative to standard training (CG).
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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 | 0.000 |
| 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".