The effect of a sand surface on physical performance responses of junior male handball players to plyometric training
Bibliographic record
Abstract
Abstract Background The effects of 7 weeks plyometric training on a stable surface and on sand were compared in junior male handball players. Methods A team of experienced players was divided randomly between three groups, undertaking a standard in-season regimen (C, n = 10), or supplementing this regimen by plyometric training on sand (PS, n = 11) or a stable surface (P, n = 10) for 7 weeks. Assessments included 20 m sprint times, change of direction tests (Modified change-of-direction T-test and Modified Illinois test), a repeated sprint T-test, jumping ability (squat, countermovement and five jump tests), and static and dynamic balance. Results After the intervention, PS showed significant increases of sprint speed relative to P and C. Change of direction scores were also improved for PS relative to P and C. Both PS and P increased vertical jump performance (squat jump, p = 0.005; ES = 0.170; counter-movement jump p < 0.001; ES = 0.247). Repeated sprint T-test scores improved in PS and P relative to C, with best times of PS > P (p < 0.05). Both plyometric groups improved their dynamic balance (p < 0.05), with three parameters of PS and only one of P being significantly greater than C. Static balance was also enhanced in both experimental groups (PS > P). Conclusions We conclude that for reasons that remain to be clarified, several performance measures in adolescent male handball players were increased more by 7 weeks of PS than by P.
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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) | 0.000 | 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) | 0.002 | 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".