Effects of Plyometric and Resistance Training on Muscle Strength and Neuromuscular Function in Young Adolescent Soccer Players
Bibliographic record
Abstract
This study examined the effect of 8-weeks of resistance (RT) and plyometric (PLYO) training on maximal strength, power and jump performance compared with no added training (CON), in young male soccer players. Forty-one 11-13 year-old soccer players were divided into three groups (RT, PLYO, CON). All participants completed 5 isometric knee extensions at 90° and 5 isokinetic knee extensions at 240°/s pre- and post-training. Peak torque (PT), peak rate of torque development (pRTD), electromechanical-day (EMD), rate of muscle activation (Q30), muscle cross-sectional area (mCSA) and jump performance were examined. Both RT and PLYO resulted in significant (p < 0.05) increases in PT, pRTD and jump performance. RT resulted in significantly greater increases in both isometric and isokinetic PT, while PLYO resulted in significantly greater increases in isometric pRTD and jump performance compared with CON (p < 0.05). Q30 increased to a greater extent in PLYO (20%) compared with RT (5%) and CON (-5%) (p = 0.1). In conclusion, 8-weeks of RT and PLYO resulted in significant improvements in muscle strength and jump performance. RT appears to be more effective at eliciting increases in maximal strength while PLYO appears to enhance explosive strength, mediated by possible increases in the rate of muscle activation.
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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) | 0.000 | 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) | 0.001 | 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".