Neuromuscular and athletic performance following core strength training in elite youth soccer: Role of instability
Bibliographic record
Abstract
Cross-sectional studies revealed that inclusion of unstable elements in core-strengthening exercises produced increases in trunk muscle activity and thus potential extra stimuli to induce more pronounced performance enhancements in youth athletes. Thus, the purpose of the study was to investigate changes in neuromuscular and athletic performance following core strength training performed on unstable (CSTU) compared with stable surfaces (CSTS) in youth soccer players. Thirty-nine male elite soccer players (age: 17 ± 1 years) were assigned to two groups performing a progressive core strength-training program for 9 weeks (2-3 times/week) in addition to regular in-season soccer training. CSTS group conducted core exercises on stable (i.e., floor, bench) and CSTU group on unstable (e.g., Thera-Band® Stability Trainer, Togu© Swiss ball) surfaces. Measurements included tests for assessing trunk muscle strength/activation, countermovement jump height, sprint time, agility time, and kicking performance. Statistical analysis revealed significant main effects of test (pre vs post) for trunk extensor strength (5%, P < 0.05, d = 0.86), 10-20-m sprint time (3%, P < 0.05, d = 2.56), and kicking performance (1%, P < 0.01, d = 1.28). No significant Group × test interactions were observed for any variable. In conclusion, trunk muscle strength, sprint, and kicking performance improved following CSTU and CSTS when conducted in combination with regular soccer training.
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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".