Balance and Core Stabilization Training with Eyes Open Versus Eyes Closed in Young Football Players
Bibliographic record
Abstract
Background. Core stability (or core strengthening) has become a well-known fitness trend that has started transcending into sports medicine. It has become a common practice to incorporate balance tasks into the training program for athletes who want to improve performance and prevent injuries. Hypothesis. We suggest that core stabilization and balance training with closed eyes will be more effective than training with open eyes. The aim of the study was to evaluate the effect of core stabilization training with open eyes versus closed eyes on balance and stability of young football players. Methods. Fourteen healthy young football players aged 10–12 years were assessed for pre and post core stabilization training using two balance tests: Stork Balance Test (SBT) and Modified Star Excursion Balance Test (mSEBT), and one test for core stability ‒ McGill Core Stability Test (MCST). The intervention included twelve twenty-minute training sessions each of them involved six core strengthening exercises. One group performed exercises with open eyes, and another with eyes closed. Results. Core stability exercises with eyes closed as well as the same exercise done with eyes open insignificantly improved dynamic balance and core stability, but significantly improved the static balance of the subjects. Conclusion. After applying training with closed eyes as well as eyes open, core stability and balance of young football players increased insignificantly. There were no significant differences in core stability and balance training between training with eyes open and eyes closed.Keywords: core stabilization training, balance, open eyes, closed eyes.
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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".