Comparison of Static Balance and the Role of Vision in Elite Athletes
Bibliographic record
Abstract
When prescribing balance exercises to athletes in different sports, it may be important to recognize performance variations. Indeed, how athletes from different sports perform on balance tests is not well understood. The goal of the present study was to compare static balance and the role of vision among elite sprinters, jumpers and rugby players. The modified clinical test of sensory interaction on balance (mCTSIB) was used to assess the velocity of the center-of-pressure (CoP) on a force platform during a 30 s bipedal quiet standing posture in 4 conditions: firm surface with opened and closed eyes, foam surface with opened and closed eyes. Three-factor ANOVA indicated a significant main effect for groups (F=21.69, df=2, p<0.001, η(2) = 0.34). Significant main effect of vision (F=43.20, df=1, p<0.001, η(2) = 0.34) and surface (F=193.41, df=1, p<0.001, η(2) = 0.70) as well as an interaction between vision (eyes open, eyes closed) and surface (firm and foam) (F=21.79, df=1, p=0.001) were reported in all groups. The subsequent Bonferroni-Dunn post hoc test indicated that rugby players displayed better static balance than sprinters and jumpers (p=0.001). The comparison of sprinters and jumpers did not reveal significant differences (p>0.05). The nature of the sport practiced and the absence of visual control are linked to modify static balance in elite athletes. Coaches and strength and conditioning professionals are recommended to use a variety of exercises to improve balance, including both exercises with opened and closed eyes on progressively challenging surfaces in order to make decisions about tasks and sensory availability during assessment and 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".