BALANCE MAINTENANCE IN SKILLED ATHLETES IS SURFACE INDEPENDENT
Bibliographic record
Abstract
Postural stability is now considered an essential component of any motor act. One often notes a somewhat standardized anticipatory response to voluntarily induced destabilizing movements. However, little attention has been given to the effect of previous experience on balance response. PURPOSE To determine whether or not ice hockey players, who practice and compete in an unstable environment, make non-standard anticipatory postural adjustments to deal with perturbations to stability. Junior ice hockey players (skilled) were compared to non-skaters (unskilled) to examine effects of previous experience on the balance response. METHODS Skin surface electrodes were placed over the muscle bellies of gastrocnemius (GA), biceps femoris (BF) and erector spinae (ES) of 6 skilled and 6 unskilled subjects. A triaxial accelerometer was affixed to the ventral surface of the right hand. Subjects performed rapid bimanual shoulder flexions to 20 degrees (Small Perturbation, Ps) and 80 degrees (Large Perturbation, PL). The task was performed on both stable (barefoot) and unstable (in-line skates) surfaces. Muscle activation response time to the self-induced balance perturbation was calculated relative to the initiation of arm movement. RESULTS Significant effects were found for both Perturbation (p <0.01) and Perturbation by Skill level (p < 0.05). BF activated first more often in PL than Ps (51±9 vs. 31±7%) across groups and surfaces. However, skilled showed the same BF response to Ps as to PL (45±10 vs. 52±12%), while unskilled showed a much lower percentage in Ps than PL (16±10 vs. 50±12%). CONCLUSION Both groups (skilled and unskilled) primarily adopted a hip strategy to compensate for the perturbation. The unskilled subjects dealt with Ps differently than PL. This was not the case for the skilled subjects. It appears that extensive training on an unstable surface contributes to skilled subjects exhibiting a balance maintenance response for large perturbations which is also utilized in the face of small perturbations. Supported by NSERC and CIHR CAR Grants 43276
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".