Effect of Custom Foot Insoles on Postural Stability in Figure Skaters While on Ice
Bibliographic record
Abstract
CONTEXT: Improvements in postural stability in figure skaters can play a significant role in performance, as well as reducing fall risk. OBJECTIVE: To explore the effect of custom foot insoles on postural stability in advanced figure skaters. DESIGN: Exploratory study. SETTING: Out of laboratory. PARTICIPANTS: Nine advanced figure skaters were recruited and 7 completed the study (age 38 ± 18.5 y, body-mass index 25 ± 3.6 kg/m2). INTERVENTION: Custom foot insoles. MAIN OUTCOME MEASURES: Primary outcome of changes in postural stability (PS) quantified by center-of-mass sway with secondary outcomes of ankle- and hip-joint sway and joint range of motion. Sway measurements were assessed using body-worn sensors while participants wore skates on ice. PS was assessed in single-leg stance, as well as during gliding on the dominant foot. RESULTS: A significant improvement in static PS was observed after 6-wk use of custom insoles. Center-of-mass sway reduced significantly on average by 48.44% (P = .023), and ankle-joint sway reduced by 45.7% (P = .05) during single-leg-stance balance measurements. During the gliding maneuver nonsignificant changes were observed for both ankle- and knee-joint range of motion. CONCLUSION: The results of this study suggest proof of concept toward benefits of custom insoles in improving postural stability in advanced figure skaters. To generalize the findings, randomized controlled trials with larger sample sizes are warranted.
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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".