The effect of footwear torsional stiffness on lower extremity kinematics and kinetics during lateral cutting movements
Bibliographic record
Abstract
Purpose: Footwear torsional stiffness affects ankle kinematics during cutting movements. The influence of torsional stiffness on lower extremity kinetics has not been studied. It was hypothesised that footwear with high torsional stiffness increases the ankle eversion, knee abduction and internal rotation moments in cutting movements. Methods: Nineteen participants performed seven repetitions of a lateral jab and a shuffle cut in two shoes with different torsional stiffness. Markers placed on forefoot, rearfoot and shank of the right leg were used to determine the kinematics. Simultaneous recordings of the ground reaction forces allowed the calculation of ground reaction impulses and internal joint moments using an inverse dynamics approach. Results: Peak torsion angles (frontal plane rotation between fore- and rearfoot) were reduced in the torsional stiff shoe (shuffle cut: 22.0° vs. 18.5°, p < 0.001; lateral jab: 22.8° vs. 20.1°, p = 0.020 flexible vs. stiff shoe). For the shuffle cut, the peak ankle eversion moment (52.9 Nm vs. 63.0 Nm, p = 0.003) and inversion angle (25.9° vs. 28.5°, p < 0.001) were higher in the stiff shoe. For the lateral jab no differences between footwear were found for the ankle kinematics or kinetics. No differences between footwear were found for knee kinematics. The ground reaction impulses were not different between shoes. Conclusions: Increased footwear torsional stiffness causes higher ankle eversion moments which may increase the risk for ankle injuries. Knee moments were not affected by footwear torsional stiffness; therefore, footwear torsional stiffness seems to have no effect on the risk for anterior cruciate ligament (ACL) injuries.
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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 | 0.002 |
| 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) | 0.002 | 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".