The effect of shoe torsional stiffness on lower extremity kinematics and biomechanical risk factors for patellofemoral pain syndrome during running
Bibliographic record
Abstract
Objective: The effect of footwear torsional stiffness on lower extremity biomechanics is not well known, although there are indications that it could affect rearfoot and ankle kinematics. These variables have previously been linked to the development of patellofemoral pain syndrome (PFPS) in runners. Therefore, the aim of this study was to compare the rearfoot and ankle frontal plane kinematics and knee abduction angular impulse between shoes with different torsional stiffness during running. Methods: Nineteen experienced runners performed heel-toe running at 3.7 m s−1 in three running shoes with different torsional stiffness. Using surface-mounted markers and a force plate, the kinematics and kinetics of the rearfoot, ankle and knee were measured. Torsion, rearfoot and ankle eversion, tibial rotation, knee abduction impulse, and vertical ground reaction force (GRF) peak were compared between footwear conditions using repeated-measures ANOVA. Results: The torsion angle was significantly different between shoes but none of the other variables showed a difference between conditions. Focusing only on the part of the stance phase with forefoot–ground contact, significant differences were reported in the range of motion (ROM) of rearfoot and ankle eversion. The differences in torsional stiffness of the running shoes did not alter the rearfoot/ankle kinematics or the knee angular impulse, which are variables that have been described as risk factors for PFPS. Conclusions: During heel-toe running, the shoe torsional stiffness does not seem to have an effect on the injury risk for PFPS. However, there are indications that, for movements performed mainly on the forefoot, this shoe characteristic could have relevance.
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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".