The Effects of Wedged Footwear on Lower Limb Frontal Plane Biomechanics During Running
Bibliographic record
Abstract
OBJECTIVE: Patellofemoral pain syndrome (PFPS), the most common running injury, has been associated with increased internal knee abduction angular impulses (KAAI). Wedged footwear can reduce these impulses during walking, but their effects during running are not well understood. The purpose of this study was to identify the effects of wedged footwear on KAAIs and describe the mechanism by which wedged footwear alters KAAIs during running. DESIGN: Controlled laboratory study. SETTING: Motion analysis laboratory. PARTICIPANTS: Nine healthy male subjects. INTERVENTIONS: Participants ran at a speed of 4 m/s with 7 different footwear conditions (3-, 6-, and 9-mm lateral wedges; 3-, 6-, and 9-mm medial wedges; neutral). MAIN OUTCOME MEASURES: Knee abduction angular impulses and 8 predictor variables were measured and compared by 1-way repeated measures analysis of variance (α = 0.05) with Bonferroni-adjusted 2-tailed paired t tests for post hoc analysis (α = 0.002). Correlation (α = 0.05) was used to determine the relationship between the mediolateral center of pressure to ankle joint center (COP-AJC) lever arm length and KAAIs. RESULTS: Laterally wedged conditions produced significantly lower KAAIs (P = 0.001) than medial wedge conditions. Peak knee abduction moments decreased (P = 0.001), whereas ankle inversion moments (P = 0.041) and the COP-AJC lever arms increased (P < 0.001) as wedges progressed from medial to lateral. KAAIs were negatively correlated with COP-AJC lever arm length (r = -0.50, P < 0.001). CONCLUSIONS: KAAIs are reduced with laterally wedged footwear because of lateral shifts in the center of pressure beneath the foot, which then increases ankle inversion moments and decreases peak knee abduction moments. Laterally wedged footwear may therefore offer greater relief to runners with PFPS than medially wedged footwear by reducing KAAIs.
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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".