Effects of Lateral and Medial Wedged Insoles on Knee and Ankle Internal Joint Moments During Walking in Healthy Men
Bibliographic record
Abstract
BACKGROUND: Wedged insoles have been used to treat knee pathologies and to prevent injuries. Although they have received much attention for the study of knee injury, the effects of wedges on ankle joint biomechanics are not well understood. This study sought to evaluate the immediate effects of lateral and medial wedges on knee and ankle internal joint loading and center of pressure (CoP) in men during walking. METHODS: Twenty-one healthy men walked at 1.4 m/sec in five footwear conditions: neutral, 6° (LW6) and 9° (LW9) lateral wedges, and 6° (MW6) and 9° (MW9) medial wedges. Peak internal knee abduction moments and angular impulses, internal ankle inversion moments and angular impulses, and mediolateral CoP were analyzed. Analysis of variance with post hoc analysis and Pearson correlations were performed to detect differences between conditions. RESULTS: No differences in internal knee joint loading were found between neutral and any of the wedge conditions. However, as the wedge angle increased from medial to lateral, the internal ankle inversion moment (LW6: P = .020; LW9: P < .001; MW6: P = .046; MW9: P < .001) and angular impulse (LW9: P = .012) increased, and the CoP shifted laterally (LW9: P < .001) and medially (MW9: P < .001) compared with the neutral condition. CONCLUSIONS: Neither lateral nor medial wedges were effective in altering internal knee joint loading during walking. However, the greater internal ankle inversion moment and angular impulse observed with lateral wedges could lead to a higher risk of ankle injury. Thus, caution should be taken when lateral wedges need to be prescribed.
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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) | 0.002 | 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".