Gait modification strategies for altering medial knee joint load: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of gait modification strategies on the external knee adduction moment (KAM), a marker of medial knee joint load; determine potentially adverse effects; assess the methodologic quality; and identify areas of future research. METHODS: Five electronic databases were searched. Studies evaluating the effects of gait modifications on the KAM in either healthy individuals or those with knee osteoarthritis (OA) were included. Methodologic quality was evaluated by 2 reviewers using the Downs and Black checklist. RESULTS: Twenty-four studies met the inclusion criteria, exploring 14 different gait modifications of varying sample sizes, age groups, and OA classifications. Contralateral cane use, increased step width, medial knee thrust, increased hip internal rotation, weight transfer to the medial foot, and increased lateral trunk lean demonstrated KAM reductions. Tai Chi gait, ipsilateral cane use, Nordic walking poles, and increased knee flexion exhibited increases in the KAM, demonstrating a potential detriment to their use. The effects of reduced stride length, as well as increases and reductions in either toe-out or gait speed, were inconsistent across the studies and gait cycle. CONCLUSION: This review demonstrates that some gait modifications have the ability to alter knee load. Future research is required to determine the magnitude of modification required to maximize beneficial effects, the best method of training, long-term patient adherence, and if these biomechanical changes can translate into clinically relevant changes in symptoms or disease progression risk.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".