Biomechanical effects of maximal footwear on running: a systematic review and network meta-analysis
Bibliographic record
Abstract
Running is widely recognised for its substantial health benefits; however, it is frequently associated with lower limb injuries caused by repetitive impact forces. To mitigate such injuries, maximal footwear has been developed; nevertheless, evidence comparing its biomechanical effects with those of other footwear types remains inconclusive. A Bayesian network meta-analysis of 14 studies (222 participants) was conducted, based on systematic searches of PubMed, Web of Science, the Cochrane Library, Scopus, and Embase (from inception to 12 November 2024). Multiple biomechanical parameters were evaluated, including vertical average loading rate, vertical instantaneous loading rate, impact peak, active peak and ankle peak eversion. The results revealed a complex and sometimes contradictory biomechanical profile for maximal footwear. Specifically, maximal footwear resulted in a significantly higher impact peak compared to both conventional and minimal footwear. In contrast, for the vertical average loading rate, it performed significantly better than minimal footwear but showed no significant difference compared to conventional footwear. For other impact metrics, no significant differences were observed. Notably, maximal footwear was associated with a significantly lower ankle peak eversion compared to minimal footwear, suggesting a potential for greater control of ankle motion.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".