A Systematic Review of Finite Element Analysis in Running Footwear Biomechanics: Insights for Running-Related Musculoskeletal Injuries
Bibliographic record
Abstract
This study presented a systematic review of recent advancements in the application of finite element (FE) methods to running and running shoe biomechanics. It focused on outlining the general approach to build foot-running shoe FE models, exploring their current applications and challenges, and providing directions for future research. The review also aimed to highlight the gap between theoretical mechanical responses in simulations and real-world manifestations of running-related musculoskeletal injuries (RRMI). A comprehensive search of electronic databases, including Web of Science, PubMed, and Scopus, identified 12 eligible articles for inclusion in this review. Current studies have examined the effects of various running shoe design features and conditions on the mechanical response of internal foot tissues using foot-running shoe FE models. These models have gradually evolved from simplified local representations to more realistic and comprehensive models, with the incorporation of experimental data enhancing simulation accuracy. However, to further improve simulation outcomes, key advancements are proposed to reduce development time and enhance model robustness. These include high-fidelity 3D model development, personalized shape transformation, AI-driven automated reconstruction, comprehensive dynamic running simulations, and improved validation methods. More importantly, future research needs to bridge the gap between FE simulations and RRMI risk by addressing the complexities of bone fracture criteria and conducting localized assessments of bone properties. Overall, this review provided valuable insights for biomedical engineers, medical professionals, and researchers, facilitating more accurate investigations of foot-running shoe FE models. Ultimately, these advancements aim to improve footwear design and training programs to reduce the risk of RRMI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".