Towards functionally individualised designed footwear recommendation for overuse injury prevention: a scoping review
Bibliographic record
Abstract
Abstract Injury prevention is essential in running due to the risk of overuse injury development. Tailoring running shoes to individual needs may be a promising strategy to reduce this risk. Novel manufacturing processes allow the production of individualised running shoes that incorporate features that meet individual biomechanical and experiential needs. However, specific ways to individualise footwear to reduce injury risk are poorly understood. Therefore, this scoping review provides an overview of (1) footwear design features that have the potential for individualisation; and (2) the literature on the differential responses to footwear design features between selected groups of individuals. These purposes focus exclusively on reducing the risk of overuse injuries. We included studies in the English language on adults that analysed: (1) potential interaction effects between footwear design features and subgroups of runners or covariates (e.g., age, sex) for running-related biomechanical risk factors or injury incidences; (2) footwear comfort perception for a systematically modified footwear design feature. Most of the included articles (n = 107) analysed male runners. Female runners may be more susceptible to footwear-induced changes and overuse injury development; future research should target more heterogonous sampling. Several footwear design features (e.g., midsole characteristics, upper, outsole profile) show potential for individualisation. However, the literature addressing individualised footwear solutions and the potential to reduce biomechanical risk factors is limited. Future studies should leverage more extensive data collections considering relevant covariates and subgroups while systematically modifying isolated footwear design features to inform footwear individualisation.
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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 | 0.000 | 0.000 |
| 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) | 0.284 | 0.002 |
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; both teacher heads agree on what is shown here.
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".