The effects of systematically altered footwear features on biomechanics, injury, performance, and preference in runners of different skill level: a systematic review
Bibliographic record
Abstract
Matching running footwear features to the functional needs of specific groups of runners stratified according to their training status or running experience has the potential to improve footwear preference, enhance running performance, and reduce the risk of running-related injuries. The complexity of modern running shoes in combination with the various functional needs of specific groups of runners, however, introduces many confounding factors, disguising what an appropriate shoe design should entail. As a result, it is crucial to identify and synthesise the effects of systematically modified running footwear features on lower extremity biomechanics, performance, injury risk, and footwear preference in runners of different skill levels to advance the knowledge of running biomechanics and footwear. The databases Scopus and PubMed were searched for related studies and 895 articles were identified. After the application of exclusion criteria, 68 articles remained and were arranged in a matrix that highlighted the investigated footwear feature and runner level. The effects of 20 footwear features on lower extremity biomechanics, injury, performance, and preference in novice, recreational, and high calibre runners were synthesised. Laced uppers with harder midsole materials in the forefoot/lateral regions of the shoe and softer materials in the rearfoot/medial regions increased comfort and thus likely preference. To increase performance, footwear should be constructed as lightweight as possible, and implement a range of different bending stiffness for individual needs. Injury risks at the knee joint were reduced in footwear with thinner midsoles. For high calibre runners, specifically, insoles should be custom moulded to the shape of the foot, and special attention needs to be payed to the elasticity of the upper, in order to avoid detrimental shifts in running patterns.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".