Running shoes and running injuries: mythbusting and a proposal for two new paradigms: ‘preferred movement path’ and ‘comfort filter’
Bibliographic record
Abstract
In the past 100 years, running shoes experienced dramatic changes. The question then arises whether or not running shoes (or sport shoes in general) influence the frequency of running injuries at all. This paper addresses five aspects related to running injuries and shoe selection, including (1) the changes in running injuries over the past 40 years, (2) the relationship between sport shoes, sport inserts and running injuries, (3) previously researched mechanisms of injury related to footwear and two new paradigms for injury prevention including (4) the 'preferred movement path' and (5) the 'comfort filter'. Specifically, the data regarding the relationship between impact characteristics and ankle pronation to the risk of developing a running-related injury is reviewed. Based on the lack of conclusive evidence for these two variables, which were once thought to be the prime predictors of running injuries, two new paradigms are suggested to elucidate the association between footwear and injury. These two paradigms, 'the preferred movement path' and 'the comfort filter', suggest that a runner intuitively selects a comfortable product using their own comfort filter that allows them to remain in the preferred movement path. This may automatically reduce the injury risk and may explain why there does not seem to be a secular trend in running injury rates.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".