Lower limb kinematic variability associated with minimal footwear during running
Bibliographic record
Abstract
Purpose: This study investigated lower limb variability when trained runners wore a minimal shoe for the first time. It was hypothesised that initial lower limb variability would be decreased in the minimal shoe condition due to lack of familiarity. It was also hypothesised that variability would increase over time as runners become more familiar with the condition. Methods: Testing included three 10 minute treadmill running trials conducted in runner's own running shoes, a pair of minimal shoes followed by runner's own shoes again. The shoe order was selected so as to establish a baseline value of variability in a runner's most familiar shoes followed by a perturbation which was the inclusion of minimal shoes. Continuous Relative Phase (CRP) relationships and kinematic values at heel strike were determined which allowed lower limb variability to be quantified. Results: Kinematic variability values were not statistically different between runner's own shoes and minimal shoes. CRP relationships did not differ between minimal shoes and runner's own shoes or over time. Conclusions: Trained runners did not change lower limb variability while wearing minimal shoes for the first time. Lack of familiarity does not appear to affect lower limb variability. The footwear included in this research study had similar cushioning properties to traditional footwear but with a different construction which may relate to similar values found between conditions. Investigating how runners of different abilities transition to minimal footwear should be focused upon to reduce risk of injury.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".