Examining injury risk and pain perception in runners using minimalist footwear
Bibliographic record
Abstract
BACKGROUND: This study examines the effect of progressive increases in footwear minimalism on injury incidence and pain perception in recreational runners. METHODS: One hundred and three runners with neutral or mild pronation were randomly assigned a neutral (Nike Pegasus 28), partial minimalist (Nike Free 3.0 V2) or full minimalist shoe (Vibram 5-Finger Bikila). Runners underwent baseline testing to record training and injury history, as well as selected anthropometric measurements, before starting a 12-week training programme in preparation for a 10 km event. Outcome measures included number of injury events, Foot and Ankle Disability (FADI) scores and visual analogue scale pain rating scales for regional and overall pain with running. RESULTS: 99 runners were included in final analysis with 23 injuries reported; the neutral shoe reporting the fewest injuries (4) and the partial minimalist shoe (12) the most. The partial minimalist shoe reported a significantly higher rate of injury incidence throughout the 12-week period. Runners in the full minimalist group reported greater shin and calf pain. CONCLUSIONS: Running in minimalist footwear appears to increase the likelihood of experiencing an injury, with full minimalist designs specifically increasing pain at the shin and calf. Clinicians should exercise caution when recommending minimalist footwear to runners otherwise new to this footwear category who are preparing for a 10 km event.
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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 | 0.001 | 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) | 0.002 | 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".