The effect of three different levels of footwear stability on pain outcomes in women runners: a randomised control trial
Bibliographic record
Abstract
BACKGROUND: The present study examines the injury status in women runners who are randomised to receive a neutral, stability or motion control running shoe. METHODS: 81 female runners were categorised into three different foot posture types (39 neutral, 30 pronated, 12 highly pronated) and randomly assigned a neutral, stability or motion control running shoe. Runners underwent baseline testing to record training history, as well as leg alignment, before commencing a 13-week half marathon training programme. Outcome measures included number of missed training days due to pain and three visual analogue scale (VAS) items for pain during rest, activities of daily living and with running. RESULTS: 194 missed training days were reported by 32% of the running population with the stability shoe reporting the fewest missed days (51) and the motion control shoe (79) the most. There was a significant main effect (p<0.001) for footwear condition in both the neutral and pronated foot types: the motion control shoe reporting greater levels of pain in all three VAS items. In neutral feet, the neutral shoe reported greater values of pain while running than the stability shoe; in pronated feet, the stability shoe reported greater values of pain while running than the neutral shoe. No significant effects were reported for the highly pronated foot, although this was limited by an inadequate sample size. CONCLUSION: The findings of this study suggest that our current approach of prescribing in-shoe pronation control systems on the basis of foot type is overly simplistic and potentially injurious.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".