PRONATION, SHOCK, AND INJURY IN THE RUNNING ATHLETE
Bibliographic record
Abstract
INTRODUCTION: Recent running injury trends suggest a predominance of lower leg impact injuries as compared to knee injuries observed in the past. Shoe desisn and training patterns based on past injury trends emphasize pronation control. However, pronation may be a positive gait characteristic, controlling impact forces experienced by the body and thus influencing injury. This relationship has implications in the industry of shoe design and ultimately the prevention of running related injuries. PURPOSE: To determine the relationship between rearfoot motion and shock attenuation during running and their link to injury in trained runners. METHODS: Ninety-two intercollegiate running athletes (40 women, 52 men, mean age = 21 years) were prospectively monitored through biomechanical analysis, survey data, daily log and physician assessment. Subjects ran on a treadmill at 3.8m/s in a fatigued state. Rearfoot motion was assessed in the coronal plane using 2D video (60Hz). Shock transmission at the tibia was measured using uniaxial accelerometers (1000Hz). Each athlete was assessed for injury by a qualified CASM physician. RESULTS: Initial assessment of the first twenty-five subjects revealed lower leg injuries in thirty-three percent. A negative correlation was found between peak tibial acceleration and magnitude of rearfoot motion (r = −0.9212) in this group, suggesting a biomechanical mechanism influencing impact forces. CONCLUSIONS: Rearfoot motion appears to affect shock transmission through the tibia in athletes reporting impact loading type injuries. Objective biomechanical parameters may be implicated, or conversely, may be predictive of running injury. This could influence the focus of future footwear design and prevention of injuries. A research grant was provided by the Nike Sports Research Laboratory, Beaverton, Oregon, USA
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".