Influence of Treadmill Design on Rearfoot Pronation During Gait at Different Speeds
Bibliographic record
Abstract
Understanding the dynamic function of the rearfoot is necessary for recognizing and treating several types of mechanical foot dysfunction. Although the motion of the rearfoot is often measured during treadmill locomotion, the effect of different types of treadmills on the motion of the foot is unclear. In this study, the kinematics of the right subtalar joint in 24 volunteers walking at three speeds on two motorized treadmills were examined. The two treadmills (a wide width and a soft surface versus a narrow width and a hard surface) were selected to maximize motion differences. Maximal change in angular position (positive: supination; negative: pronation) about each volunteer's subtalar joint axis was estimated during three gait phases: weight acceptance, midstance, and push-off. A factorial, repeated-measures analysis of variance determined that the treadmill design had a significant effect on subtalar joint position (F = 5.423; P = .029), albeit with moderate power (0.61). Descriptively, collapsed over all speeds, the subject's feet on the narrow/hard compared with the wide/soft treadmill showed more pronation (0.44 degrees ), less pronation (0.46 degrees ), and more supination (1.44 degrees ) during weight acceptance, midstance, and push-off, respectively. We conclude that treadmill design can affect an individual's rearfoot kinematics.
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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".