Surface effects on kinematics, kinetics and stiffness of habitual rearfoot strikers during running
Bibliographic record
Abstract
The surface effects on running biomechanics have been greatly investigated. However, the effects on rearfoot strike runners remain unknown. The purpose of this study was to investigate the effects of surfaces on the running kinematics, kinetics, and lower-limb stiffness of habitual rearfoot strikers. Thirty healthy male runners were recruited to run at 3.3 ± 0.2 m/s on a customized runway covered with three different surfaces (artificial grass, synthetic rubber, or concrete), and their running kinematics, kinetics, and lower-limb stiffness were compared. Differences among the three surfaces were examined using statistical parametric mapping and one-way repeated-measure analysis of variance. There were no statistical differences in the lower-limb joint motion, vertical ground reaction force (GRF), loading rates, and lower-limb stiffness when running on the three surfaces. The braking force (17%-36% of the stance phase) and mediolateral GRF were decreased when running on concrete surface compared with running on the other two surfaces. The moments of ankle joint in all three plane movement and frontal plane hip and knee joints were increased when running on concrete surface. Therefore, habitual rearfoot strikers may expose to a higher risk of running-related overuse injuries when running on a harder surface.
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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 | 0.000 | 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".