Effects of Surface Inclination on the Vertical Loading Rates and Landing Pattern during the First Attempt of Barefoot Running in Habitual Shod Runners
Bibliographic record
Abstract
Barefoot running has been proposed to reduce vertical loading rates, which is a risk factor of running injuries. Most of the previous studies evaluated runners on level surfaces. This study examined the effect of surface inclination on vertical loading rates and landing pattern during the first attempt of barefoot running among habitual shod runners. Twenty habitual shod runners were asked to run on treadmill at 8.0 km/h at three inclination angles (0°; +10°; -10°) with and without their usual running shoes. Vertical average rate (VALR) and instantaneous loading rate (VILR) were obtained by established methods. Landing pattern was decided using high-speed camera. VALR and VILR in shod condition were significantly higher (p < 0.001) in declined than in level or inclined treadmill running, but not in barefoot condition (p > 0.382). There was no difference (p > 0.413) in the landing pattern among all surface inclinations. Only one runner demonstrated complete transition to non-heel strike landing in all slope conditions. Reducing heel strike ratio in barefoot running did not ensure a decrease in loading rates (p > 0.15). Conversely, non-heel strike landing, regardless of footwear condition, would result in a softer landing (p < 0.011).
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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) | 0.001 | 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".