Plantar load characteristics among runners with different strike patterns during preferred speed
Bibliographic record
Abstract
This study aimed to compare the plantar loads between habitual rearfoot strike (RFS) and non-RFS (NRFS) during running under the participant’s preferred speed. A total of 66 (36 RFS, 30 NRFS) healthy amateur male runners were included in our study. In-shoe pressure sensors were utilised to the test plantar loads when participants were running using their preferred foot strike pattern and running speed (RFS: 3.2 ± 0.3 m/s; NRFS: 3.4 ± 0.4 m/s). Results indicated that running speed has a significant effect on the total contact area [F (1, 64) = 7.061, P = 0.01, η2 = 0.101], which also affects midfoot and forefoot regions. No significant difference was found on the total maximum force, force-time-integral, peak pressure (PP) and pressure-time-integral (PTI), but the total contact area of RFS was higher than that of NRFS runners [F (1, 64) = 77.406, P < 0.001, η2 = 0.551]. Plantar loads were mainly focused on the heel and midfoot for RFS runners in all variables, and NRFS runners experienced increased PP and PTI in medial forefoot regions. Habitual runners tend to adjust their contact area according to the running speed through midfoot and forefoot regions. RFS runners remain susceptible to high impact force on the heel and midfoot, and NRFS runners experience high impact force in the first metatarsal regions. Therefore, runners should note this situation to avoid running-related injuries.
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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) | 0.002 | 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".