The Relationship Between Foot Arch Stiffness, Midsole Bending Stiffness, And Running Economy
Bibliographic record
Abstract
A theoretical foot-shoe model has been proposed by Kelly et al. (2016), where the arch stiffness of the foot and stiffness of the shoe act as two springs in series to maintain the overall system stiffness and improve performance. Matching a runner’s arch stiffness with an appropriate midsole bending stiffness may help optimize the foot-shoe system stiffness and improve running economy. PURPOSE: To determine if a relationship exists between foot arch stiffness and performance in shoes with varying midsole bending stiffnesses. METHODS: Eighteen subjects (10 M; 8F) participated in this study. Measurements of each subject’s foot arch length and midfoot height were calculated from 3D foot scans and used to determine the arch height index (AHI = midfoot height / arch length) for seated and standing positions. An Arch Stiffness Index (ASI) was defined as the ratio of the standing position AHI divided by the seated position AHI with a value closer to 1 representing a stiffer arch. Subjects then performed a 6-minute run in three footwear conditions with varying midsole bending stiffnesses (22 N/mm; 42 N/mm; & 79 N/mm) while metabolic data was collected and analyzed for the final two minutes. The footwear condition with each subject’s lowest running economy was considered their best performing condition. A Pearson product-moment correlation was used to determine the relationship between ASI and the bending stiffness of the shoe in which the subject performed best in (α = .05). RESULTS: There was a moderate, positive correlation between ASI with the stiffness of the shoe that the runner performed best in, which was statistically significant (r = 0.579, p = .012) (Figure 1). CONCLUSIONS: With increasing ASI, runners performed best in shoes with greater midsole bending stiffness, and vice versa. This suggests that matching a runner’s arch stiffness with appropriate midsole bending stiffness can help optimize the overall system stiffness of the foot-shoe interface and improve running economy.
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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 | 0.001 | 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".