Energy Storage And Return From Footwear And Biological Structures While Running
Bibliographic record
Abstract
Energy Storage and Return from Footwear and Biological Structures While Running Extraordinary performances have been achieved by runners using advanced footwear. Previous research has found energetically favorable changes in biological joint kinetics while running in such footwear. However, there is limited research that also quantifies footwear kinetics during running. PURPOSE: To compare energy storage and return from footwear and biological structures while running in shoes of varying constructions. METHODS: Fifteen male runners ran in a motion analysis lab while kinematics and ground reaction forces were collected. Three footwear conditions of varying construction were evaluated (Shoe A, B, C). Shoe A had the greatest midsole compliance and longitudinal bending stiffness. Hip, knee, and ankle joint powers were computed using inverse dynamics to evaluate the mechanical work at biological joints. Distal rearfoot power was computed to evaluate the net mechanical work from the biological foot and footwear structures (Foot + Footwear). Kruskal-Wallis and Wilcoxon signed-rank tests were used for between footwear comparisons. RESULTS: Negative Foot + Footwear work (energy storage) was significantly greater in Shoe A (0.29 ± 0.07 J/kg) than Shoe B (0.21 ± 0.06 J/kg, p < 0.001), but not Shoe C (0.26 ± 0.08 J/kg, p = 0.055). Positive Foot + Footwear work (energy return) was significantly greater in Shoe A (0.30 ± 0.09 J/kg) than both other shoes (Shoe B: 0.19 ± 0.08 J/kg, p = 0.001; Shoe C: 0.15 ± 0.07 J/kg, p < 0.001). Positive ankle work was significantly lower in Shoe A (0.61 ± 0.08 J/kg) than both other shoes (Shoe B: 0.68 ± 0.09 J/kg, p < 0.001; Shoe C: 0.67 ± 0.06 J/kg, p = 0.003). Hip and knee work were not significantly different between shoe conditions (p > 0.05). CONCLUSION: Understanding how advanced footwear stores and returns energy during running may explain the reduction in work required by biological structures and resulting increased performance. Supported by Li-Ning.
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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 | 0.000 |
| 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".