The Isolated Effect of Midsole Compliance on Running Economy and Biomechanics in Highly Trained Runners
Bibliographic record
Abstract
PURPOSE: Running economy is a key determinant of endurance performance, with recently developed advanced footwear technologies (AFTs) improving running economy substantially. One key feature of AFTs is the thick, compliant midsole. Previously, greater surface compliance has been associated with greater leg stiffness and enhanced running economy, suggesting that increased shoe compliance could induce similar effects and therefore at least partially explain the metabolic benefit of AFTs. However, it remains unclear whether midsole compliance replicates the effects of surface compliance on running economy and leg stiffness and what biomechanical mechanisms underlie these improvements. METHODS: Nineteen well-trained male runners completed biomechanical and metabolic testing in two shoes designed to differ only in midsole compliance. Participants ran three 5-min trials at 12 and 16 km·h -1 on an instrumented treadmill in each shoe. During these trials, we collected 3D motion capture data, ground reaction forces, and whole-body metabolic rate via indirect calorimetry. RESULTS: More compliant footwear was associated with a 3.90% improvement in running economy ( P < 0.001) and a 2.98% increase in leg stiffness ( P < 0.01). Additionally, runners exhibited reduced knee flexion at midstance, leg compression, average knee extension velocity, and peak knee extensor moment with greater midsole compliance ( P < 0.05). These biomechanical changes resulted in a 9.46% decrease in average positive knee joint power ( P = 0.001). CONCLUSIONS: Greater midsole compliance improves running economy. Altering midsole compliance primarily affects knee mechanics and average positive knee joint power. While future research should explore muscle fascicle dynamics to directly determine the muscle-level effect, our results suggest that shoe compliance improves running economy by lowering knee extensor muscle metabolic demand.
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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".