Increasing the midsole bending stiffness of shoes alters gastrocnemius medialis muscle function during running
Bibliographic record
Abstract
In recent years, increasing the midsole bending stiffness (MBS) of running shoes by embedding carbon fibre plates in the midsole resulted in many world records set during long-distance running competitions. Although several theories were introduced to unravel the mechanisms behind these performance benefits, no definitive explanation was provided so far. This study aimed to investigate how the function of the gastrocnemius medialis (GM) muscle and Achilles tendon is altered when running in shoes with increased MBS. Here, we provide the first direct evidence that the amount and velocity of GM muscle fascicle shortening is reduced when running with increased MBS. Compared to control, running in the stiffest condition at 90% of speed at lactate threshold resulted in less muscle fascicle shortening (p = 0.006, d = 0.87), slower average shortening velocity (p = 0.002, d = 0.93) and greater estimated Achilles tendon energy return (p ≤ 0.001, d = 0.96), without a significant change in GM fascicle work (p = 0.335, d = 0.40) or GM energy cost (p = 0.569, d = 0.30). The findings of this study suggest that running in stiff shoes allows the ankle plantarflexor muscle-tendon unit to continue to operate on a more favourable position of the muscle's force-length-velocity relationship by lowering muscle shortening velocity and increasing tendon energy return.
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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) | 0.001 | 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".