Effect of Shoe and Surface Stiffness on Lower Limb Tendon Strain in Jumping
Bibliographic record
Abstract
BACKGROUND: Tendinopathies are painful overuse injuries observed in athletes participating in jumping sports. These injuries are heavily dependent on the resulting strain from the applied mechanical load. Therefore, mechanisms to reduce tendon strain may represent a primary prevention strategy to reduce the incidence of tendinopathy. PURPOSE: The purpose of this study was to examine the effect of shoe and surface stiffness on Achilles and patellar tendon strains during jumping. We hypothesized that less stiff shoes and surfaces would reduce Achilles and patellar tendon strains during jumping. METHODS: Thirty healthy male basketball players performed countermovement jumps in three shoes and on three surfaces with different stiffness properties while motion capture, force platform, and jump height data were collected. Magnetic resonance imaging was used to obtain participant-specific tendon morphology, and a combined dynamometry/ultrasound/electromyography session was used to obtain tendon material properties. Finally, a musculoskeletal model was used to estimate tendon strains in each surface and shoe combination. RESULTS: Achilles tendon strains during landing were reduced by 5.3% in the least stiff shoe compared with the stiffest shoe (P = 0.021) likely due to in bending stiffness altering the center of pressure location. Furthermore, Achilles tendon strains during landing were 5.7% and 8.1% lower on the stiffest surface compared with the least stiff and middle stiffness surfaces, respectively (P ≤ 0.047), because of changes in ground reaction force magnitude and center of pressure location. No effects of shoe stiffness or surface construction were observed for jump height (P > 0.243) or peak patellar tendon strains (P > 0.259). CONCLUSIONS: Changes to shoe stiffness and surface construction can alter Achilles tendon strains without affecting jump performance in athletes.
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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 | 0.000 | 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".