Cycling Cleat Positioning Influences Achilles Tendon Strains, but at What Energetic Cost?
Bibliographic record
Abstract
INTRODUCTION: Patellar and Achilles tendinopathy are overuse injuries associated with repetitive strain and often arise following sudden increases in exercise intensity and/or volume. As such, interventions that reduce tendon strain may represent effective methods for reducing overuse injury risk in this at-risk population of cyclists. One potential intervention is through the anterior/posterior positioning of the cycling cleat. METHODS: Ten recreational athletes, who were all novice cyclists, cycled on a stationary cycle ergometer at three cleat positions (neutral, 20 mm anterior, and 20 mm posterior), four power outputs (150, 200, 250, and 300 W), and two rider positions (seated and standing) for a total of 24 conditions. Motion capture and plantar-pressure data were collected, and peak Achilles and patellar tendon strains were obtained using musculoskeletal modeling. Metabolic output for each condition was also modeled using a combination of musculoskeletal modeling and a previously published metabolic model. RESULTS: Peak Achilles tendon strain was significantly reduced with a posterior cleat position compared with a neutral ( P = 0.047) and anterior ( P < 0.001) position during both standing and seated cycling. Peak patellar tendon strain ( P = 0.928) and modeled metabolic output ( P = 0.778), however, were not influenced by cleat position. CONCLUSIONS: Cycling with a 20 mm posterior cleat position represents an effective intervention for reducing the risk of developing Achilles tendinopathy without concurrently increasing patellar tendon strain or sacrificing performance.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".