Biomechanical insights into Achilles tendinopathy risk and protection in runners: a large prospective study 4HAIE
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate whether lower limb biomechanics in runners and non-runners are risk factors for the onset of Achilles tendinopathy, and to assess the contributions of age, sex, running distance and injury history to the onset of Achilles tendinopathy. METHODS: This prospective cohort study used quota sampling stratified by age, sex, region and physical activity status (runner, non-runner). Baseline assessments included questionnaires on running history and Achilles tendinopathy symptoms, running biomechanics, MRI and dual-energy X-ray absorptiometry. Participants were followed for 1 year using a Fitbit device and a custom mobile application for weekly injury reporting and orthopaedic diagnoses. Binary logistic regression identified risk factors (OR, 95% CI). Primary biomechanical variables included ankle, knee and hip kinematics and kinetics during the stance phase of running. The main outcome was a medically confirmed diagnosis of Achilles tendinopathy within 1 year. RESULTS: Our study included 911 adults (mean age 37.7±12.5 years; 429 (47%) females; 528 (58%) runners) followed for 1 year. A higher peak ankle inversion moment (OR 0.33, 95% CI 0.17 to 0.61) during the stance phase decreased the odds of developing Achilles tendinopathy, while a lower peak ankle external rotation angle (OR 2.20, 95% CI 1.23 to 4.03) and greater running distance (OR 1.67, 95% CI 1.23 to 2.22) increased the odds of onset. The findings were consistent across the full cohort and runners. CONCLUSION: We identified a lower peak ankle inversion moment, a lower peak ankle external rotation angle and a greater running volume as significant predictors of the onset of Achilles tendinopathy. Targeting biomechanical factors and running volume may help prevent Achilles tendinopathy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".