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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".