Cumulative Metrics of Tendon Load and Damage Vary Discordantly with Running Speed
Bibliographic record
Abstract
PURPOSE: Cumulative load has become a popular metric in running biomechanics research to account for potential spatiotemporal changes associated with different locomotion strategies. This study investigated how incorporating mechanical fatigue principles into Achilles tendon cumulative load measurements affected their relationship with running speed. METHODS: Achilles tendon forces and strains were estimated from a dynamometry/ultrasound session followed by a motion capture session, where participants ran at three speeds. Three cumulative measures of increasing complexity were calculated using Achilles tendon force/strain: 1) cumulative load, defined as the product of the stance phase time integral of Achilles tendon force/strain and the stride count for 1 km of running; 2) cumulative damage, which accounted for the nonlinear relationship between load magnitude and fatigue life by exponentially weighting the time integral of Achilles tendon force/strain before multiplication with stride count; and (3) the probability of fatigue failure, which expanded upon the cumulative damage measure of Achilles tendon strain by fitting a probabilistic Weibull model to existing fatigue life data to account for the inherent variability that exists in the fatigue life of biological samples. RESULTS: Cumulative load measures significantly decreased with running speed, whereas the cumulative damage and probabilistic measures either increased or did not change significantly with running speed. CONCLUSIONS: The choice of cumulative metric has an important influence on the interpretation of overuse injury risk with changes in running speed. Although cumulative load metrics certainly provide meaningful information about the load experienced over a given distance, they do not account for the tissue damage incurred by such load. Cumulative load metrics should therefore be interpreted with caution when making inferences to overuse injury risk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".