Observation of the Achilles Tendon and Gastrocnemius Muscle’s Passive Stiffness During an Incremental Running Protocol
Bibliographic record
Abstract
OBJECTIVE: Passive stiffness, a biomechanical parameter, has a potential influence on running economy, thus playing a pivotal role in performance. This study aimed to quantify passive stiffness of the Achilles tendon and gastrocnemius muscle using myotonometry throughout an incremental running protocol. METHODS: Twenty-one male participants underwent a multistage incremental test (Bordeaux test) on a treadmill until exhaustion. Passive stiffness of the gastrocnemius muscle and Achilles tendon was measured using a MyotonPRO device. Measurements were taken before, during, and after the incremental test. RESULTS: Our findings indicated that passive stiffness of the medial gastrocnemius decreased at rest between prerun and postrun assessments (-20.12 N·m-1, P = .012). Furthermore, during the test, stiffness increased at low intensity (at 50% of maximal aerobic speed: +104.8 N·m-1, P = .042), returning to baseline values as intensity increased. CONCLUSIONS: Throughout an incremental running protocol, both Achilles tendon and gastrocnemius muscle stiffness initially increased and then decreased with escalating percentages of maximal aerobic speed. A deeper understanding of stiffness in running can inform more effective recommendations for runners' strengthening and training.
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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) | 0.001 | 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) | 0.001 | 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".