Factors contributing to the fatigue-related reduction in active dorsiflexion joint range of motion
Bibliographic record
Abstract
Reductions in active joint range of motion (ROM) are responsible for decreased work-generating capacity during fatiguing repetitive isotonic shortening contractions. Factors responsible for impairing the joint-angle-specific net torque developed during muscle shortening could include fatigue-induced torque loss, shortening-induced torque depression in the agonist muscle, and opposing passive tension of the antagonists, but these have not been systematically explored. Nine men (aged 25.8 ± 2.0 years) performed a maximal-effort fatiguing task that consisted of repetitive loaded shortening dorsiflexions through a 40° ankle joint ROM until active ROM decreased by 50%. Torque developed during contractile shortening, as well as passive opposing tension, was quantified before and after the reduction in active ROM. Before fatigue, and compared with maximum voluntary isometric contraction torque at the terminal ROM, shortening-induced torque depression in the agonist muscle and passive tension from the antagonists reduced net torque developed at the end of contractile shortening by ∼42% and ∼19%, respectively. After fatigue, a steepened ascending joint torque-angle relationship remained during contractile shortening, but neither muscle coactivation nor contractile slowing contributed to the fatigue-induced torque loss. Fatigue-induced torque loss, shortening-induced torque depression in the agonist, and passive tension in the antagonist greatly depressed net torque developed at the end of contractile shortening. These contributed to the fatigue-induced reduction in active ROM by impairing the ability of the dorsiflexors to generate sufficient torque to overcome the imposed load at the end of contractile shortening.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".