Effect of Submaximal Repetitive Exercise on Knee Coactivation in Young and Middle-Aged Women
Bibliographic record
Abstract
Coactivation of the knee extensors and flexors increases knee joint contact forces, which may lead to degradation of the articular surfaces. This study investigated the effect of neuromuscular fatigue induced by submaximal, repetitive, dynamic contractions on coactivation of knee musculature in young and middle-aged women. Data from 10 young women (24.6±1.8 years) and 8 middle-aged women (55.4±4.2 years) were analyzed. Measures included peak knee extension and flexion torques and the average amplitude of surface electromyography of rectus femoris and biceps femoris. Coactivation ratios were calculated from these activations. To induce fatigue, participants completed up to ten sets of 50 concentric knee extension and flexion contractions at 60°/s. A two-factor analysis of variance was used to determine the effect of age and fatigue. The young group showed higher peak torque compared with the middle-aged group (P<.001). During flexion, biceps femoris activity increased after fatigue when both groups were considered together (P=.018). During extension, biceps femoris activity was higher in the middle-aged than young group (P=.043). Middle-aged women exhibited a trend for greater coactivation during knee extension compared with young women (P=.066). This coactivation likely contributed to extension torque decrements in middle-aged women.
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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) | 0.001 | 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".