The Effects of Knee Joint Angle and Contractor Intensity on Perceived Exertion
Bibliographic record
Abstract
This study examined knee joint angle and knee muscle contraction intensity effects on perceived exertion during isometric contractions. Fourteen healthy young adults participated in five experimental exercise sessions in which knee angles varied randomly (10°, 30°, 50°, 70°, and 90°), each separated by one week. During each session, subjects performed five isometric maximal voluntary contractions (MVCs) of knee extension, followed by nine, randomly ordered submaximal contractions (10%-90% MVC, 10% increments). The participants repeated the identical procedure for the knee flexor muscles. Immediately following each submaximal contraction, participants rated their perceived exertion using a modified Borg category-ratio scale. We found that the participants' overall ratings of perceived exertion were significantly ( p < .05) greater at the 90° than at the 70° and 10° positions during the knee extensor contractions. There were also several significant angle by contraction intensity interactions in that perceived exertion was significantly greater across 60% to 70% MVC at 30° than at 50° ( p < .01), while the opposite pattern was observed across 70% to 80% MVC ( p < .01). During knee flexor contractions, perceived exertion was significantly greater ( p < .05) at 90°, when compared with all other knee angles. There were also significant ( p < .05) angle by contraction intensity interactions between the 50° and 70° knee positions across contraction intensities of 30-40%, 40-50%, 50-60%, and 60-70% MVC. We conclude that the higher perceived exertion rating at 90° during knee extension and flexion contractions suggests different peripheral and central contributors between both muscle groups, due to differences in muscle length.
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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 | 0.001 | 0.006 |
| 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".