Effect of eccentric contraction velocity on muscle damage in repeated bouts of elbow flexor exercise
Bibliographic record
Abstract
Eccentric exercise induces muscle damage, but controversy exists concerning the effect of contraction velocity on the magnitude of muscle damage, and little is known about the effect of contraction velocity on the repeated-bout effect. This study examined slow (60 degrees.s(-1)) and fast (180 degrees.s(-1)) velocity eccentric exercises for changes in indirect markers of muscle damage following 3 exercise bouts that were performed every 2 weeks. Fifteen young men were divided into 2 groups based on the velocity of eccentric exercise: 7 in the Ecc60 (60 degrees.s(-1)) group, and 8 in the Ecc180 (180 degrees.s(-1)) group. The exercise consisted of 30 maximal eccentric contractions of the elbow flexors at each velocity, in which the elbow joint was forcibly extended from 60 degrees to 180 degrees (full extension) on an isokinetic dynamometer. Changes in maximal voluntary isometric contraction strength, range of motion, muscle soreness, and plasma creatine kinase activity before and for 4 days after the exercise were compared in the 2 groups using a mixed-model analysis (groupxboutxtime). No significant differences between groups were evident for changes in any variables following exercise bouts; however, the changes were significantly smaller (p<0.05) after the second and third bouts than after the first bout. These results indicate that the contraction velocity does not influence muscle damage or the repeated-bout effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".