Voluntary muscle activation varies with age and muscle group
Bibliographic record
Abstract
The consistency and the number of attempts required to achieve maximal voluntary muscle activation have not been documented and compared between young and old adults. Furthermore, few studies have contrasted activation between functional pairs of muscle groups, and no study has tested upper limb muscles. The purpose of this study was to measure and compare voluntary muscle activation of the elbow flexors and extensors in young and old men over two separate test sessions. With the method of twitch interpolation to measure activation, six young (24 +/- 1 yr) and six old (83 +/- 4 yr) men performed five maximal voluntary contractions (MVC) during each session for each muscle group. Elbow flexion and extension MVC was less (43 and 47%, respectively) in the old men, yet the best maximal voluntary muscle activation was similar between age groups. However, when all 10 attempts at MVC were compared, the mean activation scores were slightly less (approximately 5%) in the elbow extensors but were approximately 11% less (P < 0.001) in the elbow flexors of old men, compared with young men. During the second session, there was a significant improvement of 13% (P < 0.005) in mean elbow flexor activation in the old men. There were no session differences for either muscle group for the young men. The results indicate that, for aged men, elbow flexor maximal activation is achieved less frequently compared with elbow extensors, and thus mean activation for elbow flexors is less than for elbow extensors. However, if sufficient attempts are provided, the best effort for the old men is not different from that of the young men for either muscle group.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".