Effect of age and sex on strength and spatial electromyography during knee extension
Bibliographic record
Abstract
BACKGROUND: Multichannel surface electromyography (EMG) is a method to examine properties of motor unit (MU) activity using multiple electrodes arranged on a two-dimensional grid. This technique can be used to examine alterations in EMG activity distribution due to contraction intensity as well as due to physiological differences such as age or sex. Therefore, the purpose of this study was to compare strength and high-density surface EMG (HDsEMG) features during isometric and isokinetic knee extensions between older and younger men and women. METHODS: Twenty younger (ages 19-25 years) and twenty older (ages 64-78) men and women performed submaximal and maximal isometric (at a joint angle of 90°) and isokinetic knee extensions, while HDsEMG was recorded from the vastus lateralis. Spatial distribution was estimated using the root mean square (RMS), and 2-dimensional (2D) maps were developed to examine spatial features. Coefficient of variation (CV) and modified entropy were used to examine alterations in muscle heterogeneity and pattern. Peak torque and HDsEMG parameters were compared across age and gender. RESULTS: Younger males and females produced significantly higher mean torque than the older group (p < 0.001) for all contractions. Both age- and sex-related significant differences (p < 0.05) were found for EMG spatial features suggesting neuromuscular differences. Modified entropy was significantly higher and CV was lower for young females compared to young males (p < 0.05) across both isometric and isokinetic contractions. CONCLUSIONS: We found that isometric and isokinetic knee extension strength, spatial distribution, and intensity differ as a function of age and sex during knee extensions. While there were no differences detected in entropy between age groups, there were sex-related differences in the younger age category. The lack of age-related differences in entropy was surprising given the known effects of aging on muscle fiber composition. However, it is often reported that muscle coactivation increases with age and this work was limited to the study of one muscle of the knee extensors (vastus lateralis) which should be addressed in future work. The findings suggest while both age and sex affect muscle activation, sex had a greater effect on heterogeneity. The results obtained will help to develop improved rehabilitation programs for aging men and women.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".