Muscle Activation of the Elbow Flexor and Extensor Muscles During Self-Resistance Exercises
Bibliographic record
Abstract
Muscle activation during self-resistance exercises was studied in 18 subjects performing (a) maximal unilateral isometric cocontractions of flexor and extensor muscles of the right elbow (UNI); (b) bilateral exercises consisting of maximal isometric extensions of the right elbow against the left elbow flexors (BiExt) and maximal isometric flexion of the right elbow against the left elbow extensors (BiFlex). Force production by the biceps brachii (BB), brachioradialis (BR), and triceps brachii (TB) during UNI, BiFlex, and BiExt were estimated by comparing the integrated surface electromyograms (iEMG) of BB, BR, and TB during UNI, BiExt, and BiFlex with the individual iEMG-force relationship determined from isometric contractions at 30, 60, and 100% maximal voluntary contraction during elbow flexion (MVCflex) or extension (MVCext) against a force transducer. During BiFlex for BB or BR and BiExt for TB, the values (mean ± SE) of BB-iEMG, BR-iEMG, and TB-iEMG were 74.0 ± 4.5, 76.6 ± 5.7, and 84.4 ± 4.5% iEMG at MVC (% iEMGmax). The forces were 86.0 ± 3.7% TB-Forcemax during BiExt, 74.1 ± 3.6% BB-Forcemax and 71.8 ± 4.0% BR-Forcemax during BiFlex. During UNI, BB-iEMG, BR-iEMG, and TB-iEMG were 59.9 ± 4.6, 53.4 ± 4.0, and 66.3 ± 4.7% iEMGmax, respectively. The forces during UNI (70.4 ± 4.0% TB-Forcemax, 60.4 ± 4.3% BB-Forcemax, and 49.2 ± 3.1% BR-Forcemax) were significantly lower than those during bilateral exercises. A 2-way analysis of variance (Muscle × Exercise) indicated that the effects of Muscle and Exercise upon % iEMGmax were significant (p < 0.05; p < 0.001, respectively). In conclusion, bilateral opposition exercises should be more effective in developing strength than cocontraction exercises, which correspond to a moderate activation level even for weak agonist muscle groups.
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 | 0.000 | 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".