Upper body muscle activation during low-versus high-load resistance exercise in the bench press
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to compare activation of the upper body musculature during the barbell bench press at varying training intensities. METHODS: Twelve young, resistance-trained men performed sets of the bench press to momentary muscular failure with two different loads: a high-load(HIGH) set at 80% of 1RM and a low-load (LOW) set at 50% 1RM. Exercise order was counterbalanced so that half the subjects performed the LOW condition first and the other half performed the HIGH first. Surface electromyography (EMG) was used to assess mean, peak, and iEMG muscle activation of the anterior deltoid, triceps brachii, and sternal and clavicular heads of the pectoralis major. RESULTS: The main effects for trials were significant for mean EMG (p < 0.001) and iEMG matched (p < 0.001) favoring HIGH and iEMG total favoring LOW(p = 0.001) across all muscle groups in both conditions with varying effect sizes. All other main effects and interactions were not statistically significant. CONCLUSION: Despite similarities in peak EMG amplitude, the greater results for mean and iEMG matched in HIGH suggests that heavier loads may produce greater muscle activation.
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) | 0.004 | 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".