The Effect of 5, 10, and 20 Repetition Maximums on the Recovery of Voluntary and Evoked Contractile Properties
Bibliographic record
Abstract
Maximal strength training has been reported to emphasize neural adaptations. The main objective of this study was to detect differences in muscle activation between 5, 10, and 20 repetition maximum (RM) sets. Fourteen subjects performed elbow flexion with 5, 10, and 20RM. Subjects were tested for maximum isometric force (maximal voluntary contraction [MVC]), twitch amplitude (peak twitch [Pt]), time to peak twitch (TPT), half relaxation time (1/2 RT), electromyography (EMG), and muscle activation (interpolated twitch). Subjects were tested preexercise and 30 seconds, 1, 2, and 3 minutes postexercise. There were no significant differences in MVC, muscle activation, or antagonist/agonist EMG after 5, 10, or 20RM. However, greater RM did have a greater detrimental effect on twitch properties than fewer RM. Peak twitch was significantly (p = 0.004) less (32.08%) for the 20 than for the 5RM, whereas TPT shortened (p < 0.05) by 7.3 and 11.1% with 10 and 20RM vs. 5RM, respectively. Half relaxation time at 20RM was shortened (p < 0.05) by 20.6 and 25.4% compared with that at 5 and 10RM, respectively. MVC, muscle activation, and temporal twitch properties did not recover within 3 minutes of recovery. In conclusion, whereas 5RM did not produce greater muscle inactivation, twitch contractile properties were affected to a greater degree by a higher number of RM.
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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 | 0.000 | 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) | 0.002 | 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".