Rest-pause and drop-set training elicit similar strength and hypertrophy adaptations compared with traditional sets in resistance-trained males
Bibliographic record
Abstract
This paper aimed to compare the effect of drop-set (DS) and rest-pause (RP) systems versus traditional resistance training (TRT) with equalized total training volume on maximum dynamic strength (1RM) and thigh muscle thickness (MT). Twenty-eight resistance-trained males were randomly assigned to either RP (n = 10), DS (n = 9) or TRT (n = 9) protocols performed twice a week for 8 weeks. 1RM and MT of the proximal, middle and distal portions of the lateral thigh were assessed at baseline and post-intervention. A significant time × group interaction was observed for 1RM (P = 0.001) in the barbell back squat after 8-weeks. Post hoc comparisons revealed that RP promoted higher 1RM than TRT (P = 0.001); no statistical differences in strength were observed between the other conditions. A significant main effect of time was revealed for MT at the proximal (P = 0.0001) and middle (P = 0.0001) aspects of the lateral thigh for all training groups; however, the distal portion did not show a time effect (P = 0.190). There were no between-group interactions for MT. Our findings suggest that RP promotes slightly superior strength-related improvements compared with TRT, but hypertrophic adaptations are similar between conditions. Novelty: Rest-pause elicited a slightly superior benefit for strength adaptations compared with traditional resistance training. Resistance training systems do not promote superior hypertrophic adaptations when total training volume is equalized. Muscle thickness in distal portion of thigh is similar to baseline. Although modest, effect sizes tended to favor rest-pause.
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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 | 0.000 |
| 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) | 0.004 | 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".