effects of a single or multi-step drop-set training compared to traditional resistance training on muscle performance and body composition
Bibliographic record
Abstract
The purpose was to examine two drop-set (DS) protocols (single step vs. multi-step) compared to traditional resistance training (TRT) over 8 weeks on changes in muscular strength, endurance and body composition. Twenty-seven trained males were randomized to one of three groups: traditional resistance training (TRT: n = 9), the single step drop set group (DS-S: n = 10) and the multi-step drop set group (DS-M: n = 8). Before and after training, body composition (percent body fat and skeletal muscle mass), and muscular strength and endurance (bench and leg press) were determined. Results: There was a significant interaction for leg press 1-RM (p < .001) and absolute change for leg-press 1-RM was significantly greater for both drop set protocols compared to TRT (p < .001). There were significant interactions for both leg press and bench press endurance (p < .001), with post hoc analyses revealing that only DS-M was superior to TRT (p < .001). There was a significant main effect of time for % body fat (p = .020), SMM (p < .001), however there were no differences between groups. Conclusions: Overall, single-step and multi-step drop-set training to failure appear to be effective strategies to enhance lower body strength, while only the multi-step drop set training enhanced muscular endurance compared to TRT.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".