Velocity-based resistance training: impact of velocity loss in the set on neuromuscular performance and hormonal response
Bibliographic record
Abstract
This study aimed to compare the effects of 2 resistance training (RT) programs with different velocity losses (VLs) allowed in each set: 10% (VL10%) versus 30% (VL30%) on neuromuscular performance and hormonal response. Twenty-five young healthy males were randomly assigned into 2 groups: VL10% (n = 12) or VL30% (n = 13). Subjects followed a velocity-based RT program for 8 weeks (2 sessions per week) using only the full-squat (SQ) exercise at 70%–85% 1-repetition maximum (1RM). Repetition velocity was recorded in all training sessions. A 20-m running sprint, countermovement jump (CMJ), 1RM, muscle endurance, and electromyogram (EMG) during SQ exercise and resting hormonal concentrations were assessed before and after the RT program. Both groups showed similar improvements in muscle strength and endurance variables (VL10%: 7.0%–74.8%; VL30%: 4.2%–73.2%). The VL10% resulted in greater percentage increments in CMJ (9.2% vs. 5.4%) and sprint performance (–1.5% vs. 0.4%) than VL30%, despite VL10% performing less than half of the repetitions than VL30% during RT. In addition, only VL10% showed slight increments in EMG variables, whereas no significant changes in resting hormonal concentrations were observed. Therefore, our results suggest that velocity losses in the set as low as 10% are enough to achieve significant improvements in neuromuscular performance, which means greater efficiency during RT. Novelty The VL10% group showed similar or even greater percentage of changes in physical performance compared with VL30%. No significant changes in resting hormonal concentrations were observed for any training group. Curvilinear relationships between percentage VL in the set and changes in strength and CMJ performance were observed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".