Hormonal responses after resistance exercise performed with maximum and submaximum movement velocities
Bibliographic record
Abstract
This study examined the effects of maximum and submaximum movement velocities after a muscular hypertrophy type resistance exercise protocol on testosterone, human growth hormone (hGH) and cortisol concentrations and on neuromuscular performance assessed with a vertical jump. Eleven males performed a control and 3 resistance exercise protocols (4 sets of squat and 4 sets of leg-press exercises, 8 repetitions/set, 10-repetition maximum load). The first exercise protocol was performed at maximum velocity (Vmax); the second at 70% of Vmax with equal training volume (70%VmaxEV) to Vmax; and the third at 70% of Vmax (70%Vmax) with a 10.6% higher training volume to Vmax. Testosterone and hGH increased after all exercise protocols (p < 0.05) compared with baseline and were higher versus control values (p < 0.05). Cortisol concentrations gradually decreased in 70%Vmax, 70%VmaxEV and control protocols following a typical circadian rhythm (p < 0.05), but remained relatively constant in Vmax protocol. Comparisons among protocols showed that hGH was higher in 70%Vmax versus Vmax (p < 0.05), while cortisol was higher in Vmax versus 70%VmaxEV and control (p < 0.05). The greatest reduction in vertical jump and increase in heart rate were observed after the Vmax protocol (p < 0.05). In conclusion, a hypertrophy type resistance exercise protocol performed at maximum movement velocity increases testosterone and hGH and generates a greater biological stress, as evident by a higher cortisol concentrations and heart rate responses, and a greater reduction in neuromuscular performance. A protocol, however, performed at submaximum movement velocity combined with greater training volume stimulates to a greater extent the hGH response with no effect on cortisol.
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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) | 0.001 | 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".