Effect of jumping interval training on neuromuscular and physiological parameters: a randomized controlled study
Bibliographic record
Abstract
This study analyzed the effect of 4 weeks of jumping interval training (JIT), included in endurance training, on neuromuscular and physiological parameters. Eighteen recreational runners, randomized in control and experimental groups, performed 40 min of running at 70% of velocity at peak oxygen uptake, for 3 times per week. Additionally, the experimental group performed the JIT twice per week, which consisted of 4 to 6 bouts of continuous vertical jumps (30 s) with 5-min intervals. Three days before and after the training period, the countermovement (CMJ) and continuous jump (CJ30), isokinetic and isometric evaluation of knee extensors/flexors, progressive maximal exercise, and submaximal constant-load exercise were performed. The JIT provoked improvement in neuromuscular performance, indicated by (i) increased jump height (4.7%; effect size (ES) = 0.99) and power output (≈ 3.7%; ES ≈ 0.82) of CMJ and rate of torque development of knee extensors in isometric contraction (29.5%; ES = 1.02); (ii) anaerobic power and capacity, represented by the mean of jump height (7.4%; ES = 0.8), and peak power output (PPO) (5.6%; ES = 0.73) of the first jumps of CJ30 and the mean of jump height (10.2%, ES = 1.04) and PPO (9.5%, ES = 1.1), considering all jumps of CJ30; and (iii) aerobic power and capacity, represented by peak oxygen uptake (9.1%, ES = 1.28), velocity at peak oxygen uptake (2.7%, ES = 1.11), and velocity corresponding to the onset of blood lactate accumulation (9.7%, ES = 1.23). These results suggest that the JIT included in traditional endurance training induces moderate to large effects on neuromuscular and physiological parameters.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".