The acute effects of performing drop jumps of different intensities on concentric squat strength.
Bibliographic record
Abstract
AIM: To investigate the acute effects of performing drop jumps of different intensities on subsequent squat 1 repetition maximum (1RM). METHODS: 14 participants with strength training experience completed two familiarization sessions to become accustomed with the testing procedures and 1RM-like loads. Following this, four different 1RM testing sessions were completed. In each testing session subjects performed a 5-min bicycle warm-up followed by a series of sets with increasingly heavier loads until squat 1RM was achieved. During the first of these four sessions squat 1RM was assessed without the addition of drop jumps to the 1RM warm-up routine, thus was designated the control (CTRL) condition. In the final 3 testing sessions, two drop jumps from either 30 (DJ30), 45 (DJ45), or 60 (DJ60) cm were added to the warm-up routine that preceded squat 1RM assessment. EMG activity of the vastus lateralis was also monitored during 1RM testing. RESULTS: Squat 1RM without prior plyometric activity was 128.4±36.1 kg. Following DJ30, DJ45, and DJ60 squat 1RM equaled 130.4±36.4 kg, 130.9±38.3 kg, 131.0±38.9 kg, respectively. A repeated measures ANOVA uncovered a significant main effect of warm-up condition (P=0.021). Post hoc analysis revealed that differences in the 1RM values were only significant between DJ30 and CTRL (P=0.002). No significant differences in muscle activation of the vastus lateralis were noted between the conditions. CONCLUSION: These findings indicate lower body strength in individuals familiar with resistance training can be acutely enhanced when preceded by a warm-up incorporating a low volume of low intensity drop jumps.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".