Acute Effects of Heavy Preloading on Vertical and Horizontal Jump Performance
Bibliographic record
Abstract
The purpose of this study was to investigate the acute effects of a heavy dynamic preload, consisting of 1 set of 5 repetition maximum (5RM) back squats, on countermovement vertical jump (VJ) and horizontal jump (HJ) performance. The study also investigated the ability of subjects to learn to apply the effects of the preload over subsequent training sessions. Nineteen (N = 19) resistance-trained men (age = 25.0 +/- 4.8 years; weight = 79.3 +/- 6.6 kg) participated in the study. Each subject took part in 4 practice and 4 testing sessions. The 4 practice sessions were included to allow for any learning effects of VJ and HJ to stabilize and to establish a true 5RM back squat. The 4 testing sessions were included to see if subjects were able to capitalize on the repeat exposure to the protocol. One practice session consisted of a 10-minute warm-up (5 minutes of cycling and 5 minutes of stretching), 2 sets of VJ and HJ (each set of VJ and HJ consisted of 4 jump repetitions) with a 5-minute rest between sets, progressive 5RM back squat evaluation, and 2 final sets of VJ and HJ. Both VJ and HJ increased approximately 2% over the 4 practice sessions, and 5RM back squat strength improved from 164.2 +/- 25.1 kg to 196.9 +/- 23.0 kg (p < or = 0.05). The 4 testing sessions each consisted of the standardized warm-up, 1 set of 4 VJs and HJs, a 5-minute rest, 5RM back squat, a 5-minute rest, and the final set of VJs and HJs. Pre- and post-5RM VJ and HJ order was randomly assigned. The results indicated no significant differences occurred between the mean or maximal values for either VJ or HJ as a consequence of the dynamic preload exercise. In addition, the results reflected an inability of subjects to benefit from the repeated exposure to the heavy dynamic preload exercise protocol.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".