The Effects of Whole Body Vibration Application on Jump and Balance Performance in University Students
Bibliographic record
Abstract
The aim of this research was to investigate the effect of vibration applications on muscle strength and balance. As the subjects to the research, nineteen male sport science students (age: 21.45±2.16 years, height: 177.55±7.24 cm, body mass: 71.62±11.02 kg) voluntarily participated in the study. The students were exposed to vibration in squat exercise position before they were exposed to vibration at 25Hz, 50Hz frequency and control (no-vibration) on three different application days. Before and after the vibration exposure, jump tests and balance tests were administered right after the 5th, 10th and 15th min in the wake of the vibration. A statistically significant difference was found in active and squat jump heights administered following the 25Hz and 50 Hz frequency applications (p≤0.05). No significant differences were found in the jump heights in control group (p>0.05). While there was no significant difference found in balance tests performed after 25Hz and 50Hz vibration applications (p>0.05), a significant difference was found after the control application (p≤0.05). As a result, there was an increase in jump heights following the acute vibration application however, no change was seen in balance scores. Practicing acute vibration applications can be recommended to increase jump height especially before the competitions and applications.
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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) | 0.002 | 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".