Whole-Body Vibration Applied During Upper Body Exercise Improves Performance
Bibliographic record
Abstract
Whole-body vibration (WBV) training has exercisers perform static and dynamic resistance training exercises on a ground-based platform. Exposure to WBV exposure has demonstrated benefits and no effect on lower body strength, power, and performance. The aim of this study was to determine if WBV exposure (50 Hz, 2.51 mm) has any potentiating effects postexercise by measuring the kinematic variables of a set of upper body elbow-extensor exercise (70% one-repetition maximum [1RM]) to volitional exhaustion. Sixteen recreationally active students (12 male and 4 female) performed 3 different experimental conditions on separate days. Each condition had the subjects perform 1 set of elbow-extension exercise to fatigue with 1 of 3 WBV treatments: WBV simultaneously during the set (AE); 60 seconds after application of WBV for 30 seconds (RE); and no WBV (CTRL). Kinematic parameters of each repetition were monitored by linking a rotary encoder to the highest load plate. The mean velocity and acceleration throughout the set and perceived exertion were analyzed. A significant increase (p < 0.05) was observed in the mean velocity for the whole set in the AE condition vs. the CTRL condition. The mean acceleration was significantly higher (p < 0.05) in the AE condition in comparison with RE (increased by 45.3%) and CTRL (increased by 50.4%) conditions. The positive effect induced by WBV on upper-limb performance is only achieved when the stimulus is applied during the exercise. However, WBV applied 60 seconds before upper body exercise results in no benefit.
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 | 0.000 |
| 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".