Addition of Synchronous Whole-Body Vibration to Body Mass Resistive Exercise Causes Little or No Effects on Muscle Damage and Inflammation
Bibliographic record
Abstract
The purpose of this study was to determine if a moderate intensity whole-body vibration (WBV) body mass resistive exercise session causes additional muscle damage, soreness, and inflammation compared with the same exercise session without vibration (NoV). Ten recreationally active male university students completed 2 separate 24-hour study periods incorporating an exercise session with WBV or NoV. Muscle torque was measured (at 0, 60, and 240°·s-1 angular velocities), soreness (10-point scale) in the upper (UE [triceps]) and lower (LE [quadriceps]) extremities, and muscle inflammation markers (interleukin [IL]-1β, IL-6, IL-10) were measured at 4 time points (preexercise, immediately postexercise, 4 hours post, and 24 hours post). Diet was controlled. Compared with NoV, WBV increased (p < 0.01) muscle soreness at 24 hours postexercise in both the UE (2.2 ± 1.7 vs. 0.6 ± 0.9) and LE (2.0 ± 1.5 vs. 0.7 ± 0.7). Muscle torque was decreased immediately postexercise (p < 0.05) in the UE and LE at 0°·s and in the UE at 240°·s, but there was no difference between exercise treatments. The exercise session caused significant but small increases in IL-1β and IL-6 but with no differences between exercise treatments. Interleukin-10 was increased with WBV (2.9 ± 2.0 to 3.6 ± 1.9 pg·ml-1; p < 0.03). These data suggest that the addition of WBV to exercise has little effect on muscle function and damage, soreness, or inflammation.
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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) | 0.001 | 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 | 0.000 |
| 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".