Autonomic Nervous System Responses to Whole-Body Vibration and Mental Workload: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Whole-body vibration (WBV) and mental workload (MWL) are common stressors among drivers who attempt to control numerous variables while driving a car, bus, or train. OBJECTIVE: To examine the individual and combined effects of the WBV and MWL on the autonomic nervous system. METHODS: ; frequency 3-20 Hz). Each condition was examined for 5 min individually and combined. Inter-beat intervals were extracted from ECG records. The time-domain and frequency-domain heart rate variability parameters were then extracted from the inter-beat intervals data. RESULTS: A significant (p=0.008) increase was observed in the mean RR interval while the participants were exposed to WBV; there was a significant (p=0.02) reduction in the mean RR interval while the participants were performing the MWL. WBV (p=0.02) and MWL significantly (p<0.001) increased the standard deviation of normal-to-normal intervals with a moderate-to-large effect size. All active periods increased the low-frequency component and low-frequency/high-frequency ratio. However, only the WBV significantly increased the highfrequency component. A significant (p=0.01) interaction was observed between the WBV and MWL on low-frequency component and low-frequency/high-frequency ratio. CONCLUSION: Exposure to WBV and MWL can dysregulate the autonomic nervous system. WBV stimulates both sympathetic and parasympathetic nervous system; MWL largely affects sympathetic nervous system. Both variables imbalance the sympatho-vagal control as well.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".