The impact of different seats and whole-body vibration exposures on truck driver vigilance and discomfort
Bibliographic record
Abstract
Laboratory studies have shown that exposure to whole-body vibration (WBV) increases physical and mental fatigue, which are common issues professional drivers face. The objective of this study was to determine whether altering WBV exposures had any effect on driver vigilance and discomfort. A repeated measures crossover design of five truck drivers with regular 10-h routes was used. Active and passive suspension truck seats were evaluated. For each seat, WBV exposures were measured. Participants completed a discomfort questionnaire and a reaction time task before and after their shift for two weeks, one week per seat. Compared with the passive seat, the active seat significantly reduced WBV exposures, decrements in the optimal and mean reaction times (p = 0.02, 0.047, respectively), and discomfort in the lower back and wrist(s)/forearm(s) (p < 0.01, 0.01, respectively). Study results indicated that reducing WBV helps reduce discomfort and maintain vigilance, which may improve drivers' health and reduce the risk of truck collisions. Practitioner Summary: The active suspension seat used in this study reduced truck drivers' exposure to whole-body vibration (WBV) by over 33% in relation to their current industry standard passive suspension seat. This study demonstrated that reducing truck drivers' exposure to WBV reduced fatigue and discomfort development over a workday.
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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 | 0.002 |
| 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".