The effects of standing foot-transmitted vibration on self-reported discomfort ratings
Bibliographic record
Abstract
BACKGROUND: Occupational foot-transmitted vibration (FTV) exposure is common in industries like mining, construction, and agriculture, often leading to acute and chronic injuries. Vibration assessments require technical expertise and equipment which can be costly for employers to perform. Alternatively, researchers have observed that self-reported discomfort can be used as an effective indicator of injury risk. OBJECTIVE: This study aimed to investigate the effect of standing FTV exposure on self-reported ratings of discomfort, and whether these subjective ratings differed by body area and exposure frequency. METHODS: Participants (n = 30) were randomly exposed to standing FTV at six frequencies (25, 30, 35, 40, 45, and 50 Hz) for 20-45 seconds. Following each exposure, participants rated discomfort on a scale of 0-9 in four body areas: head and neck (HN), upper body (UB), lower body (LB), and total body. RESULTS: Results indicated that participants experienced the most discomfort in the LB at higher frequencies (p < 0.001), consistent with the resonance of foot structures. The HN discomfort tended to decrease as the exposure frequency increased, although not statistically significant (p > 0.0167). The UB discomfort remained relatively low across all frequencies. CONCLUSIONS: The study suggests a potential connection between resonant frequencies and discomfort, potentially indicating injury risk. Although self-reported discomfort is insufficient for directly assessing injury risk from FTV, it provides a simple method for monitoring potential musculoskeletal risks related to vibration exposure at resonant frequencies. While professional vibration assessment remains necessary, self-reported discomfort may act as an early indicated of vibration-induced injuries, aiding in implementing mitigation strategies.
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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 | 0.005 |
| 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) | 0.004 | 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".