Relationships between Height, Mass, Body Mass Index, and Trunk Muscle Activation during Seated Whole-Body Vibration Exposure
Bibliographic record
Abstract
Operators of heavy equipment are often exposed to high levels of whole-body vibration (WBV), which has been associated with a variety of adverse health outcomes. Although anthropometric factors are known to impact vibration dose and risk of low back pain, studies have yet to investigate the influence of anthropometric factors on muscle activation during WBV exposure. This study quantified the relationships between muscle activation, vibration frequency, body mass, body mass index (BMI), and height both pre- and post-fatigue. Muscle activation of the external oblique (EO), internal oblique (IO), lumbar erector spinae (LE) and thoracic erector spinae (TE) were quantified using surface electromyography. Results indicate increased activation with increased mass, BMI, and frequency for the LE, TE, and IO, which may be a result of increased activation to stabilize the spine. Decreased muscle activation with increased height was seen in the TE, IO, and pre-fatigue EO, which could indicate higher risk for low back injury since height is associated with increased forces on the spine. This may contribute to the association between increased low back pain incidence and increased height. Results suggest that ISO 2631-1 health guidance should incorporate anthropometric factors, as these may influence muscle activation and back injury risk.
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 | 0.001 |
| 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) | 0.001 | 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".