Effect of fatigue on muscle latency, muscle activation and perceived discomfort when exposed to whole-body vibration
Bibliographic record
Abstract
Whole-body vibration and muscle fatigue have both been shown to delay the trunk muscle reflex response and increase trunk muscle activation, leading to an increased risk of low back injuries. However, the effects of whole-body vibration on previously fatigued trunk muscles have never been tested, despite studies showing that prolonged exposure to whole-body vibration can lead to muscle fatigue. The purpose of this research was to investigate the effects of muscle fatigue on muscle latency, muscle activation and perceived discomfort when exposed to whole-body vibration. The results showed that a fatigued muscle state resulted in increased muscle latency, muscle activation and perceived discomfort, which all escalate the risk of low back injuries. Additionally, the ISO 2631-1 comfort ratings did not increase with fatigue, showing a disconnect between these comfort ratings and the perceived discomfort ratings in a fatigued muscle state. Practitioner summary: When exposed to whole-body vibration, fatigued back muscles result in delayed muscle contraction, higher overall muscle activation and increased perceived discomfort, all of which are known to increase low back injury risk. ISO 2631-1 comfort ratings are unable to increase with fatigue, showing a disconnect with perceived discomfort ratings. Abbreviations: EMG: electromyography; EO: external oblique; IO: internal oblique; LE: lumbar erector spinae; LEO: left externaloblique; LIO: left internal oblique; LLE: left lumbar erector spinae; LTE: left thoracic erector spinae; MVC: maximum voluntarycontraction; REO: right external oblique; RIO: right internal oblique; RLE: right lumbar erector spinae; RTE: right thoracicerector spinae; SEAT: Seat Effective Amplitude Transmissibility; TE: thoracic erector spinae; WBV: whole body vibration
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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".