Occupational exposure to whole‐body vibration is associated with the development of musculoskeletal, neurological, and other ailments. Low back pain and other spine disorders are prevalent among those exposed to whole‐body vibration in occupational and military settings. Although standards for limiting exposure to whole‐body vibration have been in place for decades, there is a lack of understanding of whole‐body vibration‐associated risks among safety and healthcare professionals. Consequently, disorders associated with whole‐body vibration exposure remain prevalent in the workforce and military. The relationship between whole‐body vibration and low back pain in humans has been established largely through cohort studies, for which vibration inputs that lead to symptoms are rarely, if ever, quantified. This gap in knowledge highlights the need for the development of relevant in vivo, ex vivo, and in vitro models to study such pathologies. The parameters of vibrational stimuli (eg, frequency and direction) play critical roles in such pathologies, but the specific cause‐and‐effect relationships between whole‐body vibration and spinal pathologies remain mostly unknown. This paper provides a summary of whole‐body vibration parameters; reviews in vivo, ex vivo, and in vitro models for spinal pathologies resulting from whole‐body vibration; and offers suggestions to address the gaps in translating injury biomechanics data to inform clinical practice.
Traumatic brain injury is highly prevalent in the United States. However, despite its frequency and significance, there is little understanding of how the brain responds during injurious loading. A confounding problem is that because testing conditions vary between assessment methods, brain biomechanics cannot be fully understood. Data mining techniques, which are commonly used to determine patterns in large datasets, were applied to discover how changes in testing conditions affect the mechanical response of the brain. Data at various strain rates were collected from published literature and sorted into datasets based on strain rate and tension vs. compression. Self-organizing maps were used to conduct a sensitivity analysis to rank the testing condition parameters by importance. Fuzzy C-means clustering was applied to determine if there were any patterns in the data. The parameter rankings and clustering for each dataset varied, indicating that the strain rate and type of deformation influence the role of these parameters in the datasets.
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