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Characteristics Of Whole-Body Vibration Frequencies And Low Back Pain In Urban Taxi Drivers - Introduction; Proceedings Of The First American Conference On Human Vibration
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    Occupational exposures to whole-body vibration (WBV) at different frequency domains may differentially affect human comfort and the musculoskeletal system. Under this presumption, a frequency-based weighting scheme has been adapted in many widely accepted standards for WBV measurement. However, there is very little human data showing a direct link between WBV frequency and musculoskeletal disorders. We conducted an epidemiologic study to examine the association between WBV frequency and prevalence of low back pain (LBP) and to identify determinants of specific frequencies associated with LBP in urban taxi drivers. Methods The WBV frequency data were collected from 247 professional drivers (aged 44.6±8.3) who participated in an exposure validation study 1 of the Taxi Drivers’ Health Study (TDHS) in 2000. 2 In accordance with the ISO 2631-1 (1997) methods, we measured the frequency-weighted acceleration over drivers’ seat surface, under conditions representing randomly assigned destinations. We developed a WBV record-replay system at the Liberty Mutual Research Institute (LMRI) in Hopkinton, MA, USA. This system includes two tri-axial accelerometers (PCB Piezotronics, NY, USA), one RD-130T PCM data recorder (TEAC, Tokyo, Japan), and one LMWBV meter 2.0 (LMRI, MA, USA). Only the vertical axis of seat-surface WBV frequency was used in this study. To characterize the WBV frequency curve, we manually identified the presence of any peak within each of the following frequency range: <4, 4-10, 10-20, and >20 Hz. Information about the operating vehicles and driving environment was either collected from the vehicle registration record (manufacturer, year of make, transmission, engine size, etc.) or directly measured (wheel-base length, seat inclination, etc.). Structured interviews were conducted by an occupational physician to gather information on LBP that had led to medical attention or absence from driving in past year. We used multiple logistic regression to estimate the prevalence odds ratio (OR) associated with the presence of each index peak frequency, adjusting for age, body mass index, professional seniority, daily driving hours, seat inclination, and the intensity of predicted root-mean-square WBV exposure in m/sec2. For any revealed WBV frequency that was associated with LBP, we constructed a multiple logistic regression model to identify the personal and vehicle characteristics associated with the presence of WBV peak within the indicated frequency range.
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