Modeling Predictors of Whole Body Vibration Exposure among Saskatchewan Farmers: a Key Step in Low Back Disorder Prevention
Date
2016-08-12
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ORCID
0000-0003-1813-1159
Type
Thesis
Degree Level
Masters
Abstract
Background
Farmers experience a high rate of low back pain (LBP), with a lifetime prevalence of up to 75%. Whole body vibration exposure has been recognized as a significant physical risk factor associated with LBP. The agriculture sector has high whole body vibration exposures related to various machine types; however, little research has assessed vibration exposure in farming due to the inconvenience and cost of direct data collection. Prediction modelling is potentially a cost-efficient way to estimate directly measured exposure.
Objectives
The objectives of this study are to 1) measure the physical exposure of whole body vibration in Saskatchewan farmers and understand its magnitude and variability between farm machinery; and 2) use farm, vehicle, and task characteristics to determine any predictive relationship with directly-measured whole body vibration exposures among Saskatchewan farmers.
Methods
A 1-year field study with 3 repeated farm visits was conducted for whole body vibration measurements on 21 farms within a 400 km distance of Saskatoon. Whole body vibration was assessed using a tri-axial accelerometer embedded in a standard rubber seat pad according to international standards (ISO 2631-1). Whole body vibration data were summarized by machinery type into standardized metrics of root-mean-squared accelerations (RMS), peak, crest factor, and vibration dose value (VDV). Vehicle characteristics were gathered by on-site observations supplemented by open access vehicle descriptions through manufacturers. Farm characteristics and farmer’s self-reported whole body vibration exposure were collected via questionnaires. A manually stepwise method was conducted to build mixed-effects models for both RMS and VDV outcomes.
Results
A total of 87 whole body vibration measurements were gathered from 8 machine types: tractor, combine, pickup truck, grain truck, sprayer, swather, all-terrain vehicle, and skid steer. The average measurement duration was 85 minutes. The mean vector sums were RMS 0.78 m/s², peak 19.34 m/s², crest factor 27.64, and VDV 10.02 m/s1.75. The fixed effects of ‘horsepower’, ‘vehicle transmission type’, ‘farm size’, and ‘farm commodity’ explained 44% of the variance in RMS; while ‘horsepower’, ‘seat suspension type’, ‘loading frequency’, ‘tire tread type’, ‘jerk/jolt frequency’, ‘seat bottom-out frequency’, ‘farm commodity’, and ‘farm size’ explained only 20% of VDV variance.
Conclusion
High mechanical vibration and shocks from a range vehicle types call for action to reduce agricultural whole body vibration. Although VDV is relatively difficult to predict through farm and vehicle features collected in the present study, RMS can be predicted to a moderately useful degree. Predictors identified via modeling can help explain the variances of whole body vibration exposures and may also serve as new surrogates for future whole body vibration exposure assessment.
Description
Keywords
agriculture, farm machinery, hazard, mechanical shock, occupational exposure, exposure assessment, prediction modeling
Citation
Degree
Master of Science (M.Sc.)
Department
Community Health and Epidemiology
Program
Community and Population Health Science