Abstract
This study examined the agreement of subjective ratings of upper extremity exposures with corresponding direct measurements obtained simultaneously from workers. Psychophysical ratings of exposure, based on the Borg CR-10 scale, were obtained for the period of time in which direct measurements were acquired using electrogoniometers (wrist), electroinclinometers (shoulder) and electromyography (grip force). Subjects were selected from workers at two automobile manufacturing plants. Significant relationships between subjective ratings of wrist position and measured wrist posture or motion and between ratings of shoulder position and measured shoulder posture were not found. Ratings of manual effort were significantly correlated with directly measured grip force (% maximum voluntary contraction). Ratings of pace were significantly correlated with directly measured wrist motion and this relationship was strengthened with the addition of relative grip force as a covariate. Workers with hand/wrist symptoms provided ratings that were more strongly related to the directly measured exposures than those without symptoms. Self-report by workers is an alternative to more resource-intensive and invasive exposure assessment methods. However, the validity of workers' self-reported exposure assessments has been questioned. The objective of this study was to examine the agreement of selected questionnaire items with corresponding direct measurements from bioinstrumentation and to provide a better understanding of worker self-reports.
Acknowledgements
This work was funded by grant number R01-OH03514 from the National Institute for Occupational Safety and Health (NIOSH). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH. This research could not be possible without the assistance of the United Auto Workers and the company. Participation of the individual workers is greatly appreciated. The authors thank John Cotnam for his help with data collection and Drs. Rebecca Gore, Chang Deok Won and Kwangseog Ahn for their help with data analysis.