A system for predicting and preventing work-related musculoskeletal disorders among dentists

Abstract

Work-related musculoskeletal disorders (WMSDs) have become increasingly common among dentists and initiate a series of events that could result in a career ending. This study aims to construct a system for predicting and preventing WMSD among dentists. We used Bayesian network (BN) that describes the mutual relationships among multiple variables contributing to WMSDs. The data-sets were prepared from direct measurements of dentist's movements and a questionnaire survey. We applied BN learning algorithms to the training data-sets to develop WMSD prediction model using 10-fold cross-validation. To evaluate the system performance, 16 dentists were randomly assigned into a 2 × 2 crossover trial scheduled to each of two sequences of dental working: receiving feedback or no feedback including the probability of WMSD and related risk factors from the system. The group that received feedback decreased significantly (t-test, p < 0.05) the extensions of neck and upper back in the y-axis as well as the WMSD probability on the post-test. In conclusion, the system for predicting and preventing WMSD aids the correction of neck and upper back extensions and reduction in WMSD probability, which may potentially contribute to reduce the risk of WMSD among dentists.