Discriminating between individuals with and without musculoskeletal disorders of the upper extremity by means of items related to computer keyboard use.

INTRODUCTION

Identifying postures and behaviors during keyboard use that can discriminate between individuals with and without musculoskeletal disorders of the upper extremity (MSD-UE) is important for developing intervention strategies. This study explores the ability of models built from items of the Keyboard-Personal Computer Style instrument (K-PeCS) to discriminate between subjects who have MSD-UE and those who do not.

METHODS

Forty-two subjects, 21 with diagnosed MSD-UE (cases) and 21 without MSD-UE (controls), were videotaped while using their keyboards at their onsite computer workstations. These video clips were rated using the K-PeCS. The K-PeCS items were used to generate models to discriminate between cases and controls using Classification and Regression Tree (CART) methods.

RESULTS

Two CART models were generated; one that could accurately discriminate between cases and controls when the cases had any diagnosis of MSD-UE (69% accuracy) and one that could accurately discriminate between cases and controls when the cases had neck-related MSD-UE (93% accuracy). Both models had the same single item, "neck flexion angle greater than 20 degrees ". In both models, subjects who did not have a neck flexion angle of greater than 20 degrees were accurately identified as controls.

CONCLUSIONS

The K-PeCS item "neck flexion greater than 20 degrees " can discriminate between subjects with and without MSD-UE. Further research with a larger sample is needed to develop models that have greater accuracy.