Distinction between computer workers with and without work-related neck-shoulder complaints based on multiple surface EMG parameters

The aim of this study was to investigate whether it is possible to distinguish between computer workers with (cases) and without (controls) work-related neck–shoulder complaints by combining multiple surface electromyography (EMG) parameters in a logistic regression model. Fourteen controls and 13 cases performed five tasks: a unilateral dynamic hand task, a typing task, an editing task, a mouse task and a stress task. EMG of the trapezius muscle was measured using multi-channel electrode arrays. Root-mean-square value and median power frequency, the number of motor unit action potentials (MUAPs) per second and MUAP shape properties were assessed. Logistic regression models were developed for each task with data from ten controls and nine cases. The mouse task resulted in the most discriminative model with correct classification of 89% (jackknife evaluation). MUAP-related parameters were selected as most discriminative. Explorative evaluation with the remaining subjects resulted in a sensitivity and specificity of 3 out of 4 in both groups. In conclusion, a combination of multiple surface EMG parameters was capable of distinguishing computer workers with and without neck–shoulder complaints in a small pilot sample. Relevance to industry The results are promising for development of an objective assessment method that can identify workers who are at risk to develop chronic neck–shoulder complaints.