Use of a Spinal Traction Device during Work Shift in Assembly Line Workers.

Increasing back discomfort and spinal shrinkage during the workday is a problem that affects assembly line workers. The aim of this research was to analyze the effect of a spinal traction system on discomfort, spinal shrinkage, and spinal sagittal alignment in assembly line workers, who are in prolonged standing conditions during a workday. A total of 16 asymptomatic males were recruited to assess spinal shrinkage, spinal sagittal alignment, and back discomfort during the workday. The measurement was carried out in two days of work, a normal day, and the other using a spinal traction device utilized in two breaks during the workday. Assembly line workers lost height significantly on both control and intervention days. No differences were found between days. No changes were found in spinal sagittal alignment on the control day. Lumbar lordosis angle increased significantly at the end of the intervention day. The use of a spinal traction device during the workday in two breaks time did not significantly reduce the spinal shrinkage of healthy workers. Lumbar lordosis angle increased significantly at the end of the spinal traction intervention day. Prospective studies would be necessary to clarify the possible benefits of the traction device.

A model to differentiate WAD patients and people with abnormal pain behaviour based on biomechanical and self-reported tests.

The prevalence of malingering among individuals presenting whiplash-related symptoms is significant and leads to a huge economic loss due to fraudulent injury claims. Various strategies have been proposed to detect malingering and symptoms exaggeration. However, most of them have been not consistently validated and tested to determine their accuracy in detecting feigned whiplash. This study merges two different approaches to detect whiplash malingering (the mechanical approach and the qualitative analysis of the symptomatology) to obtain a malingering detection model based on a wider range of indices, both biomechanical and self-reported. A sample of 46 malingerers and 59 genuine clinical patients was tested using a kinematic test and a self-report questionnaire asking about the presence of rare and impossible symptoms. The collected measures were used to train and validate a linear discriminant analysis (LDA) classification model. Results showed that malingerers were discriminated from genuine clinical patients based on a greater proportion of rare symptoms vs. possible self-reported symptoms and slower but more repeatable neck motions in the biomechanical test. The fivefold cross-validation of the LDA model yielded an area under the curve (AUC) of 0.84, with a sensitivity of 77.8% and a specificity of 84.7%.