Musculoskeletal disorders prediagnosis by infrared thermography in CNC machinery operators: Regression models approaches.

BACKGROUND: Musculoskeletal System Disorders (MSDs) are a group of injuries that represent common occupational diseases and should be evaluated for prevention purposes because an increase has been observed due to the repetitive movements performed in the industry. This research was carried out in a manufacturing industry where metal parts are manufactured, and workers experience back and wrist pain. OBJECTIVE: To prediagnose Musculoskeletal System Disorders (MSDs) and examine the relationship between temperature, demographic, and physiological factors in workers through predictive models, contributing to MSD prevention. METHODS: Information from 36 operators was used to obtain vital signs and somatometry data, and thermograms of their hands in the dorsal, palmar, and back areas were collected and analyzed to determine the relationship between temperature and demographic and physiological factors. RESULTS: The ergonomic evaluations proved that the operators were at high risk owing to repetitive movements and postures adopted during work. Eighty-six percent of cases with injuries were identified using infrared thermograms, proving their high level of effectiveness. When studying the relationship between temperature behavior during recovery from repetitive activities and demographic and physiological factors, it was determined that age, dominant hand, respiratory frequency, and BMI were the most significant. CONCLUSIONS: Nine regression models were obtained, with coefficients of determination between 0.17 and 0.71. The significant factors for worker injuries were age, dominant hand, respiratory rate, and BMI. However, the sample size and variability in work activities should be extended to generalize the findings.

Quantitative Models for Prediction of Cumulative Trauma Disorders Applied to the Maquiladora Industry.

Temperature gradient changes on the surface of the skin or in the middle of the body are signs of a disease. The aim of this study is to develop quantitative models for the prediction of cumulative trauma disorders (CTDs) arising from highly repetitive activities, considering risk factors, such as age, gender, body mass index (BMI), blood pressure (BP), respiratory rate (RR), and heart rate, to prevent injuries in manufacturing factory operators. This research involved 19 individuals from the area of sanding and 14 individuals from the area of tolex in manufacturing factories who had their vital signs and somatometry taken, as well as thermal images of their hands in the dorsal and palmar areas; an evaluation by the OCRA method was also applied. Factors such as BP and heart rate were determined to significantly influence the injuries, but no strong association with BMI was found. Quadratic regression models were developed, the estimates of which were adequately adjusted to the variable (R2 and R2 adjusted > 0.70). When integrating the factors of the OCRA method to the generated models, a better fit was obtained (R2 and adjusted R2 > 0.80). In conclusion, the participants who present levels out of the normal range in at least one of the factors have high probabilities of developing injuries in their wrists.