Affiliations: [a]
Federal University of Alagoas – Campus do Sertão – Delmiro Gouveia, Alagoas, Brazil
| [b] Postgraduate Program in Production Engineering, Federal University of Rio Grande do Sul, Rio Grande do Sul, Brazil
Correspondence:
[*]
Address for correspondence: Jonhatan Magno Norte da Silva, Federal University of Alagoas –Campus do Sertão –Delmiro Gouveia, Highway AL-145, Cidade Universitária, Delmiro Gouveia, Alagoas, Brazil. E-mail: jonhatan.silva@delmiro.ufal.br.
Abstract: BACKGROUND:
Work-related musculoskeletal disorders (WMSD) encompass a range of conditions affecting muscles, tendons, and nerves. Visual diagrams are widely used to identify symptoms and to generate musculoskeletal discomfort metrics. However, there is no consensus on the number of discomfort dimensions that can originate from self-reported musculoskeletal symptoms by individuals. OBJECTIVE:
This study aimed to test the fit of WMSD symptom models from workers in two samples of different sizes. METHODS:
A combination of Full-Information Item Factor Analysis (FIFA) and Item Response Theory (IRT) was utilized to analyze and test the models. The study was conducted in two samples of workers (n1 = 6944 and n2 = 420) who had their symptoms identified with the aid of a human body diagram. An analysis was conducted considering each sample’s unidimensional and three multidimensional models. RESULTS:
The unidimensional model (general musculoskeletal discomfort), bi-dimensional model (discomfort in upper and lower body), and tridimensional model (discomfort in the upper limbs, lower limbs, and trunk) showed good values of factor loading and communalities, along with satisfactory item discrimination ability. Regardless of sample size, parameter estimation for IRT and FIFA proceeded without issues, presenting suitable fit parameters. CONCLUSION:
Three models were valid and reliable for more extensive and smaller samples. However, the tridimensional model was best for generating discomfort scores in body regions. Companies and safety professionals can use these findings to devise strategies to mitigate musculoskeletal pains based on perceived symptom locations.
