Translation, cross-cultural adaptation and validation of the Persian version of the physical workload questionnaire (PWQ)

Abstract

Background: Musculoskeletal disorders in manual workers are often linked to physical workload factors such as heavy lifting, repetitive movements, and awkward postures. These factors can negatively impact health and work efficiency. Simple tools like questionnaires are essential for studying physical workload. This study aimed to translate, culturally adapt, and evaluate the psychometric properties of the Physical Workload Questionnaire for Persian-speaking populations (PWQ-Pr). Methods: The study involved 255 participants and followed standard guidelines for translation and cultural adaptation. Various psychometric properties were assessed, including face validity through interviews, construct validity via hypothesis testing and factor analysis, content validity using the Content Validity Index (CVI), convergent validity through Pearson’s correlation with the Spine Functional Index (SFI), internal consistency using Cronbach’s alpha, and test-retest reliability assessed through the intra-class correlation coefficient (ICC) and standard measurement error over a 10-day period. Results: Good internal consistency (α = 0.89) and high test-retest reliability (ICC = 0.90, 95% confidence interval: 0.81–0.94) were obtained. Convergent validity was indicated by a correlation of r = 0.41 (P < 0.001) with the SFI questionnaire. Content Validity (CVI = 0.93) was approved. Factor analysis identified two distinct factors within the PWQ-Pr, explaining 52.07% of the variance. Hypothesis testing indicated significant differences in physical workload across job types and among individuals with and without spinal musculoskeletal disorders (MSDs), with eight hypotheses confirmed out of twelve tested. Conclusions: The Persian version of the Physical Workload Questionnaire (PWQ-Pr) is a reliable and valid tool for assessing physical workload among Persian-speaking individuals. It is effective in evaluating workload across various job tasks.