Work addiction in Chinese white-collar workers: the psychometric properties of its measure and its comorbidity with general anxiety in network analysis

Abstract

AbstractBackgroundWork addiction (WA) threatens occupation-related health in many countries including China. This research aims to evaluate the psychometric properties of the Chinese version of Bergen Work Addiction Scale (BWAS), the most common measure of WA, to facilitate relevant studies in Chinese workers. A network analysis was further conducted to identify central and bridge symptoms within the WA-anxiety network to improve intervention practices.MethodsA total of 694 Chinese white-collar workers completed an online questionnaire survey in March of 2022, and the responses to BWAS from a subsample of 50 participants one month after this survey were also collected.ResultsThe unidimensionality of BWAS was supported by results of exploratory factor analysis, exploratory graph analysis, and confirmatory factor analysis and we found satisfactory internal consistency and acceptable test-retest reliability. Multiple-group factor analyses confirmed the measurement invariance of BWAS across genders, districts (i.e., central China, eastern China, western China, and northeastern China), and age groups (i.e., young and middle-aged adults) while the convergent validity of BWAS was demonstrated by its significant correlations with Dutch Work Addiction Scale (r = 0.62,p < 0.001) and its criterion validity was indicated by its significant correlations with general anxiety, weekly work hours, and health status (r =-0.16 to 0.31,p < 0.001–0.01). Network analysis further revealed two central symptoms (WA-tolerance and WA-problems) and three bridge symptoms (WA-problems, WA-mood modification, and mouth dryness of general anxiety) maintaining the WA-anxiety comorbidity.ConclusionsOur findings suggest that BWAS is a valid measure of WA in Chinese workers and interventions should put special attention to the identified central and bridge symptoms underlying the WA-anxiety network.