| الملخص: | In this study, we aimed to develop and test the psychometric properties of an online 24-hr movement behavior questionnaire (24 hr-MBQ). We recruited a sample of 195 undergraduate students. We developed a questionnaire based on 19 items extracted from previously validated questionnaires. We conducted reliability and construct validity assessment by classical test theory and structural validity assessment using unsupervised machine learning. In the classic test, we identified seven factors where the explained variance was 66.80% in the exploratory factor analysis, with no item excluded. We identified three clusters using unsupervised machine learning and this structure was able to distinguish differences in physical activity (physically active vs. long sleeper), sedentary behavior (all cluster comparison), and sleep time duration (all cluster comparison). Our findings suggest that the online 24 hr-MBQ is a reliable and structured construct for assessing 24-hr movement behaviors in college students.
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