Faeze Ghasemi Seproo - MSc, Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
Leila Janani - PhD, Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
Seyed Abbas Motevalian - PhD, Research Center for Addiction and Risky Behaviors (ReCARB), Psychosocial Health Research Institute (PHRI), Iran University of Medical Sciences, Tehran, Iran
Abbas Abbasi-Ghahramanloo - PhD, Department of Public Health, School of Health, Ardabil University of Medical Sciences, Ardabil, Iran

Background: Dangerous behaviors adversely affect the health of adolescents and young adults. This study aimed to identify the subgroups of college students based on the parameters of risky behavior and analyze the impact of demographic factors and internet gaming disorder (IGD) belonging to each class. Study Design: A cross-sectional study. Methods: The study was conducted on ۱۳۵۵ students through a multi-stage random sampling method in ۲۰۲۰. A survey questionnaire was used to collect data, and all students completed ۱۲۹۴ sets of questionnaires. The data were analyzed using t test and latent class analysis (LCA) through SPSS and PROC LCA in SAS ۹.۲ software. Results: Three latent classes have been identified as low-risk (۷۵%), tobacco smoker (۸%), and highrisk (۱۷%). There was a high possibility of risky behavior in the third class. Marital status (being single) (OR = ۲.۲۸, ۹۵% CI: ۱.۱۹-۴.۳۷), unemployment (having no job) along with education (OR = ۱.۵۶, ۹۵% CI: ۱.۰۴-۲.۳۳), and IGD (OR = ۱.۰۶, ۹۵% CI: ۱.۰۴-۱.۰۹) increased the risk of inclusion in the tobacco smoker class. Moreover, unemployment (having no job) along with education (OR = ۱.۴۳, ۹۵% CI: ۱.۱۱-۱.۸۴) increased the chance of being in the high-risk class. Conclusion: According to the findings of this study, ۲۵% of the students were tobacco smokers or were in the high-risk class. The results of this study may help develop and evaluate preventive strategies that simultaneously take into account different behaviors.

Internet gaming disorder, Latent class analysis, Risk-taking behaviors, University students