Introduction: Widespread use of the internet is a relatively recent phenomenon that is affecting different cultures worldwide in both positive and negative ways. This study aimed to determine the gender differences in the internet addiction among medical students in public university. Methods: 426 medical students from year one to four were invited to participate in the study. For data collection, we used a self-administered questionnaire consisting of socio-demographic and internet usage questions, the Internet Addiction Test (IAT) and the Depression Anxiety and Stress Scales 21 (DASS-21). Results: There were a total of 426 (270 female and 156 male) medical students included in this study. IA was high in both male and female students. There was a higher proportion of male students (44.9%) found to be addicted compared to female students (32.2%). There were positive correlations between the DASS21 and IAT scores for both male and female students. For female students, longer hours spent online daily (p-value = 0.02), as well as high anxiety (p-value < 0.001) and depression scores (p-value = 0.037) were found to be most strongly associated with IA. Among male students, factors associated with IA were the availability of internet access at home (p-value=0.02) and high anxiety scores (p<0.001). Conclusion: IA was high in both male and female medical students, with higher anxiety raising the risk in both groups. In female students, a higher depression score was also found to be associated with IA
Internet addiction

INTRODUCTION: The social environment of adolescents plays a significant role in their psychological wellbeing, which in turn contributes to their personal development as individuals. This research aimed to determine the correlation between internet addiction and the psychological well-being of high school students in private schools in Metro Manila for the school year 2020-2021. METHODS: High school students from Grades 7-12 in private schools in Metro Manila, with at least one account in any social media platform participated. The Internet Addiction Test and The Flourishing Scale were used to determine internet addiction and psychological well-being, respectively. Spearman’s rank-order correlation was used to determine the magnitude of correlation between internet addiction and psychological well-being. RESULTS: The prevalence of internet addiction was 46.1% among 128 respondents. The mean psychological well-being score of the participants was 45.9 ± 7.84. There was weak statistically significant negative correlation between psychological well-being and internet addiction (rs(126) = -0.346, p < 0.001). CONCLUSION Students with higher scores of internet addiction were more likely to have lower scores in psychological well-being. There was weak statistically significant negative correlation between psychological well-being and internet addiction.
Humans ; Adolescent ; Internet Addiction Disorder ; Dependency, Psychological ; Internet

Objective: To understand the associations between internet addiction, screen time (computer/mobile devices use and television watching time) and depressive symptoms in adults. Methods: A total of 6 932 adults aged <60 years from the Tianjin Chronic Low-grade Sgstemic Inflammation and Health (TCLSIH) Cohort of 2013-2019 were surveyed. The information about their computer/mobile devices use and television watching time were collected by using a self-reported questionnaire. The depressive symptoms were assessed using the self-rating depression scale (SDS). The adults surveyed were divided into two groups: non-depressive symptom group (SDS score <45) and depressive symptom group (SDS score ≥45). The associations between internet addiction, screen time and depressive symptoms were estimated using Cox proportional hazard regression models, with adjusting for multiple confounders. Results: After adjusting for confounding factors, the hazard ratios (HRs) of depressive symptom in the adults who had internet addiction before, had light internet addiction and had moderate or severe internet addiction were 0.83 (95%CI: 0.56-1.23) , 1.20 (95%CI: 1.03-1.41) for light and 1.48 (95%CI: 1.16-1.89), respectively, compared with those without internet addiction. The linear trend test results of the association between internet addiction and depressive symptoms was significant (trend P<0.001). Compared with the adults who used computer/mobile devices for <1 hour/day, the HRs of depressive symptoms in those who used computer/mobile devices for >1 hour, >3 hours, >5 hours and >10 hours were 0.59 (95%CI: 0.40-0.88), 0.58 (95%CI: 0.40-0.85), 0.52 (95%CI: 0.36-0.76) and 0.69 (95%CI: 0.45-1.05) respectively, a U-shaped association was found between computer/mobile devices use time and depressive symptoms (trend P<0.001). Compared with the adults who never watch TV, the HR of depressive symptoms was 1.36 (95%CI:1.09-1.69) for those watching TV for ≥3 hours/day in crude model and 1.34 (95%CI: 1.07-1.68) for those watching TV for ≥3 hours/day in adjusted model (trend P<0.001). Conclusion: Our findings suggested that internet addiction and television watching time were associated with an increased risk of depressive symptoms, while computer/mobile device use time was associated with a reduced risk of depressive symptoms.
Adult ; Humans ; Screen Time ; Internet Addiction Disorder ; Self Report