Objectives:

This study was conducted to evaluate the internet addiction status of Ankara University Faculty of Medicine students, and to determine the relationship between this status and depression and other factors.

Materials and Methods:

This cross-sectional study conducted between May and June 2017. Population of the study consisted of 1st, 2nd and 3rd year students of Ankara University Faculty of Medicine. Minimum sample size was calculated as 523. Stratified random sampling was used according to classes. 60% of each class with 796 individuals in total were aimed, 734 were reached. Data were collected by conducting surveys under observation after necessary explanation was made, written consent was obtained. Questionnaire forms including socio-economic elements, general health status and internet usage habits, Internet Addiction Test and Beck Depression Inventory were used. Scoring 50 points and over in Internet Addiction Test was accepted as risky/addictive internet usage while scoring 17 points and over in Beck Depression Inventory as the existence of depressive symptom. Data were evaluated in SPSS 21.0. The chi-square and Mann-Whitney U tests were used for one-way and multivariate logistics regression analysis for multiple-way analysis.

Results:

In research group, risky/addictive internet usage frequency was found to be 20.0%. The mean internet usage time was 18.74 hours per week during school period and 28.73 hours during holiday period. The prevalence of depression in students was 27.2%. According to Logistic Regression Analysis results, depressive symptom status [odds ratio (OR): 3.24], class of students (OR: 2.07, OR: 1.78), academic success (OR: 2.16), problems within family (OR: 1.54) and weekly internet usage duration during holidays (OR: 2.05) were found to be related to internet usage.

Conclusion:

In this study, risky/addictive internet usage proportion was found to be high and the effects of depression and other factors were detected. In addition to preventative approaches for these factors, it is necessary to identify individuals at risk and to guide them for appropriate approaches when necessary.

Keywords: Risky Internet Use, Medical School Students, Risk Factors