Internet addiction, perceived sleep quality and its determinants in a sample of Iranian medical university students

Abstract

Background: Today, using the Internet is an integral part of daily life activities. Increasing of Internet use has occurred in different age groups, but it is more obvious among teenagers and young adults. The purpose of this study was to study of association between internet addiction and sleep quality in medical university students. Methods: 400 students of Ilam University of Medical Sciences participated in this descriptive analytical study. Students were selected using simple random sampling method and according to the study entry criteria. Data were collected using Young's Internet Addiction test (AIT) and Pittsburgh Sleep Quality Index (PSQI). Simple linear regression and multiple regression tests were used to analyze the data. A significance level was considered as α = 0.05. Results: The findings of study showed that 80% of students have mild, moderate and sever internet addiction. The average total sleep quality of students was in poor range (6.30±2.51). The mean of subjective sleep quality was in poor range (2.03 ±0.662) compared to other dimensions (p>0.05). Based on the results of simple linear regression, compared to normal people, mild (b:1.03;p:0.002) and moderate (b: 2.05; p<0.001) internet addiction had a significant association with poor total sleep quality. Also, graduate students had better sleep quality compared to undergraduate students (b:-1.03; p: 0.016). In addition, based on the multiple linear regression model adjusted for educational level compared to normal people, mild (b: 0.98; p: 0.003) and moderate (b: 1.97; p<0.001) addiction to the Internet has a significant association with poor total sleep quality. Conclusions: Internet addiction is associated with poor sleep quality in college students. This disorder is more common in undergraduate students. Therefore, it seems necessary for this group of students to implement interventions related to modification of excessive Internet use behaviors.