This study was carried out in order to detect the prevalence of IA among university students and investigate the relationship between sleep quality as well as IA.
In our study, the prevalence of IA was 4.95% and 39.62% for severe as well as moderate addiction, respectively. Our findings were consistent with the (IA) prevalence results revealed by two studies in Menoufia University [13, 14]. The former found that 48.5% of medical students were pathological users of the Internet. In contrast, the second noted that 13.2% of both non-medical and medical students were problematic users of the Internet, whereas 39.1% were potential problematic Internet users. Other studies done in Palestine and Greece illustrated that the pervasiveness of IA was 30.1% and 34.7%, respectively [15, 16]. The disparity between our findings and those of other research might be attributed to the unavailability of a specific definition and accurate assessment of (IA), besides different samples and methods used.
Internet addiction is significantly more among younger age than normal Internet users. This finding is in agreement with many studies reporting that IA was more frequent among young people [13, 17, 18]. This does not agree with lee and Stapinski [19], who found no significant association between age and Internet addiction.
Regarding gender, Internet addiction was more prevalent among males compared to females, which is consistent with most previous studies, suggesting that the male gender is a predictor of IA [20, 21]. Chou et al. [22] found that male users of the Internet were riskier to have IA due to regular use of sexual issues; however, female users either were asymptomatic or may present with mild symptoms. However, another study did not detect any gender differences regarding IA [23].
We found that fathers of students with IA were significantly more employed than those with normal Internet users. IA is more elevated among students whose mothers and fathers are employed due to increased access to the Internet and the absence of control over their Internet use [24].
Our study demonstrated that the incidence of IA was higher among students with low academic performance. The current study’s findings agreed with [17, 25], who detected a significant negative association between IA and the students’ academic performance.
Students with Internet addiction significantly had computers and the Internet in their homes than students of normal Internet use. The results of our study were consistent with [25], who showed that most students with home Internet access with cell phones as well as computers.
Students with Internet addiction significantly used the Internet in chat as well as games more compared with students with normal Internet use. Many studies have shown a link between excessive video game playing and IA [3, 26, 27]. Internet chatting has been revealed as a risk factor for IA [26].
In this study, it was found that students with Internet addiction significantly had poor sleep quality. This finding is in agreement with a study in KSA, where 511 subjects were included. Sleep disturbance was detected in approximately 50% of the study sample, markedly correlated with IA. In that study, poor quality of sleep was related to high Internet use [28].
The current study’s findings on the adverse effect of IA on the sleep quality of medical students in current research are comparable to research carried out in India and China [29]. Research performed on university students in Taiwan illustrated that the quality of sleep who were addicted to the Internet slept 1.4 times worse than students who were not addicted to the Internet [30]. Another research in Canada revealed a positive correlation between IA as well as poor quality of sleep [31]. Another research on university students found that the median scores of IA in students with sleeping disorders were more elevated than in the group without sleep disorders [32].
The mechanisms underlying the link between IA and sleep disorders have not been conclusively established [33]. The most probable is a multifactorial and two-sided model of mutual influence. Sleep disorders, reflecting psychosocial problems, depression, and anxiety-phobic disorders, can precede and contribute to the formation of IA [34]. Internet addiction has been found to contribute to disturbed circadian rhythm [34] that may negatively influence bedtime and sleep duration. Another possible explanation is the emission of blue light through the screens that are known to suppress melatonin secretion from the pineal gland, leading to prolongation of sleep latency [35].
Limitations
There are certain limitations to the present research. First, data was gathered from limited research locations. The research could not include all medical students. Furthermore, data collection has relied on self-reported questionnaires, which resulted in memory bias. In addition, a cross-sectional investigation was unable to establish a cause-and-effect link.