Internet Addiction and Its Associated Factors Among African High School and University Students: Systematic Review and Meta-Analysis

Abstract

IntroductionInternet addiction is characterized by excessive and uncontrolled use of the internet affecting everyday life. Adolescents are the primary risk group for internet addiction. Data on internet addiction is lacking in Africa. Thus, this review aimed to determine the pooled prevalence of internet addiction and its associated factors among high school and university students in Africa.MethodsA comprehensive literature search was conducted using electronic databases (PubMed/MEDLINE, Web of science, Hinari, and Google scholar) to locate potential studies. Heterogeneity between studies was checked using Cochrane Q test statistics and I2 test statistics and small-study effects were checked using Egger's statistical test at a 5% significance level. A sensitivity analysis was performed. A random-effects model was employed to estimate the pooled prevalence and associated factors of internet addiction among students. The primary outcome of measure of this review was the prevalence of internet addiction and the secondary outcome of measures are the factors associated with internet addiction.ResultsA total of 5,562 studies were identified among the five databases. Of these, 28 studies from 10 countries with 14,946 high school and university students were included in this review. The overall pooled prevalence of internet addiction among the students was 34.53% [95% Confidence Interval (CI): 26.83, 42.23, I2 = 99.20%]. Male sex [Pooled Odds Ratio (POR) = 1.92, 95% CI:1.43, 2.57 I2 = 0.00], urban residence (POR = 2.32, 95% CI:1.19, 4.53, I2 = 59.39%), and duration of daily internet use for more than 4 h (POR = 2.25, 95% CI:1.20, 4.21, I2 = 0.00%, were significantly associated with internet addiction among adolescents.ConclusionAlmost one-third of university and high school students in Africa are addicted to the internet. Male students, those from urban areas, and those who use the internet for more than 4 h per day have higher odds of internet addiction. Thus, we recommend that health planners and policymakers pay attention to the use of the internet and internet addiction in Africa.

Internet Addiction in Socio-Demographic, Academic, and Psychological Profile of College Students During the COVID-19 Pandemic in the Czech Republic and Slovakia

“…The identified discrepancy between the analyzed countries requires further investigation. The results can be compared to those of Zewde et al (40) who found in their study that students from urban areas are more likely to be addicted to the internet. It is generally known that urban areas are more internet accessible and urban individuals make greater use of the internet, which may explain the higher rates of addictive behavior in urban areas (80)(81)(82).…”

“…In both countries, male students were more likely to suffer from internet addiction than female students. Other studies also revealed a higher risk of problematic internet use or internet addiction in male students compared to female students (18,38,(40)(41)(42)(43). The predominance of males in this type of problematic behavior is evident (77).…”

“…In this case, respondents could select one of the following answers for each of the 10 items: never-−0, almost never-−1, sometimes-−2, fairly often-−3, very often-−4. The ranges of the total PSS-10 score indicated: low stress (0-13), moderate stress (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26), and high stress (27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40). Accordingly, the higher the total score, the more severe the psychological distress.…”

“…The scale consisted of 20 items with the following answers: not applicable-−0, rarely-−1, occasionally-−2, frequently-−3, often-−4, always-−5. Internet addiction was identified as follows: normal level of internet usage (0-30), mild addiction symptoms (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49), moderate addiction symptoms , and severe addiction symptoms (80-100). The IAT scale was composed of six subscales for which precise interval thresholds are not defined.…”

“…Based on these findings, it can be assumed that depressive symptoms, anxiety symptoms and perceived stress may also prove to be significant predictors in other countries, such as the Czech Republic and Slovakia. In addition, studies show that individual aspects such as gender, age, residence and academic characteristics also play an important role in problematic internet use (18,32,(36)(37)(38)(39)(40)(41)(42)(43).…”