Systematic review and meta-analysis of epidemiology of internet addiction

•This is the first meta-analysis comparing prevalence of generalized Internet addiction and Internet gaming disorder.•The rate of generalized Internet addiction is higher than the rate of Internet gaming disorder.•The prevalence of generalized Internet addiction varied with year.•The prevalence of g...

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Published inNeuroscience and biobehavioral reviews Vol. 118; pp. 612 - 622
Main Authors Pan, Yuan-Chien, Chiu, Yu-Chuan, Lin, Yu-Hsuan
Format Journal Article
LanguageEnglish
Published United States Elsevier Ltd 01.11.2020
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Summary:•This is the first meta-analysis comparing prevalence of generalized Internet addiction and Internet gaming disorder.•The rate of generalized Internet addiction is higher than the rate of Internet gaming disorder.•The prevalence of generalized Internet addiction varied with year.•The prevalence of generalized Internet addiction differed among assessment tools. The field of internet addiction has experienced significant debates on conflicting epidemiology. This meta-analysis investigated the prevalence rates of generalized internet addiction (GIA) and internet gaming disorder (IGD). We included 113 epidemiologic studies covering 693,306 subjects published from 1996 to 2018 (for 31 nations) that reported prevalence rates for GIA or IGD. We examined pooled prevalence of GIA and IGD and the hypothesized moderators including year, geographic regions, types of scales, and sample representativeness. All 133 effect sizes included 53,184 subjects with GIA or IGD. Weighted average prevalence for GIA and IGD were 7.02 % (95 % CI, 6.09 %–8.08 %) and 2.47 % (95 % CI, 1.46 %–4.16 %) respectively. For GIA, prevalence was increased over time and prevalence rates variated among different scales. IGD prevalence was neither moderated by year, regions, nor sample representativeness. The prevalence of GIA was higher than the prevalence of IGD. The GIA prevalence was increasing over time and variated with different assessments. Our results reveal that GIA may reflect a pattern of increasing human-machine interaction.
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ISSN:0149-7634
1873-7528
1873-7528
DOI:10.1016/j.neubiorev.2020.08.013