Table 1.
Study (first author, year) | Geographic area | Age category | Gender mix | PUI definition | PUI type | Control definition | PUI | Control | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
N | Mean age (SD) | % M | N | Mean age (SD) | % M | |||||||
Choi et al. (2017) [33] | South Korea | Adults | Male only | Males in their 20 and 30 s who mostly played League of Legends, FIFA, or Sudden Attack; fulfilled the proposed DSM criteria. | IGD | Non-gaming users | 22 | 29.5 (4.7) | 100 | 24 | 27.2 (4.9) | 100 |
Han et al. (2012) [34] | South Korea | Youth | Male only | YIAT > 50; game play time >4 h/day/30 h/week; impaired behaviors or distress | OGA | Healthy comparison group; game play time <3 h/day and <3 day/week | 20 | 20.9 (2.0) | 100 | 18 | 20.9 (2.1) | 100 |
Horvath et al. (2020) [35] | Germany | Youth | Mixed | Smartphone owners aged 18–30 expressing interest in a study of “dysfunctional smartphone use”; SAS-SV > 31 (males), >33 (females) | SPA | Smartphone owners aged 18–30 expressing interest in a study of “dysfunctional smartphone use”; SAS-SV below cut-off | 22 | 22.5 (3.0) | 32 | 26 | 23.0 (3.2) | 31 |
Jin et al. (2016) [36] | China | Youth | Mixed | Participated in online games such as League of Legends as major online behavior; fulfilled proposed DSM criteria; YIAT > 50 | IGD | Healthy control group with Internet use; participated in online games such as League of Legends as major online behavior; YIAT 20–30 | 25 | 19.1 (1.1) | 64 | 21 | 18.8 (1.8) | 67 |
Ko et al. (2015) [37] | Taiwan | Youth/ Adultsa | Male only | IGD diagnosis for >2 years according to DCIA; participated in online gaming for an average of ≥4 h/day on weekdays and ≥8 h/day on weekends | IGD | Never fulfilled DCIA | 30 | 23.6 (2.5) | 100 | 30 | 24.2 (2.5) | 100 |
Lee, Namkoong et al. (2018)b [50] | South Korea | Youth/ Adultsa | Male only | YIAT > 50; reported gaming as the primary purpose of their Internet use; fulfilled proposed DSM criteria | IGD | Healthy controls; YIAT < 50; spent <2 h/day on online gaming | 31 | 24.0 (2.6) | 100 | 30 | 23.0 (2.8) | 100 |
Lee, Park et al. (2018)c [38] | South Korea | Youth | Male only | YIAT ≥ 50; reported main use of Internet was playing games; clinician-administered interview to assess the core components of addiction | IGD | Healthy controls | 45 | 23.8 (1.5) | 100 | 35 | 23.4 (1.7) | 100 |
Lin et al. (2015) [39] | China | Youth | Male only | YIAT ≥ 50; reported “spending most of their online time playing online games (>50 %)” | IGA | Healthy controls | 35 | 22.2 (3.1) | 100 | 36 | 22.3 (2.5) | 100 |
Seok and Sohn (2018) [40] | South Korea | Youth | Male only | Fulfilled proposed DSM criteria | IGD | Healthy controls | 20 | 21.7 (2.7) | 100 | 20 | 22.4 (2.6) | 100 |
Sun et al. (2014) [41] | China | Youth | Mixed | Fulfilled modified YDQ criteria; subjects characterized as the IGA subtype (mostly focused on online gaming when using the Internet) | IGA | Healthy controls; sometimes played online/mobile games but did not meet diagnostic criteria for IGAd | 18 | 20.5 (3.6) | 83 | 21 | 22.0 (2.4) | 86 |
C. Wang et al. (2021) [42] | China | Youth | Mixed | YIAT ≥ 50; fulfilled ≥ 5 proposed DSM criteria | IGD | Healthy controls; YIAT < 50; fulfilled <5 proposed DSM criteria; never played online games or spent <2 h/day playing online games in the last 2 yearsd | 26 | 23.2 (2.5) | 54 | 28 | 23.4 (2.8) | 54 |
S. Wang et al. (2018)c [43] | China | Youth | Male only | Fulfilled ≥ 5 proposed DSM criteria; YIAT ≥ 50; reported Internet gaming as primary online activity | IGD | Normal controls; fulfilled <4 proposed DSM criteria; YIAT < 30 | 48 | 20.6 (1.0) | 100 | 32 | 21.1 (2.2) | 100 |
Y. Wang et al. (2016) [44] | China | Youth | Mixed | MPAI > 51 | MPD | Non-MPD | 34 | 21.6 (2.1) | 38 | 34 | 21.7 (1.9) | 38 |
Z. Wang et al. (2018)c [45] | China | Youth | Mixed | Regularly played “League of Legends” for at least a year; fulfilled ≥ 5 proposed DSM criteria; YIAT ≥ 50 | IGD | Recreational game users; regularly played “League of Legends” for at least a year and as frequently as the IGD subjects (at least 5/7 days and >14 h/week); fulfilled <4 proposed DSM criteria; YIAT < 50 | 38 | 20.7 (2.1) | 71 | 66 | 21.3 (2.0) | 56 |
Weng et al. (2013) [46] | China | Youth | Mixed | Fulfilled modified YDQ criteria; playing online games was the primary Internet activity | OGA | Healthy individuals without OGA | 17 | 16.3 (3.0) | 24 | 17 | 15.5 (3.2) | 12 |
Yoon et al. (2017) [47] | South Korea | Youth/ Adultsa | Male only | Fulfilled proposed DSM criteria; YIAT ≥ 50; spent > 4 h/day and > 30 h/week involved in Internet gaming | IGD | Healthy controls; used the Internet <2 h/day | 19 | 22.9 (5.2) | 100 | 25 | 25.4 (3.8) | 100 |
Yuan et al. (2011) [48] | China | Youth | Mixed | Fulfilled modified YDQ criteria | IAD | Healthy controls; spent <2 h/day on the internet | 18 | 19.4 (3.1) | 67 | 18 | 19.5 (2.8) | 67 |
Zhou et al. (2011) [49] | China | Youth | Mixed | Fulfilled modified YDQ criteria | IA | Healthy individuals sometimes played games but did not meet diagnostic criteria for IAd | 18 | 17.2 (2.6) | 89 | 15 | 17.8 (2.6) | 87 |
DCIA diagnostic criteria for Internet addiction, DSM Diagnostic and Statistical Manual of Mental Disorders (5th ed.), IA Internet addiction, IAD Internet addiction disorder, IGA Internet gaming addiction, IGD Internet gaming disorder, M male, MPAI mobile phone addiction index, MPD mobile phone dependence, OGA online game addiction, PUI Problematic Usage of the Internet, SAS-SV short version of the Smartphone Addiction Scale, SPA smartphone addiction, YDQ Young’s diagnostic questionnaire, YIAT Young’s online Internet addiction test.
aMean ages reported in the two samples fell into both the Youth and Adults categories.
bIncluded in the voxel-based morphometry meta-analysis but not in secondary analyses.
cSurface-based morphometry studies included only in secondary analyses.
dIncludes information from unpublished sources.