Availability of Data and Material
All data generated or analyzed during the study are included in this published article.
Conflicts of Interest
The authors have no potential conflicts of interest to disclose.
Author Contributions
Conceptualization: Patria Yudha Putra, Izzatul Fithriyah. Formal analysis: Patria Yudha Putra. Investigation: Patria Yudha Putra, Izzatul Fithriyah. Methodology: all authors. Validation: all authors. Writing—original draft: Patria Yudha Putra. Writing—review & editing: all authors.
Funding Statement
None
Study | Selection | Comparability | Results | Total |
---|---|---|---|---|
Dong et al., [15] 2020 | ☆☆☆☆☆ | ☆ | ☆☆ | 8 |
Fazeli et al., [10] 2020 | ☆☆☆☆☆ | ☆ | ☆☆ | 8 |
Teng et al., [17] 2021 | ☆☆☆☆☆ | ☆☆ | ☆☆☆ | 10 |
Lin, [16] 2020 | ☆☆☆☆☆ | ☆ | ☆☆ | 8 |
Mulyadi et al., [18] 2020 | ☆☆☆☆ | ☆ | ☆☆ | 7 |
Study | Study type | Population description | Mean age (yr) | Sample size | Context/setting |
Types of outcomes and instruments used |
||
---|---|---|---|---|---|---|---|---|
Internet addiction | Online gaming disorder | Other psychological problems | ||||||
Dong et al., [15] 2020 | Cross-sectional | School-age children and adolescents from North, East, and Middle China | 12.34 (SD=4.67) | 2,050 (1,057 male, 993 female) | During the COVID-19 pandemic in China (Feb 2020–Mar 2020) | Y-IAT | DASS-21 | |
Fazeli et al., [10] 2020 | Cross-sectional | Adolescents aged 13–18 years from 25 high schools in Qazvin (Iran) | 15.51 (SD=2.75) | 1,512 (853 male, 659 female) | During the COVID-19 pandemic in Iran (May 2020–Aug 2020) | IGDS9-SF | DASS-21, ISI, PedsQL 4.0 SF15 | |
Teng et al., [17] 2021 | Longitudinal study | Children and adolescents who were part of the Project of School Mental Health in Southwest China | NR | 1,778 (901 male, 877 female) | Before the COVID-19 pandemic compared with wave 3 (Oct 2019–Nov 2019) and wave 4 (Apr 2020–May 2020) COVID-19 pandemic in China | IGDS9-SF | CES-D, STAI | |
Lin, [16] 2020 | Cross-sectional | Junior high school students from three junior high schools located in northern Taiwan | 14.66 (SD=0.86) | 1,060 (542 male, 504 female, 14 NR) | During the COVID-19 outbreak in Taiwan (Mar 2020) | CIAS | Shortened Chinese Version of Five-Factor Inventory—Neuroticism Subscale, BIS-short form, DASS (depression only), RSES, TAS-20, CHI, SSS, VSSS | |
Mulyadi et al., [18] 2020 | Cross-sectional | University students from 21 provinces in Indonesia and Sydney, Australia | 20.6 | 991 (298 male, 683 female, 10 NR) | During the COVID-19 pandemic in Indonesia | Online questionnaires asking about internet usage activity and duration | Anxiety scale developed by Afandi (2007), an online questionnaire asking about sleep duration during the night |
SD, standard deviation; COVID-19, coronavirus disease-2019; Y-IAT, Young’s Internet Addiction Test; DASS-21, Depression, Anxiety and Stress Scale; IGDS9-SF, Internet Gaming Disorder Scale-Short Form; ISI, Insomnia Severity Index; PedsQL 4.0 SF15, Pediatric Quality of Life InventoryTM 4.0 Short Form; NR, not reported; CES-D, Center for Epidemiologic Studies Depression Scale; STAI, State-Trait Anxiety Inventory; CIAS, Chen Internet Addiction Scale; BIS, Barratt Impulsivity Scale; RSES, Rosenberg Self-Esteem Scale; TAS-20, Toronto Alexithymia Scale-20; CHI, Chinese Happiness Inventory; SSS, Social Support Scale; VSSS, Virtual Social Support Scale
Study | Assessment of internet addiction | Main findings on internet addiction prevalence during the COVID-19 pandemic |
---|---|---|
Dong et al., [15] 2020 | Y-IAT score: | 2.68% (male, 3.50%; female, 1.81%)=AIU; |
≥70=AIU | 33.37% (male, 35.10%; female, 31.52%)=PIU; | |
40–69=PIU | Age, gender, and education status were significantly different among AIU, PIU, and NIU (p<0.001) | |
≥39=NIU | ||
Lin, [16] 2020 | CIAS score: | 24.4% (130 male and 125 female)=IA |
≥64=IA | Age and all psychosocial risk factors were significantly different in the non-IA and IA groups (p<0.01) | |
<63=non-IA | ||
Mulyadi et al., [18] 2020 | Duration of internet usage per day: | 55.6% (551 respondents)=IA |
≥6 hrs=IA | Average internet usage duration per day=6.96 hrs | |
<6 hrs=non-IA |
Study | Online gaming disorder assessment | Main findings on online gaming disorder prevalence during the COVID-19 pandemic |
---|---|---|
Fazeli et al., [10] 2020 | IGDS9-SF score: no further explanation | Mean score=19.07 (SD=7.31) |
Teng et al., [17] 2021 | IGDS9-SF: at least five questions were answered “often or very often” | A significant difference in online gaming disorder prevalence before the pandemic: 3.6% (55 boys and 9 girls) compared with during the pandemic: 5.0% (72 boys and 17 girls) (p=0.025); |
Higher severity of online gaming disorder symptoms in adolescents during the pandemic compared to that before the pandemic (p=0.035), but not in children (p=0.287) |
Study | IA/OGD | Dep | Anx | Str | Ins | QoL/SWB | Neu | Imp | SE | Alx |
---|---|---|---|---|---|---|---|---|---|---|
Dong et al., [15] 2020 | IA | Sig** | Sig**† | Sig* | ||||||
Fazeli et al., [10] 2020 | OGD | Sig** | Sig** | Sig** | Sig** | Sig**‡ | ||||
Teng et al., [17] 2021 | OGD | Sig** | Sig** | |||||||
Lin, [16] 2020 | IA | NS | Sig**‡ | NS | Sig** | NS | Sig* | |||
Mulyadi et al., [18] 2020 | IA | Sig** |
COVID-19, coronavirus disease-2019; IA, internet addiction; OGD, online gaming disorder; Dep, depression; Anx, anxiety; Str, stress; Ins, insomnia; QoL, quality of life; SWB, subjective well-being; Neu, neuroticism; Imp, impulsivity; SE, self-esteem; Alx, alexithymia; Sig, significant correlation; NS, no significant correlation