https://orcid.org/0000-0003-0787-2656
Figures
Abstract
Internet gaming disorder (IGD) has been rising in recent years. The COVID-19 pandemic has led to a noticeable shift in the way people interact with technology, which could have further contributed to an increase in IGD. Post-pandemic, the concern for IGD is likely to continue as people have become increasingly reliant on online activities. Our study aimed to assess the prevalence of IGD among the general population globally during the pandemic. Relevant studies that assessed IGD during COVID-19 were identified using PubMed, EMBASE, Scopus, CINAHL, and PsycNET between 2020, Jan 1 and 2022, May 23. We used NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies to assess the risk of bias, and GRADEpro for the certainty of the evidence. Three separate meta-analyses were performed using Comprehensive meta-analysis software and Revman 5.4. In total, 362 studies were identified, of which 24 observational (15 cross-sectional and 9 longitudinal) studies among 83,903 population were included in the review, and 9 studies in the meta-analysis. The risk of bias assessment showed an overall fair impression among the studies. The meta-analysis for a single group of 3 studies showed the prevalence rate of 8.00% for IGD. Another meta-analysis of 4 studies for a single group showed a pooled mean of 16.57 which was lower than the cut-off value of the IGDS9-SF tool. The two-group meta-analysis of 2 studies showed no significant difference between the groups before and during COVID-19. Our study showed no clear evidence of increased IGD during COVID-19 due to limited number of comparable studies, substantial heterogeneity, and low certainty of evidence. Further well-designed studies are needed to provide stronger evidence to implement suitable interventions to address IGD worldwide. The protocol was registered and published in the International Prospective Register for Systematic Review (PROSPERO) with the registration number CRD42021282825.
Citation: Gopali L, Dhital R, Koirala R, Shrestha T, Bhusal S, Rimal R, et al. (2023) Effect of COVID-19 pandemic on internet gaming disorder among general population: A systematic review and meta-analysis. PLOS Glob Public Health 3(4): e0001783. https://doi.org/10.1371/journal.pgph.0001783
Editor: Carl Abelardo T. Antonio, UP Manila: University of the Philippines Manila, PHILIPPINES
Received: July 22, 2022; Accepted: March 9, 2023; Published: April 7, 2023
Copyright: © 2023 Gopali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
In recent years, internet gaming has become a popular leisure activity with 2.96 billion gamers worldwide [1]. An internet game, often synonymized with an online game, is a video game that is either partially or primarily played through the internet or any other computer network available [2]. With its exponential growth, there is an emerging concern that internet gaming leads to behavioral addiction [3].
Earlier days of the COVID-19 pandemic led to the closure of many social and recreational physical activities, resulting in a shift towards online games. People started seeking social interaction through online gaming, such as massively multiplayer online role-playing games [4]. Moderate gaming is associated with relaxation and stress reduction, positively enhancing learning ability and increasing cognitive skills [5]. However, excessive gaming can lead to mental health issues such as depression and insomnia, lower academic achievement and psychosocial well-being, and lead to changes in eating habits, musculoskeletal problems, and have a negative impact on quality of life [6]. The addictive nature of internet gaming may also lead to internet gaming disorder (IGD).
IGD was recently recognized as a mental health disorder by World Health Organization (WHO) in 2018 and is included in the latest revision of the International Classification of Diseases (ICD-11) [7]. The American Psychiatric Association defined IGD as “persistent and recurrent use of the internet to engage in games, often with other players, leading to clinically significant distress” [8]. IGD resembles symptoms of a substance use disorder, such as loss of control, functional impairment, distress, and interference with daily activities resulting in poor physical and psychosocial health [9].
Research on IGD has been emerging before the pandemic [10]. Since the pandemic saw a significant increase in online gaming, it is essential to understand the extent of its impact on IGD, especially in the light that IGD was grouped under mental health conditions recently [7]. Systematic reviews that gave a comprehensive understanding of how the pandemic has affected IGD was limited. Therefore, this study aimed to bridge this knowledge gap and assess the prevalence of IGD among the general population during the COVID-19 period. Information obtained from this study may help identify the vulnerable groups and promote the regulatory use of internet gaming to improve the physical, mental, and psychosocial health of gamers. It will aid in raising public awareness about the disorder beyond the pandemic period since IGD is likely to persist due to increased digital reliance. This study will provide valuable insights for concerned researchers, policymakers, and program managers to develop targeted interventions to prevent and treat IGD.
Methods
Protocol
The manuscript was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (S1 Table) [11]. The protocol was registered and published in the International Prospective Register for Systematic Review (PROSPERO) with the registration number CRD42021282825.
Information source
We searched databases of PubMed, Excerpta Medica dataBASE (EMBASE), Scopus, Cumulated Index to Nursing and Allied Health Literature (CINAHL), and PsycNet from January 1, 2020, to May 23, 2022. The Boolean search strategy was used with different terms to find the exposure (online gaming, internet gaming, computer gaming, gaming behavior, digital gaming, video gaming, online role-playing game), outcomes (abuse, addiction, disorder, effects, overuse, problematic, compulsive, excessive, pathological) and time period (COVID-19, COVID 19 pandemic, coronavirus disease). The details of the search terms used are outlined in (S2 Table).
Eligibility criteria
Studies on IGD consisting of a global population of all ages and genders were included in this study. Studies on online internet gaming, solo or multiplayer, during the COVID-19 pandemic were included. Studies on offline games, i.e., games that do not require internet access, were excluded. Original studies estimating the prevalence of IGD during the COVID-19 period with or without a comparison group that fit the inclusion criteria were included. Studies that defined and diagnosed IGD using the definition and proposed criteria based on DSM-5 were included. Qualitative studies, case studies, viewpoints, letters to the editors, and review articles were excluded from this study. Studies with unavailable full text or missing relevant data were excluded. Only studies that were published in English were taken into consideration.
Data extraction
The data extraction table included the following; the name of the author, article URL, decision to include or exclude the article, reason for exclusion, name of the author, year of publication, country, study design, the objective of the study, sampling, sample size, exposure, comparison group, type of analyses, internet gaming disorder values and the IGD scale. The table was adapted from the format of the data extraction table recommended by Cochrane [12]. Each study that was included after the title abstract screening was read several times by four authors (LG, RD, RK, and TS), and the information was updated accordingly on the data extraction table and a joint decision was made by the four authors regarding whether to include or exclude the study. A reason for exclusion was provided for each article that was excluded in the process.
Data collection and analysis
Two authors (LG, RD) independently searched different databases using the search terms (S2 Table). The retrieved studies from each database were uploaded to the citation manager software, Zotero [13]. Four authors (LG, RD, RK, TS) equally divided the responsibilities to remove the duplicates. The remaining studies were screened for title and abstract to exclude irrelevant studies not fitting the inclusion criteria. While excluding the irrelevant studies, two groups were formed (TS, RD) and (RK, LG), and each group screened the studies. Full text of relevant titles and abstracts were then searched. Again, all the included studies were divided into equal halves, and the authors worked in two groups (RD, TS) and (RK, LG) while extracting the data from the full text. In case of disparities during the process, it was resolved by discussion between all the authors. The final decision was made based on the majority’s decision. Study characteristics and outcomes were independently extracted from the eligible studies by all the authors.
Primary outcomes
IGD was the primary outcome. It was measured using tools such as the Internet Gaming Disorder Scale-Short Form (IGDS9-SF), Maladaptive game use scale (MGUS) scales, Internet gaming disorder scale (developed by Cui), Internet Gaming Disorder Test (IGD-20 test), and Internet Gaming Disorder Questionnaire (IGDQ). According to the DSM-5, the diagnostic criteria of IGD include the following clinical symptoms, five out of the nine scores are considered supportive for the diagnosis of IGD [14].
- preoccupation with videogames (“preoccupation”)
- experiencing unpleasant symptoms when playing videogames (“withdrawal”)
- the need to spend an increased amount of time involved in video games (“tolerance”)
- failed attempts to control participation in videogames (“lose control”)
- losing interest in past hobbies and entertainment as a result of and with the exception of, videogames (“surrender from other activities”)
- continue to use videogames despite having knowledge of psychosocial problems (“continuation”)
- deceiving family members, therapists or others regarding the number of videogames (“fraud”)
- using videogames to escape or eliminate negative feelings (“escape”)
- harm or lose relationships, work, or education or significant career opportunities because of participation with videogames (“negative consequences”)
Risk of bias within studies
NIH Quality Assessment Tools for Observational Cohort and Cross-Sectional Studies was used [15]. Four reviewers in two groups (RD, TS) and (RK, LG) equally divided the studies for the quality assessment. Each reviewer worked independently, and later the results were compared. Any disagreements were resolved by further discussions among all the authors.
Narrative synthesis
A systematic narrative synthesis of the findings from the included studies was done. The following information was extracted from each article: author name, publication year, country of study, study design, objective, population, sample size, comparison group, outcome, and type of IGD scale used. The values for mean and standard deviation scores for continuous outcomes and proportions for dichotomous outcomes were recorded for IGD diagnosis. The values for the comparison groups, if any, were also recorded.
Meta-analysis
Datasets were analyzed using Comprehensive Meta-Analysis (CMA) for single group pooled mean and pooled prevalence and Review Manager (RevMan 5.4) for the mean difference between the two groups accordingly. Dichotomous variables and continuous variables were used whenever applicable. The random-effects model was chosen to analyze the pooled data for IGD scores. The heterogeneity of the studies was quantified by the I2 statistic. I2 more than 50% was considered substantial heterogeneity across studies [16].
Certainty of evidence
Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) approach was used to assess the certainty of the evidence for eligible studies. The GRADEpro tool was used for scoring and summarizing the certainty of evidence [17]. Five domains were assessed to downgrade the scores, which included risk of bias, precision, inconsistency, indirectness, and publication bias. As the included studies were observational in design, three domains were assessed to upgrade the scores based on the magnitude of effect, opposing plausible residual bias of confounding and dose-response gradient. As all studies were observational, the initial score for the GRADE assessment was low. The scores were either downgraded from low to very low or upgraded to moderate or high quality. Depending upon the severity, the scores were devalued by one for serious concerns and two for very serious concerns.
Results
Study selection and characteristics
A total of 362 studies were identified from five databases using the search terms and 149 duplicates were removed. From 213 studies screened for title and abstract, only 63 reports were included for retrieval. One study could not be retrieved; hence only 62 studies were assessed for eligibility. Further, 38 studies were excluded by full-text screening, and 24 studies were included in the final review. Among the excluded studies, 28 did not have IGD as the study outcome, seven were not original research, two did not mention the tool used to measure IGD, and one did not mention COVID-19 as the exposure period. The process of screening and inclusion of the studies are outlined in the PRISMA flowchart (Fig 1).
Table 1 shows the characteristics of all the studies included in this review. Out of the total studies, two studies [18,19] were conducted globally while eight studies were from China [20–27], three studies were from Hong Kong [28–30], two studies were from India [31,32], and one study was each from South Korea [33], Vietnam [34], Italy [35], Iran [36], Nepal [37], Japan [38], and Malaysia [39]. One multi-country study included participants from New Zealand, Australia, and the USA [40], and another study included participants from Sri Lanka, Turkey, Australia, and the USA [41]. The study designs employed were cross-sectional (15/24) and longitudinal (9/24).
Out of 24 studies, 20 studies used the Internet Gaming Disorder Scale-Short Form (IGDS9-SF), while the rest of the studies used Maladaptive Game Use Scale (MGUS), Internet gaming disorder scale (developed by Cui), Internet Gaming Disorder Test (IGD-20 test), and 11-item Internet Gaming Disorder Questionnaire (IGDQ-11) to measure IGD. Twelve studies reported single group means; seven studies reported comparative means for longitudinal data, three studies reported proportions, and two studies reported median values.
The total sample size was 83,903 from all 24 studies. Eight studies included adolescents, and five studies included adults only. Three studies included only children, while the remaining included either children and adolescents or adolescents and adults.
Risk of bias
The risk of bias assessment within the studies was performed using the NIH quality assessment tool for observational studies (Table 2). The research objective and population studied were stated clearly in all included studies. All studies except one mentioned the inclusion and exclusion criteria. However, sample size justification, power description, and effect estimate were not reported in the majority of the studies. None of the studies were blinded. Studies were categorized into poor, fair, and good on the basis of the scoring system (Table 2).
Certainty of evidence
Table 3 shows the assessment of the certainty of evidence conducted using GRADE for three different group of studies. All the groups were graded as very low owing to the risk of bias, inconsistency, and indirectness. GRADE was not performed for single studies. There was no upgrade in the scores based on large effect size, opposing plausible residual bias, and dose gradient relationship.
Meta-analysis
Fig 2 shows the two-group meta-analysis for the mean difference using two longitudinal studies among 2353 participants. The comparison was done between the IGDS9-SF scores before and during the COVID-19 pandemic. A random-effects model was used, and the total overall effect for the mean difference is found to be -0.05 (95% CI -0.24,0.13). There was no significant difference between the groups before and during COVID-19. The value of I2 was 95.00% which represents substantial heterogeneity between the selected studies.
Fig 3 shows the single group meta-analysis for proportion using three cross-sectional studies among 2943 participants. The calculation was done using the event rate for the prevalence rate of IGD and the total sample size of each study. A random-effects model was used, and the total overall effect for the event rate is found to be 0.08 (95%CI 0.05,0.13). The value of I2 was 85.80% which represents a substantial amount of heterogeneity between the selected studies.
Fig 4 shows a single-group meta-analysis of pooled means using four studies among 3667 participants using the random effects model. The pooled mean is found to be 16.57 (95% CI 13.42, 19.71), which is below the cut-off scores (36–45) of the IGDS9-SF [41] with an I2 of 99.50%.
Discussion
This systematic review included 24 studies that aimed to identify the prevalence of IGD during the COVID-19 pandemic among 83,903 participants. The meta-analyses included 9 studies through the comparison of mean scores of IGD before and during COVID-19 and pooled effects of prevalence rate and means for single-group analysis. The results of the meta-analyses demonstrated no significant difference in mean scores of IGD before and during COVID-19, with single-group pooled means below the cut-off score for IGDS9-SF scales. However, a pooled prevalence of 8% IGD during COVID-19 was identified from studies conducted in three Asian countries among 2943 population [28,34,39].
This systematic review showed emerging research from different regions and economies. The representation of different countries indicates that IGD is considered an emerging mental health problem irrespective of the region or the economy [42]. However, the majority of the included studies in this systematic review were conducted in China. China witnessed a substantial increase in the popularity of internet games among youth and adolescents in the past two decades with 666 million online gamers in 2022, most of whom were aged more than 18 years [43]. A cross-sectional study conducted before the COVID-19 pandemic among male college students in Nanchong, China, found that one in ten students exhibited clinically significant symptoms of IGD [44]. Due to this, China has recognized IGD as a significant risk factor for mental illness and implemented numerous interventions to combat IGD and addiction, including 250 gaming detox/rehabilitation centers all over the country and restrictions on gaming hours for children under the age of 18 years [45]. Systematic and scoping reviews before COVID-19 have also shown a higher representation of data from China than in other countries [10,42].
The majority of the study population in this systematic review were adolescents. Adolescents and youth are considered to be more technology-friendly, and the use of electronic gadgets and the internet for gaming purposes is more popular among them. However, younger people are also at risk of addictive behaviors and, thus, are considered a high-risk group for IGD [46]. They are also more prone to social isolation and lack of environmental control, which are considered significant predictors of IGD [47]. According to a systematic review from 2018, the population aged between 12 and 20 years in East Asian countries had the highest prevalence of IGD [10].
The meta-analysis of single groups showed a pooled prevalence of 8% from studies conducted in Hongkong, Vietnam, and Malaysia among 2943 adolescents and young adults [28,34,39]. The highest prevalence among the three studies was observed in the study conducted among 2084 adolescents from Vietnam, with a prevalence of 11.6% [34]. A scoping review reporting the prevalence of IGD before COVID-19 suggested a wide range of prevalence between 21% and 57.5% in the general population, with most of the studies from China, Korea, and the USA [42]. Another systematic review focusing on adolescents reported a global prevalence of 5.5% among children and adolescents before COVID-19, representing East Asia, Europe, North America, and Australia [10]. The findings of this meta-analysis indicate that the prevalence of IGD during COVID-19 may have risen in South-East Asian regions and not just limited to countries with higher economies. However, there is still a paucity of long-term data from LMICs from different regions. The findings highlight the need for more prevalence studies from different regions of the world during COVID-19.
The meta-analysis of a single group’s mean IGD score was 16.3 among four studies from India, Italy, Iran and Nepal [31,35–37]. The pooled mean was lower than the mean cut-off score of 36–45 for IGD to be considered prevalent [48]. However, the studies showed substantial heterogeneity and therefore, the impression of prevalence based on pooled mean scores remains inconclusive. The two-group analysis of the mean difference based on two longitudinal studies also did not show a significant difference before and during the pandemic. There could be a lack of longitudinal studies due to a lack of baseline data before COVID-19 and feasibility issues during the pandemic resulting in underreporting of IGD.
Among the included 24 studies in this systematic review, only one study conducted in South Korea showed the highest mean for IGD before and during COVID-19, which also increased during the pandemic among high school students using MGUS (mean score of 40.69 in 2018 and 42.78 in 2020) [33]. This is consistent with the systematic review before COVID-19 that found Korea to be high, reaching an extreme 50% prevalence [10]. However, due to the use of different assessment tools for IGD, the data was not comparable with included studies from other countries. A systematic review conducted in 2018 showed that the prevalence of IGD varies widely between studies from various countries which may be due to significant differences in assessment tools, research populations, and IGD diagnostic criteria used [10].
This systematic review has certain limitations. The GRADE assessment indicated very low evidence, mostly attributed to indirectness and inconsistency. The majority of the included studies were of fair quality in the risk of bias assessment. The most common factors among these 24 studies are that most of these studies lacked a clear justification for sample size calculation and lack of association between exposure and outcome as most of the studies were cross-sectional. Thus, the findings of the systematic review highlight research gaps in this area during COVID-19 and the results could have been under-reported. We could perform meta-analysis only in 9 studies due to a lack of comparable data, and all the meta-analyses showed substantial heterogeneity. Though our studies included participants from various parts of the world, the majority of the studies were conducted in Asia, and the highest number of studies were concentrated in China. Thus, the results may not be representative of global participants. IGD was the only outcome that was measured in this study using various tools, and the results do not reflect other mental and emotional issues associated with internet gaming. Moreover, we only included results published in English, due to which the findings could be prone to language bias. Furthermore, this systematic review was conducted amid the COVID-19 pandemic and may not have captured ongoing studies that may have the potential to yield stronger evidence.
Despite the limitations, this systematic review represented the evidence from different countries with different economies indicating emerging interest in IGD globally during the pandemic. Even though we have transitioned to normal lifestyles, the risk of IGD remains because of its addictive nature. The pandemic has changed how people interact with technology and the internet. There has been a significant increase in the use of online platforms for work, education, socialization, and entertainment during the pandemic, resulting in a rise in excessive gaming. Now that the world is adapting to the new normal after the COVID-19 pandemic, the findings of this study can be used to guide future research on IGD. Additionally, a better understanding of IGD and its prevalence during periods of stress like the COVID-19 pandemic can better help individuals who rely on the internet and online gaming for entertainment or coping with stress. The findings also highlight the research gaps globally. The findings indicate a need for better-designed studies minimizing the risk of biases and more longitudinal studies to reflect the long-term perspective of different countries and diverse groups of researchers. More robust findings on IGD during and beyond COVID-19 will provide baseline evidence to design sustainable interventions and implementation for prevention and rehabilitation programs against IGD on a global scale.
Conclusion
This systematic review represented studies from diverse regions and economies. However, the studies were mostly from Asia, concentrating on China. Our study showed a pooled prevalence of IGD at 8% during COVID-19; however, it did not show any clear evidence of increased IGD during the pandemic. The lack of clear evidence is mainly due to limited studies with comparable data, very low certainty of the evidence of included studies, and substantial heterogeneity in the studies that were included in the meta-analysis. Nevertheless, this systematic review and meta-analyses highlight the research gap during COVID-19 for IGD, a recently recognized mental health condition. Further research on IGD is necessary to identify vulnerable groups, raise awareness, promote regulatory use of internet gaming interventions, and prevent and treat IGD beyond the pandemic period, given the likelihood of its persistence due to increased digital reliance. Better-designed studies that are comparable, reduce the risk of biases, and provide a long-term perspective should be carried out to help establish stronger evidence.
Supporting information
S1 Table. PRISMA statement for systematic reviews and meta-analysis.
https://doi.org/10.1371/journal.pgph.0001783.s001
(DOCX)
S2 Table. Search strategies in different search engines and registry database.
https://doi.org/10.1371/journal.pgph.0001783.s002
(DOCX)
References
- 1.
Clement J. Number of gamers worldwide from 2017 to 2024. 2022 Nov 11 [cited 13 Nov 2022]. In: Statista [Internet]. Available from: https://www.statista.com/statistics/748044/number-video-gamers-world/.
- 2.
Adams E. Fundamentals of game design. 3rd ed. Berkeley CA: New Riders; 2013.
- 3. Chen KH, Oliffe JL, Kelly MT. Internet gaming disorder: an emergent health issue for men. Am J Mens Health. 2018;12: 1151–1159. pmid:29606034
- 4. King DL, Delfabbro PH, Billieux J, Potenza MN. Problematic online gaming and the COVID-19 pandemic. J Behav Addict. 2020;9: 184–186. pmid:32352927
- 5. Jones CM, Scholes L, Johnson D, Katsikitis M, Carras MC. Gaming well: links between videogames and flourishing mental health. Front Psychol. 2014;5: 260. pmid:24744743
- 6. Wei Q, Zhang S, Pan Y, Hu H, Chen F, Yin W, et al. Epidemiology of gaming disorder and its effect on anxiety and insomnia in Chinese ethnic minority adolescents. BMC Psychiatry. 2022;22: 260. pmid:35413829
- 7.
World Health Organization. Addictive behaviours: Gaming disorder. In: World Health Organization [Internet]. 2022 [cited 14 Jul 2022]. Available from: https://www.who.int/news-room/questions-and-answers/item/addictive-behaviours-gaming-disorder.
- 8. Vahia VN. Diagnostic and statistical manual of mental disorders 5: a quick glance. Indian J Psychiatry. 2013;55: 220–223. pmid:24082241
- 9. Starcevic V, Billieux J. Does the construct of Internet addiction reflect a single entity or a spectrum of disorders? Clin Neuropsychiatry. 2017;14: 5–10.
- 10. Paulus FW, Ohmann S, von Gontard A, Popow C. Internet gaming disorder in children and adolescents: a systematic review. Dev Med Child Neurol. 2018;60: 645–659. pmid:29633243
- 11. Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372: n160. pmid:33781993
- 12.
Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al, editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.3. 2022 Feb [cited 2023 February 9]. Cochrane [Internet]. Available from: https://training.cochrane.org/handbook.
- 13. Corporation for Digital Scholarship. Zotero application. Version 6.0.22. [Software]. 2023 [cited 2023 Mar 5]. Available from: https://www.zotero.org/.
- 14. Nasution FA, Effendy E, Amin MM. Internet Gaming Disorder (IGD): a case report of social anxiety. Open Access Maced J Med Sci. 2019;7: 2664–2666. pmid:31777629
- 15. Heart National, Lung, and Blood Institute, National Institutes of Health. Study Quality Assessment Tools [Internet]. Study quality assessment tools. 2021 Jul [cited 2022 Jul 14]. Available from: https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools.
- 16.
Deeks JJ, Higgins JPT, Altman DG, editors. Chapter 10: Analysing data and undertaking meta-analyses. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al, editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.3 2022 Feb [cited 2023 February 9]. Cochrane [Internet]. Available from www.training.cochrane.org/handbook.
- 17. University McMaster and Prime Evidence. GRADEpro GDT: GRADEpro guideline development tool [Software]. 2022. Available from: https://gradepro.org.
- 18. Rozgonjuk D, Pontes HM, Schivinski B, Montag C. Disordered gaming, loneliness, and family harmony in gamers before and during the COVID-19 pandemic. Addict Behav Rep. 2022; 100426. pmid:35434242
- 19. Sallie SN, Ritou VJE, Bowden-Jones H, Voon V. Assessing online gaming and pornography consumption patterns during COVID-19 isolation using an online survey: highlighting distinct avenues of problematic internet behavior. Addict Behav. 2021;123: 107044. pmid:34311186
- 20. Chen IH, Lin YC, Lin CY, Wang WC, Gamble JH. The trajectory of psychological distress and problematic Internet gaming among primary school boys: a longitudinal study across different periods of COVID-19 in China. J Men’s Health. 2022;18: 70.
- 21. Wu Q, Luo T, Tang J, Wang Y, Wu Z, Liu Y, et al. Gaming in China before the COVID-19 pandemic and after the lifting of lockdowns: a nationwide online retrospective survey. Int J Ment Health Addict. 2022; 1–13. pmid:35382159
- 22. Chang CI, Fong Sit H, Chao T, Chen C, Shen J, Cao B, et al. Exploring subtypes and correlates of internet gaming disorder severity among adolescents during COVID-19 in China: a latent class analysis. Curr Psychol. 2022; 1–12. pmid:35505828
- 23. Xiang GX, Gan X, Jin X, Zhang YH, Zhu CS. Developmental assets, self-control and internet gaming disorder in adolescence: testing a moderated mediation model in a longitudinal study. Front Public Health. 2022;10: 808264. pmid:35186844
- 24. Wang Y, Liu B, Zhang L, Zhang P. Anxiety, depression, and stress are associated with internet gaming disorder during COVID-19: fear of missing out as a mediator. Front Psychiatry. 2022;13: 827519. pmid:35222126
- 25. Teng Z, Pontes HM, Nie Q, Griffiths MD, Guo C. Depression and anxiety symptoms associated with internet gaming disorder before and during the COVID-19 pandemic: a longitudinal study. J Behav Addict. 2021;10: 169–180. pmid:33704085
- 26. Chen IH, Chen CY, Liu C, Ahorsu DK, Griffiths MD, Chen YP, et al. Internet addiction and psychological distress among Chinese schoolchildren before and during the COVID-19 outbreak: a latent class analysis. J Behav Addict. 2021;10: 731–746. pmid:34529588
- 27. Chen CY, Chen IH, Hou WL, Potenza MN, O’Brien KS, Lin CY, et al. The relationship between children’s problematic internet-related behaviors and psychological distress during the onset of the COVID-19 pandemic: a longitudinal study. J Addict Med. 2022;16(2):e73–e80. pmid:33770003
- 28. Yang X, Yip BHK, Lee EKP, Zhang D, Wong SYS. The relationship between technology use and problem technology use and potential psychosocial mechanisms: population-based telephone survey in community adults during COVID-19. Front Psychol. 2021;12. Available from: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.696271 pmid:34434146
- 29. She R, Wong K, Lin J, Leung K, Zhang Y, Yang X. How COVID-19 stress related to schooling and online learning affects adolescent depression and Internet gaming disorder: testing conservation of resources theory with sex difference. J Behav Addict. 2021;10: 953–966. pmid:34665762
- 30. Chen IH, Chen CY, Pakpour AH, Griffiths MD, Lin CY, Li XD, et al. Problematic internet-related behaviors mediate the associations between levels of internet engagement and distress among schoolchildren during COVID-19 lockdown: a longitudinal structural equation modeling study. J Behav Addict. 2021;10: 135–148. pmid:33570506
- 31. Balhara YPS, Kattula D, Singh S, Chukkali S, Bhargava R. Impact of lockdown following COVID-19 on the gaming behavior of college students. Indian J Public Health. 2020;64: S172–S176. pmid:32496250
- 32. Singh S, Mani Pandey N, Datta M, Batra S. Stress, internet use, substance use and coping among adolescents, young-adults and middle-age adults amid the ‘new normal’ pandemic era. Clin Epidemiol Glob Health. 2021;12: 100885.
- 33. Kim D, Lee J. Addictive internet gaming usage among Korean adolescents before and after the outbreak of the COVID-19 pandemic: a comparison of the latent profiles in 2018 and 2020. Int J Environ Res Public Health. 2021;18: 7275. pmid:34299725
- 34. Cuong VM, Assanangkornchai S, Wichaidit W, Hanh VTM, Hanh HTM. Associations between gaming disorder, parent-child relationship, parental supervision, and discipline styles: findings from a school-based survey during the COVID-19 pandemic in Vietnam. J Behav Addict. 2021;10: 722–730. pmid:34564065
- 35. Volpe U, Orsolini L, Salvi V, Albert U, Carmassi C, Carrà G, et al. COVID-19-related social isolation predispose to problematic internet and online video gaming use in Italy. Int J Environ Res Public Health. 2022;19: 1539. pmid:35162568
- 36. Fazeli S, Mohammadi Zeidi I, Lin C-Y, Namdar P, Griffiths MD, Ahorsu DK, et al. Depression, anxiety, and stress mediate the associations between internet gaming disorder, insomnia, and quality of life during the COVID-19 outbreak. Addict Behav Rep. 2020;12: 100307. pmid:33110934
- 37. Shrestha M, Manandhar N, Sharma S, Joshi SK. Gaming disorder among medical college students during COVID-19 pandemic lockdown. Kathmandu Univ Med J. 2020;70: 48–52. pmid:33605238
- 38. Oka T, Hamamura T, Miyake Y, Kobayashi N, Honjo M, Kawato M, et al. Prevalence and risk factors of internet gaming disorder and problematic internet use before and during the COVID-19 pandemic: a large online survey of Japanese adults. J Psychiatr Res. 2021;142: 218–225. pmid:34385071
- 39. Ismail N, Tajjudin AI, Jaafar H, Nik Jaafar NR, Baharudin A, Ibrahim N. The relationship between internet addiction, internet gaming and anxiety among medical students in a Malaysian public university during COVID-19 pandemic. Int J Environ Res Public Health. 2021;18: 11870. pmid:34831627
- 40. Hall LC, Drummond A, Sauer JD, Ferguson CJ. Effects of self-isolation and quarantine on loot box spending and excessive gaming—results of a natural experiment. PeerJ. 2021;9: e10705. pmid:33604174
- 41. Ali AM, Al-Amer R, Atout M, Ali TS, Mansour AMH, Khatatbeh H, et al. The nine-item Internet Gaming Disorder Scale (IGDS9-SF): its psychometric properties among Sri Lankan students and measurement invariance across Sri Lanka, Turkey, Australia, and the USA. Healthcare. 2022;10: 490. pmid:35326968
- 42. Darvesh N, Radhakrishnan A, Lachance CC, Nincic V, Sharpe JP, Ghassemi M, et al. Exploring the prevalence of gaming disorder and Internet gaming disorder: a rapid scoping review. Syst Rev. 2020;9: 68. pmid:32241295
- 43.
Thomala LL. Gaming in China—statistics & facts. 2022 May 4 [cited 2022 Jul 15]. In: Statista [Internet]. Available from: https://www.statista.com/topics/4642/gaming-in-china/#topicHeader__wrapper.
- 44. Liu F, Deng H, Zhang Q, Fang Q, Liu B, Yang D, et al. Symptoms of internet gaming disorder among male college students in Nanchong, China. BMC Psychiatry. 2022;22: 142. pmid:35193532
- 45. Henken W. Video Game Addiction: China Fights “Electronic Drugs.” Addiction Center. 2021 Sep 22 [Cited 2022 Jul 15]. Available from: https://www.addictioncenter.com/community/video-game-addiction-china-fights-electronic-drugs/.
- 46. Dirandeh E, Sohrabi M-R, Dirandeh A, Kaghazloo L, Hajihashemi Z, Pouriran R. The effect of video games on teenagers’ behavior and performance: a cross-sectional study in Tehran. SDH. 2015;1.
- 47. Warburton WA, Parkes S, Sweller N. Internet gaming disorder: evidence for a risk and resilience approach. Int J Environ Res Public Health. 2022;19: 5587. pmid:35564981
- 48. Qin L, Cheng L, Hu M, Liu Q, Tong J, Hao W, et al. Clarification of the cut-off score for nine-item Internet Gaming Disorder Scale–Short Form (IGDS9-SF) in a Chinese context. Front Psychiatry. 2020;11: 470. pmid:32528331