what is the literature review about video game addiction brainly

Systematic literature review online gaming addiction among children and young adults: A framework and research agenda

Affiliations.

• 1 Colegio Universitario de Estudios Financieros (CUNEF), Madrid, Spain. Electronic address: [email protected].
• 2 ITM Business School, ITM University, India. Electronic address: [email protected].
• 3 Southampton Business School, University of Southampton, Southampton, UK. Electronic address: [email protected].
• PMID: 35104738
• DOI: 10.1016/j.addbeh.2022.107238

Online gaming addiction refers to a persistent and recurrent use of internet to engage in games leading to significant impairment or distress in a person's life. With the current pandemic, media reports suggest that the greater access of online devices among children and young adults has intensified online gaming addiction. However, the domain of online gaming addiction is a relatively new phenomenon with disparate studies examining various facets of it. Hence, the purpose of this research is to analyze the existing literature in order to identify the emerging trends in this area and to provide a systematic review that can be used as guidance for future research in this emerging field. Starting from the gaps that this review highlights, the proposed directions will help scholars find issues and gaps not sufficiently explored that can constitute the bases for further research pathways.

Keywords: Addiction; Children; Games; Internet; Online gaming disorders; Review; Young adults.

Copyright © 2022 Elsevier Ltd. All rights reserved.

Publication types

• Systematic Review
• Behavior, Addictive*
• Video Games*
• Young Adult

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Internet Gaming Addiction: A Systematic Review of Empirical Research

• Published: 16 March 2011
• Volume 10 , pages 278–296, ( 2012 )

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• Daria Joanna Kuss 1 &
• Mark D. Griffiths 1  

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The activity of play has been ever present in human history and the Internet has emerged as a playground increasingly populated by gamers. Research suggests that a minority of Internet game players experience symptoms traditionally associated with substance-related addictions, including mood modification, tolerance and salience. Because the current scientific knowledge of Internet gaming addiction is copious in scope and appears relatively complex, this literature review attempts to reduce this confusion by providing an innovative framework by which all the studies to date can be categorized. A total of 58 empirical studies were included in this literature review. Using the current empirical knowledge, it is argued that Internet gaming addiction follows a continuum, with antecedents in etiology and risk factors, through to the development of a “full-blown” addiction, followed by ramifications in terms of negative consequences and potential treatment. The results are evaluated in light of the emergent discrepancies in findings, and the consequent implications for future research.

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Kuss, D.J., Griffiths, M.D. Internet Gaming Addiction: A Systematic Review of Empirical Research. Int J Ment Health Addiction 10 , 278–296 (2012). https://doi.org/10.1007/s11469-011-9318-5

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• Published: 28 November 2022

Psychological treatments for excessive gaming: a systematic review and meta-analysis

• Jueun Kim 1 ,
• Sunmin Lee 1 ,
• Dojin Lee 1 ,
• Sungryul Shim 2 ,
• Daniel Balva 3 ,
• Kee-Hong Choi 4 ,
• Jeanyung Chey 5 ,
• Suk-Ho Shin 6 &
• Woo-Young Ahn 5  

Scientific Reports volume  12 , Article number:  20485 ( 2022 ) Cite this article

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Despite widespread public interest in problematic gaming interventions, questions regarding the empirical status of treatment efficacy persist. We conducted pairwise and network meta-analyses based on 17 psychological intervention studies on excessive gaming ( n  = 745 participants). The pairwise meta-analysis showed that psychological interventions reduce excessive gaming more than the inactive control (standardized mean difference [SMD] = 1.70, 95% confidence interval [CI] 1.27 to 2.12) and active control (SMD = 0.88, 95% CI 0.21 to 1.56). The network meta-analysis showed that a combined treatment of Cognitive Behavioral Therapy (CBT) and Mindfulness was the most effective intervention in reducing excessive gaming, followed by a combined CBT and Family intervention, Mindfulness, and then CBT as a standalone treatment. Due to the limited number of included studies and resulting identified methodological concerns, the current results should be interpreted as preliminary to help support future research focused on excessive gaming interventions. Recommendations for improving the methodological rigor are also discussed.

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Introduction.

Excessive gaming refers to an inability to control one’s gaming habits due to a significant immersion in games. Such an immersion may result in experienced difficulties in one’s daily life 1 , including health problems 2 , poor academic or job performance 3 , 4 , and poor social relationships 5 . Although there is debate regarding whether excessive gaming is a mental disorder, the 11th revision of the International Classification of Diseases (ICD-11) included Gaming Disorder as a disorder in 2019 6 . While there is no formal diagnosis for Gaming Disorder listed in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), the DSM-5 included Internet Gaming Disorder (IGD) as a condition for further study 7 . In the time since the DSM-5’s publication, research on excessive gaming has widely continued. Although gaming disorder’s prevalence appears to be considerably heterogeneous by country, results from a systematic review of 53 studies conducted between 2009 and 2019 indicated a global prevalence of excessive gaming of 3.05% 8 . More specifically, a recent study found that Egypt had the highest IGD prevalence rate of 10.9%, followed by Saudi Arabia (8.8%), Indonesia (6.1%), and India (3.8%) among medical students 9 .

While the demand for treatment of excessive gaming has increased in several countries 10 , standard treatment guidelines for problematic gaming are still lacking. For example, a survey in Australia and New Zealand revealed that psychiatrics— particularly child psychiatrists, reported greater frequency of excessive gaming in their practice, yet 43% of the 289 surveyed psychiatrists reported that they were not well informed of treatment modalities for managing excessive gaming 11 . Similarly, 87% of mental health professionals working in addiction-related institutions in Switzerland reported a significant need for professional training in excessive gaming interventions 12 . However, established services for the treatment of gaming remain scarce and disjointed.

Literature has identified a variety of treatments for excessive gaming, but no meta-analysis has yet been conducted on effectiveness of the indicated interventions. The only meta-analysis to date has focused on CBT 13 , and while results demonstrated excellent efficacy in reducing excessive gaming. However, the study did not compare the intervention with other treatment options. Given that gaming behavior is commonly affected by cognitive and behavioral factors as well as social and familial factors 14 , 15 , 16 , it would also be important to examine the effectiveness of treatment approaches that reflect social and familial influences. While two systematic reviews examined diverse therapeutic approaches, they primarily reported methodological concerns of the current literature and did not assess the weight of evidence 17 , 18 . Given that studies in this area are rapidly evolving and studies employing rigorous methodological approaches have since emerged 19 , 20 , a meta-analytic study that analyzes and synthesizes the current stage of methodological limitations while also providing a comprehensive comparison of intervention options is warranted.

In conducting such a study, undertaking a traditional pairwise meta-analysis is vital to assess overall effectiveness of diverse interventions. Particularly, moderator and subgroup analyses in pairwise meta-analysis provide necessary information as to whether effect sizes vary as a function of study characteristics. Furthermore, to obtain a better understanding of the superiority and inferiority of all clinical trials in excessive gaming psychological interventions, it is useful to employ a network meta-analysis, which allows for a ranking and hierarchy of the included interventions. While a traditional pair-wise analysis synthesizes direct evidence of one intervention compared with one control condition, a network meta-analysis incorporates multiple comparisons in one analysis regardless of whether the original studies used them as control groups. It enters all treatment and control arms of each study, and makes estimates of the differences in interventions by using direct evidence (e.g., direct estimates where two interventions were compared) and indirect evidence (e.g., generated comparisons between interventions from evidence loops in a network 21 . Recent meta-analytic studies on treatments for other health concerns and disorders have used this analysis to optimize all available evidence and build treatment hierarchies 22 , 23 , 24 .

In this study, the authors used a traditional pairwise meta-analysis and network meta-analysis to clarify the overall and relative effectiveness of psychological treatments for excessive gaming. The authors also conducted a moderator analysis to examine potential differences in treatment efficacy between Randomized Controlled Trials (RCTs) and non-RCTs, age groups, regions, and research qualities. Finally, the authors examined follow-up treatment efficacy and treatment effectiveness on common comorbid symptoms and characteristics (e.g., depression, anxiety, and impulsivity).

The protocol for this review has been registered in the International Prospective Register of Systematic Review (PROSPERO 2021: CRD 42021231205) and is available for review via the following link: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=231205 . Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) network meta-analysis checklist 25 is included in Supplementary Material 1 .

Identification and selection of studies

The authors searched seven databases, which included ProQuest, PubMed, Scopus, Web of Science, PsycINFO, Research Information Sharing Service (RISS), and DBpia. Given that a substantial number of studies have been published particularly in East Asia and exclusion of literature from the area in languages other than English has been discussed as a major limitation in previous reviews 17 , 18 , the authors gave special attention to gaming treatment studies in English and other languages from that geographical area. Additionally, the authors searched Google Scholar to ensure that no studies were accidentally excluded. The authors conducted extensive searches for studies published in peer-reviewed journals between the first available year (year 2002) and October 31, 2022, using the following search terms: “internet”, or “video”, or “online”, or “computer”, and “game”, or “games”, or “gaming”, and “addiction”, or “addictions”, or “disorder”, “disorders”, or “problem”, or “problems”, or “problematic”, or “disease”, or “diseases”, or “excessive”, or “pathological”, or “addicted”, and “treatment”, or “treatments”, or “intervention”, or “interventions”, or “efficacy”, or “effectiveness”, or “effective”, or “clinical”, or “therapy”, or “therapies”. Search strategies applied to each database is provided in Supplementary Material 2 .

The authors included studies that recruited individuals who were excessively engaging in gaming, according to cutoff scores for different game addiction scales. Since there is not yet an existing consensus on operational definitions for excessive gaming, the authors included studies that recruited individuals who met high-risk cutoff score according to the scales used in each respective study (e.g., Internet Addiction Test [modified in game environments] > 70). The authors also sought studies that provided pretest and posttest scores from the game addiction scales in both the intervention and control groups. Studies meeting the following criteria were excluded: (a) the study targeted excessive Internet use but did not exactly target excessive gaming; (b) the study provided a prevention program rather than an intervention program; (c) the study provided insufficient data to perform an analysis of the effect sizes and follow-up contact to the authors of such studies did not yield the information necessary for inclusion within this paper; and (d) the study conducted undefinable types of intervention with unclear psychological orientations (e.g., art therapy with an undefined psychological intervention, fitness programs, etc.).

Two authors (D.L. and S.L.) independently screened the titles and abstracts of articles identified by the electronic searches and excluded irrelevant studies. A content expert (J.K.) examined the intervention descriptions to determine intervention types that were eligible for this review. All treatments were primarily classified based on the treatment theory, protocol, and descriptions about the procedures presented in each paper. D.L. and S.L.—both of whom have been in clinical training for 2 years categorized treatment type, to which J.K., a licensed psychologist, cross-checked and confirmed the categorization. The authors resolved disagreements through discussion. The specific example of intervention type classification is provided in Supplementary Material 3 .

Risk of bias and data extraction

Three independent authors assessed the following risks of bias among the included studies. The authors used the Risk of Bias 2.0 (RoB 2) tool for RCT studies and the Risk Of Bias In Non-Randomized Studies of Intervention (ROBINS-I) tool for non-RCT studies. The RoB 2 evaluates biases of (a) randomization processes; (b) deviations from intended interventions; (c) missing outcome data; (d) measurement of the outcome; and (e) selection of the reported result, and it categorizes the risk of bias in each dimension into three levels (low risk, moderate risk, and high risk). The ROBINS-I evaluates biases of (a) confounding variables; (b) selection of participants; (c) classification of interventions; (d) deviations from intended interventions; (e) missing data; (f) measurement of outcomes; and (g) selection of the reported result, and it categorizes the risk of bias in each dimension into five levels (low risk, moderate risk, serious risk, critical risk, and no information). After two authors (D.L. and S.L.) assessed each study, another author (J.K.) cross-checked the assessment.

For each study, the authors collected descriptive data, which included the sample size as well as participants’ ages, and regions where the studies were conducted. The authors also collected clinical data, including whether the study design was a RCT, types of treatment and control, treatment duration, and the number of treatment sessions. Finally, the authors collected data on the follow-up periods and the measurement tools used in each study.

Data analysis

The authors employed separate pairwise meta-analyses in active control and inactive control studies using R-package “meta” 26 and employed a random-effects model due to expected heterogeneity among studies. A random-effects model assumes that included studies comprise random samples from the larger population and attempt to generalize findings 27 . The authors categorized inactive control groups including no treatment and wait-list control and categorized active control groups including pseudo training (e.g., a classic stimulus-control compatibility training) and other types of psychological interventions (e.g., Behavioral Therapy, CBT, etc.). The authors also used the bias-corrected standardized mean change score (Hedges’ g ) due to small sample sizes with the corresponding 95% confidence interval 28 . The authors’ primary effectiveness outcome was a mean score change on game addiction scales from pre-treatment to post-treatment. Hedges’ g effect sizes were interpreted as small ( g  = 0.15), medium ( g  = 0.40) and large ( g  = 0.75), as suggested by Cohen 29 . The authors used a conservative estimate of r  = 0.70 for the correlation between pre-and post-treatment measures 30 , and to test heterogeneity, the authors calculated Higgins’ I 2 , which is the percentage of variability in effect estimates due to heterogeneity among studies rather than chance. I 2  > 75% is considered substantial heterogeneity 31 .

The authors conducted moderator analyses as a function of RCT status (RCT versus non-RCT), age group (adolescents versus adults), region (Eastern versus Western), and research quality (high versus low). The authors divided high versus low quality studies using median values of research quality scores (RCT: low [0–2] versus high [3–5], non-RCT: low [0–4] versus high [5]). The authors calculated Cochran’s Q for heterogeneity: A significant Q value indicates a potentially important moderator variable. For the subgroup analyses of follow-up periods and other outcomes, the authors conducted separate pairwise analyses in 1- to 3-month follow-up studies and in 4- to 6-month follow-up studies and separate analyses in depression, anxiety, and impulsivity outcome studies.

The authors sought to further explore relative effectiveness of treatment types and performed a frequentist network meta-analysis using the R-package “netmeta” 4.0.4 version 26 . To examine whether transitivity and consistency assumptions for network meta-analysis were met, the authors assessed global and local inconsistency. To test network heterogeneity, the authors calculated Cochran’s Q to compare the effect of a single study with the pooled effect of the entire study. The authors drew the geometry plot of the network meta-analysis through the netgraph function in “netmeta”, and the thicker lines between the treatments indicated a greater number of studies.

The authors presented the treatment rankings based on estimates using the surface area under the cumulative ranking curve (SUCRA) 32 . The SUCRA ranged from 0 to 100%, with higher scores indicating greater probability of more optimal treatment. The authors also generated a league table to present relative effectiveness between all possible comparisons between treatments. When weighted mean difference for pairwise comparisons is bigger than 0, it favors the column-defining treatment. Finally, funnel plots and Egger’s test were used to examine publication bias.

Included studies and their characteristics

Figure  1 presents the flow diagram of the study selection process. The authors identified 1471 abstracts in electronic searches and identified an additional seven abstracts through secondary/manual searches (total n  = 1478). After excluding duplicates ( n  = 765) and studies that did not meet the inclusion criteria based on the study abstract ( n  = 550), the authors retrieved studies with potential to meet the inclusion criteria for full review ( n  = 163). Of these, 144 studies were excluded due to not meeting inclusion criteria based on full-text articles, leaving 19 remaining studies. Of the 19, two studies did meet this paper’s inclusion criteria but were excluded from this network meta-analysis 33 , 34 because the consistency assumption between direct and indirect estimates was not met at the time of this study's consideration based on previous studies 35 , 36 . Therefore, a total of 17 studies were included in this network meta-analysis, covering a total of 745 participants 36 .

Flow diagram of the study selection process.

Table 1 lists the characteristics of the 17 included studies. CBT ( n  = 4), Behavioral Treatment (BT) + Mindfulness ( n  = 4), and BT only ( n  = 4) were most frequently studied, followed by CBT + Family Intervention ( n  = 1), CBT + Mindfulness ( n  = 1), virtual reality BT ( n  = 1), Mindfulness ( n  = 1), and Motivational Interviewing (MI) + BT ( n  = 1). Seven studies were conducted in Korea and six were conducted in China, followed by Germany and Austria ( n  = 1), Spain ( n  = 1), the United States ( n  = 1), and the Philippines ( n  = 1). Twelve articles were written in English, and five articles were written in a language other than English. Nine studies conducted a follow-up assessment with periods ranging from one to three months, and two studies conducted a follow-up assessment with periods ranging four to six months. In one study 20 , the authors described their 6-month follow-up but did not present their outcome value, and thus only two studies were included in the four- to six-month follow-up analysis. Among the 17 included studies, eight had no treatment control group, five had an active control group (e.g., pseudo training, BT, and CBT), and four had a wait-list control group. Seven of the studies were RCT studies, and 10 were non-RCT studies.

Pairwise meta-analysis

The results of meta-analyses showed a large effect of all psychological treatments when compared to any type of comparison groups ( n  = 17, g  = 1.47, 95% CI [1.07, 1.86]). The treatment effects were separately provided according to active versus inactive comparison groups in Fig.  2 . The effects of psychological treatments were large when compared to the active control ( n  = 5, g  = 0.88, 95% CI [0.21, 1.56]) or inactive control ( n  = 12, g  = 1.70, 95% CI: [1.27, 2.12]). Substantial heterogeneity was evident in studies that were compared to both the active controls (I 2  = 72%, < 0.01) and inactive controls at p -value level of 0.05 (I 2  = 69%, p  < 0.001).

Pairwise Meta-analysis. Psychological treatment effects on excessive gaming by comparison group type (active and inactive controls). SMD standardized mean difference, SD standard deviation,  CI confidence interval, I 2  = Higgins' I 2 .

Moderator analysis

As shown in Table 2 , the moderator analysis suggested that effect sizes were larger in non-RCT studies ( n  = 10, g  = 1.60, 95% CI [1.36, 1.84]) than RCT studies ( n  = 7, g  = 1.26, 95% CI [0.30, 2.23]). However, the results of a Q-test for heterogeneity yielded insignificant results (Q = 0.44, df[Q] = 1, p  = 0.51), indicating that no statistically significant difference in treatment efficacy at p level of 0.05 between RCT and non-RCT studies.

The results of Q-test for heterogeneity did not yield any significant results, indicating no significant differences in treatment efficacy between adults and adolescents (Q = 2.39, df[Q] = 1, p  = 0.12), Western and Eastern regions (Q = 0.40, df[Q] = 1, p  = 0.53), or low and high research qualities among RCT studies (Q = 2.25, df[Q] = 1, p  = 0.13) and non-RCT studies (Q = 3.06, df[Q] = 1, p  = 0.08).

Subgroup analysis

The results demonstrated that the treatment effect was Hedges’ g  = 1.54 (95% CI [0.87, 2.21]) at 1-to-3-month follow-up and Hedges’ g  = 1.23 (95% CI [0.77, 1.68]) 4- to-6-month follow-up. The results also showed that the treatment for excessive gaming was also effective on depression and anxiety. Specifically, treatment on depression was Hedges’ g  = 0.52 (95% CI: [0.22, 0.81], p  < 0.001), and anxiety was Hedges’ g  = 0.60 (95% CI [0.11, 1.08], p  = 0.02), which are medium and significant effects. However, the effect on impulsivity was insignificant, Hedges’ g  = 0.26 (95% CI [− 0.14, 0.67], p  = 0.20).

Network meta-analysis

As shown in Fig.  3 , a network plot represents a connected network of eight intervention types (CBT, BT + Mindfulness, BT, Virtual Reality BT, CBT + Mindfulness, CBT + Family, MI + BT, and Mindfulness) and three control group types (wait-list control, no treatment, treatment as usual). The widest width of nodes was observed when comparing BT + Mindfulness and no treatment, indicating that those two modules were most frequently compared. No evidence of global inconsistency based on a random effects design-by-treatment interaction model was found (Q = 8.5, df[Q] = 7, p  = 0.29). Further, local tests of loop-specific inconsistency did not demonstrate inconsistency, indicating that the results from the direct and indirect estimates were largely in agreement ( p  = 0.12- 0.78).

Network plot for excessive gaming interventions. Width of lines and size of circles are proportional to the number of studies in each comparison. BT behavioral therapy, CBT cognitive behavioral therapy, Family family intervention, MI motivational interviewing, TAU treatment as usual.

As shown in Fig.  4 , according to SUCRA, a combined intervention of CBT and Mindfulness ranked as the most optimal treatment (SUCRA = 97.1%) and demonstrated the largest probability of effectiveness when compared to and averaged over all competing treatments. A combined treatment of CBT and Family intervention ranked second (SUCRA = 90.2%), and Mindfulness intervention ranked third (SUCRA = 82.1%). As shown in Table 3 , according to league table, CBT + Mindfulness intervention showed positive weighted mean difference values in the lower diagonal, indicating greater effectiveness over all other interventions. The CBT + Mindfulness intervention was more effective than CBT + Family or Mindfulness interventions, but their differences were not significant (weighted mean differences = 0.23–1.11, 95% CI [− 1.39 to 2.68]). The top three ranked interventions (e.g., CBT + Mindfulness, CBT + Family intervention, and Mindfulness in a row) were statistically significantly superior to CBT as a standalone treatment as well as the rest of treatments.

Surface under the cumulative ranking curve (SUCRA) rankogram of excessive gaming. BT behavioral therapy, CBT cognitive behavioral therapy, Family family intervention, MI motivational interviewing, TAU treatment as usual.

Risk of bias

Figure  5 displays an overview of the risk of bias across all included studies. Of note was that in the RCT studies, bias due to missing outcome data was least problematic, indicating a low dropout rate (six out of seven studies). In contrast, bias due to deviations from intended interventions was most problematic, indicating that, in some studies, participants and trial personnel were not blinded and/or there was no information provided as to whether treatments adhered to intervention protocols (six out of seven studies). In the non-RCT studies, bias in the selection of participants in the study was least problematic, indicating that researchers did not select participants based on participant characteristics after the start of intervention (10 out of 10 studies). In contrast, bias in the measurement of outcomes was most problematic, indicating that participants and outcome assessors were not blinded and/or studies used self-reported measures without clinical interviews (10 out of 10 studies).

Overview of risk of bias results across all included studies. Cl bias in classification of interventions, Co bias due to confounding, De bias due to deviations from intended interventions, Me bias in measurement of the outcome, Mi bias due to missing outcome data, R bias arising from the randomization process, RoB risk of bias, ROBINS-I risk of bias in non-randomized studies of intervention, Sp bias in selection of participants in the study, Sr bias in selection of the reported result.

Funnel plots and Egger’s test showed no evidence of publication in network meta-analyses. Funnel plots were reasonably symmetric and the result from Egger’s test for sample bias were not significant ( p  = 0.22; see Supplementary Material 4 ).

In this pairwise and network meta-analyses, the authors assessed data from 17 trials and analyzed the overall and relative effectiveness of eight types of psychological treatments for reducing excessive gaming. The pairwise meta-analysis results indicated large overall effectiveness of psychological treatments in reducing excessive gaming. Although the effectiveness was smaller when compared to the active controls than when compared to the inactive controls, both effect sizes were still large. However, this result needs to be interpreted with caution because there are only seven existing RCT studies and several existing low-quality studies. Network meta-analysis results indicated that a combined treatment of CBT and Mindfulness was the most effective, followed by a combined therapy of CBT and Family intervention, Mindfulness, and then CBT as a standalone treatment, however, this finding was based on a limited number of studies. Overall, the findings suggest that psychological treatments for excessive gaming is promising, but replications are warranted, with additional attention being placed on addressing methodological concerns.

The large effect of psychological treatments in reducing excessive gaming seems encouraging but the stability and robustness of the results need to be confirmed. These authors’ moderator analysis indicated that the effect size of non-RCT studies was not significantly different from that of RCT studies. The authors conducted a moderator analysis using the research quality score (high vs low) and found that research quality did not moderate the treatment effect. The authors also examined publication bias using both funnel plots and Egger’s test and found no evidence of publication bias in network meta-analysis. Because most of the studies included in the review were from Asian countries, the authors examined the generalizability of the finding by testing moderator analysis by regions and found no significant difference of treatment effect sizes between Eastern and Western countries. Finally, although limited studies exist, treatment benefits did not greatly diminish after 1–6 months of follow-ups, indicating possible lasting effects.

Network meta-analysis findings provide some preliminary support for the notion that a combined treatment of CBT and Mindfulness and a combined treatment of CBT and Family intervention are most effective in addressing individuals’ gaming behaviors. These combined therapies were significantly more effective than the CBT standalone approach. CBT has been studied and found to be highly effective in addiction treatment—particularly in reducing excessive gaming due to its attention to stimulus control and cognitive restructuring 13 . However, adding Mindfulness and family intervention may have been more effective than CBT alone, given that gaming is affected not only by individual characteristics, but also external stress or family factors.

Mindfulness generally focuses on helping individuals to cope with negative affective states through mindful reappraisal and aims to reduce stress through mindful relaxation training. The effectiveness of Mindfulness has been validated in other substance and behavioral addiction studies such as alcohol 37 , gambling 38 , and Internet 39 addiction treatments. Indulging in excessive gaming is often associated with the motivation to escape from a stressful reality 40 , and mindful exercises are likely to help gamers not depend on gaming as a coping strategy.

Because excessive gaming is often entangled with family environments or parenting-related concerns—particularly with adolescents, addressing appropriate parent–adolescent communication and parenting styles within excessive gaming interventions are likely to increase treatment efficacy 41 , 42 , 43 . Based on a qualitative study focused on interviews with excessive gamers 43 , and per reports from interviewed gamers, parental guidance to support regulatory control and encouragement to participate in other activities are important factors to reduce excessive gaming. However, at the same time, if parents excessively restrict their children’s behavior, children will feel increased stress and may further escape into the online world through gaming 44 as a means of coping with their stress. Our study indicates that appropriate communication among parents and adolescents in addition to parenting styles with respect to game control must be discussed in treatment. However, because only two studies examined the top two ranked combined interventions within this paper, such findings warrant replication.

Limitations and future directions

These authors identified methodological limitations and future directions in the reviewed studies, which include the following. The authors included non-RCTs to capture data on emerging treatments, but a lack of RCT studies contributes to this paper’s identified methodological concerns. Of 17 studies included, seven were RCT studies and 10 were non-RCT studies. The lack of RCT studies has been repeatedly mentioned in previous review studies 17 , 18 . In fact, one of the two identified reviews 17 made the criticism that even CBT (the most widely studied treatment for excessive gaming) was mostly conducted in non-RCT studies, which was commensurate with this paper’s data (only one out of four CBT studies included in this review is a RCT). Including non-RCTs may be likely to increase selection bias by employing easily accessible samples and assigning participants with more willingness (which is an indicator of better treatment outcome) to intervention groups. Selection bias may have increased the effect size of treatments than what is represented in reality and may limit the generalizability of this finding. Thus, more rigorous evaluation through RCTs is necessary in future studies.

While there are concerns surrounding assessment tools, given that all included studies used self-report measures without clinical interviews, this may lead to inaccurate results due to perceived stigma. Additionally, 11 self-reported measurement tools were employed in the included studies—and some of those tools may have poor sensitivity or specificity. A previous narrative review 45 and a recent meta-analytic review 46 suggested that the Game Addiction Scale-7, Assessment of Internet and Computer Addiction Scale-Gaming, Lemmens Internet Gaming Disorder Scale-9, Internet Gaming Disorder Scale 9- Short Form, and Internet Gaming Disorder Test-10 have good internal consistency and test–retest reliability. Thus, there is a need for studies to employ clinical interviews and self-report measures with good psychometric features.

Many studies in this included review did not describe whether participants and experimenters were blinded and there was no information about whether treatments adhered to intervention protocols. Although blinding of participants and personnel may be impossible in most psychotherapy studies, it is crucial to evaluate possible performance biases such as social desirability. Also, a fidelity check by content experts is needed to confirm whether treatments adhered to intervention protocols.

Finally, future studies need to examine treatment efficacy in treating both excessive gaming and its comorbid psychiatric symptoms. Internet/gaming addiction has been reported to have a high comorbidity with attention deficit hyperactivity disorder, depression, anxiety, and other substance abuse 47 , 48 . Our results showed that CBT, BT, and BT + Mindfulness may be effective in reducing depression or anxiety symptoms of excessive gamers. However, other psychological and/or pharmacological treatments such as CBT + Bupropion or Bupropion as a standalone treatment have been also reported as potentially effective treatments for excessive gamers with major depressive disorder 49 , 50 . Thus, it would be worthwile to examine efficacy of treatments on excessive gamers with dual diagnoses.

TO the best of the authors’ knowledge, this is the first pairwise meta-analytic and network meta-analytic study that examined the overall effectiveness of psychological treatments and compared the relative effectiveness of diverse treatment options for excessive gaming. Although the authors intentionally used network meta-analysis because of its usefulness in comparing relative effectiveness of currently existing literature, this finding should be interpreted with caution due to the small number of studies. However, as previously indicated, the global prevalence of excessive gaming highlights the need for greater attention to this topic. Studies focused on the effectiveness of diverse gaming interventions help meet the call for further inquiry and study on this topic placed by the DSM-5 7 , and allow greater advances to be made in treating individuals who may have difficulty controlling excessive gaming habits. As such, this study can provide preliminary support for beneficial treatment interventions for excessive gaming as well as recommendations for more rigorous studies to be directed at helping those who have excessive gaming habits.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

•Indicates studies used in the meta-analysis

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This research was supported by the project investigating scientific evidence for registering gaming disorder on Korean Standard Classification of Disease and Cause of Death funded by the Ministry of Health and Welfare and Korea Creative Content Agency.

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Kim, J., Lee, S., Lee, D. et al. Psychological treatments for excessive gaming: a systematic review and meta-analysis. Sci Rep 12 , 20485 (2022). https://doi.org/10.1038/s41598-022-24523-9

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2 A History and Overview of Video Game Addiction

Mark D. Griffiths, PhD, International Gaming Research Unit, Psychology Department, Nottingham Trent University, UK

Halley M. Pontes, PhD, MSc, University of Tasmania, Australia; The International Cyberpsychology and Addictions Research Laboratory, University of Tasmania, Australia

• Published: 08 October 2020
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The past decade has witnessed a significant increase in the number of empirical studies examining various aspects of problematic video game play, video game addiction, and, more recently, gaming disorder. This chapter begins with a brief past history of how research into video game addiction has developed during the past four decades in the 1980s (arcade video game addiction), 1990s (home console video game addiction), and 2000s and beyond (online video game addiction). The chapter also overviews the features of gaming addiction, its prevalence rates, demographics and gaming addiction, negative consequences of excessive video game use, Internet gaming disorder and the DSM-5, and treatment of gaming addiction. Based on the published evidence, particularly from studies conducted in the past decade, it appears that, in extreme cases, excessive gaming can have potentially damaging effects on individuals who appear to display compulsive and/or addictive behavior similar to other more traditional addictions. However, the field has been hindered by the use of inconsistent and nonstandardized criteria to assess and identify problematic and/or addictive video game use.

According to the Entertainment Software Association (ESA, 2014 ), 59% of the entire American population plays video games, with an average of two gamers in each game-playing household. Additionally, among US households 68% play video games on consoles, 53% play on smartphones, and 41% play on wireless devices (ESA, 2014 ). During 2012, playing video games via smartphones and wireless devices increased by 22% and 37%, respectively. The average video game player is 31 years old - and about 52% of these video game players are male and 48% are female (ESA, 2014 ).

Given the pervasiveness of gaming across several countries and different segments of the population, the study of its associated effects on general human behavior, physical and mental health has become an important topic for dedicated research agendas from various scientific domains in addition to psychology and psychiatry (e.g., Blocher, 2015 ; Dreier, Wölfling, & Müller, 2013 ; Johnston, Boyle, MacArthur, & Manion, 2013 ). Taken together, findings in this developing field suggest both favorable and unfavorable effects of gaming, outcomes that could inform decisions made by healthcare professionals, parents, policymakers (Przybylski, 2014 ) and other stakeholders such as researchers and the video game industry (Yousafzai, Hussain, & Griffiths, 2013 ).

One topic that has become of increasing research interest is gaming addiction and Internet Gaming Disorder (IGD). The past decade has witnessed a significant increase in the number of empirical studies examining various aspects of problematic video gameplay and video game addiction (Pontes & Griffiths, 2014 ). This chapter begins with a brief past history of how research into video game addiction has changed over the past three decades (i.e., the 1980s, 1990s, and 2000s). The chapter addresses concerns related to video game addiction and how it made its way into the latest (fifth) edition of the DSM (DSM-5; American Psychiatric Association, 2013 ). The chapter also briefly examines features of video game addiction and examines the contemporary research literature by analyzing the prevalence of video game addiction, factors associated with video game addiction, and the treatment of video game addiction.

Gaming Addiction in the 1980s

Following the release of the first commercial video games in the early 1970s, it took until the 1980s for the first reports of video game addiction to appear in the psychological and psychiatric literature. In the early 1980s, Ross, Finestone, and Lavin reported three cases of “Space Invaders obsession” and Nilles ( 1982 ) described a similar phenomenon but called it “computer catatonia.” Arguably the first reference to “video game addiction” was by Soper and Miller ( 1983 ) who, based on their observations as school counselors, claimed the disorder was like any other behavioral addiction and consisted of a compulsive behavioral involvement, a lack of interest in other activities, association mainly with other addicts, and physical and mental symptoms when attempting to stop the behavior (e.g., the shakes). Some credence was given to these claims that video game addiction existed following papers on the seemingly successful treatment of video game addiction using cognitive behavioral therapy (CBT) (Keepers, 1990 ; Kuczmierczyk, Walley, & Calhoun, 1987 ). However, all of these studies were somewhat observational, anecdotal, and/or case studies, primarily based on teenage males and all based on a particular type of video game in a particular medium (i.e., “pay-to-play” arcade video games).

Shotton ( 1989 ) carried out the first empirical study specifically on gaming addiction on a relatively small sample of 127 people (almost all teenage or young adult males) who described themselves as “hooked’’ on home video games for at least 5 years. Shotton’s conceptualization of gaming addiction was more positive than negative, and she reported that her “addicts” were on the whole highly intelligent, motivated, and achieving people who were often misunderstood by others in society. Despite this, the main problem with the study was that no standardized measure of addiction was actually used. The only criterion for being “addicted” was the individuals own admission that they were “hooked” on computer games. Despite this major shortcoming, recent research by Widyanto, Griffiths, and Brunsden ( 2011 ) reported that a person’s positive self-diagnosis to the Internet was correlated highly with more standardized measures of Internet addiction.

Gaming Addiction in the 1990s

The 1990s saw a small but significant increase of research into video game addiction, with almost all of these studies being carried out by surveying children in school settings in the United Kingdom (e.g., Brown & Robertson, 1993 ; Fisher, 1994 ; Griffiths, 1997 ; Griffiths & Hunt, 1995 , 1998 ; Parsons, 1995 ; Phillips, Rolls, Rouse, & Griffiths, 1995 ). In contrast to studies from the early 1980s, these studies mainly examined non-arcade video game playing (i.e., home console games, handheld games, PC gaming). However, all of these studies were self-report surveys, relatively small in scale, and all of them assessed video game addiction using adapted versions of the Diagnostic and Statistical Manual of Mental Disorders (DSM), such as the DSM-III or the DSM-IV criteria for pathological gambling (American Psychiatric Association, 1987 , 1994 ). Based on further analysis of the adapted DSM criteria used, these studies were later criticized as being more likely to be assessing video game preoccupation rather than video game addiction (Charlton, 2002 ).

Gaming Addiction in the 2000s and Beyond

The 2000s saw a substantial growth in the number of studies on video game addiction, particularly as gaming expanded into the new online medium where games could be played as part of a gaming community (i.e., massively multiplayer online role playing games [MMORPGs] such as World of Warcraft and Everquest). Approximately 60 studies were published on gaming addiction between 2000 and 2010 (Kuss & Griffiths, 2012 ) and a vast majority of these studies examined MMORPG addiction and were not limited to only studying adolescent males. Furthermore, many of these studies collected their data online, and a significant number of studies examined various aspects of video game addiction using non–self-report methodologies. These include studies using polysomnographic measures and visual and verbal memory tests (Dworak, Schierl, Bruns, & Struder, 2007 ); medical examinations including the patient’s history, and physical, radiologic, intraoperative, and pathologic findings (Cultrara & Har-El, 2002 ); functional magnetic resonance imaging (fMRI; Han, Hwang, & Renshaw, 2010 ; Hoeft, Watson, Kesler, Bettinger, & Reiss, 2008 ; Ko et al., 2009 ); electroencephalography (Thalemann, Wölfling, & Grüsser, 2007 ); and genotyping (Han et al., 2007 ). Given the methodological shortcomings of the studies published prior to 2000 and the fact that gaming has evolved substantially over the past decade, the remainder of this chapter will mainly focus on studies published in the past decade or so (i.e., post-2000 studies) with the exception of those concerning the health and medical consequences of excessive video game play.

Features of Gaming Addiction

There are a multitude of psychological perspectives on addiction, which has led to addiction being defined in many different ways. However, most models of addictive behavior refer to a persistent and uncontrollable urge to consume a substance or engage in an activity that results in significant personal harm and interpersonal conflict for the user (King, Delfabbro, & Griffiths, 2013 ). Thus, gaming addiction is often said to be present when individuals have completely lost control over gaming and the excessive playing behavior has had a detrimental effect on all aspects of the individuals life, compromising job and/or educational activities, interpersonal relationships, hobbies, general health, and psychological well-being (King, Delfabbro, & Griffiths, 2013 ). These two criteria (impaired control and harmful consequences) are regarded as fundamentally important criteria for addiction. An alternative model of addictive behavior has proposed six features or components of gaming addiction (Griffiths, 2005 ). To indicate addiction, it is thought that these criteria must be sustained for at least 3–6 months. Otherwise, they may simply indicate a temporary absorption in video games. These criteria include:

• Salience. This occurs when gaming becomes the most important activity in a person’s life, dominating thoughts (preoccupation and cognitive distortions), emotions (cravings), and behavior (deterioration of normal behaviors). An addicted gamer is obsessed with all aspects of video games and, when not playing, will be anticipating or planning the next playing session. • Mood modification. This refers to changes in a person’s mood state that occur as a result of gaming. Mood change may involve a subjective feeling of euphoria as well as an increase in physiological arousal (increased heart rate, muscle tension, or shaky hands) or, alternatively, a tranquilizing feeling of calm or a numbing sensation. • Tolerance. This refers to the process whereby increasing amounts of gaming are required to achieve the former mood-modifying effects. This means that players gradually increase the amount of time they spend engaged in gaming. It could be argued that addicted gamers build up their tolerance to the point that they will end a playing session only when they have become mentally or physically exhausted. • Withdrawal. These are the aversive mood states and/or physical effects that occur when gaming is suddenly discontinued or reduced. Psychological withdrawal symptoms include feelings of frustration, irritability, and flattened affect. Withdrawal motivates the individual to play video games on a regular basis and to minimize periods of absence from a video game in order to alleviate these unpleasant feeling states. • Relapse. This refers to the tendency for the player to make repeated reversions to earlier patterns of gaming and for even the most extreme patterns typical of the height of excessive gaming to be quickly restored after periods of abstinence or moderation. Relapse usually indicates that the individual has lost personal agency over the behavior. • Conflict (harm). This refers to the negative consequences of excessive gaming. Harm includes conflicts between the addicted video game player and other people (family members and friends), other activities (job, school, social life, hobbies and interests), and from within the addict him/herself (psychological distress).

Charlton ( 2002 ) suggests that three of these features may not be reliable indicators of video game addiction. His research suggests that cognitive salience (preoccupation), euphoria (mood modification), and tolerance also indicate high engagement, or a type of healthy obsession, with gaming. Therefore, studies may overestimate the prevalence of problem video game play if high engagement with gaming is not properly distinguished from gaming addiction. Given these issues of reliability, many addiction specialists maintain that impaired control and harmful effects are the most appropriate criteria for identifying gaming addiction.

Prevalence of Problematic Video Game Use and Gaming Addiction

At present, it is quite difficult to estimate the prevalence of problematic gaming due to the lack of a clear definition, the application of measures without proper psychometric characteristics, and studies using different samples and different research methodologies. Large-sample studies generally report prevalence values of lower than 10%. A study conducted in the United States on a national representative sample of teenagers (Gentile, 2009 ), as well as on a large sample of Singaporean children (Gentile et al., 2011 ) both reported a problematic game use of approximately 9%. Results of another representative study in Germany showed that 3% of the male and 0.3% of the female students studied were diagnosed as dependent on video games, while another 4.7% of male and 0.5% of female students were at risk of becoming dependent (Rehbein et al., 2010 ). On a large Hungarian online gamer sample 3.4% of gamers belonged to the high-risk group of problematic gaming and another 15.2% to the medium-risk group (Demetrovics et al., 2012 ). A proportion of 4.6% of Hungarian adolescents (approximately 16 years old) belonging to a national sample were classified as high-risk users (Pápay et al., 2013 ) (see Table 2.1 ).

Demographics and Gaming Addiction

According to an online survey examining all types of online gamers (Nagygyörgy et al., 2013 ) ( N = 4,374), the mean age was 21 years, and participants were mostly male (91%) and single (66%). Their average weekly game time varied between less than 7 hours (10%) and more than 42 hours (also 10%) with most of the gamers playing 15–27 hours weekly (35%). Furthermore, 16% of all gamers were playing either professionally (i.e., they make a living off of sponsorships and money won from tournaments) or competitively (i.e., they participate in competitions and earn money if they win). The majority of the sample (79%) had a clear gaming preference: namely, they played one single game type most of the time.

Data regarding the three main game types give a more nuanced view. The proportion of female gamers is the lowest in the case of massively multiplayer online first-person shooter (MMOFPS) games (1–2%) (Jansz & Tanis, 2007 ; Nagygyörgy et al., 2013 ) and the highest between MMORPG users (15–30%) (Cole & Griffiths, 2007 ; Nagygyörgy et al., 2013 ; Yee, 2006a ). MMOFPS users are the youngest (18–19.8 years) (Jansz & Tanis, 2007 ; Nagygyörgy, et al., 2013 ), while both massively multiplayer online real-time strategy (MMORTS) (22 years) (Nagygyörgy et al., 2013 ) and MMORPG players (21–27 years) (Nagygyörgy et al., 2013 ; Yee, 2006a ) are significantly older. Among the three main groups, MMORPG gamers spend the most time playing (Nagygyörgy et al., 2013 ). Since MMORPGs are the most researched games (most likely because they allow players to interact to form friendships, create communities, and work together to accomplish a variety of goals [Barnett & Coulson, 2010 ]), there is additional information regarding such players that is still unknown in the case of other game types. For instance, half of MMORPG players work full time, 22.2% are students, and 14.8% are homemakers (89.9% of whom were female). Furthermore, 36% of the gamers are married and 22% of them have children (Yee, 2006a , 2006b ). Overall, the demographic composition of MMORPG users is quite varied and probably more diverse than the composition of MMORTS and MMOFPS users (although this needs to be empirically established).

From a substantive perspective, there are some generalizations that can be made with regard to the demographic characteristics of gamers and problem gamers. The literature to date suggests that adolescent males and young male adults appear to be at greater risk of experiencing problematic video game play. However, the course and severity of these problems is not well known (King, Delfabbro, & Griffiths, 2012 ) and the finding that this group is more at risk may be a consequence of sampling bias and the fact that this group plays video games more frequently than do other sociodemographic groups. It has also been suggested that university students may be vulnerable to developing problematic video gaming. Reasons for this include their flexible tuition and study hours, ready access to high-speed broadband on a 24/7 basis, and multiple stressors associated with adjusting to new social obligations and/or living out-of-home for the first time (King, Delfabbro et al., 2012 ; Young, 1998a ).

Negative Consequences of Excessive Video Game Use

Irrespective of whether problematic video game play can be classed as an addiction, there is now a relatively large number of studies all indicating that excessive video game play can lead to a wide variety of negative psychosocial consequences for a minority of affected individuals. These include sacrificing work, education, hobbies, socializing, time with partner/family, and sleep (Batthyány, Müller, Benker, & Wölfling, 2009 ; Griffiths, Davies, & Chappell, 2004 ; King & Delfabbro, 2009 ; Liu & Peng, 2009 ; Peng & Liu, 2010 ; Peters & Malesky, 2008 ; Rehbein et al., 2010 ; Yee 2006a , 2006b ), increased stress (Batthyány et al., 2009 ), an absence of real-life relationships (Allison, von Wahlde, Shockley, & Gabbard, 2006 ), lower psychosocial well-being and loneliness (Lemmens, Valkenburg, & Peter, 2011 ), poorer social skills (Griffiths, 2010 ; Zamani, Kheradmand, Cheshmi, Abedi, & Hedayati, 2010 ), decreased academic achievement (Chiu, Lee, & Huang, 2004 ; Jeong & Kim, 2011 ; Rehbein et al., 2010 ; Skoric, Teo, & Neo, 2009 ), increased inattention (Batthyány et al., 2009 ; Chan & Rabinowitz, 2006 ), aggressive/oppositional behavior and hostility (Chan & Rabinowitz, 2006 ; Chiu et al., 2004 ), maladaptive coping (Batthyány et al., 2009 ; Hussain & Griffiths, 2009a , 2009b ), decreased verbal memory performance (Dworak et al., 2007 ), maladaptive cognitions (Peng & Liu, 2010 ), and suicidal ideation (Rehbein et al., 2010 ).

In addition to the reported negative psychosocial consequences, there are also many reported health and medical consequences that may result from excessive video game playing. These include epileptic seizures (Chuang, 2006 ; Graf, Chatrian, Glass, & Knauss, 1994 ; Harding & Jeavons, 1994 ; Maeda et al., 1990 ; Millett, Fish, & Thompson, 1997 ; Quirk et al., 1995 ); auditory hallucinations (Ortiz de Gortari & Griffiths, 2014a ; Spence, 1993 ); visual hallucinations (Ortiz de Gortari & Griffiths, 2014b ); enuresis (Schink, 1991 ); encopresis (Corkery, 1990 ); obesity (Deheger, Rolland-Cachera, & Fontvielle, 1997 ; Johnson & Hackett, 1997 ; Shimai, Yamada, Masuda, & Tada, 1993 ; Vandewater, Shim, & Caplovitz, 2004 ); wrist pain (McCowan, 1981 ); neck pain (Miller, 1991 ); elbow pain (Miller, 1991 ); tenosynovitis—also called “nintendinitis” (Brasington, 1990 ; Casanova & Casanova, 1991 ; Reinstein, 1983 ; Siegal, 1991 ); blisters, calluses, sore tendons, and numbness of fingers (Loftus & Loftus, 1983 ); hand-arm vibration syndrome (Cleary, McKendrick, & Sills, 2002 ); sleep abnormalities (Allison et al., 2006 ; Dworak et al., 2007 ); psychosomatic challenges (Batthyány et al., 2009 ); and repetitive strain injuries (Mirman & Bonian, 1992 ).

Taken together, this relatively long list of potential psychosocial and medical negative consequences indicates that excessive gaming is an issue irrespective of whether it is an addiction. It also suggests that more extensive recognition is needed of the wide range of potential negative and life-limiting consequences of excessive video play.

Factors Associated with Problematic Video Game Use and Video Game Addiction

A number of studies have examined the role of different personality factors, comorbidity factors, and biological factors and their association with gaming addiction. In relation to personality traits, gaming addiction has been shown to have association with neuroticism (Mehroof & Griffiths, 2010 ; Peters & Malesky, 2008 ), aggression and hostility (Caplan, Williams, & Yee, 2009 ; Chiu et al., 2004 ; Kim, Namkoong, Ku, & Kim, 2008 ; Mehroof & Griffiths, 2010 ), avoidant and schizoid interpersonal tendencies (Allison et al., 2006 ), loneliness and introversion (Caplan et al., 2009 ), social inhibition (Porter, Starcevic, Berle, & Fenech, 2010 ), boredom inclination (Chiu et al., 2004 ), sensation-seeking (Chiu et al., 2004 ; Mehroof & Griffiths, 2010 ), diminished agreeableness (Peters & Malesky, 2008 ), diminished self-control and narcissistic personality traits (Kim et al., 2008 ), low self-esteem (Ko, Yen, Chen, Chen, & Yen, 2005 ), state and trait anxiety (Mehroof & Griffiths, 2010 ), and low emotional intelligence (Parker et al., 2008 ). It is hard to assess the etiological significance of these associations with gaming addiction as they may not be unique to the disorder. Further research is therefore needed.

Research has also shown gaming addiction to be associated with a variety of comorbid disorders. This includes attention deficit hyperactivity disorder (Allison et al., 2006 ; Batthyány et al., 2009 ; Chan & Rabinowitz, 2006 ; Han et al., 2009 ) and symptoms of generalized anxiety disorder, panic disorder, depression, social phobia (Allison et al., 2006 ), school phobia (Batthyány et al., 2009 ), and various psychosomatic symptoms (Batthyány et al., 2009 ).

Through use of fMRI, biological research has shown that gaming addicts show similar neural processes and increased activity in brain areas associated with substance-related addictions and other behavioral addictions, such as pathological gambling (significant activation in the left occipital lobe, parahippocampal gyrus, dorsolateral prefrontal cortex, nucleus accumbens, right orbitofrontal cortex, bilateral anterior cingulate, medial frontal cortex, and the caudate nucleus (Han et al., 2010 ; Hoeft et al., 2008 ; Ko et al., 2009 ). It has also been reported that gaming addicts (like substance addicts) have a higher prevalence of two specific polymorphisms of the dopaminergic system (i.e., Taq1A1 allele of the dopamine D 2 receptor and the Val158Met in the catecholamine-o-methyltransferase receptor) (Han et al., 2007 ).

Internet Gaming Disorder and the DSM-5

Prior to the publication of the DSM-5 (American Psychiatric Association, 2013 ), there had been some debate as to whether “Internet addiction” should be introduced into the text as a separate disorder (Block, 2008 ; Petry & O’Brien, 2013 ). Alongside this, there was debate as to whether those researching the online addiction field should be researching generalized Internet use and/or the potentially addictive activities that can be engaged on the Internet (e.g., gambling, video gaming, sex, shopping, etc.) (Griffiths, 2000 ; Griffiths, King, & Demetrovics, 2014 ). Following these debates, the Substance Use Disorder Work Group (SUDWG) recommended that the DSM-5 include a subtype of problematic Internet use (i.e., Internet gaming disorder [IGD]) in Section 3 (“Emerging Measures and Models”) as an area that needed future research before being included in future editions of the DSM (Petry & O’Brien, 2013 ). According to Petry and O’Brien ( 2013 ), IGD will not be included as a separate mental disorder until the (1) defining features of IGD have been identified, (2) reliability and validity of specific IGD criteria have been obtained cross-culturally, (3) prevalence rates have been determined in representative epidemiological samples across the world, and (4) etiology and associated biological features have been evaluated.

One of the key reasons that IGD was not included in the main text of the DSM-5 was that the SUDWG concluded that no standard diagnostic criteria were used to assess gaming addiction across these many studies (Griffiths et al., 2014 ). A review of instruments assessing problematic, pathological, and/or addictive gaming by King and colleagues ( 2013 ) reported that 18 different screening instruments had been developed and that these had been used in 63 quantitative studies comprising 58,415 participants. This comprehensive review identified both strengths and weaknesses of these instruments. The main strengths of the instrumentation included (1) the brevity and ease of scoring; (2) excellent psychometric properties, such as convergent validity and internal consistency; and (3) robust data that will aid the development of standardized norms for adolescent populations. However, the main weaknesses identified in the instrumentation included (1) core addiction indicators being inconsistent across studies, (2) a general lack of any temporal dimension, (3) inconsistent cutoff scores relating to clinical status, (4) poor and/or inadequate interrater reliability and predictive validity; and (5) inconsistency and/or dimensionality. It has also been noted by a number of authors that the criteria for IGD assessment tools are theoretically based on a variety of different potentially problematic activities including substance use disorders, pathological gambling, and/or other behavioral addiction criteria (King et al., 2013 ; Petry & O’Brien, 2013 ). There are also issues surrounding the settings in which diagnostic screens are used, as those used in clinical practice settings may require a different emphasis than those used in epidemiological, experimental, and neurobiological research settings (King et al., 2013 ; Koronczai et al., 2011 ).

A recent review by Pápay and colleagues ( 2014 ) argued that some researchers consider video games as the starting point for examining the characteristics of IGD, while others consider the Internet as the main platform that unites different addictive Internet activities, including online games. Recent studies (Demetrovics et al., 2012 ; Kim & Kim, 2010 ) have made an effort to integrate both approaches. Consequently, IGD can either be viewed as a specific type of video game addiction, as a variant of Internet addiction, or as an independent diagnosis (Griffiths et al., 2014 ).

Griffiths ( 2005 ) has argued that although all addictions have particular and idiosyncratic characteristics, they share more commonalities than differences (i.e., salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse) and likely reflect a common etiology of addictive behavior. Consequently, online game addiction is viewed as a specific type of video game addiction. Similarly, Porter and colleagues ( 2010 ) do not differentiate between problematic video game use and problematic online game use. They conceptualized problematic video game use as excessive use of one or more video games resulting in a preoccupation with and a loss of control over playing video games and their various negative psychosocial and/or physical consequences. However, unlike Griffiths ( 2005 ), their criteria for problematic video game use does not include other features usually associated with dependence or addiction (e.g., tolerance, physical symptoms of withdrawal) as they say there is no clear evidence that problematic gaming is associated with such phenomena. Researchers such as Young ( 1998b ) view online gaming addiction as a subtype of Internet addiction, as they believe the Internet itself provides situation-specific characteristics that facilitate gaming becoming problematic and/or addictive.

Kim and Kim’s ( 2010 ) Problematic Online Game Use (POGU) model takes a more integrative approach and claims that neither of the approaches just outlined adequately capture the unique features of online games such as MMORPGs. They argue that the Internet is just one channel where people may access the content they want (e.g., gambling, shopping, sex, etc.) and that such users may become addicted to the particular content rather than to the channel itself. This is analogous to the argument by Griffiths ( 2000 ) that there is a fundamental difference between addiction to the Internet and addictions on the Internet. MMORPGs also differ from single-player video games as there are social and/or role-playing dimensions that allow interaction with other gamers.

The POGU model resulted in five underlying dimensions of addictive gameplay (i.e., euphoria, health problems, conflict, failure of self-control, and preference of virtual relationships). Demetrovics and colleagues ( 2012 ) also support the integrative approach and stress the need to include all types of online games in addiction models in order to make comparisons between genres and gamer populations possible (such as those who play online real-time strategy (RTS) games and online first-person shooter (FPS) games in addition to the widely researched MMORPG players). Their model comprises six dimensions (i.e., preoccupation, overuse, immersion, social isolation, interpersonal conflicts, and withdrawal).

Irrespective of approach or model, the components and dimensions that comprise online gaming addiction just outlined are very similar to the IGD criteria in Section 3 of the DSM-5. For instance, Griffiths’s ( 2005 ) six addiction components directly map onto the nine proposed criteria for IGD (of which five or more need to be endorsed and result in clinically significant impairment). More specifically: (1) preoccupation with Internet games (salience); (2) withdrawal symptoms when Internet gaming is taken away (withdrawal); (3) the need to spend increasing amounts of time engaged in Internet gaming (tolerance), (4) unsuccessful attempts to control participation in Internet gaming (relapse/loss of control); (5) loss of interest in hobbies and entertainment as a result of, and with the exception of, Internet gaming (conflict); (6) continued excessive use of Internet games despite knowledge of psychosocial problems (conflict); (7) deception of family members, therapists, or others regarding the amount of Internet gaming (conflict); (8) use of the Internet gaming to escape or relieve a negative mood (mood modification); and (9) loss of a significant relationship, job, or educational or career opportunity because of participation in Internet games (conflict).

Treatment of Gaming Addiction

Clinical interventions and treatment for problematic and/or addictive gaming vary considerably in the literature, with most of the very few published studies employing some type of CBT, pharmacotherapy, and/or self-devised psychological interventions (Griffiths & Meredith, 2009 ; Han et al., 2009 , 2010 ; King, Delfabbro, & Griffiths, 2010 , 2012 ). Han et al. ( 2010 ) presented some successful case studies regarding pharmacotherapeutic treatment. After a 6-week (Han et al., 2010 ) and a 12-week (Han & Renshaw, 2012 ) period of bupropion sustained-release treatment, problematic gamers showed significant improvement, evident in both decreased problem behavior and decreased depression scores. The researchers’ pharmacological choice had been driven by the similarities in neurological activity of different behavioral addictions (Han et al., 2010 ; Ko et al., 2009 ; Kuss & Griffiths, 2012 ).

Currently, the evidence base on the treatment of problematic and/or addictive gaming is limited. Furthermore, the lack of consistent approaches to treating problematic video game playing and video game addiction makes it difficult to produce any definitive conclusions as to the efficacy of treatment, although at this stage CBT (as with the treatment efficacy of other addictions) appears to show good preliminary support (King et al., 2012 ). There remains a need for controlled, comparative studies of psychological and pharmacological treatments, administered individually and in combination with each other, to determine the optimal treatment approach.

The lack of comparative treatment studies might suggest that there is a general lack of demand for psychological services for problematic video game play and/or video game addiction (King et al., 2010 ). However, this may not necessarily be the case. For instance, Woog ( 2004 ) surveyed a random sample of 5,000 US mental health professionals. Although only 229 participants completed the questionnaire, two-thirds had treated someone with excessive computer use problems in the year prior to the survey. Woog also reported that problematic gaming was most common among 11- to 17-year-old clients. However, this may not be truly representative as this age group may be more likely to present in therapy. Anecdotal evidence suggests that 11- to 17-year-old clients are typically forced by concerned parents to attend treatment. Adult gaming addicts may not seek treatment or may seek treatment at a later stage for other psychological problems (e.g., depression) that develop after experiencing the severe negative consequences of gaming.

In South East Asia there appears to be significant demand for treatment for online-related problems, including gaming addiction. The South Korean government has reportedly established a network of more than 140 counseling centers for treatment of online addiction (Kim, 2008 ). In Western countries, gaming addiction clinics have also started to emerge in places such as Holland and the United Kingdom (Griffiths & Meredith, 2009 ; King, Delfabbro, Griffiths, & Gradisar, 2011 ). There are also treatment groups that are modeled on 12-step self-help treatment (e.g., Online Gamers Anonymous) (Griffiths & Meredith, 2009 ). However, little detail is known about the treatment protocols or their efficacy.

Based on the published empirical studies, and particularly those published over the past decade, it appears that, in extreme cases, excessive gaming can have potentially damaging effects on individuals who appear to display compulsive and/or addictive behavior similar to other more traditional addictions. However, the field has been hindered by the use of inconsistent and nonstandardized criteria to assess and identify problematic and/or addictive video game use. Furthermore, most studies’ recruitment methods have serious sampling biases, with an overreliance on self-selected samples.

Despite these shortcomings, there are several noticeable trends that can be drawn from this review of problematic video game play and gaming addiction.

There has been a significant increase in empirical research decade by decade since the early 1980s.

There has been a noticeable (and arguably strategic) shift in researching the mode of video game play. In the 1980s, research mainly concerned “pay-to-play” arcade video games. In the 1990s, research mainly concerned stand-alone (offline) video games played at home on consoles, PCs or handheld devices. In the 2000s, research mainly concerned online massively multiplayer video games.

There has been a noticeable shift in how data are collected. Up until the early 2000s, data about video game behavior were typically collected face to face, whereas contemporary studies collect data online, strategically targeting online forums where gamers are known to (virtually) congregate. These samples are typically self-selecting and (by default) unrepresentative of the general population. Therefore, generalization is almost always one of the methodological shortcomings of this data collection approach.

Survey study sample sizes have generally increased. In the 1980s and 1990s, sample sizes were typically in the low hundreds. In the 2000s, sample sizes in the thousands—even if unrepresentative—are not uncommon.

There has been a diversification in the way data are collected, including experiments, physiological investigations, secondary analysis of existing data (such as data collected from online forums), and behavioral tracking studies.

There has been increased research on adult (i.e., non-child and non-adolescent) samples, reflecting the fact that the demographics of gaming have changed.

There has been increasing sophistication in relation to issues concerning assessment and measurement of problematic video game play and video game addiction. In the past few years, instruments have been developed that have more robust psychometric properties in terms of reliability and validity. However, there are still some concerns as many of the most widely used screening instruments were adapted from adult screens and much of the video game literature has examined children and adolescents. King et al. ( 2012 ) assert that to enable future advances in the development and testing of interventions for video game–related problems, there must be some consensus among clinicians and researchers as to the precise classification of these problems.

The fact that IGD was included in Section 3 of the DSM-5 appears to have been well received by researchers and clinicians in the gaming addiction field (and by those individuals who have sought treatment for such disorders and had their experiences psychiatrically validated). However, for IGD to be included in the section on “Substance-Related and Addictive Disorders” along with “Gambling Disorder,” the gaming addiction field must unite and start using the same assessment measures so that comparisons can be made across different demographic groups and different cultures. For epidemiological purposes, Koronczai and colleagues ( 2011 ) assert that the most appropriate measures in assessing problematic online use (including Internet gaming) should meet six requirements. Such an instrument should have (1) brevity (to make surveys as short as possible and help overcome question fatigue), (2) comprehensiveness (to examine all core aspects of problematic gaming as possible), (3) reliability and validity across age groups (e.g., adolescents vs. adults), (4) reliability and validity across data collection methods (e.g., online, face-to-face interview, paper-and-pencil), (5) cross-cultural reliability and validity, and (6) clinical validation. It was also noted that an ideal assessment instrument should serve as the basis for defining adequate cutoff scores in terms of both specificity and sensitivity.

Clearly, there exist a number of gaps in our current understanding of problematic video game play and gaming addiction. King, Delfabbro, and Griffiths ( 2013 ) note a need for epidemiological research to determine the incidence and prevalence of clinically significant problems associated with video game play in the broader population. There are too few clinical studies that describe the unique features and symptoms of problematic video game play and/or video game addiction. Most of the studies tend to examine problematic video play from the perspective of the individual. However, there is a small body of research suggesting that the characteristics of the video games themselves may have a role in the acquisition, development, and maintenance of video game addiction. These studies have investigated the role of structural characteristics of video games in maintaining problem playing behavior (King et al., 2011 ; Westwood & Griffiths, 2010 ; Wood, Griffiths, Chappell, & Davies, 2004 ), but there is little empirical research that examines why some individuals may be protected from developing excessive playing habits or whether some individuals simply mature out of their problem playing behavior.

Another growing concern is the recent explosion of online and mobile gaming although, as yet, little research has been done. There are also strong links between online gaming, gambling, non-gambling fantasy games, role-playing games, board games, and card games. These may be an additional cause for concern as youth migrate from free gaming sites to online gambling sites. It should also be noted that video game playing does not occur in a vacuum, but is one behavior engaged in alongside many others. To date, very few studies have been used to examine links between video games and other risk behaviors (e.g., gambling, drug and alcohol use, seatbelt use, poor school performance, conduct problems, truancy, delinquency, violence and sexual activity).

Acknowledgments

Some of the material in this chapter was previously published in the following previous works:

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• v.9(1); 2020

Online Game Addiction and the Level of Depression Among Adolescents in Manila, Philippines

Ryan v. labana.

1 Department of Biology, College of Science, Polytechnic University of the Philippines, Manila, Philippines

Jehan L. Hadjisaid

2 Senior High School, Polytechnic University of the Philippines, Manila, Philippines

Adrian R. Imperial

Kyeth elmerson jumawid, marc jayson m. lupague, daniel c. malicdem, introduction:.

World Health Organization recognizes online game addiction as a mental health condition. The rise of excessive online gaming is emerging in the Philippines, with 29.9 million gamers recorded in the country. The incidence of depression is also increasing in the country. The current correlational analysis evaluated the association between online game addiction and depression in Filipino adolescents.

A paper-and-pencil self-administered questionnaire assessing depression and online game addiction was distributed from August to November, 2018. The questionnaire included socio-demographic profiles of the respondents, and the 14-item Video Game Addiction Test (VAT) (Cronbach's α=0.91) and the Patient Health Questionnaire-9 (Cronbach's α=0.88) to determine levels of online game addiction and depression, respectively. Multiple regression analyses were used to test the association between depression and online game addiction.

Three hundred adolescents (59% males, 41% females) participated in the study. Fifty-three out of 300 respondents (12.0% males, 5.7% females) had high level of online game addiction as reflected in their high VAT scores. In this study, 37 respondents (6.7% males, 5.7% females) had moderately severe depression and 6 (2.0%) females had severe depression. Online game addiction was positively correlated with depression in this study ( r =0.31; p <0.001). When multiple regression analysis was computed, depression was found to be a predictor of online game addiction ( Coefficient =0.0121; 95% CI-8.1924 - 0.0242; p =0.05).

Conclusions:

Depression, as associated with online game addiction, is a serious threat that needs to be addressed. High level of online game addiction, as positively correlated to the rate of depression among adolescents in Manila, could potentially be attributed to the booming internet industry and lack of suffiicent mental health interventions in the country. Recommended interventions include strengthening depression management among adolescents and improving mental health services for this vulnerable population groups in schools and within the communities.

Based on the report of the European Mobile Game Market in 2016, there were more than 2.5 billion video gamers across the globe. 1 Several studies have found that the majority of these players were adolescents aged 12-17 years, 2 - 5 with more usage among males than females. 6 In 2017, newzoo.com reported that the active gamers in the Philippines were 52% males and 48% females. 7 In the US, 60% of the video gamers were males and 40% are females. 6 Studies have shown that there are similarities between males and females in regard to choice of games, behavior toward video gaming, and motives for engaging in this activity. 8 Some of the reported reasons to engage in video games include having fun and for recreation, 9 - 10 to de-stress, 11 – 12 and to avoid real life issues. 13 – 14 The prevalence of video gaming addiction varies from region to region based on the socio-cultural context and the criteria used for the assessment. 15 However, it is well established that video gaming is addictive, 16 – 18 and there is clinical evidence for the symptoms of biopsychosocial problems among video game addicts. 19 It is a serious threat to the mental and psychosocial aspects of an individual, as it lead to stress, loss of control, aggression, anxiety, and mood modification. 20 – 21

In the Philippines, online gaming is an emerging industry. The country ranks 29 th in game revenues across the globe. In 2017, there were more than 29.9 million gamers recorded in the country. Most of the gamers were 21–35 years of age, followed by the adolescents 10–20 years of age. 7 Adolescents accounted for 30.5% of the total population in the country. 22 In general, this age group is already facing mental health issues, such as anxiety, mood disorders, and depression. This concern gets more alarming as rates of suicide among high school and college students are growing worldwide. 23

World Health Organization lists video game addiction as a mental health problem. 24 Psychiatric research reported evidence on the links between depression and video game addiction. Among the findings are MRI scans of video game addicts showing disruption of some brain parts and overriding of the 'emotional' part with the 'executive' part. 25 A study in China has also reported that gamers are at increased risk of being depressed in comparison to those who did not play video games. 26 In the field of neuroscience, depression caused by online game addiction is explained as a reduction of synaptic activities due to permanent changes in the dopaminergic pathways. This means that long exposure to online gaming causes changes in a person's sense of natural rewards, often making activities less pleasurable. This neuroadaptation is also associated with chronic depression. 27

There is a paucity of studies on video game addiction in the Philippines, making its implications not well understood. There are reports of the impact of video game addiction on the academic performance of the gamers, 28 – 30 but no study has been found associating video game addiction and depression in the Philippine setting. Based on the 2004 report from the Department of Health in the Philippines, over 4.5 million cases of depression were reported in the country. Recently, World Health Organization reported that 11.6% of the 8,761 surveyed young Filipinos considered committing suicide; 16.8% of them (of 8,761) had attempted it. 31 This phenomenon is said to be instigated by several factors, including the individual's exposures to technology. Video game addiction and depression are two emerging public health issues among adolescents in the Philippines. 31 – 32 This small-scale study aims to understand the association between these two factors and produce baseline information that can be used in formulating evidence-based public health policies in the country.

Research site and participants

This study was conducted in the months of August-November 2018 in the city of Manila, the capital of the Philippines. Manila is situated on the eastern shores of Manila Bay, on the western edge of Luzon (14∘35’45”N 120∘58’38”E). It is one of the most urbanized areas and the center of technological innovation in the country. It has a population of 1.78 million, based on 2016 census. 33 Manila covers 896 barangays (villages), which are grouped into six districts. Based on the 2010 census, the total population of Filipino adolescents, regardless of sex, was 166,391. 34 This population estimate was used for computing the sample size needed for this study. Sample size calculation was estimated using the online calculator from OpenEpi. 35 The completion rate of the questionnaires was 78.13%, for a total of 300 consenting respondents who were all online video gamers. They were selected if they were residents of Manila City and reported playing video games on the regular basis.

Map of Manila from the National Capital Region of the Philippines

Instruments

The study used a paper-and-pencil self-administered questionnaire. To determine the level of online game addiction of the respondents, the study used the Video Game Addiction Test (VAT) developed by van Rooij et al . 36 from the 14-item version of the Compulsive Internet Use Scale (CIUS). 37 VAT was utilized in several studies among adolescents in the past, and it has demonstrated excellent reliability and validity. The scale outcomes were found to be comparable across gender, ethnicity, and learning year, making it a helpful tool in studying video game addiction among various subgroups. 36 The survey contains questions in five categories: loss of control, conflict, salience, mood modification, and withdrawal symptoms. Each question was measured on a 5-point scale: 0–never to 4–very often. The results were then used as an indicator of the level of addiction. This study adapted the calculations conducted by van Rooij et al. 38 wherein the average scale scores of all the respondents were arranged from 0-4 and then were divided into two groups. The first group had an average of 0-2 or 'never' to 'sometimes', while the second group had an average of 3-4 or 'often' to 'very often'. The latter group was considered to have the highest level of problematic gaming or, in this study, with online game addiction. 38 The internal reliability of the VAT in this study was excellent at Cronbach's α of 0.91.

The level of depression of the respondents was determined by using the Patient Health Questionnaire-9 (PHQ-9). 39 It is a 9-item depression module taken from the full PHQ. The questionnaire allows the respondents to rate their health status in the past six weeks. There are 9 diagnostic questions in which the respondents rated 0 for 'not at all', 1 for 'several days', 2 for 'more than half the days', and 3 for 'nearly every day'. The total of the PHQ-9 scores was used to measure severity of depression. Since there are 9 items in the questionnaire and each question can be rated from 0-3, the PHQ-9 scores can range from 0-27. The score was interpreted as ‘no depression’ (0-4 points), ‘mild depression’ (5-9 points), ‘moderate depression’ (10-14 points), ‘moderately severe depression’ (15-19 points), and ‘severe depression’ (20-27 points). 39 In this study, the internal reliability of PHQ-9 had a Cronbach's α of 0.88.

Data gathering procedure

The study randomly surveyed gamers in various parts of Manila. Since there are no reliable records of the gamers in the area available for research, various sampling techniques were utilized. A convenience sampling was done by visiting internet cafes in the city and requesting the gamers to answer the questionnaire during their time-out (from the game). A verbal consent was provided by each respondent after hearing a brief explanation of the research objectives and the necessary instructions. While answering the questionnaire, the respondents were assisted by the investigator for any clarifications and questions. The questionnaire was completed by the respondents in approximately 2.5 minutes. Other procedures included snowball sampling, accidental, and voluntary response sampling after the distribution of invitation to respond among internet cafes, gamers’ social media groups/sites, and online gamers’ organizations. The study was approved by the ethical board of the Polytechnic University of the Philippines.

Statistical analysis

All the responses from the questionnaires were inputted into MS Excel and into SPSS version 23.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics of responses were computed and included the frequencies ( f ), percentages (%), averages x and standard deviations (SD). The association between online game addiction and depression was analyzed using Pearson's correlation and was further analyzed using a multiple regression analysis. The study hypothesized that there is no significant correlation between online game addiction and level of depression among adolescents in the City of Manila, Philippines. All statistical results were considered significant at the p value <0.05.

Profile of the respondents

A total of 300 consenting adolescents participated in the study. There were more males ( n =176; 59%) than females ( n =124; 41%) who participated in the study. Most of the respondents were adolescents (aged less than 19 years), except for the six respondents who were already 20 years old during the data gathering. The mean age of the participants was 17 years old (SD=0.90). Figure 2 presents the profiles of the respondents based on their gender and age characteristics. The VAT analysis shows that there were more males (12.0%) who were addicted to online games than females (5.7%). Meanwhile, 15-, 17-, and 18-year old respondents had the highest VAT scores among the six age groups.

Profiles of the respondents based on gender and age

Level of online game addiction

The 14-item VAT was ranked from the highest to the lowest mean score to understand the common conditions experienced by the respondents. The item with the highest mean was No. 13: Do you game because you are feeling down? ( x =2.1, SD=1.40). This question had the third greatest number of “4-very often” ratings ( N =46/300). It was followed by the item No. 3: Do others (e.g., parents or friends) say you should spend less time on games? ( x =2.06, SD=1.41).. The third item with the highest mean score was item No. 7: Do you look forward to the next time you can game? ( x =2.0, SD=1.27). The item with the highest number of “4-very often” rating was item No. 14: Do you game to forget about problem? ( N =67/300). Items 12 and 2 also had high mean scores: Do you neglect to do your homework because you prefer to game? (Item 12; x =1.98, SD=1.34); and Do you continue to use the games despite your intention to stop ? (Item 2; x =1.84, SD=1.20).

Levels of online game addiction based on gender and age

Level of depression

The PHQ-9 was used to quantify the symptoms of depression of the respondents and identify its severity. The majority of the respondents demonstrated no depression (47%), followed by having mild depression (22%), and moderate depression (17%). Of note, the current study revealed 12% of the respondents had moderately severe depression and 2% had severe depression. We found that higher PHQ-9 scores were associated with decreased functional status. The most common symptoms reported by the respondents based on the mean scores of each item in PHQ-9 include …feeling tired or having little energy ( x =1.89, SD=1.30), …poor appetite or overeating ( x =1.87, SD=1.37), … feeling down, depressed or hopeless ( x =1.81, SD=1.18), …trouble falling or staying asleep, or sleeping too much ( x =1.78, SD=1.33), and …trouble concentrating on things, such as reading newspaper or watching television ( x =1.75, SD=1.40). Interestingly, the six respondents who were identified to have “severe” depression were all females, and four of them had high VAT scores.

Level of depression of the respondents based on the PHQ-9 scores

Association between online game addiction and depression

The association between online game addiction based on the VAT scores and the level of depression among the respondents was evaluated through Pearson's correlation analysis. Results ( Table 3 ) show that the level of online game addiction was positively correlated with the level of depression ( r =0.31, p <0.001) but was not significantly correlated with age or gender ( r =-0.80, p <0.171 and r = 0.10, p <0.097, respectively).

Pearson's correlation coefficient among gender, age, online game addiction, and depression of the adolescents in Manila

A multiple linear regression was calculated to predict online game addiction based on gender and depression. This regression analysis was performed with all participants and with the subset of participants with high VAT scores, which indicated online game addiction. A significant regression equation was found (F(2.50)= 2.247, 0.10), with an R 2 of 0.082. Table 4 shows that depression was a significant predictor of online game addiction.

Multiple regression analysis for prediction of online game addiction based on age and level of depression

The correlation between online game addiction and the levels of depression in this study was weak but statistically significant. This positive correlation was previously reported in other research studies across the globe. 40 – 41 In a study conducted by Rikkers et al. 40 among children and adolescents (11-17 years old) in Australia, electronic gaming was positively associated with emotional and behavioral problems including depression. Longer gaming hours were also associated with severe depressive symptoms, somatic symptoms, and pain symptoms among young people in Taiwan. 41 Online game addiction was associated by Zamani et al. 42 not only with depression but also with sleep disorder, physical complaints, and social dysfunctions of students in Iran. In a study conducted by Dong et al., 43 depression came out as one of the outcomes of the internet addiction disorder.

In the current study, most of the respondents looked forward to the next time they would game, with the most common reason of engaging in games reported to be easing the moments of feeling down. Another reason of the respondents’ addiction to online games was that they want to forget about problems. It is considered as one of the core symptoms of addiction as described by Brown. 44 The second most common experience of the respondents was the 'inability to voluntarily reduce the time spent on online games', which is another core symptom of addiction. 45 Most of the respondents admitted that they were getting advice from their parents or friends to spend less time on games, but they could not control it, despite their intention to stop. In fact, gaming negatively affected homework completion among many study participants. This effect was previously studied among high school students in Los Baños, Philippines, where the video gamers had 39% probability to fail in school. In this previously published study, 6 out of 10 video gamers spent their daily allowances on computer games, giving them access to continuously spend their time playing. 29 The addiction of the adolescents in Manila could have been influenced by the ubiquitous nature of internet in the city. Internet cafes are very accessible in the country, and they are thriving in almost all corners of the city. In addition, the rent for internet and online games in Metro Manila costs 10 to 20 pesos per hour only (US $0.19 to US $0.38 per hour), making playing video games affordable. Some internet hubs are even offering discounts and promotions for longer stays of 10-12 straight hours of playing online games.

Based on the most cited symptoms of the respondents in this study, it could be implied that adolescents cope with their emotional distress by playing online games. This means that the high occurrence of online game addiction goes along with the high occurrence of depression among the same group. In regard to depression, most respondents in this study were feeling tired, having poor appetite, feeling hopeless, having trouble falling asleep, or having trouble concentrating on things that require enough attention, like reading books. These symptoms were also reported by Schmit et al. 45 as related to online game addiction, where the people who spent longer hours playing online games got higher scores for loneliness and isolation. This study did not capture the number of hours spent by the respondents in online games, which could be incorporated in the next study for further analysis.

Depression, as associated to online game addiction, may lead to anxiety, compulsion, and suicide ideations. 46 This is a serious threat to the population health that needs to be addressed. Interventions may include strengthening depression management among adolescents, either in school or in the community. There are several ways to manage depression. The schools and the community should reinforce sports by making it more challenging, engaging, and motivating. In the Philippines, numerous factors make receiving mental health care a challenge. There is only one psychiatrist for every 250,000 mentally ill patients, budget dedicated to mental health interventions is limited, 47 a guidance and counseling system has not yet matured, 48 and there was even a report that online counseling was preferred by the students than its face-to-face counterpart. 49 The poor availability of the mental health interventions in the country may lead to upsurge of depression cases among adolescents. Meanwhile, the booming online game industry in the country leads to the increased numbers of addicted adolescents to online game addiction. Policy makers, the government, and its stakeholders should start addressing these issues before it becomes an even bigger health concern, especially in the face of ongoing COVID-19 pandemic.

The Philippine government should also assess their existing intervention programs in mental health issues. In 2016, “Hopeline” was launched in the Philippines. It was a national hotline for mental health assistance for the prevention of depression and suicide cases in the country. The hotline is equipped with a professional team of counselors as responders. 50 No study has been found to assess the effectiveness of this intervention for depression. National trainings and workshop programs have been implemented in other countries to empower the people in dealing with the stigma of depression which includes mental health literacy campaign, peer services, and advocacies. 51 This is an essential step to correct various misconceptions on depression, especially among adolescents.

This study was cross-sectional and cannot determine causality. This is the first report on the association between online game addiction and the levels of depression among adolescents in the city of Manila, Philippines. Despite the small sample and the limited scope of the research, the current study has shown interesting preliminary results that could be instrumental in the conduct of a bigger scale study in the country. To facilitate participation of the larger number of respondents, the future investigators are suggested to coordinate with various high schools in Metro Manila and use these schools as a sampling frame for a robust sampling technique. In this study, the level of online game addiction has no statistically significant association with age and gender. The association between age and online game addiction could have been improved by including older age groups in this study. Data from a group of young adults (college students), who are also exposed to online gaming, could be compared to these data for further analysis. Gender is commonly associated with the level of online game addiction in many studies, but it is not statistically significant in this present study. The sample size in this study was only 300 and may not have been representative enough of a general population. Also, our sample size was not large enough to capture distinctions between males and females. This could also be addressed by a wider scale of surveys in the future research.