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. 2024 Dec 20;9:100565. doi: 10.1016/j.puhip.2024.100565

Burden of gaming disorder among adolescents: A systemic review and meta-analysis

Prakasini Satapathy a, Mahalaqua Nazli Khatib b,1, Ashok Kumar Balaraman c, Roopashree R d, Mandeep Kaur e, Manish Srivastava f, Amit Barwal g, GV Siva Prasad h, Pranchal Rajput i, Rukshar Syed j, Gajendra Sharma k, Sunil Kumar l, Mahendra Pratap Singh m, Ganesh Bushi n, Nagavalli Chilakam o, Sakshi Pandey p, Manvinder Brar q, Rachana Mehta r,s, Sanjit Sah t,u, AbhayM Gaidhane v, Hashem Abu Serhan w,⁎, Shailesh Kumar Samal x
PMCID: PMC11925544  PMID: 40115446

Abstract

Objectives

Integrating digital technology into daily life has made video games a primary form of entertainment for adolescents worldwide. Despite their benefits, excessive gaming has emerged as a significant public health issue, recognized as a gaming disorder by the World Health Organization in the ICD-11. This study aims to assess the prevalence of gaming disorders among adolescents through a systematic review and meta-analysis.

Study design

Systematic review and meta-analysis.

Methods

A search was conducted across multiple databases until February 15, 2024. Observational studies that assessed the prevalence of gaming disorder were included. Nested Knowledge software was used for screening and data extraction. The quality assessment was performed using the Joanna Briggs Institute tool. Meta-analysis using a random effect model was used to synthesize prevalence rates. Statistical analyses were performed in R software version 4.3.

Results

The meta-analysis included 84 studies covering a diverse geographical scope totaling 641,763 individuals. The pooled prevalence of gaming disorder was 8.6 % (95 % CI: 6.9 %–10.8 %), (I2 = 100 %). Subgroup analysis revealed varying prevalence rates by country, with China reporting the highest rate at 11.7 % (95 % CI: 8.6 %–15.7 %). Meta-regression analysis highlighted an increasing trend in the prevalence of gaming disorder over the years, underscoring the growing impact of digital technologies.

Conclusion

A significant prevalence of gaming disorder among adolescents is observed. With an increasing trend, fostering healthy gaming habits, enhancing awareness, and implementing effective intervention programs are crucial. This emphasizes the importance of global efforts in combating the growing challenge of gaming disorder among adolescents.

Keywords: Gaming disorder, Adolescents, Mental health, Digital health, Meta-analysis

1. Introduction

Digital technology has seamlessly integrated into every aspect of human life in recent years, with video games emerging as a dominant form of entertainment among adolescents worldwide [[1], [2], [3]]. These games serve as a source of entertainment and offer educational and social benefits, fostering cognitive skills, strategic thinking, and teamwork [4]. However, the flip side of this digital revolution has been the emergence of excessive gaming [5]. Such excessive engagement has raised significant concerns about its potential adverse effects on mental health and social well-being, catalyzing a global debate among healthcare professionals, educators, and policymakers [6,7]. The recognition of gaming disorder as a significant public health issue marks a critical point in this ongoing discussion. In 2018, the World Health Organization (WHO) officially recognized gaming disorder in the 11th Revision of the International Classification of Diseases (ICD-11) [8], describing it as a pattern of persistent or recurrent gaming behavior so severe that it takes precedence over other life interests and daily activities, despite the occurrence of negative consequences [9].

The burgeoning prevalence of gaming disorder, particularly among adolescents, is alarming [10]. This age group is at a critical developmental stage marked by significant psychological, social, and physical changes [11]. The compulsive use of video games during this vulnerable period can have profound implications, potentially stalling or derailing these developmental processes [12]. The resultant negative outcomes, such as increased anxiety, depression, social isolation, and poor academic performance, are not only immediate concerns but also pose long-term risks for mental health and social integration [13,14]. Furthermore, the immersive nature of today's video games, combined with the social and competitive aspects facilitated by online platforms, can exacerbate these effects, making it increasingly difficult for affected individuals to disengage [15,16].

Understanding the epidemiological burden of gaming disorder and its impact on youth globally is paramount. A complex interplay of psychological, social, and environmental factors influences adolescents' engagement with digital gaming environments [17,18]. The digital age has brought about unprecedented access to gaming, with smartphones and portable gaming devices facilitating constant connectivity. This accessibility, coupled with the social validation found in online gaming communities, can contribute to the compulsive nature of gaming behaviors [19]. Moreover, the heterogeneity in the recognition and diagnosis of gaming disorder, stemming from varying cultural attitudes towards gaming and differing healthcare frameworks, complicates the global response to this issue. Recognizing the signs of gaming disorder early, fostering healthy gaming habits, and promoting alternative activities that support adolescents' development are crucial steps [20].

Despite the growing body of literature on gaming disorder, there remains a need for a comprehensive synthesis of evidence to understand its global prevalence, risk factors, and health consequences among adolescents. Variability in study methodologies, definitions of gaming disorder, and population characteristics have resulted in a wide range of prevalence estimates and outcomes, making it challenging to draw generalizable conclusions. A systematic review and meta-analysis can address these challenges by aggregating data from multiple studies, providing a more accurate estimation of the burden of gaming disorder and identifying patterns and gaps in the current research landscape.

This study aims to conduct a systematic review and meta-analysis to evaluate the burden of gaming disorder among adolescents. By examining the prevalence, this study seeks to provide a comprehensive overview of the current state of research, offer insights into the global impact of gaming disorder on adolescent health, and suggest directions for future research and interventions.

2. Methods

The study design of the present study is systematic review and meta-analysis. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Table S1) [21]. Nested Knowledge web software (Nested-Knowledge, MN, USA) was used for screening and data extraction. The Protocol has been registered in International Prospective Register of Systematic Reviews (PROSPERO) under the register number: CRD42024512385.

2.1. Eligibility criteria

Studies were eligible for inclusion if they were observational in nature, and if they assessed the prevalence of gaming disorder using any recognized diagnostic criteria, such as the DSM-5 or ICD-11. Any other standard scales were also considered. Additionally, these studies needed to report specifically on adolescent populations, defined as individuals between 9 and 21 years, and be published in English. Exclusions were applied to reviews, commentaries, and any studies that did not contain primary data. Table S2 shows the eligibility criteria.

2.2. Search strategy

An experienced librarian searched databases up to January 10, 2024 with subsequent update on February 15, 2024, including PubMed, EMBASE, and Web of Science, for articles published from 2000 to the search date. The search strategy employed a combination of keywords related to gaming disorders, such as "internet gaming," "problematic gaming," "addictive gaming," and "gaming disorder," along with terms targeting the adolescent demographic, including "adolescents," "teenage population," "young individuals," and "juveniles." Initially, there were no restrictions on the publication date or language. However, subsequent filtering was applied to select only articles published in English. The search strategies were customized to match each database's unique syntax and requirements (Table S3).

2.3. Study selection

Two reviewers (PS, MNK) independently conducted the screening process, initially reviewing titles and abstracts for eligibility, followed by a comprehensive full-text review of the potentially relevant articles. Any disagreements encountered during the screening process were resolved through discussion or, if necessary, by consulting a third reviewer (AMG). This screening was executed in two distinct phases: first, the screening of titles and abstracts, and subsequently, the full-text screening of selected articles. The Nested Knowledge Web software facilitated the screening process, ensuring a structured and efficient review [22,23].

2.4. Data extraction and quality assessment

A standardized data extraction form was employed to gather specific details from each study, such as the author(s), year of publication, country of origin, study design, sample size, age range of participants, mean age, diagnostic criteria used for gaming disorder, and the number of adolescents diagnosed with gaming disorder. Two reviewers (PS, MNK) independently performed the data extraction process to ensure accuracy and reliability. Any discrepancies that arose during this process were settled through consensus between the reviewers. The tagging function of Nested Knowledge software was utilized to facilitate and streamline the data extraction process [23].

The quality of the studies included in this review was rigorously evaluated using the Joanna Briggs Institute (JBI) tool for assessing the quality of prevalence studies [24]. This comprehensive tool is designed to appraise various aspects of study design, methodology, and reporting, thoroughly examining the potential biases and the reliability of the findings reported in each study.

2.5. Statistical analysis

A random-effects meta-analysis was employed to synthesize prevalence rates of gaming disorder among adolescents from the collected data, which allowed for the consideration of between-study heterogeneity. This heterogeneity was quantified using the I2 statistic and tau estimator [25,26]. Furthermore, subgroup analyses were planned to explore variations by country. Meta-regression analyses were performed to examine the association between study-level variables (e.g., publication year, age, and sample size) and the prevalence of gaming disorder. A Doi plot was utilized to assess the potential for publication bias [27,28]. Sensitivity analyses were performed using a leave-one-out approach to ensure the robustness of the results [29]. A 95 % prediction interval was calculated to provide a range within which the true effect size is expected to lie in similar future studies [30]. All statistical analyses were performed using the "meta" and "metaphor" packages in R software, version 4.3 [31].

3. Results

3.1. Literature search

The search across multiple databases yielded a total of 3515 records. Initial screening led to the removal of 727 duplicate records. After this step, 1176 records were screened for relevance, excluding 919 records that did not meet the inclusion criteria. These records underwent a full-text assessment for eligibility, which led to a further exclusion of 167 records based on specific reasons: 29 were reviews, 59 did not pertain to the outcome of interest, 21 involved populations not of interest, and 60 were deemed not relevant to the research question. Following the full-text assessment, 84 studies were included in the meta-analysis (Fig. 1).

Fig. 1.

Fig. 1

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PRISMA flowchart depicting article selection process.

3.2. Characteristics of included studies

The characteristics of the included studies are summarized in Table 1. These studies predominantly utilize a cross-sectional design, although a handful employ longitudinal and prospective designs, highlighting a focus on immediate associations and the potential for long-term patterns relating to gaming disorder. Thirty-two studies were conducted in China [[32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63]], demonstrating a substantial regional research focus. Spain contributed 5 studies [[64], [65], [66], [67], [68]], and the United States contributed two studies [69,70], respectively. Notably, Sweden [[71], [72], [73]], and the Netherlands [[74], [75], [76]] have each contributed three studies, indicating a keen research interest in Europe. Germany [77,78], Finland [[74], [75], [76]] and Korea [79,80] both have two studies. Saudi Arabia is represented with three studies [[81], [82], [83]], illustrating growing attention in the Middle East. Australia contributed three studies [9,84,85]. Six studies were conducted in multiple European countries [[86], [87], [88], [89], [90], [91]]. Countries like Taiwan [92], Ireland [93], Vietnam [94], Israel [95], Tunisia [96], Lebanon [97], Egypt [98], Iran [99], Malaysia [100], Kyrgyzstan [101], Norway [102], Brazil [103], Croatia [104], Indonesia [105], and Turkey [106] each contributed one study, showcasing the worldwide interest in the prevalence of gaming disorder among adolescents.

Table 1.

Characteristics of included studies.

Study Country Study Design Age range Mean age Sample Size Adolescents with Gaming disorder Criteria used for assessing gaming disorder
Alfaifi 2022 [81] Saudi Arabia Cross-sectional 12 and 18 years NA 450 132 20-item IGDA
Amudhan 2022 [111] India Cross-sectional 11 and above 12.58 1729 447 GAS - Short Version
André 2022 [71] Sweden Cross-sectional 8–18 years NA 144 47 Game Addiction Scale for Adolescents
Bumozah 2023 [82] Saudi Arabia Cross-sectional 16.91 16.91 400 76 IGDS9-SF
Buren 2023 [86] European countries Cross-sectional 15 years 15 626 50 GSMQ-9
Castren 2022 [112] Finland Cross-sectional 15–16 years NA 4160 551 Custom Questionnaire
Chang 2022 [32] China Cross-sectional 13–21 years 15.2 1305 53 IGDS- 9
Chen 2023 [45] China Longitudinal study 10 to 18 NA 2148 277 DSM-5, GAS-7
Chiu 2018 [92] Taiwan Cross-sectional 10–18 years 13.17 76 8 DSM-5, IGDT-10
Colasante 2022 [87] European countries Cross-sectional 15–16 years NA 88998 17,800 Perceived problem scale (PPS)
Colder Carras 2018 [88] European countries Cross-sectional 14–18 years 15.8 13,460 403 AICA-S
Columb 2021 [93] Ireland Cross-sectional 12–18 years 14.2 234 4 IGDS9-SF
Cuong 2022 [94] Vietnam Cross-sectional NA 14.5 2084 242 IGD-20 Test
Desai 2010 [69] USA Cross-sectional 14–18 years NA 4028 197 Minnesota Impulse Disorder Inventory
Efrati 2023 [95] Israel Prospective study 13 to 18 15.77 1056 23 IGDS9-SF
Esteve 2022 [64] Spain Cross-sectional 13 and 17 years NA 397 65 GASA
Fekih-Romdhane 2023 [96] Tunisia Cross-sectional 21.26 ± 1.68 years 21.26 851 212 DSM-5, IGD-20
García-Gil 2023 [65] Spain Cross-sectional 12–18 years 14.5 1448 469 VGEQ
Gerdner 2022 [72] Swedish Cross-sectional 12–18 years NA 387 9 IGDS
Greenberg 2022 [70] USA Cross-sectional NA NA 3657 79 Minnesota Impulsive Disorders Inventory
Haagsma 2013 [74] Netherlands Longitudinal study 12–22 years 15.7 810 16 DSM-IV, GAS
Hawi 2018 [97] Lebanon Cross-sectional 15–19 years 16.2 524 48 DSM-5, IGD-20
Hing 2023 [84] Australia Cross-sectional 12–17 years 14.8 646 89 IGDS9-SF
Huang 2022 [46] China Cross-sectional 9–18 years NA 10,479 334 IGDS9-SF
Jo 2020 [79] Korea Cross-sectional 13–18 years 13.14 139 51 DSM-5
Junus 2023 [47] China Cross-sectional 11–17 years NA 1510 86 IGDS9-SF
Kewitz 2021 [77] Germany Cross-sectional 11–17 years NA 177 7 CSAS
Khalil 2022 [98] Egypt Cross-sectional 14–18 years 16.1 700 429 IGD Scale
Kim 2020 [80] Korea Cross-sectional 12–18 years NA 2,23,542 20,797 Internet Addiction Proneness Scale for Youth-Short Form (KS scale)
Kim 2022 [113] Australia Longitudinal study 13–14 years 13.7 4968 770 IGD scale
King 2017 [9] Australia Cross-sectional 12–17 years 14.1 799 25 IGD-checklist
Lan 2022 [85] Taiwan Cross-sectional 10–18 years 15.4 8446 109 NA
Liang 2019 [48] China Cross-sectional 9–16 years 11.63 2423 72 IGD Questionnaire
Lin 2023 [49] China Cross-sectional 13.5 years old 13.5 4835 813 DSM-5, GAS-7
Lin 2023 [99] Iran Cross-sectional 16.02 ± 1.40 16.02 3837 1268 DSM-5, GDT
Liu 2023 [50] China Longitudinal study 12.33–15.33 years 13.18 910 107 IGDS
Luo 2021 [51] China Cross-sectional 12–19 years 15.61 28,689 1322 IGDS9-SF
Ma 2021 [52] China Cross-sectional 15–18 years 16.5 200 86 ICD-11 diagnostic guidelines for GD
Machimbarrena 2023 [66] Spain Cross-sectional 11–18 years 14.2 2024 66 IGD-20
Macur 2021 [114] Slovenia Cross-sectional 12–16 years 13.44 1071 48 IGDS9-SF
Männikkö 2020 [115] Finland Cross-sectional 12–16 years 13.9 560 5 POGQ
Mohamed 2023 [100] Malaysia Cross-sectional 13 years and above NA 5290 185 IGDS9-SF
Muhametjanova 2023 [101] Kyrgyzstan Cross-sectional 11–21 years 17.7 195 16 IGD-10
Müller 2015 [89] European countries Cross-sectional 14–17 years 15.8 12,938 209 DSM-5, AICA-S
Myrseth 2018 [102] Norway Longitudinal study aged 17.5years 17.5 2055 24 GASA
Nogueira-López 2023 [67] Spain Cross-sectional 11 and 18 years old 13.81 41507 747 PIUS-A
Olsen 2024 [73] Sweden Cross-sectional 9–15 years 12 541 48 DSM
Ong 2016 [53] China Retrospective cohort study Aged 19 and below 15.48 260 77 Problematic Internet Use Questionnaire
Peeters 2019 [75] Netherlands Longitudinal study 12–16 years 13.3 3348 83 DSM-5, IGDS
Peng 2023 [54] China Cross-sectional 14.3 years old 14.3 63205 1813 DSM-5, IGDS9-SF
Pontes 2016 [116] Slovenia Cross-sectional 12–16 years 13.44 1071 27 IGDS9-SF
Rajab 2020 [83] Saudi Arabia Cross-sectional 10–19 years 16.1 2537 126 GAS
Rikkers 2016 [117] Australia Cross-sectional 11–17 years NA 2967 127 EU Kids Online Survey
Roza 2023 [103] Brazil Longitudinal cohort study NA 10.2 1557 192 GAS-7
Sánchez-Llorens 2023 [68] Spain Cross-sectional NA NA 119 23 GASA
She 2022 [55] China Cross-sectional NA 13.6 3136 1368 Problem Behaviour Scale
Šincek 2017 [104] Croatia Cross-sectional 11–21 years 14.44 1150 71 Problematic Online Gaming (POGQ)
Siste 2022 [105] Indonesia Cross-sectional 20.3 to 1.9 20.3 1233 23 IGDT-10
Spilková 2017 [90] European countries Cross-sectional NA 16.7 4887 535 ESPAD study questionnaire
Thakur 2023 [118] India Cross-sectional 12–19 years 15.01 707 5 IGDS9-SF
Tsui 2021 [56] China Cross-sectional 10–17 years 13.23 1099 44 Korean Internet Addiction Proneness Scale
Uçur 2021 [106] Turkey Cross-sectional 12 and 18 14.7 1291 144 GAS
Van Den Eijnden 2018 [76] Netherlands Longitudinal study 12–15 years 12.9 538 53 IGD scale, DSM-5
Van Der Neut 2023 [91] European countries Cross-sectional NA 13.47 14,398 1283 IGDS
Wang 2014 [57] China Cross-sectional NA 14.58 503 79 DSM-5, GAS
Wang 2023 [58] China Cross-sectional 13–15 years NA 2902 408 DSM-5IGD, (DISCA)
Wartberg 2023 [78] Germany Cross-sectional 15–19 years 16.84 480 53 IGD scale
Wei 2023 [59] China Cross-sectional 12 to 18 14.9 1494 356 POGQ-SF
Xie 2023 [60] China Cross-sectional 12–17 years 14.09 1906 327 IGA questionnaire
Xu 2023 [61] China Cross-sectional 9–18 years NA 10,479 334 IGDS9-SF
Yang 2020 [62] China Cross-sectional NA 12.77 2666 346 IGDS9-SF
Yang 2023 [63] China Cross-sectional under 18 years 14.79 110 45 IAT, DSM-IV
Yang 2023 [33] China Cross-sectional NA 13.9 2102 268 24-item IGCS
Yang 2023 [42] China Longitudinal study 10–18 years 14.36 459 151 SAS-SV scores
Yu 2019 [43] China Cross-sectional 12–17 years 13.59 500 27 Problematic Online Gaming (POGQ)
Yu 2020 [44] China Cross-sectional NA 13 1066 145 DSM-V
Yu 2021 [34] China Cross-sectional NA NA 3087 418 9-item DSM-5
Yu 2022 [35] China Cross-sectional 10–18 years NA 2503 444 15-item Chinese version of the Revised IGCS, DSM-5
Yu 2022 [36] China Cross-sectional NA 13.1 2573 337 IGDS9-SF
Zhang 2022 [37] China Cross-sectional 12–18 years 9.48 7790 1777 VGDS
Zhang 2023 [38] China Cross-sectional 12–18 years 14.98 5988 582 DSM-5, GDSQ-21
Zhang 2023 [39] China Cross-sectional 16–19 years 16.89 283 45 DSM-5
Zhu 2021 [40] China Cross-sectional 8–17 years 12.6 2863 153 GAS-7
Zou 2022 [41] China Cross-sectional 11–16 years 13.58 1053 81 11-item Internet Gaming Addiction Scale
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Abbreviations: AICA-S: Assessment of Internet and Computer game Addiction-Scale, DSM-IV: Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, DSM-5: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, DSM-V: ESPAD: European School Survey Project on Alcohol and Other Drugs, GAS: Gaming Addiction Scale, GASA: Gaming Addiction Scale for Adolescents, GDT: Gaming Disorder Test, GSMQ-9: Game Social Media Questionnaire-9, IAT: Internet Addiction Test, ICD-11: International Classification of Diseases, Eleventh Revision, IGCS: Internet Gaming Cognition Scale, IGDA: Internet Gaming Disorder Assessment, IGD-10: Internet Gaming Disorder Scale-10, IGD-20: Internet Gaming Disorder Scale-20, IGDS: Internet Gaming Disorder Scale, IGDS9-SF: Internet Gaming Disorder Scale-Short Form (9 items), IGDT-10: Internet Gaming Disorder Test-10, IGS: Internet Gaming Scale, NA: Not Applicable or Not Available, POGQ: Problematic Online Gaming Questionnaire, POGQ-SF: Problematic Online Gaming Questionnaire-Short Form, PPS: Perceived Problem Scale, SAS-SV: Self-Rating Anxiety Scale-Short Version, VGDS: Video Game Dependency Scale, VGEQ: Video Game Engagement Questionnaire.

The age range of participants in these studies varies, with some focusing on a narrower spectrum, such as 12–18 years, while others encompass a broader range from 8 to 21 years. Mean ages across the studies typically fall within the early to mid-teen years, mirroring the adolescent-centric focus of the research. Sample sizes demonstrate considerable variability, ranging from as few as 76 participants to as many as 63,205, enabling a broad investigation into the prevalence and criteria of gaming disorder.

The criteria employed to assess gaming disorder are diverse, with many studies using established measures like the 20-item Internet Gaming Disorder Assessment (IGDA), the Game Addiction Scale (GAS) short version, and the DSM-5 criteria. Others have opted for custom questionnaires or less common scales, revealing different approaches to defining and measuring gaming disorder. The proportion of adolescents diagnosed with gaming disorder shows notable variation, which may be attributed to cultural, methodological, and criteria-related differences across studies. The quality assessment of the studies is given in Table S4.

3.3. Prevalence of gaming disorder among adolescents

We performed a meta-analysis to determine the pooled prevalence of gaming disorders among adolescents (Fig. 2). The analysis incorporated data from a wide range of studies, yielding a total sample size of 641,763 individuals. Based on the random effects model the pooled prevalence estimates of gaming disorder was found to be 8.6 % (95 % CI: 6.9 %–10.8 %). The prediction interval, which estimates where the true prevalence might fall in similar but yet-to-be-studied populations, ranged from 1.0 % to 48.1 %. It should be noted that the heterogeneity across the included studies was high (I2 = 100 %).

Fig. 2.

Fig. 2

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Forest plot showing pooled prevalence of Gaming disorder among adolescents.

3.4. Country-wise prevalence

We conducted a group analysis to estimate the country's prevalence of adolescent gaming disorders (Fig. S1). The subgroup analysis revealed significant trends in the prevalence of gaming disorder among adolescents, with marked differences across various countries (Table 2). China led the data with the most considerable number of studies [32] and the largest sample size (170,526), showing a substantial prevalence rate of 11.7 % (95 % CI: 8.6 to 15.7). This suggests a significant impact of gaming disorder on its youth population. Spain's five studies revealed a notable prevalence of 9.6 % (95 % CI: 2.2 to 33.2), indicating that gaming disorder is also a significant concern in European countries. A remarkable finding across the studies was the high degree of heterogeneity; several countries exhibited nearly complete heterogeneity (I2 close to or at 100 %). This points to considerable variability in prevalence estimates across different populations, possibly due to varied methodologies. Countries with a single study, such as Egypt and Iran, reported very high prevalence rates of 61.3 % (95 % CI: 57.6 to 64.9) and 33 % (95 % CI: 31.6 to 34.6), respectively. These figures suggest that gaming disorder could be an under-researched area in these regions. On the other hand, countries with multiple studies like Sweden, with three studies reporting a prevalence of 9.5 % (95 % CI: 0.5 to 69.2), and the Netherlands, with three studies and a prevalence of 3.7 % (95 % CI: 0.6 to19.9), still showed significant heterogeneity, which implies that substantial differences in findings can exist even within countries with more research data.

Table 2.

Prevalence of gaming disorder among various countries.

Country Number of studies Sample size Prevalence (95 % CI) Heterogeneity (I2)
Saudi Arabia 3 3387 14.6 (1.9–60.0) 99 %
India 2 2436 4.8 (0.00–10.0) 99 %
Sweden 3 1072 9.5 (0.5–69.2) 98 %
Finland 2 4720 3.7 (0.00–10.0) 97 %
China 32 170526 11.7 (8.6–15.7) 100 %
Taiwan 2 8522 3.4 (0.00–99.8) 97 %
Ireland 1 234 1.7 (0.5–4.3) NA
Vietnam 1 2084 11.6 (10.3–13.1) NA
USA 2 7685 3.3 (0.1–60.5) 97 %
Israel 1 1056 2.2 (1.4–3.3) NA
Spain 5 45495 9.6 (2.2–33.2) 100 %
Tunisia 1 851 24.9 (22.0–28.0) NA
Netherlands 3 4696 3.7 (0.6–19.9) 97 %
Lebanon 1 524 9.2 (6.8–12.0) NA
Australia 1 9380 7.2 (2.4–21.5) 99 %
Korea 2 223681 19.3 (0.00–99.8) 99 %
Germany 2 657 7.2 (0.1–92.2) 86 %
Egypt 1 700 61.3 (57.6–64.9) NA
Iran 1 3837 33 (31.6–34.6) NA
Slovenia 2 2142 3.4 (0.2–34.0 83 %
Malaysia 1 5290 3.5 (3.0–4.0) NA
Kyrgyzstan 1 195 8.2 (4.8–13) NA
Norway 1 2055 1.2 (0.7–1.7) NA
Brazil 1 1557 12.3 (10.7–14.1) NA
Croatia 1 1150 6.2 (4.9–7.7) NA
Indonesia 1 1233 1.9 (1.2–2.8) NA
Turkey 1 1291 11.2 (9.5–13.0) NA
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3.5. Sensitivity analysis

Sensitivity analysis was conducted to evaluate the influence of individual studies on the pooled prevalence rate of gaming disorder among adolescents (Fig. S2). This analysis involved the systematic omission of each study to observe the effect on the overall meta-analytic estimate. The findings demonstrated that the pooled prevalence remained stable across the analyses, indicating that no single study unduly influenced the meta-analytic results. The recalculated proportions and 95 % confidence intervals were consistent with the overall pooled prevalence rate when all studies were included, affirming the robustness of our meta-analysis.

3.6. Meta-regression

A meta-regression analysis was performed to determine the effects of mean age, sample size, and year of publication on the prevalence rate of gaming disorder among adolescents. It was found that neither mean age (p = 0.71) nor sample size (p = 0.65) was significantly associated with prevalence rates. However, a significant positive association was noted with the year of publication (p = 0.016), suggesting that more recent studies tend to report higher rates of gaming disorder. The accompanying bubble plot (Fig. 3) illustrates this meta-regression based on the year of publication. The size of the bubbles corresponds to the weight of each study, and the plotted regression line indicates an ascending trend in the prevalence of gaming disorder over the years. Despite some variability, the general direction points to an increasing prevalence in more recent times, highlighting the escalating concern regarding gaming disorder among the youth as we move forward.

Fig. 3.

Fig. 3

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Bubble plot showing the association of prevalence of gaming disorder and publication year.

3.7. Publication bias

Our assessment for publication bias using the Doi plot revealed an LFK index of −1.47 (Fig. 4). This value, being outside the acceptable range of −1 to +1, suggests the presence of asymmetry and hints at potential publication bias in our meta-analysis. The plot depicts a visual assessment of this bias, with the distribution of studies suggesting that smaller studies with less favorable outcomes may be underrepresented. However, given the limitations of visual assessments, we corroborated these findings with Egger's test, which did not show statistical evidence of publication bias (p = 0.1857). These results suggest that while the Doi plot indicates potential bias, it is not statistically significant in our analysis.

Fig. 4.

Fig. 4

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Doi plot assessing publication bias.

4. Discussion

The systematic review and meta-analysis conducted in this study provide a comprehensive assessment of the burden of gaming disorder among adolescents. Our findings corroborate the significant prevalence of gaming disorder, evidenced by a pooled prevalence rate of 8.6 %. This rate reflects a substantial public health concern, given the negative consequences associated with excessive gaming, such as social isolation, academic difficulties, and mental health issues, such as anxiety and depression. The prevalence observed in our study is concerning but consistent with the emerging recognition of gaming disorder as a distinct psychopathology that merits attention from clinicians and policymakers alike. The high degree of heterogeneity observed across studies in our meta-analysis indicates a considerable variation in prevalence rates, which could be attributed to differences in cultural contexts, diagnostic criteria, and methodological approaches. This suggests that gaming disorder is perceived and reported differently across countries, which may influence the overall understanding of the disorder's impact on adolescent populations. For example, the significantly higher prevalence rates reported in studies from Egypt and Iran point to possible regional nuances in the manifestation or reporting of gaming disorder. However, due to the limited number of studies from these countries, further research is warranted to explore these regional differences comprehensively. Our subgroup analysis by country revealed that China has the most substantial body of research on gaming disorder, with a prevalence rate of 11.7 %. This is indicative of the country's recognition of the issue and the measures being taken to address it. In contrast, Sweden and the Netherlands reported lower prevalence rates, yet the high heterogeneity suggests a complexity within these populations that cannot be overlooked. The variability in findings, even within countries with multiple studies, underscores the need for standardized diagnostic criteria and study methodologies to enable more accurate comparisons and conclusions. The ascending trend in the prevalence of gaming disorder over the years, as indicated by our meta-regression analysis, raises concerns about the increasing engagement of adolescents with digital gaming environments. The significant positive association with the year of publication (p = 0.016) implies that the disorder's prevalence is growing, a trend that must be monitored closely given the rapid evolution of digital technologies and gaming platforms. This increasing trend also highlights the integration of digital technology into daily life has had profound effects on adolescents, with gaming emerging as a significant leisure activity. It was found that neither mean age nor sample size significantly influenced the prevalence rates, suggesting that gaming disorder affects a broad age range of adolescents and is not constrained to particular sample sizes or study designs.

A prior meta-analysis assessing the prevalence of gaming disorder among the general population reported an overall prevalence rate of 3.3 %, which is notably lower than our findings [107]. This discrepancy may due to the former analysis incorporating only a limited number of studies, whereas our research focused exclusively on adolescents and included a significantly larger and more recent collection of studies. This difference suggests that the prevalence of gaming disorder may have risen in recent years. Another meta-analysis that concentrated on video game addiction, encompassing 27 studies, determined a prevalence rate of 5 % [108]. This analysis also highlighted various factors influencing gaming disorders, including psychological, social, and personal aspects.

The implications of these findings are far-reaching. With gaming disorder recognized by the WHO as a significant health concern, there is a clear need for the development of public health strategies to address the issue [8]. Prevention and intervention programs that focus on fostering healthy gaming habits and providing alternatives to gaming could play a crucial role in mitigating the effects of gaming disorder [109]. Moreover, increased awareness and education about gaming disorder among parents, educators, and healthcare providers are essential to ensure early identification and support for affected individuals. The research also highlights several areas for future investigation. Longitudinal studies are needed to understand the long-term implications of gaming disorder on adolescent development and mental health. Furthermore, research exploring the efficacy of various intervention strategies could inform best practices for managing gaming disorder As digital technology continues to evolve, ongoing research into new forms of gaming and their potential impact on health is necessary. Virtual reality, augmented reality, and other emerging platforms present new challenges and opportunities for engagement with gaming that must be understood in the context of gaming disorder [110].

Despite the comprehensive nature of this review and meta-analysis on the burden of gaming disorder among adolescents, several limitations must be acknowledged. The high degree of heterogeneity observed across included studies poses a significant challenge. The potential for publication bias, as indicated by the Doi plot, cannot be overlooked. Although Egger's test did not show statistical evidence of bias, the presence of asymmetry in the plot suggests that smaller studies with less favorable outcomes might be underrepresented. This could lead to an overestimation of the prevalence of gaming disorder among adolescents. The reliance on cross-sectional data for the majority of included studies limits the ability to infer causality between gaming and its adverse outcomes. Longitudinal studies are necessary to better understand the directionality of associations and the long-term impact of gaming disorder on adolescent development. The significant positive association found with the year of publication suggests an increasing trend in the prevalence of gaming disorder over time. However, this observation could also reflect the growing awareness and evolving definitions of gaming disorder, rather than an actual increase in its occurrence. As such, these findings should be interpreted with caution, acknowledging the evolving nature of gaming disorder diagnostics and the influence of digital technology trends on adolescent behaviours.

5. Conclusion

A significant prevalence of gaming disorder among adolescents has been revealed, indicative of a substantial public health concern. The increasing trend in the prevalence of gaming disorder, alongside the rapid evolution of digital technologies, necessitates ongoing monitoring and the development of targeted public health strategies to mitigate its effects. Fostering healthy gaming habits, enhancing awareness, and implementing effective intervention programs are essential steps toward addressing the global challenge of gaming disorder among adolescents.

Ethical approval

Not required.

Data availability

The data is with the authors and available on request.

Funding

This study received no funding

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests.

Acknowledgements

The authors acknowledge the Nested-Knowledge, MN, USA for providing the access to the software. The publication of this article was funded by Qatar National Library.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.puhip.2024.100565.

Contributor Information

Prakasini Satapathy, Email: prakasini.satapathy@gmail.com.

Mahalaqua Nazli Khatib, Email: nazlikhatib@dmiher.edu.in.

Ashok Kumar Balaraman, Email: ashok@cyberjaya.edu.my.

Roopashree R, Email: r.roopashree@jainuniversity.ac.in.

Mandeep Kaur, Email: mkphd2024@gmail.com.

Manish Srivastava, Email: manish.srivastava1@nimsuniversity.org.

Amit Barwal, Email: Amit2475.research@cgcjhanjeri.in.

G.V. Siva Prasad, Email: sivaprasad.gv@raghuenggcollege.in.

Pranchal Rajput, Email: pranchalr2529@gmail.com.

Rukshar Syed, Email: Rukshar.research@iesuniversity.ac.in.

Gajendra Sharma, Email: gajendra.sharma@ndimdelhi.org.

Sunil Kumar, Email: sunilkumar.mb@geu.ac.in.

Mahendra Pratap Singh, Email: thesinghmp@gmail.com.

Ganesh Bushi, Email: ganeshbushi313@gmail.com.

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Sakshi Pandey, Email: sakshi.pandey.orp@chitkara.edu.in.

Manvinder Brar, Email: manvinder.brar.orp@chitkara.edu.in.

Rachana Mehta, Email: mehtarachana89@gmail.com.

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Appendix A. Supplementary data

The following is/are the supplementary data to this article.

Multimedia component 1
mmc1.docx (1.1MB, docx)

References

  • 1.Alfredsson Ågren K., Kjellberg A., Hemmingsson H. Digital participation? Internet use among adolescents with and without intellectual disabilities: a comparative study. New Media Soc. 2020;22(12):2128–2145. [Google Scholar]
  • 2.Gómez-Gonzalvo F., Devís-Devís J., Molina-Alventosa P. Video game usage time in adolescents' academic performance. Comunicar. 2020;28(65):89–99. [Google Scholar]
  • 3.Li Y., Wang C., Liu J. A systematic review of literature on user behavior in video game live streaming. Int. J. Environ. Res. Publ. Health. 2020;17(9):3328. doi: 10.3390/ijerph17093328. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Reynaldo C., Christian R., Hosea H., Gunawan A.A. Using video games to improve capabilities in decision making and cognitive skill: a literature review. Procedia Computer Science. 2021;179:211–221. [Google Scholar]
  • 5.Drummond A., Sauer J.D., Ferguson C.J., Hall L.C. The relationship between problem gambling, excessive gaming, psychological distress and spending on loot boxes in Aotearoa New Zealand, Australia, and the United States—a cross-national survey. PLoS One. 2020;15(3) doi: 10.1371/journal.pone.0230378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Kowal M., Conroy E., Ramsbottom N., Smithies T., Toth A., Campbell M. Gaming your mental health: a narrative review on mitigating symptoms of depression and anxiety using commercial video games. JMIR Serious Games. 2021;9(2) doi: 10.2196/26575. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Price A. Online gambling in the midst of COVID-19: a nexus of mental health concerns, substance use and financial stress. Int. J. Ment. Health Addiction. 2022;20(1):362–379. doi: 10.1007/s11469-020-00366-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Gaming disorder: World Health Organisation; [Available from: https://www.who.int/standards/classifications/frequently-asked-questions/gaming-disorder.
  • 9.King D.L., Potenza M.N. Not playing around: gaming disorder in the International classification of Diseases (ICD-11) J. Adolesc. Health. 2019;64(1):5–7. doi: 10.1016/j.jadohealth.2018.10.010. [DOI] [PubMed] [Google Scholar]
  • 10.Darvesh N., Radhakrishnan A., Lachance C.C., Nincic V., Sharpe J.P., Ghassemi M., et al. Exploring the prevalence of gaming disorder and Internet gaming disorder: a rapid scoping review. Syst. Rev. 2020;9:1–10. doi: 10.1186/s13643-020-01329-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Andrews J.L., Ahmed S.P., Blakemore S.-J. Navigating the social environment in adolescence: the role of social brain development. Biol. Psychiatr. 2021;89(2):109–118. doi: 10.1016/j.biopsych.2020.09.012. [DOI] [PubMed] [Google Scholar]
  • 12.Smirni D., Garufo E., Di Falco L., Lavanco G. The playing brain. the impact of video games on cognition and behavior in pediatric age at the time of lockdown: a systematic review. Pediatr. Rep. 2021;13(3):401–415. doi: 10.3390/pediatric13030047. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.González-Bueso V., Santamaría J.J., Fernández D., Merino L., Montero E., Ribas J. Association between internet gaming disorder or pathological video-game use and comorbid psychopathology: a comprehensive review. Int. J. Environ. Res. Publ. Health. 2018;15(4):668. doi: 10.3390/ijerph15040668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Loton D., Borkoles E., Lubman D., Polman R. Video game addiction, engagement and symptoms of stress, depression and anxiety: the mediating role of coping. Int. J. Ment. Health Addiction. 2016;14:565–578. [Google Scholar]
  • 15.Heuer A., Mennig M., Schubö A., Barke A. Impaired disengagement of attention from computer-related stimuli in Internet Gaming Disorder: behavioral and electrophysiological evidence. Journal of behavioral addictions. 2021;10(1):77–87. doi: 10.1556/2006.2020.00100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Kou Y., editor. Proceedings of the Annual Symposium on Computer-Human Interaction in Play. 2020. Toxic behaviors in team-based competitive gaming: the case of league of legends. [Google Scholar]
  • 17.Meriläinen M., Ruotsalainen M. The light, the dark, and everything else: making sense of young people's digital gaming. Front. Psychol. 2023;14 doi: 10.3389/fpsyg.2023.1164992. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Rho M.J., Lee H., Lee T.-H., Cho H., Jung D., Kim D.-J., Choi I.Y. Risk factors for internet gaming disorder: psychological factors and internet gaming characteristics. Int. J. Environ. Res. Publ. Health. 2018;15(1):40. doi: 10.3390/ijerph15010040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Gong X., Cheung C.M., Zhang K.Z., Chen C., Lee M.K. A dual-identity perspective of obsessive online social gaming. J. Assoc. Inf. Syst. Online. 2021;22(5):1245–1284. [Google Scholar]
  • 20.Mohammad S., Jan R.A., Alsaedi S.L., Symptoms Jan R. Mechanisms, and treatments of video game addiction. Cureus. 2023;15(3) doi: 10.7759/cureus.36957. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Page M.J., Moher D., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. bmj. 2021;372 doi: 10.1136/bmj.n160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Awad A.A., Itumalla R., Gaidhane A.M., Khatib M.N., Ballal S., Bansal P., et al. Association of electronic cigarette use and suicidal behaviors: a systematic review and meta-analysis. BMC Psychiatr. 2024;24(1):608. doi: 10.1186/s12888-024-06012-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Awad A.A., Khatib M.N., Gaidhane A.M., Ballal S., Bansal P., Srivastava M., et al. Predictors of acute kidney injury in dengue patients: a systematic review and meta-analysis. Virol. J. 2024;21(1):1–11. doi: 10.1186/s12985-024-02488-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Munn Z., Moola S., Lisy K., Riitano D., Tufanaru C. Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data. JBI Evidence Implementation. 2015;13(3):147–153. doi: 10.1097/XEB.0000000000000054. [DOI] [PubMed] [Google Scholar]
  • 25.Langan D., Higgins J.P., Jackson D., Bowden J., Veroniki A.A., Kontopantelis E., et al. A comparison of heterogeneity variance estimators in simulated random‐effects meta‐analyses. Res. Synth. Methods. 2019;10(1):83–98. doi: 10.1002/jrsm.1316. [DOI] [PubMed] [Google Scholar]
  • 26.Gandhi A.P., Shamim M.A., Padhi B.K. Steps in undertaking meta-analysis and addressing heterogeneity in meta-analysis. The Evidence. 2023;1(1):44–59. [Google Scholar]
  • 27.Pandey P., Shabil M., Bushi G. Comment on “Sodium fluorescein and 5-aminolevulinic acid fluorescence-guided biopsy in brain lesions: a systematic review and meta-analysis”. J. Neuro Oncol. 2024;170(1):11–29. doi: 10.1007/s11060-024-04820-1. [DOI] [PubMed] [Google Scholar]
  • 28.Shabil M., Bushi G., Khatib M.N. A commentary on “Psychological health among healthcare professionals during COVID-19 pandemic: an updated meta-analysis”. Indian J. Psychiatr. 2024;66(8):763–764. doi: 10.4103/indianjpsychiatry.indianjpsychiatry_496_24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Shabil M., Bushi G., Beig M.A., Rais M.A., Ahmed M., Padhi B.K. Cardiovascular manifestation in tuberculosis cases: a systematic review and meta-analysis. Curr. Probl. Cardiol. 2023;48(7) doi: 10.1016/j.cpcardiol.2023.101666. [DOI] [PubMed] [Google Scholar]
  • 30.Shabil M., Yadav A., Shamim M.A., Ahmed M., Satapathy P., Zaidan A.A., et al. Prevalence of hepatitis B and C infections among HIV‐positive men who have sex with men: a systematic review and meta‐analysis. Health Science Reports. 2024;7(6) doi: 10.1002/hsr2.2206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Wen X., Thomas M.A., Liu L., Moe A.A., Duong P.H., Griffiths M.E., Munlyn A.L. Association between maternal e-cigarette use during pregnancy and low gestational weight gain. Int. J. Gynaecol. Obstet. 2023;162(1):300–308. doi: 10.1002/ijgo.14672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Chang C.I., Fong Sit H., Chao T., Chen C., Shen J., Cao B., et al. Exploring subtypes and correlates of internet gaming disorder severity among adolescents during COVID-19 in China: a latent class analysis. Curr. Psychol. 2023;42(23):19915–19926. doi: 10.1007/s12144-022-03133-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Yang X., Ebo T.O., Wong K., Wang X. Relationships between psychological flexibility and internet gaming disorder among adolescents: mediation effects of depression and maladaptive cognitions. PLoS One. 2023;18(2) doi: 10.1371/journal.pone.0281269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Yu Y., Mo P.K.-H., Zhang J., Li J., Lau J.T.-F. Impulsivity, self-control, interpersonal influences, and maladaptive cognitions as factors of internet gaming disorder among adolescents in China: cross-sectional mediation study. J. Med. Internet Res. 2021;23(10) doi: 10.2196/26810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Yu Y., Mo P.K., Zhang J., Li J., Lau J.T. Maladaptive cognitions, loneliness, and social anxiety as potential moderators of the association between internet gaming time and internet gaming disorder among adolescent internet gamers in China. Addict. Behav. 2022;129 doi: 10.1016/j.addbeh.2022.107239. [DOI] [PubMed] [Google Scholar]
  • 36.Yu Y., Peng L., Mo P.K., Yang X., Cai Y., Ma L., et al. Association between relationship adaptation and Internet gaming disorder among first-year secondary school students in China: mediation effects via social support and loneliness. Addict. Behav. 2022;125 doi: 10.1016/j.addbeh.2021.107166. [DOI] [PubMed] [Google Scholar]
  • 37.Zhang L., Luo T., Hao W., Cao Y., Yuan M., Liao Y. Gaming disorder symptom questionnaire: the development and validation of a screening tool for ICD-11 gaming disorder in adolescents. Front. Psychiatr. 2022;13 doi: 10.3389/fpsyt.2022.848157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Zhang L., Liu M., Yuan M., Hou M., Yang C., Wang Y., et al. The latent profile analysis of Chinese adolescents' gaming disorder: examination and validation. BMC Psychiatr. 2023;23(1):833. doi: 10.1186/s12888-023-05320-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Zhang M.X., Shu M.Y., Demetrovics Z., Wu A.M. Metacognitive beliefs and anxiety symptoms could serve as mediators between fear of missing out and gaming disorder in adolescents. Addict. Behav. 2023 doi: 10.1016/j.addbeh.2023.107775. [DOI] [PubMed] [Google Scholar]
  • 40.Zhu S., Zhuang Y., Lee P., Li J.C.-M., Wong P.W. Leisure and problem gaming behaviors among children and adolescents during school closures caused by COVID-19 in Hong Kong: quantitative cross-sectional survey study. JMIR serious games. 2021;9(2) doi: 10.2196/26808. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Zou H., Deng Y., Wang H., Yu C., Zhang W. Perceptions of school climate and internet gaming addiction among Chinese adolescents: the mediating effect of deviant peer affiliation. Int. J. Environ. Res. Publ. Health. 2022;19(6):3604. doi: 10.3390/ijerph19063604. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Yang Y., Ma Y., Zhou R., Ji T., Hou C. Internet gaming addiction among children and adolescents with non-suicidal self-Injury: a network perspective. Journal of Affective Disorders Reports. 2023 [Google Scholar]
  • 43.Yu C., Li W., Liang Q., Liu X., Zhang W., Lu H., et al. School climate, loneliness, and problematic online game use among Chinese adolescents: the moderating effect of intentional self-regulation. Front. Public Health. 2019;7:90. doi: 10.3389/fpubh.2019.00090. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Yu Y., Yang X., Wang S., Wang H., Chang R., Tsamlag L., et al. Serial multiple mediation of the association between internet gaming disorder and suicidal ideation by insomnia and depression in adolescents in Shanghai, China. BMC Psychiatr. 2020;20(1):1–9. doi: 10.1186/s12888-020-02870-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Chen S., Zhu S. Unravelling the intricacies between gaming motivations and internet gaming disorder symptoms in adolescents: a network analysis of 2-year healthy and deteriorating transition profiles. Child Adolesc. Psychiatr. Ment. Health. 2023;17(1):122. doi: 10.1186/s13034-023-00671-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Huang X., Shi H-x, Li H-q, Guo W-j, Luo D., Xu J-j. Differential effects of anxiety on internet gaming disorder: a large-scale cross-sectional survey. Front. Psychiatr. 2022;12 doi: 10.3389/fpsyt.2021.802513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Junus A., Hsu Y.-C., Wong C., Yip P.S. Is internet gaming disorder associated with suicidal behaviors among the younger generation? Multiple logistic regressions on a large-scale purposive sampling survey. J. Psychiatr. Res. 2023;161:2–9. doi: 10.1016/j.jpsychires.2023.02.038. [DOI] [PubMed] [Google Scholar]
  • 48.Liang Q., Yu C., Chen Q., Xie X., Wu H., Xing J., et al. Exposure to community violence, affiliations with risk-taking peer groups, and internet gaming disorder among Chinese adolescents: the moderating role of parental monitoring. Front. Psychol. 2019;10:2074. doi: 10.3389/fpsyg.2019.02074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Lin L., Ding R., Ni S. How does parents' psychological distress relate to adolescents' problematic gaming? The roles of parent-adolescent relationship and adolescents' emotion regulation. Journal of Behavioral Addictions. 2023;12(4):953–963. doi: 10.1556/2006.2023.00060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Liu Y., Wang R., Gong R., Yu Y., Xu C., Yu X., et al. The trajectories and associations of insomnia symptoms with addictive behaviours in adolescents: a two‐year longitudinal study. Journal of Sleep Research. 2023 doi: 10.1111/jsr.13817. [DOI] [PubMed] [Google Scholar]
  • 51.Luo T., Wei D., Guo J., Hu M., Chao X., Sun Y., et al. Diagnostic contribution of the DSM-5 criteria for internet gaming disorder. Front. Psychiatr. 2022;12 doi: 10.3389/fpsyt.2021.777397. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Ma C., Wang Z., Li C., Lu J., Long J., Li R., et al. The clinical consistency and utility of ICD-11 diagnostic guidelines for gaming disorder: a field study among the Chinese population. Front. Psychiatr. 2021;12 doi: 10.3389/fpsyt.2021.781992. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Ong R.H., Peh C.X., Guo S. Differential risk factors associated with adolescent addictive disorders: a comparison between substance use disorders and internet/gaming addiction. Int. J. Ment. Health Addiction. 2016;14:993–1002. [Google Scholar]
  • 54.Peng P., Chen Z., Ren S., Liu Y., He R., Liang Y., et al. Determination of the cutoff point for Smartphone Application-Based Addiction Scale for adolescents: a latent profile analysis. BMC Psychiatr. 2023;23(1):675. doi: 10.1186/s12888-023-05170-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.She R., Wong K., Lin J., Zhang Y., Leung K., Yang X. Profiles of stress and coping associated with mental, behavioral, and Internet use problems among adolescents during the COVID-19 pandemic: a stratified random sampling and cluster analysis. Front. Public Health. 2022;10 doi: 10.3389/fpubh.2022.826911. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Tsui YY-y, Cheng C. Internet gaming disorder, risky online behaviour, and mental health in Hong Kong adolescents: the beneficial role of psychological resilience. Front. Psychiatr. 2021;12 doi: 10.3389/fpsyt.2021.722353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Wang C.-W., Chan C.L., Mak K.-K., Ho S.-Y., Wong P.W., Ho R.T. Prevalence and correlates of video and internet gaming addiction among Hong Kong adolescents: a pilot study. Sci. World J. 2014;2014 doi: 10.1155/2014/874648. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Wang X., Liu Y., Chu HK-c, Wong SY-s, Yang X. Relationships of internet gaming engagement, history, and maladaptive cognitions and adolescent internet gaming disorder: a cross-sectional study. PLoS One. 2023;18(9) doi: 10.1371/journal.pone.0290955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Wei Q., Zhang S., Pan Y., Hu H., Chen F., Yin W., et al. Epidemiology of gaming disorder and its effect on anxiety and insomnia in Chinese ethnic minority adolescents. BMC Psychiatr. 2022;22(1):1–9. doi: 10.1186/s12888-022-03894-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Xie Y., Yang Q., Lei F. The relationship of internet gaming addiction and suicidal ideation among adolescents: the mediating role of negative emotion and the moderating role of hope. Int. J. Environ. Res. Publ. Health. 2023;20(4):3375. doi: 10.3390/ijerph20043375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Xu P., Hao X., Luo D., Lai M., Sun X., Xu J. Problematic internet gaming and non-suicidal self-injury in Chinese adolescents: moderating and mediating roles of anxiety. Compr. Psychiatr. 2023;125 doi: 10.1016/j.comppsych.2023.152398. [DOI] [PubMed] [Google Scholar]
  • 62.Yang X., Jiang X., Mo PK-h, Cai Y., Ma L., Lau JT-f. Prevalence and interpersonal correlates of internet gaming disorders among Chinese adolescents. Int. J. Environ. Res. Publ. Health. 2020;17(2):579. doi: 10.3390/ijerph17020579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Yang Q., Wang H., Wu H., Li W., Zhang Y., Yao Y., et al. Effect of online game policy on smartphone game play time, addiction, and emotion in rural adolescents of China. BMC Psychiatr. 2023;23(1):814. doi: 10.1186/s12888-023-05325-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Esteve A., Jovani A., Benito A., Baquero A., Haro G., Rodríguez-Ruiz F. Dual diagnosis in adolescents with problematic use of video games: beyond substances. Brain Sci. 2022;12(8):1110. doi: 10.3390/brainsci12081110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.García-Gil M.Á., Fajardo-Bullón F., Rasskin-Gutman I., Sánchez-Casado I. Problematic video game use and mental health among Spanish adolescents. Int. J. Environ. Res. Publ. Health. 2022;20(1):349. doi: 10.3390/ijerph20010349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.MACHIMBARRENA J., Beranuy M., Vergara-Moragues E., Fernández-González L., Calvete E., González-Cabrera J. Problematic Internet use and Internet gaming disorder: overlap and relationship with healthrelated quality of life in adolescents. Adicciones. 2023;35(2) doi: 10.20882/adicciones.1494. [DOI] [PubMed] [Google Scholar]
  • 67.Nogueira-López A., Rial-Boubeta A., Guadix-García I., Villanueva-Blasco V.J., Billieux J. Prevalence of problematic internet use and problematic gaming in Spanish adolescents. Psychiatr. Res. 2023;326 doi: 10.1016/j.psychres.2023.115317. [DOI] [PubMed] [Google Scholar]
  • 68.Sánchez-Llorens M., Marí-Sanmillán M.I., Benito A., Rodríguez-Ruiz F., Castellano-García F., Almodóvar I., Haro G. Personality traits and psychopathology in adolescents with videogame addiction. Rasgos de personalidad y psicopatología en adolescentes con adicción a videojuegos. Adicciones. 2021;35(2):151–164. doi: 10.20882/adicciones.1629. 1 Jul. 2023. [DOI] [PubMed] [Google Scholar]
  • 69.Desai R.A., Krishnan-Sarin S., Cavallo D., Potenza M.N. Video-gaming among high school students: health correlates, gender differences, and problematic gaming. Pediatrics. 2010;126(6):e1414–e1424. doi: 10.1542/peds.2009-2706. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Greenberg N.R., Zhai Z.W., Hoff R.A., Krishnan-Sarin S., Potenza M.N. An exploratory study of problematic shopping and problematic video gaming in adolescents. PLoS One. 2022;17(8) doi: 10.1371/journal.pone.0272228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.André F., Håkansson A., Johansson B.A., Claesdotter-Knutsson E. The prevalence of gaming and gambling in a child and adolescent psychiatry unit. Journal of Public Health Research. 2022;11(2) [Google Scholar]
  • 72.Gerdner A., Håkansson A. Prevalence and comorbidity in a Swedish adolescent community sample–gambling, gaming, substance use, and other psychiatric disorders. BMC Psychiatr. 2022;22(1):594. doi: 10.1186/s12888-022-04218-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Olsen M., André F., Delfin C., Håkansson A., Claesdotter-Knutsson E. Children and adolescent's self-reported gaming habits–An exploratory, cross-sectional study of gaming among 9–15-year-old school children. Emerging Trends in Drugs, Addictions, and Health. 2024;4 [Google Scholar]
  • 74.Haagsma M.C., King D.L., Pieterse M.E., Peters O. Assessing problematic video gaming using the theory of planned behavior: a longitudinal study of Dutch young people. Int. J. Ment. Health Addiction. 2013;11:172–185. [Google Scholar]
  • 75.Peeters M., Koning I., Lemmens J., Rvd Eijnden. Normative, passionate, or problematic? Identification of adolescent gamer subtypes over time. Journal of behavioral addictions. 2019;8(3):574–585. doi: 10.1556/2006.8.2019.55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Van Den Eijnden R., Koning I., Doornwaard S., Van Gurp F., Ter Bogt T. The impact of heavy and disordered use of games and social media on adolescents' psychological, social, and school functioning. Journal of behavioral addictions. 2018;7(3):697–706. doi: 10.1556/2006.7.2018.65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Kewitz S., Vonderlin E., Wartberg L., Lindenberg K. Estimated prevalence of unreported IGD cases in routine outpatient children and adolescent psychotherapy. Int. J. Environ. Res. Publ. Health. 2021;18(13):6787. doi: 10.3390/ijerph18136787. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Wartberg L., Kramer M., Potzel K., Kammerl R. Problematic use of video games, social media, and Alcohol: associations with mother-child relationship. Psychopathology. 2023;56(1–2):117–126. doi: 10.1159/000525148. [DOI] [PubMed] [Google Scholar]
  • 79.Jo Y.S., Bhang S.Y., Choi J.-S., Lee H.K., Lee S.Y., Kweon Y.-S. Internet, gaming, and smartphone usage patterns of children and adolescents in Korea: a c-CURE clinical cohort study. Journal of Behavioral Addictions. 2020;9(2):420–432. doi: 10.1556/2006.2020.00022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Kim K.M., Kim H., Choi J.-W., Kim S.Y., Kim J.W. What types of internet services make adolescents addicted? Correlates of problematic internet use. Neuropsychiatric Dis. Treat. 2020:1031–1041. doi: 10.2147/NDT.S247292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Alfaifi A.J., Mahmoud S.S., Elmahdy M.H., Gosadi I.M. Prevalence and factors associated with Internet gaming disorder among adolescents in Saudi Arabia: a cross-sectional study. Medicine. 2022;101(26) doi: 10.1097/MD.0000000000029789. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Bumozah H.S., Al-Quwaidhi A.J., Rahmah A.-G., Al-Quwaidhi A. Prevalence and risk factors of internet gaming disorder among female secondary school students in Al-ahsa, kingdom of Saudi arabia. Cureus. 2023;15(6) doi: 10.7759/cureus.40375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Rajab A.M., Zaghloul M.S., Enabi S., Rajab T.M., Al-Khani A.M., Basalah A., et al. Gaming addiction and perceived stress among Saudi adolescents. Addictive Behaviors Reports. 2020;11 doi: 10.1016/j.abrep.2020.100261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Hing N., Russell A.M., King D.L., Rockloff M., Browne M., Newall P., Greer N. Not all games are created equal: adolescents who play and spend money on simulated gambling games show greater risk for gaming disorder. Addict. Behav. 2023;137 doi: 10.1016/j.addbeh.2022.107525. [DOI] [PubMed] [Google Scholar]
  • 85.Lan Y.-T., Pan Y.-C., Lin Y.-H. Association between adolescents' problematic online behaviors and self-harm risk. J. Affect. Disord. 2022;317:46–51. doi: 10.1016/j.jad.2022.08.073. [DOI] [PubMed] [Google Scholar]
  • 86.Burén J., Nutley S.B., Crisci G., Thorell L.B. Evidence of a two-factor structure for Internet Gaming Disorder and Social Media Disorder: psychometric properties of a new screening instrument for adolescents and adults. J. Psychopathol. Behav. Assess. 2023;45(3):702–715. [Google Scholar]
  • 87.Colasante E., Pivetta E., Canale N., Vieno A., Marino C., Lenzi M., et al. Problematic gaming risk among European adolescents: a cross‐national evaluation of individual and socio‐economic factors. Addiction. 2022;117(8):2273–2282. doi: 10.1111/add.15843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Colder Carras M., Kardefelt-Winther D. vol. 27. European child & adolescent psychiatry; 2018. pp. 513–525. (When Addiction Symptoms and Life Problems Diverge: A Latent Class Analysis of Problematic Gaming in a Representative Multinational Sample of European Adolescents). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Müller K.W., Janikian M., Dreier M., Wölfling K., Beutel M.E., Tzavara C., et al. vol. 24. European child & adolescent psychiatry; 2015. pp. 565–574. (Regular Gaming Behavior and Internet Gaming Disorder in European Adolescents: Results from a Cross-National Representative Survey of Prevalence, Predictors, and Psychopathological Correlates). [DOI] [PubMed] [Google Scholar]
  • 90.Spilkova J., Chomynova P., Csemy L. Predictors of excessive use of social media and excessive online gaming in Czech teenagers. Journal of behavioral addictions. 2017;6(4):611–619. doi: 10.1556/2006.6.2017.064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.van der Neut D., Peeters M., Boniel-Nissim M., Klanšček H.J., Oja L., van den Eijnden R. A cross-national comparison of problematic gaming behavior and well-being in adolescents. Journal of Behavioral Addictions. 2023;12(2):448–457. doi: 10.1556/2006.2023.00010. 14 Apr. 2023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92.Chiu Y.-C., Pan Y.-C., Lin Y.-H. Chinese adaptation of the Ten-Item Internet Gaming Disorder Test and prevalence estimate of Internet gaming disorder among adolescents in Taiwan. Journal of Behavioral Addictions. 2018;7(3):719–726. doi: 10.1556/2006.7.2018.92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Columb D., Keegan E., Griffiths M.D., O'Gara C. A descriptive pilot survey of behavioural addictions in an adolescent secondary school population in Ireland. Ir. J. Psychol. Med. 2021:1–13. doi: 10.1017/ipm.2021.40. [DOI] [PubMed] [Google Scholar]
  • 94.Cuong V.M., Assanangkornchai S., Wichaidit W., Minh Hanh V.T., My Hanh H.T. Associations between gaming disorder, parent-child relationship, parental supervision, and discipline styles: findings from a school-based survey during the COVID-19 pandemic in Vietnam. Journal of behavioral addictions. 2021;10(3):722–730. doi: 10.1556/2006.2021.00064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Efrati Y., Spada M.M. “I have no control over how much time I play” the metacognitions about online gaming scale: evidence from a cross-cultural validation among Israeli adolescents. Addict. Behav. 2023;141 doi: 10.1016/j.addbeh.2023.107638. [DOI] [PubMed] [Google Scholar]
  • 96.Fekih-Romdhane F., Lamloum E., Loch A.A., Cherif W., Cheour M., Hallit S. The relationship between internet gaming disorder and psychotic experiences: cyberbullying and insomnia severity as mediators. BMC Psychiatr. 2023;23(1):857. doi: 10.1186/s12888-023-05363-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.Hawi N.S., Samaha M., Griffiths M.D. Internet gaming disorder in Lebanon: relationships with age, sleep habits, and academic achievement. Journal of behavioral addictions. 2018;7(1):70–78. doi: 10.1556/2006.7.2018.16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Khalil S.A., Kamal H., Elkholy H. The prevalence of problematic internet use among a sample of Egyptian adolescents and its psychiatric comorbidities. Int. J. Soc. Psychiatr. 2022;68(2):294–300. doi: 10.1177/0020764020983841. [DOI] [PubMed] [Google Scholar]
  • 99.Lin C.-Y., Potenza M.N., Pontes H.M., Pakpour A.H. Psychometric properties of the Persian Gaming Disorder Test and relationships with psychological distress and insomnia in adolescents. BMC psychology. 2023;11(1):326. doi: 10.1186/s40359-023-01368-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Mohamed N.F., Ab Manan N., Muhammad Firdaus Chan MF., Rahmatullah B., Abd Wahab R., Baharudin S.N.A., et al. The prevalence of internet gaming disorders and the associated psychosocial risk factors among adolescents in Malaysian secondary schools. Clin. Child Psychol. Psychiatr. 2023 doi: 10.1177/13591045231164870. [DOI] [PubMed] [Google Scholar]
  • 101.Muhametjanova G., Adanır G.A., Arpaci I. Investigation of gaming habits, personality traits, and internet gaming disorder among Kyrgyz adolescents. Int. J. Ment. Health Addiction. 2023;21(2):869–877. [Google Scholar]
  • 102.Myrseth H., Notelaers G. A latent class approach for classifying the problem and disordered gamers in a group of adolescence. Front. Psychol. 2018;9:2273. doi: 10.3389/fpsyg.2018.02273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103.Roza T.H., Rabelo-da-Ponte F.D., Spritzer D.T., Hoffmann M.S., Massuda R., Salum G.A., et al. Depression in mothers at childhood: direct and indirect association with problematic gaming in late adolescence/young adulthood. J. Psychiatr. Res. 2023;159:14–21. doi: 10.1016/j.jpsychires.2023.01.022. [DOI] [PubMed] [Google Scholar]
  • 104.Šincek D., Tomašić Humer J., Duvnjak I. Correlates of problematic gaming–is there support for proneness to risky behaviour? Psychiatr. Danub. 2017;29(3):302–312. doi: 10.24869/psyd.2017.302. [DOI] [PubMed] [Google Scholar]
  • 105.Siste K., Hanafi E., Sen L.T., Damayanti R., Beatrice E., Ismail R.I. Psychometric properties of the Indonesian Ten-item Internet Gaming Disorder Test and a latent class analysis of gamer population among youths. PLoS One. 2022;17(6) doi: 10.1371/journal.pone.0269528. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 106.Uçur Ö., Dönmez Y.E. Problematic internet gaming in adolescents, and its relationship with emotional regulation and perceived social support. Psychiatr. Res. 2021;296 doi: 10.1016/j.psychres.2020.113678. [DOI] [PubMed] [Google Scholar]
  • 107.Kim H.S., Son G., Roh E.-B., Ahn W.-Y., Kim J., Shin S.-H., et al. Prevalence of gaming disorder: a meta-analysis. Addict. Behav. 2022;126 doi: 10.1016/j.addbeh.2021.107183. [DOI] [PubMed] [Google Scholar]
  • 108.Limone P., Ragni B., Toto G.A. The epidemiology and effects of video game addiction: a systematic review and meta-analysis. Acta Psychol. 2023;241 doi: 10.1016/j.actpsy.2023.104047. [DOI] [PubMed] [Google Scholar]
  • 109.Lindenberg K., Kindt S., Szász-Janocha C. Effectiveness of cognitive behavioral therapy–based intervention in preventing gaming disorder and unspecified internet use disorder in adolescents: a cluster randomized clinical trial. JAMA Netw. Open. 2022;5(2) doi: 10.1001/jamanetworkopen.2021.48995. e. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 110.Lundin R.M., Yeap Y., Menkes D.B. Adverse effects of virtual and augmented reality interventions in psychiatry: systematic review. JMIR Mental Health. 2023;10 doi: 10.2196/43240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111.Amudhan S., Prakasha H., Mahapatra P., Burma A.D., Mishra V., Sharma M.K., Rao G.N. Technology addiction among school-going adolescents in India: epidemiological analysis from a cluster survey for strengthening adolescent health programs at district level. Journal of Public Health. 2022;44(2):286–295. doi: 10.1093/pubmed/fdaa257. [DOI] [PubMed] [Google Scholar]
  • 112.Castrén S., Mustonen T., Hylkilä K., Männikkö N., Kääriäinen M., Raitasalo K. Risk factors for excessive social media use differ from those of gambling and gaming in Finnish youth. Int. J. Environ. Res. Publ. Health. 2022;19(4):2406. doi: 10.3390/ijerph19042406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 113.Kim S., Champion K.E., Gardner L.A., Teesson M., Newton N.C., Gainsbury S.M. The directionality of anxiety and gaming disorder: an exploratory analysis of longitudinal data from an Australian youth population. Front. Psychiatr. 2022;13 doi: 10.3389/fpsyt.2022.1043490. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 114.Macur M., Pontes H.M. Internet Gaming Disorder in adolescence: investigating profiles and associated risk factors. BMC Publ. Health. 2021;21:1–9. doi: 10.1186/s12889-021-11394-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 115.Männikkö N., Demetrovics Z., Ruotsalainen H., Myllymäki L., Miettunen J., Kääriäinen M. Psychometric properties of the Problematic Gaming Questionnaire used to assess Finnish adolescents. Int. J. Ment. Health Addiction. 2020;18:103–111. [Google Scholar]
  • 116.Pontes H.M., Macur M., Griffiths M.D. Internet gaming disorder among Slovenian primary schoolchildren: findings from a nationally representative sample of adolescents. Journal of behavioral addictions. 2016;5(2):304–310. doi: 10.1556/2006.5.2016.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 117.Rikkers W., Lawrence D., Hafekost J., Zubrick S.R. Internet use and electronic gaming by children and adolescents with emotional and behavioural problems in Australia–results from the second Child and Adolescent Survey of Mental Health and Wellbeing. BMC Publ. Health. 2016;16:1–16. doi: 10.1186/s12889-016-3058-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 118.Thakur P.C., Sharma M.K., Mohan V., Kommu J.V.S., Anand N., Marimuthu P. Gaming among female adolescents: profiling and psychopathological characteristics in the Indian context. Front. Psychiatr. 2023;14 doi: 10.3389/fpsyt.2023.1081764. [DOI] [PMC free article] [PubMed] [Google Scholar]

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