Prevalence and risk factors for internet gaming disorder

Severo, Rovena Batista; Soares, Jennifer Mendes; Affonso, Josiara P.; Giusti, Daniela A.; Souza, Adão Antonio de; Figueiredo, Vera L. de; Pinheiro, Karen Amaral Tavares; Pontes, Halley M.

doi:10.1590/1516-4446-2019-0760

Cited by 50 publications

(75 citation statements)

References 13 publications

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“…Prevalence rates of problematic gaming varied widely in the samples ranging from 1.2 to 73.9% (weighted mean = 16.0%), while three studies had comparison groups of similar sample size ( 41 , 71 , 76 ). The majority of samples ( n = 18) were recruited from primary- / high schools, while eight samples were recruited from college/universities ( 41 , 53 – 55 , 66 , 75 , 76 , 80 ), three were recruited from gaming communities ( 24 , 52 , 74 ), one was recruited from two pediatric lipid and obesity treatment clinics ( 77 ), one was recruited through social media ( 61 ), one was recruited in “non-working contexts” [e.g., pubs, sports associations, recreational places; ( 59 )], and two samples were recruited using random population sampling ( 56 , 79 ).…”

Section: Resultsmentioning

confidence: 99%

Problematic Gaming and Sleep: A Systematic Review and Meta-Analysis

et al. 2021

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Problematic gaming has been linked to poor sleep outcomes; however, these associations have not yet been synthesized quantitatively. This review employed a meta-analysis to investigate the relationship between problematic gaming and sleep-related outcomes. A search of Medline, Embase, Web of Science, PsycINFO, and Google Scholar identified a total of 763 studies, including 34 studies (n = 51,901 participants) eligible for inclusion. Papers were included if available in any European language, addressed problematic gaming, contained original data, and provided sufficient data for calculation of effect sizes. Two researchers independently extracted data using pre-defined fields including quality assessment. Sleep-related outcomes were meta-analyzed for sleep parameters that were reported by 5 or more papers. Significant overall effects were found for sleep duration (g = −0.238, 95% CI = −0.364, −0.112), poor sleep quality (OR = 2.02, 95% CI = 1.47, 2.78), daytime sleepiness (OR = 1.57, 95% CI = 1.00, 2.46) and sleep problems (OR = 2.60, 95% CI = 1.94, 3.47). Between-study heterogeneity was detected for all meta-analyses. Subgroup analyses showed a higher inverse effect size for adolescent samples compared to adult or non-specific age samples in terms of sleep duration. For daytime sleepiness, a larger effect size was found for studies based on single-item sleep measures compared to multi-item sleep measures. For sleep problems, the subgroup analysis showed the opposite with a higher effect size for studies based on single-item sleep measures than multi-item sleep measures. Across all sleep parameters, problematic gamers consistently reported a more adverse sleep status than non-problematic gamers.Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/; record ID: CRD42020158955.

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Section: Resultsmentioning

confidence: 99%

Problematic Gaming and Sleep: A Systematic Review and Meta-Analysis

et al. 2021

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“…Disordered gaming has also been linked to several negative health-related outcomes and psychosocial detriments, such as increased stress, increased obesity, decreased job performance and/or job loss, decreased academic achievement, sleep abnormalities, relationship problems, depression, lower psychosocial wellbeing, and anxiety [ 7 ] alongside other similar addictive disorders involving excessive technology use [ 17 ]. Additionally, Severo et al [ 18 ] recently found that increased depressive symptoms, poor sleeping, male gender, greater time spent gaming, and total free-time spent gaming are key IGD risk factors. In addition, other adolescent studies reported increased time spent gaming among disordered gamers [ 19 ], a phenomenon that is arguably reflected within the IGD definition itself through the operationalization of tolerance symptoms in IGD (i.e., “tolerance—the need to spend increasing amounts of time engaged gaming”) [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Internet Gaming Disorder in adolescence: investigating profiles and associated risk factors

Macur

Pontes

2021

BMC Public Health

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Background Electronic gaming is a popular free-time activity and its deleterious effects have been considered by the American Psychiatric Association and World Health Organization. More recently ‘Gaming Disorder’ (GD) has been added to the 11th revision of the International Classification of Diseases, while ‘Internet Gaming Disorder’ (IGD) remains as a tentative disorder in the 5th revision of the Diagnostic and Statistical Manual of Mental Disorders. This study aimed to explore the characteristics of young gamers at risk for developing IGD. Methods To achieve this, a quantitative and nationally representative study was conducted in primary schools in Slovenia with eighth grade as the primary sampling unit (N = 1071, Meanage = 13.44 years, SDage = 0.59). Psychometric testing assessing IGD was conducted to identify participants’ IGD risk levels and to compare ‘high risk gamers’, ‘low risk gamers’, and ‘non-gamers’ in relation to free-time activities, self-control, and parent-child relationship. A one-way ANOVA analysis was conducted with Games-Howell post-hoc test to compare the three groups of participants. Statistically significant IGD factors were then included in a multinomial logistic regression analysis to identify the most relevant predictors of IGD. Results About 4.7% (n = 48) [95% CI: 3.4–6.0%] of Slovenian adolescents were found to be ‘high risk gamers’ when considering risk of IGD. These were mostly males (n = 42, 87.5%), and their preferred leisure activities involved more screen time activities (e.g., watching TV, playing video games, using social media). Moreover, ‘high risk gamers’ showed significantly lower levels of self-control compared to ‘low risk gamers’, and poorer understanding with their parents. Perceived satisfaction with life and mental health did not differ significantly between the three groups. The multinomial logistic regression identified four key predictors of IGD: male gender, gaming as a frequent free-time activity, attending music school or a choir and self-control. Conclusion Public health measures should target adolescents at increased risk of developing IGD in early age because they are particularly drawn to excessive gaming behaviors and present greater IGD vulnerability.

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“…A recent study conducted by Moore, Satel and Pontes (in press) investigated whether disordered gaming could be predicted by health and wellbeing factors such as depression, anxiety, loneliness, attention problems, physical health problems, and psychological wellbeing in a sample of young adults and found that about 15% of the variability in GD could be explained by these predictors. In a similar vein, a recent study by Severo et al (2020) reported that IGD was associated with severe depressive symptoms, poor sleep quality, and increased time spent gaming in a sample of 555 individuals.…”

Section: Introductionmentioning

confidence: 71%