Dataset Information

Brain Activity toward Gaming-Related Cues in Internet Gaming Disorder during an Addiction Stroop Task.

ABSTRACT:

Background and aims

Attentional bias for drug-related stimuli is a key characteristic for drug addiction. Characterizing the relationship between attentional bias and brain reactivity to Internet gaming-related stimuli may help in identifying the neural substrates that critical to Internet gaming disorder (IGD).

Methods

19 IGD and 21 healthy control (HC) subjects were scanned with functional magnetic resonance imaging while they were performing an addiction Stroop task.

Results

Compared with HC group, IGD subjects showed higher activations when facing Internet gaming-related stimuli in regions including the inferior parietal lobule, the middle occipital gyrus and the dorsolateral prefrontal cortex. These brain areas were thought to be involved in selective attention, visual processing, working memory and cognitive control.

Discussion and conclusions

The results demonstrated that compared with HC group, IGD subjects show impairment in both visual and cognitive control ability while dealing with gaming-related words. This finding might be helpful in understanding the underlying neural basis of IGD.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC4872468 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Brain Activity toward Gaming-Related Cues in Internet Gaming Disorder during an Addiction Stroop Task.

Zhang Yifen Y Lin Xiao X Zhou Hongli H Xu Jiaojing J Du Xiaoxia X Dong Guangheng G

Frontiers in psychology 20160519

<h4>Background and aims</h4>Attentional bias for drug-related stimuli is a key characteristic for drug addiction. Characterizing the relationship between attentional bias and brain reactivity to Internet gaming-related stimuli may help in identifying the neural substrates that critical to Internet gaming disorder (IGD).<h4>Methods</h4>19 IGD and 21 healthy control (HC) subjects were scanned with functional magnetic resonance imaging while they were performing an addiction Stroop task.<h4>Results ...[more]

PMID: 27242623

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Project description:BackgroundDigital technology use is nearly ubiquitous among young adults; this use provides both benefits and risks. The risks of technology use include maladaptive technology use or technology addiction. Several conceptualizations of these addictions have emerged, each with its own assessment tools. These conditions include problematic internet use (PIU), internet gaming disorder (IGD), and social media addiction (SMA). These conditions have been associated with health outcomes such as problematic alcohol use, sleep disorders, and mental illness. These maladaptive technology conditions have been most commonly studied in isolation from each other.ObjectiveThe aim of this study is to examine PIU, IGD, and SMA together to better inform future research approaches and provider screening practices for young adults.MethodsThis cross-sectional survey study was conducted using Qualtrics panel-based recruitment and survey hosting. We recruited US young adults aged 18-25 years. The survey assessed PIU, IGD, and SMA. Survey measures also included assessments of problematic alcohol use, sleep, depression, and anxiety. We evaluated the frequency of and overlap in positive screening scores among PIU, IGD, and SMA and modeled each condition using multivariate logistic regression. Finally, we calculated sensitivity and specificity, as well as the positive predictive value and negative predictive value of the screening tools using the most prevalent maladaptive technology type.ResultsOur 6000 participants had an average age of 21.7 (SD 2.5) years. Of these 6000 participants, 3062 (51.03%) were female, 3431 (57.18%) were Caucasian, 1686 (28.1%) were in a 4-year college program, and 2319 (38.65%) worked full time. The mean PIU score was 3.5 (SD 3.1), and 53.58% (3215/6000) of participants met the criteria for PIU. The mean IGD score was 2.7 (SD 2.6), and 24.33% (1460/6000) of participants met the criteria for IGD. The mean SMA score was 7.5 (SD 5.7), and 3.42% (205/6000) met the criteria for SMA. Across all 3 maladaptive technology use diagnoses, there were varied associations with demographic variables and similar overlap with health outcomes. The sensitivity of PIU screening to detect IGD was 82% and to detect SMA was 93%, whereas the specificity and positive predictive value were much lower (37%-54% specificity; 6%-37% positive predictive value).ConclusionsThis cross-sectional survey screened a large national sample of adolescents and young adults for PIU, IGD, and SMA to determine prevalence and overlap, demographic associations with each, and associations between these technology-related conditions and health outcomes. There was overlap across PIU, IGD, and SMA in some associated demographic variables and health outcomes. However, the patterns in the associated variables demonstrated unique qualities of each of these conditions.

Project description:BackgroundInternet gaming disorder (IGD) was included in the DSM-5 in 2013 as a condition requiring further research, and gaming disorder (GD) was included in the ICD-11 in 2018. Given the importance of including these conditions in diagnostic guidelines, a review was conducted to describe their prevalence.MethodsUsing guidance from the Joanna Briggs Institute and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), we conducted a rapid scoping review. MEDLINE, Embase, PsycINFO, and the Cochrane library were searched for literature published from inception to July 2018. All review stages were pilot-tested to calibrate reviewers. The titles/abstracts and full-text articles were screened by one reviewer to include quantitative primary studies that reported GD or IGD prevalence. Excluded citations were screened by a second reviewer to confirm exclusion. Charting was conducted by one reviewer and verified by another, to capture relevant data. Results were summarized descriptively in tables or text.ResultsWe assessed 5550 potentially relevant citations. No studies on GD were identified. We found 160 studies of various designs that used 35 different methods to diagnose IGD. The prevalence of IGD ranged from 0.21-57.50% in general populations, 3.20-91.00% in clinical populations, and 50.42-79.25% in populations undergoing intervention (severe cases). Most studies were conducted in the Republic of Korea (n = 45), China (n = 29), and the USA (n = 20). Results are also presented for severe IGD and by geographic region, gender/sex, and age groups (child, adolescent, adult). The five most frequently reported health-related variables were depression (67 times), Internet addiction (54 times), anxiety (48 times), impulsiveness (37 times), and attention-deficit hyperactivity disorder (24 times).ConclusionsDue to the variability in diagnostic approaches, knowledge users should interpret the wide IGD prevalence ranges with caution. In addition to further research on GD, consensus on the definition of IGD and how it is measured is needed, to better understand the prevalence of these conditions.

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Dataset Information

Brain Activity toward Gaming-Related Cues in Internet Gaming Disorder during an Addiction Stroop Task.

Background and aims

Methods

Results

Discussion and conclusions

Publications

Brain Activity toward Gaming-Related Cues in Internet Gaming Disorder during an Addiction Stroop Task.

Similar Datasets

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