The aim of the present study was to test the impulsivities and compulsivities of behavioral addictions, including Internet gaming disorder (IGD) and gambling disorder (GD), by directly comparing them with alcohol use disorder (AUD) and a healthy control (HC) group. We enrolled male patients who were diagnosed with IGD, GD or AUD, with 15 patients per group, as well as 15 HCs. Trait impulsivity was measured using the Barratt Impulsiveness Scale version 11 (BIS-11). The stop-signal test (SST) from the Cambridge Neuropsychological Test Automated Battery (CANTAB) was used to assess the patients’ abilities to inhibit prepotent responses. Compulsivity was measured using the intra–extra dimensional set shift (IED) test from the CANTAB. The Trail Making Test (TMT) was also used in this study. The IGD and AUD groups scored significantly higher on the BIS-11 as a whole than did the HC group (p = 0.001 and p = 0.001, respectively). The IGD and AUD groups also scored significantly higher on the BIS-11 as a whole than did the GD group (p = 0.006 and p = 0.001, respectively). In addition, the GDgroup made significantly more errors (p = 0.017 and p = 0.022, respectively) and more individuals failed to achieve criterion on the IED test compared with the IGD and HC groups (p = 0.018 and p = 0.017, respectively). Discussion: These findings may aid in the understanding of not only the differences in categorical aspects between individuals with IGD and GD but also in impulsivity–compulsivity dimensional domains. Additional studies are needed to elucidate the neurocognitive characteristics of behavioral addictive disorders in terms of impulsivity and compulsivity.

Background: This study aimed to explore relationships between impulsivity, interpersonal relationships, depression, and Internet Gaming Disorder (IGD) symptoms. Methods: A total of 118 young adults participated in this study: 67 IGD patients who met five or more of the DSM-5 diagnostic criteria for IGD and 56 healthy controls. We administered questionnaires to assess IGD symptoms (Young’s Internet Addiction Test; Y-IAT), impulsivity (Barratt Impulsiveness Scale; BIS-11), interpersonal relationship (Relationship Change Scale; RCS), and depression (Beck Depression Inventory; BDI). We used PROCESS macro in SPSS to perform mediation analysis. Results: IGD symptom was positively related to depression and impulsivity, and negatively related to the quality of interpersonal relationships. Mediation analysis revealed full mediation effects of interpersonal relationships and depression on the association between impulsivity and IGD symptoms in the IGD group. Specifically, even after adjusting for gender as a covariate, high impulsivity was associated with greater difficulty with interpersonal relationships; which further affected depression and increased the risk of IGD. Conclusions: These results demonstrate the importance of early intervention in IGD patients, particularly in young adults with high impulsivity. When intervening in adults’ IGD, we should consider not only individual factors (e.g., depression) but also socioenvironmental factors (e.g., interpersonal relationships).

Background and objectives: The ubiquitous Internet connections by smartphones weakened the traditional boundaries between computers and mobile phones. We sought to explore whether smartphone-related problems differ from those of computer use according to gender using latent class analysis (LCA). Methods: After informed consents, 555 Korean middle-school students completed surveys on gaming, Internet use, and smartphone usage patterns. They also completed various psychosocial instruments. LCA was performed for the whole group and by gender. In addition to ANOVA and χ² tests, post-hoc tests were conducted to examine differences among the LCA subgroups. Results: In the whole group (n = 555), four subtypes were identified: dual-problem users (49.5%), problematic Internet users (7.7%), problematic smartphone users (32.1%), and “healthy” users (10.6%). Dual-problem users scored highest for addictive behaviors and other psychopathologies. The gender-stratified LCA revealed three subtypes for each gender. With dual-problem and healthy subgroup as common, problematic Internet subgroup was classified in the males, whereas problematic smartphone subgroup was classified in the females in the gender-stratified LCA. Thus, distinct patterns were observed according to gender with higher proportion of dual-problem present in males. While gaming was associated with problematic Internet use in males, aggression and impulsivity demonstrated associations with problematic smartphone use in females. Conclusions: An increase in the number of digital media-related problems was associated with worse outcomes in various psychosocial scales. Gaming may play a crucial role in males solely displaying Internet-related problems. The heightened impulsivity and aggression seen in our female problematic smartphone users requires further research.

We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameters including power spectrum density (PSD) and functional connectivity (FC) at frequency bands was used to establish models for the binary classification between patients with each disorder and HCs. The support vector machine, random forest, and elastic net ML methods were applied, and prediction performances were compared. The elastic net model with IQ adjustment showed the highest accuracy. The best feature combinations and classification accuracies for discrimination between patients and HCs with adjusted IQ were as follows: schizophrenia = alpha PSD, 93.83%; trauma and stress-related disorders = beta FC, 91.21%; anxiety disorders = whole band PSD, 91.03%; mood disorders = theta FC, 89.26%; addictive disorders = theta PSD, 85.66%; and obsessive–compulsive disorder = gamma FC, 74.52%. Our findings suggest that ML in EEG may predict major psychiatric disorders and provide an objective index of psychiatric disorders.

Previous studies have reported associations between aggression and Internet addiction disorder (IAD), which has also been linked with anxiety, depression, and impulsiveness. However, the causal relationship between aggression and IAD has thus far not been clearly demonstrated. This study was designed to (a) examine the association between aggression and IAD and (b) investigate the mediating effects of anxiety, depression, and impulsivity in cases in which IAD predicts aggression or aggression predicts IAD. A total of 714 middle school students in Seoul, South Korea, were asked to provide demographic information and complete the Young's Internet Addiction Test (Y-IAT), the Buss–Perry Aggression Questionnaire, the Barratt Impulsiveness Scale-11, the State–Trait Anger Expression Inventory-2, the Beck Anxiety Inventory, the Beck Depression Inventory, and the Conners–Wells Adolescent Self-Report Scale. Three groups were identified based on the Y-IAT: the usual user group ( n =487, 68.2%), the high-risk group ( n =191, 26.8%), and the Internet addiction group ( n =13, 1.8%). The data revealed a linear association between aggression and IAD such that one variable could be predicted by the other. According to the path analysis, the clinical scales (BAI, BDI, and CASS) had partial or full mediating effects on the ability of aggression to predict IAD, but the clinical scales had no mediating effect on the ability of IAD to predict aggression. The current findings suggest that adolescents with IAD seem to have more aggressive dispositions than do normal adolescents. If more aggressive individuals are clinically prone to Internet addiction, early psychiatric intervention may contribute to the prevention of IAD.