The aim of this study was to develop a self-diagnostic scale that could distinguish smartphone addicts based on the Korean self-diagnostic program for Internet addiction (K-scale) and the smartphone's own features. In addition, the reliability and validity of the smartphone addiction scale (SAS) was demonstrated. A total of 197 participants were selected from Nov. 2011 to Jan. 2012 to accomplish a set of questionnaires, including SAS, K-scale, modified Kimberly Young Internet addiction test (Y-scale), visual analogue scale (VAS), and substance dependence and abuse diagnosis of DSM-IV. There were 64 males and 133 females, with ages ranging from 18 to 53 years (M = 26.06; SD = 5.96). Factor analysis, internal-consistency test, t-test, ANOVA, and correlation analysis were conducted to verify the reliability and validity of SAS. Based on the factor analysis results, the subscale “disturbance of reality testing” was removed, and six factors were left. The internal consistency and concurrent validity of SAS were verified (Cronbach's alpha = 0.967). SAS and its subscales were significantly correlated with K-scale and Y-scale. The VAS of each factor also showed a significant correlation with each subscale. In addition, differences were found in the job (p<0.05), education (p<0.05), and self-reported smartphone addiction scores (p<0.001) in SAS. This study developed the first scale of the smartphone addiction aspect of the diagnostic manual. This scale was proven to be relatively reliable and valid.

The purpose of this study was to identify personality factor-associated predictors of smartphone addiction predisposition (SAP). Participants were 2,573 men and 2,281 women (n = 4,854) aged 20–49 years (Mean ± SD: 33.47 ± 7.52); participants completed the following questionnaires: the Korean Smartphone Addiction Proneness Scale (K-SAPS) for adults, the Behavioral Inhibition System/Behavioral Activation System questionnaire (BIS/BAS), the Dickman Dysfunctional Impulsivity Instrument (DDII), and the Brief Self-Control Scale (BSCS). In addition, participants reported their demographic information and smartphone usage pattern (weekday or weekend average usage hours and main use). We analyzed the data in three steps: (1) identifying predictors with logistic regression, (2) deriving causal relationships between SAP and its predictors using a Bayesian belief network (BN), and (3) computing optimal cut-off points for the identified predictors using the Youden index. Identified predictors of SAP were as follows: gender (female), weekend average usage hours, and scores on BAS-Drive, BAS-Reward Responsiveness, DDII, and BSCS. Female gender and scores on BAS-Drive and BSCS directly increased SAP. BAS-Reward Responsiveness and DDII indirectly increased SAP. We found that SAP was defined with maximal sensitivity as follows: weekend average usage hours > 4.45, BAS-Drive > 10.0, BAS-Reward Responsiveness > 13.8, DDII > 4.5, and BSCS > 37.4. This study raises the possibility that personality factors contribute to SAP. And, we calculated cut-off points for key predictors. These findings may assist clinicians screening for SAP using cut-off points, and further the understanding of SA risk factors.

Background: Understanding the risk factors associated with Internet gaming disorder (IGD) is important to predict and diagnose the condition. The purpose of this study is to identify risk factors that predict IGD based on psychological factors and Internet gaming characteristics; Methods: Online surveys were conducted between 26 November and 26 December 2014. There were 3568 Korean Internet game users among a total of 5003 respondents. We identified 481 IGD gamers and 3087 normal Internet gamers, based on Diagnostic and Statistical Manual for Mental Disorders (DSM-5) criteria. Logistic regression analysis was applied to identify significant risk factors for IGD; Results: The following eight risk factors were found to be significantly associated with IGD: functional and dysfunctional impulsivity (odds ratio: 1.138), belief self-control (1.034), anxiety (1.086), pursuit of desired appetitive goals (1.105), money spent on gaming (1.005), weekday game time (1.081), offline community meeting attendance (2.060), and game community membership (1.393; p < 0.05 for all eight risk factors); Conclusions: These risk factors allow for the prediction and diagnosis of IGD. In the future, these risk factors could also be used to inform clinical services for IGD diagnosis and treatment.

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.

Aims: Ghrelin is a peptide hormone that antagonizes the action of leptin and is thereby thought to regulate feeding behaviour. The actions of ghrelin and leptin appear to be mediated by the neuropeptide Y (NPY) and Agouti-related protein (AGRP) system. Recent studies have suggested that leptin and NPY play significant roles in the pathophysiology of alcoholism. The aim of this study was to determine whether ghrelin is associated with the state and duration of abstinence in individuals with alcohol dependence. Methods: Fasting plasma ghrelin levels were compared between 47 individuals with chronic alcoholism during a period of abstinence and 50 control subjects. Results: Fasting plasma ghrelin levels were higher in alcohol abstainers than those in controls. Furthermore, a positive correlation was observed between ghrelin levels and the duration of abstinence. In addition, daily alcohol intake prior to abstinence was inversely related to ghrelin levels. Conclusions: These findings suggest that ghrelin plays a role in the pathogenesis of alcohol dependence, particularly during the abstinence period, in individuals with chronic alcoholism.