With widespread use of the smartphone, clinical evidence for smartphone addiction remains unclear. Against this background, we analyzed the effect of smartphone use patterns on smartphone addiction in Korean adolescents. A total of 370 middle school students participated. The severity of smartphone addiction was measured through clinical interviews and the Korean Smartphone Addiction Proneness Scale. As a result, 50 (13.5%) were in the smartphone addiction group and 320 (86.5%) were in the healthy group. To investigate the effect of smartphone use patterns on smartphone addiction, we performed self-report questionnaires that assessed the following items: smartphone functions mostly used, purpose of use, problematic use, and parental attitude regarding smartphone use. For smartphone functions mostly used, the addiction group showed significantly higher scores in “Online chat.” For the purpose of use, the addiction group showed significantly higher “habitual use,” “pleasure,” “communication,” “games,” “stress relief,” “ubiquitous trait,” and “not to be left out.” For problematic use, the addiction group showed significantly higher scores on “preoccupation,” “tolerance,” “lack of control,” “withdrawal,” “mood modification,” “conflict,” “lies,” “excessive use,” and “loss of interest.” For parental attitude regarding children's smartphone use, the addiction group showed significantly higher scores in “parental punishment.” Binary logistic regression analysis indicated that “female,” “use for learning,” “use for ubiquitous trait,” “preoccupation,” and “conflict” were significantly correlated with smartphone addiction. This study demonstrated that the risk factors for smartphone addiction were being female, preoccupation, conflict, and use for ubiquitous trait; the protective factor was use for learning. Future studies will be required to reveal the additional clinical evidence of the disease entity for smartphone addiction.

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This study revealed that factors known to influence suicide ideation in the elderly from previous studies, such as residence area, separation from a spouse, education level, religion, and drinking, show changed influence as the elderly reach their 70s and 80s. However, a negative perception of one's own health status was a risk factor that encompassed most ages and sexes.

This study investigated quantitative electroencephalography (QEEG) subtypes as auxiliary tools to assess Attention Deficit Hyperactivity Disorder (ADHD). A total of 74 subjects (58 male and 16 female) were assessed using the Korean version of the Diagnostic Interview Schedule for Children Version IV and were assigned to one of three groups: ADHD, ADHD-Not Otherwise specified (NOS), and Neurotypical (NT). We measured absolute and relative EEG power in 19 channels and conducted an auditory continuous performance test. We analyzed QEEG according to the frequency range: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), and beta (13.5–30 Hz). The subjects were then grouped by Ward’s method of cluster analysis using the squared Euclidian distance to measure dissimilarities. We discovered four QEEG clusters, which were characterized by: (a) elevated delta power with less theta activity, (b) elevated slow alpha relative power, (c) elevated theta with deficiencies of alpha and beta relative power, and (d) elevated fast alpha and beta absolute power. The largest proportion of participants in clusters (a) and (c) were from the ADHD group (48% and 47%, respectively). Conversely, group (b) mostly consisted of the participants from the NOS group (59%), while group (d) had the largest proportion of participants from the NT group (62%). These results indicate that children with ADHD does not neurophysiologically constitute a homogenous group. We also identified a new subtype with increased alpha power in addition to those commonly reported in ADHD. Given the QEEG characteristics with increased alpha power, we should consider the possibility that this subtype may be caused by childhood depression. In conclusion, we believe that these QEEG subtypes of ADHD are expected to provide valuable information for accurately diagnosing ADHD.

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Attention-deficit/hyperactivity disorder (ADHD) leads to functional decline in academic performance, interpersonal relationships, and development in school-aged children. Early diagnosis and appropriate intervention can significantly reduce the functional decline caused by ADHD. Currently, there is no established biological marker for ADHD. Some studies have suggested that various indicators from the quantitative electroencephalogram (QEEG) may be useful biological markers for the diagnosis of ADHD. Until the 2010s, theta/beta ratio (TBR) was a biomarker candidate for ADHD that consistently showed high diagnostic value. However, limitations of TBR have recently been reported. Studies have demonstrated that phase-amplitude coupling, especially theta phase-gamma amplitude coupling, are related to cognitive dysfunction and may assist in the diagnosis of ADHD. As yet, the underlying mechanism is not clearly established, and the clinical efficacy of these biomarkers needs to be proven through well-controlled studies. Based on the heterogeneous characteristics of ADHD, subgrouping through QEEG plays a key role in diagnosis and treatment planning. Sophisticated, welldesigned studies and meta-analyses are necessary to confirm these findings.

Purpose: We aimed to compare the Korean version of the ADHD Rating Scale (K-ARS) and Integrated Visual and Auditory Plus (IVA+Plus), a continuous performance test, by analyzing their abilities to distinguish different groups (attention-deficit/hyperactivity disorder [ADHD], ADHD-not otherwise specified [NOS], and normal control [NC]). Patients and Methods: Individuals of 7-12 years of age who visited our child and adolescent psychiatric clinic were recruited. Seventy-four participants (58 males, 16 females) were classified into three groups according to results from the Korean Version of Diagnostic Interview Schedule for Children Version IV. The K-ARS and IVA+Plus were administered. An analysis of covariance (ANCOVA) was conducted. The tools' accuracy in discriminating patients with ADHD or NOS from NCs was evaluated using a receiver operating characteristic (ROC) curve analysis. Results: ANCOVA revealed significant differences in the K-ARS results of the three groups (ADHD [n=29], NOS [n=33], NC [n=12]), whereas a difference in IVA+Plus results was observed only between the ADHD and NC groups. In the ROC curve analysis of the K-ARS, the areas under the curve (AUCs) for each group were 0.960 (ADHD vs NC), 0.885 (NOS vs NC), 0.920 (ADHD+NOS vs NC), and 0.779 (ADHD vs NOS+NC). In the ROC curve analysis for the IVA+Plus hyperactivity-impulsiveness scale, the AUCs for each group were 0.740 (ADHD vs NC), 0.643 (NOS vs NC), 0.688 (ADHD+NOS vs NC), and 0.626 (ADHD vs NOS+NC); those for the inattention scale were 0.731 (ADHD vs NC), 0.658 (NOS vs NC), 0.692 (ADHD+NOS vs NC), and 0.625 (ADHD+NOS vs NC). Conclusion: The K-ARS was useful to distinguish the ADHD and NOS groups from the NC group, while the IVA+Plus was useful to distinguish the ADHD group from the NC group. Clinicians should ensure they understand the properties of each tool and apply them appropriately in the diagnosis of ADHD.

Objective Diagnosis of anxiety has relied primarily on self-report. This study aimed to investigate the neural correlates of anxiety with quantitative electroencephalography (qEEG) focusing on the state and trait anxiety defined according to the Research Domain Criteria framework existing across the differential diagnosis, rather than focusing on the diagnosis.Methods A total of 41 participants who visited a psychiatric clinic underwent resting state EEG and completed the State-Trait Anxiety Inventory. The absolute power of six frequency bands were analyzed: delta (1–4 Hz), theta (4–8 Hz), alpha (8–10 Hz), fast alpha (10–13.5 Hz), beta (13.5–30 Hz), and gamma (30–80 Hz).Results State anxiety scores were significantly negatively correlated with absolute gamma power in frontal (Fz, r=-0.484) and central (Cz, r=-0.523) regions, while trait anxiety scores were significantly negatively correlated with absolute gamma power in frontal (Fz, r= -0.523), central (Cz, r=-0.568), parietal (P7, r=-0.500; P8, r=-0.541), and occipital (O1, r=-0.510; O2, r=-0.480) regions.Conclusion The present study identified the significantly negative correlations between the anxiety level and gamma band power in fronto-central and posterior regions assessed at resting status. Further studies to confirm our findings and identify the neural correlates of anxiety are needed.