Objective Problematic internet gaming is an increasing mental health concern in adolescence; however, not all individuals exposed to known risk factors develop maladaptive gaming behaviors. Although aggression and self-control have each been linked to gaming-related problems, their interactive effects remain insufficiently understood. Methods This study examined whether self-control moderates the association between aggression and problematic internet gaming among adolescents. Baseline data from 1,914 first-grade middle school students participating in the internet gaming disorder cohort study (Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence) were analyzed. Gaming-related problems were assessed using the Internet Gaming Use–Elicited Symptom Screen. Aggression and self-control were measured with validated self-report scales. Depressive symptoms, trait anxiety, and attention-deficit/hyperactivity disorder symptoms were included as covariates. Group differences were examined using t-tests and chi-square tests. Results Moderation analyses tested whether self-control altered the association between aggression and gaming-related problems. Self-control significantly moderated the association between aggression and gaming-related problems, such that higher self-control attenuated the positive relationship between aggression and problematic gaming. Gaming-related problems were more prevalent among males, adolescents whose parents had a high school education or lower, and those not residing in two-parent households. Conclusion Self-control influences the strength of the association between aggression and maladaptive gaming behaviors in adolescence, underscoring its role as a protective factor in problematic internet gaming.
Objective This study was conducted to examine the effect of social media addiction and emotional intelligence on ghosting among adolescents. Methods This descriptive cross-sectional study was conducted between June and September 2025 using an online survey method. The study was conducted with 425 adolescents aged 10–19 years living in Batman, Türkiye. R programming language 4.1.3, G*Power 3.1, and SPSS-22 programmes were used in the analysis of the study. Results It has been determined that the model is meaningful and usable in hierarchical regression analysis (F(2,422)=30.392, p=0.001). The level of social media addiction and emotional intelligence levels together explain 12.2% of the total variance in ghosting levels (R2=0.122). It was determined that an increase in participants’ social media addiction levels (t=4.895, p<0.001) and a decrease in their emotional intelligence levels (t=-4.021, p<0.001) were significantly associated with higher levels of ghosting. SHAP values show the extent to which each variable influences the model’s predictions and the magnitude of this influence. According SHAP values, social media addiction is identified as the most important variable in predicting the ghosting variable. Conclusion Participants’ increased social media addiction and decreased emotional intelligence were found to be associated with higher levels of ghosting. Longitudinal studies on ghosting among adolescents are recommended. Furthermore, the implementation of educational and intervention programs aimed at regulating social media use and enhancing emotional intelligence skills in adolescents is suggested.
Objective Narcolepsy often begins in adolescence, yet diagnosis is frequently delayed. This study aimed to identify factors associated with delayed diagnosis of narcolepsy, defined as more than 1 year between symptom onset and diagnosis, focusing on age at onset, clinical symptoms, and family history.Methods We recruited 235 patients with clinically diagnosed narcolepsy from St.Vincent’s Hospital in South Korea. Participants completed an online survey assessing age at symptom onset, diagnostic timing, clinical features, and family history of sleep disorders. Patients were classified into a non-delay group (diagnosis within 1 year, n=53) and a delay group (diagnosis after 1 year, n=182). Group differences were analyzed using independent t-tests and chi-square tests.Results The mean diagnostic delay was 0.49±0.50 years in the non-delay group and 10.96±8.37 years in the delay group. The delay group had a significantly younger age at symptom onset than the non-delay group (15.13±4.88 vs. 17.89±7.91 years, p=0.002). Diagnosis in the delay group typically occurred only after marked functional impairment due to excessive daytime sleepiness. Cataplexy showed a nonsignificant trend toward earlier diagnosis, while other narcolepsy symptoms and family history were not associated with diagnostic timing.Conclusion Earlier age at onset is the strongest risk factor for delayed diagnosis of narcolepsy. Neither symptom profile nor family history reliably facilitates early recognition. Proactive screening and improved awareness among clinicians and families are needed to reduce diagnostic delay and its psychosocial burden.
Objective Diabetes mellitus (DM) and cognitive impairment (CoI) are correlated, but the combined impact of depressive symptoms on CoI risk in older adults remains unclear. Methods This study included 1,200 U.S. adults aged ≥60 years (National Health and Nutrition Examination Survey, NHANES) and 1,500 Chinese adults (China Health and Retirement Longitudinal Study, CHARLS). DM was defined by laboratory measures or self-report; depressive symptoms were assessed via the Patient Health Questionnaire-9 (PHQ-9) (NHANES) and CES-D-10 (CHARLS); CoI was measured using the CERAD battery (NHANES) and situational memory/mental integrity scores (CHARLS). Multivariate logistic regression estimated associations of DM and depressive symptoms with CoI. Additive interaction was evaluated by the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S). Restricted cubic spline (RCS) analyses examined nonlinear DM–depressive symptoms interactions. Results Both DM and depressive symptoms were independently and significantly associated with CoI. After multivariate adjustment, diabetic patients exhibited significantly increased CoI risk; depressive symptoms similarly elevated CoI risk. Interaction analysis revealed an additive effect when DM co-occurred with depressive symptoms in CHARLS but not in NHANES. Mediation analyses further suggest bidirectional associations: in CHARLS, diabetes and depressive symptoms appear to mediate each other’s effects on CoI. Additionally, RCS analysis in NHANES indicated a nonlinear interaction between depressive symptom severity (PHQ-9) and DM on CoI (p<0.05). Conclusion The observed associations varied across cohorts, with a significant additive interaction between diabetes and depressive symptoms found only in the CHARLS cohort. Integrating metabolic and mental health screening and interventions may optimize cognitive outcomes in high-risk older adult populations.
Objective Individuals under community correction are a high-risk population for anxiety and depression, which frequently co-occur. However, the symptom network structures of anxiety, depression, and their comorbidity in this population remained unexplored. This study addresses this gap using a network analysis approach. Methods A total of 818 Chinese individuals under community correction (134 females, 684 males; over 95% aged 18–60 years) completed the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS). Separate symptom networks for anxiety and depression were estimated to identify central symptoms using expected influence (EI), while a comorbidity network was estimated to identify bridge symptoms using bridge EI and bridge betweenness. Directed acyclic graph (DAG) analyses were then conducted to identify potential trigger symptoms and further clarify the roles of central and bridge symptoms. Results A total of 36.7% and 13.1% of participants reported depression and anxiety symptoms, respectively, with 11.0% exhibiting comorbidity. In the anxiety network, “panic” (SAS-03) was the central symptom (highest EI) and the key trigger symptom in the DAG. In the depression network, “emptiness” (SDS-18) was the central symptom, while “fatigue” (SDS-10) was the key trigger. In the comorbidity network, “insomnia” (SAS-19), “sleep disturbance” (SDS-04), and “fatigue” were identified as bridge symptoms. In the comorbidity DAG, “fatigue” acted as a key trigger, facilitating the transmission from depression to anxiety via “sleep disturbance” and “insomnia.” Conclusion The study preliminarily examined the phenomenon and mechanisms of anxiety, depression, and their comorbidity among individuals under community correction, providing new insights for intervention planning.
