Rejective parenting is a major antecedent of emotional instability and aggressive behavioral problems. Previous studies have reported that emotional problems, such as depression, anxiety, aggression, and conduct problems in children and adolescents, improve through interventions that enhance emotional recognition competency. In this study, we explored whether the emotional recognition ability levels of individual adolescents moderated the pathway of negative parenting on aggression mediated by depression The moderated mediating effect of emotional recognition competency was investigated through examining 2265 first-year high school students using the 2021 data from the Korean Children and Youth Panel Survey 2018. There was no significant moderating effect on the direct pathway from rejective parenting to aggression. The moderating effect of emotional recognition competency on the indirect pathway leading to aggression through depression in rejective parenting was significant. These results suggest that the ability to correctly understand signals related to the emotions of others can play an important role in reducing depression and aggressive behavior by reducing conflict with people around them and experiencing more support.

Our research investigated how a smartphone application utilizing behavioral activation principles affects depression levels in young adult populations. A total of 67 participants aged 20–30 years with clinically significant depressive symptoms (CESD-11 ≥ 16) were divided into treatment (n = 31) and comparison conditions (n = 36) through randomization procedures. Participants in the experimental group engaged with a BA-based mobile application (Maummove) over an eight-week period, while those in the control group completed weekly assessments without intervention. Depression, perceived stress, and life satisfaction were measured at baseline and postintervention using the CESD-11, PSS, and SWLS, respectively. The results indicated that the experimental group exhibited significant reductions in depression (Cohen’s d = 1.03) and stress (Cohen’s d = 0.99) compared to the control group, which showed minimal changes. Improvements in life satisfaction were observed in the experimental group, with a smaller effect size (Cohen’s d = 0.23). Time-series analyses demonstrated that depressive symptoms decreased progressively throughout the intervention period, falling below the clinical cutoff by the seventh week. These findings provide preliminary evidence that BA-based mobile applications may offer a promising, accessible approach to reducing depressive symptoms and perceived stress in young adults, though replication in larger samples with longer follow-up periods is needed to establish generalizability. This study highlights the potential of digitally delivered BA interventions as a viable alternative or complement traditional mental health services, particularly for populations facing barriers to face-to-face care.

This exploratory study investigated whether voice-derived acoustic features reflect depressive symptom severity and whether they carry preliminary predictive signal for distinguishing individuals with Major Depressive Disorder (MDD) from healthy controls (HC). Using the publicly available MODMA dataset (23 MDD; 29 HC), 6553 acoustic features were extracted with openSMILE. Spearman correlation and group-difference analyses identified several MFCC-derived spectral features as moderately and systematically associated with PHQ-9 scores, indicating their potential relevance as severity-linked acoustic markers. To complement these findings, a supplementary severity-based classification using a PHQ-9 ≥ 10 threshold showed that a logistic regression model trained on the top five correlated MFCC features achieved a cross-validated AUC of 0.78 (SD = 0.15), supporting their association with clinically defined symptom burden. Four machine learning pipelines were further evaluated for an exploratory MDD–HC classification task. Among them, the PCA + XGBoost model demonstrated the most stable generalization (test AUC = 0.60), although predictive performance remained limited within the constraints of the small and high-dimensional dataset. SHAP analysis highlighted MFCC-derived features as key contributors to model decisions, providing transparent interpretability. Overall, the study presents preliminary evidence linking acoustic characteristics to depressive symptoms and outlines a reproducible analytical workflow, while underscoring the need for substantially larger and more diverse datasets to establish clinically meaningful predictive validity.