The Korea National Health and Nutrition Examination Survey (KNHANES) is a national health survey mandated by Article 16 of the National Health Promotion Act to assess the health and nutritional status of the Korean population. Over the past 2 decades, the survey has continuously introduced timely survey components while strengthening its survey methodology and operational systems to enhance both the policy relevance and scientific utility of its findings. The 10th KNHANES (2025–2027) preserves the statistical representativeness of its sampling design while expanding the use of web-based self-administered questionnaires to improve participant convenience. In response to Korea’s transition into a super-aged society, the 10th KNHANES incorporates enhanced older-adult health surveys, including osteoporosis assessment and older adults’ life functioning, and newly introduces items addressing social support as part of the social determinants of health. In addition, a longitudinal follow-up survey system has been established to monitor long-term changes in population health. Collectively, these changes are expected to improve understanding of aging-related health issues, support evidence-based national health policy development, and expand the applicability of KNHANES data for academic research.

Objective: Recently, various mental health promotion programs using virtual reality (VR) technology have been tried in the psychiatric field. Considering the importance of mental health in pregnant women, VR-assisted mental health promotion programs for pregnant women are needed to be developed. This study aimed to prospectively investigate the efficacy of a VR-assisted mental health promotion program for pregnant women. Methods: Fifty-five pregnant women were participated in this study. Mental health states such as depression, anxiety, perceived stress, and quality of life were assessed by Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale, and World Health Organization Quality of Life Scale Abbreviated Version (WHOQOL-BREF) before and after using VR program treatment. Based on the mental health types of pregnant women classified by cluster analysis, the effects of the VR program were analyzed by paired t test and Wilcoxon signed rank test. Results: The VR program significantly reduced PHQ-9 (p = 0.033) and GAD-7 (p = 0.046) scores, and significantly increased WHOQOL-BREF (p = 0.026) score of the participants. Additionally, when classifying the mental health types of pregnant women, the VR program was significantly effective to improve depression, anxiety, and quality of life in the most severe group based on the cluster analysis. Conclusion The results of this study suggest that VR program may be effective to improve the mental health of pregnant women. Particularly, VR programs for the high-risk group of depression might be the most effective. Therefore, VR-assisted mental health promotion programs could be useful for managing the mental health in pregnant women.

Objective: Recently, various mental health promotion programs using virtual reality (VR) technology have been tried in the psychiatric field. Considering the importance of mental health in pregnant women, VR-assisted mental health promotion programs for pregnant women are needed to be developed. This study aimed to prospectively investigate the efficacy of a VR-assisted mental health promotion program for pregnant women. Methods: Fifty-five pregnant women were participated in this study. Mental health states such as depression, anxiety, perceived stress, and quality of life were assessed by Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale, and World Health Organization Quality of Life Scale Abbreviated Version (WHOQOL-BREF) before and after using VR program treatment. Based on the mental health types of pregnant women classified by cluster analysis, the effects of the VR program were analyzed by paired t test and Wilcoxon signed rank test. Results: The VR program significantly reduced PHQ-9 (p = 0.033) and GAD-7 (p = 0.046) scores, and significantly increased WHOQOL-BREF (p = 0.026) score of the participants. Additionally, when classifying the mental health types of pregnant women, the VR program was significantly effective to improve depression, anxiety, and quality of life in the most severe group based on the cluster analysis. Conclusion The results of this study suggest that VR program may be effective to improve the mental health of pregnant women. Particularly, VR programs for the high-risk group of depression might be the most effective. Therefore, VR-assisted mental health promotion programs could be useful for managing the mental health in pregnant women.

Objective: Recently, various mental health promotion programs using virtual reality (VR) technology have been tried in the psychiatric field. Considering the importance of mental health in pregnant women, VR-assisted mental health promotion programs for pregnant women are needed to be developed. This study aimed to prospectively investigate the efficacy of a VR-assisted mental health promotion program for pregnant women. Methods: Fifty-five pregnant women were participated in this study. Mental health states such as depression, anxiety, perceived stress, and quality of life were assessed by Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale, and World Health Organization Quality of Life Scale Abbreviated Version (WHOQOL-BREF) before and after using VR program treatment. Based on the mental health types of pregnant women classified by cluster analysis, the effects of the VR program were analyzed by paired t test and Wilcoxon signed rank test. Results: The VR program significantly reduced PHQ-9 (p = 0.033) and GAD-7 (p = 0.046) scores, and significantly increased WHOQOL-BREF (p = 0.026) score of the participants. Additionally, when classifying the mental health types of pregnant women, the VR program was significantly effective to improve depression, anxiety, and quality of life in the most severe group based on the cluster analysis. Conclusion The results of this study suggest that VR program may be effective to improve the mental health of pregnant women. Particularly, VR programs for the high-risk group of depression might be the most effective. Therefore, VR-assisted mental health promotion programs could be useful for managing the mental health in pregnant women.

Objective: Recently, various mental health promotion programs using virtual reality (VR) technology have been tried in the psychiatric field. Considering the importance of mental health in pregnant women, VR-assisted mental health promotion programs for pregnant women are needed to be developed. This study aimed to prospectively investigate the efficacy of a VR-assisted mental health promotion program for pregnant women. Methods: Fifty-five pregnant women were participated in this study. Mental health states such as depression, anxiety, perceived stress, and quality of life were assessed by Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale, and World Health Organization Quality of Life Scale Abbreviated Version (WHOQOL-BREF) before and after using VR program treatment. Based on the mental health types of pregnant women classified by cluster analysis, the effects of the VR program were analyzed by paired t test and Wilcoxon signed rank test. Results: The VR program significantly reduced PHQ-9 (p = 0.033) and GAD-7 (p = 0.046) scores, and significantly increased WHOQOL-BREF (p = 0.026) score of the participants. Additionally, when classifying the mental health types of pregnant women, the VR program was significantly effective to improve depression, anxiety, and quality of life in the most severe group based on the cluster analysis. Conclusion The results of this study suggest that VR program may be effective to improve the mental health of pregnant women. Particularly, VR programs for the high-risk group of depression might be the most effective. Therefore, VR-assisted mental health promotion programs could be useful for managing the mental health in pregnant women.

Objective: Suicide is a significant public health issue, particularly among older adults, where the risk is heightened.Early identification of individuals at risk for suicidal ideation is essential for timely interventions, greatly improving prevention efforts. This study aimed to develop a predictive model for suicidal ideation in community-dwelling older adults using psychiatric self-report scales and machine learning classifiers. Methods: A total of 238 older adults were assessed using the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7, Perceived Stress Scale-10, and World Health Organization Quality of Life Scale – Abbreviated Version.A nested 5-fold cross-validation procedure repeated 100 times was used for feature selection and model evaluation.Various classifiers—including support vector machines, random forest, logistic regression, linear discriminant analysis, and gradient boosting—were employed. Results: As the number of PHQ-9 items increased from two to six, the area under the curve (AUC) rose from 0.835 to 0.892. When a set of nine features—selected based on feature stability across iterations—was used, the AUC further improved to 0.904. This progression indicates that inclusion of additional informative items enhances classification performance. Conclusion This study demonstrates that psychiatric self-report scales can effectively predict suicidal ideation risk in community-dwelling older adults. By utilizing efficient features, the predictive accuracy of the model can be enhanced, offering valuable insights for developing early identification systems for high-risk groups. These findings suggest that a community-based suicide prevention program could be promoted by implementing a screening system to identify individuals at high risk for suicidal ideation among the elderly.

Background: Although exercising can be effective in alleviating mood symptoms, implementing an exercise regimen can be challenging. This study aims to evaluate the subjective experience and satisfaction with an exercise program that uses augmented reality (AR) developed to improve exercise adherence in patients with mood disorders. Methods: Thirty patients diagnosed with mood disorders participated in this study. Before and after the AR-based exercise program, psychological states were assessed using the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale (PSS), and World Health Organization Quality of Life Scale Abbreviated Version (WHOQOL-BREF). In addition, the satisfaction survey conducted after participating in the exercise program was analyzed. Results: The mean score of PHQ-9 decreased significantly following the exercise program. Moreover, the mean scores for GAD-7 and PSS significantly decreased when compared before and after the exercise program. Meanwhile, the mean score of WHOQOL-BREF for physical and social satisfaction increased significantly. Furthermore, this program received positive responses on the satisfaction survey. Conclusion In patients with mood disorders, the AR-based exercise program showed remarkable positive effects on psychological states and satisfaction levels. The exercise program using AR technology may effectively improve exercise program adherence in patients with mood disorders.

Background: Stress management is an important component of management of mental health. Stress processing can be understood as the appraisal of and coping with stress. This study aimed to compare positive and negative perceptions of stress according to patterns of coping strategies. Methods: The study recruited 116 college students who completed the Perceived Stress Scale and Coping Inventory for Stressful Situations using a tablet PC or smartphone application. K-means cluster analysis was used to categorize the subjects into three clusters. Results: Positive perceptions were highest in Cluster 2 (i.e., task and avoidance dominant) at 3.18±0.66 followed by 2.30±0.65 in Cluster 1 (passive coping) and lowest in Cluster 3 (emotion dominant) at 1.75±0.63. Negative perceptions were highest in Cluster 3 at 2.45±0.66 followed by Cluster 1 at 1.66±0.65 and Cluster 2 at 1.07±0.68. Conclusion Differences in coping patterns can reduce the negative effects of stress. Thus, assessing the perception and management of stress can be useful and help people change their coping styles to more adaptive ones. Toward this end, a mobile application can be an appropriate tool.

Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier.The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively.The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.

Objective: Although maintenance treatment for mood disorders is important, the treatment discontinuation rate is reported to be high. This study aimed to investigate the dropout rates and associated factors in mood disorders. Methods: The patients in a mood disorder clinic (n = 535) were examined. Demographic and clinical factors, scores of psychometric scales, time to dropout from initial treatment in patients with bipolar disorder (BP) (n = 288) and depressive disorder (DD) (n = 143) were evaluated based on database of the mood disorder clinic. Results: Among the studied patients with BP and DD, 50% showed dropout in 4.05 and 2.17 years, respectively. The mean survival times were 8.90 years in bipolar disorder I (BP-I), 5.19 years in bipolar II disorder, 3.22 years in bipolar disorder not otherwise specified, 4.24 years in major depressive disorder, and 4.03 years in other depressive disorders.In the multivariate Cox proportional hazards regression model in the BP group, diagnosis BP-I was found to be significantly related to the decrease in dropout rate (hazard ratio [HR] = 0.22, p = 0.001); however, increased past suicide attempt number was significantly related to the increase in dropout rate (HR = 1.13, p = 0.017). In the DD group, none of anxiety disorders as comorbidity, increased scores of openness, and extraversion personality were related to the increase in dropout rate. Conclusion Patients with BP, especially BP-I, showed a lower dropout rate as compared to patients with other mood disorders.