Background: Community acquired lower respiratory tract infection (LRTI) is a leading cause for hospitalization in children and important cause for antibiotic prescription. We aimed to describe the aetiology of LRTI in children and analyse factors associated with bacterial or viral infection. Methods: Patients aged < 19 years with a diagnosis of LRTI were identified from the Observational Medical Outcomes Partnership Common Data Model Database of Seoul National University Bundang Hospital from January 2005–July 2019, and their clinical characteristics were obtained from the electronic medical records and retrospectively reviewed. Results: Among 5,924 cases of LRTI, 74.2% were pneumonia and 25.8% were bronchiolitis/ bronchitis. Patients’ median age was 1.8 (interquartile range, 3.1) years and 79.9% were < 5 years old. Pathogens were identified in 37.8%; 69.1% were viral and 30.9% were bacterial/ Mycoplasma pneumoniae. Respiratory syncytial virus was most common (70.9%) among viruses and M. pneumoniae (94.6%) was most common among bacteria. Viral LRTI was associated with winter, age < 2 years, rhinorrhoea, dyspnoea, lymphocytosis, thrombocytosis, wheezing, stridor, chest retraction, and infiltration on imaging. Bacteria/ M. pneumoniae LRTI was associated with summer, age ≥ 2 years, fever, decreased breathing sounds, leucocytosis, neutrophilia, C-reactive protein elevation, and positive imaging findings (consolidation, opacity, haziness, or pleural effusion). Conclusion In children with LRTI, various factors associated with viral or bacterial/ M. pneumoniae infections were identified, which may serve as guidance for antibiotic prescription.

Background: Community acquired lower respiratory tract infection (LRTI) is a leading cause for hospitalization in children and important cause for antibiotic prescription. We aimed to describe the aetiology of LRTI in children and analyse factors associated with bacterial or viral infection. Methods: Patients aged < 19 years with a diagnosis of LRTI were identified from the Observational Medical Outcomes Partnership Common Data Model Database of Seoul National University Bundang Hospital from January 2005–July 2019, and their clinical characteristics were obtained from the electronic medical records and retrospectively reviewed. Results: Among 5,924 cases of LRTI, 74.2% were pneumonia and 25.8% were bronchiolitis/ bronchitis. Patients’ median age was 1.8 (interquartile range, 3.1) years and 79.9% were < 5 years old. Pathogens were identified in 37.8%; 69.1% were viral and 30.9% were bacterial/ Mycoplasma pneumoniae. Respiratory syncytial virus was most common (70.9%) among viruses and M. pneumoniae (94.6%) was most common among bacteria. Viral LRTI was associated with winter, age < 2 years, rhinorrhoea, dyspnoea, lymphocytosis, thrombocytosis, wheezing, stridor, chest retraction, and infiltration on imaging. Bacteria/ M. pneumoniae LRTI was associated with summer, age ≥ 2 years, fever, decreased breathing sounds, leucocytosis, neutrophilia, C-reactive protein elevation, and positive imaging findings (consolidation, opacity, haziness, or pleural effusion). Conclusion In children with LRTI, various factors associated with viral or bacterial/ M. pneumoniae infections were identified, which may serve as guidance for antibiotic prescription.

Background: Community acquired lower respiratory tract infection (LRTI) is a leading cause for hospitalization in children and important cause for antibiotic prescription. We aimed to describe the aetiology of LRTI in children and analyse factors associated with bacterial or viral infection. Methods: Patients aged < 19 years with a diagnosis of LRTI were identified from the Observational Medical Outcomes Partnership Common Data Model Database of Seoul National University Bundang Hospital from January 2005–July 2019, and their clinical characteristics were obtained from the electronic medical records and retrospectively reviewed. Results: Among 5,924 cases of LRTI, 74.2% were pneumonia and 25.8% were bronchiolitis/ bronchitis. Patients’ median age was 1.8 (interquartile range, 3.1) years and 79.9% were < 5 years old. Pathogens were identified in 37.8%; 69.1% were viral and 30.9% were bacterial/ Mycoplasma pneumoniae. Respiratory syncytial virus was most common (70.9%) among viruses and M. pneumoniae (94.6%) was most common among bacteria. Viral LRTI was associated with winter, age < 2 years, rhinorrhoea, dyspnoea, lymphocytosis, thrombocytosis, wheezing, stridor, chest retraction, and infiltration on imaging. Bacteria/ M. pneumoniae LRTI was associated with summer, age ≥ 2 years, fever, decreased breathing sounds, leucocytosis, neutrophilia, C-reactive protein elevation, and positive imaging findings (consolidation, opacity, haziness, or pleural effusion). Conclusion In children with LRTI, various factors associated with viral or bacterial/ M. pneumoniae infections were identified, which may serve as guidance for antibiotic prescription.

Background: Community acquired lower respiratory tract infection (LRTI) is a leading cause for hospitalization in children and important cause for antibiotic prescription. We aimed to describe the aetiology of LRTI in children and analyse factors associated with bacterial or viral infection. Methods: Patients aged < 19 years with a diagnosis of LRTI were identified from the Observational Medical Outcomes Partnership Common Data Model Database of Seoul National University Bundang Hospital from January 2005–July 2019, and their clinical characteristics were obtained from the electronic medical records and retrospectively reviewed. Results: Among 5,924 cases of LRTI, 74.2% were pneumonia and 25.8% were bronchiolitis/ bronchitis. Patients’ median age was 1.8 (interquartile range, 3.1) years and 79.9% were < 5 years old. Pathogens were identified in 37.8%; 69.1% were viral and 30.9% were bacterial/ Mycoplasma pneumoniae. Respiratory syncytial virus was most common (70.9%) among viruses and M. pneumoniae (94.6%) was most common among bacteria. Viral LRTI was associated with winter, age < 2 years, rhinorrhoea, dyspnoea, lymphocytosis, thrombocytosis, wheezing, stridor, chest retraction, and infiltration on imaging. Bacteria/ M. pneumoniae LRTI was associated with summer, age ≥ 2 years, fever, decreased breathing sounds, leucocytosis, neutrophilia, C-reactive protein elevation, and positive imaging findings (consolidation, opacity, haziness, or pleural effusion). Conclusion In children with LRTI, various factors associated with viral or bacterial/ M. pneumoniae infections were identified, which may serve as guidance for antibiotic prescription.

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.

Objective: This study aimed to develop a machine learning-based classification model to differentiate bipolar disorder from major depressive disorder using self-report scales, including the Mood Disorder Questionnaire (MDQ), Bipolar Spectrum Diagnostic Scale (BSDS), and Anger Coping Scale (ACS). Methods: A total of 122 bipolar and 67 depressive patients participated. Recursive feature elimination with 1,000 iterations was used to identify the most informative features. Machine learning classifiers assessed combinations of MDQ, BSDS, and ACS items for classification performance. Results: The AUC values for MDQ and BSDS were 0.8212 and 0.7934, respectively. Combining MDQ and BSDS increased the AUC to 0.8477, which improved further to 0.8548 when ACS was included. For MDQ, the best performance was achieved when all 13 items were included. In contrast, the combined model of MDQ, BSDS, and ACS showed optimal performance when BSDS items 18 (conflicts with colleagues or police), 19 (alcohol or substance use), and ACS item 15 (beating others) were excluded. Conclusion Integrating anger coping styles with mood symptoms enhanced diagnostic accuracy, particularly when items related to undesirable behaviors were excluded. This machine learning approach shows potential for effectively evaluating bipolarity and underscores the importance of refining self-report scales to optimize diagnostic tools. Future research should incorporate clinical and objective data to enhance classification models.

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: 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: 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.

Objective: Due to the high frequency of depressive symptoms associated with breast cancer, it is crucial to screen for depression in breast cancer patients. While numerous screening tools are available for depression in this population, there is a need for a brief and convenient tool to enhance clinical use. This study aims to investigate the psychometric properties of the Patient Health Questionnaire-9 (PHQ-9) in patients with breast cancer. Methods: Patients with breast cancer (n=327) who visited the Breast Cancer Clinic were included in this study. The reliability of the PHQ-9 was analyzed by Cronbach’s α, and the construct validity of the PHQ-9 was explored by factor analysis. The concurrent validity of the PHQ-9 was evaluated by Pearson correlation analysis with the Hospital Anxiety and Depression Scale (HADS) and Perceived Stress Scale (PSS). Results: The values of Cronbach’s α ranged from 0.800 to 0.879 was acceptable. The exploratory factor analysis revealed that the one-factor model and two-factor model of the PHQ-9 explained 46% and 57% of the variance, respectively. The PHQ-9 were significantly correlated with those of HADS (r=0.702, p<0.001) and PSS (r=0.466, p<0.001). Consequently, the PHQ-9 demonstrated acceptable reliability and validity in breast cancer patients. Conclusion The findings of this study indicate that the PHQ-9 exhibits acceptable reliability and validity in patients with breast cancer. The convenience of this brief self-report questionnaire suggests its potential as a reliable and valid tool for assessing depression in breast cancer clinics.