BackgroundWith the rapidly aging population, mental health among older adults has received growing attention. Although the likelihood of experiencing depressive symptoms is higher in late adulthood, older adults are more reluctant to visit a clinic due to the stigma surrounding mental health issues, and many remain undiagnosed and untreated. Digital phenotyping has emerged as a promising approach to mitigate this problem. Longitudinal monitoring via wearable devices can facilitate the timely identification of depressive symptoms in older adults. However, there has not been sufficient investigation to develop a machine learning approach that accounts for between-person and within-person characteristics. ObjectiveThis study aimed to investigate the utility of active and passive digital phenotyping data collected via wearable devices for monitoring the probability and severity of depressive symptoms. Specifically, we applied multilevel hurdle modeling within a machine learning framework to enable efficient depression screening in the general population, with a focus on community-dwelling older adults. MethodsWe analyzed 1011 cases reported by 147 older Korean adults for 2 years. Participants were asked to complete the 9-item Patient Health Questionnaire (PHQ-9) items in our mobile app during the last week of each month. In addition to the annual in-person data collection, we also collected active and passive sensing data from participants via smartphones and smartwatches. For dimensionality reduction on 44 features, parallel analysis and principal component analysis were used. With the extracted 6 principal components (PCs), a Bayesian multilevel hurdle model was used. ResultsWhen constructing PCs, the weekly stress rating from active data and sleep-related features from passive data were the top 5 contributing features. Among the 6 PCs, the PC consisting of low psychological distress and high social support was significantly associated with depressive symptoms in community-dwelling older adults. This Bayesian multilevel hurdle model showed good performance in screening for depressive symptom severity (R2=0.53) and in distinguishing between those with and without symptoms (area under the receiver operating characteristic curve=0.88 and F1-score=0.75) on the test data. The between-person variance was larger than the within-person variance, especially in explaining the probability of depressive symptoms. ConclusionsIn mental health screening, active and passive digital phenotyping data can be used in conjunction with traditional clinical screening tools to monitor depressive symptoms among community-dwelling older adults. Dimensionality reduction via parallel analysis and principal component analysis can help identify latent risk profiles. Given the nested data structure and heterogeneity in depressive symptoms, a Bayesian multilevel hurdle model within a machine learning framework may be helpful for depression screening. Overall, digital phenotyping can be a useful tool for personalized, within-person health tracking, even after accounting for substantial between-person variance. We recommend future work to address data imbalance to further strengthen this approach.

Depressive symptoms are common yet often underrecognized in routine care, underscoring the need for scalable screening approaches beyond episodic self-report assessments. Wearable actigraphy can passively and continuously capture daily activity and 24-hour rest–activity rhythms associated with depressive symptom burden. However, the performance of artificial intelligence (AI) models that leverage actigraphy data for depression screening remains insufficiently established. This study aimed to develop and evaluate AI-based models for passive screening of depressive symptoms from daily wrist actigraphy data. We analyzed actigraphy recordings from 1,160 Hispanic/Latino adults in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) who completed the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), a self-reported depressive symptom screening scale. Multichannel actigraphy data, including activity counts, light exposure, and wake status, were used as inputs to five deep learning architectures to classify CESD-10–defined depressive symptom groups, comparing mild and higher symptoms with the normal group. Actigraphy-derived behavioral markers differed across depressive symptom groups, showing lower daytime activity and altered circadian rest–activity organization with increasing symptom burden. In held-out testing, the best-performing models achieved AUROCs of 0.791 for mild symptoms and 0.832 for higher depressive symptoms. Our study suggests that actigraphy-derived data can support AI-based classification of depressive symptoms. An actigraphy-based AI model may serve as a scalable, passive, and noninvasive complementary signal to aid early screening alongside traditional depression assessments before clinical diagnosis.

Background:Depressive symptoms are common yet often underrecognized in routine care, underscoring the need for scalable screening approaches beyond episodic self-report assessments. Wearable actigraphy can passively and continuously capture daily activity and 24-hour rest-activity rhythms associated with depressive symptom burden. However, the performance of artificial intelligence (AI) models that leverage actigraphy data for depressive symptom screening remains insufficiently established. Objective:This study aimed to develop and evaluate AI-based models for passive screening of depressive symptoms from daily wrist actigraphy data. Methods:We analyzed actigraphy recordings from 1160 Hispanic/Latino adults in the Hispanic Community Health Study/Study of Latinos who completed the 10-item Center for Epidemiologic Studies Depression scale (CESD-10), a self-reported depressive symptom screening scale. Multichannel actigraphy data, including activity counts, light exposure, and wake status, were used as inputs to 5 deep learning architectures to classify CESD-10-defined depressive symptom groups, comparing mild and higher symptoms with the normal group. Results:Actigraphy-derived behavioral markers differed across depressive symptom groups, showing lower daytime activity and altered circadian rest-activity organization with increasing symptom burden. Among the 5 deep learning architectures evaluated, the long short-term memory model consistently demonstrated the strongest overall discrimination. In held-out testing, the long short-term memory model achieved a macro-averaged area under the receiver operating characteristic curve of 0.80, with the strongest discrimination observed for the higher depressive symptom group (area under the receiver operating characteristic curve 0.889). These findings indicate improved model discrimination with increasing symptom severity, although false-positive rates remained notable across both classification tasks. Conclusions:Our study suggests that actigraphy-derived data can support AI-based classification of depressive symptoms. An actigraphy-based AI model may serve as a scalable, passive, and noninvasive complementary signal to aid early screening alongside traditional depressive symptom assessments before clinical diagnosis.

Abstract Background Although selective serotonin reuptake inhibitors (SSRIs) are the treatment of choice for dialysis patients, tricyclic antidepressants (TCAs) are still frequently prescribed in clinical practice. No studies have compared the cardiovascular outcomes of patients with kidney failure (KF) and depression prescribed SSRIs and those of patients with KF and depression prescribed TCAs. Methods Data of patients who were diagnosed with depression after being diagnosed with KF between January 2003 and December 2022 were extracted from the Korean National Health Institute Database System. The primary outcome was incident heart failure. Secondary outcomes included all-cause mortality and other cardiovascular outcomes. Results We extracted the data of 3096 patients who were prescribed a single type of antidepressant that was continued throughout the follow-up period. Among these patients, 2169 were prescribed TCAs (mean age, 59.3 years; 57.4% were male), and 927 were prescribed SSRIs (mean age, 60.5 years; 48.8% were male) after KF was diagnosed. After 1:1 propensity score matching, 844 patients remained in each group, and no significant difference in all-cause mortality was observed between patients prescribed TCAs and those prescribed SSRIs. However, the risk of incident heart failure associated with TCA use was higher than that associated with SSRI use (adjusted hazard ratio (HR) 1.21, 95% confidence interval (CI) 1.07–1.36). This result was consistent with those indicated by the unmatched multivariate adjusted analysis and landmark analysis. Conclusions Among patients who were diagnosed with depression after being diagnosed with KF, SSRI use was associated with a lower risk of incident heart failure than TCA use. Individualized antidepressant treatment strategies should be considered for patients at high risk.

Despite the high suicide rate in South Korea, older adults are reluctant to see a psychiatrist. Recently, text mining has gained popularity to detect depression in social media posts, but older adults rarely use social media. However, more than 90

Introduction and Objectives: Major depressive disorder (MDD) is a major psychiatric complication of liver transplantation (LT). Here, we aimed to analyze the impact of de novo MDD on survival post-LT and identify risk factors for this disorder among LT recipients. Materials and Methods: A retrospective analysis was conducted on 1350 LT recipients at Severance Hospital, Korea, from July 2005 to December 2022. Patients with MDD were matched 1:5 with controls using a nested case-control design to control for immortal time bias. Results: During follow-up post-LT, 58 patients (4.3 %) were newly diagnosed with MDD. The median time from LT to MDD diagnosis was 316 (interquartile range 46–920) days. Patients with MDD had significantly lower graft survival rates than controls at 1, 3, and 5 years after matching (89.5 %, 75.3 %, and 66.5 % vs. 95.5 %, 91.5 %, and 86.4 %, respectively; P = 0.003). Multivariable Cox regression identified de novo MDD as an independent risk factor for reduced graft survival (hazard ratio 2.39, 95 % confidence interval [CI] 1.15–4.98, P = 0.003). Independent risk factors for de novo MDD included female sex (odds ratio [OR] 2.29, 95 % CI 1.16–4.53, P = 0.017), alcoholic liver disease (OR 2.36, 95 % CI 1.16–4.75, P = 0.016), pre-transplant encephalopathy (OR 2.95, 95 % CI 1.49–5.79, P = 0.002), and lower hemoglobin levels (OR 0.85, 95 % CI 0.73–0.98, P = 0.025). Conclusions: In our matched population of nested case controls, de novo MDD significantly reduced the survival of LT recipients. Screening and early intervention are required for LT recipients with risk factors for MDD.

Background:The prevalence of depression is high among patients with end-stage kidney disease (ESKD). Recent studies have indicated under-recognition and -treatment of depression in this population, and little is known about how the specialty of the prescribing clinician may influence clinical outcomes. This study aimed to evaluate whether the prescribing clinician's specialty (psychiatrist vs. non-psychiatrist) is associated with clinical outcomes in patients with ESKD and comorbid depression who receive antidepressant treatment. Methods:We extracted data from the Korean National Health Institute Database System from January 2004 to December 2022. Patients with ESKD and depression who underwent antidepressant therapy after their ESKD diagnosis were included. Patients were followed up for 4.7 ± 3.5 years. Results:Among 16 756 patients with ESKD and depression [mean age, 67.3 years; 8614 (51.4%) men], 7841 (46.8%) patients were prescribed antidepressants by psychiatrists. After propensity score matching, the 5-year mortality was significantly lower in the psychiatrist (25.8%) than in the non-psychiatrist group (38.2%). After multivariable adjustment, prescription by a psychiatrist remained significantly associated with lower mortality (adjusted hazard ratio, 0.66; 95% confidence interval, 0.62-0.70; P < .001). All-cause mortality was consistent across various subgroups, such as age (above or below 75 years), sex, time from dialysis initiation to depression diagnosis, income level, region of residence, and comorbidity status. This trend remained in 6-month, 1-year, 2-year, and 3-year landmark analyses. Conclusions:Our findings suggest a potential benefit of specialty psychiatric care for improving clinical outcomes in patients with ESKD and depression.

COVID-19 has brought about disruptions in the lives of adolescents, which pose a threat to mental health. While multiple studies have suggested a trend of increased depression during COVID-19, only few have explored the protective factors that could support their mental health during this critical period, highlighting a significant gap in the literature. The current study investigated the association between lifestyle modifications and changes in COVID-19-induced depressive mood. By analyzing the data of 54,848 adolescents from a nationally representative sample of the Korea Youth Risk Behavior Web-based Survey (KYRBS), we examined the associations between four lifestyle behavior changes – increased physical activity, increased breakfast consumption, decreased alcohol use, and decreased smoking – and decreased depressive mood during COVID-19 using multinomial logistic regression. Our results indicated that all four lifestyle behavioral changes were associated with the decreased depressive mood in adolescents. Logistic regression model illustrated that the odds ratios (ORs) of decreased depressive mood for each lifestyle behavior change are (1) increased physical activity 2.481 (95 • COVID-19 brought a number of changes in the lives of Korean adolescents’, which led to psychological distress. • We employed four different lifestyle behavior modifications – increased physical activity, increased breakfast consumption, decreased alcohol consumption, and decreased smoking – and found that healthier modifications in adolescents’ lifestyle behavior are positively associated with a decrease in the depressive mood of adolescents. • Even in a high-risk group of clinical depression, a relationship between lifestyle behavior modification and a decrease in adolescents’ depressive mood remained robust and positive. • Physical activity and breakfast consumption are well-known for its positive impact on adolescents’ mental health beforehand COVID-19, and regarding decreased alcohol consumption and smoking, whether the adolescent is in an adverse surrounding must be accounted. • In an advent of pandemics, such as COVID-19, policymakers should take it into consideration that healthier lifestyle modifications can be used to mitigate the negative impact of pandemic on adolescents’ mental health and employ various tools to safeguard adolescents’ mental well-being.

Background:Despite the high suicide rates among patients with end-stage kidney disease (ESKD), there is no suicide prediction model specifically designed for this vulnerable population. Herein, we aimed to develop and validate a novel suicide risk score for ESKD patients. Methods:We analyzed data from the National Health Insurance Service (NHIS) of South Korea, including 251 819 patients aged above 18 years diagnosed with ESKD between 2007 and 2022 in South Korea. The mean follow-up duration was 6.6 years. The cohort was randomly divided into derivation (70%) and validation (30%) sets. Using multivariate Cox proportional hazard regression, key variables were incorporated to develop the suicide risk score, which was converted into a 48-point scoring system, which is composed of easily identifiable clinical parameters. Results:Among 176 273 patients in the derivation cohort, 1126 (0.64%) patients committed suicide. The suicide risk score demonstrated moderate discrimination in both the derivation (C-statistic, 0.694) and validation (C-statistic, 0.709) cohorts, with good calibration. In the validation cohort, patients scoring below 16, 17-32 and 33-48 had predicted 10-year suicide risk of 0.2%, 1.2% and 7.7%, respectively, while the observed 10-year risk were 0.3%, 0.8% and 3.9%. These findings highlight the model's ability to effectively stratify risk using routinely available clinical data. Conclusions:The suicide risk score is a significant advancement in suicide risk prediction for ESKD patients. It is based on simple, routinely collected clinical indicators and provides an actionable tool for risk stratification and early intervention in daily practice.

Depressive symptoms and stress exposure fluctuate over time in community-dwelling older adults, but they are frequently assessed using one-time retrospective self-report measures. Social support viewed as a multifaceted construct can play diverse moderating roles in this association although it is typically gauged through the measure of perceived social support. This study aims to explore the relationships between stress, social support, and depressive symptoms among older adults by utilizing the longitudinal data collected through a smartphone application and supplemented by annual face-to-face interviews conducted over a 2-year period. Using longitudinal multilevel analysis, we analyzed the data on PHQ-9, stress exposure, and four distinct measures of social support collected from 354 community-dwelling older adults in South Korea. The results demonstrated that 59% of the variability in depressive symptoms was attributable to differences between individuals. Stress exposure was a strong predictor (γ = 3.01⁣ ∗∗∗ , 95% CI = 2.34-3.67). As expected, positive functional social support alleviated the effects of stress on depression (γ = -1.12⁣ ∗∗ , 95% CI = -1.92 ~ -0.32) while negative functional social support (γ = 2.36⁣ ∗∗∗ , 95% CI = 1.29-3.44) and negative structural social support (γ = 3.22⁣ ∗ , 95% CI = 0.79-5.64) worsened the effects of stress on depression. A notable finding is that stress-amplifying effects from the negative functional and structural social support, in addition to well-known stress-buffering effects from positive functional social support, should be regarded as indispensable components in safeguarding the mental health of older adults. Considering the decline in social interactions and the lower probability of older adults establishing new social connections, it is essential to consider approaches that prevent a lack of functional and structural social support and foster a high-quality of functional and structural social support, particularly for those facing greater stressors, as a preventative method against depressive symptoms.

BackgroundAs life expectancy increases, understanding the mechanism for late-life depression and finding a crucial moderator becomes more important for mental health in older adults. Childhood adversity increases the risk of clinical depression even in old age. Based on the stress sensitivity theory and stress-buffering effects, stress would be a significant mediator, while social support can be a key moderator in the mediation pathways. However, few studies have tested this moderated mediation model with a sample of older adults. This study aims to reveal the association between childhood adversity and late-life depression in older adults, taking into consideration the effects of stress and social support.MethodsThis study used several path models to analyze the data from 622 elderly participants who were never diagnosed with clinical depression.ResultsWe found that childhood adversity increases the odds ratio of depression by approximately 20% in older adults. Path model with mediation demonstrates that stress fully mediates the pathway from childhood adversity to late-life depression. Path model with moderated mediation also illustrates that social support significantly weakens the association between childhood adversity and perceived stress.ConclusionThis study provides empirical evidence to reveal a more detailed mechanism for late-life depression. Specifically, this study identifies one crucial risk factor and one protective factor, stress and social support, respectively. This brings insight into prevention of late-life depression among those who have experienced childhood adversity.

(1) Background: The global threat of Coronavirus disease 2019 (COVID-19) continues. The diversity of clinical characteristics and progress are reported in many countries as the duration of the pandemic is prolonged. We aimed to perform a novel systematic review and meta-analysis focusing on findings about correlations between clinical characteristics and laboratory features of patients with COVID-19. (2) Methods: We analyzed cases of COVID-19 in different countries by searching PubMed, Embase, Web of Science databases and Google Scholar, from the early stage of the outbreak to late March. Clinical characteristics, laboratory findings, and treatment strategies were retrospectively reviewed for the analysis. (3) Results: Thirty-seven (n = 5196 participants) COVID-19-related studies were eligible for this systematic review and meta-analysis. Fever, cough and fatigue/myalgia were the most common symptoms of COVID-19, followed by some gastrointestinal symptoms which are also reported frequently. Laboratory markers of inflammation and infection including C-reactive protein (CRP) (65% (95% confidence interval (CI) 56–81%)) were elevated, while lymphocyte counts were decreased (63% (95% CI 47–78%)). Meta-analysis of treatment approaches indicated that three modalities of treatment were predominantly used in the majority of patients with a similar prevalence, including antiviral agents (79%), antibiotics (78%), and oxygen therapy (77%). Age was negatively correlated with number of lymphocytes, but positively correlated with dyspnea, number of white blood cells, neutrophils, and D-dimer. Chills had been proved to be positively correlated with chest tightness, lung abnormalities on computed tomography (CT) scans, neutrophil/lymphocyte/platelets count, D-dimer and CRP, cough was positively correlated with sputum production, and pulmonary abnormalities were positively correlated with CRP. White blood cell (WBC) count was also positively correlated with platelet counts, dyspnea, and neutrophil counts with the respective correlations of 0.668, 0.728, and 0.696. (4) Conclusions: This paper is the first systematic review and meta-analysis to reveal the relationship between various variables of clinical characteristics, symptoms and laboratory results with the largest number of papers and patients until now. In elderly patients, laboratory and clinical characteristics indicate a more severe disease course. Moreover, treatments such as antiviral agents, antibiotics, and oxygen therapy which are used in over three quarters of patients are also analyzed. The results will provide “evidence-based hope” on how to manage this unanticipated and overwhelming pandemic.

Background Many potential environmental risk factors, environmental protective factors, and peripheral biomarkers for ADHD have been investigated, but the consistency and magnitude of their effects are unclear. We aimed to systematically appraise the published evidence of association between potential risk factors, protective factors, or peripheral biomarkers, and ADHD. Methods In this umbrella review of meta-analyses, we searched PubMed including MEDLINE, Embase, and the Cochrane Database of Systematic Reviews, from database inception to Oct 31, 2019, and screened the references of relevant articles. We included systematic reviews that provided meta-analyses of observational studies that examined associations of potential environmental risk factors, environmental protective factors, or peripheral biomarkers with diagnosis of ADHD. We included meta-analyses that used categorical ADHD diagnosis criteria according to DSM, hyperkinetic disorder according to ICD, or criteria that were less rigorous than DSM or ICD, such as self-report. We excluded articles that did not examine environmental risk factors, environmental protective factors, or peripheral biomarkers of ADHD; articles that did not include a meta-analysis; and articles that did not present enough data for re-analysis. We excluded non-human studies, primary studies, genetic studies, and conference abstracts. We calculated summary effect estimates (odds ratio [OR], relative risk [RR], weighted mean difference [WMD], Cohen's d, and Hedges' g), 95% CI, heterogeneity I-2 statistic, 95% prediction interval, small study effects, and excess significance biases. We did analyses under credibility ceilings, and assessed the quality of the meta-analyses with AMSTAR 2 (A Measurement Tool to Assess Systematic. Reviews 2). This study is registered with PROSPERO, number CRD42019145032. Findings We identified 1839 articles, of which 35 were eligible for inclusion. These 35 articles yielded 63 meta analyses encompassing 40 environmental risk factors and environmental protective factors (median cases 16 850, median population 91954) and 23 peripheral biomarkers (median cases 175, median controls 187). Evidence of association was convincing (class I) for maternal pre-pregnancy obesity (OR 1.63, 95% CI 1.49 to 1.77), childhood eczema (1.31, 1.20 to 1.44), hypertensive disorders during pregnancy (1.29, 1.22 to 1.36), pre-eclampsia (1.28, 1.21 to 1.35), and maternal acetaminophen exposure during pregnancy (RR 1.25, 95% CI 1.17 to 1.34). Evidence of association was highly suggestive (class II) for maternal smoking during pregnancy (OR 1.6, 95% CI 1.45 to 1.76), childhood asthma (1.51, 1.4 to 1.63), maternal pre-pregnancy overweight (1.28, 1. 21 to 1.35), and senim vitamin D (WMD 6.93, 95% CI 9.34 to 4-51). Interpretation Maternal pre-pregnancy obesity and overweight; pre-eclampsia, hypertension, acetaminophen exposure, and smoking during pregnancy; and childhood atopic diseases were strongly- associated with ADHD. Previous familial studies suggest that maternal pre-pregnancy obesity-, overweight, and smoking during pregnancy are confounded by familial or genetic factors, and further high-quality studies are therefore required to establish causality. Copyright (C) 2020 Elsevier Ltd. All rights reserved.

Smartphones are adopted as a universal tool for information, communication, education, and entertainment, especially among adolescents. We aimed to investigate the association of smartphone use with depressive symptoms and suicidal behaviors in a large, representative Korean adolescent population.