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Despite the advent of heated tobacco products (HTPs), their relationship to mental health remains unclear. This study aimed to determine associations between the use of combustible cigarettes (CCs) and HTPs with depressive symptoms. This descriptive-analytical cross-sectional study was conducted in March 2023. Using the 8th Korea National Health and Nutrition Examination Survey, 5,349 adults aged 19 years or older were classified into four groups: non-users, CC-only users, HTP-only users, and dual users. Relationships between exclusive or dual use of CCs and HTPs and depressive symptoms were analyzed using item scores and total scores of the Patient Health Questionnaire-9 (PHQ-9). To examine associations between exclusive or dual use of CCs and HTPs and depressive symptoms, a multinomial regression analysis was performed using the PHQ-9 total score. HTP-only users had the highest proportion of those with anhedonia and depressed mood. CC-only users had the highest proportion of individuals with trouble sleeping, while dual users had a higher proportion of those with fatigue and appetite problems. After adjusting for general characteristics, compared to non-users, CC-only users were more likely to have mild and moderate to severe depressive symptoms. HTP-only users and dual users were also more likely to have moderate to severe depressive symptoms. All smokers have a higher risk of depression than non-smokers. Health care providers should closely monitor depressive symptoms, especially in HTP users and dual users of tobacco products.

Sleep quality is a multidimensional construct encompassing the effectiveness and restorativeness of sleep. The pre-sleep arousal scale is a widely used instrument for evaluating aspects of arousal that are closely related to sleep quality. This study aimed to evaluate the psychometric properties of the Korean version of the pre-sleep arousal scale (K-PSAS). We performed a secondary analysis of cross-sectional data from 286 adults aged 19-70 years who used electronic cigarettes or heated tobacco products. The original PSAS was translated into Korean, with content validity assessed by experts. Construct validity was evaluated via exploratory factor analysis, and concurrent validity was assessed by correlating the K-PSAS with the Insomnia Severity Index, Pittsburgh Sleep Quality Index, and Hospital Anxiety and Depression Scale. Reliability was examined using Cronbach's α, and split-half reliability coefficient. Both the item-level content validity index for all items and the scale-level content validity index average for the K-PSAS-16 were 1.0. After removing the survey item on \"being mentally alert and active at bedtime\" (item 13) due to low factor loading, the K-PSAS-15 demonstrated a two-factor structure, with somatic and cognitive arousal factors explaining 42.36% and 10.19% of the variance, respectively. A significant positive correlation was observed between the two factors (ρ = 0.61, p < .001). Item 13 also showed a low corrected item-total correlation coefficient of -0.10. The correlation coefficients between K-PSAS-15 and other validated scales ranged from 0.49 to 0.71. The K-PSAS-15 showed good internal consistency, with a Cronbach's α of.91 for the total scale, and α = .87 and.90 for the somatic and cognitive subscales, respectively. Split-half reliability was also acceptable (total = .90; somatic = .86; cognitive = .88). The K-PSAS-15, which excludes one poorly performing item from the original scale, is a reliable and valid tool for assessing pre-sleep arousal.

Along with positive SARS-CoV-2 RNA in nasopharyngeal swabs, viral RNA was detectable at high concentration for >3 weeks in fecal samples from 12 mildly symptomatic and asymptomatic children with COVID-19 in Seoul, South Korea. Saliva also tested positive during the early phase of infection. If proven infectious, feces and saliva could serve as transmission sources.

Cognitive frailty (CF), a clinical syndrome involving both physical frailty (PF) and impaired cognition (IC), is associated with adverse health outcomes in older adults. This study aimed to identify key predictors of CF and develop a machine learning-based model for CF risk assessment using data from 2404 community-dwelling older adults in the Korean Frailty and Aging Cohort Study (2016–2017). PF was evaluated using Fried frailty phenotype, while IC was assessed using Mini-Mental State Examination (MMSE). Participants exhibiting at least one frailty phenotype and MMSE score ≤ 24 were classified as having CF. A comprehensive analysis encompassing sociodemographic, clinical, and health status characteristics was conducted. A machine learning approach incorporating recursive feature elimination and bootstrapping was employed to develop the prediction model. Among the diverse CF-associated characteristics, the machine learning-based model identified six optimal features (key predictors): motor capacity, education level, physical function limitation, nutritional status, balance confidence, and activities of daily living. The model demonstrated robust predictive performance, achieving an area under the curve of 84.34%, with high sensitivity, specificity, and accuracy. These findings underscore the importance of comprehensive health assessments for early CF detection and demonstrate the potential of predictive modeling in facilitating personalized interventions for at-risk older adults.

Background In countless number of clinical trials, measurements of outcomes rely on instrument questionnaire items which however often suffer measurement error problems which in turn affect statistical power of study designs. The Cronbach alpha or coefficient alpha, here denoted by C α , can be used as a measure of internal consistency of parallel instrument items that are developed to measure a target unidimensional outcome construct. Scale score for the target construct is often represented by the sum of the item scores. However, power functions based on C α have been lacking for various study designs. Methods We formulate a statistical model for parallel items to derive power functions as a function of C α under several study designs. To this end, we assume fixed true score variance assumption as opposed to usual fixed total variance assumption. That assumption is critical and practically relevant to show that smaller measurement errors are inversely associated with higher inter-item correlations, and thus that greater C α is associated with greater statistical power. We compare the derived theoretical statistical power with empirical power obtained through Monte Carlo simulations for the following comparisons: one-sample comparison of pre- and post-treatment mean differences, two-sample comparison of pre-post mean differences between groups, and two-sample comparison of mean differences between groups. Results It is shown that C α is the same as a test-retest correlation of the scale scores of parallel items, which enables testing significance of C α . Closed-form power functions and samples size determination formulas are derived in terms of C α , for all of the aforementioned comparisons. Power functions are shown to be an increasing function of C α , regardless of comparison of interest. The derived power functions are well validated by simulation studies that show that the magnitudes of theoretical power are virtually identical to those of the empirical power. Conclusion Regardless of research designs or settings, in order to increase statistical power, development and use of instruments with greater C α , or equivalently with greater inter-item correlations, is crucial for trials that intend to use questionnaire items for measuring research outcomes. Discussion Further development of the power functions for binary or ordinal item scores and under more general item correlation strutures reflecting more real world situations would be a valuable future study.

The present study examined the daily well-being of Koreans ( n = 353,340) for 11 weeks during the COVID-19 pandemic (January 20 –April 7). We analyzed whether and how life satisfaction, positive affect, negative affect, and life meaning changed during the outbreak. First, we found that the well-being of Koreans changed daily in a cubic fashion, such that it declined and recovered during the early phase but declined substantially during the later phase (after COVID- 19 was declared world pandemic by WHO). Second, unlike other emotions, boredom displayed a distinctive pattern of linear increase, especially for younger people, suggesting that boredom might be, in part, responsible for their inability to comply with social distancing recommendations. Third, the well-being of older people and males changed less compared to younger people and females. Finally, daily well-being dropped significantly more in the hard-hit regions than in other regions. Implications and limitations are discussed.

Cerebral atherosclerosis contributes to dementia via unclear processes. We performed proteomic sequencing of dorsolateral prefrontal cortex in 438 older individuals and found associations between cerebral atherosclerosis and reduced synaptic signaling and RNA splicing and increased oligodendrocyte development and myelination. Consistently, single-cell RNA sequencing showed cerebral atherosclerosis associated with higher oligodendrocyte abundance. A subset of proteins and modules associated with cerebral atherosclerosis was also associated with Alzheimer’s disease, suggesting shared mechanisms.Proteome-wide association studies of brain samples from older adults revealed effects of cerebral atherosclerosis and Alzheimer’s disease. A subset of proteins and protein co-expression modules were associated with both, suggesting shared mechanisms.

Falls are a critical concern in older adults with cognitive frailty (CF). However, previous studies have not fully examined whether machine learning models can predict falls in older individuals with CF. The 2-year longitudinal data set from the Korean Frailty and Aging Cohort Study and machine learning approach were utilized to predict fall risk. We analyzed multidimensional health data, including demographics, clinical conditions, as well as the physical and psychological health factors of 443 older adults with CF identified out of 2,404 older adults. For fall risk prediction, we developed a machine learning framework incorporating logistic regression, bootstrapping, and recursive feature elimination. Statistical analysis revealed significant differences between the non-faller and faller groups for nine clinical conditions as well as physical and psychological variables. Using nine significant variables, our machine-learning-based model demonstrated good predictive performance with an area under the curve (AUC) exceeding 80%. Furthermore, our machine learning framework identified four optimal variables: the number of Fried physical frailty (PF) phenotypes, PF-Mobility scores, scores from the Korean version of the Short Geriatric Depression Scale, and scores from SARC-F (consisting of five components: strength, assistance with walking, rising from a chair, climbing stairs, and experiencing falls). It demonstrated excellent predictive performance, with an AUC, sensitivity, specificity, and accuracy exceeding 95%. These variables reflect the critical association between physical and psychological health and fall risk. These findings underscore the importance of integrating multidimensional health data with machine learning methodologies to accurately predict fall risk in older adults with CF, design targeted interventions, and enable healthcare professionals to implement strategies to reduce and prevent such falls.

Background As the number of individuals living alone increases, it becomes clear that health disparities vary according to a person’s living arrangement. However, very few studies have investigated the characteristics of individuals who improve or maintain multiple healthy behaviors based on their living arrangements. This study aimed to explore the differing individual characteristics and multiple health behaviors in Korean adults living alone compared to those living with others and to identify the factors significantly associated with these behaviors. Methods This study utilized a secondary analysis, using 2013–2015 Korea National Health and Nutrition Examination Survey data, with a cross-sectional and descriptive correlational design ( N  = 15,934). Multiple health behaviors, based on the comparison of past and present behaviors, included smoking, alcohol consumption, and weight control. The total number of health behaviors was calculated as the sum of each single health behavior. The different numbers of health behaviors were categorized into four levels: from 0, none of the three health behaviors to 3, all three health behaviors. Descriptive statistics and generalized ordinal logistic regression analysis were used. Results People living alone engaged in fewer healthy behaviors ( p  <  0.05) and reported lower rates of maintenance of abstinence from smoking and weight control compared to those living with others, but they maintained a status of abstaining from alcohol consumption more than those living with others ( p  ≤ 0.001). In particular, higher self-rated health statuses (Adjusted Odds Ratio [aOR] = 2.03, 95% Confidence Interval [CI] = 1.04–3.97), being overweight (aOR = 1.46, 95% CI = 1.11–1.92), and having shorter sleep durations per day (aOR = 0.74, 95% CI = 0.55–0.99) were significantly associated with 0, 1 versus 2, 3 levels of healthy behaviors in those living alone. Conclusions Korean adults who lived alone had different factors associated with different combinations of multiple healthy behaviors compared to those living with others. Therefore, we need to manage healthy behaviors by considering associated factors for those living alone. Specifically, clinicians should consider the vulnerability of health behaviors in people living alone and provide customized approaches and multidimensional interventions based on their living arrangements.

Background Archaea are one of the least-studied members of the gut-dwelling autochthonous microbiota. Few studies have reported the dominance of methanogens in the archaeal microbiome (archaeome) of the human gut, although limited information regarding the diversity and abundance of other archaeal phylotypes is available. Results We surveyed the archaeome of faecal samples collected from 897 East Asian subjects living in South Korea. In total, 42.47% faecal samples were positive for archaeal colonisation; these were subsequently subjected to archaeal 16S rRNA gene deep sequencing and real-time quantitative polymerase chain reaction-based abundance estimation. The mean archaeal relative abundance was 10.24 ± 4.58% of the total bacterial and archaeal abundance. We observed extensive colonisation of haloarchaea (95.54%) in the archaea-positive faecal samples, with 9.63% mean relative abundance in archaeal communities. Haloarchaea were relatively more abundant than methanogens in some samples. The presence of haloarchaea was also verified by fluorescence in situ hybridisation analysis. Owing to large inter-individual variations, we categorised the human gut archaeome into four archaeal enterotypes. Conclusions The study demonstrated that the human gut archaeome is indigenous, responsive, and functional, expanding our understanding of the archaeal signature in the gut of human individuals. C2ZBUs6SCY_qq35PWZmhrc Video Abstract