Factors Predicting Older People's Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study.

BACKGROUND: Mobile health (mHealth) services enable real-time measurement of information on individuals' biosignals and environmental risk factors; accordingly, research on health management using mHealth is being actively conducted. OBJECTIVE: The study aims to identify the predictors of older people's intention to use mHealth in South Korea and verify whether chronic disease moderates the effect of the identified predictors on behavioral intentions. METHODS: A cross-sectional questionnaire study was conducted among 500 participants aged 60 to 75 years. The research hypotheses were tested using structural equation modeling, and indirect effects were verified through bootstrapping. Bootstrapping was performed 10,000 times, and the significance of the indirect effects was confirmed through the bias-corrected percentile method. RESULTS: Of 477 participants, 278 (58.3%) had at least 1 chronic disease. Performance expectancy (ß=.453; P=.003) and social influence (ß=.693; P<.001) were significant predictors of behavioral intention. Bootstrapping results showed that facilitating conditions (ß=.325; P=.006; 95% CI 0.115-0.759) were found to have a significant indirect effect on behavioral intention. Multigroup structural equation modeling testing the presence or absence of chronic disease revealed a significant difference in the path of device trust to performance expectancy (critical ratio=-2.165). Bootstrapping also confirmed that device trust (ß=.122; P=.039; 95% CI 0.007-0.346) had a significant indirect effect on behavioral intention in people with chronic disease. CONCLUSIONS: This study, which explored the predictors of the intention to use mHealth through a web-based survey of older people, suggests similar results to those of other studies that applied the unified theory of acceptance and use of technology model to the acceptance of mHealth. Performance expectancy, social influence, and facilitating conditions were revealed as predictors of accepting mHealth. In addition, trust in a wearable device for measuring biosignals was investigated as an additional predictor in people with chronic disease. This suggests that different strategies are needed, depending on the characteristics of users.

Association between smoking and all-cause mortality in Parkinson's disease.

We aimed to investigate the association between smoking status and all-cause mortality of Parkinson's disease (PD). Among the whole nationwide population data from Korea National Health Insurance Service, newly diagnosed PD was selected, and all-cause mortality was evaluated. The systematic review was performed through a literature search on the PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases. Among 26,080 individuals with PD, there was no significant association between smoking status and all-cause mortality in a nationwide cohort study (ex-smoker, HR 0.1.03, 95% CI 0.97-1.10; current smoker, HR 1.06, 95% CI 0.96-1.16). The systematic review, including six prospective cohort studies, also found a nonsignificant association. PD smokers tended to have fewer deaths from neurologic causes but were significantly more likely to die from smoking-related cancers such as lung cancer. We presented a nonsignificant association between smoking and mortality of PD, and cigarette smoking is not recommended in individuals with PD.

Machine learning based risk prediction for Parkinson's disease with nationwide health screening data.

Although many studies have been conducted on machine learning (ML) models for Parkinson's disease (PD) prediction using neuroimaging and movement analyses, studies with large population-based datasets are limited. We aimed to propose PD prediction models using ML algorithms based on the National Health Insurance Service-Health Screening datasets. We selected individuals who participated in national health-screening programs > 5 times between 2002 and 2015. PD was defined based on the ICD-code (G20), and a matched cohort of individuals without PD was selected using a 1:1 random sampling method. Various ML algorithms were applied for PD prediction, and the performance of the prediction models was compared. Neural networks, gradient boosting machines, and random forest algorithms exhibited the best average prediction accuracy (average area under the receiver operating characteristic curve (AUC): 0.779, 0.766, and 0.731, respectively) among the algorithms validated in this study. The overall model performance metrics were higher in men than in women (AUC: 0.742 and 0.729, respectively). The most important factor for predicting PD occurrence was body mass index, followed by total cholesterol, glucose, hemoglobin, and blood pressure levels. Smoking and alcohol consumption (in men) and socioeconomic status, physical activity, and diabetes mellitus (in women) were highly correlated with the occurrence of PD. The proposed health-screening dataset-based PD prediction model using ML algorithms is readily applicable, produces validated results, and could be a useful option for PD prediction models.

Feeding Practices and Early Childhood Caries in Korean Preschool Children.

OBJECTIVES: This study aimed to investigate the association between feeding practices and early childhood caries using representative Korean national survey data. METHODS: Data of 2772 children (aged 1-3 years) from the fourth to seventh Korean National Health and Nutrition Examination Surveys (2007-2018) were analysed. Data on decayed-filled teeth (dft) were obtained, and early childhood caries was determined as dft ≥1. Chi-squared test was performed to examine the experiencing of dental caries by participant characteristics, whilst analysis of variance was performed to compare the difference in dft values amongst the 3 groups (breast-feeding, mixed-feeding, and formula-feeding groups). Logistic regression analysis was performed to evaluate the association between feeding practice and early childhood caries. RESULTS: Comparison of the mean dft values amongst the 3 groups showed the highest value in the breast-feeding group. Multiple logistic regression analysis results showed that the likelihood of experiencing dental caries was significantly lower in the mixed-feeding group than in the breast-feeding group, whereas no significant difference was seen between the formula-feeding and breast-feeding groups. The likelihood of early childhood caries was higher in the group that introduced weaning food at ≥6 months than that at 4- to 6-month age, whilst the introduction of cow's milk and use of nutritional supplements had no significant association with the likelihood of early childhood caries. CONCLUSIONS: Breast-feeding and delayed introduction of weaning food were associated with a higher likelihood of early childhood caries.

Lifestyle Factors and Parkinson Disease Risk: Korean Nationwide Cohort Study With Repeated Health Screening Data.

BACKGROUND AND OBJECTIVES: Many previous studies, mostly performed in Western countries, on the effects of lifestyle factors on Parkinson disease (PD) used baseline lifestyle characteristics without directly accounting for changes in covariate values over time. The objective of this study was to evaluate the association of repeatedly measured lifestyle factors with PD risk in a Korean population. METHODS: We conducted a nationwide population-based cohort study. Among 512,836 Koreans in the national health checkup database, we selected individuals who underwent health screening ≥3 times between 2002 and 2015 and followed up until December 31, 2015. PD was defined using the ICD-10 code G20 (with ≥3 times clinic visits for PD, to increase the diagnostic validity). Data on lifestyle factors such as smoking, alcohol consumption, and physical activity were collected using self-reported questionnaires. Logistic regression analysis with time-dependent covariates using generalized estimation equation models was performed to determine PD development. RESULTS: During the 14-year follow-up, 2,665 patients developed PD. Smoking showed a dose-response inverse association with PD only in men (ex-smoker, odds ratio [OR] 0.782, 95% confidence interval [CI] 0.713-0.858; current smoker, OR 0.556, 95% CI 0.488-0.632). Alcohol consumption and regular physical activity were related to reduced PD development in both sexes; however, alcohol consumption in men (≤3 per week, OR 0.717, 95% CI 0.658-0.780; ≥4 per week, OR 0.745, 95% CI 0.644-0.861) and physical activity in women (moderate, OR 0.792, 95% CI 0.748-0.840; vigorous, OR 0.830, 95% CI 0.756-0.911) had more consistent associations with PD development compared to those of the other sex. Participants with regular health screening showed a consistent relationship between lifestyle factors and PD development, whereas lifestyle factors in those without regular health screening had a decreased relationship with PD, even smoking habit. CONCLUSIONS: Analysis using repeatedly measured lifestyle factors showed an association between lifestyle factors and PD development. Characteristics of lifestyle data including repeated measurements, timing, or regularity might influence results, and future studies with appropriate lifestyle factors could increase PD risk prediction. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that smoking, alcohol use, and physical activity are associated with reduced risk of PD in a Korean population.

Comparison of Risk of Pneumonia Caused by Fluticasone Propionate versus Budesonide in Chronic Obstructive Pulmonary Disease: A Nationwide Retrospective Cohort Study.

INTRODUCTION: Inhaled corticosteroids (ICSs) play an important role in lowering the risk of acute exacerbation of chronic obstructive pulmonary disease (COPD). However, ICSs are known to increase the risk of pneumonia. Moreover, previous studies have shown that the incidence rate of pneumonia varies depending on the type of ICS. In this study, the risk of pneumonia according to the type of ICS was investigated in a population-based cohort. METHODS: A retrospective cohort study was conducted using claims data of the entire population from the Korean National Health Insurance Service. Patients who were newly diagnosed with COPD and prescribed fluticasone propionate or budesonide were enrolled as study subjects. Cumulative doses of ICSs were classified into categorical variables to analyze the risk of pneumonia within identical ICS doses. RESULTS: A total of 47,473 subjects were identified and allocated as 14,518 fluticasone propionate and 14,518 budesonide users through 1:1 propensity score matching. Fluticasone propionate users were more likely to develop pneumonia than budesonide users (14.22% vs 10.66%, p<0.0001). The incidence rate per 100,000 person-years was 2,914.77 for fluticasone propionate users and 2,102.90 for budesonide users. The hazard ratio (HR) of pneumonia in fluticasone propionate compared to budesonide was 1.34 (95% CI 1.26-1.43, p<0.0001). The risk of pneumonia for fluticasone propionate compared to budesonide increased with higher ICS cumulative doses: 1.06 (0.93-1.21), 1.41 (1.19-1.66), 1.41 (1.23-1.63), and 1.49 (1.33-1.66) from the lowest to highest quartiles, respectively. CONCLUSION: ICS types and doses need to be carefully considered during treatment with ICSs in patients with COPD.

Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms.

In this study, we developed machine learning-based prediction models for early childhood caries and compared their performances with the traditional regression model. We analyzed the data of 4195 children aged 1-5 years from the Korea National Health and Nutrition Examination Survey data (2007-2018). Moreover, we developed prediction models using the XGBoost (version 1.3.1), random forest, and LightGBM (version 3.1.1) algorithms in addition to logistic regression. Two different methods were applied for variable selection, including a regression-based backward elimination and a random forest-based permutation importance classifier. We compared the area under the receiver operating characteristic (AUROC) values and misclassification rates of the different models and observed that all four prediction models had AUROC values ranging between 0.774 and 0.785. Furthermore, no significant difference was observed between the AUROC values of the four models. Based on the results, we can confirm that both traditional logistic regression and ML-based models can show favorable performance and can be used to predict early childhood caries, identify ECC high-risk groups, and implement active preventive treatments. However, further research is essential to improving the performance of the prediction model using recent methods, such as deep learning.

Statin Supply and Polydrug Use in Older Adults: A Focus on Drug Combinations that Reduce Bone Density.

BACKGROUND: We investigated the comorbidities of individuals who were prescribed statins to identify the use of bone mineral density (BMD)-reducing drugs, examine polydrug use trends involving these drugs, and explore their relationship with osteoporosis. METHODS: We analyzed claims data from the Korean National Health Insurance Service (January 2014-December 2018). We sampled 20% of 8,379,419 patients aged ≥50 years who were prescribed statins. Among them, we analyzed the data of those who were administered two or more prescriptions for 14 days or longer within 6 months of the initial date of statin prescription. Data on comorbidities and drugs that can potentially reduce BMD were obtained. Osteoporosis-related diagnoses were obtained as an outcome measure. The relationship between statins and BMD-reducing drugs was analyzed using logistic regression. RESULTS: Among the 4,138 statin users aged 50 years or older, 552 were diagnosed with osteoporosis. The most common comorbidity in statin users was hypertension, followed by ischemic heart disease, diabetes mellitus, and stroke. The most frequently administered BMD-reducing drugs were proton pump inhibitors (PPIs). The osteoporosis diagnosis rate was higher in patients who were prescribed both statins and PPIs or both statins and levothyroxine than in those using only a statin. CONCLUSION: PPIs and levothyroxine should be prescribed cautiously in statin users and bone densitometry should be proactively performed considering the increased risk of osteoporosis.

Risk of Pneumonia Associated with Inhaled Corticosteroid in Patients with Chronic Obstructive Pulmonary Disease: A Korean Population-Based Study.

Introduction: Inhaled corticosteroids (ICSs) are recommended for patients with frequent exacerbation of chronic obstructive pulmonary disease (COPD). However, accumulating evidence has indicated the risk of pneumonia from the use of ICS. This study aimed to investigate the association between ICS and pneumonia in the real-world clinical setting. Methods: A retrospective cohort study was performed using nationwide population data from the Korea National Health Insurance Service. Subjects who had a new diagnosis of COPD and who received inhaled bronchodilators without a diagnosis of pneumonia before the initiation of bronchodilators were identified. Subjects were followed up until their first diagnosis of pneumonia. The risk of pneumonia in ICS users was compared to that in non-ICS users. Results: A total of 87,594 subjects were identified and 1:1 matched to 22,161 ICS users and non-ICS users. More ICS users were diagnosed with pneumonia compared to non-ICS users (33.73% versus 24.51%, P<0.0001). The incidence rate per 100,000 person-years was 8904.98 for ICS users and 6206.79 for non-ICS users. The hazard ratio (HR) of pneumonia for ICS users was 1.62 (95% CI 1.54-1.70). The HR of subjects prescribed with the lowest ICS cumulative dose was 1.35 (1.27-1.43). The HR increased to 1.51 (1.42-1.60), 1.96 (1.85-2.09), and 2.03 (1.89-2.18) as the cumulative dose increased. Pneumonia was strongly associated with fluticasone propionate (1.79 (1.70-1.89)) and fluticasone furoate (1.80 (1.61-2.01)) use, compared to the use of other types of ICS. Conclusion: ICS increases the risk of pneumonia in patients with COPD. Hence, ICS should be carefully prescribed in patients with risk factors for pneumonia while considering the cumulative doses and subtypes of ICS.