Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: Patient-generated health data (PGHD), especially lifelog data, are important for managing chronic diseases. Additionally, personal health records (PHRs) have been considered an effective tool to engage patients more actively in the management of their chronic diseases. However, no PHRs currently integrate PGHD directly from Samsung S-Health and Apple Health apps. Objective: The purposes of this study were (1) to demonstrate the development of an electronic medical record (EMR)–tethered PHR system (Health4U) that integrates lifelog data from Samsung S-Health and Apple Health apps and (2) to explore the factors associated with the use rate of the functions. Methods: To upgrade conventional EMR-tethered PHRs, a task-force team (TFT) defined the functions necessary for users. After implementing a new system, we enrolled adults aged 19 years and older with prior experience of accessing Health4U in the 7-month period after November 2017, when the service was upgraded. Results: Of the 17,624 users, 215 (1.22%) integrated daily steps data, 175 (0.99%) integrated weight data, 51 (0.29%) integrated blood sugar data, and 90 (0.51%) integrated blood pressure data. Overall, 61.95% (10,919/17,624) had one or more chronic diseases. For integration of daily steps data, 48.3% (104/215) of patients used the Apple Health app, 43.3% (93/215) used the S-Health app, and 8.4% (18/215) entered data manually. To retrieve medical documentation, 324 (1.84%) users downloaded PDF files and 31 (0.18%) users integrated their medical records into the Samsung S-Health app via the Consolidated-Clinical Document Architecture download function. We found a consistent increase in the odds ratios for PDF downloads among patients with a higher number of chronic diseases. The age groups of ≥60 years and ≥80 years tended to use the download function less frequently than the others. Conclusions: This is the first study to examine the factors related to integration of lifelog data from Samsung S-Health and Apple Health apps into EMR-tethered PHRs and factors related to the retrieval of medical documents from PHRs. Our findings on the lifelog data integration can be used to design PHRs as a platform to integrate lifelog data in the future.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Improvement of dietary habits is recommended for the management of cerebrovascular and cardiovascular diseases (CCVD). This study aimed to evaluate the dietary habits of CCVD patients and compare them with the general population by using the Diet Quality Index—International (DQI-I). Data from the Korean National Health and Nutrition Examination Surveys (2013–2016) were used. Cardiovascular diseases included myocardial infarction, angina pectoris, and heart failure; and cerebrovascular diseases included stroke, cerebral infarction, and hemorrhage. In total, 12,683 subjects over 20 years old were included, comprising 718 CCVD patients and 11,965 non-CCVD subjects. Survey-weighted multiple linear regression analyses with adjustment for covariates were used to compare DQI-I scores. The mean total DQI-I scores for the CCVD and non-CCVD groups were 66.7 ± 9.2 and 67.8 ± 9.2, respectively. After adjusting for covariates, the CCVD group had DQI-I scores significantly lower than the non-CCVD group (coefficient −1.13, p-value = 0.011). In the analysis of each DQI-I component, the CCVD group had lower scores for variety (coefficient −0.54, p-value = 0.004) and adequacy (coefficient −0.86, p-value = 0.001). In this study, using nationally representative data, dietary habits of CCVD patients were shown to be lower in quality than non-CCVD subjects. Therefore, evaluation and education of adequate dietary habits are needed in the management of CCVD patients.

Background: This study aimed to explore the relationship between fruit intake, changes in fruit intake, and changes in cardiometabolic factors in people with obesity. Methods: A total of 21,270 subjects (8,718 men, 12,552 women) aged 40 years and over, from the Korean-based Genome and Epidemiology Study, were followed up for an average of 4.4 years. Fruit intake was assessed using a food frequency questionnaire at baseline and the second follow-up. The beta coefficient and confidence intervals for changes in cardiometabolic risk factors according to fruit consumption were calculated using a linear regression model. Results: In men, the abdominal circumference decreased with changes in fruit intake (P=0.029). Fruit intake and increased fruit intake in men were associated with a lower systolic blood pressure (P=0.012 and P=0.02, respectively) and lower triglyceride levels (P=0.002 and P<0.001, respectively). In women, abdominal circumference decreased with both fruit intake and increased fruit intake (P<0.001 and P=0.013, respectively). Systolic blood pressure and triglycerides tended to decrease only with fruit intake (P=0.048 and P<0.001, respectively). Unlike in men, fasting blood glucose tended to decrease in women with both fruit intake and increased fruit intake (P=0.011 and P=0.005, respectively). Conclusion: Fruit intake and increased fruit intake may have beneficial effects on cardiometabolic risk factors among individuals who are obese.

Objective: Depression among non-smokers at risk of second-hand smoke (SHS) exposure has been a neglected public health concern despite their vulnerability. The objective of this study was to develop high-performance machine-learning (ML) models for the prediction of depression in non-smokers and to identify important predictors of depression for second-hand smokers. Methods: ML algorithms were created using demographic and clinical data from the Korea National Health and Nutrition Examination Survey (KNHANES) participants from 2014, 2016, and 2018 ( N = 11,463). The Patient Health Questionnaire was used to diagnose depression with a total score of 10 or higher. The final model was selected according to the area under the curve (AUC) or sensitivity. Shapley additive explanations (SHAP) were used to identify influential features. Results: The light gradient boosting machine (LGBM) with the highest positive predictive value (PPV; 0.646) was selected as the best model among the ML algorithms, whereas the support vector machine (SVM) had the highest AUC (0.900). The most influential factors identified using the LGBM were stress perception, followed by subjective health status and quality of life. Among the smoking-related features, urine cotinine levels were the most important, and no linear relationship existed between the smoking-related features and the values of SHAP. Conclusions: Compared with the previously developed ML models, our LGBM models achieved excellent and even superior performance in predicting depression among non-smokers at risk of SHS exposure, suggesting potential goals for depression-preventive interventions for non-smokers during public health crises.