BackgroundPersonal health record (PHR)–based health care management systems can improve patient engagement and data-driven medical diagnosis in a clinical setting.ObjectiveThe purpose of this study was (1) to demonstrate the development of an electronic health record (EHR)–tethered PHR app named MyHealthKeeper, which can retrieve data from a wearable device and deliver these data to a hospital EHR system, and (2) to study the effectiveness of a PHR data-driven clinical intervention with clinical trial results.MethodsTo improve the conventional EHR-tethered PHR, we ascertained clinicians’ unmet needs regarding PHR functionality and the data frequently used in the field through a cocreation workshop. We incorporated the requirements into the system design and architecture of the MyHealthKeeper PHR module. We constructed the app and validated the effectiveness of the PHR module by conducting a 4-week clinical trial. We used a commercially available activity tracker (Misfit) to collect individual physical activity data, and developed the MyHealthKeeper mobile phone app to record participants’ patterns of daily food intake and activity logs. We randomly assigned 80 participants to either the PHR-based intervention group (n=51) or the control group (n=29). All of the study participants completed a paper-based survey, a laboratory test, a physical examination, and an opinion interview. During the 4-week study period, we collected health-related mobile data, and study participants visited the outpatient clinic twice and received PHR-based clinical diagnosis and recommendations.ResultsA total of 68 participants (44 in the intervention group and 24 in the control group) completed the study. The PHR intervention group showed significantly higher weight loss than the control group (mean 1.4 kg, 95% CI 0.9-1.9; P<.001) at the final week (week 4). In addition, triglyceride levels were significantly lower by the end of the study period (mean 2.59 mmol/L, 95% CI 17.6-75.8; P=.002).ConclusionsWe developed an innovative EHR-tethered PHR system that allowed clinicians and patients to share lifelog data. This study shows the effectiveness of a patient-managed and clinician-guided health tracker system and its potential to improve patient clinical profiles.Trial RegistrationClinicalTrials.gov NCT03200119; https://clinicaltrials.gov/ct2/show/NCT03200119 (Archived by WebCite at http://www.webcitation.org/6v01HaCdd)

Background Although using the technologies for a variety of chronic health conditions such as personal health record (PHR) is reported to be acceptable and useful, there is a lack of evidence on the associations between the use of the technologies and the change of health outcome and patients’ response to a digital health app. Objective This study aimed to examine the impact of the use of PHR and wearables on health outcome improvement and sustained use of the health app that can be associated with patient engagement. Methods We developed an Android-based mobile phone app and used a wristband-type activity tracker (Samsung Charm) to collect data on health-related daily activities from individual patients. Dietary record, daily step counts, sleep log, subjective stress amount, blood pressure, and weight values were recorded. We conducted a prospective randomized clinical trial across 4 weeks on those diagnosed with obstructive sleep apnea (OSA) who had visited the outpatient clinic of Seoul National University Bundang Hospital. The trial randomly assigned 60 patients to 3 subgroups including 2 intervention groups: (1) mobile app and wearable device users (n=20), (2) mobile app–only users (n=20), and (3) controls (n=20). The primary outcome measure was weight change. Body weights before and after the trial were recorded and analyzed during clinic visits. Changes in OSA–related respiratory parameters such as respiratory disturbance, apnea-hypopnea, and oxygenation desaturation indexes and snoring comprised the secondary outcome and were analyzed for each participant. Results We collected the individual data for each group during the trial, specifically anthropometric measurement and laboratory test results for health outcomes, and the app usage logs for patient response were collected and analyzed. The body weight showed a significant reduction in the 2 intervention groups after intervention, and the mobile app–only group showed more weight loss compared with the controls ( P =.01). There were no significant changes in sleep-related health outcomes. From a patient response point of view, the average daily step counts (8165 steps) from the app plus wearable group were significantly higher than those (6034 steps) from the app-only group because they collected step count data from different devices ( P =.02). The average rate of data collection was not different in physical activity ( P =.99), food intake ( P =.98), sleep ( P =.95), stress ( P =.70), and weight ( P =.90) in the app plus wearable and app-only groups, respectively. Conclusions We tried to integrate PHR data that allow clinicians and patients to share lifelog data with the clinical workflow to support lifestyle interventions. Our results s...

The choice of levofloxacin or moxifloxacin made no difference to the final treatment outcome among patients with fluoroquinolone-sensitive MDR-TB. Clinical trial registered with www.clinicalrials.gov (NCT01055145).

ObjectivesTo investigate the short-term effects of a lifestyle modification intervention based on a mobile application (app) linked to a hospital electronic medical record (EMR) system on weight reduction and obstructive sleep apnea (OSA).MethodsWe prospectively enrolled adults (aged >20 years) with witnessed snoring or sleep apnea from a sleep clinic. The patients were randomized into the app user (n=24) and control (n=23) groups. The mobile app was designed to collect daily lifestyle data by wearing a wrist activity tracker and reporting dietary intake. A summary of the lifestyle data was displayed on the hospital EMR and was reviewed. In the control group, the lifestyle modification was performed as per usual practice. All participants underwent peripheral arterial tonometry (WatchPAT) and body mass index (BMI) measurements at baseline and after 4 weeks of follow-up.ResultsAge and BMI did not differ significantly between the two groups. While we observed a significant decrease in the BMI of both groups, the decrease was greater in the app user group (P <0.001). Apnea-hypopnea index, respiratory distress index, and oxygenation distress index did not change significantly in both groups. However, the proportion of sleep spent snoring at >45 dB was significantly improved in the app user group alone (P =0.014). In either group, among the participants with successful weight reduction, the apnea-hypopnea index was significantly reduced after 4 weeks (P =0.015). Multiple regression analyses showed that a reduction in the apnea-hypopnea index was significantly associated with BMI.ConclusionAlthough a short-term lifestyle modification approach using a mobile app was more effective in achieving weight reduction, improvement in OSA was not so significant. Long-term efficacy of this mobile app should be evaluated in the future studies.

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ObjectivesWe aimed to develop a common health information exchange (HIE) platform that can provide integrated services for implementing the HIE infrastructure in addition to guidelines for participating in an HIE network in South Korea.MethodsBy exploiting the Health Level 7 (HL7) Clinical Document Architecture (CDA) and Integrating the Healthcare Enterprise (IHE) Cross-enterprise Document Sharing-b (XDS.b) profile, we defined the architectural model, exchanging data items and their standardization, messaging standards, and privacy and security guidelines, for a secure, nationwide, interoperable HIE. We then developed a service-oriented common HIE platform to minimize the effort and difficulty of fulfilling the standard requirements for participating in the HIE network. The common platform supports open application program interfaces (APIs) for implementing a document registry, a document repository, a document consumer, and a master patient index. It could also be used for testing environments for the implementation of standard requirements.ResultsAs the initial phase of implementing a nationwide HIE network in South Korea, we built a regional network for workers' compensation (WC) hospitals and their collaborating clinics to share referral and care record summaries to ensure the continuity of care for industrially injured workers, using the common HIE platform and verifying the feasibility of our technologies.ConclusionsWe expect to expand the HIE network on a national scale with rapid support for implementing HL7 and IHE standards in South Korea.

Trans-Scirpusin A (TSA) is a resveratrol oligomer found in Borassus flabellifer L. We found that TSA inhibited the growth of colorectal cancer Her2/CT26 cells in vivo in mice. Although some cytotoxic T lymphocytes (CTLs) were induced against the tumor-associated antigen Her2, TSA treatment did not significantly increase the level of Her2-specific CTL response compared to that with vehicle treatment. However, there was a significant increase in the level of TNF-α mRNA in tumor tissue and Her2-specific Ab (antibody) production. More importantly, we found that TSA overcomes the tumor-associated immunosuppressive microenvironment by reducing the number of CD25+FoxP3+ regulatory T cells and myeloid-derived suppressor cells (MDSCs). We detected the induction of autophagy in TSA-treated Her2/CT26 cells, based on the increased level of the mammalian autophagy protein LC3 puncta, and increased conversion of LC3-I to LC3-II. Further, TSA induced 5' AMP-activated protein kinase (p-AMPK) (T172) and inhibited mammalian target of rapamycin complex 1 (mTORC1) activity as estimated by phosphorylated ribosomal protein S6 kinase beta-1 (p-p70S6K) levels, thereby suggesting that TSA-mediated AMPK activation and inhibition of mTORC1 pathway might be associated with autophagy induction. TSA also induced apoptosis of Her2/CT26 cells, as inferred by the increased sub-G1 mitotic phases in these cells, Annexin V/PI-double positive results, and TUNEL-positive cells. Finally, we found that the combined treatment of mice with docetaxel and TSA successfully inhibited tumor growth to a greater extent than docetaxel alone. Therefore, we propose the use of TSA for supplementary anticancer therapy to support anti-neoplastic drugs, such as docetaxel, by inducing apoptosis in cancer cells and resulting in the induction of neighborhood anti-cancer immunity.

Background Prevention and management of chronic diseases are the main goals of national health maintenance programs. Previously widely used screening tools, such as Health Risk Appraisal, are restricted in their achievement this goal due to their limitations, such as static characteristics, accessibility, and generalizability. Hypertension is one of the most important chronic diseases requiring management via the nationwide health maintenance program, and health care providers should inform patients about their risks of a complication caused by hypertension. Objective Our goal was to develop and compare machine learning models predicting high-risk vascular diseases for hypertensive patients so that they can manage their blood pressure based on their risk level. Methods We used a 12-year longitudinal dataset of the nationwide sample cohort, which contains the data of 514,866 patients and allows tracking of patients’ medical history across all health care providers in Korea (N=51,920). To ensure the generalizability of our models, we conducted an external validation using another national sample cohort dataset, comprising one million different patients, published by the National Health Insurance Service. From each dataset, we obtained the data of 74,535 and 59,738 patients with essential hypertension and developed machine learning models for predicting cardiovascular and cerebrovascular events. Six machine learning models were developed and compared for evaluating performances based on validation metrics. Results Machine learning algorithms enabled us to detect high-risk patients based on their medical history. The long short-term memory-based algorithm outperformed in the within test (F1-score=.772, external test F1-score=.613), and the random forest-based algorithm of risk prediction showed better performance over other machine learning algorithms concerning generalization (within test F1-score=.757, external test F1-score=.705). Concerning the number of features, in the within test, the long short-term memory-based algorithms outperformed regardless of the number of features. However, in the external test, the random forest-based algorithm was the best, irrespective of the number of features it encountered. Conclusions We developed and compared machine learning models predicting high-risk vascular diseases in hypertensive patients so that they may manage their blood pressure based on their risk level. By relying on the prediction model, a government can predict high-risk patients at the nationwide level and establish health care policies in advance.