Health Management Of Chronic Diseases Research Articles

Digital Health Platform for Improving the Effect of the Active Health Management of Chronic Diseases in the Community: Mixed Methods Exploratory Study

Background China is vigorously promoting the health management of chronic diseases and exploring digital active health management. However, as most medical information systems in China have been built separately, there is poor sharing of medical information. It is difficult to achieve interconnectivity among community residents’ self-testing information, community health care information, and hospital health information, and digital chronic disease management has not been widely applied in China. Objective This study aimed to build a digital health platform and improve the effectiveness of full-cycle management for community chronic diseases through digital active health management. Methods This was a single-arm pre-post intervention study involving the development and use of a digital health platform (2-year intervention; 2020 to 2022). The digital health platform included the “i Active Health” applet for residents and the active health information system (cardio-cerebrovascular disease risk management system) for medical teams. The digital active health management of chronic diseases involved creating health streets, providing internet-assisted full-cycle active health services for residents, implementing internet-based community management for hypertension and diabetes, and performing real-time quantitative assessment and hierarchical management of residents’ risks of cardio-cerebrovascular disease. After the 2-year intervention, management effectiveness was evaluated. Results We constructed a digital health platform with interconnected health information and implemented a digital active health management model. After the intervention, the 2-way referral between community health care institutions and hospitals increased. Residents’ health literacy rate increased from 30.6% (3062/10,000) in 2020 to 49.9% (4992/10,000) in 2022, with improvements in health knowledge, health behavior, and health skills. Moreover, the risk of cardio-cerebrovascular disease decreased after the intervention. The community hypertension and diabetes standardized management rates increased from 59.6% (2124/3566) and 55.8% (670/1200) in 2020 to 75.0% (3212/4285) and 69.4% (1686/2430) in 2022, respectively. The control rates of blood pressure in patients with hypertension and blood sugar in patients with diabetes increased from 51.7% (1081/2091) and 42.0% (373/888) in 2020 to 81.2% (1698/2091) and 73.0% (648/888) in 2022, respectively. The intervention improved patients’ BMI, waist circumference, blood uric acid levels, and low-density lipoprotein cholesterol levels. The drug compliance rate of patients with hypertension and diabetes increased from 33.6% (703/2091) and 36.0% (320/888) in 2020 to 73.3% (1532/2091) and 75.8% (673/888) in 2022, respectively. The intervention greatly improved the diet behavior, exercise behavior, and drinking behavior of patients with hypertension and diabetes. Conclusions Our digital health platform can effectively achieve the interconnection and exchange of different health information. The digital active health management carried out with the assistance of this platform improved the effectiveness of community chronic disease management. Thus, the platform is worth promoting and applying in practice.

Effect of Telehealth Services on Mitral and Tricuspid Regurgitation Progression: Retrospective Study.

Mitral regurgitation (MR) and tricuspid regurgitation (TR) are common cardiac conditions with high mortality risks, which can be improved through early intervention. Telehealth services, which allow for remote monitoring of patient conditions, have been proven to improve the health management of chronic diseases, but the effects on MR and TR progression are unknown. This study aimed to explore whether patients receiving telehealth services have less MR and TR progression compared with a control group. We also aimed to identify the determinants of MR and TR progression. This single-center retrospective study conducted at the National Taiwan University Hospital compared MR and TR progression (defined as either progression to moderate or greater MR and TR or MR and TR progression by ≥2 grades during the study period) between the telehealth and control groups. Patients had a minimum of 2 transthoracic echocardiograms at least 6 months apart; baseline mild-moderate MR and TR or lower; and no prior surgeries on the mitral or tricuspid valve. Telehealth patients were defined as those who received telehealth services for at least 28 days within 3 months of baseline. Basic demographics, baseline blood pressure measurements, prescribed medication, and Charlson Comorbidity Index components were obtained for all patients. A total of 1081 patients (n=226 in the telehealth group and n=855 in the control group) were included in the study analyses. The telehealth group showed significantly lower baseline systolic blood pressure (P<.001), higher Charlson Comorbidity Index (P=.02), higher prevalence of prior myocardial infarction (P=.01) and heart failure (P<.001), higher beta-blocker (P=.03) and diuretic (P=.04) use, and lower nitrate use (P=.04). Both groups showed similar cardiac remodeling conditions at baseline. Telehealth was found to be neutral for both MR (hazard ratio 1.10, 95% CI 0.80-1.52; P=.52) and TR (hazard ratio 1.27, 95% CI 0.92-1.74; P=.14) progression. Determinants for moderate or greater MR progression included older age, female sex, diuretic use, larger left atrial dimension, left ventricular end-diastolic dimension, left ventricular end-systolic dimension, and lower left ventricular ejection fraction. Determinants of moderate or greater TR progression included older age, female sex, diuretic use, presence of atrial fibrillation, LA dimension, left ventricular end-systolic dimension, and lower left ventricular ejection fraction; statin use was found to be protective. This is the first study to assess the association between telehealth services and the progression of MR and TR. Telehealth patients, who had more comorbidities, displayed similar MR and TR progression versus control patients, indicating that telehealth may slow MR and TR progression. Determinants of MR and TR progression included easy-to-measure traditional echo parameters of cardiac function, older age, female sex, and atrial fibrillation, which can be incorporated into a telehealth platform and advanced alert system, improving patient outcomes through personalized care.

Retracted: Analysis of the Effect of Health Management of Chronic Diseases and Metabolic Disorders under the Guidance of B-Ultrasound.

[This retracts the article DOI: 10.1155/2021/6648611.].

Health Data-Driven Machine Learning Algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease.

PurposeAs global aging progresses, the health management of chronic diseases has become an important issue of concern to governments. Influenced by the aging of its population and improvements in the medical system and healthcare in general, Taiwan’s population of patients with chronic kidney disease (CKD) has tended to grow year by year, including the incidence of high-risk cases that pose major health hazards to the elderly and middle-aged populations.MethodsThis study analyzed the annual health screening data for 65,394 people from 2010 to 2015 sourced from the MJ Group – a major health screening center in Taiwan – including data for 18 risk indicators. We used five prediction model analysis methods, namely, logistic regression (LR) analysis, C5.0 decision tree (C5.0) analysis, stochastic gradient boosting (SGB) analysis, multivariate adaptive regression splines (MARS), and eXtreme gradient boosting (XGboost), with estimated glomerular filtration rate (e-GFR) data to determine G3a, G3b & G4 stage CKD risk factors.ResultsThe LR analysis (AUC=0.848), SGB analysis (AUC=0.855), and XGboost (AUC=0.858) generated similar classification performance levels and all outperformed the C5.0 and MARS methods. The study results showed that in terms of CKD risk factors, blood urea nitrogen (BUN) and uric acid (UA) were identified as the first and second most important indicators in the models of all five analysis methods, and they were also clinically recognized as the major risk factors. The results for systolic blood pressure (SBP), SGPT, SGOT, and LDL were similar to those of a related study. Interestingly, however, socioeconomic status-related education was found to be the third important indicator in all three of the better performing analysis methods, indicating that it is more important than the other risk indicators of this study, which had different levels of importance according to the different methods.ConclusionThe five prediction model methods can provide high and similar classification performance in this study. Based on the results of this study, it is recommended that education as the socioeconomic status should be an important factor for CKD, as high educational level showed a negative and highly significant correlation with CKD. The findings of this study should also be of value for further discussions and follow-up research.

Health Analysis of Footballer Using Big Data and Deep Learning

With the development of information technology, health management and big data have risen and developed in recent years. Big data need proper analysis and shape in order to extract meaningful information from it. This paper analyzes the application status and prospects of big data in the field of health management. The results show that the most widely used big data in health management are intelligent wearable devices. Big data applications in football players’ mental health monitoring systems and chronic disease health management systems also have a good prospect. The intelligent wearable device is applied to several aspects of sports work: teaching and sports training, real-time monitoring of football players’ physical exercise process, collecting football players’ heart rate, calorie consumption, exercise steps, and track, blood pressure, blood oxygen, and other physical exercise data; through monitoring the heart rate, we can get the intensity and duration of football players’ physical exercise in school; through the calculation, we can also get the football players’ time energy consumption and understand the overall situation of football players’ physical exercise in school; through step counting and track monitoring, we can master the number of steps and track of football players; by monitoring the changes of blood oxygen and blood pressure of football players, we need to build a third-party residents’ health information storage and analysis system and further realize the marketization of residents’ health big data. The experimental results of the proposed study show the effectiveness of the proposed work.

Analysis of the Effect of Health Management of Chronic Diseases and Metabolic Disorders under the Guidance of B-Ultrasound.

Chronic diseases are diseases with insidious onset, prolonged duration, and intractability and are associated with complex etiology and multiple morbidities, including hypertension, obesity, liver disease, kidney disease, cancer, cardiovascular disease, and chronic lung disease. For patients with chronic diseases, although the cause of the disease is not in the spleen and stomach, medication or further development of the disease will affect the function of the spleen and stomach disorders, leading to worsening of the disease. In this article, ultrasound is used to detect health disorders of the spleen and stomach. Patients should eat a light diet and exercise more. Elderly patients can increase abdominal massage and other health care. It should be emphasized that the prevalence of chronic diseases is not lower in people with high education or high economic income but is closely related to people's daily living habits; secondly, we should enhance physical exercise and advocate annual physical examination. Finally, we need to develop different measures according to different regions to prevent and treat chronic diseases and give timely feedback to the interventions. New preventive and curative measures are given in time to improve public health.

Analysis of community chronic disease health management mode under the background of big data

In recent years, the “Internet + medical” exploration and the country’s vigorously promoted hierarchical diagnosis and treatment system have provided an opportunity to improve the status quo of diabetes. Some scholars have proposed “one-to-one binding community nurses” (Wang Li et al., 2016) and personalized treatment based on big data (He Ting et al., 2016). New chronic disease management concepts such as an integrated chronic disease management model for the elderly based on mobile medical technology (Che Fengyuan et al., 2016). Although different names are used, the core point of view is that patients and community doctors complete the contract, the community doctors will take care of the patients, and the hospital doctors will take care of the patients. The patient’s blood glucose data can be shared with relatives and friends, community doctors, and hospital doctors in real time with the help of platform tools such as blood glucose meters, mobile apps, and cloud medical platforms. And community and hospital doctors’ feedback on patients can also be sent to patients and relatives and friends in real time, thereby realizing hierarchical diagnosis and treatment of diabetic patients when medical resources are scarce and unevenly distributed. This article refers to this model as the “family-style chronic disease management model”. The interaction between patients, relatives and friends, community doctors, and hospital doctors is shown in Figure 1.

Smartphone interventions for long-term health management of chronic diseases: an integrative review.

Long-term health management is challenging for the rapidly growing number of patients with chronic diseases. Smartphone interventions offer promising solutions. This article presents features of smartphone interventions for long-term chronic condition management, illustrating how these applications benefit patients with chronic diseases. Systematic searches for smartphone health interventions were conducted in five publication databases. Articles were included only if (1) the smartphone application (app) was exclusively developed for patients with chronic diseases and (2) the article incorporated a defined outcome measurement to evaluate the effects of the implemented intervention. Sixteen articles were included in the final review, including studies in diabetes, mental health problems, overweight, cancer, and chronic obstructive pulmonary disease. These studies found that the smartphone intervention was a completely or at least partially effective tool to assist in managing some chronic diseases. With the help of health-related smartphone apps, patients with chronic conditions (1) felt secure in the knowledge that their illnesses were closely monitored, (2) participated in their own health management more effectively, and (3) felt that they had not been forgotten by their doctors and were taken good care of even outside the hospital/clinic. However, there are limited smartphone apps for the long-term health management of chronic diseases. More smartphone apps need to be developed to help people manage chronic diseases.

A Risk-Based Evaluation Methodology for Cost Effectiveness of Chronic Condition Health Management Programs

A financial evaluation of the effectiveness of chronic disease health management programs would ideally take into account the health care costs of managed chronic member populations compared against the health care costs of comparable nonmanaged chronic member populations. This paper considers one approach when a comparison chronic member population is not readily available. The approach uses a nonmanaged nonchronic member population as the comparison group, applying a claims normalization methodology to the medical claims costs of the chronic and nonchronic populations. The normalization process includes stratification by demographic and risk factors. The resulting savings factors are then modified to identify expected savings for single-employer groups. The analysis develops savings factors at the book-of-business level, which are then applied to groups to estimate the savings at the group level. Separate savings factors are developed by age and sex, condition, and duration from initial chronic diagnosis. Results are generally intuitively reasonable: Savings are higher for members with longer duration since identification, as well as for members with more costly conditions (e.g., heart disease, comorbid conditions). The effect-on-savings estimates of underlying factors such as age, duration, and condition have not been extensively examined in the health services literature. Our paper adds to the literature by examining the effect of duration, risk, and condition on savings from a management program while providing a methodology that may be followed by other practitioners to examine these effects in other populations.