BackgroundA number of studies have examined the influence of self-efficacy, social support and patient-provider communication (PPC) on self-care and glycemic control. Relatively few studies have tested the pathways through which these constructs operate to improve glycemic control, however. We used structural equation modeling to examine a conceptual model that hypothesizes how self-efficacy, social support and patient-provider communication influence glycemic control through self-care behaviors in Chinese adults with type 2 diabetes.MethodsWe conducted a cross-sectional study of 222 Chinese adults with type 2 diabetes in one primary care center. We collected information on demographics, self-efficacy, social support, patient-provider communication (PPC) and diabetes self-care. Hemoglobin A1c (HbA1c) values were also obtained. Measured variable path analyses were used to determine the predicted pathways linking self-efficacy, social support and PPC to diabetes self-care and glycemic control.ResultsDiabetes self-care had a direct effect on glycemic control (β = −0.21, p = .007), No direct effect was observed for self-efficacy, social support or PPC on glycemic control. There were significant positive direct paths from self-efficacy (β = 0.32, p < .001), social support (β = 0.17, p = .009) and PPC (β = 0.14, p = .029) to diabetes self-care. All of them had an indirect effect on HbA1c (β =–0.06, β =–0.04, β =–0.03 respectively). Additionally, PPC was positively associated with social support (γ = 0.32, p < .001).ConclusionsHaving better provider-patient communication, having social support, and having higher self-efficacy was associated with performing diabetes self-care behaviors; and these behaviors were directly linked to glycemic control. So longitudinal studies are needed to explore the effect of self-efficacy, social support and PPC on changes in diabetes self-care behaviors and glycemic control.

cardiovascular disease (cVD) is the leading cause of death worldwide and a major public health concern. CVD prediction is one of the most effective measures for CVD control. In this study, 29930 subjects with high-risk of CVD were selected from 101056 people in 2014, regular follow-up was conducted using electronic health record system. Logistic regression analysis showed that nearly 30 indicators were related to CVD, including male, old age, family income, smoking, drinking, obesity, excessive waist circumference, abnormal cholesterol, abnormal low-density lipoprotein, abnormal fasting blood glucose and else. Several methods were used to build prediction model including multivariate regression model, classification and regression tree (CART), Naïve Bayes, Bagged trees, Ada Boost and Random forest. We used the multivariate regression model as a benchmark for performance evaluation (Area under the curve, AUC = 0.7143). The results showed that the Random Forest was superior to other methods with an AUC of 0.787 and achieved a significant improvement over the benchmark. We provided a CVD prediction model for 3-year risk assessment of CVD. It was based on a large population with high risk of CVD in eastern China using Random Forest algorithm, which would provide reference for the work of cVD prediction and treatment in china. Cardiovascular disease (CVD) is a series of diseases involving the circulatory system, including angina pectoris, myocardial infarction, coronary heart disease, heart failure, arrhythmia and else, which is generally related to atherosclerosis. With the social economy development, the population aging and the urbanization acceleration in China, some changes have taken place in national lifestyles, which leading to a rise of CVD prevalence. In 2016, there were more than 290 million cases of CVD in China, and 4.344 million deaths from it, including 2.098 million deaths from stroke and 1.736 million deaths from coronary heart disease, which bringing heavy social and economic burden 1. CVD is a disease that can be prevented and controlled, and early intervention can effectively control its progress 2. In recent years, many achievements have been made in the study of CVD risk prediction model, but the effect of epidemiological risk factors and biomarkers may be different in different populations, the CVD model has certain population specificity. In addition, there has been no study on CVD risk prediction model based on large cohort population in eastern China. At the same time, a large number of the existing CVD prediction models use multivariable regression method to build prediction models in a linear fashion, but it generally exhibit modest predictive performance, especially for certain sub-populations 3,4. Machine learning (ML) such as random forest (RF) can improve the performance of risk predictions by exploiting large data repositories to identify novel risk predictors and more complex interactions between them 3. In this study, we conducted a CVD prediction model research based on a specific c...

Introduction: We examined: (a) current (past 30-day) smokers' interest in using or switching to electronic nicotine delivery systems (ENDS) or smokeless tobacco for various reasons; (b) correlates of interest in these products; and (c) subgroups of current smokers in relation to interest in these products. Methods: We conducted a cross-sectional survey assessing sociodemographics, tobacco use, interest in ENDS and smokeless tobacco among smokers, and knowledge about ENDS among 2,501 US adults recruited through an online consumer panel. We oversampled tobacco users (36.7% current cigarette smokers), ethnic minorities, and southeastern US state residents. Results: On average, participants were more interested in ENDS than smokeless tobacco across all reasons provided. Additionally, they were less interested in either product because of their potential use in places prohibiting smoking or due to curiosity and more interested in reducing health risk or cigarette consumption or to aid in cessation. We documented high rates (27.9%) of misbeliefs about Food and Drug Administration approval of ENDS for cessation, particularly among current smokers (38.5%). Also, 27.2% of current smokers had talked with a health care provider about ENDS, with 18.0% reporting that their provider endorsed ENDS use for cessation. Furthermore, cluster analyses revealed 3 groups distinct in their interest in the products, sociodemographics, and smoking-related characteristics. Conclusions: This study highlights higher interest in ENDS versus smokeless tobacco and greater interest in both for harm reduction and cessation than due to novelty or smoking restrictions. Developing educational campaigns and informing practitioners about caveats around ENDS as cessation or harm reduction aids are critical.

Background The control of vaccine hesitancy and the promotion of vaccination are key protective measures against COVID-19. Objective This study assesses the prevalence of vaccine hesitancy and the vaccination rate and examines the association between factors of the health belief model (HBM) and vaccination. Methods A convenience sample of 2531 valid participants from 31 provinces and autonomous regions of mainland China were enrolled in this online survey study from January 1 to 24, 2021. Multivariable logistic regression was used to identify the associations of the vaccination rate and HBM factors with the prevalence of vaccine hesitancy after other covariates were controlled. Results The prevalence of vaccine hesitancy was 44.3% (95% CI 42.3%-46.2%), and the vaccination rate was 10.4% (9.2%-11.6%). The factors that directly promoted vaccination behavior were a lack of vaccine hesitancy (odds ratio [OR] 7.75, 95% CI 5.03-11.93), agreement with recommendations from friends or family for vaccination (OR 3.11, 95% CI 1.75-5.52), and absence of perceived barriers to COVID-19 vaccination (OR 0.51, 95% CI 0.35-0.75). The factors that were directly associated with a higher vaccine hesitancy rate were a high level of perceived barriers (OR 1.63, 95% CI 1.36-1.95) and perceived benefits (OR 0.51, 95% CI 0.32-0.79). A mediating effect of self-efficacy, influenced by perceived barriers (standardized structure coefficient [SSC]=−0.71, P<.001), perceived benefits (SSC=0.58, P<.001), agreement with recommendations from authorities (SSC=0.27, P<.001), and agreement with recommendations from friends or family (SSC=0.31, P<.001), was negatively associated with vaccination (SSC=−0.45, P<.001) via vaccine hesitancy (SSC=−0.32, P<.001). Conclusions It may be possible to increase the vaccination rate by reducing vaccine hesitancy and perceived barriers to vaccination and by encouraging volunteers to advocate for vaccination to their friends and family members. It is also important to reduce vaccine hesitancy by enhancing self-efficacy for vaccination, due to its crucial mediating function.

Background Well-designed mobile health (mHealth) interventions support a positive user experience; however, a high rate of disengagement has been reported as a common concern regarding mHealth interventions. To address this issue, it is necessary to summarize the design features that improve user engagement based on research over the past 10 years, during which time the popularity of mHealth interventions has rapidly increased due to the use of smartphones. Objective The aim of this review was to answer the question “Which design features improve user engagement with mHealth interventions?” by summarizing published literature with the purpose of guiding the design of future mHealth interventions. Methods This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. Databases, namely, PubMed, Web of Science, Cochrane Library, Ovid EMBASE, and Ovid PsycINFO, were searched for English and Chinese language papers published from January 2009 to June 2019. Thematic analysis was undertaken to assess the design features in eligible studies. The Mixed Methods Appraisal Tool was used to assess study quality. Results A total of 35 articles were included. The investigated mHealth interventions were mainly used in unhealthy lifestyle (n=17) and chronic disease (n=10) prevention programs. Mobile phone apps (n=24) were the most common delivery method. Qualitative (n=22) and mixed methods (n=9) designs were widely represented. We identified the following 7 themes that influenced user engagement: personalization (n=29), reinforcement (n=23), communication (n=20), navigation (n=17), credibility (n=16), message presentation (n=16), and interface aesthetics (n=7). A checklist was developed that contained these 7 design features and 29 corresponding specific implementations derived from the studies. Conclusions This systematic review and thematic synthesis identified useful design features that make an mHealth intervention more user friendly. We generated a checklist with evidence-based items to enable developers to use our findings easily. Future evaluations should use more robust quantitative approaches to elucidate the relationships between design features and user engagement.

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BackgroundTB and HIV co-epidemic is a major public health problem in many parts of the world. But the prevalence of TB/HIV co-infection was diversified among countries. Exploring the reasons of the diversity of TB/HIV co-infection is important for public policy, planning and development of collaborative TB/HIV activities. We aimed to summarize the prevalence of TB and HIV co-infection worldwide, using meta-analysis based on systematic review of published articles.MethodsWe searched PubMed, Embase, and Web of Science for studies of the prevalence of TB/HIV co-infection. We also searched bibliographic indices, scanned reference lists, and corresponded with authors. We summarized the estimates using meta-analysis and explored potential sources of heterogeneity in the estimates by metaregression analysis.ResultsWe identified 47 eligible studies with a total population of 272,466. Estimates of TB/HIV co-infection prevalence ranged from 2.93% to 72.34%; the random effects pooled prevalence of TB/HIV co-infection was 23.51% (95% CI 20.91–26.11). We noted substantial heterogeneity (Cochran’s χ 2 = 10945.31, p<0.0001; I 2 = 99.58%, 95% CI 99.55–99.61). Prevalence of TB/HIV co-infection was 31.25%(95%CI 19.30–43.17) in African countries, 17.21%(95%CI 9.97–24.46) in Asian countries, 20.11%(95%CI 13.82–26.39) in European countries, 25.06%(95%CI 19.28–30.84) in Latin America countries and 14.84%(95%CI 10.44–19.24) in the USA. Prevalence of TB/HIV co-infection was higher in studies in which TB diagnosed by chest radiography and HIV diagnosis based on blood analyses than in those which used other diagnostic methods, and in countries with higher prevalence HIV in the general population than in countries with lower general prevalence.ConclusionsOur analyses suggest that it is necessary to attach importance to HIV/TB co-infection, especially screening of TB/HIV co-infection using methods with high sensitivity, specificity and predictive values in the countries with high HIV/AIDS prevalence in the general population.

The results suggest that the GV model is an acceptable and effective model for managing Chinese hypertensive patients in primary health care centers, and it could be a complement to the traditional individual office visit.