BackgroundObesity is known to be a risk factor to a variety of chronic diseases. Weight misperception has an impact on weight-loss attitude and behavior. We aimed to investigate factors associated with weight underestimation, and to assess the effect of hypertension, diabetes and dyslipidemia awareness on weight underestimation and weight management for overweight and obese adults.MethodsData was obtained from the 2011 Beijing Non-communicable disease and risk factors Surveillance (BJNCDRS). A total of 19,932 participants with measures of weight and height were included in the analysis. Self-perception of weight was obtained by asking, “How do you describe your weight?”, and the question for weight management was “Are you taking any actions to control your body weight?”. Multiple logistic regression was used to investigate factors related to weight underestimation.ResultsFor the underweight, normal weight, and overweight/obese categories, more than half of the participants perceived their weight accurately (63.6, 53.8, 66.2%, respectively). For overweight and obese adults, older age, male, rural residence, lower level of education, lower level of income, absence of hypertension, presence of diabetes and absence of dyslipidemia positively associated with weight underestimation, and awareness of having hypertension and dyslipidemia were negatively associated with weight underestimation (Adjusted OR(95%CI) were 0.70(0.61~ 0.79) and 0.71(0.62~ 0.80), respectively). Awareness of having hypertension and dyslipidemia were significantly associated with weight management (Adjusted OR (95%CI) were 1.42(1.25~ 1.62) and 1.53(1.36~ 1.72), respectively). There was no significant association between awareness of diabetes and weight underestimation(P > 0.05) or weight management(P > 0.05).ConclusionsMore than half of the participants perceived their weight accurately. For overweight/obese population, awareness of having hypertension and dyslipidemia could improve weight perception and weight management, whereas awareness of having diabetes might not.

<b><i>Objective:</i></b> Metabolic syndrome (MetS) is one of the major public health problems worldwide. The aim of this study was to investigate the prevalence and associated risk factors of MetS in Beijing to formulate targeted policies. <b><i>Methods:</i></b> Data from the 2017 Beijing Chronic Disease and Risk Factors Surveillance were used in this study, in which multistage stratified cluster sampling was adopted to collect a representative sample of 12,597 Beijing residents aged from 18 to 79 years. According to the definition of the International Diabetes Federation, the weighted prevalence of MetS and clustering of MetS components were estimated. The Rao-Scott adjusted χ² test was used to test differences in MetS and components rates, and complex sampling unconditional logistic regression was used to explore influencing factors of MetS. <b><i>Results:</i></b> The prevalence of MetS was 25.59% (95% CI 23.77–27.41), and the proportion of men and women was 30.53% (95% CI 28.32–32.75) and 20.44% (95% CI 18.29–22.58), respectively. The proportion of central obesity, high fasting plasma glucose, high triglyceride, low high-density lipoprotein cholesterol, and high blood pressure (BP) was 42.02, 27.96, 32.87, 27.25, and 43.06%, respectively. A total of 29.60% (95% CI 27.55–31.74) participants presented at least three components of MetS. The results from complex sampling unconditional logistic regression revealed that the risk factors for having MetS included being 45–59 years old, being 60–79 years old, being male, living in a rural area, education with junior middle school level, education with primary school or below level, harmful use of alcohol, inappropriate sleep time, and having an unhealthy waist-to-height ratio (WHtR); the OR values were 1.55 (95% CI 1.32–1.81), 1.94 (95% CI 1.62–2.31), 1.51 (95% CI 1.34–1.70), 1.27 (95% CI 1.06–1.52), 1.38 (95% CI 1.13–1.68), 1.44 (95% CI 1.13–1.84), 1.50 (95% CI 1.14–1.99), 1.23 (95% CI 1.10–1.37), and 238.20 (95% CI 92.54–613.12), respectively. <b><i>Conclusions:</i></b> The prevalence of MetS is still in a rising trend in Beijing. Strategies aimed at prevention and control of high BP should be prioritized to reduce the occurrence of MetS. WHtR is more important to evaluate MetS. Health education and personalized lifestyle intervention should be promoted to keep a healthy WHtR and waist circumference. An appropriate sleep time should be kept, and harmful alcohol drinkers should limit or abstain from alcohol.

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Background Patients with type 2 diabetes (T2DM) have an increasing need for personalized and Precise management as medical technology advances. Artificial intelligence (AI) technologies on mobile devices are being developed gradually in a variety of healthcare fields. As an AI field, knowledge graph (KG) is being developed to extract and store structured knowledge from massive data sets. It has great prospects for T2DM medical information retrieval, clinical decision-making, and individual intelligent question and answering (QA), but has yet to be thoroughly researched in T2DM intervention. Therefore, we designed an artificial intelligence-based health education accurately linking system (AI-HEALS) to evaluate if the AI-HEALS-based intervention could help patients with T2DM improve their self-management abilities and blood glucose control in primary healthcare. Methods This is a nested mixed-method study that includes a community-based cluster-randomized control trial and personal in-depth interviews. Individuals with T2DM between the ages of 18 and 75 will be recruited from 40-45 community health centers in Beijing, China. Participants will either receive standard diabetes primary care (SDPC) (control, 3 months) or SDPC plus AI-HEALS online health education program (intervention, 3 months). The AI-HEALS runs in the WeChat service platform, which includes a KBQA, a system of physiological indicators and lifestyle recording and monitoring, medication and blood glucose monitoring reminders, and automated, personalized message sending. Data on sociodemography, medical examination, blood glucose, and self-management behavior will be collected at baseline, as well as 1,3,6,12, and 18 months later. The primary outcome is to reduce HbA1c levels. Secondary outcomes include changes in self-management behavior, social cognition, psychology, T2DM skills, and health literacy. Furthermore, the cost-effectiveness of the AI-HEALS-based intervention will be evaluated. Discussion KBQA system is an innovative and cost-effective technology for health education and promotion for T2DM patients, but it is not yet widely used in the T2DM interventions. This trial will provide evidence on the efficacy of AI and mHealth-based personalized interventions in primary care for improving T2DM outcomes and self-management behaviors. Trial registration Biomedical Ethics Committee of Peking University: IRB00001052-22,058, 2022/06/06; Clinical Trials: ChiCTR2300068952, 02/03/2023.

To improve the overall performance of polyurethane acrylic (PUAs) coatings applied to an iron or wood substrate, a modifier, trivinylisooctyl polyhedral oligomeric silsesquioxane (TVi7iso–POSS), was successfully synthesized by a polycondensation reaction in the presence of an organotin catalyst. TVi7iso–POSS is a POSS derivative possessing three olefin and seven isooctyl bonds; its molecular structure was confirmed by FT-IR, 1H-NMR, and mass spectrometry. The synthesized TVi7iso–POSS was then used as a modifier with butyl methacrylate (BMA), dodecafluoroheptyl methacrylate (DFMA), difunctional PUA (PUA–2), and photo-initiator 1173 to produce a novel polyurethane coating (PFMPUAs) via UV-curing. The performance of the obtained PFMPUAs coating was analyzed via X-ray photoelectron spectrometry, SEM, atomic force microscopy, TGA, and differential scanning calorimetry. The newly synthesized modifier, TVi7iso–POSS, enhanced the thermal stability, hardness, flexibility, impact resistance, and adhesion of the PUAs coating and maintained its good light transmittance. Moreover, the PFMPUAs coating exhibited better overall performance compared to the previously studied PUAs coating when the addition of TVi7iso–POSS and DFMA was 15 wt.% of PUA–2. Therefore, the PFMPUAs coating has potential applications in the field of environmentally friendly coatings.

Background: Cigarette smoking, including secondhand exposure, is a leading cause of several atherosclerotic diseases (AS). Non-high-density lipoprotein cholesterol (non-HDL-C) is considered as a valuable predictor for dyslipidemia and AS. There is a need to identify the association of tobacco smoke exposure (TSE) and the marker of sub-clinical AS.Objective: To examine the association of TSE, including exposed status, cigarette smoking intensity and burden with serum non-HDL-C level.Methods: A cross-sectional study was carried out on 18-79 years old residents in 2017 in Beijing. All participants were interviewed on their tobacco exposed status (unexposed, passively exposed, actively exposed), smoking intensity (number of cigarettes smoked per week) and smoking burden (pack-years of smoking) among current smokers. Non-HDL-C was calculated by subtracting high density lipoprotein cholesterol (HDL-C) from total cholesterol (TC). Covariates were age, education status, alcohol drinking status within 30 days, hypertension, type 2 diabetes, physical inactivity and BMI. We plot graphs to show the tendency between the number of cigarettes smoked per week, passively exposed days per week and non-HDL-C levels, respectively. Multiple regression models were used to assess the association between non-HDL-C level and TSE after the adjustment for covariates. Results: Of the 12,798 participants, mean age was 44.9±15.4 years, 46.8% were male. The prevalence of actively exposed, passively exposed and unexposed to tobacco smoke was 25.7%, 35.7% and 38.6%, respectively. Of the tobacco passively exposed subjects, 21.7% had to exposed to second-hand smoke every day. Among current smokers, 41.2% were smoking more than 120 cigarettes per week, and 56.6% were smoking more over 20 pack-years. With the increasing of the number of cigarettes smoked per week, the serum level of non-HDL-C in male increased gradually (Male: F=2.83, P=0.04; Female: F=1.23, P=0.32; Total: F=3.29, P=0.02). After multivariable adjustment, smokers had a higher non-HDL-C level (OR=1.34, 95%CI: 1.20,1.59, P=0.001) compared with tobacco unexposed subjects. However, passively smokers were not associated with non-HDL-C levels regardless of gender (OR=0.98, 95%CI: 0.78,1.23, P=0.87). Compared with lighter smokers, male smokers who smoked more than 140 cigarettes per week had significantly higher non-HDL-C levels (OR=1.30, 95%CI: 1.00,1.70, P=0.04). Male higher burden smokers (>20 pack-years) had more risk of having higher non-HDL-C level (OR=1.88, 95%CI: 1.46,2.43, P<0.001) compared with other smokers. However, there were no significant differences between non-HDL-C and smoking intensity or burden among female smokers.Conclusions: This study showed that strong associations between TSE and non-HDL-C levels, especially in male smokers. Findings of this work emphasize the importance of encouragement to focus on blood lipid levels among smokers.