Objective:To understand the potential categories of e-health literacy of elderly diabetic patients in community, and analyze the relationship between each category and physical activity level, in order to provide theoretical basis for formulating intervention measures to improve e-health literacy of elderly diabetic patients.Methods:This study was across-sectional survey. Elderly diabetic patients from 3 community health service centers in Fenyang City, Shanxi Province were selected by convenient sampling method from January to June 2023. General data questionnaire, e-Health Literacy Scale and International Physical Activity Questionnaire were used to investigate the elderly diabetic patients. Mplus8.0 was used for potential profile analysis, and SPSS 26.0 software was used to compare the differences in physical activity levels among different categories of e-health literacy.Results:Totally 248 elderly diabetes patients in the community aged 60-88 (63.8 ± 6.4) years, 135 males (54.4%) and 113 females (45.6%). The e-health literacy scores of 248 elderly diabetic patients in community were (17.70 ± 5.30), which could be divided into three potential categories: general low literacy group (56.0%, 139/248), high evaluation ability-medium literacy group (18.2%, 45/248) and high access ability-high literacy group (25.8%, 64/248). Three potential categories of physical activity levels were (1 464.74 ± 190.37), (2 094.40 ± 139.70), (2 368.44 ± 157.27) metabolic equivalent-min per week, and the difference was statistically significant ( F=118.83, P<0.001). Conclusions:The e-health literacy of elderly diabetic patients in community can be divided into three potential categories, and the physical activity level of patients with different e-health literacy categories is different. It is suggested that targeted intervention should be conducted based on the homogenous group of elderly diabetic patients with e-health literacy to improve their physical activity level.

Objective:To investigate the symptoms and symptom clusters of elderly stroke patients in the acute phase, analyze the factors influencing the symptom clusters and to provide a basis for the implementation of targeted symptom management in elderly stroke patients.Methods:Convenience sampling method was used to select 257 acute-phase elderly stroke patients who attended Fenyang Hospital in Shanxi Province from October 2022 to January 2023, and a cross-sectional survey was conducted by the General Information Questionnaire, Memory Symptom Evaluation Inventory, Modified Barthel Index Scale (MBI), and the National Institutes of Health Stroke Scale (NIHSS), and the symptom clusters were extracted by means of exploratory factor analysis, and the factors influencing symptom clusters were explored by means of binary logistic regression analysis.Results:Of the 257 patients, 138 were male and 119 were female, aged 60 to 90 (70.34 ± 6.94) years old. Factor analysis yielded four symptom clusters, which were named oral-intestinal symptom cluster, negative emotional symptom cluster, fatigue symptom cluster, and perceptual symptom cluster according to the symptom characteristics; regression analysis showed that: stroke site and NIHSS scores were the influencing factors of the oral-intestinal symptom cluster (all P<0.05); per capita monthly family income, number of episodes and MBI scores were the influencing factors of the negative emotional symptom cluster (all P<0.05); number of chronic diseases, type of stroke, and gender were the influencing factors of the fatigue emotional symptom cluster (all P<0.05); and literacy and stroke site were influencing factors of the perception symptom cluster (all P<0.05). Conclusions:Elderly stroke patients in the acute phase of the existence of more symptoms, symptoms interact with each other to form symptom clusters, health care personnel should be symptom clusters as a unit, the implementation of interventions for their influencing factors, effective symptom management, in order to improve the quality of life of patients.

Objective To evaluate the accuracy of simple anthropometric parameters in diagnosing obesity in children in Guangzhou. Methods A cross-sectional study, including 465 children aged 6-9 years, was carried out in Guangzhou. Their body height and weight, waist circumference (WC) and hip circumference were measured according to standard procedure. Body mass index (BMI), waist to hip ratio (WHR) and waist-to-height ratio (WHtR) were calculated. Body fat percentage (BF%)was determined by dual-energy X-ray absorptiometry. Multiple regression analysis was applied to evaluate the correlations between age-adjusted physical indicators and BF%, after the adjustment for age. Obesity was defined by BF%. Receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic accuracy of the indicators for childhood obesity. Area under-ROC curves (AUCs) were calculated and the best cut-off point that maximizing'sensitivity+specificity-1'was determined. Results BMI showed the strongest association with BF% through multiple regression analysis. For'per-standard deviation increase' of BMI, BF%increased by 5.3%(t=23.1, P<0.01) in boys and 4.6%(t=17.5, P<0.01) in girls, respectively. The ROC curve analysis indicated that BMI exhibited the largest AUC in both boys (AUC=0.908) and girls (AUC=0.895). The sensitivity was 80.8% in boys and 81.8% in girls, and the specificity was 88.2%in boys and 87.1%in girls. Both the AUCs for WHtR and WC were less than 0.8 in boys and girls. WHR had the smallest AUCs (<0.8) in both boys and girls. Conclusion BMI appeared to be a good predicator for BF%in children aged 6-9 years in Guangzhou.

【Objective】 To analyze the association between the composition of gut microbiota and blood pressure in children aged 6 - 9 years, in order to provide new ideas for childhood hypertension prevention and treatment. 【Methods】 A total of 411 children aged 6 - 9 years were recruited in Guangzhou from December 2015 to March 2017. The gut microbiota was characterized by 16S ribosomal RNA amplicon sequencing. The multivariate methods with unbiased variable selection in R (MUVR) were performed to identify the significant OTUs. Spearman correlation as well as multiple linear regression were used to explore the relationship between gut microbiota and blood pressure in children. 【Results】 Significant difference in β diversity index was observed between children with normal blood pressure and those with abnormal blood pressure (R²weighted=0.015,Pweighted=0.01; R²unweighted=0.027, Punweighted=0.001). After adjustment for covariates and controlling the false discovery rate (FDR), multiple linear regression analysis showed that blood pressure level in children decreased with the increasing abundance of OTU_3 (genus Blautia), OTU_131 (genus [Clostridium]_innocuum_group), OTU_1776 (genus Blautia), OTU_2159 (genus Prevotella) and OTU_91 (species Bifidobacterium_breve) (β:-0.18 --0.14; PFDR<0.05. In contrast, blood pressure in children increased with the increasing abundance of OTU_108(genus Sutterella), OTU_1(species Faecalibacterium_prausnitzii)、OTU_8(genus Roseburia), OTU_48 (genus Lachnoclostridium), OTU_81 (genus Coprococcus), OTU_401 (family Lachnospiraceae), OTU_1284 (family Lachnospiraceae) and OTU_2793 (family Lachnospiraceae)(β: 0.14 - 0.21; PFDR<0.05. The influence of gut microbiota on systolic pressure and diastolic pressure may be mediated by the increase of body mass index (BMI). 【Conclusions】 Pediatric blood pressure level is associated with the composition of gut microbiota, while BMI partially plays a mediating role in the relationship. It is recommended to modulate the abundance of microbiota as genus Blautia, Sutterella and so on to prevent hypertension in children.