Abstract
Objective: To determine the characteristics of members of online diabetes communities as well as those factors affecting the provision and acceptance of social support. Methods: A cross-sectional STAR questionnaire survey was conducted among patients with diabetes who were members of online diabetes groups. Univariate and multivariate binary logistic regression analysis were adopted to explore the relative analysis of providing and accepting social support compared with the characteristics of members in virtual diabetics’ groups. Results: A total of 1297 respondents were collected. The map distribution of patients in China was mainly located in the Guangdong, Jiangsu, Shandong, Henan, and Hebei provinces. As for their demographic characteristics, respondents had diabetes or prediabetes and were between the ages of 21 and 50 years (Median age was 35.0 (interquartile range from 28.0 to 44.0)). Most respondents were married and lived in cities. The education level of patients was mainly distributed throughout junior high, technical secondary, high school, junior college, and undergraduate levels. Age, marital status, and education level varied by gender, and the total score of the patients aged 41 to 50 for social support had a statistical significance between male and female. In addition, when group members were in junior high school or below, or were undergraduate students, their total social support scores varied by gender. Binary logistic regression showed that in 21 independent variables the total score and the total score grade of relationship intensity in the online group and reorganize of age were significant. The patients’ social support acceptance of the map of respondents score grading of relationship intensity in the online group was 5.420 times higher than that of the lower score grading of relationship intensity in the group. At the same time, the patients’ social support acceptance of the patients at the age of less than or equal to 31 years old was 19.608 times higher than that of group members aged more than 31 years old. Conclusion: Age and education background of the patients affects scores of social supports between males and females. The higher the total score and the score grade of relationship intensity in the online group, the higher the patients’ social support acceptance. The younger patients had a better utilization of social support.
Highlights
With rapid developments in mobile Internet technology and the promotion and evaluation of the progress made by traditional mutual aid organizations, more people are participating in onlineInt
54% and 46% of respondents were male and female, respectively; 72.3% and 27.7% were married and single, respectively; and most respondents were between 21 and 50 years of age (The age distribution of the subjects did not conform to the normal distribution, because tests of normality (The test of using observation data to judge whether the population obeys normal distribution) showed the statistic for Shapiro-Wilk was 0.977 and p = 0.000), and median age was 35.0 (Interquartile ranged from 28.0 to 44.0), the age range in which diabetes is most likely to occur [25]
No statistical significance was found in place of residence after participating in a diabetics’ group, the degree of help that improved their own diseases, the average number of members in various diabetics’ groups that participated with great attention and their own condition about diabetes (Table 1)
Summary
With rapid developments in mobile Internet technology and the promotion and evaluation of the progress made by traditional mutual aid organizations, more people are participating in onlineInt. The formation of online groups where people with diabetes can interact with each other has been a consequential development in diabetes management as it has been an “expansion and innovation” of the professional-led mode of intervention in traditional diabetes care Considering these developments, we conducted a large-scale population survey of people with diabetes from China who were members of online diabetes communities (ODCs), we systematically collected data on the target population for empirical analysis, and the healthy social characteristics of the ODCs population were explored. This improved understanding of the relationship of personal and online interaction characteristics with acceptance of online social support in people with diabetes, thereby aiding diabetes treatment in general and the promoting the development of diabetes health groups in particular
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