期刊文章%@ 1438-8871 %I JMIR出版物%V 23 %N卡塔尔世界杯8强波胆分析 9 %P 26881 %T普通人群中糖尿病护理电子保健服务的准备和接受情况:横线研究%A AshaRani,PV %A juue Hua,Lau %A Roystonn,Kumarasan %A Siva Kumar,Fiona Devi %A Peizhi,Wang %A Ying Jie,Soo %A Shafie,Saleha %A Chang,Sherilyn %A Jeyagurunathan,Anitha %A Boon Yiang,Chua %A Abdin,Edimansyah %A Ajit Vaingankar,Janhavi %A Sum,Chee Fang %A Lee,Eng Sing %A Chong,Siow Ann %A Subramaniam,Mythily %+精神健康研究所研究部，新加坡Buangkok View 10, 539747，新加坡，65 63892961，asharani_pezhummoottil_vasudevan_n@imh.com.sg eHealth %K糖尿病%K普通人群%K接受%K准备%D 2021 %7 2.9.2021 %9原始论文%J J医学互联网Res %G英文%X背景:糖尿病管理是世界范围内日益增长的卫生保健挑战。电子保健可以彻底改变糖尿病护理，其成功与否取决于最终用户的接受程度。目的:本研究旨在了解在一个多民族的亚洲国家中，普通人群对糖尿病治疗的电子健康服务的准备程度和接受程度、感知的电子健康的优势和劣势，以及与电子健康准备程度和接受程度相关的因素。方法:在横断面流行病学研究中，参与者(N=2895)通过不成比例的分层随机抽样从人口登记中选择。招募年龄在18岁以上的新加坡公民或永久居民。这些数据是通过计算机辅助的个人访谈获得的。根据参与者的喜好，使用四种当地语言(英语、汉语、马来语或泰米尔语)中的一种进行电子健康问卷调查。采用双变量卡方分析比较糖尿病组和非糖尿病组之间的社会人口学特征和对电子健康服务的优缺点的感知。 Multivariable logistic regression models were used to determine factors associated with eHealth readiness and acceptance. All analyses were weighted using survey weights to account for the complex survey design. Results: The sample comprised participants with (n=436) and without (n=2459) diabetes. eHealth readiness was low, with 47.3% of the overall sample and 75.7% of the diabetes group endorsing that they were not ready for eHealth (P<.001). The most acceptable eHealth service overall was booking appointments (67.4%). There was a significantly higher preference in the diabetes group for face-to-face sessions for consultation with the clinician (nondiabetes: 83.5% vs diabetes: 92.6%; P<.001), receiving prescriptions (61.9% vs 79.3%; P<.001), referrals to other doctors (51.4% vs 72.2%; P<.001), and receiving health information (34% vs 63.4%; P<.001). The majority of both groups felt that eHealth requires users to be computer literate (90.5% vs 94.3%), does not build clinician-patient rapport compared with face-to-face sessions (77.5% vs 81%), and might not be credible (56.8% vs 64.2%; P=.03). Age (≥35 years), ethnicity (Indian), and lower education status had lower odds of eHealth readiness. Age (≥35 years), ethnicity (Indian), lower education status (primary school), BMI (being underweight), and marital status (being single) were associated with a lower likelihood of eHealth acceptance. Among only those with diabetes, a longer duration of diabetes (4-18 years), higher education (degree or above), and younger age (23-49 years) were associated with eHealth readiness, whereas younger age and income (SGD 2000-3999 [US $1481-$2961]) were associated with acceptance. Conclusions: Overall, an unfavorable attitude toward eHealth was observed, with a significantly higher number of participants with diabetes reporting their unwillingness to use these services for their diabetes care. Sociodemographic factors associated with acceptance and readiness identified a group of people who were unlikely to accept the technology and thus need to be targeted for eHealth literacy programs to avoid health care disparity. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2020-037125 %M 34473062 %R 10.2196/26881 %U //www.mybigtv.com/2021/9/e26881 %U https://doi.org/10.2196/26881 %U http://www.ncbi.nlm.nih.gov/pubmed/34473062