Factors Influencing the Behavioral Intentions and Use Behaviors of Telemedicine in Patients With Diabetes: Web-Based Survey Study

被引:1
作者
Shao, Huige [1 ]
Liu, Chaoyuan [2 ]
Tang, Li [1 ]
Wang, Bian [1 ]
Xie, Hebin [3 ]
Zhang, Yiyu [1 ,4 ]
机构
[1] Univ South China, Affiliated Changsha Cent Hosp, Hengyang Med Sch, Dept Endocrinol, Changsha, Peoples R China
[2] Cent South Univ, Xiangya Hosp 2, Dept Oncol, Changsha, Peoples R China
[3] Univ South China, Hengyang Med Sch, Sci & Educ Dept, Affiliated Changsha Cent Hosp, Changsha, Peoples R China
[4] Univ South China, Affiliated Changsha Cent Hosp, Hengyang Med Sch, Dept Endocrinol, 161 Shaoshan South Rd, Changsha 410000, Peoples R China
来源
JMIR HUMAN FACTORS | 2023年 / 10卷
关键词
diabetes mellitus; telemedicine; survey; China; behavioral intention; acceptance; technology; technology use; diabetic; outpatient; eHealth; remote care; older adult patients; low income; diabetes; type; 1; 2; INFORMATION-TECHNOLOGY; ACCEPTANCE; SATISFACTION; MANAGEMENT; EXTENSION; ADOPTION; MHEALTH;
D O I
10.2196/46624
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Telemedicine has great potential for diabetes management. The COVID-19 pandemic has boosted the development of telemedicine. However, the factors influencing the behavioral intentions to use and use behaviors of telemedicine in patients with diabetes in China are not clear. Objective: We aimed to understand the determinants of behavioral intention to use telemedicine based on an extended Unified Theory of Acceptance and Use of Technology model and to identify demographic factors associated with telemedicine use in patients with diabetes in China. Methods: Patients with diabetes who are aged >= 18 years were surveyed from February 1 to February 7, 2023. We distributed the survey link in 3 WeChat groups including a total of 988 patients with diabetes from the outpatient department or patients discharged from Changsha Central Hospital. Structural equation modeling was used to understand the determinants of behavioral intention. A multivariate logistic regression analysis was used to identify the demographic factors associated with telemedicine use. Results: In total, 514 questionnaires were collected. Of the respondents, 186 (36.2%) were diagnosed with COVID-19. The measurement model showed acceptable reliability, convergent validity, discriminant validity, and data fit indices. The model explained 63.8% of the variance in behavioral intention. Social influence, performance expectancy, and facilitating conditions positively influenced behavioral intention (beta=.463, P<.001; beta=.153, P=.02; and beta=.257, P=.004, respectively). Perceived susceptibility, perceived severity, and effort expectancy had no significant impact on behavioral intention (all P>.05). The overall use of telemedicine was 20.6% (104/514). After adjusting for the behavioral intention score, the multivariate regression analysis showed that age, education, and family income were associated with telemedicine use. Telemedicine use was higher in the 40 to 59 years and 18 to 39 years age groups than in the >= 60 years age group (odds ratio [OR] 4.35, 95% CI 1.84-10.29, P=.001; OR 9.20, 95% CI 3.40-24.88, P<.001, respectively). Telemedicine use was higher in the senior high school and the university and more groups than in junior high school education and less group (OR 2.45, 95% CI 1.05-5.73, P=.04; OR 2.63, 95% CI 1.11-6.23, P=.03, respectively). Patients with a higher family income used telemedicine more often than the patients who had an annual family income <= yen 10,000 (CNY yen 1=US $0.1398; yen 10,000- yen 50,000 group: OR 3.90, 95% CI 1.21-12.51, P=.02; yen 50,000- yen 100,000 group: OR 3.91, 95% CI 1.19-12.79, P=.02; > yen 100,000 group: OR 4.63, 95% CI 1.41-15.27, P=.01). Conclusions: Social influence, performance expectancy, and facilitating conditions positively affected the behavioral intention of patients with diabetes to use telemedicine. Young patients, highly educated patients, and patients with high family income use telemedicine more often. Promoting behavioral intention and paying special attention to the needs of older adult patients, patients with low income, and patients with low levels of education are needed to encourage telemedicine use.
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