Factors Influencing Patients' Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey

Yiyu Zhang, Chaoyuan Liu, Shuoming Luo, Yuting Xie, Fang Liu, Xia Li, Zhiguang Zhou, Yiyu Zhang, Chaoyuan Liu, Shuoming Luo, Yuting Xie, Fang Liu, Xia Li, Zhiguang Zhou

Abstract

Background: Diabetes poses heavy social and economic burdens worldwide. Diabetes management apps show great potential for diabetes self-management. However, the adoption of diabetes management apps by diabetes patients is poor. The factors influencing patients' intention to use these apps are unclear. Understanding the patients' behavioral intention is necessary to support the development and promotion of diabetes app use.

Objective: This study aimed to identify the determinants of patients' intention to use diabetes management apps based on an integrated theoretical model.

Methods: The hypotheses of our research model were developed based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT). From April 20 to May 20, 2019, adult patients with diabetes across China, who were familiar with diabetes management apps, were surveyed using the Web-based survey tool Sojump. Structural equation modeling was used to analyze the data.

Results: A total of 746 participants who met the inclusion criteria completed the survey. The fitness indices suggested that the collected data fit well with the research model. The model explained 62.6% of the variance in performance expectancy and 57.1% of the variance in behavioral intention. Performance expectancy and social influence had the strongest total effects on behavioral intention (β=0.482; P=.001). Performance expectancy (β=0.482; P=.001), social influence (β=0.223; P=.003), facilitating conditions (β=0.17; P=.006), perceived disease threat (β=0.073; P=.005), and perceived privacy risk (β=-0.073; P=.012) had direct effects on behavioral intention. Additionally, social influence, effort expectancy, and facilitating conditions had indirect effects on behavioral intention that were mediated by performance expectancy. Social influence had the highest indirect effects among the three constructs (β=0.259; P=.001).

Conclusions: Performance expectancy and social influence are the most important determinants of the intention to use diabetes management apps. Health care technology companies should improve the usefulness of apps and carry out research to provide clinical evidence for the apps' effectiveness, which will benefit the promotion of these apps. Facilitating conditions and perceived privacy risk also have an impact on behavioral intention. Therefore, it is necessary to improve facilitating conditions and provide solid privacy protection. Our study supports the use of UTAUT in explaining patients' intention to use diabetes management apps. Context-related determinants should also be taken into consideration.

Keywords: China; diabetes mellitus; mobile applications; structural equation modeling; survey.

Conflict of interest statement

Conflicts of Interest: None declared.

©Yiyu Zhang, Chaoyuan Liu, Shuoming Luo, Yuting Xie, Fang Liu, Xia Li, Zhiguang Zhou. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.08.2019.

Figures

Figure 1
Figure 1
Research model. UTAUT: Unified Theory of Acceptance and Use of Technology.
Figure 2
Figure 2
Sampling procedure and results.
Figure 3
Figure 3
Research model explaining performance expectancy and behavioral intention (direct effects). H1: Performance expectancy positively influences the behavioral intention of patients to use diabetes management apps, H2: Effort expectancy positively influences the behavioral intention of patients to use diabetes management apps, H3: Effort expectancy positively influences performance expectancy, H4: Facilitating conditions positively influence the behavioral intention of patients to use diabetes management apps, H5: Facilitating conditions positively influence performance expectancy, H6: Social influence positively influences the behavioral intention of patients to use diabetes management apps, H7: Social influence positively influences performance expectancy, H8: Perceived disease threat positively influences the behavioral intention of patients to use diabetes management apps, H9: Perceived disease threat positively influences performance expectancy.

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