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

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.

Barriers for User Acceptance of Mobile Health Applications for Diabetic Patients: Applying the UTAUT Model

“…The positive impact of effort expectancy on behavioural intention is supported by other studies that analysed consumer usage of health informatics [25]. However, the impact of effort expectancy on behavioural intention is more prominent for older users as a study with younger and well-educated users found that this relationship was not significant [25]. Therefore, diabetes applications should be designed so that they are easy to use and understand, especially for older users.…”

“…For patients aged 50 years or older, effort expectancy was identified as a key factor for diabetes applications [24]. The positive impact of effort expectancy on behavioural intention is supported by other studies that analysed consumer usage of health informatics [25]. However, the impact of effort expectancy on behavioural intention is more prominent for older users as a study with younger and well-educated users found that this relationship was not significant [25].…”

“…Research indicates that performance expectancy is an important determinant of the intention to use diabetes management apps [25]. The finding is supported by a study that indicates performance expectancy's significant impact on the users' behavioural intention to adopt mHealth services in Bangladesh [19].…”

“…Social influence is found to be a significant determinant of the intention to use diabetes management apps [25]. This finding is supported by the fact that supportive health care professionals and family members are core to m-health acceptance [27].…”

Differential Influences of Social Support on App Use for Diabetes Self-Management – a Mixed Methods Approach

“…While theoretical models of technology adoption (51) are one step towards understanding factors that in uence (mobile) technology adoption and use, there is a gap in proving both signi cant and substantive usage effects for mHealth (52,53). Previous studies investigated factors in uencing mHealth use in speci c target groups (e.g., 54,55), for speci c diseases or health topics (56)(57)(58)(59)(60), and for sustained mHealth engagement (61). This study aims to explicate the effect of social support.…”