Introduction
mHealth acceptance
Mobile health (mHealth) applications, especially the so-called lifestyle apps such as fitness apps, have become increasingly popular, specifically among younger people, due to growing health awareness.1 2 Besides lifestyle apps, mHealth applications such as continuous glucose monitoring systems (CGMs) are instrumental for the self-management of chronic diseases like diabetes mellitus.3 4 Different studies have shown that using mHealth applications leads to improved self-management and better health among people with chronic diseases.5 6 This is especially true in the case of diabetes, which is one of the most frequently occurring chronic diseases worldwide.7 mHealth applications for patients with diabetes can support sustained self-management and help maintain lower long-term glucose levels.3 6 8
Despite the benefits associated with mHealth applications, there are several reasons why they are not used, such as difficulties in their control9 or acceptance problems.10–12 User acceptance can be described as ‘the demonstrable willingness within a user group to employ information technology for the tasks it is designed to support’.13 Several studies have shown that, especially for people with type 2 diabetes, the acceptance of mHealth self-management applications is noticeably low.8 14
Theoretical background
Technology acceptance models such as the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) have been developed to predict the acceptance of information technologies in health informatics and other fields of application.15–17
The UTAUT2 model was established in 2012 for use in a consumer context.18 In contrast to the previous technology acceptance models, UTAUT2 used additional exogenous constructs “habit”, “hedonic motivation” and “price value” to predict the endogenous construct “behavioral intention”, which is understood as an expression of technology acceptance.18 With the focus on the individuals and their needs, UTAUT2 is particularly suitable for predicting the acceptance of mHealth applications such as mobile diabetes applications.18 19 However, it is still not as widely used in mHealth acceptance studies as other technology acceptance models. Some studies using UTAUT2 have pointed out that essential aspects such as health-related factors19 20 or factors related to trust in the data collected2 21 are missing. In a previous qualitative study, we could confirm the general suitability of the UTAUT2 model in the field of mHealth self-management applications but identified some missing aspects, such as the awareness of the perceived threat of disease and credibility in the data collected by the application for predicting mHealth acceptance.22 Therefore, we proposed adding the following constructs to the UTAUT2 model: “perceived disease threat” and “trust”. The construct “trust” is associated with the belief that people accept uncertainties due to positive expectations.2 It is used to determine the data credibility and trustworthiness of the mobile health application, which is particularly important for behavioural intention and long-term use of mHealth applications.2 22
Furthermore, when patients face health-threatening situations, they are more open to new health technologies.11 12 Especially with chronic diseases like diabetes, the individual awareness of the risk and limitations for their health, reflected by the construct “perceived disease threat”, is a significant driver for acceptance and use of mHealth applications.20 22 Few studies have used the UTAUT2 model to predict mHealth acceptance to date, and these studies have not yet considered the two constructs of “perceived disease threat” and “trust”.19 22
Study objectives
This study aims to validate whether the exogenous UTAUT2 constructs, combined with the additional constructs “perceived disease threat” and “trust”, can predict mHealth acceptance using mobile diabetes applications as an example.
Hypotheses development and proposed research model
Figure 1 shows the proposed research model using the exogenous UTAUT2 constructs and additional constructs “perceived disease threat” (PDT) and “trust” (TR) for predicting the endogenous construct “behavioral intention” (BI) to use mHealth applications. Although this study focused on the acceptance (BI) of mobile diabetes applications, we also included the endogenous construct “use behavior” (UB) in the analysis to validate the exogenous constructs in the complete UTAUT2 model.
Based on the existing UTAUT2 model, we adopted the relationships between the exogenous constructs “performance expectancy” (PE), “effort expectancy” (EE), “social influence” (SI), “facilitating conditions” (FC), “hedonic motivation” (HM), “price value” (PV) and “habit” (HT) and the endogenous construct “behavioral intention” (BI).18 In addition, the factors PDT and TR have been shown in various studies to predict the acceptance of mHealth applications.1 2 11 12 20 22 This leads to the following hypothesis: PE, EE, SI, FC, HM, PV, HT, PDT and TR affect the BI to use mobile diabetes applications.