Page 50 - Exploring the Potential of Self-Monitoring Kidney Function After Transplantation - Céline van Lint

48 Chapter 3
increasing insulin dose [17]. For patients with depression or with an increased risk of cardiovascular problems, the level of interest in using a telehealth application was found to be related to confidence and perceived advantages and disadvantages of the application [18]. Furthermore, studies of internet-based testing for sexually transmitted diseases [19] and the use of personal electronic health records and secure messaging [20] put forward internet and technology usage, health care access, provider satisfaction, interactions between environmental factors, and interactions between patient activation and tool empowerment potential as key factors determining people’s use of SMSSs. Arning and Karsh have also noticed that the current IT acceptance models were insufficient to understand patients [21, 22], and various researchers have worked on determining relevant factors that explain patients’ behavioural intention to use eHealth technology [22-25].
Renal transplant patients, however, might be at more risk than the previous examples of chronic patients, as rejection can occur acutely with the risk of losing the transplanted kidney. Although other domains such as office applications or e-commerce, even the eHealth domain in general, have received substantial research attention, less is known about patient acceptance of a SMSS in general and more specifically, the acceptance of a SMSS by renal transplant patients.
Objective
To better understand the renal transplant patients and their acceptance of using a SMSS, this paper studies their intention of using a SMSS and the underlying factors that explain this use intention. This understanding would allow system designers and health program managers to direct their attention and effort effectively and efficiently.
Literature Review
The most well-known models or theories that have been used to explain peoples’ acceptance of technology are the theory of reasoned action (TRA) [10], the theory of planned behaviour (TPB) [11], the technology acceptance model (TAM) [12], and their extensions, such as TAM2 [26], the unified theory of acceptance and use of technology (UTAUT) [27], and TAM3 [28]. These models are used widely, and their coefficient of determination (R2) ranged from 17% to 70%. In other words, the factors in these models can explain this amount of variation between people’s intentions to use information technology [27]. R2 is calculated by the squaring the correlation between the predicted behavioural intention by the model and the actual behavioural intention reported by the individuals. Further meta-analysis and review showed that TAM and its extensions are valid and robust, but more variables should be integrated to enhance the explained