JMIR Publications

ABSTRACT

Background:

The advent of telerehabilitation has created new opportunities for the care of patients with metabolic syndrome. In distant rehabilitation, technology acceptance is particularly important because home-based project is based on digital devices and many patient are less familiar with the use of them.

Objective:

Our aim was to explore technology acceptance among patients undergoing a three-month complex, telemedicine-supported metabolic rehabilitation. We were curious to see how different factors influence the intention to use rehabilitation technologies and how this changes through the rehabilitation process.

Methods:

Participants were selected from the patients of the metabolic telerehabilitation programme at the University. Our model was based on UTAUT2, which we supplemented with various other constructs. A paper-pencil questionnaire survey was administered on the last day of the preparatory week of the rehabilitation programme (T1) and at the follow-up visit after the closing (T2). We used structural equation modelling with the least squares method (SEM-PLS) to explore the relationships between model variables. Respondent segments also were identified by performing a hierarchical cluster analysis using Ward's method.

Results:

Facilitating conditions have the greatest impact on the behaviroal intention (BI) to use technology. Effort expectancy has no direct effect on BI, only through performance expectancy which may be due to the fact that in telerehabilitation settings patients are rather goal-driven than experience-driven. The analysis of the data in T2 shows that the direct impact of social influence on BI has disappeared by the end of the rehabilitation process. This can be explained by that during device usage it becomes clear that the devices are secure and the data are safe, making this factor implicit in the patient's behaviour. Only two constructs appeared in both the T1 and T2 models: performance expectancy and facilitating conditions. By comparing the two datasets, we have provided empirical support for an old hypothesis: the experience of using the tool for a time has led to a significant reduction in the impact of facilitating conditions and a corresponding increase in the dominance of performance expectancy, which has 'absorbed' the impact of some other constructs. Based on the attitudes of the respondents we found three clusters. The rehabilitation programme itself has a significant impact on the patients' BI, because the relative share of “enthusiastic users” (50.3%) increased by about 20%, while the share of “distrustful reluctants” (17.3%) decreased to a quarter at the end of the program.

Conclusions:

This behaviour-based functional approach enables treatments to be tailored to actual technology usage demands rather than presumptive societal features. This means that before beginning rehabilitation, attempts should be undertaken to identify patients’ clusters in clinical practice and rehabilitation should be planned according to individual's attitude towards technology.