Determining the Acceptance of Digital Cardiac Rehabilitation and Its Influencing Factors among Patients Affected by Cardiac Diseases

Background: Cardiac diseases are a major global health issue with an increasing prevalence of affected people. Rehabilitation following cardiac events is underutilized, despite its proven effectiveness. Digital interventions might present a useful addition to traditional cardiac rehabilitation. Aims...

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Bibliographic Details
Main Authors: Alexander Bäuerle, Charlotta Mallien, Tienush Rassaf, Lisa Jahre, Christos Rammos, Eva-Maria Skoda, Martin Teufel, Julia Lortz
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Journal of Cardiovascular Development and Disease
Subjects:
Online Access:https://www.mdpi.com/2308-3425/10/4/174
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Summary:Background: Cardiac diseases are a major global health issue with an increasing prevalence of affected people. Rehabilitation following cardiac events is underutilized, despite its proven effectiveness. Digital interventions might present a useful addition to traditional cardiac rehabilitation. Aims: This study aims to assess the acceptance of mobile health (mHealth) cardiac rehabilitation and to investigate the underlying factors of acceptance in patients with ischemic heart disease and congestive heart failure. Methods: A cross-sectional study was conducted from November 2021 to September 2022 with <i>N</i> = 290 patients. Sociodemographic, medical, and eHealth-related data were assessed. The Unified Theory of Acceptance and Use of Technology (UTAUT) was applied. Group differences in acceptance were examined and a multiple hierarchical regression analysis was conducted. Results: The overall acceptance of mHealth cardiac rehabilitation was high (<i>M</i> = 4.05, <i>SD</i> = 0.93). Individuals with mental illness reported significantly higher acceptance (<i>t</i>(288) = 3.15, <i>p<sub>adj</sub></i> = 0.007, <i>d</i> = 0.43). Depressive symptoms (β = 0.34, <i>p</i> < 0.001); digital confidence (β = 0.19, <i>p</i> = 0.003); and the UTAUT predictors of performance expectancy (β = 0.34, <i>p</i> < 0.001), effort expectancy (β = 0.34, <i>p</i> < 0.001), and social influence (β = 0.26, <i>p</i> < 0.001) significantly predicted acceptance. The extended UTAUT model explained 69.5% of the variance in acceptance. Conclusions: As acceptance is associated with the actual use of mHealth, the high level of acceptance found in this study is a promising basis for the future implementation of innovative mHealth offers in cardiac rehabilitation.
ISSN:2308-3425