Virtual Cardiac Rehabilitation in a Pandemic Scenario: A Review of HCI Design Features, User Acceptance and Barriers

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Abstract

We report on the literature review of best practices in virtual cardiac rehabilitation (VCR), with a focus on technology usability, acceptance, and adoption barriers. We reviewed recent papers published in scientific conferences and journals on the topics of virtual cardiac rehabilitation and remote cardiac monitoring, with a publication dates from 2019 to 2021. Cardiovascular disease is a leading cause of mortality in elderly populations worldwide, and older adults are at an increased risk of COVID-19. Before the pandemic, the uptake of VCR technologies was slow due to concerns about technology effectiveness and cost. Since early 2020, better acceptance and adoption of VCR into routine care have been observed in many countries, including Canada. Some VCR components, like patient education or consultations are digitized relatively easy with online education sessions and resource repositories available for patients. Other elements such as supervised exercise and safe physical activity are more difficult to implement and require use of home cardiac monitoring technology. A proper HCI design of VCR services can contribute to better technology adoption by both service provider s and patients, and lead to improved patient outcomes. Design recommendations to increase adoption and improve user engagement with VCR by older adults include using multimodal interfaces, providing structured training and support, tailoring content and mode of delivery to the user, enabling automatic data transfer and easy integration across various systems and devices, improving accuracy of home cardiac monitoring devices, and conducting systematic technology validation studies, including remote usability evaluations for VCR technologies as part of the technology adoption life cycle.

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APA

Kondratova, I., & Fournier, H. (2022). Virtual Cardiac Rehabilitation in a Pandemic Scenario: A Review of HCI Design Features, User Acceptance and Barriers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13330 LNCS, pp. 485–499). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-05581-2_34

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