Acceptability of Telerehabilitation: Experiences and Perceptions by Individuals with Stroke and Caregivers in an Early Supported Discharge Program

Abstract

Introduction: Telerehabilitation (TR) is a promising method for facilitating the delivery and access to post-stroke rehabilitation services. Objective: The aim of this study was to explore the acceptability of TR and factors influencing its adoption by individuals with stroke and caregivers. Methods: A qualitative descriptive approach was used. Six individuals with stroke and three caregivers participated in individual online interviews. An abductive thematic analysis was employed to analyze the qualitative data, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) model. Results: Participants reported positive experiences with TR, resulting in improvements in functional abilities, such as manual dexterity, balance, and positive interactions with therapists. They found the technology easy to learn and use, facilitating engagement in TR. Participants’ prior experiences with technology, along with support from caregivers and therapists, facilitated acceptance and the use of TR. The COVID-19 pandemic also motivated participants to accept TR. However, technical issues, unstable internet connections, and lack of feedback were barriers to the use of TR. Conclusion: Despite existing obstacles, TR can be used to provide rehabilitation services for individuals with stroke. Addressing these barriers is necessary to promote the widespread and effective use of TR in the context of stroke recovery.

Emerging Post-Hospital Models of Care: A Primer for Stroke Center Leaders

“…They utilized technology such as wearable devices, tablets, virtual reality, and photos to provide rehabilitation services remotely. While it was a small exploratory study, the intervention was positively perceived by both patients and rehabilitation providers, and improved functional ability defined as meeting rehabilitation goals 17 . Cui and colleagues 18 also reported applying machine learning to predict patients at increased need for support at discharge.…”