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Evaluating the GRACE voice assistant for dementia care among caregivers and healthcare professionals: An interview study.

Walliser, F.; Biller-Andorno, N.; Kowatsch, T.; Vinay, R.

2025-10-09 public and global health
10.1101/2025.10.08.25337586 medRxiv
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BackgroundConsidering rising dementia rates, voice assistants are being investigated to support people with dementia (PWD) and reduce the burden on caregivers (CGs). GRACE, an intervention-delivering voice assistant, provides cognitive support and assistance with daily living for people with early-stage dementia. Initial pilot trials showed promising user experience among healthy adults but required further evaluation by care professionals working with PWD as a next reiteration process. ObjectiveThis study investigated three main research questions: (1) Which intervention components of GRACE do healthcare professionals (HCPs) and CGs find most valuable for supporting the daily lives of PWD? (2) What key constraints do HCPs and CGs expect when implementing GRACE in dementia care settings? and (3) What changes or additions do HCPs and CGs recommend for optimizing the functionality of GRACE? MethodsTwelve semi-structured interviews were conducted (CG: n=4, HCP: n=8). Anonymized transcripts were coded and analyzed thematically to directly answer the research questions. ResultsRespondents found the onboarding activity and memory exercise to be most valuable, due to its relevance in everyday life. Identified barriers for implementation included practical, technical and ethical challenges. Recommendations for successful implementation and further development of GRACE included improving PWD acceptance and greater AI-supported personalized content. Finally, a list of future exercises for incorporation and other use cases of GRACE beyond dementia care were also identified. ConclusionsGRACE has great potential as a complementary tool in dementia care. Ensuring personalization, ease of use, and alignment with care processes will be critical for adoption and long-term success.

Published in DIGITAL HEALTH (predicted rank #3) · training set

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