Authors:
Gergely Magyar
1
;
João Balsa
2
;
Ana Cláudio
2
;
Maria Carmo
2
;
Pedro Neves
2
;
Pedro Alves
2
;
Isa Félix
3
;
Nuno Pimenta
4
;
5
and
Mara Guerreiro
3
;
6
Affiliations:
1
Department of Cybernetics and Artificial Intelligence, Technical University of Kosice, Letna 9, Kosice, Slovakia
;
2
Biosystems & Integrative Sciences Institute (BioISI), Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
;
3
Unidade de Investigação e Desenvolvimento em Enfermagem (ui&de), Escola Superior de Enfermagem de Lisboa, Lisboa, Portugal
;
4
Sport Sciences School of Rio Maior – Polytechnic Institute of Santarém, Rio Maior, Portugal
;
5
Exercise and Health Laboratory, Interdisciplinary Centre for the Study of Human Performance, ULisboa, Cruz-Quebrada, Portugal
;
6
Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Instituto Universitário Egas Moniz, Monte de Caparica, Portugal
Keyword(s):
Virtual Humans, Relational Agents, Artificial Intelligence, Health Care, Behaviour Change, Type 2 Diabetes, Older People.
Abstract:
The global prevalence of diabetes is escalating. Attributable deaths and avoidable health costs related to diabetes represent a substantial burden and threaten the sustainability of contemporary healthcare systems. Information technologies are an encouraging avenue to tackle the challenge of diabetes management. Anthropomorphic virtual assistants designed as relational agents have demonstrated acceptability to older people and may promote long-term engagement. The VASelfCare project aims to develop and test a virtual assistant software prototype to facilitate the self-care of older adults with type 2 diabetes mellitus. The present position paper describes key aspects of the VASelfCare prototype and discusses the potential use of artificial intelligence. Machine learning techniques represent promising approaches to provide a more personalised user experience with the prototype, by means of behaviour adaptation of the virtual assistant to users’ preferences or emotions or to develop ch
atbots. The effect of these sophisticated approaches on relevant endpoints, such as users’ engagement and motivation, needs to be established in comparison to less responsive options.
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