Automatic adaptation in self-monitoring systems for MCI patients: conceptual approach and a software prototype
Abstract
With the growth of access to mobile devices, more applications are emerging for the most diverse purposes. In the health area, applications for monitoring patients with some type of dementia facilitate the day-to-day tasks of both patients and caregivers. However, a remaining challenge is to maintain patient loyalty in using the application so that their data can be constantly collected and analyzed, and treatments and follow-ups can be effective. One approach to trying to improve this situation is to customize and adapt the system, bringing it closer to the preferences of each user. This paper presents the conceptual approach and a prototype of a mobile self-monitoring system for patients with Mild Cognitive Impairment (MCI), a pre-dementia condition, which aims to maintain user loyalty in using the system.
Keywords:
software adaptation, mobile, self-monitoring, mHealth
References
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Sandra Baldassarri, Isabelle Hupont, David Abad a, and Eva Cerezo. 2015. Affective-aware tutoring platform for interactive digital television. Multimedia Tools and Applications 74 (5 2015), 3183–3206. Issue 9. https://doi.org/10.1007/S11042-013-1779-Z/TABLES/2
Vlado Glavinic, Sandi Ljubic, and Mihael Kukec. 2008. Transformable Menu Component for Mobile Device Applications:Working with both Adaptive and Adaptable User Interfaces. International Journal of Interactive Mobile Technologies (iJIM) 2 (5 2008), 22–27. Issue 3. https://doi.org/10.3991/IJIM.V2I3.285
Karl-Ebbe Jönsson, Kajsa Ornstein, Jonas Christensen, and Jeanette Eriksson. 2019. A reminder system for independence in dementia care: A case study in an assisted living facility. In ACM International Conference Proceeding Series (PETRA ’19). Association for Computing Machinery, New York, NY, USA, 176–185. https://doi.org/10.1145/3316782.3321530
Evdoxia Eirini Lithoxoidou, Ioannis Paliokas, Ioannis Gotsos, Stelios Krinidis, Athanasios Tsakiris, Konstantinos Votis, and Dimitrios Tzovaras. 2018. A Gamification Engine Architecture for Enhancing Behavioral Change Support Systems. In Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference (Corfu, Greece) (PETRA ’18). Association for Computing Machinery, New York, NY, USA, 482–489. https://doi.org/10.1145/3197768.3201561
Nelly Condori-Fernandez ANDFranci Suni Lopez. 2017. Using Emotions to Empower the Self-Adaptation Capability of Software Services. In 2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering (SEmotion). 15–21. https://doi.org/10.1109/SEmotion.2017.8
David C. Mohr, Ken R. Weingardt, Madhu Reddy, and Stephen M. Schueller. 2017. Three problems with current digital mental health research. and three things we can do about them. Psychiatric Services 68, 5 (5 2017), 427–429. https://doi.org/10.1176/appi.ps.201600541
Maurice Mulvenna, Suzanne Martin, Stefan S venstedt, Johan Bengtsson, Franka Meiland, Rose Marie Dr es, Marike Hettinga, Ferial Moelaert, and David Craig. 2010. Designing & evaluating a cognitive prosthetic for people with mild dementia. In Proceedings of the 28th Annual European Conference on Cognitive Ergonomics (Delft, Netherlands) (ECCE ’10). Association for Computing Machinery, New York, NY, USA, 11–18. https://doi.org/10.1145/1962300.1962306
Laila Oksnebjerg, Bob Woods, Kathrine Ruth, Annette Lauridsen, Susanne Kristiansen, Helle Dalsgaard Holst, and Gunhild Waldemar. 2020. A tablet app supporting self-management for people with dementia: Explorative study of adoption and use patterns. JMIR mHealth and uHealth 8, 1 (1 2020), e14694. https://doi.org/10.2196/14694
Published
2022-06-22
How to Cite
CRUZ, Davi de Oliveira; CHECHETTI JUNIOR, Carlos Alberto; BRUCKI, Sonia Maria Dozzi; TAKADA, Leonel Tadao; NUNES, Fátima L. S..
Automatic adaptation in self-monitoring systems for MCI patients: conceptual approach and a software prototype. In: LIFE IMPROVEMENT IN QUALITY BY UBIQUITOUS EXPERIENCES WORKSHOP (LIQUE), 2. , 2022, Aveiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
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p. 1-4.
DOI: https://doi.org/10.5753/lique.2022.19994.
