Author:
Wang Wei,Khalajzadeh Hourieh,Grundy John,Madugalla Anuradha,McIntosh Jennifer,Obie Humphrey O.
Abstract
AbstracteHealth technologies have been increasingly used to foster proactive self-management skills for patients with chronic diseases. However, it is challenging to provide each user with their desired support due to the dynamic and diverse nature of the chronic disease and its impact on users. Many such eHealth applications support aspects of “adaptive user interfaces”—interfaces that change or can be changed to accommodate the user and usage context differences. To identify the state of the art in adaptive user interfaces in the field of chronic diseases, we systematically located and analysed 48 key studies in the literature with the aim of categorising the key approaches used to date and identifying limitations, gaps, and trends in research. Our data synthesis is based on the data sources used for interface adaptation, the data collection techniques used to extract the data, the adaptive mechanisms used to process the data, and the adaptive elements generated at the interface. The findings of this review will aid researchers and developers in understanding where adaptive user interface approaches can be applied and necessary considerations for employing adaptive user interfaces to different chronic disease-related eHealth applications.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Human-Computer Interaction,Education
Reference118 articles.
1. Abowd, G.D., Dey, A.K., Brown, P.J., et al.: Towards a better understanding of context and context-awareness. In: International Symposium on Handheld and Ubiquitous Computing. Springer, pp 304–307 (1999)
2. Abrahão, S., Insfran, E., Sluÿters, A., et al.: Model-based intelligent user interface adaptation: challenges and future directions. Softw. Syst. Model. 20(5), 1335–1349 (2021)
3. Abras, C., Maloney-Krichmar, D., Preece, J., et al.: User-centered design. In: Bainbridge, W. (ed.) Encyclopedia of Human–Computer Interaction, vol. 37, pp. 445–456. Sage Publications, Thousand Oaks (2004)
4. Akiki, P.A., Bandara, A.K., Yu, Y.: Adaptive model-driven user interface development systems. ACM Comput. Surv. (2014). https://doi.org/10.1145/2597999
5. Alaa, M., Zaidan, A.A., Zaidan, B.B., et al.: A review of smart home applications based on internet of things. J. Netw. Comput. Appl. 97, 48–65 (2017)