Journal article
Wen Wang, Hourieh Khalajzadeh, Anuradha Madugalla, Jennifer McIntosh, Humphrey O. Obie
arXiv.org, 2022
arXiv.org, 2022
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ArXiv
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APA
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Wang, W., Khalajzadeh, H., Madugalla, A., McIntosh, J., & Obie, H. O. (2022). Adaptive user interfaces in systems targeting chronic disease: a systematic literature review. ArXiv.org.
Chicago/Turabian
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Wang, Wen, Hourieh Khalajzadeh, Anuradha Madugalla, Jennifer McIntosh, and Humphrey O. Obie. “Adaptive User Interfaces in Systems Targeting Chronic Disease: a Systematic Literature Review.” arXiv.org (2022).
MLA
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Wang, Wen, et al. “Adaptive User Interfaces in Systems Targeting Chronic Disease: a Systematic Literature Review.” ArXiv.org, 2022.
BibTeX Click to copy
@article{wen2022a,
title = {Adaptive user interfaces in systems targeting chronic disease: a systematic literature review},
year = {2022},
journal = {arXiv.org},
author = {Wang, Wen and Khalajzadeh, Hourieh and Madugalla, Anuradha and McIntosh, Jennifer and Obie, Humphrey O.}
}
Abstract
eHealth 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 the desired support due to the dynamic and diverse nature of the chronic disease. Many such eHealth applications support aspects of ’adaptive user interfaces’ – that change or can be changed to accommodate the user and usage context differences. To identify the state-of-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, revolves around 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. the oxygen level in the blood (S42) are the physiological data addressed in the articles we included.Theremaining identified subcategories of user characteristics belong to user’s demographics (15%), user’s psychological characteristics (8%) and user’s social activity (4%). Studies primarily employed data on users’ cognitive features (S5, S12 and S43) and personality factors while examining psychological characteristics (S10). In terms of demographic characteristics, researchers mostly employ age (S1, S24, and S27), gender (S27), and literacy data (S15, S17, S24, S26 and S47). Each of these subcategories specifies one dimension of the user characteristics. This paper also investigates in terms of the variety of dimensions used for user characteristics data. Our analysis revealed that over 80% of included articles used at least one of the above-mentioned user characteristic subcategories; a number of articles (29%) applied more than one subcategory of user characteristics to achieve a higher understanding of users.
