Modelling mobile-based technology adoption among people with dementia

The work described in this paper builds upon our previous research on adoption modelling and aims to identify the best subset of features that could offer a better understanding of technology adoption. The current work is based on the analysis and fusion of two datasets that provide detailed informa...

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Bibliographic Details
Published in:Personal and ubiquitous computing 2022-04, Vol.26 (2), p.365-384
Main Authors: Chaurasia, Priyanka, McClean, Sally, Nugent, Chris D., Cleland, Ian, Zhang, Shuai, Donnelly, Mark P., Scotney, Bryan W., Sanders, Chelsea, Smith, Ken, Norton, Maria C., Tschanz, JoAnn
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computer Science
Dementia
Empirical analysis
Mobile Computing
Modelling
Original
Original Article
Personal Computing
Technology adoption
Technology utilization
User Interfaces and Human Computer Interaction
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Summary:The work described in this paper builds upon our previous research on adoption modelling and aims to identify the best subset of features that could offer a better understanding of technology adoption. The current work is based on the analysis and fusion of two datasets that provide detailed information on background, psychosocial, and medical history of the subjects. In the process of modelling adoption, feature selection is carried out followed by empirical analysis to identify the best classification models. With a more detailed set of features including psychosocial and medical history information, the developed adoption model, using k NN algorithm, achieved a prediction accuracy of 99.41% when tested on 173 participants. The second-best algorithm built, using NN, achieved 94.08% accuracy. Both these results have improved accuracy in comparison to the best accuracy achieved (92.48%) in our previous work, based on psychosocial and self-reported health data for the same cohort. It has been found that psychosocial data is better than medical data for predicting technology adoption. However, for the best results, we should use a combination of psychosocial and medical data where it is preferable that the latter is provided from reliable medical sources, rather than self-reported.
ISSN:1617-4909
1617-4917
DOI:10.1007/s00779-021-01572-x