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. 2022;26(2):365-384.
doi: 10.1007/s00779-021-01572-x. Epub 2021 May 3.

Modelling mobile-based technology adoption among people with dementia

Priyanka Chaurasia  1 , Sally McClean  2 , Chris D Nugent  2 , Ian Cleland  2 , Shuai Zhang  2 , Mark P Donnelly  2 , Bryan W Scotney  2 , Chelsea Sanders  3 , Ken Smith  4 , Maria C Norton  5 , JoAnn Tschanz  3
Affiliations

Modelling mobile-based technology adoption among people with dementia

Priyanka Chaurasia et al. Pers Ubiquitous Comput. 2022.

Abstract

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 kNN 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.

Keywords: Assistive technologies; Dementia; Medical history; Reminder application; Technology adoption.

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Figures

Fig. 1
Fig. 1
An overview of the TAUT, indicating sources of the data and set of features used in the project
Fig. 2
Fig. 2
Screenshots from the TAUT app showing (a) upcoming reminders list and (b) reminder creation screens
Fig. 3
Fig. 3
Influence diagram of features impacting on technology adoption [8]
Fig. 4
Fig. 4
Influence diagram based on the combined features from the CCSMA and UPDB datasets
Fig. 5
Fig. 5
(a) Distribution of male and female in the adopter and refuser class. (b) Educational information of adopters and refuser class
See this image and copyright information in PMC

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