Modelling assistive technology adoption for people with dementia

Priyanka Chaurasia, Sally I McClean, Chris Nugent, Ian Cleland, Shuai Zhang, Mark Donnelly, Scotney Bryan, Chelsea Sanders, Ken Smith, Maria C. Norton, JoAnn Tschanz

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Purpose: Assistive technologies have been identified as a potential solution for the provision of elderly care. Such technologies have in general the capacity to enhance the quality of life and increase the level of independence among their users. Nevertheless, the acceptance of these technologies is crucial to their success. Generally speaking, the elderly are not well-disposed to technologies and have limited experience; these factors contribute towards limiting the widespread acceptance of technology. It is therefore important to evaluate the potential success of technologies prior to their deployment.Materials and methods: The research described in this paper builds upon our previous work on modeling adoption of assistive technology, in the form of cognitive prosthetics such as reminder apps and aims at identifying a refined sub-set of features which offer improved accuracy in predicting technology adoption. Consequently, in this paper, an adoption model is built using a set of features extracted from a user’s background to minimise the likelihood of non-adoption. The work is based on analysis of data from the Cache County Study on Memory and Aging (CCSMA) with 31 features covering a range of age, gender, education and details of health condition. In the process of modelling adoption, feature selection and feature reduction is carried out followed by identifying the best classification models.Findings: With the reduced set of labelled features the technology adoption model built achieved an average prediction accuracy of 92.48% when tested on 173 participants.Conclusions: We conclude that modelling user adoption from a range of parameters such as physical, environmental and social perspectives is beneficial in recommending a technology to a particular user based on their profile.
LanguageEnglish
Pages235-248
JournalJournal of Biomedical Informatics
Volume63
Early online date30 Aug 2016
DOIs
Publication statusPublished - 31 Oct 2016

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Self-Help Devices
Dementia
Technology
Health Education
Prosthetics
Application programs
Quality of Life
Feature extraction
Aging of materials
Education
Health

Keywords

  • Assistive technology
  • Technology adoption
  • Prediction modelling
  • Dementia

Cite this

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title = "Modelling assistive technology adoption for people with dementia",
abstract = "Purpose: Assistive technologies have been identified as a potential solution for the provision of elderly care. Such technologies have in general the capacity to enhance the quality of life and increase the level of independence among their users. Nevertheless, the acceptance of these technologies is crucial to their success. Generally speaking, the elderly are not well-disposed to technologies and have limited experience; these factors contribute towards limiting the widespread acceptance of technology. It is therefore important to evaluate the potential success of technologies prior to their deployment.Materials and methods: The research described in this paper builds upon our previous work on modeling adoption of assistive technology, in the form of cognitive prosthetics such as reminder apps and aims at identifying a refined sub-set of features which offer improved accuracy in predicting technology adoption. Consequently, in this paper, an adoption model is built using a set of features extracted from a user’s background to minimise the likelihood of non-adoption. The work is based on analysis of data from the Cache County Study on Memory and Aging (CCSMA) with 31 features covering a range of age, gender, education and details of health condition. In the process of modelling adoption, feature selection and feature reduction is carried out followed by identifying the best classification models.Findings: With the reduced set of labelled features the technology adoption model built achieved an average prediction accuracy of 92.48{\%} when tested on 173 participants.Conclusions: We conclude that modelling user adoption from a range of parameters such as physical, environmental and social perspectives is beneficial in recommending a technology to a particular user based on their profile.",
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Modelling assistive technology adoption for people with dementia. / Chaurasia, Priyanka; McClean, Sally I; Nugent, Chris; Cleland, Ian; Zhang, Shuai; Donnelly, Mark; Bryan, Scotney; Sanders, Chelsea; Smith, Ken; Norton, Maria C.; Tschanz, JoAnn.

In: Journal of Biomedical Informatics, Vol. 63, 31.10.2016, p. 235-248.

Research output: Contribution to journalArticle

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AU - Chaurasia, Priyanka

AU - McClean, Sally I

AU - Nugent, Chris

AU - Cleland, Ian

AU - Zhang, Shuai

AU - Donnelly, Mark

AU - Bryan, Scotney

AU - Sanders, Chelsea

AU - Smith, Ken

AU - Norton, Maria C.

AU - Tschanz, JoAnn

N1 - UIR Compliant - evidence uploaded to Other files

PY - 2016/10/31

Y1 - 2016/10/31

N2 - Purpose: Assistive technologies have been identified as a potential solution for the provision of elderly care. Such technologies have in general the capacity to enhance the quality of life and increase the level of independence among their users. Nevertheless, the acceptance of these technologies is crucial to their success. Generally speaking, the elderly are not well-disposed to technologies and have limited experience; these factors contribute towards limiting the widespread acceptance of technology. It is therefore important to evaluate the potential success of technologies prior to their deployment.Materials and methods: The research described in this paper builds upon our previous work on modeling adoption of assistive technology, in the form of cognitive prosthetics such as reminder apps and aims at identifying a refined sub-set of features which offer improved accuracy in predicting technology adoption. Consequently, in this paper, an adoption model is built using a set of features extracted from a user’s background to minimise the likelihood of non-adoption. The work is based on analysis of data from the Cache County Study on Memory and Aging (CCSMA) with 31 features covering a range of age, gender, education and details of health condition. In the process of modelling adoption, feature selection and feature reduction is carried out followed by identifying the best classification models.Findings: With the reduced set of labelled features the technology adoption model built achieved an average prediction accuracy of 92.48% when tested on 173 participants.Conclusions: We conclude that modelling user adoption from a range of parameters such as physical, environmental and social perspectives is beneficial in recommending a technology to a particular user based on their profile.

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