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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| Published in | Personal and ubiquitous computing Vol. 26; no. 2; pp. 365 - 384 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.04.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1617-4909 1617-4917 |
| DOI | 10.1007/s00779-021-01572-x |
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| 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
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. |
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| AbstractList | 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
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. 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.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. 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. 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. |
| Author | Donnelly, Mark P. Tschanz, JoAnn McClean, Sally Sanders, Chelsea Nugent, Chris D. Norton, Maria C. Zhang, Shuai Smith, Ken Cleland, Ian Chaurasia, Priyanka Scotney, Bryan W. |
| Author_xml | – sequence: 1 givenname: Priyanka orcidid: 0000-0003-4249-3678 surname: Chaurasia fullname: Chaurasia, Priyanka email: p.chaurasia@ulster.ac.uk organization: School of Computing and Intelligent Systems, Ulster University – sequence: 2 givenname: Sally surname: McClean fullname: McClean, Sally organization: School of Computing, Ulster University – sequence: 3 givenname: Chris D. surname: Nugent fullname: Nugent, Chris D. organization: School of Computing, Ulster University – sequence: 4 givenname: Ian surname: Cleland fullname: Cleland, Ian organization: School of Computing, Ulster University – sequence: 5 givenname: Shuai surname: Zhang fullname: Zhang, Shuai organization: School of Computing, Ulster University – sequence: 6 givenname: Mark P. surname: Donnelly fullname: Donnelly, Mark P. organization: School of Computing, Ulster University – sequence: 7 givenname: Bryan W. surname: Scotney fullname: Scotney, Bryan W. organization: School of Computing, Ulster University – sequence: 8 givenname: Chelsea surname: Sanders fullname: Sanders, Chelsea organization: Department of Psychology, Utah State University – sequence: 9 givenname: Ken surname: Smith fullname: Smith, Ken organization: Department of Family and Consumer Studies, University of Utah – sequence: 10 givenname: Maria C. surname: Norton fullname: Norton, Maria C. organization: Department of Family, Consumer, and Human Development, Utah State University – sequence: 11 givenname: JoAnn surname: Tschanz fullname: Tschanz, JoAnn organization: Department of Psychology, Utah State University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35368316$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1007_s00779_021_01638_w crossref_primary_10_1177_20552076231162985 crossref_primary_10_2196_57940 crossref_primary_10_3390_ijerph19031133 crossref_primary_10_1007_s13755_023_00257_4 |
| Cites_doi | 10.1016/j.socscimed.2009.02.010 10.1080/17483100600845414 10.1016/j.chb.2010.02.005 10.1037/0882-7974.21.2.333 10.3109/09540261.2013.849663 10.1016/j.pmcj.2009.04.001 10.1613/jair.953 10.1016/j.chb.2010.06.020 10.3233/THC-2012-0677 10.1109/JBHI.2013.2267549 10.1186/s12916-014-0192-4 10.1109/TNSRE.2004.828423 10.1207/s15327752jpa6803_3 10.1038/nrneurol.2012.263 10.1093/ageing/afu189 10.1093/iwc/iwt041 10.1016/j.jbi.2016.08.021 10.1016/S0895-4356(99)00103-1 10.2307/30036540 10.1258/135763307779701158 10.1002/9781118490082 10.1109/EMBC.2016.7591704 10.1007/978-3-319-13105-4_39 10.1145/1456659.1456684 10.4324/9781315787954 10.4017/gt.2011.10.01.006.00 10.1109/IEMBS.2011.6091310 10.1007/978-1-4615-5725-8_3 10.1007/978-3-319-48799-1_12 |
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| Keywords | Technology adoption Assistive technologies Medical history Dementia Reminder application |
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| SubjectTerms | 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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| Title | Modelling mobile-based technology adoption among people with dementia |
| URI | https://link.springer.com/article/10.1007/s00779-021-01572-x https://www.ncbi.nlm.nih.gov/pubmed/35368316 https://www.proquest.com/docview/2640565322 https://www.proquest.com/docview/2646941348 https://pubmed.ncbi.nlm.nih.gov/PMC8933362 |
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