Abstract
In this paper we study the use of medical history information extracted from the Utah Population Database (UPDB) to predict adoption of a reminder solution for people with dementia. The adoption model was built using 24 categorised features. The kNN classification algorithm gave the best performance with 85.8 % accuracy. Whilst data from the UPDB is more readily available than that in our previous work, the results highlight the benefit of including psychosocial and background information within an adoption model.
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Chaurasia, P., McClean, S.I., Nugent, C.D., Scotney, B.W.: A duration-based online reminder system. Int. J. Pervasive Comput. Commun. 10(3), 337–366 (2014)
Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mob. Comput. 5(4), 277–298 (2009)
Hartin, P.J., Nugent, C.D., McClean, S.I., Cleland, I., Norton, M.C., Sanders, C., Tschanz, J.T.: A smartphone application to evaluate technology adoption and usage in persons with dementia. In: Proceeding of Annual International Conference on IEEE Engineering in Medicine and Biology Society, vol. 2014, pp. 5389–5392 (2014)
Zhang, S., McClean, S.I., Nugent, C.D., Donnelly, M.P., Galway, L., Scotney, B.W., Cleland, I.: A predictive model for assistive technology adoption for people with dementia. IEEE J. Biomed. Heal. Inf. 18(1), 375–383 (2014)
O’Neill, S.A., Parente, G., Donnelly, M.P., Nugent, C.D., Beattie, M.P., McClean, S.I., Scotney, B.W., Mason, S.C., Craig, D.: Assessing task compliance following mobile phone-based video reminders. In: Proceedings of Annual International Conference on IEEE Engineering and Medical Biology Society, EMBS, pp. 5295–5298 (2011)
Chaurasia, P., McClean, S.I., Nugent, C.D., Cleland, I., Zhang, S., Donnelly, M.P., Scotney, B.W., Sanders, C., Smith, K., Norton, M.C., Tschanz, J.: Technology adoption and prediction tools for everyday technologies aimed at people with dementia. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Florida, USA, 16–20 August 2016 (2016)
Day, H., Jutai, J.: Measuring the psychosocial impact of assistive devices: the PIADS. Can. J. Rehabil. 9(2), 159–168 (1996)
Scherer, M., Jutai, J., Fuhrer, M., Demers, L., Deruyter, F.: A framework for modelling the selection of assistive technology devices (ATDs). Disabil. Rehabil. Assist. Technol. 2(1), 1–8 (2007)
Venkatesh, V., Morris, M.G., Gordon, B.D., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478 (2003)
Chen, K., Chan, A.H.S.: A review of technology acceptance by older adults. Gerontechnology 10(1), 1–12 (2011)
Mitzner, T.L., Boron, J.B., Fausset, C.B., Adams, A.E., Charness, N., Czaja, S.J., Dijkstra, K., Fisk, A.D., Rogers, W.A., Sharit, J.: Older adults talk technology: technology usage and attitudes. Comput. Hum. Behav. 26(6), 1710–1721 (2010)
Czaja, S.J., Charness, N., Fisk, A.D., Hertzog, C., Nair, S.N., Rogers, W.A., Sharit, J.: Factors predicting the use of technology: findings from the center for research and education on aging and technology enhancement (CREATE). Psychol. Aging 21(2), 333–352 (2006)
Peek, S.T.M., Wouters, E.J.M., van Hoof, J., Luijkx, K.G., Boeije, H.R., Vrijhoef, H.J.M.: Factors influencing acceptance of technology for aging in place: a systematic review. Int. J. Med. Inf. 83(4), 235–248 (2014)
Tschanz, J.T., Norton, M.C., Zandi, P.P., Lyketsos, C.G.: The cache county study on memory in aging: factors affecting risk of Alzheimer’s disease and its progression after onset. Int. Rev. Psychiatry 25(6), 673–685 (2013)
Chaurasia, P., McClean, S.I., Nugent, C.D., Cleland, I., Zhang, S., Donnelly, M.P., Scotney, B.W., Sanders, C., Smith, K., Norton, M.C., Tschanz, J.: Modelling assistive technology adoption for people with dementia. J. Biomed. Inform. 63, 235–248 (2016)
Acknowledgements
The Alzheimer’s Association is acknowledged for supporting the TAUT project under the research grant ETAC-12-242841. Partial support for all data sets within the UPDB was provided by the University of Utah Huntsman Cancer Institute and the Huntsman Cancer Institute Cancer Center Support grant, P30 CA42014 from the National Cancer Institute.
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Chaurasia, P. et al. (2016). Impact of Medical History on Technology Adoption in Utah Population Database. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. IWAAL AmIHEALTH UCAmI 2016 2016 2016. Lecture Notes in Computer Science(), vol 10070. Springer, Cham. https://doi.org/10.1007/978-3-319-48799-1_12
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DOI: https://doi.org/10.1007/978-3-319-48799-1_12
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