Predicting Technology Adoption in People with Dementia; Initial Results from the TAUT Project

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8868)


The acceptance of technology is a crucial factor in successfully deploying technology solutions in healthcare. Our previous research has highlighted the potential of modelling user adoption from a range of environmental, social and physical parameters. This current work aims to build on the notion of predicting technology adoption through a study investigating the usage of a reminding application deployed through a mobile phone. The TAUT project is currently recruiting participants from the Cache County Study on Memory in Aging (CCSMA) and will monitor participants over a period of 12 months. Information relating to participants’ compliance with usage of the reminding application, details of cognitive assessments from the CCSMA and medical and genealogical related details from the Utah Population Database (UPDB) will be used as inputs to the development of a new adoption model. Initial results show, that with an unscreened dataset, it is possible to predict refusers and adopters with an F-measure of 0.79.


Technology adoption Assistive technology dementia Reminding Technology 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kane, M., Cook, L.: Dementia 2013: The hidden voice of loneliness. Alzheimer’s Society, London (2013)Google Scholar
  2. 2.
    Chuttur, M.: Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. Sprouts: Working Papers on Information Systems 9, Article 37 (2009)Google Scholar
  3. 3.
    Wilkowska, W., Gaul, S., Ziefle, M.: A Small but Significant Difference – The Role of Gender on Acceptance of Medical Assistive Technologies. In: Leitner, G., Hitz, M., Holzinger, A. (eds.) USAB 2010. LNCS, vol. 6389, pp. 82–100. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Day, H., Jutai, J.: Measuring the Psychosocial Impact of Assistive Devices: the PIADS. Canadian Journal of Rehabilitation 9(2), 159–168 (1996)Google Scholar
  5. 5.
    Yen, D.C., Wu, C., Cheng, F., Huang, Y.: Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Comput. Hum. Behav. 26, 906–915 (2010)CrossRefGoogle Scholar
  6. 6.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science 35(8), 982–1003 (1989)Google Scholar
  7. 7.
    Kowalewski, S., Wilkowska, W., Ziefle, M.: Accounting for User Diversity in the Acceptance of Medical Assistive Technologies. In: Szomszor, M., Kostkova, P. (eds.) e-Health. LNICST, vol. 69, pp. 175–183. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Scherer, M.J., Jutai, J., Fuhrer, M., Demers, L., Deruyter, F.: A framework for modelling the selection of assistive technology devices (ATDs). Disab. & Rehab.: Assis. Tech. 2, 1–8 (2007)Google Scholar
  9. 9.
    Stronge, A.J., Rogers, W.A., Fisk, A.D.J.: Human factors considerations in implementing telemedicine systems to accommodate older adults. Telemed Telecare 13, 1–3 (2007)CrossRefGoogle Scholar
  10. 10.
    Ziefle, M.: Age perspectives on the usefulness on e-health applications. In: International Conference on Health Care Systems, Ergonomics, and Patient Safety (HEPS), Straßbourg, France (2008)Google Scholar
  11. 11.
    Arning, K., Ziefle, M.: Different Perspectives on Technology Acceptance: The Role of Technology Type and Age. In: Holzinger, A., Miesenberger, K. (eds.) USAB 2009. LNCS, vol. 5889, pp. 20–41. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Cartwright, M., Hirani, S.P., Rixon, L., Beynon, M., Doll, H., Bower, P., et al.: Effect of telehealth on quality of life and psychological outcomes over 12 months (Whole Systems Demonstrator telehealth questionnaire study): nested study of patient reported outcomes in a pragmatic, cluster randomized controlled trial. BMJ 346, 653 (2013)CrossRefGoogle Scholar
  13. 13.
    Zhang, S., McClean, S.I., Nugent, C.D., et al.: A predictive model for assistive technology adoption for people with dementia. IEEE Journal of Biomedical and Health Informatics 18(1), 375 (2014)CrossRefGoogle Scholar
  14. 14.
    O’Neill, S.A., Parente, G., Donnelly, et al.: Assessing task compliance following mobile phone-based video reminders. In: Proceedings of the IEEE EMBC 2011, pp. 5295–5298 (2011)Google Scholar
  15. 15.
    Hartin, P.J., Nugent, C.D., McClean, S.I., et al.: A smartphone application to evaluate technology adoption and usage in persons with dementia. In: 2014 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, Chicago, pp. 5389–5392 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Science Research Institute and School of Computing and MathematicsUniversity of UlsterNewtownabbeyUK
  2. 2.Computer Science Research Institute and School of Computing and Information EngineeringUniversity of UlsterColeraineUK
  3. 3.Department of PhycologyUtah State UniversityLoganUSA
  4. 4.Population Sciences, Huntsman Cancer InstituteUniversity of UtahSalt Lake CityUSA

Personalised recommendations