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 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.

[1]  Karen Renaud,et al.  Predicting technology acceptance and adoption by the elderly: a qualitative study , 2008, SAICSIT '08.

[2]  Chris D. Nugent,et al.  A Predictive Model for Assistive Technology Adoption for People With Dementia , 2014, IEEE Journal of Biomedical and Health Informatics.

[3]  Wim Van Biesen,et al.  Diagnosis and treatment of hyponatremia: a systematic review of clinical practice guidelines and consensus statements , 2014, BMC Medicine.

[4]  Martina Ziefle,et al.  A Small but Significant Difference - The Role of Gender on Acceptance of Medical Assistive Technologies , 2010, USAB.

[5]  Ron Kohavi,et al.  The Wrapper Approach , 1998 .

[6]  Greta Rait,et al.  Comorbidity and dementia: a scoping review of the literature , 2014, BMC Medicine.

[7]  Riccardo Russo,et al.  A Student's Guide to Analysis of Variance , 1999 .

[8]  Ken R. Smith,et al.  Effects of childhood and middle-adulthood family conditions on later-life mortality: evidence from the Utah Population Database, 1850-2002. , 2009, Social science & medicine.

[9]  N. Charness,et al.  Factors Predicting the Use of Technology: Findings From the Center for Research and Education on Aging and Technology Enhancement (CREATE) , 2006 .

[10]  Chris D. Nugent,et al.  A smartphone application to evaluate technology adoption and usage in persons with dementia , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Chris D. Nugent,et al.  Assessing task compliance following mobile phone-based video reminders , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  J. Jutai,et al.  A framework for modelling the selection of assistive technology devices (ATDs) , 2007, Disability and rehabilitation. Assistive technology.

[13]  David C. Yen,et al.  Determinants of users' intention to adopt wireless technology: An empirical study by integrating TTF with TAM , 2010, Comput. Hum. Behav..

[14]  Maria C. Norton,et al.  The Cache County Study on Memory in Aging: Factors affecting risk of Alzheimer's disease and its progression after onset , 2013, International review of psychiatry.

[15]  Diane J. Cook,et al.  Author's Personal Copy Pervasive and Mobile Computing Ambient Intelligence: Technologies, Applications, and Opportunities , 2022 .

[16]  K. Offord,et al.  Logistic regression : Advances in statistical methods for personality assessment research , 1997 .

[17]  Huaxi Xu,et al.  Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy , 2013, Nature Reviews Neurology.

[18]  Chris D. Nugent,et al.  Predicting Technology Adoption in People with Dementia; Initial Results from the TAUT Project , 2014, IWAAL.

[19]  E W Steyerberg,et al.  Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis. , 1999, Journal of clinical epidemiology.

[20]  Mohammad Chuttur,et al.  Overview of the Technology Acceptance Model: Origins, Developments and Future Directions , 2009 .

[21]  Bryan Scotney,et al.  Duration discretisation for activity recognition. , 2012, Technology and health care : official journal of the European Society for Engineering and Medicine.

[22]  A. D. Fisk,et al.  Human factors considerations in implementing telemedicine systems to accommodate older adults , 2007, Journal of Telemedicine and Telecare.

[23]  Chris D. Nugent,et al.  Modelling assistive technology adoption for people with dementia , 2016, J. Biomed. Informatics.

[24]  Robert M. Groves,et al.  Nonresponse in Household Interview Surveys: Groves/Nonresponse , 1998 .

[25]  Chris D. Nugent,et al.  Development of a Technology Adoption and Usage Prediction Tool for Assistive Technology for People with Dementia , 2014, Interact. Comput..

[26]  Chris D. Nugent,et al.  Technology adoption and prediction tools for everyday technologies aimed at people with dementia , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[27]  Pouyan Esmaeilzadeh,et al.  The Limitations of Using the Existing TAM in Adoption of Clinical Decision Support System in Hospitals: An Empirical Study in Malaysia , 2016 .

[28]  Miriam Sander,et al.  The challenges of human population ageing , 2014, Age and ageing.

[29]  Arthur D. Fisk,et al.  Older adults talk technology: Technology usage and attitudes , 2010, Comput. Hum. Behav..

[30]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[31]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[32]  A. Chan,et al.  A review of technology acceptance by older adults , 2011 .

[33]  D.H. Stefanov,et al.  The smart house for older persons and persons with physical disabilities: structure, technology arrangements, and perspectives , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.