A Predictive Model for Assistive Technology Adoption for People With Dementia

• Published in: IEEE journal of biomedical and health informatics 2014-01, Vol.18 (1), p.375-383
• Main Authors: Zhang, Shuai, McClean, Sally I., Nugent, Chris D., Donnelly, Mark P., Galway, Leo, Scotney, Bryan W., Cleland, Ian
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Assistive technology; Cell Phone; classification; Dementia; Dementia - rehabilitation; Female; Home Care Services; Humans; Informatics; Male; Middle Aged; Models, Statistical; Prediction algorithms; prediction models; Predictive models; Reminder Systems; Self-Help Devices; Streaming media; technology adoption; Young Adult
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2013.2267549

Assistive technology has the potential to enhance the level of independence of people with dementia, thereby increasing the possibility of supporting home-based care. In general, people with dementia are reluctant to change; therefore, it is important that suitable assistive technologies are selected for them. Consequently, the development of predictive models that are able to determine a person's potential to adopt a particular technology is desirable. In this paper, a predictive adoption model for a mobile phone-based video streaming system, developed for people with dementia, is presented. Taking into consideration characteristics related to a person's ability, living arrangements, and preferences, this paper discusses the development of predictive models, which were based on a number of carefully selected data mining algorithms for classification. For each, the learning on different relevant features for technology adoption has been tested, in conjunction with handling the imbalance of available data for output classes. Given our focus on providing predictive tools that could be used and interpreted by healthcare professionals, models with ease-of-use, intuitive understanding, and clear decision making processes are preferred. Predictive models have, therefore, been evaluated on a multi-criterion basis: in terms of their prediction performance, robustness, bias with regard to two types of errors and usability. Overall, the model derived from incorporating a k-Nearest-Neighbour algorithm using seven features was found to be the optimal classifier of assistive technology adoption for people with dementia (prediction accuracy 0.84 ± 0.0242).