title: Predicting the role of assistive technologies in the lives of people with dementia using objective care recipient factors.
abstract: The population of people with dementia is not homogeneous. People with dementia exhibit a wide range of needs, each characterized by diverse factors including age, sex, ethnicity, and place of residence. These needs and characterizing factors may influence the applicability, and ultimately the acceptance, of assistive technologies developed to support the independence of people with dementia. Accordingly, predicting the needs of users before developing the technologies may increase the applicability and acceptance of assistive technologies. Current methods of prediction rely on the difficult collection of subjective, potentially invasive information. We propose a method of prediction that uses objective, unobtrusive, easy to collect information to help inform the development of assistive technologies.
  
  
  
  We develop a set of models that can predict the level of independence of people with dementia during 20 activities of daily living using simple, objective information. Using data collected from a Canadian survey conducted with caregivers of people with dementia, we create an ordered logistic regression model for each of the twenty daily tasks in the Bristol ADL scale.
  
  
  
  Data collected from 430 Canadian caregivers of people with dementia were analyzed to reveal: most care recipients were mothers or husbands, married, living in private housing with their caregivers, English-speaking, Canadian born, clinically diagnosed with dementia 1 to 6 years prior to the study, and were dependent on their caregiver. Next, we developed models that use 13 factors to predict a person with dementia's ability to complete the 20 Bristol activities of daily living independently. The 13 factors include caregiver relation, age, marital status, place of residence, language, housing type, proximity to caregiver, service use, informal primary caregiver, diagnosis of Alzheimer's disease or dementia, time since diagnosis, and level of dependence on caregiver. The resulting models predicted the aggregate level of independence correctly for 88 of 100 total responses categories, marginally for nine, and incorrectly for three.
  
  
  
  Objective, easy to collect information can predict caregiver-reported level of task independence for a person with dementia. Knowledge of task independence can then inform the development of assistive technologies for people with dementia, improving their applicability and acceptance.
authors.affiliation: Department of Electrical and Computer Engineering and Discipline of Emergency Medicine, Memorial University, S.J. Carew Building, St. John's, NL, A1B 3X5, Canada. sczarnuch@mun.ca.
authors.forename: Stephen
authors.identifier: 0000-0002-9192-4107
authors.initials: S
authors.lastname: Czarnuch
authors.affiliation: Department of Sociology, Memorial University, Arts and Administration Building, St. John's, NL, A1C 5S7, Canada.
authors.forename: Rose
authors.initials: R
authors.lastname: Ricciardelli
authors.affiliation: Department of Occupational Science and Occupational Therapy & Institute of Biomaterials and Biomedical Engineering, University of Toronto, 160-500 University Ave., Toronto, ON, M5G 1V7, Canada.|Toronto Rehabilitation Institute-University Health Network, 550 University Avenue, Toronto, ON, M5G 2A2, Canada.
authors.forename: Alex
authors.initials: A
authors.lastname: Mihailidis
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D000544:Alzheimer Disease / Q000175:diagnosis / Q000453:epidemiology / Q000534:rehabilitation; D002170:Canada / Q000453:epidemiology; D017028:Caregivers / Q000523:psychology; D003704:Dementia / Q000175:diagnosis / Q000453:epidemiology / Q000534:rehabilitation; D005260:Female; D006801:Humans; D057187:Independent Living / Q000523:psychology / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D020380:Needs Assessment; D012656:Self-Help Devices / Q000706:statistics & numerical data*; D013647:Task Performance and Analysis
keywords: Assistive technology; Canadian population; Needs assessment; Predictive model; Technology acceptance