mHealth technology for dementia prevention: Advancing design for behaviour changee

Background

Over the next generation, the prevalence of dementia in Canada is expected to double, with associated direct and indirect costs reaching $153 billion. Considering there are no currently available drug treatments, strategies to prevent the impending dementia epidemic are critical. Optimistically, by delaying the onset of dementia by just 5 years, we may reduce the prevalence of the disease by 50%. Adopting a healthy lifestyle to optimize brain health, such as engaging in physical, social, nutritional, and cognitively stimulating activities, is a key to preventing or slowing the progression toward dementia. Despite growing scientific evidence, many older Canadians do not meet recommended guidelines due to a combination of environmental, social, and/or functional barriers.

The emergence of mobile computing and communication technologies in health care (mHealth) has shown promise in supporting prevention of chronic diseases, such as weight loss, smoking cessation, and diabetes management. The long-term objective of this research is to develop mHealth systems to promote a healthy lifestyle conducive to preventing cognitive decline and dementia. This project builds upon foundational work from a pilot project that developed and tested a new mHealth system, called the Adaptive Prompting in Retirement Living (APRiL) project. The APRiL system reminded residents in retirement living communities of upcoming activities, services, and events provided by the residence. As a proof-of-concept project, the APRiL system successfully applied a machine learning algorithm to learn the most effective combination of cues to deliver notifications, such as tactile, audio, and/or visual cues. While the residents testing the app found the reminders to be useful and expressed interest in the system beyond the 2-4 week study period, standard design of mobile device apps presented older adults with usability challenges. For example, older adult participants experienced difficulty using standard touchscreen navigation gestures. These challenges are mirrored in reports from other researchers evaluating mHealth systems for older adults.

Goals

To address these challenges, the goal of the current project is to advance the design of the APRiL system to promote healthy activities associated with dementia prevention in older adults. The effectiveness of the re-designed system (APRiL 2.0) to modify participation in healthy activities will be evaluated in a small group of residents in retirement living (n=10) and older adults living independently at home (n=10) across a two-week period.

The undergraduate co-op student was responsible for the development of the smart phone application (or “app”) targeted to prompt older adults to participate in organized social and physical activities. The app applies machine learning to optimize the type and frequency of reminders to maximize their effectiveness and indoor geolocation to detect their location and activities. The student also integrated peripherals, such as location beacons and/or activity monitors, into the system. The student worked closely with an experienced product manager from industry, as well as other faculty in computer science, kinesiology, and pharmacy to design, develop, and implement the app.

Summary of findings

We will produce three primary deliverables at the end of the project:

  1. A mobile device app, APRiL 2.0, that will prompt and encourage activity participation and service use among retirement living residents at-risk for dementia
  2. Research article describing the participatory design approach of the APRiL 2.0 system (Phase 1)
  3. Research article reporting the initial evaluation of effectiveness of the APRiL 2.0 app to promote participation and factors influencing behaviour (Phase 2)
Project members: 
Undergraduate Fellow
Faculty Supervisor
Last updated: January 08, 2017