Technology for Maintaining Good Mental and Cognitive Health

Currently, 747,000 Canadians have some type of cognitive impairment, including dementia. This number is expected to double to 1.4 million by 2031. Furthermore, 20% of Canadian seniors are living with a mental illness, anxiety and depression. Pain tends to be under-reported and not treated, resulting in agitation and aggression, while mood disorders often go untreated. Researchers in TECH-MCH are developing software applications for screening and assessment, interventions to enhance mental health and cognitive function, and tools that can automatically detect behaviours that lead to poor cognitive and mental health. TECH-MCH will result in new technologies in an area that has largely been ignored in the technology and aging field.

Workpackage Leads
  • Lili Liu, University of Alberta
  • Eleni Stroulia, University of Alberta


Core Research Projects

ICT applications for Screening, Assessment and Interventions to Enhance Mental Health – 6.1 MEN-ASSESS

This project focuses on the use of information communication technologies (ICTs) to provide older adults in the community with access to information that helps them manage the stressors of caregiving. It also provides access to a suite of applications that help older adults to manage their own signs and symptoms of depression and anxiety. These apps may also be in the form of games on mobile devices that older adults can use in their own homes to practice cognitive skills including those prescribed by their health professionals.

 

Project Leads
  • Mark Chignell, University of Toronto
  • Lili Liu, University of Alberta
Researchers
  • Adriana Rios-Rincon, University of Alberta
  • Antonio Miguel-Cruz, University of Alberta
  • Eleni Stroulia, University of Alberta
  • Jacqueline Rousseau, University of Montreal
  • Jacques Lee, Sunnybrook Health Sciences
  • Leon Zucherman, University of Toronto
  • Marc Kanik, Keebee Play

 

Automated Assessments of Cognitive Impairment using Environment-based Sensing – 6.2 COG-ASSESS

How can we monitor a person’s daily-life activities through an easily available and inexpensive hardware-software system, in order to recognize changes that predict future cognitive decline? That is the broad research question being investigated by this project. To that end, we will (a) use a variety of commercial off-the-shelf sensors (from infrared sensors to cameras) and sensor-embedded “smart” devices, (b) design algorithms for analyzing and fusing the sensor data-streams of these devices, as well as the resulting data archives, and (c) develop software systems implementing these algorithms and integrating the physical infrastructure to monitor and predict if an older adult will suffer from cognitive decline. This work is at the core of the AGE-WELL vision “to help older Canadians to maintain their independence, health and quality of life through accessible information communication technologies that increase their safety and security”. Cost-effective technical solutions for recognizing – ahead of time – indicators of potential future cognitive decline will enable independent living for older adults, providing peace of mind for seniors and their families.

Games to help assess and treat cognitive decline

Project Leads
  • Eleni Stroulia, University of Alberta
Researchers
  • Adriana Rios-Rincon, University of Alberta
  • Antonio Miguel-Cruz, University of Alberta
  • Herbert Yang, University of Alberta
  • Ioanis Nikolaidis, University of Alberta
  • Lili Liu, University of Alberta
  • Norm O'Rourke, Simon Fraser University
  • Rasit Eskicioglu, University of Manitoba

 

Improving Pain care for Older Adults through the Use of Advanced Technologies – 6.3 PAIN CARE

Pain is very common in older populations. However, older adults are often undertreated for pain, especially those with serious dementia who live in nursing homes and cannot report their pain because of cognitive impairments that accompany dementia. The goal of this program is to improve pain care through the use of technology. A key specific goal is to develop and evaluate an affordable technology that will facilitate regular pain assessment with minimal resources. Our project involves an inexpensive vision-based sensor that can be easily implemented in most long-term care facilities. The system is being designed to assist health-care staff with pain assessment while at the same time addressing limitations due to staffing shortages. The plan is to test and evaluate the complete system in at least two long-term care facilities and determine its impact. Additional projects involve use of a web-based platform to facilitate pain management including pain self-management in older adults. 

Project Leads
  • Thomas Hadjistavropoulos, University of Regina
  • Babak Taati, Toronto Rehab Institute/University of Toronto
Researchers
  • Greg Marchildon, University of Toronto
  • Kenneth Prkachin, University of Northern British Columbia

 

Assessing Cognitive Ability using Automated Assessment of Speech – 6.5-CAT

Clinical measures of cognition typically rely on time-consuming, subjective and expensive assessments. However, our recent advances in computational linguistics, signal processing, and machine learning now allow for objective, automatic, and rapid analysis of cognition, through speech. Our prior work has focused on binary classification problems between people with or without a particular disorder, such as Alzheimer’s disease. In this grant, we will use these modern tools to objectively assess cognition, differentially in people with post-stroke aphasia and memory impairment, by measures of speech and language at 5 time points. This will be applied to a new technological medium – the telephone, which will allow for broader data collection and unique insights in human-computer interaction.

Dr. Frank Rudzicz leads this project, with two industrial partners: WinterLight Labs, and CBI Health Group. WinterLight Labs will supply speech-based assessment to support data collection and data analysis. Patients will be recruited at CBI Health Group clinics, and clinical assessment will be provided by Drs. Regina Jokel and Andrea Iaboni. Research will be conducted at the Toronto Rehabilitation Institute, the Rotman Research Institute at Baycrest, and the University of Toronto.

Our primary objective is to validate the computational speech-based assessment techniques relative to current gold standard assessments. This will involve modern machine learning that is more incisive than the current state-of-the-art. This will be accomplished within two elicitation platforms: a traditional web-based interface, and a phone-based interface. Optimizing the latter is crucial in order to establish the feasibility of using this platform across as wide a population as possible.

In the short term, this project will 1) validate current automatic cognitive assessments, 2) create a new industrial collaboration between a large healthcare network and a local technology startup in Canada, and 3) further our understanding of cognitive function in different patient populations among older Canadians.

Project Leads
  • Frank Rudzicz, Toronto Rehab Institute/University of Toronto
Researchers
  • Regina Jokel, University of Toronto

 

Product adaptation and verification of a technology to monitor cognition in older adults – 6.8-SIP A1

Problem: According to the Alzheimer's Association, about 600,000 Canadians have some form of dementia, costing more than $10.4 billion annually. In 15 years, this number will climb to 937,000. Early diagnosis and ongoing validation of treatments with respect to cognitive impairment is critical to promote healthy aging amongst this population. Thus, there is increasing demand for rapid, user-friendly technologies to identify early decline in brain function. Yet, there are currently no cost-effective ways to monitor the physiological impacts of treatments for cognitive decline. Research suggests evoked potentials using electroencephalography (EEG), may provide such a measure. However, current state-of-the-art requires numerous 'leads' and extensive clinical training. Standard EEG testing puts strain on the cognitivelyimpaired, who have trouble sitting still for typical 1-hour examination periods: 25 minutes of EEG cap set-up and multiple paradigms each taking some 10 minutes. 

Purpose: We will validate the use of a low-cost, 'rapid output' EEG platform for the diagnosis and assessment of cognitive impairment. NeuroCatchTM is a clinician-friendly software tool, translating established brain waves into a clinicallyaccessible, understandable framework. NeuroCatchTM outperforms existing EEG tools by extracting critical brain data in about 5 minutes. NeuroCatchTM has been tested amongst people with brain injuries and concussions. Given that the cognitively-impaired have similar challenges, reworking the product for this population is a logical next step.

Impact: We will verify NeuroCatch's capacity to assess functional brain status amongst people with cognitive impairment, establishing a new product for healthy aging. Funding will support minor design changes, field-testing and deliver a validated prototype. Led by trainees, this project delivers a cheaper accessible tool for clinicians, saves healthcare costs, improves diagnosis and treatment monitoring and reaches the global cognition market, ultimately improving the lives of aging adults in Canada and internationally.

Project Leads
  • Frank Knoefel,
Researchers
  • Rafik Goubran, Carleton University

 

ALADIN: Adaptive Lighting for Alzheimer and Dementia Intervention – 6.10-CAT ALADIN

Project Leads
  • M. Cynthia Goh, University of Toronto
Researchers
  • Venkat Venkataramanan, University of Toronto

 

Cycled lighting in the senior home: Effect on rest-activity, sleep, performance and psychological well-being – 6.11-CAT

Project Leads
  • Ralph Mistlberger,

 

Technology-enhanced multimodal meditation for enhancing wellness in long term care – 6.12-SIP A4

Ethical wellness care for the elderly is urgently needed as many residents in long term care are anxious, lethargic or depressed. Our society urgently requires more, and better, therapeutic options to deal with our aging, and increasingly long-lived population. Stats Canada estimates that close to 25% of Canadians will be over the age of 65 by 2036, increasing health-care costs and socioeconomic burdens. To deal with this challenge, Canada needs to develop innovative, evidence-based methods that can maintain wellness in the aging population without requiring an unsustainable increase in the number of caregivers. The Interactive Media Lab at the University of Toronto, with its extensive experience in human factors engineering and technology evaluation, will assist industry partner Praxis in evaluating its virtual reality (VR) for neurocognitive rehabilitation and preparing that technology for commercialization. Praxis has developed technology-enhanced mindfulness meditation (TEMM), which has shown promising results within a psychiatric practice. The motivation behind TEMM is to utilize knowledge of brain properties to provide immersive experiences that promote calm awareness, positive affect and sensory receptivity to external/environmental input. In this project we will evaluate the use and effectiveness of TEMM for seniors with cognitive impairment and dementia as a necessary step towards commercializing the technology. Praxis will refine its immersive multimedia suite based on our research results from field testing TEMM in long term care. The first phase of work will involve field testing and the second phase will involve scientific evaluation of TEMM in long term care and a comparative analysis of how TEMM outcomes vary across different settings (e.g., acute care vs. neuro-rehabilitation care vs. standard long term care). Outcomes assessed will include wellbeing, amount of stress-reduction and changes in cognitive function for people using the technology.
Project Leads
  • Mark Chignell, University of Toronto

 

Feasibility study of an interactive digital technology in reducing bathing-related agitation in a residential care facility – 6.13-SIP A4

The MindfulGarden project comprises three feasibility studies exploring the use of interactive digital interventions to arrest and de-escalate anxiety and aggression in frail elderly suffering from hyperactive delirium and/or dementia. Two studies have been approved for implementation in September 2018. Fraser Health has approved a pilot study for Peace Arch Hospital to determine the efficacy of MindfulGarden in reducing hyperactive delirium. A concurrent study at Delta View will address hyperactive dementia in long term care. This application to AGE-WELL is in support of a third study at Delta View Rehabilitation Centre validating MindfulGarden as an important treatment in reducing bathing-related agitation in dementia residents. Bathing is well recognized as an activity associated with resident stress and aggression and violence against caregivers. In a study of 1,565 ‘bath sessions’ administered to long-term care dementia residents, 46.8% involved some degree of agitated behaviour, with 27.5% involving actual physical resistance (Cooke, 2006; Cooke & Gutman, 2005). A 2014 report from the University of Lethbridge also shows aggression and caregiver distress, with 40% of staff reporting feeling powerless and emotionally drained on a routine basis. (Source: Spenceley, S. 2015). There is an urgent need for new tools and treatments that can reduce residents’ stress in the bathing environment. In Cooke’s study, of six factors examined including air and water temperature and type of bathtub, increased privacy and the presence of windows in the bathing area were statistically significant in reducing physical agitation during bathing. MindfulGarden offers a digital approximation of the natural environment that might be viewed from a window. It combines a waterproof TV screen with sensors that respond to patient vocalization and movements to trigger a visual ‘garden’ that has been shown at proof-of-concept in 2016 to de-escalate challenging behaviours associated with delirium and dementia so that care can continue (demo: https://vimeo.com/296919077).
Project Leads
  • Gloria Gutman, Simon Fraser University

 

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