Disturbances in sleep and daily activity patterns can lead to safety risks and declining well-being for people living with dementia. However, clinicians lack practical tools to monitor these changes over time. Researchers at UHN’s KITE Research Institute (KITE) found that routinely collecting safety data from wearable, real-time location systems (RTLS) can be used to monitor daily activity and sleep to support people living with dementia.
In this study, led by KITE graduate student Yasser Karam, 47 residents of a specialized dementia care unit wore RTLS bracelets that tracked their movements for an average of nine weeks. The research team analyzed this data to calculate how much participants moved, how regular their daily activity was, and how much time they spent in bed. These digital markers were then analyzed by machine learning models to group participants into six distinct categories, ranging from well-regulated daily activity to severe disturbances.
The researchers confirmed that higher nighttime movement and less regular daily activity were linked to greater sleep difficulties and increased motor agitation, such as restlessness or fidgeting, which can indicate distress. Participants with more disrupted activity were found to be older with more severe cognitive impairment, reduced independence in daily activities, and more mood-related symptoms than those with fewer activity disruptions.
These findings suggest that RTLS can be used as a continuous monitoring tool for sleep and activity disturbances. By identifying meaningful patterns in daily behaviour, this approach could help clinicians detect emerging problems earlier and develop more personalized care plans to support better sleep, mood, and overall wellbeing for people living with dementia.
At the time of the study, first author, Yasser Karam, was a Master’s student co-supervised by Drs. Andrea Iaboni and Shehroz Khan at UHN’s KITE Research Institute. This work was completed as part of his Master’s thesis.
Dr. Andrea Iaboni, co-senior author of the study, is currently a Scientist at UHN’s KITE Research Institute. At the University of Toronto, Dr. Iaboni is an Associate Professor in the Department of Psychiatry and a Faculty Member of the Rehabilitation Sciences Institute.
At the time of the study, Dr. Shehroz Khan, co-senior author of the study, was a Scientist at UHN’s KITE Research Institute. He is currently an Assistant Professor at the College of Engineering and Technology, American University of the Middle East, Kuwait.
This work was supported by UHN Foundation, AGE-WELL, Toronto Dementia Research Alliance, Canadian Institutes of Health Research, and the Walter & Maria Schroeder Institute for Brain Innovation and Recovery.
Karam Y, Shum LC, Faruk T, Arora T, McArthur C, Chu CH, McGilton KS, Flint AJ, Lim A, Khan SS, Iaboni A. Digital markers and phenotypes of rest-activity rhythms in people with advanced dementia using real-time location data. J Gerontol A Biol Sci Med Sci. 2026 Feb 5. doi: 10.1093/gerona/glaf288.