Event Highlights:
Dr. Margie E. Lachman, PhD, Minnie and Harold L. Fierman Professor of Psychology and Director of the Lifespan Lab and Royal Center at Brandeis University, will provide a brief overview of the importance of functional health for maintaining quality of life and aging in place. Two pilot investigators funded by the Mass AITC will present their projects on the use of technology to assess frailty and function by monitoring activity. There will be time for discussion and questions.
Frailty affects 10% of community-dwelling older adults (25% in those aged 80+), leading to increased risks of mortality, disability, healthcare utilization, and loss of independence. Current clinical practices struggle to detect frailty early due to resource limitations, resulting in challenges in modifying its course.
Dr. Amanda Paluch, Assistant Professor of Kinesiology at the University of Massachusetts Amherst, will summarize her pilot project “Detecting frailty in home environments through non-invasive whole room body heat sensing in older adults.” which aims to advance frailty identification using contactless in-home assessment tools employing the heatic sensors of butlr Technologies. This spatial intelligence technology allows for real-time tracking, enabling early recognition of changes in activity and movement patterns, thus assessing the risk of frailty.
Maintaining functional independence is a high priority for people living with Alzheimer's disease. Currently, functional outcomes are assessed using self-reported tools and laboratory-based functional tests. Assessments of movement and mobility with wearable actigraphy sensors have the potential to provide a more comprehensive depiction of an individual's functional abilities in their home environment; however, methods to capture such measures have not been validated in individuals with Alzheimer's disease.
Dr. Jen Blankenship, Senior Research Scientist at VivoSense, will discuss her MassAITC pilot study, “Developing real-world digital biomarkers from wearable sensors in Alzheimer’s disease,” which aims to develop and validate machine learning algorithms to capture aspects of real-world walking behavior with actigraphy sensors in older adults with and without mild Alzheimer's disease.