Pervasive computing technologies to continuously assess Alzheimer’s disease progression and intervention efficacy
17 Oct 2018 by Kelly Doherty
Traditionally, assessment of functional and cognitive status of individuals with dementia
occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes
in individuals’ health. Conventionally, this is done using various clinical assessment tools
applied at the point of care and relies on patients’ and caregivers’ ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology ORCATECH) at the Oregon Health and Science University
have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive
computing technologies that make it possible to more accurately track activities and
behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns,
medication adherence, and computer use. Patterns of intra-individual variation detected
in each of these areas are used to predict outcomes such as low mood, loneliness, and
cognitive function. These methods have the potential to improve the quality of patient
health data and in turn patient care especially related to cognitive decline. Furthermore,
the continuous real-world nature of the data may improve the efficiency and ecological
validity of clinical intervention studies.
occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes
in individuals’ health. Conventionally, this is done using various clinical assessment tools
applied at the point of care and relies on patients’ and caregivers’ ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology ORCATECH) at the Oregon Health and Science University
have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive
computing technologies that make it possible to more accurately track activities and
behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns,
medication adherence, and computer use. Patterns of intra-individual variation detected
in each of these areas are used to predict outcomes such as low mood, loneliness, and
cognitive function. These methods have the potential to improve the quality of patient
health data and in turn patient care especially related to cognitive decline. Furthermore,
the continuous real-world nature of the data may improve the efficiency and ecological
validity of clinical intervention studies.
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