Detecting Cognitive Decline in Early Alzheimer's Patients Using Wearable Technologies

Abstract

Alzheimer’s disease (AD) is the world’s most common neurodegenerative disease. AD is characterized by a progressive decline in cognitive function. A growing body of evidence reveals that changes in cognitive, sensory, and motor functioning, along with physiological changes in the autonomic nervous system can be detected a decade or more before the first AD symptoms manifest. Early detection of AD by monitoring these changes in physiological functioning allows for intervention approaches to be applied earlier to slow down the progression and impacts of AD (e.g. cognitive training, using cholinergic enhancing drugs). Heart rate variability (HRV) has been considered a convenient, non-invasive indicator of autonomic nervous system (ANS) activity. Several lines of evidence support the use of HRV data to evaluate cognitive function. As such, we can use HRV as a novel physiological marker to identify AD. In this paper, we examine whether wearable technologies can provide objective, non-invasive methods for the early detection of cognitive decline among AD patients by capturing physiological changes such as heart rate variability (HRV). We contend that the early recognition of autonomic dysfunction may help in the early detection of dementia.