Feasibility of Using a Wearable Biosensor Device in Patients at Risk for Alzheimer’s Disease Dementia
Alzheimer’s disease (AD) is the most common and most costly chronic neurodegenerative disease globally. AD develops over an extended period prior to cognitive symptoms, leaving a “window of opportunity” for targeted risk-reduction interventions. Further, this pre-dementia phase includes early physiological changes in sleep and autonomic regulation, for which wearable biosensor devices may offer a convenient and cost-effective method to assess AD-risk.
Patients with a family history of AD and no or minimal cognitive complaints were recruited from the Alzheimer’s Prevention Clinic at Weill Cornell Medicine & New York-Presbyterian. Of the 40 consecutive patients screened, 34 (85%) agreed to wear a wearable biosensor device (WHOOP). One subject (2.5%) lost the device prior to data collection. Of the remaining subjects, 24 were classified as normal cognition and were asymptomatic, 6 were classified as subjective cognitive decline, and 3 were amyloid-positive (one with pre-clinical AD, one with pre-clinical Lewy-Body Dementia, and one with mild cognitive impairment due to AD). Sleep-cycle, autonomic (heart rate variability [HRV]) and activity measures were collected via WHOOP. Blood biomarkers and neuropsychological testing sensitive to cognitive changes in pre-clinical AD were obtained. Participants completed surveys assessing their sleep-patterns, exercise habits, and attitudes towards WHOOP. The goal of this prospective observational study was to determine the feasibility of using a wrist-worn biosensor device in patients at-risk for AD dementia. Unsupervised machine learning was performed to first separate participants into distinct phenotypic groups using the multivariate biometric data. Additional statistical analyses were conducted to examine correlations between individual biometric measures and cognitive performance.
27 (81.8%) participants completed the follow-up surveys. Twenty-four participants (88.9%) were satisfied with WHOOP after six months, and twenty-three (85.2%) wanted to continue wearing WHOOP. K-means clustering separated participants into two groups. Group 1 was older, had lower HRV, and spent more time in slow-wave sleep (SWS) than Group 2. Group 1 performed better on two cognitive tests assessing executive function: Flanker Inhibitory Attention/Control (FIAC) (p=.031), and Dimensional Change Card Sort (DCCS) (p=.061). In Group 1, DCCS was correlated with SWS (ϱ=.68, p=0.024) and HRV (ϱ=.6, p=0.019). In Group 2, DCCS was correlated with HRV (ϱ=.55, p=0.018). There were no significant differences in blood biomarkers between the two groups.
Wearable biosensor devices may be a feasible tool to assess AD-related physiological changes. Longitudinal collection of sleep and HRV data may potentially be a noninvasive method for monitoring cognitive changes related to pre-clinical AD. Further study is warranted in larger populations.
Key wordsAlzheimer’s disease actinography unsupervised machine learning early detection biosensor devices
This study was supported by NIH/NCATS UL1TR002384, NIH PO1AG026572; Zuckerman Family Foundation; Ace’s for Alzheimer’s; Hilarity for Charity; Women’s Alzheimer’s Movement; Memories for Mary; and philanthropic support from the patients of the Alzheimer’s Prevention Clinic.
Funding: The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Declaration of interests: Dr. Isaacson has served as a scientific advisor for Eisai. The remaining authors report no conflicts of interest or other relevant disclosures.
Ethical standards: The study procedures followed were in accordance with the ethical standards of the Institutional Review Board and the Principles of the Declaration of Helsinki (revised version of 2013).
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