Feasibility of Using a Wearable Biosensor Device in Patients at Risk for Alzheimer's Disease Dementia
Open accessAbstract
BACKGROUND: 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. METHODS: 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 and 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. RESULTS: 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. CONCLUSIONS: 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 non-invasive method for monitoring cognitive changes related to pre-clinical AD. Further study is warranted in larger populations.
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In some studies (15/189, 7.9%), wearable prices were not transparent.
“In some studies (15/189, 7.9%), wearable prices were not transparent, as data were provided through a variety of participant-owned wearables [87] or the wearable hardware was part of a subscription or a membership fee, that is, Whoop strap of Whoop [178].”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [178]
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The impact of wearable technologies in health research: scoping review
Huhn, S., Axt, M., Gunga, H.-C., Maggioni, M. A., Munga, S., Obor, D., Sié, A., Boudo, V., Bunker, A., Sauerborn, R., et al. (2022) · JMIR mHealth and uHealth · doi:10.2196/34384 · Cited by 379 · see citing works
- Authors
- Saif, N., Yan, P., Niotis, K., Scheyer, O., Rahman, A., Berkowitz, M., Krikorian, R., Hristov, H., Sadek, G., Bellara, S., & Isaacson, R. S.
- Affiliations
- Weill Cornell Medicine (US)
- Beth Israel Deaconess Medical Center (US)
- University of California, Los Angeles (US)
- University of Cincinnati (US)
- Corresponding author
- Isaacson, Richard S.
- Venue
- The Journal of Prevention of Alzheimer's Disease
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 7
- Issue
- 2
- Pages
- 104-111
- ISSN
- 2274-5807 (ISSN-L)
- PMC
- PMC8202529
- OpenAlex
- W2979675636
- Cited by
- 41
- References
- 40
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 13 · 2023: 8 · 2022: 7 · 2021: 3 · 2020: 1
- FWCI
- 3.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Journal of Prevention of Alzheimer s Disease (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (UL1TR002384)
- National Institute on Aging (P01AG026572)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- Alzheimer's disease, preclinical Alzheimer's disease, wearable biosensors, WHOOP, heart rate variability, slow-wave sleep, autonomic regulation, subjective cognitive decline, family history of Alzheimer's disease, executive function, blood biomarkers, amyloid, dementia with Lewy bodies, mild cognitive impairment, device feasibility, K-means clustering, risk mitigation
- MeSH terms
- Wearable Electronic Devices, Adult, Aged, Alzheimer Disease, Feasibility Studies, Female, Heart Rate, Humans, Male, Middle Aged, Surveys and Questionnaires, Sleep, Risk Assessment, Actigraphy
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