ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025
Variability of longitudinal sleep monitoring in amyloid-negative and amyloid-positive cognitively unimpaired and mildly impaired older adults.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Variability of longitudinal sleep monitoring in amyloid-negative and amyloid-positive cognitively unimpaired and mildly impaired older adults.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
introductionSleep disturbances are associated with Alzheimer's disease (AD) pathology and cognitive symptoms, but few studies have longitudinally assessed sleep, AD biomarkers, and cognition. Understanding how sleep changes over time in older adults with and without AD pathology is crucial for appropriately designing longitudinal studies of aging and AD.
methodsSleep was measured over ≈3.5 years in older adults using self-reported questionnaires and an at-home single-channel electroencephalography (scEEG) device. Participants also underwent amyloid imaging and cognitive testing. Longitudinal change of multiple sleep parameters was determined and sample sizes estimated for future clinical trials using sleep as an outcome.
resultsIn both amyloid groups, EEG spectral power measures demonstrated minimal longitudinal change, whereas self-reported and sleep parameters such as sleep efficiency demonstrated greater variability over time. DISCUSSION: This study characterized how sleep parameters change in older adults with and without AD pathology, offering important guidance for future longitudinal studies targeting sleep and neurodegeneration. HIGHLIGHTS: Low variability in electroencephalography (EEG) spectral power metrics, including delta, theta, and alpha power. Greater variability in self-report sleep metrics, including self-reported time to fall asleep and EEG-derived metrics such as sleep duration and N2 sleep. Understanding the variability in sleep measures over time will offer important guidance for designing future longitudinal studies targeting sleep and neurodegeneration.
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