Abstract
Alzheimers Dement. 2025 Oct;21(10):e70758. doi: 10.1002/alz.70758.
ABSTRACT
INTRODUCTION: Disturbed sleep patterns are common in dementia but have not been objectively quantified over long periods.
METHODS: We compared a cohort of 83 Alzheimer's disease (AD) patients to 13,588 individuals from the general population. Sleep patterns, heart rate, and breathing rate data were acquired using a zero-burden contactless, under-mattress pressure sensor. Data reduction and explainable machine learning approaches were used to identify sleep phenotypes.
RESULTS: AD was characterized by longer time in bed, more bed exits, less snoring, and changes in estimated sleep states. We derived the Dementia Research Institute Sleep Index for Alzheimer's Disease (DRI-SI-AD), a digital biomarker quantifying sleep disturbances. DRI-SI-AD detected the effects of acute clinical events and dementia progression at the individual level.
DISCUSSION: Our approach may help bridge a gap in dementia care by providing a zero-burden method for longitudinal monitoring of health events, disease progression, and dementia risk.
HIGHLIGHTS: Continuous monitoring reveals dementia-specific nocturnal sleep disturbances. We developed a novel sleep biomarker, Dementia Research Institute Sleep Index (AD), for tracking Alzheimer's disease (AD) progression. We used contactless under-mattress sensors for low-burden, long-term data collection. Prolonged bedtimes and frequent exits were identified as key dementia-related sleep traits. We demonstrated the feasibility of in-home monitoring for dementia care and risk assessment.
PMID:41137623 | DOI:10.1002/alz.70758
UK DRI Authors
