Contactless longitudinal monitoring in the home characterizes aging and Alzheimer's disease-related night-time behavior and physiology.

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 cl

SUBMITTER: Soreq E 

PROVIDER: S-EPMC12552897 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature