{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Soreq E"],"funding":["UK Dementia Research Institute","Medical Research Council","Alzheimer’s Research UK","Alzheimer's Research UK"],"pagination":["e70758"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12552897"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(10)"],"pubmed_abstract":["<h4>Introduction</h4>Disturbed sleep patterns are common in dementia but have not been objectively quantified over long periods.<h4>Methods</h4>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.<h4>Results</h4>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"],"journal":["Alzheimer's & dementia : the journal of the Alzheimer's Association"],"pubmed_title":["Contactless longitudinal monitoring in the home characterizes aging and Alzheimer's disease-related night-time behavior and physiology."],"pmcid":["PMC12552897"],"funding_grant_id":["7201","7206"],"pubmed_authors":["Lai H","Vittrant B","Soreq E","Nilforooshan R","Joffe A","Stefanos MA","de Villele P","Revell V","Kolanko MA","Wingfield D","Walsh C","CRT group","Sharp DJ","Ravindran KKG","Golemme M","Dijk DJ","Daniels S","Giovane MD","Della Monica C"],"additional_accession":[]},"is_claimable":false,"name":"Contactless longitudinal monitoring in the home characterizes aging and Alzheimer's disease-related night-time behavior and physiology.","description":"<h4>Introduction</h4>Disturbed sleep patterns are common in dementia but have not been objectively quantified over long periods.<h4>Methods</h4>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.<h4>Results</h4>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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-06-05T04:50:45.051Z","creation":"2026-05-13T14:29:19.641Z"},"accession":"S-EPMC12552897","cross_references":{"pubmed":["41137623"],"doi":["10.1002/alz.70758"]}}