Eyal Soreq, Magdalena Kolanko, CRT group, Kiran K G Ravindran, Ciro della Monica, Victoria L. Revell, Sarah Daniels, Anna Joffe, Helen Lai, Mara Golemme, Martina Del Giovane, Chloe Walsh, David Wingfield, Ramin Nilforooshan, Marie‐Ange Stefanos, Benjamin Vittrant, Paul de Villèle, Derk‐Jan Dijk, David Sharp
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.