Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study (Preprint)

Abstract

BACKGROUND Population ageing is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased healthcare expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs, and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnoea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted, evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. OBJECTIVE In this work we aim to evaluate which contactlessly measurable sleep parameter, is best suited to monitor perceived and actual health status changes in older adults. METHODS We analysed real-world longitudinal data from 37 community-dwelling older adults, including more than 6000 nights of sleep. Sleep data was measured by a pressure sensor placed beneath the mattress and corresponding health status information was acquired through weekly questionnaires and reports by healthcare personnel. Association with perceived health, evaluated by EuroQol EQ-VAS ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to real-world health incidents was investigated through manual case-by-case analysis. RESULTS Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed - measured by the number toss-and-turn events - as the most predictive sleep parameter (t-Value=-0.435, p-adj=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be linked to reported health incidents. CONCLUSIONS Our results suggest that nightly body movements in bed, such as toss and turns, could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier as well as more personalized and precise treatment options. CLINICALTRIAL