Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Sep 29, 2020
Date Accepted: Apr 23, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Sleep Restlessness as a Potential Digital Biomarker for Unobtrusively Monitoring General Health Deteriorations in Community-Dwelling Older Adults
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 contactless, pervasive computing derived sleep parameter, is best suited to monitor perceived and actual health in older adults.
Methods:
We analysed real-world longitudinal data from 36 community-dwelling older adults, including more than 7000 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 were quantitatively evaluated using L1 penalized generalized linear mixed models and the corresponding effects with regards to actual health incidents were investigated through manual case by case analysis.
Results:
Using self-reported perceived health, we identified bed restlessness - measured by the number of larger movements in bed - as the most predictive sleep parameter. Case by case analysis further revealed that increases in restlessness could often be linked to reported health incidents.
Conclusions:
Our results suggest that bed restlessness measured by the number of larger movements in bed, such as toss and turns, could be a potentially relevant as well as easy to interpret and derive digital biomarker. Furthermore, this biomarker could potentially be used to continuously screen for early sings of health issues in community-dwelling older adults.
Citation
The author of this paper has made a PDF available, but requires the user to login, or create an account.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.