Publication: Circadian monitoring as an aging predictor
Authors
Martinez-Nicolas, A ; Madrid, J A ; Garcia, F J ; Campos, M ; Moreno Casbas, M T ; Almaida Pagán, Pedro Francisco ; Lucas-Sanchez, A ; Rol, M A
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Publisher
Nature Research
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Fisiología Ciber Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain. Geriatrics Section, Hospital Virgen del Valle, Toledo, Spain Department of Computer Science and Systems, University of Murcia, IMIB-Arrixaca, Murcia, Spain Nursing and Healthcare Research Unit (Investén-isciii), Madrid, Spain
Description
© The Author(s) 2018. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/
This document is the Published version of a Published Work that appeared in final form in Scientific Reports. To access the final edited and published work see https://doi.org/10.1038/s41598-018-33195-3
Abstract
The ageing process is associated with sleep and circadian rhythm (SCR) frailty, as well as greater
sensitivity to chronodisruption. This is essentially due to reduced day/night contrast, decreased
sensitivity to light, napping and a more sedentary lifestyle. Thus, the aim of this study is to develop an
algorithm to identify a SCR phenotype as belonging to young or aged subjects. To do this, 44 young
and 44 aged subjects were recruited, and their distal skin temperature (DST), activity, body position,
light, environmental temperature and the integrated variable TAP rhythms were recorded under
free-living conditions for five consecutive workdays. Each variable yielded an individual decision tree
to differentiate between young and elderly subjects (DST, activity, position, light, environmental
temperature and TAP), with agreement rates of between 76.1% (light) and 92% (TAP). These decision
trees were combined into a unique decision tree that reached an agreement rate of 95.3% (4 errors
out of 88, all of them around the cut-off point). Age-related SCR changes were very significant,
thus allowing to discriminate accurately between young and aged people when implemented
in decision trees. This is useful to identify chronodisrupted populations that could benefit from
chronoenhancement strategies.
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