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dc.contributor.authorMartinez-Nicolas, A
dc.contributor.authorMadrid, J A
dc.contributor.authorGarcía, F J
dc.contributor.authorCampos, M
dc.contributor.authorMoreno-Casbas, Teresa 
dc.contributor.authorAlmaida-Pagán, P F
dc.contributor.authorLucas-Sánchez, A
dc.contributor.authorRol, M A
dc.date.accessioned2019-02-11T11:10:49Z
dc.date.available2019-02-11T11:10:49Z
dc.date.issued2018-10-09
dc.identifier.citationSci Rep. 2018 Oct 9;8(1):15027es_ES
dc.identifier.issn2045-2322es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/7159
dc.description.abstractThe 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.es_ES
dc.description.sponsorshipThis work was supported by the Ministry of Economy and Competitiveness, the Instituto de Salud Carlos III through a CIBERFES grant (CB16/10/00239, CB16/10/00468, CB16/10/00456) awarded to JAM, MMC and FJG respectively, and grant 19899/GERM/15 awarded to JAM (co-financed by FEDER).es_ES
dc.language.isoenges_ES
dc.publisherNature Publishing Groupes_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleCircadian monitoring as an aging predictores_ES
dc.typeArtículoes_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID30301951es_ES
dc.format.volume8es_ES
dc.format.number1es_ES
dc.format.page15027es_ES
dc.identifier.doi10.1038/s41598-018-33195-3es_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España)
dc.contributor.funderInstituto de Salud Carlos III-ISCIII
dc.description.peerreviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.1038/s41598-018-33195-3
dc.identifier.journalScientific reportses_ES
dc.repisalud.centroISCIII::Unidad de Investigación en Cuidados de Salud (Investén-isciii)es_ES
dc.repisalud.institucionISCIIIes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/CB16/10/00239es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/CB16/10/00468es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/CB16/10/00456es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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