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dc.contributor.authorKortelainen, Lauri
dc.contributor.authorHelske, Jouni
dc.contributor.authorFinni, Taija
dc.contributor.authorMehtätalo, Lauri
dc.contributor.authorTikkanen, Olli
dc.contributor.authorKärkkäinen, Salme
dc.date.accessioned2021-08-31T08:34:12Z
dc.date.available2021-08-31T08:34:12Z
dc.date.issued2021
dc.identifier.citationKortelainen, L., Helske, J., Finni, T., Mehtätalo, L., Tikkanen, O., & Kärkkäinen, S. (2021). A nonlinear mixed model approach to predict energy expenditure from heart rate. <i>Physiological Measurement</i>, <i>42</i>(3), Article 035001. <a href="https://doi.org/10.1088/1361-6579/abea25" target="_blank">https://doi.org/10.1088/1361-6579/abea25</a>
dc.identifier.otherCONVID_51753043
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/77613
dc.description.abstractObjective: Heart rate (HR) monitoring provides a convenient and inexpensive way to predict energy expenditure (EE) during physical activity. However, there is a lot of variation among individuals in the EE-HR relationship, which should be taken into account in predictions. The objective is to develop a model that allows the prediction of EE based on HR as accurately as possible and allows an improvement of the prediction using calibration measurements from the target individual. Approach: We propose a nonlinear (logistic) mixed model for EE and HR measurements and an approach to calibrate the model for a new person who does not belong to the data set used to estimate the model. The calibration utilizes the estimated model parameters and calibration measurements of HR and EE from the person in question. We compare the results of the logistic mixed model with a simpler linear mixed model for which the calibration is easier to perform. Main results: We show that the calibration is beneficial already with only one pair of measurements on HR and EE. That is an important benefit over an individual-level model fitting which requires a larger number of measurements. Moreover, we present an algorithm for calculating the confidence and prediction intervals of the calibrated predictions. The analysis was based on up to eleven pairs of EE and HR measurements from each of 54 individuals of a heterogeneous group of people, who performed a maximal treadmill test. Significance: The proposed method allows accurate energy expenditure predictions based on only a few calibration measurements from a new individual without access to the original dataset, thus making the approach viable for example on wearable computers.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Physics
dc.relation.ispartofseriesPhysiological Measurement
dc.rightsCC BY-NC-ND 4.0
dc.subject.otherenergiankulutus: sykemittaus
dc.subject.otherkalibrointi
dc.subject.otherlogistinen sekamalli
dc.subject.otherfyysinen aktiivisuus.
dc.subject.otherenergy expenditure
dc.subject.otherheart rate monitoring
dc.subject.otherindividual calibration
dc.subject.otherlogistic mixed model
dc.subject.otherphysical activity
dc.titleA nonlinear mixed model approach to predict energy expenditure from heart rate
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202108314739
dc.contributor.laitosLiikuntatieteellinen tiedekuntafi
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosFaculty of Sport and Health Sciencesen
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn0967-3334
dc.relation.numberinseries3
dc.relation.volume42
dc.type.versionacceptedVersion
dc.rights.copyright© 2021 Institute of Physics and Engineering in Medicine
dc.rights.accesslevelembargoedAccessfi
dc.relation.grantnumber311877
dc.subject.ysokalibrointi
dc.subject.ysofyysinen aktiivisuus
dc.subject.ysomittausmenetelmät
dc.subject.ysosykemittarit
dc.subject.ysotilastolliset mallit
dc.subject.ysoenergiankulutus (aineenvaihdunta)
dc.subject.ysosyke
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p13931
jyx.subject.urihttp://www.yso.fi/onto/yso/p23102
jyx.subject.urihttp://www.yso.fi/onto/yso/p20083
jyx.subject.urihttp://www.yso.fi/onto/yso/p12342
jyx.subject.urihttp://www.yso.fi/onto/yso/p26278
jyx.subject.urihttp://www.yso.fi/onto/yso/p24540
jyx.subject.urihttp://www.yso.fi/onto/yso/p3751
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1088/1361-6579/abea25
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramResearch profiles, AoFen


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