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dc.contributor.authorAadland, Eivind
dc.contributor.authorKvalheim, Olav Martin
dc.contributor.authorAnderssen, Sigmund Alfred
dc.contributor.authorResaland, Geir Kåre
dc.contributor.authorAndersen, Lars Bo
dc.date.accessioned2020-04-29T10:47:45Z
dc.date.available2020-04-29T10:47:45Z
dc.date.created2019-09-21T20:38:39Z
dc.date.issued2019
dc.identifier.citationInternational Journal of Behavioral Nutrition and Physical Activity. 2019, 16, 74.en_US
dc.identifier.issn1479-5868
dc.identifier.urihttps://hdl.handle.net/11250/2652912
dc.descriptionThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en_US
dc.description.abstractBackground: The analysis of associations between accelerometer-derived physical activity (PA) intensities and cardiometabolic health is a major challenge due to multicollinearity between the explanatory variables. This challenge has facilitated the application of different analytic approaches within the field. The aim of the present study was to compare association patterns of PA intensities with cardiometabolic health in children obtained from multiple linear regression, compositional data analysis, and multivariate pattern analysis. Methods: A sample of 841 children (age 10.2 ± 0.3 years; BMI 18.0 ± 3.0; 50% boys) provided valid accelerometry and cardiometabolic health data. Accelerometry (ActiGraph GT3X+) data were characterized into traditional (four PA intensity variables) and more detailed categories (23 PA intensity variables covering the intensity spectrum; 0–99 to ≥10,000 counts per minute). Several indices of cardiometabolic health were used to create a composite cardiometabolic health score. Multiple linear regression and multivariate pattern analyses were used to analyze both raw and compositional data. Results: Besides a consistent negative (favorable) association between vigorous PA and the cardiometabolic health measure using the traditional description of PA data, associations between PA intensities and cardiometabolic health differed substantially depending on the analytic approaches used. Multiple linear regression lead to instable and spurious associations, while compositional data analysis showed distorted association patterns. Multivariate pattern analysis appeared to handle the raw PA data correctly, leading to more plausible interpretations of the associations between PA intensities and cardiometabolic health. Conclusions: Future studies should consider multivariate pattern analysis without any transformation of PA data when examining relationships between PA intensity patterns and health outcomes.en_US
dc.language.isoengen_US
dc.subjectmultivariate pattern analysisen_US
dc.subjectcompositional data analysisen_US
dc.subjectmultiple linear regressionen_US
dc.subjectmulticollinearityen_US
dc.subjectstatisticsen_US
dc.subjectchildrenen_US
dc.subjectaccelerometeren_US
dc.subjectintensityen_US
dc.titleMulticollinear physical activity accelerometry data and associations to cardiometabolic health: Challenges, pitfalls, and potential solutionsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© The Author(s). 2019en_US
dc.source.volume16:74en_US
dc.source.journalInternational Journal of Behavioral Nutrition and Physical Activityen_US
dc.identifier.doi10.1186/s12966-019-0836-z
dc.identifier.cristin1727484
dc.description.localcodeSeksjon for idrettsmedisinske fag / Department of Sports Medicineen_US
cristin.unitcode150,34,0,0
cristin.unitnameSeksjon for idrettsmedisinske fag
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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