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Multicollinear physical activity accelerometry data and associations to cardiometabolic health: challenges, pitfalls, and potential solutions

BACKGROUND: 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...

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Autores principales: Aadland, Eivind, Kvalheim, Olav Martin, Anderssen, Sigmund Alfred, Resaland, Geir Kåre, Andersen, Lars Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712694/
https://www.ncbi.nlm.nih.gov/pubmed/31455305
http://dx.doi.org/10.1186/s12966-019-0836-z
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author Aadland, Eivind
Kvalheim, Olav Martin
Anderssen, Sigmund Alfred
Resaland, Geir Kåre
Andersen, Lars Bo
author_facet Aadland, Eivind
Kvalheim, Olav Martin
Anderssen, Sigmund Alfred
Resaland, Geir Kåre
Andersen, Lars Bo
author_sort Aadland, Eivind
collection PubMed
description BACKGROUND: 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. TRIAL REGISTRATION: The study was registered in Clinicaltrials.gov 7th of April 2014 with identification number NCT02132494. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12966-019-0836-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-67126942019-08-29 Multicollinear physical activity accelerometry data and associations to cardiometabolic health: challenges, pitfalls, and potential solutions Aadland, Eivind Kvalheim, Olav Martin Anderssen, Sigmund Alfred Resaland, Geir Kåre Andersen, Lars Bo Int J Behav Nutr Phys Act Methodology BACKGROUND: 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. TRIAL REGISTRATION: The study was registered in Clinicaltrials.gov 7th of April 2014 with identification number NCT02132494. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12966-019-0836-z) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-27 /pmc/articles/PMC6712694/ /pubmed/31455305 http://dx.doi.org/10.1186/s12966-019-0836-z Text en © The Author(s). 2019 Open AccessThis 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.
spellingShingle Methodology
Aadland, Eivind
Kvalheim, Olav Martin
Anderssen, Sigmund Alfred
Resaland, Geir Kåre
Andersen, Lars Bo
Multicollinear physical activity accelerometry data and associations to cardiometabolic health: challenges, pitfalls, and potential solutions
title Multicollinear physical activity accelerometry data and associations to cardiometabolic health: challenges, pitfalls, and potential solutions
title_full Multicollinear physical activity accelerometry data and associations to cardiometabolic health: challenges, pitfalls, and potential solutions
title_fullStr Multicollinear physical activity accelerometry data and associations to cardiometabolic health: challenges, pitfalls, and potential solutions
title_full_unstemmed Multicollinear physical activity accelerometry data and associations to cardiometabolic health: challenges, pitfalls, and potential solutions
title_short Multicollinear physical activity accelerometry data and associations to cardiometabolic health: challenges, pitfalls, and potential solutions
title_sort multicollinear physical activity accelerometry data and associations to cardiometabolic health: challenges, pitfalls, and potential solutions
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712694/
https://www.ncbi.nlm.nih.gov/pubmed/31455305
http://dx.doi.org/10.1186/s12966-019-0836-z
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