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

Eivind Aadland, Olav Martin Kvalheim, Sigmund Alfred Anderssen, Geir Kåre Resaland, Lars Bo Andersen, Eivind Aadland, Olav Martin Kvalheim, Sigmund Alfred Anderssen, Geir Kåre Resaland, Lars Bo Andersen

Abstract

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 .

Keywords: Accelerometer; Children; Compositional data analysis; Intensity; Multicollinearity; Multiple linear regression; Multivariate pattern analysis; Statistics.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Bivariate correlations (95% confidence intervals) between physical activity intensities and a composite cardiometabolic health score using the traditional description of four physical activity variables. Raw data (upper panel), compositional data (lower panel)
Fig. 2
Fig. 2
Association patterns between physical activity intensities and a composite cardiometabolic health score using the traditional description of four physical activity variables using different analytic approaches. Multiple linear regression with raw data (upper left panel), multiple linear regression with compositional data using the ilr-transformation (lower left panel), multivariate pattern analysis with raw data (upper right panel), and multivariate pattern analysis with compositional data using the clr-transformation (lower right panel). Selectivity ratio is calculated as the ratio of explained to total variance on the predictive (target projected) component. R2 = explained variance of the model
Fig. 3
Fig. 3
Bivariate correlations (95% confidence intervals) between physical activity intensities and a composite cardiometabolic health score using the spectrum description of 23 physical activity variables. Raw data (upper panel), compositional data (lower panel)
Fig. 4
Fig. 4
Association patterns between physical activity intensities and a composite cardiometabolic health score using the spectrum description of 23 physical activity variables using different analytic approaches. Multiple linear regression with raw data (upper left panel), multiple linear regression with compositional data using the ilr-transformation (lower left panel), multivariate pattern analysis with raw data (upper right panel), and multivariate pattern analysis with compositional data using the clr-transformation (lower right panel). Selectivity ratio is calculated as the ratio of explained to total variance on the predictive (target projected) component. R2 = explained variance of the model

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