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A closer look at the relationship among accelerometer-based physical activity metrics: ICAD pooled data

Research output: Contribution to journalArticle

Original languageEnglish
Article number40
JournalInternational Journal of Behavioral Nutrition and Physical Activity
Volume16
Issue number1
DOIs
DateAccepted/In press - 17 Apr 2019
DatePublished (current) - 29 Apr 2019

Abstract

Background: Accelerometers are widely used to assess child physical activity (PA) levels. Using the accelerometer data, several PA metrics can be estimated. Knowledge about the relationships between these different metrics can improve our understanding of children's PA behavioral patterns. It also has significant implications for comparing PA metrics across studies and fitting a statistical model to examine their health effects. The aim of this study was to examine the relationships among the metrics derived from accelerometers in children. Methods: Accelerometer data from 24,316 children aged 5 to 18 years were extracted from the International Children's Accelerometer Database (ICAD) 2.0. Correlation coefficients between wear time, sedentary behavior (SB), light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), moderate- and vigorous-intensity PA (MVPA), and total activity counts (TAC) were calculated. Results: TAC was approximately 22X10 3 counts higher (p < 0.01) with longer wear time (13 to 18 h/day) as compared to shorter wear time (8 to < 13 h/day), while MVPA was similar across the wear time categories. MVPA was very highly correlated with TAC (r =.91; 99% CI =.91 to.91). Wear time-adjusted correlation between SB and LPA was also very high (r = -.96; 99% CI = -.96, - 95). VPA was moderately correlated with MPA (r =.58; 99% CI =.57,.59). Conclusions: TAC is mostly explained by MVPA, while it could be more dependent on wear time, compared to MVPA. MVPA appears to be comparable across different wear durations and studies when wear time is ≥8 h/day. Due to the moderate to high correlation between some PA metrics, potential collinearity should be addressed when including multiple PA metrics together in statistical modeling.

    Research areas

  • ActiGraph, Adolescents, Children, ICAD, Physical activity measurement, Sedentary, Total activity counts

    Structured keywords

  • ALSPAC

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via Springer Nature at https://doi.org/10.1186/s12966-019-0801-x . Please refer to any applicable terms of use of the publisher.

    Final published version, 692 KB, PDF document

    Licence: CC BY

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