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

Soyang Kwon, Lars Bo Andersen, Anders Grøntved, Elin Kolle, Greet Cardon, Rachel Davey, Susi Kriemler, Kate Northstone, Angie S. Page, Jardena J. Puder, John J. Reilly, Luis B. Sardinha, Esther M. F. van Sluijs, Kathleen F. Janz

Research output: Contribution to journalArticle

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 22X103 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.
LanguageEnglish
Article number40
Number of pages9
JournalInternational Journal of Behavioral Nutrition and Physical Activity
Volume16
Issue number1
DOIs
Publication statusPublished - 29 Apr 2019

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Keywords

  • ICAD
  • children
  • adolescents
  • ActiGraph
  • total activity counts
  • sedentary
  • physical activity measurement

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Kwon, Soyang ; Andersen, Lars Bo ; Grøntved, Anders ; Kolle, Elin ; Cardon, Greet ; Davey, Rachel ; Kriemler, Susi ; Northstone, Kate ; Page, Angie S. ; Puder, Jardena J. ; Reilly, John J. ; Sardinha, Luis B. ; van Sluijs, Esther M. F. ; Janz, Kathleen F. / A closer look at the relationship among accelerometer-based physical activity metrics : ICAD pooled data. In: International Journal of Behavioral Nutrition and Physical Activity. 2019 ; Vol. 16, No. 1.
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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 22X103 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.",
keywords = "ICAD, children, adolescents, ActiGraph, total activity counts, sedentary, physical activity measurement",
author = "Soyang Kwon and Andersen, {Lars Bo} and Anders Gr{\o}ntved and Elin Kolle and Greet Cardon and Rachel Davey and Susi Kriemler and Kate Northstone and Page, {Angie S.} and Puder, {Jardena J.} and Reilly, {John J.} and Sardinha, {Luis B.} and {van Sluijs}, {Esther M. F.} and Janz, {Kathleen F.}",
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Kwon, S, Andersen, LB, Grøntved, A, Kolle, E, Cardon, G, Davey, R, Kriemler, S, Northstone, K, Page, AS, Puder, JJ, Reilly, JJ, Sardinha, LB, van Sluijs, EMF & Janz, KF 2019, 'A closer look at the relationship among accelerometer-based physical activity metrics: ICAD pooled data' International Journal of Behavioral Nutrition and Physical Activity, vol. 16, no. 1, 40. https://doi.org/10.1186/s12966-019-0801-x

A closer look at the relationship among accelerometer-based physical activity metrics : ICAD pooled data. / Kwon, Soyang; Andersen, Lars Bo; Grøntved, Anders; Kolle, Elin; Cardon, Greet; Davey, Rachel ; Kriemler, Susi ; Northstone, Kate; Page, Angie S.; Puder, Jardena J. ; Reilly, John J.; Sardinha, Luis B. ; van Sluijs, Esther M. F. ; Janz, Kathleen F.

In: International Journal of Behavioral Nutrition and Physical Activity, Vol. 16, No. 1, 40, 29.04.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A closer look at the relationship among accelerometer-based physical activity metrics

T2 - International Journal of Behavioral Nutrition and Physical Activity

AU - Kwon, Soyang

AU - Andersen, Lars Bo

AU - Grøntved, Anders

AU - Kolle, Elin

AU - Cardon, Greet

AU - Davey, Rachel

AU - Kriemler, Susi

AU - Northstone, Kate

AU - Page, Angie S.

AU - Puder, Jardena J.

AU - Reilly, John J.

AU - Sardinha, Luis B.

AU - van Sluijs, Esther M. F.

AU - Janz, Kathleen F.

PY - 2019/4/29

Y1 - 2019/4/29

N2 - 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 22X103 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.

AB - 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 22X103 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.

KW - ICAD

KW - children

KW - adolescents

KW - ActiGraph

KW - total activity counts

KW - sedentary

KW - physical activity measurement

U2 - 10.1186/s12966-019-0801-x

DO - 10.1186/s12966-019-0801-x

M3 - Article

VL - 16

JO - International Journal of Behavioral Nutrition and Physical Activity

JF - International Journal of Behavioral Nutrition and Physical Activity

SN - 1479-5868

IS - 1

M1 - 40

ER -