Following publication of the original article [1], the author reported that the name of the collaborator group was missing from the author group

Andersen, Lars Bo

Cardon, Greet

Davey, Rachel

Grøntved, Anders

ICAD Collaborators

Janz, Kathleen F

Kolle, Elin

Kriemler, Susi

Kwon, Soyang

Northstone, Kate

Page, Angie S

Puder, Jardena J

Reilly, John J

Sardinha, Luis B

van Sluijs, Esther MF

International Journal of Behavioral Nutrition and Physical Activity

English

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

Page, Angie S.

Puder, Jardena J.

Reilly, John J.

Sardinha, Luís B.

van Sluijs, Esther M.F.

Janz, Kathleen F.

NORA - Norwegian Open Research Archives

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

Kwon, S.

Andersen, L.B.

Grøntved, A.

Kolle, E.

Cardon, G.

Davey, R.

Kriemler, S.

Northstone, K.

Page, A.S.

Puder, J.J.

Reilly, J.J.

Sardinha, L.B.

van Sluijs, EMF

Janz, K.F.

Serveur académique lausannois

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

Page, Angie

Puder, Jardena

Reilly, John

Sardinha, Luis

van Sluijs, Esther

Janz, Kathleen

Ghent University Academic Bibliography

CORRECTION Open AccessCorrection to: A closer look at therelationship among accelerometer-basedphysical activity metrics: ICAD pooled dataSoyang Kwon1*, Lars Bo Andersen2, Anders Grøntved3, Elin Kolle4, Greet Cardon5, Rachel Davey6, Susi Kriemler7,Kate Northstone8, Angie S. Page9, Jardena J. Puder10, John J. Reilly11, Luis B. Sardinha12, Esther M. F. van Sluijs13,Kathleen F. Janz14 and On behalf of the ICAD CollaboratorsCorrection to: Int J Behav Nutr Phys Act (2019) 16:40https://doi.org/10.1186/s12966-019-0801-xFollowing publication of the original article [1], theauthor reported that the name of the collaborator groupwas missing from the author group.Author group in published article:Soyang Kwon1*, Lars Bo Andersen2, AndersGrøntved3, Elin Kolle4, Greet Cardon5, Rachel Davey6,Susi Kriemler7, Kate Northstone8, Angie S. Page9,Jardena J. Puder10, John J. Reilly11, Luis B. Sardinha12,Esther M. F. van Sluijs13 and Kathleen F. Janz14.Revised author group:Soyang Kwon1*, Lars Bo Andersen2, AndersGrøntved3, Elin Kolle4, Greet Cardon5, Rachel Davey6,Susi Kriemler7, Kate Northstone8, Angie S. Page9,Jardena J. Puder10, John J. Reilly11, Luis B. Sardinha12,Esther M. F. van Sluijs13 and Kathleen F. Janz14 Onbehalf of the ICAD Collaborators.Author details1Ann & Robert H. Lurie Children’s Hospital of Chicago Stanley ManneChildren’s Research Institute, 225 E Chicago Ave, Box 157, Chicago, IL 60611,USA. 2Faculty of Education, Arts and Sport, Western Norway University ofApplied Sciences, Sogndal, Norway. 3Department of Sports Science andClinical Biomechanics, University of Southern Denmark, Odense, Denmark.4Norway, Norwegian School of Sport Science, Oslo, Norway. 5Department ofMovement and Sports Sciences, Ghent University, 9000 Ghent, Belgium.6Centre for Research & Action in Public Health Health Research Institute,University of Canberra, Canberra, Australia. 7Epidemiology, Biostatistics andPrevention Institute, University of Zürich, Zürich, Switzerland. 8Bristol MedicalSchool, University of Bristol, Bristol, UK. 9Centre for Exercise, Nutrition andHealth Sciences, University of Bristol, Bristol, UK. 10Obstetric Service, LausanneUniversity Hospital, Lausanne, Switzerland. 11Physical Activity for HealthGroup, School of Psychological Sciences and Health, University ofStrathclyde, Glasgow, UK. 12Exercise and Health Laboratory, CIPER, Faculdadede Motricidade Humana, Universidade de Lisboa, Cruz-Quebrada, Portugal.13Centre for Diet and Activity Research (CEDAR) & MRC Epidemiology Unit,University of Cambridge, Cambridge, UK. 14Department of Health andHuman Physiology, University of Iowa, Iowa City, IA, USA.Reference1. Kwon, et al. A closer look at the relationship among accelerometer-basedphysical activity metrics: ICAD pooled data. Int J Behav Nutr Phys Act. 2019;16:40. https://doi.org/10.1186/s12966-019-0801-x.© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe 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.* Correspondence: skwon@luriechildrens.org1Ann & Robert H. Lurie Children’s Hospital of Chicago Stanley ManneChildren’s Research Institute, 225 E Chicago Ave, Box 157, Chicago, IL 60611,USAFull list of author information is available at the end of the articleKwon et al. International Journal of Behavioral Nutrition and Physical Activity          (2019) 16:62 https://doi.org/10.1186/s12966-019-0828-z

A closer look at the relationship among accelerometer-based physical activity metrics : ICAD pooled data (vol 16, pg 40, 2019)

Apollo (Cambridge)

Following publication of the original article [1], the author reported that the name of the collaborator group was missing from the author group.

Erratum for A closer look at the relationship among accelerometer-based physical activity metrics: ICAD pooled data. [Int J Behav Nutr Phys Act. 2019

van Sluijs, Esther M F

ZORA

Zurich Open Repository andArchiveUniversity of ZurichMain LibraryStrickhofstrasse 39CH-8057 Zurichwww.zora.uzh.chYear: 2019Correction to: A closer look at the relationship among accelerometer-basedphysical activity metrics: ICAD pooled dataKwon, 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 FAbstract: Following publication of the original article [1], the author reported that the name of thecollaborator group was missing from the author group. Erratum for A closer look at the relationshipamong accelerometer-based physical activity metrics: ICAD pooled data. [Int J Behav Nutr Phys Act.2019]DOI: https://doi.org/10.1186/s12966-019-0828-zPosted at the Zurich Open Repository and Archive, University of ZurichZORA URL: https://doi.org/10.5167/uzh-177649Journal ArticlePublished Version  The following work is licensed under a Creative Commons: Attribution 4.0 International (CC BY 4.0)License.Originally published at:Kwon, Soyang; Andersen, Lars Bo; Grøntved, Anders; Kolle, Elin; Cardon, Greet; Davey, Rachel; Kriem-ler, Susi; Northstone, Kate; Page, Angie S; Puder, Jardena J; Reilly, John J; Sardinha, Luis B; vanSluijs, Esther M F; Janz, Kathleen F (2019). Correction to: A closer look at the relationship amongaccelerometer-based physical activity metrics: ICAD pooled data. International Journal of BehavioralNutrition and Physical Activity, 16:62.DOI: https://doi.org/10.1186/s12966-019-0828-zCORRECTION Open AccessCorrection to: A closer look at therelationship among accelerometer-basedphysical activity metrics: ICAD pooled dataSoyang Kwon1*, Lars Bo Andersen2, Anders Grøntved3, Elin Kolle4, Greet Cardon5, Rachel Davey6, Susi Kriemler7,Kate Northstone8, Angie S. Page9, Jardena J. Puder10, John J. Reilly11, Luis B. Sardinha12, Esther M. F. van Sluijs13,Kathleen F. Janz14 and On behalf of the ICAD CollaboratorsCorrection to: Int J Behav Nutr Phys Act (2019) 16:40https://doi.org/10.1186/s12966-019-0801-xFollowing publication of the original article [1], theauthor reported that the name of the collaborator groupwas missing from the author group.Author group in published article:Soyang Kwon1*, Lars Bo Andersen2, AndersGrøntved3, Elin Kolle4, Greet Cardon5, Rachel Davey6,Susi Kriemler7, Kate Northstone8, Angie S. Page9,Jardena J. Puder10, John J. Reilly11, Luis B. Sardinha12,Esther M. F. van Sluijs13 and Kathleen F. Janz14.Revised author group:Soyang Kwon1*, Lars Bo Andersen2, AndersGrøntved3, Elin Kolle4, Greet Cardon5, Rachel Davey6,Susi Kriemler7, Kate Northstone8, Angie S. Page9,Jardena J. Puder10, John J. Reilly11, Luis B. Sardinha12,Esther M. F. van Sluijs13 and Kathleen F. Janz14 Onbehalf of the ICAD Collaborators.Author details1Ann & Robert H. Lurie Children’s Hospital of Chicago Stanley ManneChildren’s Research Institute, 225 E Chicago Ave, Box 157, Chicago, IL 60611,USA. 2Faculty of Education, Arts and Sport, Western Norway University ofApplied Sciences, Sogndal, Norway. 3Department of Sports Science andClinical Biomechanics, University of Southern Denmark, Odense, Denmark.4Norway, Norwegian School of Sport Science, Oslo, Norway. 5Department ofMovement and Sports Sciences, Ghent University, 9000 Ghent, Belgium.6Centre for Research & Action in Public Health Health Research Institute,University of Canberra, Canberra, Australia. 7Epidemiology, Biostatistics andPrevention Institute, University of Zürich, Zürich, Switzerland. 8Bristol MedicalSchool, University of Bristol, Bristol, UK. 9Centre for Exercise, Nutrition andHealth Sciences, University of Bristol, Bristol, UK. 10Obstetric Service, LausanneUniversity Hospital, Lausanne, Switzerland. 11Physical Activity for HealthGroup, School of Psychological Sciences and Health, University ofStrathclyde, Glasgow, UK. 12Exercise and Health Laboratory, CIPER, Faculdadede Motricidade Humana, Universidade de Lisboa, Cruz-Quebrada, Portugal.13Centre for Diet and Activity Research (CEDAR) & MRC Epidemiology Unit,University of Cambridge, Cambridge, UK. 14Department of Health andHuman Physiology, University of Iowa, Iowa City, IA, USA.Reference1. Kwon, et al. A closer look at the relationship among accelerometer-basedphysical activity metrics: ICAD pooled data. Int J Behav Nutr Phys Act. 2019;16:40. https://doi.org/10.1186/s12966-019-0801-x.© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe 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.* Correspondence: skwon@luriechildrens.org1Ann & Robert H. Lurie Children’s Hospital of Chicago Stanley ManneChildren’s Research Institute, 225 E Chicago Ave, Box 157, Chicago, IL 60611,USAFull list of author information is available at the end of the articleKwon et al. International Journal of Behavioral Nutrition and Physical Activity          (2019) 16:62 https://doi.org/10.1186/s12966-019-0828-z

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

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

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

Sardinha, Luis B.

van Sluijs, Esther M. F.

University of Strathclyde Institutional Repository

 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 &lt; 0.01) with longer wear time (13 to 18 h/day) as compared to shorter wear time (8 to &lt; 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. </p

Van Sluijs, Esther M.F.

University of Canberra Research Repository

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

Explore Bristol Research

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.
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.
TAC was approximately 22X10 &lt;sup&gt;3&lt;/sup&gt;  counts higher (p &lt; 0.01) with longer wear time (13 to 18 h/day) as compared to shorter wear time (8 to &lt; 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).
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

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

https://www.repository.cam.ac.uk/bitstream/1810/308890/2/12966_2019_Article_828.pdf

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

Abstract

Similar works

Full text

Available Versions

NORA - Norwegian Open Research Archives

Serveur académique lausannois

Ghent University Academic Bibliography

Apollo (Cambridge)

ZORA

Ghent University Academic Bibliography

University of Strathclyde Institutional Repository

University of Canberra Research Repository

Explore Bristol Research

Serveur académique lausannois