TY - JOUR
T1 - Equating accelerometer estimates among youth
T2 - The Rosetta Stone 2
AU - Brazendale, Keith
AU - Beets, Michael W
AU - Bornstein, Daniel B
AU - Moore, Justin B
AU - Pate, Russell R
AU - Weaver, Robert G
AU - Falck, Ryan S
AU - Chandler, Jessica L
AU - Andersen, Lars B
AU - Anderssen, Sigmund A
AU - Cardon, Greet
AU - Cooper, Ashley
AU - Davey, Rachel
AU - Froberg, Karsten
AU - Hallal, Pedro C
AU - Janz, Kathleen F
AU - Kordas, Katarzyna
AU - Kriemler, Susi
AU - Puder, Jardena J
AU - Reilly, John J
AU - Salmon, Jo
AU - Sardinha, Luis B
AU - Timperio, Anna
AU - van Sluijs, Esther M F
AU - On behalf of the International Children's Accelerometry Database (ICAD) Collaborators
N1 - Copyright © 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
PY - 2015/2/23
Y1 - 2015/2/23
N2 - OBJECTIVES: Different accelerometer cutpoints used by different researchers often yields vastly different estimates of moderate-to-vigorous intensity physical activity (MVPA). This is recognized as cutpoint non-equivalence (CNE), which reduces the ability to accurately compare youth MVPA across studies. The objective of this research is to develop a cutpoint conversion system that standardizes minutes of MVPA for six different sets of published cutpoints.DESIGN: Secondary data analysis.METHODS: Data from the International Children's Accelerometer Database (ICAD; Spring 2014) consisting of 43,112 Actigraph accelerometer data files from 21 worldwide studies (children 3-18 years, 61.5% female) were used to develop prediction equations for six sets of published cutpoints. Linear and non-linear modeling, using a leave one out cross-validation technique, was employed to develop equations to convert MVPA from one set of cutpoints into another. Bland Altman plots illustrate the agreement between actual MVPA and predicted MVPA values.RESULTS: Across the total sample, mean MVPA ranged from 29.7MVPAmind(-1) (Puyau) to 126.1MVPAmind(-1) (Freedson 3 METs). Across conversion equations, median absolute percent error was 12.6% (range: 1.3 to 30.1) and the proportion of variance explained ranged from 66.7% to 99.8%. Mean difference for the best performing prediction equation (VC from EV) was -0.110mind(-1) (limits of agreement (LOA), -2.623 to 2.402). The mean difference for the worst performing prediction equation (FR3 from PY) was 34.76mind(-1) (LOA, -60.392 to 129.910).CONCLUSIONS: For six different sets of published cutpoints, the use of this equating system can assist individuals attempting to synthesize the growing body of literature on Actigraph, accelerometry-derived MVPA.
AB - OBJECTIVES: Different accelerometer cutpoints used by different researchers often yields vastly different estimates of moderate-to-vigorous intensity physical activity (MVPA). This is recognized as cutpoint non-equivalence (CNE), which reduces the ability to accurately compare youth MVPA across studies. The objective of this research is to develop a cutpoint conversion system that standardizes minutes of MVPA for six different sets of published cutpoints.DESIGN: Secondary data analysis.METHODS: Data from the International Children's Accelerometer Database (ICAD; Spring 2014) consisting of 43,112 Actigraph accelerometer data files from 21 worldwide studies (children 3-18 years, 61.5% female) were used to develop prediction equations for six sets of published cutpoints. Linear and non-linear modeling, using a leave one out cross-validation technique, was employed to develop equations to convert MVPA from one set of cutpoints into another. Bland Altman plots illustrate the agreement between actual MVPA and predicted MVPA values.RESULTS: Across the total sample, mean MVPA ranged from 29.7MVPAmind(-1) (Puyau) to 126.1MVPAmind(-1) (Freedson 3 METs). Across conversion equations, median absolute percent error was 12.6% (range: 1.3 to 30.1) and the proportion of variance explained ranged from 66.7% to 99.8%. Mean difference for the best performing prediction equation (VC from EV) was -0.110mind(-1) (limits of agreement (LOA), -2.623 to 2.402). The mean difference for the worst performing prediction equation (FR3 from PY) was 34.76mind(-1) (LOA, -60.392 to 129.910).CONCLUSIONS: For six different sets of published cutpoints, the use of this equating system can assist individuals attempting to synthesize the growing body of literature on Actigraph, accelerometry-derived MVPA.
U2 - 10.1016/j.jsams.2015.02.006
DO - 10.1016/j.jsams.2015.02.006
M3 - Article (Academic Journal)
C2 - 25747468
SN - 1878-1861
JO - Journal of science and medicine in sport / Sports Medicine Australia
JF - Journal of science and medicine in sport / Sports Medicine Australia
ER -