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. 2016 Mar;19(3):242-249.
doi: 10.1016/j.jsams.2015.02.006. Epub 2015 Feb 23.

Equating accelerometer estimates among youth: The Rosetta Stone 2

Keith Brazendale  1 , Michael W Beets  2 , Daniel B Bornstein  2 , Justin B Moore  3 , Russell R Pate  2 , Robert G Weaver  2 , Ryan S Falck  2 , Jessica L Chandler  2 , Lars B Andersen  4 , Sigmund A Anderssen  5 , Greet Cardon  6 , Ashley Cooper  7 , Rachel Davey  8 , Karsten Froberg  9 , Pedro C Hallal  10 , Kathleen F Janz  11 , Katarzyna Kordas  7 , Susi Kriemler  12 , Jardena J Puder  13 , John J Reilly  14 , Jo Salmon  15 , Luis B Sardinha  16 , Anna Timperio  14 , Esther M F van Sluijs  17 ; International Children's Accelerometry Database (ICAD) Collaborators
Affiliations

Equating accelerometer estimates among youth: The Rosetta Stone 2

Keith Brazendale et al. J Sci Med Sport. 2016 Mar.

Abstract

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.

Keywords: Children; Cutpoints; MVPA; Measurement; Policy; Public health.

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Figures

Figure 1
Figure 1
Bland Altman plots of best (a) and worst (b) agreement between actual MVPA and predicted MVPA values. (a) Van Cauwenberghe MVPA predicted from Evenson MVPA. Dashed line signifies mean difference (-0.110 min.d-1). (b) Freedson (3MET) MVPA predicted from Puyau MVPA (Age not in model). Dashed line signifies mean difference (34.76 min.d-1)
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