Equating accelerometer estimates among youth: The Rosetta Stone 2.

Authors:
Keith Brazendale, Ph.D., M.Sc.
Keith Brazendale, Ph.D., M.Sc.
University of South Carolina
Research Assistant Professor
Obesogenic behaviors
Columbia, SC | United States
Michael W Beets
Michael W Beets
University of South Carolina
United States
Daniel B Bornstein
Daniel B Bornstein
University of South Carolina
Dr. Justin B Moore, PhD, MS
Dr. Justin B Moore, PhD, MS
Wake Forest School of Medicine
Associate Professor
Implementation Science, Epidemiology
Winston-Salem, NC | United States
Russell R Pate
Russell R Pate
University of South Carolina
United States
Robert G Weaver
Robert G Weaver
University of South Carolina
Ryan S Falck
Ryan S Falck
and Cognitive Neuroscience Lab
Jessica L Chandler
Jessica L Chandler
University of South Carolina
United States

J Sci Med Sport 2016 Mar 23;19(3):242-249. Epub 2015 Feb 23.

University of Cambridge, MRC Epidemiology Unit, UK.

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.

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Source
http://dx.doi.org/10.1016/j.jsams.2015.02.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5381708PMC

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March 2016
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6 Citations

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