Article open access publication

Equating accelerometer estimates among youth: The Rosetta Stone 2

Journal of Science and Medicine in Sport, Elsevier, ISSN 1878-1861

Volume 19, 3, 2016

DOI:10.1016/j.jsams.2015.02.006, Dimensions: pub.1012575409, PMC: PMC5381708, PMID: 25747468,



  1. (1) University of South Carolina, grid.254567.7
  2. (2) Norwegian School of Sport Sciences, grid.412285.8
  3. (3) University of Southern Denmark, grid.10825.3e, SDU
  4. (4) Ghent University, grid.5342.0
  5. (5) University of Bristol, grid.5337.2
  6. (6) University of Canberra, grid.1039.b
  7. (7) Federal University of Pelotas, grid.411221.5
  8. (8) University of Iowa, grid.214572.7
  9. (9) University of Zurich, grid.7400.3
  10. (10) University of Lausanne, grid.9851.5
  11. (11) University of Glasgow, grid.8756.c
  12. (12) Deakin University, grid.1021.2
  13. (13) University of Lisbon, grid.9983.b
  14. (14) University of Cambridge, grid.5335.0


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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University of Southern Denmark

Danish Open Access Indicator

2016: Realized

Research area: Medicine

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2016: Level 1

Research area: Medicine

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Times Cited: 39

Field Citation Ratio (FCR): 17.66

Relative Citation ratio (RCR): 3.31

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Green, Accepted