Keith Brazendale1, Michael W Beets2, Daniel B Bornstein2, Justin B Moore3, Russell R Pate2, Robert G Weaver2, Ryan S Falck2, Jessica L Chandler2, Lars B Andersen4, Sigmund A Anderssen5, Greet Cardon6, Ashley Cooper7, Rachel Davey8, Karsten Froberg9, Pedro C Hallal10, Kathleen F Janz11, Katarzyna Kordas7, Susi Kriemler12, Jardena J Puder13, John J Reilly14, Jo Salmon15, Luis B Sardinha16, Anna Timperio14, Esther M F van Sluijs17. 1. University of South Carolina, Department of Exercise Science, USA. Electronic address: brazendk@email.sc.edu. 2. University of South Carolina, Department of Exercise Science, USA. 3. University of South Carolina, Department of Health Promotion, Education, and Behavior, USA. 4. University of Southern Denmark, Department of Sport Science and Clinical Biomechanics, Denmark; Norwegian School of Sport Science, Norway. 5. Norwegian School of Sport Science, Norway. 6. Ghent University, Department of Movement and Sports Sciences, Belgium. 7. University of Bristol, Centre for Exercise, Nutrition and Health Sciences/School of Social and Community Medicine, UK. 8. University of Canberra, Centre for Research and Action in Public Health, Australia. 9. University of Southern Denmark, Department of Sport Science and Clinical Biomechanics, Denmark. 10. Federal University of Pelotas, Brazil. 11. University of Iowa, Department of Health and Human Physiology, USA. 12. University of Zürich, Epidemiology, Biostatistics and Public Health Institute, Switzerland. 13. University of Lausanne, Service of Endocrinology, Diabetes and Metabolism, Switzerland. 14. University of Glasgow, Division of Developmental Medicine, UK. 15. Deakin University, School of Exercise and Nutrition Sciences/Centre for Physical Activity and Nutrition Research, Australia. 16. Technical University of Lisbon, Faculty of Human Movement, Portugal. 17. University of Cambridge, MRC Epidemiology Unit, UK.
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.
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.
Authors: Margarita S Treuth; Kathryn Schmitz; Diane J Catellier; Robert G McMurray; David M Murray; M Joao Almeida; Scott Going; James E Norman; Russell Pate Journal: Med Sci Sports Exerc Date: 2004-07 Impact factor: 5.411
Authors: Calum Mattocks; Sam Leary; Andy Ness; Kevin Deere; Joanne Saunders; Kate Tilling; Joanne Kirkby; Steven N Blair; Chris Riddoch Journal: Int J Pediatr Obes Date: 2007
Authors: Philip M Dixon; Pedro F Saint-Maurice; Youngwon Kim; Paul Hibbing; Yang Bai; Gregory J Welk Journal: Med Sci Sports Exerc Date: 2018-04 Impact factor: 5.411
Authors: Donna Spruijt-Metz; Cheng K Fred Wen; Brooke M Bell; Stephen Intille; Jeannie S Huang; Tom Baranowski Journal: Am J Prev Med Date: 2018-08-19 Impact factor: 5.043
Authors: Pedro F Saint-Maurice; Youngwon Kim; Paul Hibbing; April Y Oh; Frank M Perna; Gregory J Welk Journal: Am J Prev Med Date: 2017-06 Impact factor: 5.043
Authors: Robert Glenn Weaver; Rafael M Tassitano; Maria Cecília M Tenório; Keith Brazendale; Michael W Beets Journal: J Phys Act Health Date: 2021-10-09
Authors: Justin B Moore; Michael W Beets; Keith Brazendale; Steven N Blair; Russell R Pate; Lars B Andersen; Sigmund A Anderssen; Anders Grøntved; Pedro C Hallal; Katarzyna Kordas; Susi Kriemler; John J Reilly; Luis B Sardinha Journal: Med Sci Sports Exerc Date: 2017-07 Impact factor: 5.411
Authors: Erik Sigmund; Dagmar Sigmundová; Petr Badura; Lucie Trhlíková; Andrea Madarasová Gecková Journal: BMC Public Health Date: 2016-07-13 Impact factor: 3.295
Authors: Claudia S Leeger-Aschmann; Einat A Schmutz; Annina E Zysset; Tanja H Kakebeeke; Nadine Messerli-Bürgy; Kerstin Stülb; Amar Arhab; Andrea H Meyer; Simone Munsch; Oskar G Jenni; Jardena J Puder; Susi Kriemler Journal: BMC Public Health Date: 2019-05-06 Impact factor: 3.295
Authors: Gregory J Welk; Yang Bai; Jung-Min Lee; Job Godino; Pedro F Saint-Maurice; Lucas Carr Journal: Med Sci Sports Exerc Date: 2019-08 Impact factor: 5.411
Authors: Alex V Rowlands; Nathan P Dawkins; Ben Maylor; Charlotte L Edwardson; Stuart J Fairclough; Melanie J Davies; Deirdre M Harrington; Kamlesh Khunti; Tom Yates Journal: Sports Med Open Date: 2019-12-05