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Daniel Arvidsson, Elias Johannesson, Lars Bo Andersen, Magnus Karlsson, Per Wollmer, Ola Thorsson and Magnus Dencker

. Calibration of accelerometer output for children . Med Sci Sports Exerc . 2005 ; 37 ( suppl 11 ): S523 – S530 . PubMed 16294115 10.1249/ 22. Mattocks C , Leary S , Ness A , et al . Calibration of an accelerometer during free-living activities in children . Int J Pediatr

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Anantha Narayanan, Farzanah Desai, Tom Stewart, Scott Duncan and Lisa Mackay

; activities manually labeled by a human observer Semi-free-living: activities self-annotated by the subjects 160 features from both time domain and frequency domain 128-sample sliding window, overlap = 50% C4.5 Graft, NB, BayesNET, IBI, IBK, KStar, JRip, SVM, Multi Perception, AdaBoost, AdaBoostM1, Bagging

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Jade L. Morris, Andy Daly-Smith, Margaret A. Defeyter, Jim McKenna, Steve Zwolinsky, Scott Lloyd, Melissa Fothergill and Pamela L. Graham

, Ness A , et al . Calibration of an accelerometer during free-living activities in children . Int J Pediatr Obes . 2007 ; 2 ( 4 ): 218 – 26 . PubMed ID: 17852552 doi:10.1080/17477160701408809 10.1080/17477160701408809 17852552 31. Migueles JH , Cadenas-Sanchez C , Ekelund U , et

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Albert R. Mendoza, Kate Lyden, John Sirard, John Staudenmayer, Catrine Tudor-Locke and Patty S. Freedson

; Kooiman et al., 2015 ; Montes, Young, Tandy, & Navalta, 2018 ; Nelson, Kaminsky, Dickin, & Montoye, 2016 ). Lab-based activity protocols include fixed time treadmill and overground walking and running and simulated fixed time free-living activities (e.g., vacuuming, computer work). From these studies

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Aston K. McCullough and Carol Ewing Garber

-step PHIT decision tree identified the majority of children at high risk of insufficient daily physical activity using 45 minutes of accelerometer data without the need for 7 days of free-living activity monitoring. Furthermore, nearly all children (∼90%) who received a preliminary positive PHIT

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Denise M. Jones, Harvi F. Hart, Kay M. Crossley, Ilana N. Ackerman and Joanne L. Kemp

’t currently provide a direct translation between these activity categories and METs” ( . Comparisons of step count output during free-living activities between the

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Kelli L. Cain, Edith Bonilla, Terry L. Conway, Jasper Schipperijn, Carrie M. Geremia, Alexandra Mignano, Jacqueline Kerr and James F. Sallis

accelerometer assessments of free-living activity . Med Sci Sports Exerc . 2013 ; 45 ( 1 ): 170 – 7 . PubMed doi:10.1249/MSS.0b013e31826c2cf1 10.1249/MSS.0b013e31826c2cf1 22895373 48. Winkler EAG , Paul A , Clark BK , Matthews CE , Owen NG , Healy GN . Identifying sedentary time using

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Brian M. Wood, Herman Pontzer, Jacob A. Harris, Audax Z.P. Mabulla, Marc T. Hamilton, Theodore W. Zderic, Bret A. Beheim and David A. Raichlen

.W. , Schuna , J.M. , & Hamilton , M.T. ( 2015 ). Free-living activity counts-derived breaks in sedentary time: Are they real transitions from sitting to standing? Gait & Posture, 42, 70 – 72 . doi:10.1016/j.gaitpost.2015.04.008 10.1016/j.gaitpost.2015.04.008 Blurton Jones , N. ( 2016

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Dinesh John, Qu Tang, Fahd Albinali and Stephen Intille

superior results when calibrating machine learning algorithms to estimate physical activity outcomes such as type and intensity ( Ellis et al., 2014 ; Sasaki et al., 2016 ). However, unlike the impact of free-living activity context in predicting specific physical behavior labels when training/using a

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Katja Krustrup Pedersen, Esben Lykke Skovgaard, Ryan Larsen, Mikkel Stengaard, Søren Sørensen and Kristian Overgaard

individuals. Finally, most validation studies are carried out in laboratory-based settings, which allows for good control of type, speed, and intensity of the activities performed. However, extrapolating data from treadmill to overground walking and running for use in free-living activity research requires