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Items: 1 to 20 of 123

1.

Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers.

Zakeri IF, Adolph AL, Puyau MR, Vohra FA, Butte NF.

J Nutr. 2013 Jan;143(1):114-22. doi: 10.3945/jn.112.168542. Epub 2012 Nov 28.

2.

Prediction of energy expenditure and physical activity in preschoolers.

Butte NF, Wong WW, Lee JS, Adolph AL, Puyau MR, Zakeri IF.

Med Sci Sports Exerc. 2014 Jun;46(6):1216-26. doi: 10.1249/MSS.0000000000000209.

3.

Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water.

Butte NF, Wong WW, Adolph AL, Puyau MR, Vohra FA, Zakeri IF.

J Nutr. 2010 Aug;140(8):1516-23. doi: 10.3945/jn.109.120162. Epub 2010 Jun 23.

4.

Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents.

Zakeri IF, Adolph AL, Puyau MR, Vohra FA, Butte NF.

J Appl Physiol (1985). 2010 Jan;108(1):128-36. doi: 10.1152/japplphysiol.00729.2009. Epub 2009 Nov 5.

5.

Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry.

Zakeri I, Adolph AL, Puyau MR, Vohra FA, Butte NF.

J Appl Physiol (1985). 2008 Jun;104(6):1665-73. doi: 10.1152/japplphysiol.01163.2007. Epub 2008 Apr 10. Erratum in: J Appl Physiol. 2008 Oct;105(4):1384.

6.

A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers.

Ellis K, Kerr J, Godbole S, Lanckriet G, Wing D, Marshall S.

Physiol Meas. 2014 Nov;35(11):2191-203. doi: 10.1088/0967-3334/35/11/2191. Epub 2014 Oct 23.

7.

Validation of five minimally obstructive methods to estimate physical activity energy expenditure in young adults in semi-standardized settings.

Schneller MB, Pedersen MT, Gupta N, Aadahl M, Holtermann A.

Sensors (Basel). 2015 Mar 13;15(3):6133-51. doi: 10.3390/s150306133.

8.
9.

Modeling energy expenditure in children and adolescents using quantile regression.

Yang Y, Adolph AL, Puyau MR, Vohra FA, Butte NF, Zakeri IF.

J Appl Physiol (1985). 2013 Jul 15;115(2):251-9. doi: 10.1152/japplphysiol.00295.2013. Epub 2013 May 2.

10.

Validity of hip-mounted uniaxial accelerometry with heart-rate monitoring vs. triaxial accelerometry in the assessment of free-living energy expenditure in young children: the IDEFICS Validation Study.

Ojiambo R, Konstabel K, Veidebaum T, Reilly J, Verbestel V, Huybrechts I, Sioen I, Casajús JA, Moreno LA, Vicente-Rodriguez G, Bammann K, Tubic BM, Marild S, Westerterp K, Pitsiladis YP; IDEFICS Consortium..

J Appl Physiol (1985). 2012 Nov;113(10):1530-6. doi: 10.1152/japplphysiol.01290.2011. Epub 2012 Sep 20.

11.

Predictive validity of three ActiGraph energy expenditure equations for children.

Trost SG, Way R, Okely AD.

Med Sci Sports Exerc. 2006 Feb;38(2):380-7.

PMID:
16531910
12.
13.

Distributed lag and spline modeling for predicting energy expenditure from accelerometry in youth.

Choi L, Chen KY, Acra SA, Buchowski MS.

J Appl Physiol (1985). 2010 Feb;108(2):314-27. doi: 10.1152/japplphysiol.00374.2009. Epub 2009 Dec 3.

14.

Relative validity of 3 accelerometer models for estimating energy expenditure during light activity.

Wetten AA, Batterham M, Tan SY, Tapsell L.

J Phys Act Health. 2014 Mar;11(3):638-47. doi: 10.1123/jpah.2011-0167. Epub 2013 Feb 8.

PMID:
23417054
15.

Validity of a multisensor armband in estimating 24-h energy expenditure in children.

Dorminy CA, Choi L, Akohoue SA, Chen KY, Buchowski MS.

Med Sci Sports Exerc. 2008 Apr;40(4):699-706. doi: 10.1249/MSS.0b013e318161ea8f.

16.

Predicting energy expenditure from accelerometry counts in adolescent girls.

Schmitz KH, Treuth M, Hannan P, McMurray R, Ring KB, Catellier D, Pate R.

Med Sci Sports Exerc. 2005 Jan;37(1):155-61.

17.

Simplification of the method of assessing daily and nightly energy expenditure in children, using heart rate monitoring calibrated against open circuit indirect calorimetry.

Beghin L, Budniok T, Vaksman G, Boussard-Delbecque L, Michaud L, Turck D, Gottrand F.

Clin Nutr. 2000 Dec;19(6):425-35.

PMID:
11104594
18.

Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure.

Brage S, Brage N, Franks PW, Ekelund U, Wong MY, Andersen LB, Froberg K, Wareham NJ.

J Appl Physiol (1985). 2004 Jan;96(1):343-51. Epub 2003 Sep 12.

19.

Determinants of fat mass in prepubertal children.

Müller MJ, Grund A, Krause H, Siewers M, Bosy-Westphal A, Rieckert H.

Br J Nutr. 2002 Nov;88(5):545-54.

PMID:
12425735
20.

Accuracy of accelerometer regression models in predicting energy expenditure and METs in children and youth.

Alhassan S, Lyden K, Howe C, Kozey Keadle S, Nwaokelemeh O, Freedson PS.

Pediatr Exerc Sci. 2012 Nov;24(4):519-36.

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