Literature DB >> 22995396

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.

Robert Ojiambo1, Kenn Konstabel, Toomas Veidebaum, John Reilly, Vera Verbestel, Inge Huybrechts, Isabelle Sioen, José A Casajús, Luis A Moreno, German Vicente-Rodriguez, Karin Bammann, Bojan M Tubic, Staffan Marild, Klaas Westerterp, Yannis P Pitsiladis.   

Abstract

One of the aims of Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants (IDEFICS) validation study is to validate field measures of physical activity (PA) and energy expenditure (EE) in young children. This study compared the validity of uniaxial accelerometry with heart-rate (HR) monitoring vs. triaxial accelerometry against doubly labeled water (DLW) criterion method for assessment of free-living EE in young children. Forty-nine European children (25 female, 24 male) aged 4-10 yr (mean age: 6.9 ± 1.5 yr) were assessed by uniaxial ActiTrainer with HR, uniaxial 3DNX, and triaxial 3DNX accelerometry. Total energy expenditure (TEE) was estimated using DLW over a 1-wk period. The longitudinal axis of both devices and triaxial 3DNX counts per minute (CPM) were significantly (P < 0.05) associated with physical activity level (PAL; r = 0.51 ActiTrainer, r = 0.49 uniaxial-3DNX, and r = 0.42 triaxial Σ3DNX). Eight-six percent of the variance in TEE could be predicted by a model combining body mass (partial r(2) = 71%; P < 0.05), CPM-ActiTrainer (partial r(2) = 11%; P < 0.05), and difference between HR at moderate and sedentary activities (ModHR - SedHR) (partial r(2) = 4%; P < 0.05). The SE of TEE estimate for ActiTrainer and 3DNX models ranged from 0.44 to 0.74 MJ/days or ∼7-11% of the average TEE. The SE of activity-induced energy expenditure (AEE) model estimates ranged from 0.38 to 0.57 MJ/day or 24-26% of the average AEE. It is concluded that the comparative validity of hip-mounted uniaxial and triaxial accelerometers for assessing PA and EE is similar.

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Year:  2012        PMID: 22995396     DOI: 10.1152/japplphysiol.01290.2011

Source DB:  PubMed          Journal:  J Appl Physiol (1985)        ISSN: 0161-7567


  9 in total

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2.  Assessment of 24-hour physical behaviour in children and adolescents via wearables: a systematic review of free-living validation studies.

Authors:  Marco Giurgiu; Simon Kolb; Carina Nigg; Alexander Burchartz; Irina Timm; Marlissa Becker; Ellen Rulf; Ann-Kathrin Doster; Elena Koch; Johannes B J Bussmann; Claudio Nigg; Ulrich W Ebner-Priemer; Alexander Woll
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4.  The impact of familial, behavioural and psychosocial factors on the SES gradient for childhood overweight in Europe. A longitudinal study.

Authors:  K Bammann; W Gwozdz; C Pischke; G Eiben; J M Fernandez-Alvira; S De Henauw; L Lissner; L A Moreno; Y Pitsiladis; L Reisch; T Veidebaum; I Pigeot
Journal:  Int J Obes (Lond)       Date:  2016-08-16       Impact factor: 5.095

5.  Decreased levels of physical activity in adolescents with down syndrome are related with low bone mineral density: a cross-sectional study.

Authors:  Angel Matute-Llorente; Alejandro González-Agüero; Alba Gómez-Cabello; Germán Vicente-Rodríguez; José Antonio Casajús
Journal:  BMC Endocr Disord       Date:  2013-07-04       Impact factor: 2.763

6.  Does participation in physical education reduce sedentary behaviour in school and throughout the day among normal-weight and overweight-to-obese Czech children aged 9-11 years?

Authors:  Erik Sigmund; Dagmar Sigmundová; Zdenek Hamrik; Andrea Madarásová Gecková
Journal:  Int J Environ Res Public Health       Date:  2014-01-16       Impact factor: 3.390

7.  Using hidden markov models to improve quantifying physical activity in accelerometer data - a simulation study.

Authors:  Vitali Witowski; Ronja Foraita; Yannis Pitsiladis; Iris Pigeot; Norman Wirsik
Journal:  PLoS One       Date:  2014-12-02       Impact factor: 3.240

8.  Adherence to the obesity-related lifestyle intervention targets in the IDEFICS study.

Authors:  E Kovács; A Siani; K Konstabel; C Hadjigeorgiou; I de Bourdeaudhuij; G Eiben; L Lissner; W Gwozdz; L Reisch; V Pala; L A Moreno; I Pigeot; H Pohlabeln; W Ahrens; D Molnár
Journal:  Int J Obes (Lond)       Date:  2014-09       Impact factor: 5.095

9.  Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network.

Authors:  Meina Li; Keun-Chang Kwak; Youn Tae Kim
Journal:  Sensors (Basel)       Date:  2016-09-22       Impact factor: 3.576

  9 in total

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