Literature DB >> 17852552

Calibration of an accelerometer during free-living activities in children.

Calum Mattocks1, Sam Leary, Andy Ness, Kevin Deere, Joanne Saunders, Kate Tilling, Joanne Kirkby, Steven N Blair, Chris Riddoch.   

Abstract

OBJECTIVE: The aims of this study were to develop an equation to predict energy expenditure and to derive cut-points for moderate and vigorous physical activity intensity from the Actigraph accelerometer output in children aged 12 years.
METHODS: The children performed a series of activities (lying, sitting, slow walking, fast walking, hopscotch and jogging) while wearing an Actigraph and a portable metabolic unit. The sample was divided into a developmental and a validation group. Random intercepts models were used to develop a prediction equation in the developmental group. The equation was assessed in the validation group by calculating limits of agreement (actual minus predicted energy expenditure). Thresholds for moderate and vigorous activity were derived by refitting the energy expenditure model with VO2 as the outcome.
RESULTS: The developmental group comprised 163 children, while the validation group comprised 83 children. The equation, adjusted for age and gender, adequately predicted energy expenditure from accelerometer counts. Physical activity intensity cut-points were derived from resting VO2. The lower threshold for moderate intensity (four METs), adjusted for age and gender, was 3581 counts per minute. The lower threshold for vigorous activity (six METs) was 6130 counts per minute.
CONCLUSION: The prediction equation and the derived cut-points will help to better interpret the output of the Actigraph in children aged 12 years. The cut-point for moderate to vigorous physical activity is higher than that reported previously.

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Mesh:

Year:  2007        PMID: 17852552     DOI: 10.1080/17477160701408809

Source DB:  PubMed          Journal:  Int J Pediatr Obes        ISSN: 1747-7166


  84 in total

1.  Use of a two-regression model for estimating energy expenditure in children.

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2.  Use of accelerometer data in prediction equations for capturing implausible dietary intakes in adolescents.

Authors:  Sabrina E Noel; Calum Mattocks; Pauline Emmett; Chris J Riddoch; Andrew R Ness; P K Newby
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3.  The distribution of physical activity in an after-school friendship network.

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4.  Effect of BMI on prediction of accelerometry-based energy expenditure in youth.

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Journal:  Med Sci Sports Exerc       Date:  2012-12       Impact factor: 5.411

5.  Correlation between physical activity, aerobic fitness and body fat against autonomic function profile in children.

Authors:  Tina Tanha; Per Wollmer; Artur Fedorowski; Ola Thorsson; Magnus K Karlsson; Magnus Dencker
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6.  Validity of ActiGraph child-specific equations during various physical activities.

Authors:  Scott E Crouter; Magdalene Horton; David R Bassett
Journal:  Med Sci Sports Exerc       Date:  2013-07       Impact factor: 5.411

Review 7.  A Review of Emerging Analytical Techniques for Objective Physical Activity Measurement in Humans.

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8.  The relationship between hispanic parents and their preschool-aged children's physical activity.

Authors:  Rachel Ruiz; Sabina B Gesell; Maciej S Buchowski; Warren Lambert; Shari L Barkin
Journal:  Pediatrics       Date:  2011-04-11       Impact factor: 7.124

9.  Obesogenic diet and physical activity: independent or associated behaviours in adolescents?

Authors:  R Jago; A R Ness; P Emmett; C Mattocks; L Jones; C J Riddoch
Journal:  Public Health Nutr       Date:  2009-12-03       Impact factor: 4.022

10.  Utility of accelerometers to measure physical activity in children attending an obesity treatment intervention.

Authors:  Wendy Robertson; Sarah Stewart-Brown; Elizabeth Wilcock; Michelle Oldfield; Margaret Thorogood
Journal:  J Obes       Date:  2010-10-03
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