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

PURPOSE The purpose of this study was to develop two new two-regression models (2RM), for use in children, that estimate energy expenditure (EE) using the ActiGraph GT3X: 1) mean vector magnitude (VM) counts or 2) vertical axis (VA) counts. The new 2RMs were also compared with existing ActiGraph equations for children. METHODS Fifty-seven boys and 52 girls (mean ± SD: age = 11 ± 1.7 yr, body mass index = 21.4 ± 5.5 kg·m(-2)) performed 30-min supine rest and 8 min of six different activities ranging from sedentary behaviors to vigorous physical activity. Eighteen activities were split into three routines with each routine performed by 38-39 participants. Seventy-seven participants were used for the development group, and 39 participants were used for the cross-validation group. During all testing, activity data were collected using an ActiGraph GT3X, worn on the right hip, and oxygen consumption was measured using a Cosmed K4b. All energy expenditure values are expressed as MET(RMR) (activity VO(2)/resting VO(2)). RESULTS For each activity, a coefficient of variation was calculated using 10-s epochs for the VA and VM to determine whether the activity was continuous walking/running or an intermittent lifestyle activity. Separate regression equations were developed for walking/running and intermittent lifestyle activity. In the cross-validation group, the VM and VA 2RMs were within 0.8 MET(RMR) of measured MET(RMR) for all activities except Sportwall and running (all P > 0.05). The other existing ActiGraph equations had mean errors ranging from 0.0 to 2.6 MET(RMR) for the activities. CONCLUSIONS The new 2RMs for use in children with the ActiGraph GT3X provide a closer estimate of mean measured MET(RMR) than other currently available prediction equations. In addition, they improve the individual prediction errors across a wide range of activity intensities.

[1]  Ann V Rowlands,et al.  Validation of the RT3 triaxial accelerometer for the assessment of physical activity. , 2004, Medicine and science in sports and exercise.

[2]  L. Mâsse,et al.  Physical activity in the United States measured by accelerometer. , 2008, Medicine and science in sports and exercise.

[3]  Patty Freedson,et al.  Calibration of accelerometer output for children. , 2005, Medicine and science in sports and exercise.

[4]  J M Bland,et al.  Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .

[5]  P. D. Watson,et al.  Validity of the computer science and applications (CSA) activity monitor in children. , 1998, Medicine and science in sports and exercise.

[6]  Schofield Wn,et al.  Predicting basal metabolic rate, new standards and review of previous work , 1985 .

[7]  R. Eston,et al.  Validity of heart rate, pedometry, and accelerometry for predicting the energy cost of children's activities. , 1998, Journal of applied physiology.

[8]  Maciej S Buchowski,et al.  Validation of the ActiGraph two-regression model for predicting energy expenditure. , 2010, Medicine and science in sports and exercise.

[9]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[10]  A. Beckett,et al.  AKUFO AND IBARAPA. , 1965, Lancet.

[11]  K. Janz Growth, maturation, and physical activity, 2nd edition , 2004 .

[12]  Tom Baranowski,et al.  Decision boundaries and receiver operating characteristic curves: New methods for determining accelerometer cutpoints , 2007, Journal of sports sciences.

[13]  M. Tremblay,et al.  Canadian Health Measures Survey: rationale, background and overview. , 2007, Health reports.

[14]  Stewart G Trost,et al.  Comparison of accelerometer cut points for predicting activity intensity in youth. , 2011, Medicine and science in sports and exercise.

[15]  G S Krahenbuhl,et al.  Factors Affecting Running Economy , 1989, Sports medicine.

[16]  Scott E Crouter,et al.  Refined two-regression model for the ActiGraph accelerometer. , 2010, Medicine and science in sports and exercise.

[17]  John Staudenmayer,et al.  An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. , 2009, Journal of applied physiology.

[18]  J. Staudenmayer,et al.  Development of novel techniques to classify physical activity mode using accelerometers. , 2006, Medicine and science in sports and exercise.

[19]  J. D. Janssen,et al.  Assessment of energy expenditure for physical activity using a triaxial accelerometer. , 1994, Medicine and science in sports and exercise.

[20]  T. Barstow,et al.  The level and tempo of children's physical activities: an observational study. , 1995, Medicine and science in sports and exercise.

[21]  C B Corbin,et al.  The validity of the Tritrac-R3D Activity Monitor for the assessment of physical activity in children. , 1995, Research quarterly for exercise and sport.

[22]  Issa Zakeri,et al.  Prediction of activity energy expenditure using accelerometers in children. , 2004, Medicine and science in sports and exercise.

[23]  D R Bassett,et al.  A new 2-regression model for the Actical accelerometer , 2007, British Journal of Sports Medicine.

[24]  S. Blair,et al.  Calibration of an accelerometer during free-living activities in children. , 2007, International journal of pediatric obesity : IJPO : an official journal of the International Association for the Study of Obesity.

[25]  S. Going,et al.  Defining accelerometer thresholds for activity intensities in adolescent girls. , 2004, Medicine and science in sports and exercise.

[26]  T. Takken,et al.  Review of Prediction Models to Estimate Activity-Related Energy Expenditure in Children and Adolescents , 2010, International journal of pediatrics.

[27]  Scott E. Crouter,et al.  Validity of the Actical for estimating free-living physical activity , 2011, European Journal of Applied Physiology.

[28]  O. Bar-or,et al.  Growth, Maturation and Physical Activity , 1992 .

[29]  M. Puyau,et al.  Validation and calibration of physical activity monitors in children. , 2002, Obesity research.

[30]  R. Mcmurray,et al.  Calibration of two objective measures of physical activity for children , 2008, Journal of sports sciences.

[31]  Scott E Crouter,et al.  A novel method for using accelerometer data to predict energy expenditure. , 2006, Journal of applied physiology.

[32]  Stewart G. Trost,et al.  State of the Art Reviews: Measurement of Physical Activity in Children and Adolescents , 2007 .