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Choi, An, Lee, Lee, and Kim: Accuracy of Accelerometer for the Prediction of Energy Expenditure and Activity Intensity in Athletic Elementary School Children During Selected Activities
Research Article

Korean Journal of Community Nutrition 2017; 22(5): 413-425.

Published online: 31 October 2017

DOI: https://doi.org/10.5720/kjcn.2017.22.5.413

Accuracy of Accelerometer for the Prediction of Energy Expenditure and Activity Intensity in Athletic Elementary School Children During Selected Activities

Su-Ji Choi, Hae-Sun An, Mo-Ran Lee, Jung-Sook Lee, Eun-Kyung Kim

Department of Food and Nutrition, Gangneung-Wonju National University, Gangneung, Korea.

Corresponding author: Eun-Kyung Kim. Department of Food and Nutrition, Gangneung-Wonju National University, Gangneung 25457, Korea. Tel: (033) 640-2336, Fax: (033) 640-2330, ekkim@gwnu.ac.kr

Received 24 August 2017       Revised 13 October 2017       Accepted 13 October 2017

Copyright © 2017 The Korean Society of Community Nutrition

The Korean Society of Community Nutrition

(open-access):

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives

Accurate assessment of energy expenditure is important for estimation of energy requirements in athletic children. The objective of this study was to evaluate the accuracy of accelerometer for prediction of selected activities' energy expenditure and intensity in athletic elementary school children.

Methods

The present study involved 31 soccer players (16 males and 15 females) from an elementary school (9-12 years). During the measurements, children performed eight selected activities while simultaneously wearing the accelerometer and carrying the portable indirect calorimeter. Five equations (Freedson/Trost, Treuth, Pate, Puyau, Mattocks) were assessed for the prediction of energy expenditure from accelerometer counts, while Evenson equation was added for prediction of activity intensity, making msix equations in total. The accuracy of accelerometer for energy prediction was assessed by comparing measured and predicted values, using the paired t-test. The intensity classification accuracy was evaluated with kappa statistics and ROC-Curve.

Results

For activities of lying down, television viewing and reading, Freedson/Trost, Treuth were accurate in predicting energy expenditure. Regarding Pate, it was accurate for vacuuming and slow treadmill walking energy prediction. Mattocks was accurate in treadmill running activities. Concerning activity intensity classification accuracy, Pate (kappa=0.72) had the best performance across the four intensities (sedentary, light, moderate, vigorous). In case of the sedentary activities, all equations had a good prediction accuracy, while with light activities and Vigorous activities, Pate had an excellent accuracy (ROC-AUC=0.91, 0.94). For Moderate activities, all equations showed a poor performance.

Conclusions

In conclusion, none of the assessed equations was accurate in predicting energy expenditure across all assessed activities in athletic children. For activity intensity classification, Pate had the best prediction accuracy.

Keywords: energy expenditure, accelerometer, children, athlete

Figures and Tables

Fig. 1

Comparison of predicted energy expenditure by ActiGraph with measured energy expenditure by K4b2.

* Statistically significant (p<0.001). (LD: Lying down, TV: Television viewing, RE: Reading, VA: Vacuuming, SW: Slow walking(2.5mph), BW: Brisk walking (3.5mph), SC: Stair climbing, RU: Running (5mph)).
kjcn-22-413-g001
Table 1

Description of the eight activity trials

kjcn-22-413-i001
Table 2

ActiGraph prediction models

kjcn-22-413-i002

1) SED: Sedentary activity

2) LPA: Light activity

3) MPA: Moderate activity

4) VPA: Vigorous activity

5) Per 15s, all other counts reported per minute

METS: Metabolic equivalents, VO2: Volume of oxygen consumption (ml/kg/min), AEE: activity energy expenditure (kcal/kg/min), PAEE: physical activity energy expenditure (KJ/kg/min)

Table 3

Cut off points of physical activity intensity by Trost (2011)

kjcn-22-413-i003

1) Measured by Cosmed K4b2

Table 4

Anthropometric measurements of subjects

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1) Mean±SD

2) Measured by Inbody 620

3) Body weight (kg)/[Height (m)]2

4) Body weight (kg)−Fat mass (kg)

5) Significant difference between male and female was tested by Mann-Whitney test *: p<0.01

Table 5

Descriptive statistics for VO2, EE and ActiGraph counts for eight activity trials. Comparison of measured METS with the values ofFAO/WHO/UNU and Compendium child

kjcn-22-413-i005

1) Measured by Cosmed K4b2

2) VO2: Oxygen consumption

3) EE: Energy expenditure

4) FAO/WHO/UNU (1985)

5) Compendium Child METS by Ridley and Olds (2008)

6) Mean±SD

7) Slow walking (2.5 mph), Brisk walking (3.5 mph), Running (5 mph)

8) abc: Different superscripts indicate significant difference p<0.05 by Tukey's multiple comparison test

Table 6

Sensitivity, specificity, and area under the ROC curve (ROC°©AUC) values for the classification of sedentary, light, moderate and vigorous activity

kjcn-22-413-i006

1) Se: Sensitivity

2) Sp: Specificity

3) AUC: area under curve, CI: confidence interval

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Eun-Kyung Kim
https://orcid.org/0000-0003-1292-7586

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