Advertisement

European Journal of Applied Physiology

, Volume 116, Issue 1, pp 29–38 | Cite as

Kids are not little adults: what MET threshold captures sedentary behavior in children?

  • Pedro F. Saint-MauriceEmail author
  • Youngwon Kim
  • Gregory J. Welk
  • Glenn A. Gaesser
Original Article

Abstract

Purpose

The study compares MET-defined cutpoints used to classify sedentary behaviors in children using a simulated free-living design.

Methods

A sample of 102 children (54 boys and 48 girls; 7–13 years) completed a set of 12 activities (randomly selected from a pool of 24 activities) in a random order. Activities were predetermined and ranged from sedentary to vigorous intensities. Participant’s energy expenditure was measured using a portable indirect calorimetry system, Oxycon mobile. Measured minute-by-minute VO2 values (i.e., ml/kg/min) were converted to an adult- or child-MET value using the standard 3.5 ml/kg/min or the estimated child resting metabolic rate, respectively. Classification agreement was examined for both the “standard” (1.5 adult-METs) and an “adjusted” (2.0 adult-METs) MET-derived threshold for classifying sedentary behavior. Alternatively, we also tested the classification accuracy of a 1.5 child-MET threshold. Classification accuracy of sedentary activities was evaluated relative to the predetermined intensity categorization using receiver operator characteristic curves.

Results

There were clear improvements in the classification accuracy for sedentary activities when a threshold of 2.0 adult-METs was used instead of 1.5 METs (Se1.5 METs = 4.7 %, Sp1.5 METs = 100.0 %; Se2.0 METs = 36.9 %, Sp2.0 METs = 100.0 %). The use of child-METs while maintaining the 1.5 threshold also resulted in improvements in classification (Se = 45.1 %, Sp = 100.0 %).

Conclusion

Adult-MET thresholds are not appropriate for children when classifying sedentary activities. Classification accuracy for identifying sedentary activities was improved when either an adult-MET of 2.0 or a child-MET of 1.5 was used.

Keywords

Youth Public health Measurement Resting energy expenditure 

Abbreviations

AUC

Area under the curve

CI

Confidence interval

EE

Energy expenditure

ICC

Intraclass correlation

MET

Metabolic equivalent

MVPA

Moderate-to-vigorous physical activity

OM

Oxycon mobile

PA

Physical activity

REE

Resting energy expenditure

ROC

Receiver operating characteristic

Se

Sensitivity

Sp

Specificity

Notes

Acknowledgments

No financial disclosures were reported by the authors of this paper. This work was funded by a grant received from the National Institute of Health, reference R01 HL091006.

Compliance with ethical standards

Conflict of interest

All the authors declare no conflicts of interest.

References

  1. Altman DG (1990) Practical statistics for medical research, 1st edn. Chapman and Hall/CRC, LondonGoogle Scholar
  2. Byrne NM, Hills AP, Hunter GR, Weinsier RL, Schutz Y (2005) Metabolic equivalent: one size does not fit all. J Appl Physiol (1985) 99:1112–1119CrossRefGoogle Scholar
  3. Ekelund U, Luan J, Sherar LB, Esliger DW, Griew P, Cooper A (2012) Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents. JAMA 307:704–712PubMedPubMedCentralCrossRefGoogle Scholar
  4. Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG (2008) Calibration of two objective measures of physical activity for children. J Sports Sci 26:1557–1565PubMedCrossRefGoogle Scholar
  5. Fischer C, Yildirim M, Salmon J, Chinapaw MJ (2012) Comparing different accelerometer cut-points for sedentary time in children. Pediatr Exerc Sci 24:220–228PubMedGoogle Scholar
  6. Freedson P, Pober D, Janz KF (2005) Calibration of accelerometer output for children. Med Sci Sports Exerc 37:S523–S530PubMedCrossRefGoogle Scholar
  7. Harrell JS, McMurray RG, Baggett CD, Pennell ML, Pearce PF, Bangdiwala SI (2005) Energy costs of physical activities in children and adolescents. Med Sci Sports Exerc 37:329–336PubMedCrossRefGoogle Scholar
  8. Hodges LD, Brodie DA, Bromley PD (2005) Validity and reliability of selected commercially available metabolic analyzer systems. Scand J Med Sci Sports 15:271–279PubMedCrossRefGoogle Scholar
  9. Jago R, Zakeri I, Baranowski T, Watson K (2007) Decision boundaries and receiver operating characteristic curves: new methods for determining accelerometer cutpoints. J Sports Sci 25:937–944PubMedCrossRefGoogle Scholar
  10. McMurray RG, Soares J, Caspersen CJ, McCurdy T (2014) Examining variations of resting metabolic rate of adults: a public health perspective. Med Sci Sports Exerc 46:1352–1358PubMedPubMedCentralCrossRefGoogle Scholar
  11. Morrison JA, Alfaro MP, Khoury P, Thornton BB, Daniels SR (1996) Determinants of resting energy expenditure in young black girls and young white girls. J Pediatr 129:637–642PubMedCrossRefGoogle Scholar
  12. Owen N, Sparling PB, Healy GN, Dunstan DW, Matthews CE (2010) Sedentary behavior: emerging evidence for a new health risk. Mayo Clin Proc 85:1138–1141PubMedPubMedCentralCrossRefGoogle Scholar
  13. Pate RR, Stevens J, Pratt C, Sallis JF, Schmitz KH, Webber LS, Young DR (2006) Objectively measured physical activity in sixth-grade girls. Arch Pediatr Adolesc Med 160:1262–1268PubMedPubMedCentralCrossRefGoogle Scholar
  14. Pate RR, O’Neill JR, Lobelo F (2008) The evolving definition of “sedentary”. Exerc Sport Sci Rev 36:173–178PubMedCrossRefGoogle Scholar
  15. Reilly JJ, Janssen X, Cliff DP, Okely AD (2015) Appropriateness of the definition of ‘sedentary’ in young children: whole-room calorimetry study. J Sci Med Sport 18(5):565–568. doi: 10.1016/j.jsams.2014.07.013 PubMedCrossRefGoogle Scholar
  16. Ridgers ND, Salmon J, Ridley K, O’Connell E, Arundell L, Timperio A (2012) Agreement between activPAL and ActiGraph for assessing children’s sedentary time. Int J Behav Nutr Phys Act 9:15PubMedPubMedCentralCrossRefGoogle Scholar
  17. Ridley K, Olds TS (2008) Assigning energy costs to activities in children: a review and synthesis. Med Sci Sports Exerc 40:1439–1446PubMedCrossRefGoogle Scholar
  18. Ridley K, Ainsworth BE, Olds TS (2008) Development of a compendium of energy expenditures for youth. Int J Behav Nutr Phys Act 5:45PubMedPubMedCentralCrossRefGoogle Scholar
  19. Sallis JF, Buono MJ, Freedson PS (1991) Bias in estimating caloric expenditure from physical activity in children. Implications for epidemiological studies. Sports Med 11:203–209PubMedCrossRefGoogle Scholar
  20. Schofield WN (1985) Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr 39:5–41PubMedGoogle Scholar
  21. Sedentary Behaviour Research Network (2012) Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab 37:540–542PubMedCrossRefGoogle Scholar
  22. Treuth MS, Sherwood NE, Butte NF, McClanahan B, Obarzanek E, Zhou A, Rochon J (2003) Validity and reliability of activity measures in African–American girls for GEMS. Med Sci Sports Exerc 35:532–539PubMedCrossRefGoogle Scholar
  23. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M (2008) Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 40:181–188PubMedCrossRefGoogle Scholar
  24. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA (2011) Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc 43:1360–1368PubMedCrossRefGoogle Scholar
  25. Welk GJ (2005) Principles of design and analyses for the calibration of accelerometry-based activity monitors. Med Sci Sports Exerc 37:S501–S511PubMedCrossRefGoogle Scholar
  26. Zou KH, O’Malley AJ, Mauri L (2007) Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation 115:654–657PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Pedro F. Saint-Maurice
    • 1
    • 2
    Email author
  • Youngwon Kim
    • 1
    • 3
  • Gregory J. Welk
    • 1
  • Glenn A. Gaesser
    • 4
  1. 1.Department of KinesiologyIowa State UniversityAmesUSA
  2. 2.School of Psychology: CIPsiUniversity of MinhoBragaPortugal
  3. 3.MRC Epidemiology Unit, School of Clinical MedicineUniversity of CambridgeCambridgeUK
  4. 4.School of Nutrition and Health PromotionArizona State UniversityPhoenixUSA

Personalised recommendations