Predictive validity of an age-specific MET equation among youth of varying body size

  • Eric E. Wickel
  • Joey C. Eisenmann
  • Gregory J. Welk
Original Article


The purpose of this study was to cross-validate the age-specific Freedson MET equation among children and adolescents of varying body size. Sixty-seven children (41 boys; 26 girls) between 6 and 13 years completed five 3-min trials (1.6, 3.2, 4.0, 4.8, and 6.4 km h−1) on a motorized treadmill. During each trial, participants wore an Actigraph accelerometer while oxygen consumption was assessed by indirect calorimetry. Using the Actigraph activity counts, predicted MET values were determined with the age-specific Freedson equation and were compared with measured MET values using dependent t tests. Participants were divided into body size categories based on their calculated body surface area (BSA, m2) (small: BSA ≤ 0.96 m2; medium: 0.96 m2 < BSA ≤ 1.20 m2; large: BSA > 1.20 m2) to determine if body size influenced the difference between measured and predicted MET values. The measured MET value was similar to the predicted MET value at the slowest treadmill speed (1.6 km h−1) (2.3 vs. 2.3 METs); however, the measured MET value was lower than the predicted MET value at the remaining speeds (3.2, 4.0, 4.8, and 6.4 km h−1) (P < 0.001). With the exception of the fastest treadmill speed (6.4 km h−1), the mean difference between the measured and predicted MET values was greater between the two smaller BSA categories compared to the largest BSA category. The results suggest that the age-specific Freedson child equation significantly overestimates energy expenditure (METs) during locomotor speeds between 3.2 and 6.4 km h−1. This effect was primary observed among relatively smaller children.


Accelerometer Physical activity Energy expenditure Children 


  1. Ariens GAM, van Mechelen W, Kemper HCG, Twisk JWR (1997) The longitudinal development of running economy in males and females aged between 13 and 27 years: the Amsterdam Growth and Health Study. Eur J Appl Physiol 76:214–220CrossRefGoogle Scholar
  2. Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet I:307–310Google Scholar
  3. Bratteby LE, Sandhagen B, Lotborn M, Samuelson G (1997) Daily energy expenditure and physical activity assessed by an activity diary in 374 randomly selected 15-year-old adolescents. Eur J Clin Nutr 51:592–600PubMedCrossRefGoogle Scholar
  4. Crouter SE, Clowers KG, Bassett DRJ (2006) A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol 100:1324–1331PubMedCrossRefGoogle Scholar
  5. Eaton DK, Kann L, Kinchen S, Ross J, Hawkins J, Harris WA, Lowry R, McManus T, Chyen D, Shanklin S, Lim C, Grunbaum JA, Wechsler H (2006) Youth risk behavior surveillance−United States, 2005. J Sch Health 76:353–372PubMedCrossRefGoogle Scholar
  6. Eisenmann JC, Wickel EE (2005) Moving on land: an explanation of pedometer counts in children. Eur J Appl Physiol 93:440–446PubMedCrossRefGoogle Scholar
  7. Forster MA, Hunter GR, Hester DJ, Dunaway D, Shuleva K (1994) Aerobic capacity and grade-walking economy of children 5–9 years old: a longitudinal study. Pediatr Exerc Sci 6:31–38Google Scholar
  8. Freedson PS, Melanson E, Sirard J (1998) Calibration of the computer science and applications, Inc. accelerometer. Med Sci Sports Exerc 30:777–781PubMedCrossRefGoogle Scholar
  9. Freedson P, Pober D, Janz KF (2005) Calibration of accelerometer output for children. Med Sci Sports Exerc 37:S523–530PubMedCrossRefGoogle Scholar
  10. Krahenbuhl GS, Morgan DW, Pangrazi RP (1989) Longitudinal changes in distance-running performance of young males. Int J Sports Med 10:92–96PubMedCrossRefGoogle Scholar
  11. Malina RM, Bouchard C, Bar-Or O (2004) Growth, maturation, and physical activity. Human Kinetics, ChampaignGoogle Scholar
  12. Montgomery C, Reilly JJ, Jackson DM, Kelly LA, Slater C, Paton JY, Grant S (2004) Relation between physical activity and energy expenditure in a representative sample of young children. Am J Clin Nutr 80:591–596PubMedGoogle Scholar
  13. Puyau MR, Adolph AL, Vohra FA, Butte NF (2002) Validation and calibration of physical activity monitors in children. Obes Res 10:150–157PubMedCrossRefGoogle Scholar
  14. Riddoch CJ, Boreham CA (1995) The health-related physical activity of children. Sports Med 19:86–102PubMedGoogle Scholar
  15. Rodriguez G, Moreno LA, Sarria A, Fleta J, Bueno M (2002) Resting energy expenditure in children and adolescents: agreement between calorimetry and prediction equations. Clin Nutr 21:255–260PubMedCrossRefGoogle Scholar
  16. Sallis JF (1991) Self-report measures of children’s physical activity. J Sch Health 61:215–219PubMedCrossRefGoogle Scholar
  17. Schofield WN (1985) Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr 39C:5–41Google Scholar
  18. Strong W, Malina R, Blimkie C, Daniels S, Dishman R, Gutin B, Hergenroeder A, Must A, Nixon P, Pivarnik J, Rowland T, Trost S, Trudeau F (2005) Evidence based physical activity for school-age youth. J Pediatr 146:732–737PubMedCrossRefGoogle Scholar
  19. Taylor CR, Schmidt-Nielson K, Raab JL (1970) Scaling of energetic cost of running to body size in mammals. Am J Physiol 219:1104–1107PubMedGoogle Scholar
  20. Troiano RP (2005) A timely meeting: objective measurement of physical activity. Med Sci Sports Exerc 37:S487–489PubMedCrossRefGoogle Scholar
  21. Trost SG, Ward DS, Moorehead SM, Watson PD, Riner W, Burke JR (1998) Validity of the computer science and applications (CSA) activity monitor in children. Med Sci Sports Exerc 30:629–633PubMedGoogle Scholar
  22. Trost SG, Pate RR, Sallis JF, Freedson PS, Taylor WC, Dowda M, Sirard J (2002) Age and gender differences in objectively measured physical activity in youth. Med Sci Sports Exerc 34:350–355PubMedCrossRefGoogle Scholar
  23. Trost SG, Way R, Okely AD (2006) Predictive validity of three ActiGraph energy expenditure equations for children. Med Sci Sports Exerc 38:380–387PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Eric E. Wickel
    • 1
  • Joey C. Eisenmann
    • 2
  • Gregory J. Welk
    • 2
  1. 1.Department of Exercise and Sport ScienceUniversity of TulsaTulsaUSA
  2. 2.Department of Health and Human PerformanceIowa State UniversityAmesUSA

Personalised recommendations