Journal List > J Nutr Health > v.50(6) > 1081530

J Nutr Health. 2017 Dec;50(6):552-564. Korean.
Published online December 31, 2017.
© 2017 The Korean Nutrition Society
Energy expenditure of physical activity in Korean adults and assessment of accelerometer accuracy by gender
Yeon-jung Choi,1 Mun-jeong Ju,1 Jung-hye Park,1 Jong-hoon Park,2 and Eun-kyung Kim1
1Department of Food and Nutrition, Gangneung-Wonju National University, Gangneung 25457, Korea.
2Department of Physical Education, Korea University, Seoul 02841, Korea.

To whom correspondence should be addressed. tel: +82-33-640-2336, Email:
Received August 24, 2017; Revised September 26, 2017; Accepted November 30, 2017.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.



The purpose of this study was to measure energy expenditure (EE) the metabolic equivalents (METs) of 13 common physical activities by using a portable telemetry gas exchange system (K4b2) and to assess the accuracy of the accelerometer (Actigraph GT3X+) by gender in Korean adults.


A total of 109 adults (54 males, 55 females) with normal BMI (body mass index) participated in this study. EE and METs of 13 selected activities were simultaneously measured by the K4b2 portable indirect calorimeter and predicted by the GT3X+ Actigraph accelerometer. The accuracy of the accelerometer was assessed by comparing the predicted with the measured EE and METs.


EE (kcal/kg/hr) and METs of treadmill walking (3.2 km/h, 4.8 km/h and 5.6 km/h) and running (6.4 km/h) were significantly higher in female than in male participants (p < 0.05). On the other hand, the accelerometer significantly underestimated the EE and METs for all activities except descending stairs, moderate walking, and fast walking in males as well as descending stairs in females. Low intensity activities had the highest rate of accurate classifications (88.3% in males and 91.3% females), whereas vigorous intensity activities had the lowest rate of accurate classifications (43.6% in males and 27.7% in females). Across all activities, the rate of accurate classification was significantly higher in males than in females (75.2% and 58.3% respectively, p < 0.01). Error between the accelerometer and K4b2 was smaller in males than in females, and EE and METs were more accurately estimated during treadmill activities than other activities in both males and females.


The accelerometer underestimated EE and METs across various activities in Korean adults. In addition, there appears to be a gender difference in the rate of accurate accelerometer classification of activities according to intensity. Our results indicate the need to develop new accelerometer equations for this population, and gender differences should be considered.

Keywords: estimated energy requirements; physical activity; physical intensity; indirect calorimetry; accelerometer


Fig. 1
Agreement rate of physical activity intensity classifications between K4b2 and accelerometer measurements in male and female. Activity intensity classification was described using agreement rate of physical activity intensity classification and Kappa statistics was used to describe the level of agreement rate between K4b2 and accelerometer.
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Table 1
Description of 13 physical activities investigated
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Table 2
Anthropometric measurements of the subjects
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Table 3
Oxygen consumption, carbon dioxide production, energy expenditure and METs measured by K4b2
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Table 4
Comparison of energy expenditure, METs and intensity for all activities measured by K4b2 and estimated by equations using Actigraph
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Table 5
Accuracy of Actigraph for energy expenditure measurement by gender
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Table 6
Accuracy of Actigraph for METs by gender
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This study was supported by grants from Korea Centers for Disease Control and Prevention (2014-E35002-00).

1. Ministry of Health and Welfare (KR). The Korean Nutrition Society. Dietary reference intakes for Koreans 2015. Sejong: Ministry of Health and Welfare; 2016.
2. Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr 1990;51(2):241–247.
3. Henry CJ. Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr 2005;8(7A):1133–1152.
4. Institute of Medicine Panel on Macronutrients (US). Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. Washington, D.C.: National Academies Press; 2002.
5. Ainsworth BE, Haskell WL, Leon AS, Jacobs DR Jr, Montoye HJ, Sallis JF, Paffenbarger RS Jr. Compendium of physical activities: classification of energy costs of human physical activities. Med Sci Sports Exerc 1993;25(1):71–80.
6. Lee MY. Preparation in kinesmetrics to develop physical activity guidelines for Korean. Korean Soc Meas Eval Phys Edu Sports Sci 2011;13(3):17–31.
7. Kim YJ, An HS, Kim EK. Energy expenditure of eight walking activities in normal weight and obese high school students: using an indirect calorimeter and accelerometers worn on ankle and waist. J Korean Diet Assoc 2017;23(1):78–93.
8. Kim YJ, Wang CS, Kim EK. Measurement of energy expenditure through treadmill-based walking and self-selected hallway walking of college students: using indirect calorimeter and accelerometer. Korean J Community Nutr 2016;21(6):520–532.
9. Lee MY, Lee H, Choi JY. Error rates of prediction equations and cut-points of Actigraph GT3X+. Korean Soc Meas Eval Phys Edu Sports Sci 2016;18(1):17–29.
10. Ahn HJ, Lee MC, Lee DT. Validity and energy expenditure of physical activity estimated by accelerometer. J Coaching Dev 2006;8(4):69–77.
11. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008;40(1):181–188.
12. Sirard JR, Trost SG, Pfeiffer KA, Dowda M, Pate RR. Calibration and evaluation of an objective measure of physical activity in preschool children. J Phys Act Health 2005;2(3):345–357.
13. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc 1998;30(5):777–781.
14. Lee MY. Criterion and convergent validity evidences of an accelerometer and a pedometer. Korean Soc Meas Eval Phys Edu Sports Sci 2012;14(2):1–13.
15. Crouter SE, Churilla JR, Bassett DR Jr. Estimating energy expenditure using accelerometers. Eur J Appl Physiol 2006;98(6):601–612.
16. Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport 2011;14(5):411–416.
17. Kim DY, Jeon SH, Kang SY, Kim NH. Customized estimating algorithm of physical activities energy expenditure using a tri-axial accelerometer. J Korea Contents Assoc 2011;11(12):103–111.
18. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc 2011;43(7):1360–1368.
19. Lyden K, Kozey SL, Staudenmeyer JW, Freedson PS. A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations. Eur J Appl Physiol 2011;111(2):187–201.
20. Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR Jr, Tudor-Locke C, Greer JL, Vezina J, Whitt-Glover MC, Leon AS. 2011 compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exerc 2011;43(8):1575–1581.
21. Kim MH, Kim JH, Kim EK. Accuracy of predictive equations for resting energy expenditure (REE) in non-obese and obese Korean children and adolescents. Nutr Res Pract 2012;6(1):51–60.
22. Ministry of Health and Welfare, Korea Centers for Disease Control and Prevention. Korea Health Statistics 2015: Korea National Health and Nutrition Examination Survey (KNHANES VI-3). Sejong: Korea Centers for Disease Control and Prevention; 2016.
23. McLaughlin JE, King GA, Howley ET, Bassett DR Jr, Ainsworth BE. Validation of the COSMED K4 b2 portable metabolic system. Int J Sports Med 2001;22(4):280–284.
24. Spurr GB, Prentice AM, Murgatroyd PR, Goldberg GR, Reina JC, Christman NT. Energy expenditure from minute-by-minute heart-rate recording: comparison with indirect calorimetry. Am J Clin Nutr 1988;48(3):552–559.
25. Park JY, Park ST, Jun TW, Eom WS, Lee DG, Park IR, Kang HJ. Prediction of energy expenditure during exercise through heart rate in young adult. Exerc Sci 2004;13(3):311–322.
26. Pate RR, Kriska A. Physiological basis of the sex difference in cardiorespiratory endurance. Sports Med 1984;1(2):87–98.
27. Hall KS, Howe CA, Rana SR, Martin CL, Morey MC. METs and accelerometry of walking in older adults: standard versus measured energy cost. Med Sci Sports Exerc 2013;45(3):574–582.
28. Kim JH, Son HR, Choi JS, Kim EK. Energy expenditure measurement of various physical activity and correlation analysis of body weight and energy expenditure in elementary school children. J Nutr Health 2015;48(2):180–191.
29. An JH. The model for the walking and running program for the health of the aged. Korean J Phys Educ 1996;35(3):299–308.
30. Howe CA, Staudenmayer JW, Freedson PS. Accelerometer prediction of energy expenditure: vector magnitude versus vertical axis. Med Sci Sports Exerc 2009;41(12):2199–2206.
31. Bassett DR Jr, Ainsworth BE, Swartz AM, Strath SJ, O'Brien WL, King GA. Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc 2000;32 9 Suppl:S471–S480.
32. Rowlands AV, Thomas PW, Eston RG, Topping R. Validation of the RT3 triaxial accelerometer for the assessment of physical activity. Med Sci Sports Exerc 2004;36(3):518–524.