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Energy expenditure of physical activity in Korean adults and assessment of accelerometer accuracy by gender

성인의 13가지 신체활동의 에너지 소비량 및 가속도계 정확성의 남녀비교

  • Choi, Yeon-jung (Department of Food and Nutrition, Gangneung-Wonju National University) ;
  • Ju, Mun-jeong (Department of Food and Nutrition, Gangneung-Wonju National University) ;
  • Park, Jung-hye (Department of Food and Nutrition, Gangneung-Wonju National University) ;
  • Park, Jong-hoon (Department of Physical Education, Korea University) ;
  • Kim, Eun-kyung (Department of Food and Nutrition, Gangneung-Wonju National University)
  • 최연정 (강릉원주대학교 식품영양학과) ;
  • 주문정 (강릉원주대학교 식품영양학과) ;
  • 박정혜 (강릉원주대학교 식품영양학과) ;
  • 박종훈 (고려대학교 체육교육과) ;
  • 김은경 (강릉원주대학교 식품영양학과)
  • Received : 2017.08.24
  • Accepted : 2017.11.30
  • Published : 2017.12.31

Abstract

Purpose: 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 ($K4b^2$) and to assess the accuracy of the accelerometer (Actigraph $GT3X^+$) by gender in Korean adults. Methods: 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 $K4b^2$ 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. Results: 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 $K4b^2$ 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. Conclusion: 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.

본 연구는 20 ~ 60대의 성인 109명 (남 54명, 여 55명)을 대상으로 13가지 대표적 신체활동에 대한 에너지 소비량(EE)및 신체활동 강도 (METs)를 휴대용 호흡가스 분석기($K4b^2$)를 이용하여 측정하고 이를 기준으로 가속도계(Actigraph $GT3X^+$)의 정확성을 성별에 따라 평가하였다. 그 결과를 요약하면 다음과 같다. 1. 휴대용 호흡가스 분석기로 측정한 보통 달리기를 제외한 모든 트레드밀 활동의 단위체중당 에너지 소비량과 METs값은 여자가 남자 보다 유의하게 높았다. 13가지 대표 활동 중 눕기, 앉기 및 서기와 같은 움직임이 없는 활동은 에너지 소비량이 가장 낮은 것으로 나타났고, 남자는 계단 올라가기 (에너지 소비량 포함)에서 여자는 8 km/h 속도의 보통 달리기 (에너지 소비량값 포함)에서 에너지가 많이 소비되는 고강도 활동으로 나타났다. 2. 가속도계 및 휴대용 호흡가스 분석기로 측정한 EE와 METs값을 비교한 결과, 남자는 계단 내려가기, 보통 걷기와 빠르게 걷기 활동에서 여자는 계단 내려가기를 제외한 모든 활동에서, 가속도계가 휴대용 호흡가스 분석기 보다 과소추정 하였다. 3. Compendium 2011 기준에 따라 강도를 분류하였을 때 가속도계와 휴대용 호흡가스 분석기는 저강도 활동에서 일치하는 비율이 가장 높은 (남자 88.3%, 여자 91.3%) 반면, 고강도 활동에서 가장 낮은 일치율을 보였고 여자 (58.3%)보다는 남자 (75.2%)에서 일치율이 유의하게 높았다. 4. 가속도계를 이용하여 EE 및 METs값 추정 시 준거도구와의 차이를 성별에 따라 비교하면, 계단 내려가기를 제외한 모든 활동에서 여자 보다 남자에서 오차가 작은 것으로 나타났다. 특히, 트레드밀 이외의 활동보다는 트레드밀 활동에서의 오차가 작은 것으로 나타났다. 본 연구는 다양한 연령대의 성인 남녀의 신체활동의 에너지 소비량 등을 휴대용 호흡가스 분석기로 측정하였다는데 큰 의의가 있으며 이와 같은 측정값은 한국인을 위한 활동분류표 개발 및 에너지 소비량 산출 시 활용될 수 있을 것이다. 추후 연구에서는 현재까지 개발된 다양한 가속도계 추정식의 정확성이 검증되어야 하며 향후 가속도계를 이용하여 EE 및 METs값 산출 시 필요한 추정식은 적용하는 신체활동 종류뿐만 아니라 성별에 따라 개발되어야 할 것이다.

Keywords

References

  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. https://doi.org/10.1093/ajcn/51.2.241
  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. https://doi.org/10.1249/00005768-199301000-00011
  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. https://doi.org/10.14373/JKDA.2017.23.1.78
  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. https://doi.org/10.5720/kjcn.2016.21.6.520
  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, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008; 40(1): 181-188. https://doi.org/10.1249/mss.0b013e31815a51b3
  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. https://doi.org/10.1123/jpah.2.3.345
  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. https://doi.org/10.1097/00005768-199805000-00021
  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. https://doi.org/10.1007/s00421-006-0307-5
  16. Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport 2011; 14(5): 411-416. https://doi.org/10.1016/j.jsams.2011.04.003
  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. https://doi.org/10.5392/JKCA.2011.11.12.103
  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. https://doi.org/10.1249/MSS.0b013e318206476e
  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. https://doi.org/10.1007/s00421-010-1639-8
  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. https://doi.org/10.1249/MSS.0b013e31821ece12
  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. https://doi.org/10.4162/nrp.2012.6.1.51
  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. https://doi.org/10.1055/s-2001-13816
  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. https://doi.org/10.1093/ajcn/48.3.552
  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. https://doi.org/10.2165/00007256-198401020-00001
  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. https://doi.org/10.1249/MSS.0b013e318276c73c
  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. https://doi.org/10.4163/jnh.2015.48.2.180
  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. https://doi.org/10.1249/MSS.0b013e3181aa3a0e
  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. https://doi.org/10.1097/00005768-200009001-00006
  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. https://doi.org/10.1249/01.MSS.0000117158.14542.E7

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