Journal List > Korean J Community Nutr > v.19(6) > 1038489

TOOLS
Son, Yeon, Choi, and Kim: The Measurements of the Resting Metabolic Rate (RMR) and the Accuracy of RMR Predictive Equations for Korean Farmers
Research Article

Korean Journal of Community Nutrition 2014; 19(6): 568-580.

Published online: 31 December 2014

DOI: https://doi.org/10.5720/kjcn.2014.19.6.568

The Measurements of the Resting Metabolic Rate (RMR) and the Accuracy of RMR Predictive Equations for Korean Farmers

Hee-Ryoung Son1), Seo-Eun Yeon1), Jung-Sook Choi2), Eun-Kyung Kim1)

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

2)National Academy of Agricultural Science, Rural Development Administration, Jeonbuk, Korea.

Corresponding author: Eun Kyung Kim. Department of Food & Nutrition, Gangneung-Wonju National University,7 Jukheon road, Gangneung, Gangwon 210-702, Korea. Tel: (033) 640-2336, Fax: (33) 640-2330, ekkim@gwnu.ac.kr

Received 6 August 2014       Revised 18 November 2014       Accepted 5 December 2014

Copyright © 2014 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

The purpose of this study was to measure the resting metabolic rate (RMR) and to assess the accuracy of RMR predictive equations for Korean farmers.

Methods

Subjects were 161 healthy Korean farmers (50 males, 111 females) in Gangwon-area. The RMR was measured by indirect calorimetry for 20 minutes following a 12-hour overnight fasting. Selected predictive equations were Harris-Benedict, Mifflin, Liu, KDRI, Cunningham (1980, 1991), Owen-W, F, FAO/WHO/UNU-W, WH, Schofield-W, WH, Henry-W, WH. The accuracy of the equations was evaluated on the basis of bias, RMSPE, accurate prediction and Bland-Altman plot. Further, new RMR predictive equations for the subjects were developed by multiple regression analysis using the variables highly related to RMR.

Results

The mean of the measured RMR was 1703 kcal/day in males and 1343 kcal/day in females. The Cunningham (1980) equation was the closest to measured RMR than others in males and in females (males Bias -0.47%, RMSPE 110 kcal/day, accurate prediction 80%, females Bias 1.4%, RMSPE 63 kcal/day, accurate prediction 81%). Body weight, BMI, circumferences of waist and hip, fat mass and FFM were significantly correlated with measured RMR. Thus, derived prediction equation as follow: males RMR = 447.5 + 17.4·Wt, females RMR = 684.5 - 3.5·Ht + 11.8·Wt + 12.4·FFM.

Conclusions

This study showed that Cunningham (1980) equation was the most accurate to predict RMR of the subjects. Thus, Cunningham (1980) equation could be used to predict RMR of Korean farmers studied in this study. Future studies including larger subjects should be carried out to develop RMR predictive equations for Korean farmers.

Keywords: resting metabolic rate, farmers, predictive equations, accuracy, indirect calorimetry

Figures and Tables

Fig. 1
Bland-Altman plots for measured RMR and predicted RMR derived from 5 selected equations (WHO_W, WHO_WH, Scho_W, Scho_WH, Cunningham_80) for male subjects
kjcn-19-568-g001
Fig. 2
Bland-Altman plots for measured RMR and predicted RMR derived from 5 selected equations (WHO_W, WHO_WH, Scho_W, Scho_WH, Cunningham_80) for female subjects
kjcn-19-568-g002
Table 1
Equations used to predict the resting metabolic rate in the study
kjcn-19-568-i001

Abbreviation: W, Weight in kg; H, Height in cm; A, Age in years; FFM, Fat free mass in kg.

1) Koreans Dietary Reference Intakes

2) Food and Agriculture Organization/World Health Organization/United Nations University

Table 2
Characteristics of the study subjects
kjcn-19-568-i002

1) Mean±SD

2) Weight (kg) / [Height (m)]2

3) Measured by inbody 720

4) Weight (kg) - fat mass (kg)

*: p < 0.05, ***: p < 0.001 Significantly different between male and female by t-test

Table 3
Measured resting metabolic rate and adjusted resting metabolic rate for body weight and fat free mass
kjcn-19-568-i003

1) Standard deviation

2) RMR (resting metabolic rate) adjusted for body Wt (Weight)

3) RMR adjusted for FFM (Fat Free Mass)

***: p < 0.001 Significantly different between male and female by t-test

Table 4
Assessment of resting metabolic rate predictive equations based on bias, RMSPE and accurate prediction (%) in male subjects
kjcn-19-568-i004

1) [(predicted RMR - measured RMR) / measured RMR] × 100

2)

PMSPE: Root Mean Squared Prediction Error=∑predicted RMR-measured RMR2/N)kjcn-19-568-i008

3) Percentage of subjects predicted by equation within 90% to 110% of measured RMR

4) Percentage of subjects predicted by equation < 90% of measured RMR

5) Percentage of subjects predicted by equation > 110% of measured RMR

***: p < 0.001 Significantly different between measured RMR and predicted RMR by paired t-test

Table 5
Assessment of resting metabolic rate predictive equations based on bias, RMSPE and accurate prediction (%) in female subjects
kjcn-19-568-i005

1) [(predicted RMR - measured RMR) / measured RMR] × 100

2)

PMSPE: Root Mean Squared Prediction Error=∑predicted RMR-measured RMR2/N)kjcn-19-568-i008

3) Percentage of subjects predicted by equation within 90% to 110% of measured RMR

4) Percentage of subjects predicted by equation < 90% of measured RMR

5) Percentage of subjects predicted by equation > 110% of measured RMR

*: p < 0.05, **: p < 0.01,***: p < 0.001 Significantly different between measured RMR and predicted RMR by paired t-test

Table 6
Pearson's correlation coefficient (r) between measured resting metabolic rate and related variables
kjcn-19-568-i006

*: p < 0.05, **: p < 0.01, ***: p < 0.001 by Pearson's correlation

Table 7
Development of new predictive equations for resting metabolic rate by stepwise multiple regression analysis
kjcn-19-568-i007

Abbreviation: RMR; Resting metabolic rate, Wt; weight, Ht; height, BMI; body mass index, FFM; fat free mass, WHR; wasit/hip ratio, SBP; systolic blood pressure, DBP; diastolic blood pressure

- Equation 1 : Age, Ht, Wt, BMI, Waist, Hip, WHR, Fat (%), Fat mass, FFM, SBP, DBP

- Equation 2 : Male (Ht, Wt, BMI, Waist, Hip, Fat (%), Fat mass, FFM), Female (Ht, Wt, BMI, Waist, Hip, WHR, Fat (%), Fat mass, FFM, SBP, DBP)

- Equation 3 : Age, Wt, Ht, FFM

References

1. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986; 1(8476):307–310.
2. Bleiberg FM, Brun TA, Goihman S, Gouba E. Duration of activities and energy expenditure of female farmers in dry and rainy seasons in Upper-Volta. Br J Nutr. 1980; 43(1):71–82.
3. Chang UJ, Lee KR. Correlation between measured resting energy expenditure and predicted basal energy expenditure in female college students. J Korean Soc Food Sci Nutr. 2005; 34(2):196–201.
4. Crouter SE, Antczak A, Hudak JR, DellaValle DM, Haas JD. Accuracy and reliability of the ParvoMedics TrueOne 2400 and MedGraphics VO2000 metabolic systems. Eur J Appl Physiol. 2006; 98(2):139–151.
5. Cunningham JJ. A reanalysis of the factors influencing basal metabolic rate in normal adults. Am J Clin Nutr. 1980; 33(11):2372–2374.
6. Daly JM, Heymsfield SB, Head CA, Harvey LP, Nixon DW, Katzeff H, Grossman GD. Human energy requirements: overestimation by widely used prediction equation. Am J Clin Nutr. 1985; 42(6):1170–1174.
7. Food and Agriculture Organization of the United Nations. World Health Organization. United Nations University. Energy and protein requirements: report of a joint FAO/WHO/UNU expert consultation. Geneva: World Health Organization;1985.
8. Garby L, Garrow JS, Jørgensen B, Lammert O, Madsen K, Sørensen P, Webster J. Relation between energy expenditure and body composition in man: specific energy expenditure in vivo of fat and fat-free tissue. Eur J Clin Nutr. 1988; 42(4):301–305.
9. Hayter JE, Henry CJ. Basal metabolic rate in human subjects migrating between tropical and temperate regions: a longitudinal study and review of previous work. Eur J Clin Nutr. 1993; 47(10):724–734.
10. Hayter JE, Henry CJ. A re-examination of basal metabolic rate predictive equations: the importance of geographic origin of subjects in sample selection. Eur J Clin Nutr. 1994; 48(10):702–707.
11. Henry CJ. Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr. 2005; 8(7A):1133–1152.
12. Kim EK, Kim GS, Park JS. Comparison of activity factor, predicted resting metabolic rate, and intakes of energy and nutrients between athletic and non-athletic high school students. J Korean Diet Assoc. 2009; 15(1):52–68.
13. Kim EK, Lee SH, Ko SY, Yeon SE, Choe JS. Assessment of physical activity level of Korean farmers to establish estimated energy requirements during busy farming season. Korean J Community Nutr. 2011; 16(6):751–761.
14. Kim HR. The relationship of socioeconomic position and health behaviors with morbidity in Seoul, Korea. Health Soc Welf Rev. 2005; 25(2):3–35.
15. Lee GH, Kim MH, Kim EK. Accuracy of predictive equations for resting metabolic rate in Korean college students. Korean J Community Nutr. 2009; 14(4):462–473.
16. Lee HC. Estimating optimum level of population in rural areas based on rural population forecasts and over-depopulation classification schemes. J Rural Tour. 2007; 14(1):159–181.
17. Lee JS, Lee GH, Kim EK. Comparison of measured and predicted resting metabolic rate of 30-40 aged Korean women. J Korean Diet Assoc. 2007; 13(2):157–168.
18. Lee SH, Yeon SE, Son HR, Choi JS, Kim EK. Assessment of energy intake and physical activity level for Korean farmers to establish estimated energy requirements during the off-season for farmers. Korean J Community Nutr. 2012; 17(5):652–663.
19. Yim KS. Energy metabolism in obesity. J Korean Soc Study Obes. 2001; 10(3):271–282.
20. Liu HY, Lu YF, Chen WJ. Predictive equations for basal metabolic rate in Chinese adults: a cross-validation study. J Am Diet Assoc. 1995; 95(12):1403–1408.
21. Lührmann PM, Herbert BM, Krems C, Neuhäuser-Berthold M. A new equation especially developed for predicting resting metabolic rate in the elderly for easy use in practice. Eur J Nutr. 2002; 41(3):108–113.
22. 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.
23. Miller AT Jr, Blyth CS. Lean body mass as a metabolic reference standard. J Appl Physiol. 1953; 5(7):311–316.
24. Owen OE, Holup JL, D'Alessio DA, Craig ES, Polansky M, Smalley KJ, Kavle EC, Bushman MC, Owen LR, Mozzoli MA, Kendrick ZV, Boden GH. A reappraisal of the caloric requirements of men. Am J Clin Nutr. 1987; 46(6):875–885.
25. Owen OE, Kavle E, Owen RS, Polansky M, Caprio S, Mozzoli MA, Kendrick ZV, Bushman MC, Boden G. A reappraisal of caloric requirements in healthy women. Am J Clin Nutr. 1986; 44(1):1–19.
26. Park JA, Kim KJ, Kim JH, Park YS, Koo J, Yoon JS. A comparison of the resting energy expenditure of Korean adults using indirect calorimetry. Korean J Community Nutr. 2003; 8(6):993–1000.
27. Park J, Kazuko IT, Kim E, Kim J, Yoon J. Estimating freeliving human energy expenditure: practical aspects of the doubly labeled water method and its applications. Nutr Res Pract. 2014; 8(3):241–248.
28. Schofield WN, Schofield C, James WP. Basal metabolic rate: review and prediction, together with an annotated bibliography of source material. Hum Nutr Clin Nutr. 1985; 39C:suppl. 1. 5–96.
29. Singh J, Prentice AM, Diaz E, Coward WA, Ashford J, Sawyer M, Whitehead RG. Energy expenditure of Gambian women during peak agricultural activity measured by the doubly-labelled water method. Br J Nutr. 1989; 62(2):315–329.
30. Suh MK. Health states of the elderly and policy implications. J Korea Gerontol Soc. 1995; 15(1):28–39.
31. Taaffe DR, Thompson J, Butterfield G, Marcus R. Accuracy of equations to predict basal metabolic rate in older women. J Am Diet Assoc. 1995; 95(12):1387–1392.
32. The Korean Nutrition Society. Dietary reference intakes for Koreans. Seoul: The Korean Nutrition Society;2005.
33. The Korean Nutrition Society. Dietary reference intakes for Koreans. Seoul: The Korean Nutrition Society;2010.
34. Tzankoff SP, Norris AH. Effect of muscle mass decrease on age-related BMR changes. J Appl Physiol Respir Environ Exerc Physiol. 1977; 43(6):1001–1006.
35. Tzankoff SP, Norris AH. Longitudinal changes in basal metabolism in man. J Appl Physiol Respir Environ Exerc Physiol. 1978; 45(4):536–539.
36. Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. 1949. Nutrition. 1990; 6(3):213–221.
37. World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO Consultation. Geneva: World Health Organization;2000.
38. Yamauchi T, Ohtsuka R. Basal metabolic rate and energy costs at rest and during exercise in rural- and urban-dwelling Papua New Guinea highlanders. Eur J Clin Nutr. 2000; 54(6):494–499.
39. Yeon SE, Son HR, Choi JS, Kim EK. Relationships among serum adiponectin, leptin and vitamin D concentrations and the metabolic syndrome in farmers. Korean J Community Nutr. 2014; 19(1):12–26.
TOOLS
Similar articles