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

Article information

Korean J Community Nutr. 2014;19(6):568-580

Publication date (electronic) : 2014 December 31

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

Hee-Ryoung Son ¹⁾, Seo-Eun Yeon ¹⁾, Jung-Sook Choi ²⁾, Eun-Kyung Kim ¹⁾

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 2014 August 06; Revised 2014 November 18; Accepted 2014 December 05.

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

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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

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

Table 1

Equations used to predict the resting metabolic rate in the study

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

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

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

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

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

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

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

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

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

^*: 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

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