Resting Metabolic Rate Prediction Equations Research Articles

The Development of a Resting Metabolic Rate Prediction Equation for Professional Male Rugby Union Players.

Determining resting metabolic rate (RMR) is an important aspect when calculating energy requirements for professional rugby union players. Prediction equations are often used for convenience to estimate RMR. However, the accuracy of current prediction equations for professional rugby union players remains unclear. The aims of this study were to examine the RMR of professional male rugby union players compared to nine commonly used prediction equations and develop and validate RMR prediction equations specific to professional male rugby union players. One hundred and eight players (body mass (BM) = 102.9 ± 13.3 kg; fat-free mass (FFM) = 84.8 ± 10.2 kg) undertook Dual-energy X-ray Absorptiometry scans to assess body composition and indirect calorimetry to determine RMR. Mean RMR values of 2585 ± 176 kcal∙day-1 were observed among the group with forwards (2706 ± 94 kcal·day-1), demonstrating significantly (p < 0.01; d = 1.93) higher RMR compared to backs (2465 ± 156 kcal·day-1), which appeared to be due to their higher BM and FFM measures. Compared to the measured RMR for the group, seven of the nine commonly used prediction equations significantly (p < 0.05) under-estimated RMR (-104-346 kcal·day-1), and one equation significantly (p < 0.01) over-estimated RMR (192 kcal·day-1). This led to the development of a new prediction equation using stepwise linear regression, which determined that the strongest predictor of RMR for this group was FFM alone (R2 = 0.70; SEE = 96.65), followed by BM alone (R2 = 0.65; SEE = 104.97). Measuring RMR within a group of professional male rugby union players is important, as current prediction equations may under- or over-estimate RMR. If direct measures of RMR cannot be obtained, we propose the newly developed prediction equations be used to estimate RMR within professional male rugby union players. Otherwise, developing team- and/or group-specific prediction equations is encouraged.

Validation of predictive equations to estimate resting metabolic rate of females and males across different activity levels.

Using equations to predict resting metabolic rate (RMR) has yielded different degrees of validity, particularly when sex and different physical activity levels were considered. Therefore, the purpose of the present study was to determine the validity of several different predictive equations to estimate RMR in female and male adults with varying physical activity levels. We measured the RMR of 50 adults (26 females and 24 males) evenly distributed through activity levels varying from sedentary to ultra-endurance. Body composition was measured by dual X-ray absorptiometry and physical activity was monitored by accelerometry. Ten equations to predict RMR were applied (using Body Mass [BM]: Harris & Benedict, 1919; Mifflin et al., 1990 [MifflinBM]; Pontzer et al., 2021 [PontzerBM]; Schofield, 1985; FAO/WHO/UNU, 2004; and using Fat-Free Mass (FFM): Cunningham, 1991; Johnstone et al., 2006; Mifflin et al., 1990 [MifflinFFM]; Nelson et al. 1992; Pontzer et al., 2021 [PontzerFFM]). The accuracy of these equations was analyzed, and the effect of sex and physical activity was evaluated using different accuracy metrics. Equations using BM were less accurate for females, and their accuracy was influenced by physical activity and body composition. FFM equations were slightly less accurate for males but there was no obvious effect of physical activity or other sample parameters. PontzerFFM provides higher accuracy than other models independent of the magnitude of RMR, sex, activity levels, and sample characteristics. Equations using FFM were more accurate than BM equations in our sample. Future studies are needed to test the accuracy of RMR prediction equations in diverse samples.

Accuracy of Resting Metabolic Rate Prediction Equations in Sport Climbers.

Resting metabolic rate (RMR) represents the energy required to maintain vital body functions. In dietary practice, RMR is determined by predictive equations on the basis of using body weight or fat-free mass. Our study aimed to assess whether predictive equations used to estimate RMR are reliable tools for estimating the energy requirements of sport climbers. The study included 114 sport climbers whose RMR was measured with a Fitmate WM. Anthropometric measurements were performed with X-CONTACT 356. The resting metabolic rate was measured by indirect calorimetry and was compared with the RMR estimated by 14 predictive equations on the basis of using body weight/fat-free mass. All equations underestimated RMR in male and female climbers, except for De Lorenzo's equation in the group of women. The De Lorenzo equation demonstrated the highest correlation with RMR in both groups. The results of the Bland-Altman tests revealed an increasing measurement error with increasing metabolism for most of the predictive equations in male and female climbers. All equations had low measurement reliability according to the intraclass correlation coefficient. Compared with the indirect calorimetry measurement results, none of the studied predictive equations demonstrated high reliability. There is a need to develop a highly reliable predictive equation to estimate RMR in sport climbers.

The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes

ABSTRACT Predictive resting metabolic rate (RMR) equations are widely used to determine total daily energy expenditure (TDEE). However, it remains unclear whether these predictive RMR equations accurately predict TDEE in the athletic populations. The purpose of this study was to examine the accuracy of 10 commonly used RMR prediction equations (Cunningham, De Lorenzo, Freire, Harris-Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) in collegiate men and women athletes. One-hundred eighty-seven National Collegiate Athletic Association Division III men (n = 97) and women (n = 90) athletes were recruited to participate in one day of metabolic testing. RMR was measured using indirect calorimetry and body composition was analyzed using air displacement plethysmography. A repeated measures ANOVA with Bonferroni post hoc analyses was selected to determine mean differences between measured and predicted RMR. Linear regression analysis was used to assess the accuracy of each RMR prediction method (p<0.05). All prediction equations significantly underestimated RMR (p<0.001), although there was no difference between the De Lorenzo and Watson equations and measured RMR (p = 1.00) for women, only. In men, the Tinsley and Freire equations were the most agreeable formulas with the lowest root-mean-square prediction error value of 404 and 412 kcals, respectively. In women, the De Lorenzo and Watson equations were the most agreeable equations with the lowest root-mean-squared error value of 171 and 211 kcals, respectively. The results demonstrate that such RMR equations may underestimate actual energy requirements of athletes and thus, practitioners should interpret such values with caution. Highlights All prediction equations significantly underestimated RMR in men athletes. All prediction equations, except for the De Lorenzo and Watson equations, significantly underestimated RMR in women athletes. Although a significant underestimation of RMR in men athletes, the Freire and Tinsley equations were the most agreeable prediction equations. In women athletes, the De Lorenzo and Watson equations were the most agreeable prediction equations.

Comparison of Indirect Calorimetry and Common Prediction Equations for Evaluating Changes in Resting Metabolic Rate Induced by Resistance Training and a Hypercaloric Diet.

Rodriguez, C, Harty, PS, Stratton, MT, Siedler, MR, Smith, RW, Johnson, BA, Dellinger, JR, Williams, AD, White, SJ, Benavides, ML, and Tinsley, GM. Comparison of indirect calorimetry and common prediction equations for evaluating changes in resting metabolic rate induced by resistance training and a hypercaloric diet. J Strength Cond Res 36(11): 3093-3104, 2022-The ability to accurately identify resting metabolic rate (RMR) changes over time allows practitioners to prescribe appropriate adjustments to nutritional intake. However, there is a lack of data concerning the longitudinal utility of commonly used RMR prediction equations. The purpose of this study was to evaluate the validity of several commonly used prediction equations to track RMR changes during a hypercaloric nutritional intervention and supervised resistance exercise training program. Twenty resistance-trained men completed the study. The protocol lasted 6 weeks, and subjects underwent RMR assessments by indirect calorimetry (IC) preintervention and postintervention to obtain reference values. Existing RMR prediction equations based on body mass (BM) or dual-energy X-ray absorptiometry fat-free mass (FFM) were also evaluated. Equivalence testing was used to evaluate whether each prediction equation demonstrated equivalence with IC. Null hypothesis significance testing was also performed, and Bland-Altman analysis was used alongside linear regression to assess the degree of proportional bias. Body mass and FFM increased by 3.6 ± 1.7 kg and 2.4 ± 1.6 kg, respectively. Indirect calorimetry RMR increased by 165 ± 97 kcal·d -1 , and RMR:FFM increased by 5.6 ± 5.2%. All prediction equations underestimated mean RMR changes relative to IC, with magnitudes ranging from 75 to 155 kcal·d -1 , while also displaying unacceptable levels of negative proportional bias. In addition, no equation demonstrated equivalence with IC. Common RMR prediction equations based on BM or FFM did not fully detect the increase in RMR observed with resistance training plus a hypercaloric diet. Overall, the evaluated prediction equations are unsuitable for estimating RMR changes in the context of this study.

Accuracy of resting metabolic rate prediction equations among healthy adults in Trinidad and Tobago.

Over 50% of adults in Latin America and the Caribbean have a body mass index (BMI) ≥ 25 suggesting excess energy intakes relative to energy expenditure. Accurate estimation of resting metabolic rate (RMR), the largest component of total energy requirements, is crucial to strategies aimed at reducing the prevalence and incidence of overweight and obesity. We evaluated the accuracies of established and locally developed RMR prediction equations (RMRP) among adults. Four hundred adult volunteers ages 20 to 65 years had RMR measured (RMRM) with a MedGem® indirect calorimeter according to recommended procedures. RMRP were compared to RMRM with values ± 10% of RMRM deemed accurate. Anthropometry was measured using standard procedure. Linear regression with bootstrap analyses was used to develop local RMRP equations based on anthropometric and demographic variables. The University of the West Indies Ethics Committee approved the study. Males had higher mean absolute RMR (p < 0.001) but similar mean age-adjusted measured RMR per kg of body (20.9 vs. 21.5 kcals/day; p = 0.1) to females. The top performing established anthropometry-based RMRP among participants by sex, physical activity (PA) level and BMI status subgroups were Mifflin-St Jeor, Owen, Korth, Harris-Benedict, and Livingston, while Johnstone, Cunningham, Müller (body composition (BC)), Katch and McArdle, Mifflin-St Jeor (BC) were the most accurate BC-based RMRP. Locally developed RMRP had accuracies comparable to their top-ranked established RMRP counterparts. Accuracies of established RMRP depended on habitual PA level, BMI status, BC and sex. Furthermore, locally developed RMRP provide useful alternatives to established RMRP.

A Comparison Of Female Only Predictive Equations Versus Measured Resting Metabolic Rate

Established prediction equations, such as Dore, Garrow and Weber (GW), and Cunningham (CH) were developed to predict resting metabolic rate (RMR) in females. However, these female prediction equations should be reexamined to determine if an update is required. PURPOSE: To examine several well-known female only RMR prediction equations (RMRpredict) against measured (RMRmeas) values in healthy females. METHODS: Fourteen female participants (36.5 ± 16.2 yrs, 166.8±3.6 cm, and 62.7±33.2kg) participated in this study. Each participant followed standard pre-test guidelines and underwent a single RMR assessment using a metabolic cart calibrated before each test. Each participant laid motionless in a supine position under a ventilated canopy for 25-30 minutes. The most stable 5 minutes of resting data within ≤5% of coefficient of variation was defined as the measured resting metabolic rate. A one-way repeated measures ANOVA was used to compare the RMRmeas against RMRpredict (Dore, GW, and CH). Additionally, Bland Altman Limits of Agreement (LA) were reported as frequency of subjects outside of agreement compared to RMRmeas. For significant differences, Cohen’s d effects sizes were reported. All results are expressed as M ± SD with significance set at p ≤ 0.05. RESULTS: The GW (1408.3 ± 149.3 kcal/day) and CH (1497 ± 135.8 kcal/day) prediction equations did not differ from the RMRmeas (1485.4 ± 247.2 kcal/day) (p > 0.05). However, significant differences were observed for Dore (1104.5 ± 89.3 kcal/day; d = 2.71) prediction equation when compared to RMRmeas. Dore under-predicted 100% of cases under the LA and the Garrow had the highest variability over-predicting and under-predicting 50% and 21.4%, respectively. The Dore equation significantly underestimated RMR when compared to RMRmeas by 23.8 ± 9.3 %. CONCLUSION: Our preliminary data shows that the use of the Dore prediction equation underestimated RMR by 380.9± 157.9 kcal/day. Therefore, using this equation to estimate calories in females should be interpreted with caution. Although not statistically different from the RMRmeas, the GW equation had the largest variability of RMR estimates. Our data supports the continued use of the CH prediction equation, as no significant differences were observed compared to RMRmeas in a healthy population of females.

Accuracy of Equations Used to Predict Resting Metabolic Rate (RMR) Requirements Among Healthy and Acutely Ill Females in Trinidad and Tobago; Aged 18 to 65 Years

Abstract Objectives To determine the level of accuracy of the recommended resting metabolic rate (RMR) prediction equations among healthy and acutely ill females: aged 18 to 65 years, in Trinidad and Tobago. Methods Following informed consent and enrollment, sixty female (acutely ill 30; healthy 30) volunteers had anthropometry and RMR (MedGem® indirect calorimeter, Micro life, USA) measured using recommended procedures. RMR from prediction equations were compared to RMR measured by indirect calorimetry with values between ±10% of measured RMR being considered accurate. The university's ethics committee approved the study. Results The top-two ranked recommended RMR prediction equations for acutely ill females, in decreasing order of accuracy were Owen (46.7%) and Bernstein (40%). Among the healthy females, the top-two ranked recommended RMR prediction equations with a similar level of accuracy (46.7%) were Livingston and Kohlstadt, and DeLorenzo. The population-derived RMR prediction equations had 56.7% and 70% accuracies among the acutely ill and healthy females respectively. These were significantly higher than the top-ranked recommended prediction equations for both groups. Notably, limiting the risk of malnutrition by at least 5%: through diet quality by way of accurate energy predictions could improve health-related quality of life. Increasing the predictability of energy needs within any population can also ensure accuracy of RMR per weight in kilogram (kcal/kg) needed daily and energy balance. On average, this female population utilized 18.4 kcal of energy per kilogram body weight (kcal/kg). Conclusions All the recommended RMR prediction equations resulted in biases &amp;gt; 50%, thus our population-derived RMR equations can be used as a superior alternative among participants to determining the energy-needs of acutely ill individuals as well as healthy females. Substituting the commonly used prediction equations with population-specific equations can increase the level of accuracy by at least 10%, thus limiting the risk of malnutrition by at least 5% and improving health-related quality of life. Funding Sources NIL.

The Accuracy of Equations Used To Predict the Resting Metabolic Rate (RMR) Requirements of Males: Aged 18 to 65 Years, in Trinidad and Tobago

Abstract Objectives To determine the level of accuracy of recommended resting metabolic rate (RMR) prediction equations among healthy and acutely ill males: aged 18 to 65-years, in Trinidad and Tobago. Methods Following informed consent and enrolment, sixty-six male (acutely ill 33; healthy 33) volunteers had anthropometry and RMR (MedGem® indirect calorimeter, Micro life, USA) measured using recommended procedures. RMR from prediction equations were compared to RMR measured by indirect calorimetry with values between ±10% of measured RMR being considered accurate. The university's ethics committee approved the study. Results The top-four ranked recommended RMR prediction equations for acutely ill males, in decreasing order of accuracy were Müller (39.4%), Bernstein (39.4%), Korth (36.4%) and Mifflin St. Joer (36.4%). Among the healthy males, the top-five ranked recommended RMR prediction equations were Müller (54.5%), Huang (54.5%), Lürhmann (51.5%), Korth (48.5%) and Valencia (48.5). Population-derived RMR prediction equations had 54.5% and 63.6% accuracies among the acutely ill and healthy males respectively. These were significantly higher than the top-ranked recommended prediction equations for both groups. Notably, limiting the risk of malnutrition by at least 5%: through diet quality by way of accurate energy predictions could improve health-related quality of life. Increasing the predictability of energy needs within any population can also ensure the accuracy of RMR per weight (kilogram) needed daily and energy balance. On average, this male population utilized 17.5 kcal of energy per kilogram body weight. Conclusions All other recommended RMR prediction equations except Huang, Lürhmann, and Müller resulted in biases &amp;gt;50%. Substituting the commonly used prediction equations with our population-specific equation can increase the level of accuracy by at least 10%, thus limiting the risk of malnutrition by at least 5%, and improving health-related quality of life for this male population. Funding Sources nil.

Comparison of predictive equations for resting metabolic rate in Portuguese women

Obesity represents a public health challenge and dietary interventions to prevent or treat obesity rely on the ability to accurately determine daily energy requirements - which are based on measures of total energy expenditure. Several prediction equations to estimate resting metabolic rate (RMR) have been developed, however the validity of these equations is uncertain. The present study aims to determine the accuracy of four commonly used RMR prediction equations in normal weight, overweight and obese Portuguese women aged 18 to 64 years. RMR was measured in 156 women (age: 40.3 ± 10.2 years; Body Mass Index (BMI): 20.6 ± 6.8 kg/m2) using indirect calorimetry. The resulting values were compared with the predictive values from the Harris-Benedict, FAO/WHO/UNU, Schofield and Mifflin-St. Jeor equations across BMI categories. At an individual level, the equations with the highest percentage of accurate predictions were the Mifflin-St. Jeor equation in normal weight women (41.9%) and the Harris-Benedict equation in overweight (55.4%) and obese (50.9%) women. The accuracy of the RMR prediction equations studied varied by weight status and due to the low levels of accuracy reported, the present equations might have limited applicability for Portuguese women at an individual level.

Accuracy of resting metabolic rate prediction equations in female rugby players

AbstractThis study aimed to investigate (1) the accuracy of resting metabolic rate (RMR) prediction equations in female rugby players on a group and individual level; and (2) whether individual differences in the accuracy of prediction equations is associated with muscle damage or energy availability.RMR was assessed in 14 female provincial and club rugby players (Age: 20–34 years, FFM: 47–63 kg, FM: 15–37%) training a minimum of twice per week. Participants attended the laboratory following an overnight fast and having avoided strenuous exercise for 24 hours. RMR was measured over 30 minutes by indirect calorimetry, and taken as the 10 minutes with the lowest variation. Body composition was assessed by air displacement plethysmography, muscle damage indicated by creatine kinase (CK) and risk of low energy availability assessed by the Low Energy Availability in Females Questionnaire. Accuracy of RMR prediction equations relevant to the general population and athletes were assessed including the Harris Benedict (1919), Cunningham (1980) and Ten Haaf FFM (2014) based equations.Measured RMR was 1748 ± 146 kcal/day (range: 1474–2010 kcal/day). Predicted RMR determined by the Harris-Benedict equation (1601 ± 120 kcal/day) was significantly lower than measured RMR (p &lt; 0.001), whereas predicted RMR using the Cunningham (1753 ± 146 kcal/day, p = 0.89) and the Ten Haaf (1781 ± 115 kcal/day, p = 0.33) equations did not differ from measured RMR. On an individual level, 50% (n = 7), 86% (n = 12) and 79% (n = 11) of participants fell within 10% of the measured RMR value when RMR was predicted by Harris-Benedict, Cunningham and Ten Haaf equations respectively. CK values were 182 ± 155U/L (range: 25–490U/L). When correlations of the whole group were studied, the difference between predicted and measured RMR was not associated with CK (r = 0.13). However, in the two individuals who fell outside the 10% range of that predicted by the Cunningham equation, one above and one below, CK values were 428U/L and 166U/L respectively. Muscle damage (as indicated by a high CK value) could therefore be one potential explanation for the higher measured RMR in the individual who was above the Cunningham predicted value.In this cohort of female rugby players, the Cunningham equation showed the best accuracy on a group and individual level, suggesting this may be the most suitable prediction equation for this population. Further studies with larger sample sizes and investigating underlying reasons for why RMR measured values may differ from predicted values are needed.

Determining a Resting Metabolic Rate Prediction Equation for Collegiate Female Athletes.

Watson, AD, Zabriskie, HA, Witherbee, KE, Sulavik, A, Gieske, BT, and Kerksick, CM. Determining a resting metabolic rate prediction equation for collegiate female athletes. J Strength Cond Res 33(9): 2426-2432, 2019-A lack of evidence exists regarding the accuracy of common resting metabolic rate (RMR) prediction equations in athletic female populations. The purpose of this research was to measure RMR in a large cohort of NCAA Division II female athletes and use regression techniques to develop new prediction equations. Sixty-six female athletes from 11 different sports completed this protocol, which included skinfold measurements followed by an RMR assessment using indirect calorimetry. The average RMR was 1,466 ± 150 kcal·d. Many between-sport differences in body composition were identified, with gymnastics athletes having the lowest body fat percentage (p < 0.05) and basketball athletes having the greatest absolute fat-free mass (p < 0.05). Resting metabolic rate was moderately correlated (p < 0.05) with height (r = 0.52), total mass (r = 0.59), and fat-free mass (r = 0.54). Two equations were developed, both of which were more accurate for this population than other RMR prediction equations. One of the new equations, which used height and body mass as covariates (equation 1), was slightly more accurate than the equation using body composition parameters (equation 2). The new equations were cross-validated using a randomly selected subset (n = 22) of the original sample. The subset did not show statistically different results from the remainder of the sample (n = 44) between equation 1 (p = 0.083) and equation 2 (p = 0.22). Equation 1, which had more easily measurable parameters, exhibited heightened accuracy, which has important implications for implementation among athletes, coaches, and athletic support staff.

Resting Metabolic Rate of Junior National Weightlifters in India: Development and Validation of Prediction Models

Weightlifters involve in intense strength and resistance training, thus calorie adequacy is a nutritional concern for them. Determining Resting Metabolic rate (RMR) is important to assess energy needs and there is limited scientific evidence in this area. Thus, this study aims to document physical characteristics and RMR of Indian junior weightlifters. It also attempts to identify suitable RMR prediction equations by developing new equations and comparing measured with predicted RMR from seven equations already existing world-wide. In this cross-sectional observation study, twenty one Indian junior weightlifters (Boys = 9; Girls = 12) were assessed for body composition (skinfold technique) and RMR using indirect calorimetry. Regression models for RMR were developed and measured RMR was compared with predicted RMR using paired samples t-test. The 24-hour RMR showed significant (P&amp;lt;0.01) gender differences (Boys = 1675 ± 111.7; Girls = 1425 ± 93.4), whereas, RMR per unit fatfree mass and body mass were similar across gender. The simple equation developed using body mass (RMR = 1206 + 13 × Body Mass (Kg) – 227.7 × Gender; R2 = 0.872; SEE = 60.604) showed good agreement with measured RMR. Out of seven existing RMR models, the equation of ten Haaf and Weijs (2014) showed better predictability. In conclusion, fat-free mass caused a major variation in RMR. Considering the limited scientific evidence, prediction equations newly developed and RMR equation of ten Haaf and Weijs (2014) can be used for periodical monitoring of RMR.

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes.

Jagim, AR, Camic, CL, Kisiolek, J, Luedke, J, Erickson, J, Jones, MT, and Oliver, JM. Accuracy of resting metabolic rate prediction equations in athletes. J Strength Cond Res 32(7): 1875-1881, 2018-The purpose of this study was to determine the accuracy of 5 different resting metabolic rate (RMR) prediction equations in male and female athletes. Twenty-two female (19.7 ± 1.4 years; 166.2 ± 5.5 cm; 63.5 ± 7.3 kg; 49.2 ± 4.3 kg of fat-free mass (FFM); 23.4 ± 4.4 body fat (BF) percent) and 28 male (20.2 ± 1.6 years; 181.9 ± 6.1 cm; 94.5 ± 16.2 kg; 79.1 ± 7.2 kg of FFM; 15.1 ± 8.5% BF) athletes were recruited to participate in 1 day of metabolic testing. Assessments comprised RMR measurements using indirect calorimetry, and body composition analyses using air displacement plethysmography. One-way repeated-measures analysis of variance with follow-up paired t tests were selected to determine differences between indirect calorimetry and 5 RMR prediction equations. Linear regression analysis was used to assess the accuracy of each RMR prediction method. An alpha level of p ≤ 0.05 was used to determine statistical significance. All the prediction equations significantly underestimated RMR while the Cunningham equation had the smallest mean difference (-165 kcals). In men, the Harris-Benedict equation was found to be the best prediction formula with the lowest root-mean-square prediction error value of 284 kcals. In women, the Cunningham equation was found to be the best prediction equation with the lowest root-mean-squared error value of 110 kcals. Resting metabolic rate prediction equations consistently seem to underestimate RMR in male and female athletes. The Harris-Benedict equation seems to be most accurate for male athletes, whereas the Cunningham equation may be better suited for female athletes.

Estimating the agreement between the metabolic rate calculated from prediction equations and from a portable indirect calorimetry device: an effort to develop a new equation for predicting resting metabolic rate

BackgroundMany studies have been performed over time in order to determine the reliability of metabolic rate prediction equations.PurposeTo evaluate the agreement, in terms of bias, absolute bias and accuracy between metabolic rate prediction equations and measured metabolic rate using indirect calorimetry system (IC), investigating also the factors affecting this agreement.MethodsThe anthropometric features of 383 Caucasian participants of all Body Mass Index (BMI) classes were recorded and Resting Metabolic Rate (RMR) was measured by using the IC Fitmate portable device. The resulting values were compared with the predictive values of Harris & Benedict, Schofield, Owen, FAO-WHO-UNU, Mifflin and Harrington equations.ResultsA closer approximation in agreement was obtained using the Harrington equation (based on BMI, age and gender). The equations using variables, such as weight, height, age and gender demonstrated higher agreement than the equations using merely weight and gender. Higher educational level was associated with normal weight, while higher calorific ratio was found in the class of normal-weighted individuals. An inverse relationship between ΒΜΙ and RMR was also observed and a logarithmic equation for calculating RMR was created, which was differentiated in relation to BMI classes, using the weight and gender variables.ConclusionA better measurement agreement between RMR prediction equations and IC may be achieved due to BMI consideration. The present findings contributed to a better understanding of the measured parameters, confirming the inverse relationship between BMI and RMR. Age group and gender variables may also exert significant role on the bias response of some RMR equations.

Validation of resting metabolic rate equations in obese and non-obese young healthy adults

Although validating resting metabolic rate (RMR) prediction equations in different populations is warranted to estimate total energy requirements, there are no data about the accuracy and applicability of RMR predictive equations in young healthy Jordanians. This study aimed to test the validity of predicted RMR using four prediction equations and its agreement with RMR measured using indirect calorimetry in healthy young obese and non-obese Jordanian adults. Predicted RMR was tested for agreement with indirect calorimetry. Harris-Benedict, Food and Agriculture Organization/World Health Organization/United Nations University, Mifflin and Owen equations were used to predict RMR. A total of 406 (200 females, 206 males) normal bodyweight, overweight, and obese healthy young adults aged 18-25 years were included. Of all tested equations, Harris-Benedict equation showed no significant differences compared to measured RMR using indirect calorimetry (p>0.05) in normal bodyweight (1374.1±72.5 vs. 1347.6±333.4), overweight (1513.1±66.8 vs. 1492.9±374.8) and obese (1673.5±178.4 vs. 1736.4±386.9) females. While significant differences (p<0.05) were found in all male participants. The other studied equations showed significant differences (p≤0.05) compared to measured RMR in gender based groups with different bodyweight status. Only Harris-Benedict equation was the most accurate in predicting RMR among females regardless of bodyweight status. Apart from that, these equations did not perform well at the group level. The tested prediction equations further underestimated RMR. Future studies aiming at validating RMR prediction equations in different populations are warranted in order to understand the factors that could affect the accuracy of RMR prediction.

Cross-Validation of Resting Metabolic Rate Prediction Equations

BackgroundResting metabolic rate (RMR) measurement is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, their accuracy likely varies across individuals. Understanding the factors that influence the accuracy of RMR predictions will help to revise existing, or develop new and improved, equations. ObjectiveOur aim was to test the validity of RMR predicted in healthy adults by the Harris-Benedict, World Health Organization, Mifflin-St Jeor, Nelson, Wang equations, and three meta-equations of Sabounchi. DesignPredicted RMR was tested for agreement with indirect calorimetry. Participants/settingMen and women (n=30) age 18 to 65 years from Grand Forks, ND, were recruited and included for analysis during spring/summer 2014. Participants were nonobese or obese (body mass index range=19 to 39) and primarly white. Main outcome measureAgreement between measured (indirect calorimetry) and predicted RMR was measured. Statistical analysisThe methods of Bland and Altman were employed to determine mean bias (predicted minus measured RMR, kcal/day) and limits of agreement between predicted and measured RMR. Repeated-measures analysis of variance was used to test for bias in RMR predicted from each equation vs the measured RMR. ResultsBias (mean±2 standard deviations) was lowest for the Harris-Benedict (−14±378 kcal/24 h) and World Health Organization (−25±394 kcal/24 h) equations. These equations also predicted RMR that were not different from measured. Mean RMR predictions from all other equations significantly differed from indirect calorimetry. The 2 standard deviation limits of agreement were moderate or large for all equations tested, ranging from 314 to 445 kcal/24 h. Prediction bias was inversely associated with the magnitude of RMR and with fat-free mass. ConclusionsAt the group level, the traditional Harris-Benedict and World Health Organization equations were the most accurate. However, these equations did not perform well at the individual level. As fat-free mass increased, the prediction equations further underestimated RMR.

Cross‐Validation of Recent and Longstanding Resting Metabolic Rate Prediction Equations

Resting metabolic rate (RMR) measurement is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, their accuracy likely varies across individuals. Understanding the factors that influence predicted RMR accuracy at the individual level will help to revise existing or develop new equations that are more accurate. The objective of the present study was to test the validity of established (Harris‐Benedict, WHO, Mifflin‐St. Jeor) and contemporary (3 meta equations of Sabounchi and colleagues, 1 equation of Nelson and colleagues, 1 by Wang and colleagues) RMR prediction equations in healthy adults.DesignPredicted RMR was tested for agreement with indirect calorimetry. Men and women (n=30) age 18–65y from Grand Forks, ND were recruited and included for analysis during Spring/Summer 2014. Participants were nonobese to obese, primarly Caucasian and a representive sample of a healthy, free‐living population. The main outcome measure was bias between measured (indirect calorimetry) and predicted RMR. The methods of Bland and Altman were employed to determine mean bias (predicted minus measured RMR, kcal/day) and limits of agreement between predicted and measured RMR. Repeated measures ANOVA was utilized to test for differences in RMR predicted from each equation versus the measured RMR. Mean bias was lowest for the Harris‐Benedict (4 ± (2 SD) 386 kcal/24 hrs) and WHO (25 ± 402 kcal/24 hrs) equations and these mean predicted RMR did not differ from measured. Mean RMR predictions from all other equations significantly differed from indirect calorimetry. The 2 SD limits of agreement were moderate or large for all equations tested ranging from 282 to 445 kcal/24 hrs. Prediction bias was positively associated with the magnitude of RMR and with FFM. In conclusion, the traditional Harris‐Benedict and WHO equations were the most accurate. However, even these equations did not perform well at the individual level. As FFM increased, the prediction equations further underestimated RMR.Support or Funding InformationSupport: USDA‐ARS 3062‐51000‐051‐00D

Accuracy and Applicability of Resting Metabolic Rate Prediction Equations Differ for Women Across the Lifespan

Background: Nutrition clinicians need accurate and reliable resting metabolic rate (RMR) assessments to determine energy needs and an appropriate nutrition care plan. Material/Methods: This cross-sectional study compared the accuracy of eight RMR prediction equations (Harris-Benedict, Robertson and Reid, Cunningham 1980, FAO/WHO/UNU, Owen, Mifflin-St. Jeor, Cunningham 1991, and Nelson) to measured RMR by indirect calorimetry among young (n=57; age: 25±3 years), midlife (n=57; age: 44±3 years), and older (n=46; age: 68±5 years) women. Paired t-tests examined differences between predicted and measured RMR. Statistical analyses were conducted using SPSS (version 21), with significance defined as p&lt;0.05. Bland-Altman plots displayed prediction bias and agreement. Prediction accuracy was defined when predicted RMR was ±10% of measured RMR. Serum thyroid stimulating hormone and follicle stimulating hormone concentrations were measured to assess thyroid function and ovarian reserve, respectively. Results: The difference between predicted and measured RMR ranged from +0.6% (Owen) to +17.7% (Cunningham 1980) for the young, -2.8% (Nelson) to +18.1% (Cunningham 1980) midlife, and +2.8 (Nelson) to +26.7% (Cunningham 1980) older women. For the young women, only the Owen equation predicted RMR similar to measured RMR (p=0.905). For the older women, only the Nelson equation predicted RMR similar to measured RMR (p=0.051). All estimates using prediction equations were significantly different from measured values for midlife women. Conclusion: Many RMR prediction equations have limited applicability for women at difference stages of the lifespan, thus impacting patient outcomes. Additional research is necessary to determine the appropriateness of RMR prediction equations among women of all ages.

Common Prediction Equations Overestimate Measured Resting Metabolic Rate in Young Hispanic Women.

The accuracy of 6 resting metabolic rate (RMR) prediction equations to indirect calorimetry was compared in 38 Hispanic women (age = 30 ± 7 years; body mass index = 28.9 ± 7.2 kg/m2; body fat = 42% ± 8%). Paired t tests examined differences between predicted and measured RMR; significance defined as P < 0.05. Bias and agreement were displayed using Bland-Altman plots. Accuracy was defined when the predicted RMR was ± 10% of the measured RMR. Data were analyzed with SPSS (version 19). Only the equation of Owen et al was not significantly different from the measured RMR (1336 ± 142 and 1322 ± 203 kcal/d, respectively). The equation of Owen et al was accurate for 84.2% of women; RMR prediction equations had limited applicability for young Hispanic women.