Resting Metabolic Rate Prediction Research Articles

Multiple factor assessment for determining resting metabolic rate in young adults

Existing formulas cannot fully explain the variation of resting metabolic rate (RMR). This study aims to examine potential influencing factors beyond anthropometric measurements and develop more accurate equations using accessible parameters. 324 healthy adults (230 females; 18–32 years old) participated in the study. Height, fat-free mass (FFM), fat mass (FM) and RMR were measured. Menstrual cycle, stress levels, living habits, and frequency of consuming caffeinated foods were collected. Measured RMR were compared with predictive values of the new equations and previous 11 equations. Mean RMR for men and women was 1825.2 ± 248.8 and 1345.1 ± 178.7 kcal/day, respectively. RMR adjusted for FFM0.66FM0.066 was positively correlated with BMI. The multiple regression model showed that RMR can be predicted in this population with model 1 (with FFM, FM, age, sex and daily sun exposure duration) or model 2 (with weight and height replacing FFM and FM). The accuracy was 75.31% in the population for predictive model 1 and 70.68% for predictive model 2. The new equations had overall improved performance when compared with existing equations. The predictive formula that consider daily sun exposure duration improve RMR prediction in young adults. Additional investigation is required among individuals in the middle-aged and elderly demographic.

Development of New Predictive Equations for the Resting Metabolic Rate (RMR) of Women with Lipedema.

This study aimed to develop a novel predictive equation for calculating resting metabolic rate (RMR) in women with lipedema. We recruited 119 women diagnosed with lipedema from the Angiology Outpatient Clinic at Wroclaw Medical University, Poland. RMR was assessed using indirect calorimetry, while body composition and anthropometric measurements were conducted using standardized protocols. Due to multicollinearity among predictors, classical multiple regression was deemed inadequate for developing the new equation. Therefore, we employed machine learning techniques, utilizing principal component analysis (PCA) for dimensionality reduction and predictor selection. Regression models, including support vector regression (SVR), random forest regression (RFR), and k-nearest neighbor (kNN) were evaluated in Python's scikit-learn framework, with hyperparameter tuning via GridSearchCV. Model performance was assessed through mean absolute percentage error (MAPE) and cross-validation, complemented by Bland-Altman plots for method comparison. A novel equation incorporating body composition parameters was developed, addressing a gap in accurate RMR prediction methods. By incorporating measurements of body circumference and body composition parameters alongside traditional predictors, the model's accuracy was improved. The segmented regression model outperformed others, achieving an MAPE of 10.78%. The proposed predictive equation for RMR offers a practical tool for personalized treatment planning in patients with lipedema.

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.

Development and validation of new predictive equations for the resting metabolic rate of older adults aged ≥65 y

BackgroundThe aging process alters the resting metabolic rate (RMR), but it still accounts for 50%–70% of the total energy needs. The rising proportion of older adults, especially those over 80 y of age, underpins the need for a simple, rapid method to estimate the energy needs of older adults. ObjectivesThis research aimed to generate and validate new RMR equations specifically for older adults and to report their performance and accuracy. MethodsData were sourced to form an international dataset of adults aged ≥65 y (n = 1686, 38.5% male) where RMR was measured using the reference method of indirect calorimetry. Multiple regression was used to predict RMR from age (y), sex, weight (kg), and height (cm). Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split and leave one out cross-validation were performed. The newly generated prediction equations were compared with the existing commonly used equations. ResultsThe new prediction equation for males and females aged ≥65 y had an overall improved performance, albeit marginally, when compared with the existing equations. It is described as follows: RMR (kJ/d) = 31.524 × W (kg) + 25.851 × H (cm) − 24.432 × Age (y) + 486.268 × Sex (M = 1, F = 0) + 530.557. Equations stratified by age (65–79.9 y and >80 y) and sex are also provided. The newly created equation estimates RMR within a population mean prediction bias of ∼50 kJ/d (∼1%) for those aged ≥65 y. Accuracy was reduced in adults aged ≥80 y (∼100 kJ/d, ∼2%) but was still within the clinically acceptable range for both males and females. Limits of agreement indicated a poorer performance at an individual level with 1.96-SD limits of approximately ±25%. ConclusionsThe new equations, using simple measures of weight, height, and age, improved the accuracy in the prediction of RMR in populations in clinical practice. However, no equation performs optimally at the individual level.

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.

Cross-Validation of a New General Population Resting Metabolic Rate Prediction Equation Based on Body Composition

Current prediction equations for resting metabolic rate (RMR) were validated in a relatively small sample with high-individual variance. This study determined the accuracy of five common RMR equations and proposed a novel prediction equation, including body composition. A total of 3001 participants (41 ± 13 years; BMI 28.5 ± 5.5 kg/m2; 48% males) from nutrition clinics in Israel were measured by indirect calorimetry to assess RMR. Dual-energy X-ray absorptiometry were used to evaluate fat mass (FM) and free-fat mass (FFM). Accuracy and mean bias were compared between the measured RMR and the prediction equations. A random training set (75%, n = 2251) and a validation set (25%, n = 750) were used to develop a new prediction model. All the prediction equations underestimated RMR. The Cunningham equation obtained the largest mean deviation [-16.6%; 95% level of agreement (LOA) 1.9, -35.1], followed by the Owen (-15.4%; 95% LOA 4.2, -22.6), Mifflin-St. Jeor (-12.6; 95% LOA 5.8, -26.5), Harris-Benedict (-8.2; 95% LOA 11.1, -27.7), and the WHO/FAO/UAU (-2.1; 95% LOA 22.3, -26.5) equations. Our new proposed model includes sex, age, FM, and FFM and successfully predicted 73.5% of the explained variation, with a bias of 0.7% (95% LOA -18.6, 19.7). This study demonstrates a large discrepancy between the common prediction equations and measured RMR and suggests a new accurate equation that includes both FM and FFM.

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.

Predictive Equation to Estimate Resting Metabolic Rate in Older Chilean Women.

Resting metabolic rate (RMR) depends on body fat-free mass (FFM) and fat mass (FM), whereas abdominal fat distribution is an aspect that has yet to be adequately studied. The objective of the present study was to analyze the influence of waist circumference (WC) in predicting RMR and propose a specific estimation equation for older Chilean women. This is an analytical cross-sectional study with a sample of 45 women between the ages of 60 and 85 years. Weight, height, body mass index (BMI), and WC were evaluated. RMR was measured by indirect calorimetry (IC) and %FM using the Siri equation. Adequacy (90% to 110%), overestimation (>110%), and underestimation (<90%) of the FAO/WHO/UNU, Harris–Benedict, Mifflin-St Jeor, and Carrasco equations, as well as those of the proposed equation, were evaluated in relation to RMR as measured by IC. Normal distribution was determined according to the Shapiro–Wilk test. The relationship of body composition and WC with RMR IC was analyzed by multiple linear regression analysis. The RMR IC was 1083.6 ± 171.9 kcal/day, which was significantly and positively correlated with FFM, body weight, WC, and FM and inversely correlated with age (p < 0.001). Among the investigated equations, our proposed equation showed the best adequacy and lowest overestimation. The predictive formulae that consider WC improve RMR prediction, thus preventing overestimation in older women.

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.

Development of a New Equation for the Prediction of Resting Metabolic Rate in Sri Lankan Adults.

Resting metabolic rate (RMR) is the key determinant of the energy requirement of an individual. Measurement of RMR by indirect calorimetry is not feasible in field settings and therefore equation-based calculations are used. Since a valid equation is not available for Sri Lankans, it is important to develop a new population-specific equation for field use. The study objective was to develop a new equation for the prediction of RMR in healthy Sri Lankans using a reference method, indirect calorimetry. RMR data were collected from fifty-seven (male 27) adults aged 19 to 60 years. They were randomly assigned to validation (n = 28) and cross-validation (n = 19) groups using the statistical package R (version 3.6.3). Height, weight, and RMR were measured. Multivariable fractional polynomials (MFP) were used to determine explanatory variables and their functional forms for the model. A variable shrinkage method was used to find the best fit predictor coefficients of the equation. The developed equation was cross-validated on an independent group. Weight and sex code (male = 1; female = 0) were identified as reliable independent variables. The new equation developed was RMR (kcal/day) = 284.5 + (13.2 x weight) + (133.0 x sex code). Independent variables of the prediction equation were able to predict 88.5% of the variance. Root mean square error (RMSE) of the prediction equation in validation and cross-validation was 88.11 kcal/day and 79.03 kcal/day, respectively. The equation developed in this study is suitable for predicting RMR in Sri Lankan adults.

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.

Indices of Resting Metabolic Rate Accurately Reflect Energy Deficiency in Exercising Women.

Energy deficiency in exercising women can lead to physiological consequences. No gold standard exists to accurately estimate energy deficiency, but measured-to-predicted resting metabolic rate (RMR) ratio has been used to categorize women as energy deficient. The purpose of the study was to (a)evaluate the accuracy of RMR prediction methods, (b)determine the relationships with physiological consequences of energy deficiency, and (c)evaluate ratio thresholds in a cross-sectional comparison of ovulatory, amenorrheic, or subclinical menstrual disturbances in exercising women (n = 217). Dual-energy X-ray absorptiometry (DXA) and indirect calorimetry provided data on anthropometrics and energy expenditure. Harris-Benedict, DXA, and Cunningham (1980 and 1991) equations were used to estimate RMR and RMR ratio. Group differences were assessed (analysis of variance and Kruskal-Wallis tests); logistic regression and Spearman correlations related ratios with consequences of energy deficiency (i.e.,low total triiodothyronine; TT3). Sensitivity and specificity calculations evaluated ratio thresholds. Amenorrheic women had lower RMR (p < .05), DXA ratio (p < .01), Cunningham1980 (p < .05) and Cunningham1991 (p < .05) ratio, and TT3 (p < .01) compared with the ovulatory group. Each prediction equation overestimated measured RMR (p < .001), but predicted (p < .001) and positively correlated with TT3 (r = .329-.453). A 0.90 ratio threshold yielded highest sensitivity for Cunningham1980 (0.90) and Harris-Benedict (0.87) methods, but a higher ratio threshold was best for DXA (0.94) and Cunningham1991 (0.92) methods to yield a sensitivity of 0.80. In conclusion, each ratio predicted and correlated with TT3, supporting the use of RMR ratio as an alternative assessment of energetic status in exercising women. However, a 0.90 ratio cutoff is not universal across RMR estimation methods.

Validation of Equations for the Prediction of Resting Metabolic Rate in Sri Lankan Adults

Validation of Equations for the Prediction of Resting Metabolic Rate in Sri Lankan Adults

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.