Resting metabolic rate in muscular physique athletes validity of existing methods and

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Resting metabolic rate in muscular physique athletes: validity of existing methods and 
development of new prediction equations
Grant M. Tinsley*, Austin J. Graybeal, M. Lane Moore
Affiliation for all authors: Department of Kinesiology & Sport Management, Texas Tech 
University, Lubbock, TX, USA
*Corresponding author: Grant M. Tinsley, Department of Kinesiology & Sport Management, 
Texas Tech University, Lubbock, TX, 79424, USA. grant.tinsley@ttu.edu (806) 834-5895.
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Abstract
Estimation of resting metabolic rate (RMR) is an important step for prescribing an individual’s 
energy intake. The purpose of this study was to evaluate the validity of portable indirect 
calorimeters and RMR prediction equations in muscular physique athletes. Twenty-seven males 
(n=17; BMI: 28.8±2.0 kg/m2; body fat: 12.5±2.7%) and females (n=10; BMI: 22.8±1.6 kg/m2; 
body fat: 19.2±3.4%) were evaluated. The reference RMR value was obtained from the 
ParvoMedics TrueOne® 2400 indirect calorimeter, and the Cosmed FitmateTM and Breezing® 
Metabolism Tracker provided additional RMR estimates. Existing RMR prediction equations 
based on body weight (BW) or dual-energy x-ray absorptiometry (DXA) fat-free mass (FFM) 
were also evaluated. Errors in RMR estimates were assessed using validity statistics, including t-
tests with Bonferroni correction, linear regression, and calculation of the SEE, total error, and 
95% limits of agreement. Additionally, new prediction equations based on BW (RMR [kcal/d] = 
24.8*BW [kg] + 10) and FFM (RMR [kcal/d] = 25.9*FFM [kg] + 284) were developed using 
stepwise linear regression and evaluated using leave-one-out cross-validation. Nearly all existing 
BW- and FFM-based prediction equations, as well as the Breezing® Tracker, did not exhibit 
acceptable validity and typically underestimated RMR. The ten Haaf (2014) and Cunningham 
(1980) FFM-based equations may produce acceptable RMR estimates, although the Cosmed 
FitmateTM and newly developed BW- and FFM-based equations may be most suitable for RMR 
estimation in male and female physique athletes. Future research should provide additional 
external cross-validation of the newly-developed equations in order to refine the ability to predict 
RMR in physique athletes.
Keywords: resting energy expenditure, metabolism, bodybuilders, Harris-Benedict, Mifflin, body 
composition, Cunningham, ten Haaf, indirect calorimetry
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Introduction
Measurement or estimation of resting metabolic rate (RMR) is frequently the first step in 
prescribing energy intake, both in the general population and athletes (Thomas et al. 2016). Even 
in highly active individuals, RMR represents a substantial contribution to total daily energy 
expenditure (TDEE). While laboratory methods, namely indirect calorimetry, are commonly 
utilized for RMR measurement, most individuals rely on prediction equations to estimate RMR. 
Prediction equations based on body weight (BW) have been utilized for over 100 years (Harris 
and Benedict 1918), and numerous distinct equations are presently employed (Flack et al. 2016). 
However, due to the known differences in the metabolic activity of fat mass and fat-free mass 
(FFM), several other equations predict RMR based on FFM rather than BW (Hayes et al. 2002). 
A detailed analysis of energy expenditure at the organ/tissue level of the body demonstrated a 
linear relationship between FFM and RMR within the range of FFM typically observed in 
humans, and equations typically possess a slope that ranges from 19.7 to 24.5 and a positive 
intercept of approximately 200 to 700 kcal/day (Wang et al. 2000).
In athletes, some advocate the use of FFM-based prediction equations due to the relatively 
greater proportion of FFM in these individuals (ten Haaf and Weijs 2014). Several investigations 
have examined the validity of BW- or FFM-based equations in athletic populations, with some 
leading to the development of new athlete-specific equations. These investigations have 
examined a variety of athletic groups, including endurance athletes (Thompson and Manore 
1996), a mixed group of athletes including waterpolo, judo and karate (De Lorenzo et al. 1999), 
rowers and canoeists (Carlsohn et al. 2011), a variety of team sport athletes (i.e. football, track 
and field, baseball, swimming and soccer) (Jagim et al. 2017) and a mixed group of individual 
and team sport athletes (ten Haaf and Weijs 2014). While some of the aforementioned prediction 
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equations were developed in narrowly defined groups of athletes, others included substantial 
heterogeneity in an attempt to produce a generalizable equation. It is recognized that the 
accuracy of RMR prediction equations may be population-specific, indicating that these 
equations cannot be indiscriminately applied groups that are dissimilar to those in which they 
were developed (da Rocha et al. 2005). Additionally, although generalizable equations are 
convenient, they may mask actual differences between specific sub-populations of athletes, who 
may vary in body composition and training practices that could impact RMR. For example, the 
groups of athletes included in the aforementioned equations do not typically exhibit the degree of 
muscularity observed in competitive physique athletes, whereas physique athletes may have 
lower energy intake and TDEE than athletes in some traditional sports (Slater and Phillips 2011).
Proper prescription of energy intake to facilitate fat loss, while promoting the retention of 
FFM and physical performance, is a major goal of physique athletes preparing for competition 
(Helms et al. 2014). However, most of these athletes do not have access to traditional indirect 
calorimeters for measurement of RMR. Currently, there are several portable indirect calorimeters 
available, which may be a more accessible option to this athletic population. However, limited 
information is available concerning the validity of these devices for RMR estimation in athletes, 
and the price and complexity of the devices varies widely. Despite the existence of these portable 
devices, the most common method of RMR estimation remains the use of prediction equations. It 
is unclear if FFM-based equations, which necessitate the estimation of body composition, are 
superior to BW-based equations in this population. Theoretically, FFM-based equations could be 
advantageous, although this has not previously been examined. 
To our knowledge, no previous investigations have examined the validity of portable indirect 
calorimetry and BW- or FFM-based RMR prediction equations in physique athletes. Therefore, 
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the purpose of this study was to evaluate the utility of several practical methods of RMR 
estimation in male and female physique athletes and to produce preliminary RMR prediction 
equations for this population. 
Materials and Methods
At a single research visit, the RMR of physique athletes was measured via three indirect 
calorimeters. Body weight and composition were also assessed to allow for estimation of RMR 
using prediction equations. Twenty-seven physique athletes volunteered to participate in this 
study as previously described (Graybeal et al. 2018). To be eligible for inclusion in this analysis, 
participants were required to meet at least one of the following criteria: 1) have competed in a 
bodybuilding or physique competition within the last year; 2) have plans to compete within the 
next year; or 3) self-identify as a bodybuilder and exhibit a physique commensurate with 
competitive physique athletes, as evaluated by study investigators. Additionally, prospective 
participants were required to be between the ages of 18 and 50, generally healthy, and report the 
completion of ≥ 3 sessions per week of resistance training, continuously for ≥ 3 years, prior to 
screening. This study was approved by the Texas Tech University institutional review board, and 
all participants signed the informed consent document prior to participation.
Participants reported to the laboratory in the morning after an overnight (≥ 8 hours) 
abstention from food, fluid, supplement or medication ingestion, and exercise. Body weight and 
height were assessed using a digital scale and stadiometer (Seca 769, Hamburg, Germany). FFM 
was estimated via dual-energy x-ray absorptiometry (DXA). DXA scans were performed on a 
calibrated GE Lunar Prodigy scanner with enCORE software (v. 16.2), and participant 
positioning was conducted according to manufacturer recommendations. Due to the large body 
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size of many participants, it was necessary to perform the reflection scanning technique in which 
the unobserved portion of the body (i.e. the left arm) is estimated from the observed portion of 
the body. This technique was performed in accordance with manufacturer recommendations and 
has been reported to induce minimal error (Moco et al. 2018; Tinsley et al. 2018b). In order to 
further reduce potential errors caused by this procedure, scans were conducted in duplicate and 
averaged for analysis. DXA lean soft tissue and bone mineral content were summed to provide 
an estimate of FFM. 
Following body composition assessment, RMR was assessed by three indirect 
calorimetry devices. The TrueOne® 2400 (ParvoMedics, Sandy, UT, USA) was designated as 
the reference method, and additional methods were a portable research-grade device (FitmateTM, 
Cosmed, Rome, Italy) and a portable consumer-grade device (Breezing® Metabolism Tracker, 
Breezing, Tempe, AZ, USA). The TrueOne® 2400 was selected as the reference method due to 
its demonstrated accuracy (Cooper et al. 2009; Kaviani et al. 2018). A recent study evaluated the 
accuracy and reliability of 12 indirect calorimeters using methanol combustion (Kaviani et al. 
2018). Of the 12 devices, two separate TrueOne® 2400 systems were ranked 1st and 2nd for CO2 
recovery, 2nd and 5th for O2 recovery and 2nd and 4th for RER accuracy. Furthermore, both of the 
evaluated TrueOne® 2400 units measured CO2 recovery, O2 recovery and RER within 2% of 
theoretical values, unlike most other devices. The TrueOne® 2400 unit used in this study was 
less than 2 years old at study commencement, and regular maintenance and calibrations were 
performed according to manufacturer instructions throughout this time period. A new Cosmed 
FitmateTM device was purchased for this study, and the oxygen sensor remained in the “optimal” 
state throughout data collection. A new Breezing® Tracker device was purchased from the 
manufacturer approximately 3 months prior to study commencement, and all testing was 
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completed in less than 7 months after receipt of the device and its associated supplies (e.g. sensor 
cartridges).
Pre-assessment standardization and testing were conducted according recommended 
procedures (Compher et al. 2006). Briefly, the participant was rested and fasted overnight prior 
to each assessment and was instructed to remain motionless, but awake, throughout testing. Each 
participant was offered a blanket at each assessment in order to promote a comfortable body 
temperature, and all testing took place in the same climate-controlled room with the lights 
dimmed. Due to previous laboratory assessments conducted at the research visit, each participant 
rested in the supine position for approximately 30 minutes prior to the commencement of the 
first RMR assessment. The order of RMR assessments was randomly determined using the 
random integer set generator available at random.org. For each device, manufacturer procedures 
were followed. RMR via TrueOne® 2400 (RMRPARVO) and FitmateTM (RMRCOSMED) was 
assessed in the supine position, while RMR via Breezing® (RMRBREEZING) was assessed in the 
seated position per manufacturer instructions. Regardless of assessment order, each participant 
moved from the supine to seated position for a period of approximately two minutes between 
RMR assessments.
Prior to TrueOne® 2400 assessments, daily gas and flow calibrations were performed. 
Prior to FitmateTM assessments, daily flow and oxygen sensor calibrations were performed. For 
both the TrueOne® 2400 and FitmateTM assessments, the first five minutes of each test were 
discarded, and the assessment continued until there was a period of 5 consecutive minutes with a 
coefficient of variation (CV) for RMR of ≤ 10%. Using 1-minute averaging, the average CVs in 
this study were 4.2 ± 1.5% and 4.6 ± 1.9% for RMRPARVO and RMRCOSMED, respectively. The 
Breezing® device utilizes a sensor cartridge and flow meter to evaluate expired air as previously 
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described (Xian et al. 2015). The device is synced with a phone or tablet, and each single-use 
sensor has a QR code that is scanned by the associated phone or tablet to provide calibration 
information for the sensor. During each assessment, the participant breathes through a disposable 
mouthpiece for 1 to 2 minutes, until 6 L of air has been expired during the assessment. RMR is 
then estimated from VO2 and VCO2 using the Weir equation (Xian et al. 2015) and reported in 
kcal/d.
In addition to the indirect calorimetry assessments, RMR was predicted via five BW-
based equations, five FFM-based equations and one organ/tissue-based equation (Table 1). BW-
based equations (Harris and Benedict 1918; FAO 1985; Mifflin et al. 1990; De Lorenzo et al. 
1999; ten Haaf and Weijs 2014) utilized BW obtained on a digital scale (Seca 769, Hamburg, 
Germany), while FFM-based equations (Cunningham 1980; Owen et al. 1987; Mifflin et al. 
1990; Cunningham 1991; ten Haaf andWeijs 2014) utilized DXA FFM, and the organ/tissue 
model used various components of DXA output as previously described (Hayes et al. 2002).
Statistical Analysis
Potential differences in RMR between the reference method and alternative methods 
were analyzed using dependent t-tests with a Bonferroni-adjusted alpha level due to multiple 
comparisons (p ≤ 0.0033). The constant error (CE) was determined as the mean difference 
between an alternate RMR assessment and the reference method (e.g., RMRALTERNATE – 
RMRPARVO). Additionally, the Pearson product moment correlation coefficient (r), coefficient of 
determination (R2), standard error of the estimate (SEE), and total error (TE) were calculated. 
The TE, also known as the root mean square error (RMSE), was calculated as:
𝑇𝐸 = Σ(𝑅𝑀𝑅𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 ― 𝑅𝑀𝑅𝑃𝐴𝑅𝑉𝑂)2/𝑛 
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The TE represents the average deviation of individual scores from the line of identity 
between the reference method and each alternative method, whereas the SEE indicates the 
deviation of individual data points around the line of best fit for the reference method and each 
alternative method (Heyward and Wagner 2004). The following thresholds were used to describe 
the r values: trivial (<0.1), small (0.1 to 0.29), moderate (0.30 to 0.49), large (0.50 to 0.69), very 
large (0.70 to 0.89), and extremely large or “near perfect” (0.90 to 1.00) (Hopkins et al. 2009). 
The effect size (ES) of the differences between methods was determined using Cohen’s d. The 
magnitude of the ES was interpreted as: very small (<0.2), small (0.2 – 0.59), moderate (0.6 – 
1.19), large (1.2 – 2.0), and very large (>2.0) (Hopkins, Marshall et al. 2009). The Bland-Altman 
method (Bland and Altman 1986) was used to identify the 95% limits of agreement (LOA) 
between the reference and alternative methods. The 95% LOA indicate the individual predictive 
accuracy of a method based on a 95% confidence interval. Linear regression was utilized to 
evaluate proportional bias between the reference method and alternative methods (i.e. varying 
discrepancies between reference and alternative methods based on RMR values) as previously 
described (Tinsley 2017). Additionally, stepwise linear regression was utilized to develop RMR 
prediction equations from relevant variables (i.e. BW, FFM, age, sex and height). Due to the 
relatively small sample size, leave-one-out cross-validation was utilized to evaluate the newly 
developed equations (Ivanescu et al. 2016). This procedure involves sequentially removing each 
participant’s data, developing linear regression equations using the remaining data, and 
calculating the error produced when the regression equations are applied to the excluded data. 
The TE (i.e. RMSE) of the leave-one-out analysis was calculated using the prediction errors (i.e. 
CE) observed when regression equations were applied to excluded data. These leave-one-out TE 
values were compared to the TE values of the regression equations developed in the entire 
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sample (Lohman et al. 2000). Data were analyzed using IBM SPSS (v. 25) and Microsoft Excel 
(v. 16.11).
Results
All participants self-identified as bodybuilders, with 48% reporting participation in a 
physique contest in the past year. All participants reported practicing high-volume resistance 
training for ≥ 3 years, with current training of 5.7 ± 0.9 days per week. The rates of self-reported 
anabolic androgenic steroid (AAS) use were 26% (M: 35%, F: 10%) for current use and 41% 
(M: 59%, F: 10%) for use in the previous 3 years. However, it is believed that under-reporting of 
AAS usage may have occurred. The DXA fat-free mass index (FFMI) of male participants (24.2 
± 1.3 kg/m2) was approximately 2 SD greater than reference values from the National Health and 
Nutrition Examination Survey (NHANES), and the DXA FFMI of female participants (17.7 ± 
0.9 kg/m2) was approximately 1 SD greater than NHANES reference values (Kelly et al. 2009). 
Conversely, the DXA fat-mass index (FMI) of male and female participants (M: 3.6 ± 0.9 kg/m2; 
F: 4.4 ± 1.0 kg/m2) was approximately 1 SD below NHANES reference values (Kelly et al. 
2009). Participant characteristics are displayed in Table 2.
Validity of the evaluated RMR methods for males and females combined are presented in 
Table 3, while individual results for males and females are presented in Tables 4 and 5, 
respectively. RMRCOSMED was not significantly different from RMRPARVO in males, females or 
males and females combined (+3.0 to 4.3%; trivial to small ES). Additionally, proportional bias 
was not present, and the LOAs were narrow relative to other methods (Figure 1A). In contrast, 
RMRBREEZING was 14.5% lower (moderate ES) in males and females combined when compared 
to RMRPARVO. The level of disagreement was much larger in males (-22.0%; large ES) than in 
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females (+4.3%; small ES). However, in both males and females, LOAs for RMRBREEZING were 
wide relative to other methods (Figure 1B).
In general, BW-based equations underestimated RMR in males and females combined by 
4.5 to 15.1% (small to moderate ES) (Figure 2; Table 3). In males, all BW-based equations 
underestimated RMR by ≥ 10.1% (medium to large ES), with the exception of the ten Haaf 
equation, which did not differ significantly from the reference method (-6.2%; small ES). 
However, all BW-based equations demonstrated statistically significant negative proportional 
bias with regression coefficients of ≥ -0.65. In females, RMR estimates from BW equations were 
not statistically different from the reference method. However, three BW-based equations 
underestimated RMR by ≥ 7.1% (moderate to large ES), while one overestimated RMR by 7.1% 
(DeLorenzo; moderate ES) and one (ten Haaf) displayed no CE (trivial ES). Although not 
statistically significant, BW-based equations demonstrated possible negative proportional bias, 
with regression coefficients varying from -0.28 to -1.0. 
 In males and females combined, three FFM-based equations underestimated RMR by 
10.2 to 17.4% (small to moderate ES), while two equations (Cunningham [1980] and ten Haaf) 
did not differ significantly from RMRPARVO (-0.8 to -2.6%; trivial ES) and exhibited relatively 
low TE (Figure 3). In males, three FFM-based equations underestimated RMR by 10.9 to 15.5% 
(moderate ES), while two other equations (ten Haaf, Cunningham [1980]) exhibited 
underestimations of RMR relative to RMRPARVO (-2.0 to 3.9%; trivial to small ES) that were not 
statistically significant. Statistically significant negative proportional bias was seen for all FFM-
based equations in males (Table 4). In females, two FFM-based equations significantly 
underestimated RMR by 11.7 to 16.8% (moderate to very large ES), while three equations 
exhibited deviations (-8.6 to +2.2%; trivial to moderate ES) that were not statistically significant. 
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Although not statistically significant, all FFM-based equations demonstrated possible negative 
proportional bias, with regression coefficients ≥ -0.24 (Table 5). For males, females, and the 
entire sample, RMR estimated by the organ/tissue equation (RMRHAYES; Figure 1C) did not 
outperform several predictive equations based solely on FFM or BW (Tables 3 – 5).
New BW- and FFM-based RMR prediction equations were developed within the entire 
sample (Figure 4). In the BW-based model (BWTINSLEY), BW was the only statistically 
significant predictor of RMR (B: 24.78; p < 0.0001). Excluded variables included sex (B: 0.094; 
p = 0.57), age (B: -0.035; p = 0.66) and height (B: -0.070; p = 0.68). Overall, BWTINSLEY had an r 
of 0.921 and R2 of 0.849. The final BWTINSLEY equation is: , with RMR 𝑅𝑀𝑅 = 24.8 ∗ 𝐵𝑊 + 10
calculated in kcal/d and BW in kg. In the FFM-based model (FFMTINSLEY), DXA FFM was the 
only statistically significant predictor of RMR (B: 25.94; p < 0.0001). Excluded variables 
included sex (B: -0.066; p = 0.73), age (B: 0.005; p = 0.95), height (B: -0.115; p = 0.52) and 
weight (B: 0.417; p = 0.37). Overall, the FFM-based model had an r of 0.923 and R2 of 0.851. 
The final FFMTINSLEY equation is: , with RMR calculated in kcal/d 𝑅𝑀𝑅 = 25.9 ∗ 𝐹𝐹𝑀 + 284
and FFM in kg. In males, females and the entire sample, the average leave-one-out TE for both 
the BW- and FFM-based equations were ≤ 15 kcal/d higher than the TEs when linear regression 
was performed using the entire dataset (Table 6). The newly developed BWTINSLEY equation 
generally minimized CE, ES, TE, and LOA relative to other BW-based equations. Additionally, 
it was the only model without statistically significant proportional bias (Figure 2F). The newly 
developed FFMTINSLEY equation also generally minimized CE, ES, TE, and LOA relative to other 
FFM-based equations and exhibited less proportional bias (Figure 3F).
Additional sub-analysis with the newly developed equations was performed on 
participants reporting current AAS usage (6 M, 1 F) versus those reporting no current usage of 
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AAS. Although these results should be interpreted with caution due to the self-reported nature of 
AAS usage and the substantial differences in sample size between those reporting current AAS 
usage (n=7) and those reporting no current AAS usage (n=20), the newly developed equations 
produced CE and r values of similar magnitudes in users and non-users when compared to the 
reference method. When using the BWTINSLEY equation, the CE was 29 kcal/d (r = 0.93) in non-
users vs. – 32 kcal/d (r = 0.88) in users. Additionally, there was no difference in CE when users 
and non-users were compared via independent samples t-test (p = 0.44). When using the 
FFMTINSLEY equation, the CE was 9 kcal/d (r = 0.92) in non-users vs. -36 kcal/d (r = 0.91) in 
users. Additionally, there was no difference in CE when users and non-users were compared via 
independent samples t-test (p = 0.57).
Discussion
The purpose of this study was to evaluate the utility of several practical methods of RMR 
estimation in male and female physique athletes, including two portable indirect calorimeters and 
several commonly used BW- and FFM-based prediction equations. Additionally, preliminary 
RMR prediction equations based on BW or FFM were developed for this population. The major 
finding of this study was that the Cosmed FitmateTM portable indirect calorimeter and the newly 
developed BW- and FFM-based prediction equations produced less error than all other methods 
for RMR estimation in male and female physique athletes. No other BW-based RMR prediction 
equations performed acceptably in males, although the ten Haaf (2014) BW equation produced 
relatively low errors in females. The existing Cunningham (1980) and ten Haaf (2014) FFM-
based equations produced fairly accurate RMR estimates in males and females, although 
substantial proportional bias was present, particularly when examining males alone. All other 
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RMR prediction equations evaluated in this study are apparently unsuitable for use in muscular 
physique athletes.
For this investigation, the ParvoMedics TrueOne® 2400 indirect calorimeter was 
designated as the reference method of assessment due to its documented accuracy and reliability 
(Cooper et al. 2009; Kaviani et al. 2018). Of the methods evaluated in this study, the Cosmed 
FitmateTM portable indirect calorimeter produced RMR estimates with the narrowest 95% LOA 
and highest correlation when compared to the reference RMR. The Cosmed FitmateTM and the 
newly developed BW- and FFM-based equations exhibited relatively low TE and generally 
exhibited smaller regression coefficients than most existing prediction equations, indicative of 
less proportional bias. The CE for the FitmateTM was similar in males in females (~69 kcal/d; 
trivial to small ES), and RMR estimates were not significantly different than the reference 
method. Previous research also supports the validity and reliability of the FitmateTM system 
(Nieman et al. 2006; Vandarakis et al. 2013). The other portable indirect calorimetry device 
evaluated in this study (Breezing® Tracker) utilizes a portable metabolic analyzer that was 
previously deemed to produce valid RMR estimates relative to the Douglas bag method (CE: 59 
± 31 kcal/d; 95% LOA: ±215 kcal/d) in a sample of 17 healthy adults, ranging from underweight 
to obese (Zhao et al. 2014). Another report derived from the same study found a CE of 6 kcal/d 
when >300 tests were performed in 12 adults across a range of energy expenditures from 1,000 
to 4,000 kcal/d (Xian, Quach et al. 2015). However, despite these results, the Breezing® device 
performed very poorly in the present investigation. The device produced alarming 
underestimations of RMR in males (CE: -513 kcal/d; large ES), a trivial correlation with the 
reference method (r: 0.02), and wholly unacceptable LOA (±919 kcal/d). In females, Breezing® 
produced the same CE as Cosmed (67 kcal/d) but only a moderate correlation (r: 0.38) with the 
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reference method. Additionally, it produced a large regression coefficient (B: 0.569), indicative 
of proportional bias, and the widest LOA of any method in females. Overall, this device 
performed worse than all other methods in males, including prediction equations, as well as 
performing poorly in females. Based on these results, the use of this device for estimating RMR 
in this population is strongly discouraged.
In males, most existing BW- and FFM-based equations substantially underestimated 
RMR by up to 393 kcal/d, while also exhibiting unacceptable levels of negative proportional bias(i.e. greater underestimation of RMR in individuals with higher RMR). Based on the results of 
this investigation, none of the existing BW-based equations can be deemed acceptable for use in 
male physique athletes. However, when applied to males, the newly developed BW-based 
equation demonstrated low error, a high correlation with the reference method, and no 
statistically significant proportional bias. Overall, the performance of the new BW-based 
equation was superior to other BW-based equations and similar to the Cosmed FitmateTM in 
males. When considering FFM-based equations, the Cunningham (1980) and ten Haaf (2014) 
FFM-based equations produced less error than other equations in males, with the exception of 
the newly-developed FFM-based equation. While FFMTINSLEY demonstrated a smaller magnitude 
of proportional bias than the other methods, statistically significant bias was still observed. The 
frequent negative proportional bias observed in males may be partially attributed to one 
participant with a very high RMR assessed by the reference method (2,998 kcal/d) that was 
consistently underestimated by prediction equations. When a FFM-based equation is utilized for 
RMR prediction in male physique athletes, the newly developed equation may be suitable. 
However, appropriate caution should be employed when interpreting RMR estimates, 
particularly in very large individuals with high RMR.
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In females, the ten Haaf (2014) equation was the only existing BW-based prediction 
equation deemed acceptable for use. Both the ten Haaf and newly-developed BW-based equation 
can potentially be used in female physique athletes similar to those examined in this 
investigation. As in males, the Cunningham (1980) and ten Haaf (2014) equations produced 
lower error than other FFM-based equations. Overall, these two equations, along with the newly-
developed FFM-based equation, may be acceptable for use in female physique athletes similar to 
those evaluated in the present study.
It is noteworthy that, although the Cunningham (1980) FFM-based equation produced 
relatively low error in males and females, individuals who were trained athletes were specifically 
excluded during the development of this equation, and the researchers’ goal was to develop an 
equation for a population of normal adults (Cunningham 1980). Additionally, since body 
composition was not available for the participants used in equation development, it was predicted 
from the participants’ body mass and age. Therefore, the accuracy of this equation is likely 
serendipitous rather than due to similarities between the populations. A more recent equation 
developed by Cunningham et al. (1991) produced relatively higher error than the older equation 
in the present investigation. In contrast, ten Haaf et al. (2014) developed their BW- and FFM-
based equations in a group of male and female athletes from a variety of sports, including long 
distance running and cycling, gymnastics, sprinting, rowing, swimming, fitness, hockey, soccer, 
volleyball, dancing, martial arts, skating and tennis. Despite the diverse nature of the participants 
used in equation development, the ten Haaf FFM-based prediction equation generally performed 
well in the present sample of physique athletes. Additionally, the BW-based equation performed 
well in females. While these results are promising for the application of the ten Haaf equations in 
diverse athletic populations, the new BWTINSLEY and FFMTINSLEY equations were developed for a 
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specific target population of athletes (i.e. muscular physique competitors) rather than broad 
application to athletes in general. Nonetheless, cross validation of the newly developed equations 
in sports with the most muscular athletes (e.g. American football) may be warranted.
Within the participants of this study, the new BW- and FFM-based equations performed 
very similarly when compared to the reference RMR value. Although this indicates that either 
could be used in individuals similar to the participants of the present study, an argument can be 
made that the BW-based equation could be preferable for several reasons. First, the new FFM-
based equation was developed using DXA FFM, and DXA is often unavailable for body 
composition assessment. The utilization of FFM estimates from other devices could impact this 
equations accuracy due to discrepancies between DXA and other methods in athletic populations 
(Moon et al. 2009; Graybeal et al. 2018). We performed additional analysis in our sample 
indicating that the magnitude of difference in RMR with FFM estimates from alternative 
methods (i.e. multi-compartment models, multiple bioelectrical impedance analysis devices and 
bioimpedance spectroscopy) ranges from -30 to +74 kcal/d on average (data not shown). 
Additionally, much lower assessment error can be expected when evaluating BW within a single 
individual as compared to FFM. For both body composition assessment and RMR predictions 
based on body composition, caution should be employed when evaluating a single individual due 
to the distinct possibility of over- or under-estimation of body compartments (e.g. FFM) in any 
given individual. Lastly, performing a simple BW measurement is much more feasible than 
accurate body composition assessment in most settings. For these reasons, the most practical 
option may be to employ the newly developed BW-based equation, provided that the individual 
being evaluated exhibits similar characteristics to those used for equation development in this 
population (Table 2). 
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In the present investigation, all participants except one reported that they were in their 
offseason from competitions or not currently in a specific phase of their competitive cycle. This 
may be attributable to the fact that the data collection for this study was performed in the fall (i.e. 
late August to mid-December). As such, our results are generalizable to physique athletes who 
are not currently in the preparatory phase prior to a competition. It has been documented that 
decreases in RMR are observed in the competition preparation periods, but that RMR is 
recovered relatively quickly after competition as energy intake increases (Trexler et al. 2017; 
Tinsley et al. 2018a).
 The self-reported nature of AAS usage and limited number of individuals reporting 
current AAS usage did not allow for comprehensive evaluation of possible differences between 
AAS users and natural competitors. However, our preliminary evaluation did not reveal 
appreciable differences in the performance of the newly developed equations in AAS users 
versus non-users. Based on the available information, it is believed that the developed equations 
can be used in athletes similar to those in our sample regardless of current AAS usage. There are 
several other limitations to the present investigation. Our sample size is small for the 
development ofnew predictive equations, although relatively few prospective participants were 
available for evaluation due to the special population being assessed. However, we performed 
leave-one-out cross-validation in accordance with recommendations for small samples sizes. 
Nonetheless, we encourage additional external cross-validation of our newly developed 
equations in order to more fully determine their utility. Our analysis did not reveal improved 
utility of separate RMR prediction equations for male and female physique athletes (data not 
shown). However, future research should continue to investigate whether a sex term improves 
prediction of RMR in athletes. Although all participants reported to the laboratory after an 
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overnight period of fasting and resting, it is possible that the frequent training sessions of the 
participants could have altered RMR due to prior exercise. However, this limitation was deemed 
necessary due to the unwillingness of prospective participants to abstain from exercise for longer 
periods of time. In this respect, we believe our sample is representative of physique athletes in 
general.
In conclusion, the Cosmed FitmateTM portable indirect calorimeter and the newly 
developed BW- and FFM-based prediction equations may be suitable for RMR estimation in 
male and female physique athletes similar to those in the present study. Additionally, the ten 
Haaf (2014) and Cunningham (1980) FFM-based equations may be acceptable for use in male 
and female physique athletes, with the ten Haaf BW-based equation being suitable in females 
only. All other BW- and FFM-based prediction equations that were evaluated, as well as the 
Breezing® Tracker, do not exhibit acceptable validity and frequently underestimate RMR in 
physique athletes. Future research should externally cross-validate the developed equations in 
order to refine the ability to practically produce valid estimates of RMR in this unique athletic 
population.
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Acknowledgements
The authors would like to thank Megan Cruz, Alfred Kankam, and Michael Villarreal for their 
assistance with data collection for this study.
Conflict of Interest Statement
The authors have no conflicts of interest to report. 
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Table 1. Resting metabolic rate prediction equations.
1Units for these equations are: RMR (kcal/d), FFM (kg), BW (kg), H (cm), age (y); 2Newly-developed equations presented in this article
Type of equation Reference Equation1
Organ/tissue equation Hayes (2002) 𝑅𝑀𝑅 = 𝑅𝑀𝑅𝐵 + 𝑅𝑀𝑅𝑆𝑀 + 𝑅𝑀𝑅𝑏𝑜𝑛𝑒 + 𝑅𝑀𝑅𝐴𝑇 + 𝑅𝑀𝑅𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙
FFM-based equations Cunningham (1991) 𝑅𝑀𝑅 = 21.6 ∗ 𝐹𝐹𝑀 + 370
Cunningham (1980) 𝑅𝑀𝑅 = 22 ∗ 𝐹𝐹𝑀 + 500
Mifflin (1990) 𝑅𝑀𝑅 = 19.7 ∗ 𝐹𝐹𝑀 + 413
Owen (1987) M: 𝑅𝑀𝑅 = 22.3 ∗ 𝐹𝐹𝑀 + 290
F: 𝑅𝑀𝑅 = 19.7 ∗ 𝐹𝐹𝑀 + 334
ten Haaf (2014) 𝑅𝑀𝑅 = 0.239(95.272 ∗ 𝐹𝐹𝑀 + 2026.161)
Tinsley2 𝑅𝑀𝑅 = 25.9 ∗ 𝐹𝐹𝑀 + 284
BW-based equations Mifflin (1990) 𝑅𝑀𝑅 = 9.99 ∗ 𝐵𝑊 + 6.25 ∗ 𝐻 ― 4.92 ∗ 𝑎𝑔𝑒 + 166 ∗ 𝑠𝑒𝑥 ― 161
Harris-Benedict (1918) M: 𝑅𝑀𝑅 = 13.75 ∗ 𝐵𝑊 + 5 ∗ 𝐻 ― 6.76 ∗ 𝑎𝑔𝑒 + 66.47
F: 𝑅𝑀𝑅 = 9.56 ∗ 𝐵𝑊 + 1.85 ∗ 𝐻 ― 4.68 ∗ 𝑎𝑔𝑒 + 655.1
FAO (1985) M: 𝑅𝑀𝑅 = 15.3 ∗ 𝐵𝑊 + 679
F: 𝑅𝑀𝑅 = 14.7 ∗ 𝐵𝑊 + 496
De Lorenzo (1999) 𝑅𝑀𝑅 = 9 ∗ 𝐵𝑊 + 11.7 ∗ 𝐻 ― 857
ten Haaf (2014) 𝑅𝑀𝑅 = 0.239(49.94 ∗ 𝐵𝑊 + 24.59 ∗ 𝐻 ― 34.014 ∗ 𝑎𝑔𝑒 + 799.257 ∗ 𝑠𝑒𝑥 + 122.502)
Tinsley2 𝑅𝑀𝑅 = 24.8 ∗ 𝐵𝑊 + 10
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Table 2. Participant Characteristics 
Data presented as mean ± SD
All (n=27) Males (n=17) Females (n=10)
Age (y) 25.9 ± 6.0 26.0 ± 6.5 25.8 ± 5.4
Height (cm) 175.6 ± 9.2 180.4 ± 7.2 167.5 ± 5.7
Weight (kg) 82.9 ± 17.0 94.0 ± 9.7 63.8 ± 5.7
BMI (kg/m2) 26.6 ± 3.5 28.8 ± 2.0 22.8 ± 1.6DXA Body fat (%) 15.0 ± 4.4 12.5 ± 2.7 19.2 ± 3.4
DXA FFMI (kg/m2) 21.8 ± 3.4 24.2 ± 1.3 17.7 ± 0.9
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Table 3. Validity of resting metabolic rate estimates in males and females combined (n = 27).
Method Mean ± SD p-value 
(t-test)
CE ± SD Cohen's 
d
r p-value 
(correlation)
R2 SEE TE 95% 
LOA
B p-value 
(linear 
regression)
ICPARVO 2051 ± 457 --- --- --- --- --- --- --- --- --- --- ---
ICCOSMED 2121 ± 482 0.037 69 ± 164 0.15t 0.94p < 0.0001* 0.88 167 175 ± 321 0.054 0.45
ICBREEZING 1753 ± 319 0.003* -298 ± 479 -0.76m 0.28s 0.16 0.08 312 557 ± 940 -0.549 0.067
OTHAYES 1896 ± 398 0.0002* -155 ± 188 -0.36s 0.91p < 0.0001* 0.83 166 241 ± 369 -0.146 0.10
FFMCUNN1 1999 ± 358 0.16 -53 ± 188 -0.13t 0.92p < 0.0001* 0.85 141 192 ± 368 -0.253 0.004†
FFMCUNN2 1842 ± 352 <0.0001* -210 ± 190 -0.51s 0.92p < 0.0001* 0.85 138 281 ± 372 -0.272 0.002†
FFMMIFFLIN 1755 ± 321 <0.0001* -296 ± 204 -0.75m 0.92p < 0.0001* 0.85 126 357 ± 399 -0.365 < 0.001†
FFMOWEN 1778 ± 400 <0.0001* -273 ± 179 -0.64m 0.92p < 0.0001* 0.85 158 325 ± 351 -0.14 0.094
FFMT-H 2036 ± 371 0.66 -16 ± 184 -0.04t 0.92p < 0.0001* 0.85 146 181 ± 360 -0.218 0.011†
BWMIFF 1741 ± 297 <0.0001* -310 ± 232 -0.80m 0.90p <0.0001* 0.80 134 385 ± 455 -0.446 <0.0001†
BWH-B 1852 ± 343 <0.0001* -199 ± 209 -0.49s 0.90p <0.0001* 0.81 151 286 ± 411 -0.302 0.002†
BWFAO 1848 ± 363 <0.0001* -203 ± 215 -0.49s 0.89p <0.0001* 0.79 170 293 ± 421 -0.244 0.017†
BWDEL 1943 ± 252 0.04 -108 ± 259 -0.29s 0.89v <0.0001* 0.80 116 276 ± 508 -0.611 <0.0001†
BWTH 1960 ± 342 0.04 -92 ± 221 -0.23s 0.89v <0.0001* 0.79 161 235 ± 432 -0.307 0.004†
For RMR prediction, the following equations were used: Hayes et al. (2002), Cunningham (1980) [CUNN1], Cunningham (1991) [CUNN2], Mifflin 
et al. (1990), Owen et al. (1987), ten Haaf (2014) [TH], Harris and Benedict (1918) [H-B], FAO (1985) and DeLorenzo (1999) [DEL]. 
*statistically significant at p < 0.0033 (adjustment for multiple comparisons); †statistically significant at p < 0.05
Italic superscripts indicate magnitude of effect size or correlation (t: trivial, s: small, m: moderate, l: large, v: very large, p: near perfect).
Abbreviations: B (regression coefficient), BW (body weight model), CE (constant error), F (female), FFM (fat-free mass model), IC (indirect 
calorimetry), LOA (limits of agreement), M (male), OT (organ/tissue model), SEE (standard error of the estimate), TE (total error). 
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Table 4. Validity of resting metabolic rate estimates in males (n = 17).
Method Mean ± SD p-value 
(t-test)
CE ± SD Cohen's 
d
r p-value 
(correlation)
R2 SEE TE 95% 
LOA
B p-value 
(linear 
regression)
ICPARVO 2337 ± 310 --- --- --- --- --- --- --- --- --- --- ---
ICCOSMED 2408 ± 350 0.15 71 ± 192 0.21s 0.84v < 0.0001* 0.70 198 199 ± 376 0.134 0.396
ICBREEZING 1824 ± 359 0.0003* -513 ± 469 -1.53l 0.02t 0.93 0.00 370 686 ± 919 0.286 0.576
OTHAYES 2166 ± 199 0.005 -172 ± 217 -0.66m 0.72v 0.001* 0.51 143 272 ± 426 -0.502 0.023†
FFMCUNN1 2245 ± 170 0.09 -92 ± 212 -0.37s 0.76v 0.0003* 0.57 115 226 ± 416 -0.655 0.002†
FFMCUNN2 2083 ± 167 0.0002* -254 ± 213 -1.02m 0.76v 0.0003* 0.57 113 328 ± 418 -0.673 0.001†
FFMMIFFLIN 1975 ± 152 < 0.0001* -362 ± 218 -1.48m 0.76v 0.0003* 0.57 103 419 ± 428 -0.763 < 0.001†
FFMOWEN 2058 ± 172 0.0001* -279 ± 212 -1.11m 0.76v 0.0003* 0.57 116 346 ± 415 -0.641 0.002†
FFMT-H 2290 ± 176 0.37 -47 ± 210 -0.19t 0.76v 0.0003* 0.57 119 210 ± 413 -0.620 0.003†
BWMIFF 1944 ± 144 <0.0001* -393 ± 238 -1.63l 0.67l 0.003* 0.45 110 456 ± 467 -0.854 0.0005†
BWH-B 2086 ± 176 0.0004* -251 ± 233 -1.00m 0.67l 0.003* 0.44 136 338 ± 457 -0.650 0.008†
BWFAO 2102 ± 160 0.001* -235 ± 241 -0.95m 0.64l 0.005 0.41 128 332 ± 473 -0.758 0.003†
BWDEL 2032 ± 180 <0.0001* -305 ± 224 -1.20l 0.70v 0.001* 0.49 133 374 ± 438 -0.724 0.001†
BWTH 2192 ± 168 0.02 -145 ± 241 -0.58s 0.63l 0.006 0.40 135 275 ± 473 -0.709 0.006†
See footnotes on Table 3.
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Table 5. Validity of resting metabolic rate estimates in females (n = 10).
Method Mean ± SD p-value 
(t-test)
CE ± SD Cohen's 
d
r p-value 
(correlation)
R2 SEE TE 95% 
LOA
B p-value 
(linear 
regression)
ICPARVO 1566 ± 133 --- --- --- --- --- --- --- --- --- --- ---
ICCOSMED 1633 ± 182 0.08 67 ± 109 0.42s 0.80l 0.003 0.65 115 124 ± 214 0.343 0.169
ICBREEZING 1633 ± 200 0.30 67 ± 194 0.4s 0.38m 0.28 0.14 196 196 ± 380 0.569 0.24
OTHAYES 1438 ± 126 0.01 -128 ± 131 -0.99m 0.49m 0.14 0.24 116 178 ± 257 -0.078 0.855
FFMCUNN1 1581 ± 107 0.68 16 ± 116 0.13t 0.55l 0.09 0.30 95 111 ± 228 -0.283 0.471
FFMCUNN2 1432 ± 105 0.005 -134 ± 116 -1.12m 0.55l 0.09 0.30 93 173 ± 227 -0.307 0.436
FFMMIFFLIN 1381 ± 96 0.0006* -184 ± 113 -1.59l 0.55l 0.09 0.30 85 213 ± 222 -0.421 0.285
FFMOWEN 1302 ± 96 < 0.0001* -263 ± 113 -2.27v 0.55l 0.09 0.30 85 284 ± 222 -0.421 0.285
FFMT-H 1604 ± 110 0.33 38 ± 117 0.31s 0.55l 0.09 0.30 98 118 ± 230 -0.240 0.542
BWMIFF 1396 ± 95 0.004 -169 ± 139 -1.47l 0.29s 0.41 0.08 96 215 ± 273 -0.519 0.332
BWH-B 1454 ± 70 0.02 -111 ± 128 -1.04m 0.33s 0.02 0.11 71 165 ± 252 -0.883 0.076
BWFAO 1417 ± 77 0.01 -149 ± 156 -1.37l -0.04t 0.92 0.00 82 210 ± 306 -1.03 0.142
BWDEL 1677 ± 107 0.04 111 ± 142 0.92m 0.31s 0.37 0.10 108 175 ± 279 -0.333 0.526
BWTH 1566 ± 112 0.99 0 ± 142 0.00t 0.26s 0.46 0.07 114 142 ± 294 -0.277 0.619
See footnotes on Table 3.
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Table 6. Preliminary evaluation of new resting metabolic rate prediction equations. 
Method Mean ± SD p-value 
(t-test)
CE ± SD Cohen's 
d
r p-value 
(correlation)
R2 SEE TE TE 
(LOO)
95% 
LOA
B p-value 
(linear 
regression)
All (n=27) 2051 ± 457 --- --- --- --- --- --- --- --- --- --- --- ---
M (n=17) 2337 ± 310 --- --- --- --- --- --- --- --- --- --- --- ---ICPARVO
F (n=10) 1566 ± 133 --- --- --- --- --- --- --- --- --- --- --- ---
All (n=27) 2049 ± 422 0.93 -3 ± 176 -0.01t 0.92p < 0.0001* 0.85 166 173 187 ± 346 -0.084 0.31
M (n=17) 2338 ± 200 0.99 1 ± 205 0.00t 0.76v 0.0003* 0.57 135 199 214 ± 402 -0.484 0.018†FFMTINSLEY
F (n=10) 1557 ± 126 0.83 -9 ± 123 -0.07t 0.55l 0.09 0.30 111 117 129 ± 241 -0.074 0.852
All (n=27) 2065 ± 422 0.70 13 ± 180 0.03t 0.92p <0.0001* 0.85 169 177 188 ± 352 -0.083 0.32
M (n=17) 2342 ± 241 0.92 5 ± 204 0.02t 0.75v 0.0003* 0.56 164 198 211 ± 401 -0.283 0.159BWTINSLEY
F (n=10) 1593 ± 140 0.50 28 ± 137 0.20s 0.50s 0.13 0.25 129 132 140 ± 268 0.070 0.868
*statistically significant at p < 0.0033 (adjustment for multiple comparisons); †statistically significant at p < 0.05
Italic superscripts indicate magnitude of effect size or correlation (t: trivial, s: small, m: moderate, l: large, v: very large, p: near perfect).
Abbreviations: B (regression coefficient), BW (body weight model), CE (constant error), F (female), FFM (fat-free mass model), IC (indirect 
calorimetry),LOA (limits of agreement), LOO (leave-one-out cross-validation), M (male), OT (organ/tissue model), SEE (standard error of the 
estimate), TE (total error). 
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FIGURE LEGENDS
Figure 1. Bland-Altman plots for resting metabolic rate estimation via portable indirect calorimeters and an 
organ/tissue equation. Results are shown for Cosmed FitmateTM (A), Breezing® (B), and the Hayes et al. (2002) 
equation (C). The middle horizontal line indicates the constant error (i.e. mean difference), while the upper and 
lower horizontal lines indicate the 95% limits of agreement. The regression line best fitting the data is shown, 
with larger slopes indicating greater proportional bias.
Figure 2. Bland-Altman plots for resting metabolic rate estimation via body weight equations. Results are 
shown for Mifflin (A), Harris-Benedict (B), FAO (C), DeLorenzo (D), ten Haaf (E) and Tinsley (F). The middle 
horizontal line indicates the constant error (i.e. mean difference), while the upper and lower horizontal lines 
indicate the 95% limits of agreement. The regression line best fitting the data is shown, with larger slopes 
indicating greater proportional bias.
Figure 3. Bland-Altman plots for resting metabolic rate estimation via fat-free mass equations. Results are 
shown for Cunningham (1991) (A), Cunningham (1980) (B), Mifflin (C), Owen (D), ten Haaf (E) and Tinsley 
(F). The middle horizontal line indicates the constant error (i.e. mean difference), while the upper and lower 
horizontal lines indicate the 95% limits of agreement. The regression line best fitting the data is shown, with 
larger slopes indicating greater proportional bias.
Figure 4. Newly developed resting metabolic rate prediction equations. A strong linear relationship was 
observed between resting metabolic rate and body weight (A) and between resting metabolic rate and fat-free 
mass (B). The newly developed equations produced strong correlations with reference resting metabolic rate 
estimates (body weight: r = 0.921, R2 = 0.849 [C]; fat-free mass: r = 0.923, R2 = 0.851 [D]).
Page 32 of 37
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AVERAGED RMR (kcal/d)
30002500200015001000
M
E
A
N
 D
IF
F
E
R
E
N
C
E
 (
k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
E
A
N
 D
IF
F
E
R
E
N
C
E
 (
k
c
a
l/
d
)
5 0 0
0
- 5 0 0
- 1 0 0 0
- 1 5 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
E
A
N
 D
IF
F
E
R
E
N
C
E
 (
k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
A B C Page 33 of 37
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AVERAGED RMR (kcal/d)
30002500200015001000
M
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A
N
 D
IF
F
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R
E
N
C
E
 (
k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
E
A
N
 D
IF
F
E
R
E
N
C
E
 (
k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
E
A
N
 D
IF
F
E
R
E
N
C
E
 (
k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
E
A
N
 D
IF
F
E
R
E
N
C
E
 (
k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
E
A
N
 D
IF
F
E
R
E
N
C
E
 (
k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
E
A
N
 D
IF
F
E
R
E
N
C
E
 (
k
c
a
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)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
D E F 
A B C Page 34 of 37
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AVERAGED RMR (kcal/d)
30002500200015001000
M
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A
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IF
F
E
R
E
N
C
E
 (
k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
E
A
N
 D
IF
F
E
R
E
N
C
E
 (
k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
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A
N
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IF
F
E
R
E
N
C
E
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k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
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A
N
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IF
F
E
R
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N
C
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 (
k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
E
A
N
 D
IF
F
E
R
E
N
C
E
 (
k
c
a
l/
d
)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
AVERAGED RMR (kcal/d)
30002500200015001000
M
E
A
N
 D
IF
F
E
R
E
N
C
E
 (
k
c
a
l/
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)
6 0 0
3 0 0
0
- 3 0 0
- 6 0 0
- 9 0 0
D E F 
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BODY WEIGHT (kg)
1 1 01 0 09 08 07 06 05 0
R
M
R
 (
k
c
a
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3000
2500
2000
1500
1000
FFM (kg)
1 0 09 08 07 06 05 04 0
R
M
R
 (
k
c
a
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d
)
3000
2500
2000
1500
1000
R
E
F
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R
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N
C
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 R
M
R
 (
kc
al
/d
)
3000
2500
2000
1500
1000
BW-PREDICTED RMR (kcal/d)
30002500200015001000
FFM-PREDICTED RMR (kcal/d)
30002500200015001000
R
E
F
E
R
E
N
C
E
 R
M
R
 (
kc
al
/d
)
3000
2500
2000
1500
1000
A B 
C D 
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