MEASUREMENT OF AGREEMENT OF RESTING METABOLIC RATE BETWEEN
INDIRECT CALORIMETERY AND MULTIPLE ESTIMATION MODELS IN
ADULTS USING AIR DISPLACEMENT PLETHYSMOGRAPHY
A Thesis
Presented to
The Graduate Faculty of The University of Akron
In Partial Fulfillment
Of the Requirements for the Degree
Master of Science
Brian Miller
December, 2013
MEASUREMENT OF AGREEMENT OF RESTING METABOLIC RATE BETWEEN
INDIRECT CALORIMETERY AND MULTIPLE ESTIMATION MODELS IN
ADULTS USING AIR DISPLACEMENT PLETHYSMOGRAPHY
Brian Miller
Thesis
Approved:
Accepted:
_______________________________
Advisor
Dr. Ronald Otterstetter
____________________________
Department Chair
Dr. Victor Pinheiro
_______________________________
Committee Member
Mrs. Michelle Boltz
____________________________
Interim Dean of the College
Dr. Susan G. Clark
_______________________________
Committee Member
Dr. Mark Fridline
____________________________
Dean of the Graduate School
Dr. George R. Newkome
______________________________
Date
ii
ABSTRACT
PURPOSE: Air displacement plethysmography (ADP) is a well validated method for
estimating body composition. Similarly, predictive equations derived from regression
techniques based on large samples are extensively utilized in estimating resting metabolic
rate (RMR). The Cos Med BOD POD System®, which operates via ADP, utilizes a
predictive equation to estimate RMR based on the Nelson (1992) model. However, the
agreement of this predictive model with indirect calorimetery (IC) has come into
question. The aim of this study is to investigate the agreement of RMR estimation models
using ADP measures against a gas exchange IC system in addition to other predictive
RMR models.
METHODS: Sixty-six apparently healthy subjects (25 men, 41 women) participated in
and completed the study. RMR measurements were obtained from the Parvomedics
TrueOne 2400 System® and The Cos Med BOD POD System® within 10 minutes of one
another. IC RMR estimates were tested against nine other validated models using ADP
measures via ANOVA techniques with multiple comparisons testing and Bland Altman
analysis.
RESULTS: The Nelson (1992) model under-predicted RMR compared to IC (P<0.001).
The Dore et al. (1982) model was the best predictor of RMR compared to the IC
measures (p=0.907). However, by sex, the Dore et al. (1982) model significantly underpredicted RMR for men and over-predicted for women when compared to the IC measure
(men: -231±87kcal, p<0.014 and women: 150±43kcal, p <0.001, respectively).
iii
CONCLUSIONS: The current RMR estimation model using ADP measures underpredicts total caloric needs. The Dore et al. (1982) model more accurately predicted RMR
in the entire sample but significantly varied when split by sex.
iv
DEDICATION
This manuscript is dedicated to the Memory of my late grandmother, Rita M.
Janke (March 31, 1930 – September 12, 2013 R.I.P.).
v
ACKNOWLEDGEMENTS
I would like to thank my mentors from The University of Akron including Dr.
Deborah Marino and Mrs. Michelle Boltz from nutrition, Dr. Ronald Otterstetter from
exercise physiology, and Dr. Mark Fridline from statistics. Without their devotion and
friendship, this journey would have been impossible. I would have not been able to
complete this thesis without the love and support from my wife Elise, my mother-in-law
Denise, and my Basset Hound, Sprinkles.
vi
TABLE OF CONTENTS
Page
LIST OF TABLES ............................................................................................ ix
LIST OF FIGURES ........................................................................................... x
CHAPTER
I.
INTRODUCTION .................................................................................... 1
II.
BACKGROUND OF THE STUDY ......................................................... 3
Air Displacement Plethysmography ................................................... 3
Estimation of Resting Metabolic Rate ................................................ 6
Indirect Calorimetery .......................................................................... 9
Agreement between Estimation Methods ......................................... 10
III
METHODOLOGY ................................................................................. 13
Hypothesis and Statement of Objectives .......................................... 13
Subjects and Inclusion Criteria ......................................................... 13
Testing Procedure ............................................................................. 14
Statistical Analysis ............................................................................ 15
IV.
RESULTS ............................................................................................... 16
V.
DISCUSSION ......................................................................................... 21
vii
Limitations ........................................................................................ 24
VI.
CONCLUSION ...................................................................................... 25
REFERENCES ................................................................................................ 26
APPENDICES ................................................................................................. 29
APPENDIX A: LETTER OF INFORMED CONSENT ........................ 30
APPENDIX B: IRB LETTER OF APPROVAL .................................... 32
viii
LIST OF TABLES
Table
Page
1
RMR Estimation Models and Derivation Specifics ................................8
2
Sample Characteristics ...........................................................................16
3
Agreement between Estimation Models and RMR-C ............................17
4
Results of Repeated Measures ANOVA ................................................18
5
Paired Sample t-test and Limits of Agreement .......................................20
ix
LIST OF FIGURES
Figure
1
Page
Box Plots of Estimated RMR by Model .................................................19
x
CHAPTER I
INTRODUCTION
Accurate resting metabolic rate (RMR) measurements are necessary for
professionals to provide appropriate nutrition and exercise recommendations. These
measurements are an important component of clinical and professional settings to provide
important information regarding energy requirements and macronutrient utilization at rest
(Cooper et al., 2009). Inaccurate measurements of RMR can lead to negative
consequences of the resulting malnutrition. RMR is the amount of energy (kcal) a person
uses per day to sustain metabolism at rest (Mifflin, St Jeor, Hill, Scott, Daugherty & Koh,
1990).
Resting metabolic rate can be measured via indirect calorimetery (IC) or
estimated by predictive equations. Portable indirect calorimeters that measure gas
exchange provide accurate readouts within 5% of direct calorimetery. IC is a reliable and
well documented method of estimating a person’s metabolic demands by analyzing the
ratio of carbon dioxide and oxygen throughout respiration (Cooper et al., 2009).
However, predictive equations are most often utilized due to their ease of use and low
cost. In many cases these equations employ surrogate variables acquired from other
anthropometric measurement methods. Consequently, large variances are possible based
on which method that measurement was acquired from. Here, many of the predictive
models used to estimate RMR are not derived using body composition measurements
1
from air displacement plethysmography (ADP) (Nelson, Weinsier, Long & Schutz,
1992). ADP determines body volume on the basis of the pressure/volume relationship in
tandem with models that estimate body composition based on body density. The Cos Med
BOD POD System® (BOD POD) estimates RMR via ADP (Life Measurement Inc.
[LMI], 2004).
The majority of research in ADP is based on the accuracy of determining body
composition measures, including fat mass (FM) and fat-free mass (FFM) rather than
applicability of these measures in estimating RMR. Thus, limited research has been
conducted regarding the BOD POD’s accuracy in determining RMR. The BOD POD
employs the RMR estimation model derived by Nelson et al. (1992) (LMI, 2004). The
Nelson equation is a validated and cross-validated model that includes both FM and FFM
(Nelson, Weinsier, Long & Schutz, 1992). However, this model might present with large
variances that might not optimally predict RMR for a total population or when split by
sex. Furthermore, the derivation of this model is not based on measures obtained from the
BOD POD.
The central hypothesis states that the current methodology of estimating RMR
using the measurements obtained from ADP will result in large variances that are
inappropriate for patient recommendations. The aim of this study is to compare the
agreement between multiple RMR estimation models using body composition measures
obtained from an ADP against IC via metabolic cart.
2
CHAPTER II
BACKGROUND OF THE STUDY
Air Displacement Plethysmography
Air displacement plethysmography (ADP) uses the principle of whole body
densitometry to estimate the amount of fat and lean tissue in the body. ADP was an
instrument created in cooperation with the National Institute of Health designed to
replace the gold standard measure of hydro-densitometry. In addition to ADP, body
composition can be estimated by a variety of methods including skin folds, bioelectric
impedance, hydro-densitometry, and dual energy x-ray absorptiometry (DEXA).
However, each of these methods has potential physical and financial limitations such as
the user bias, invasiveness, and discomfort of hydro-densitometry and the cost of the
DEXA (Hoffman & Hilderbrant, 2001).
ADP has been shown to be equivocal to hydrostatic weighing and DEXA
techniques in estimating body composition (Fields, Goran, & McCroy, 2002). This
technique is a two-compartment estimation method that analyzes the components of the
body including fat mass and fat free mass (water, bone, non-bone mineral, and protein).
The universal density of fat and fat-free compartments based on cadaver analysis is
0.9g/cc and 1.1g/cc, respectively. ADP employs the use of multiple body composition
estimation equations using body density (LMI, 2004).
3
Here, body density is determined by the relationship of the density of an object
proportional to it mass divided by its volume (Density = Mass/Volume). First, a subject is
weighed. Then the subject’s volume is determined from pressure exerted by that body in
a closed chamber. Here, the test chamber pressure (Ptest) and volume (Vtest) are compared
to a reference pressure (Preference) and volume (Vreference) using Boyle’s Gas Law illustrated
below (LMI, 2004).
The Cos Med BOD POD System® (BOD POD) is beneficial for the exercise
specialist and/or dietitian in that it can: measure the amount of lean and body fat tissue,
measure the effectiveness of a lifestyle modification, provide motivational feedback for
clients, and discourage the use of rapid weight loss programs that result in depletion of
metabolically active tissue rather than body fat (Hoffman & Hilderbrant, 2001).
The BOD POD is comprised of a cabin, integrated computer system with data
interface board and monitor, software, scale, and calibration standard. The subject is
prompted to wear a tight-fitting synthetic swimsuit and cap to prevent and/or reduce
erroneous air displacement. Air displacement is then measured by the movement of a
mounted diaphragm within the cabin as air is pumped into the cabin equal to the volume
of the empty cabin. A pressure gradient is created with the addition of a subject’s
displacement that causes outward movement of the diaphragm. This displacement is
compared against a volume standard to determine the subject’s total volume. Body
density is determined from the division of the subject’s weight by their volume. Body
composition is then determined using one of multiple body composition estimation
equations using body density. The default model is based on the Siri 1961 equation,
4
illustrated below, which is designed for body fat percentage estimation in the general
population (Hoffman & Hilderbrant, 2001; LMI, 2004).
Body Fat % =
The BOD POD system is designed to correct for the isothermal effect, the
variance in body mass from hair, lung volume, clothing, and body surface area. Thoracic
lung gas volume is a critical component to the variance in predicted body density. If left
uncorrected the estimated lung volume can vary by up to 40% of the actual lung volume.
The thoracic lung volume correction factor is a built in function derived from the Crapo
Equation that estimates lung volume as the sum of forced reserve capacity and half of the
tidal volume (LMI, 2004).
The BOD POD employs the RMR estimation model derived by Nelson et al. 1992
(LMI, 2004). The Nelson equation is a validated and cross-validated model that includes
both FM and FFM (Nelson, Weinsier, Long & Schutz, 1992). However, this model might
present with large variances that might not optimally predict RMR for a total population
or when split by sex.
Predictive equations, including Nelson et al. (1992) are most often utilized due to
their ease of use and low cost. In many cases these equations employ surrogate variables
acquired from other anthropometric measurement methods. Consequently, large
variances are possible based on which method each measurement is acquired from. Here,
many of the predictive models used to estimate RMR are not derived using body
composition measurements from ADP (Nelson, Weinsier, Long & Schutz, 1992).
Due to the strict protocol and technical difficulties associated with obtaining an
accurate measurement of RMR, prediction equations have been developed to estimate
5
RMR based on parameters more easily measured. BOD POD uses prediction equations
which account for both FM and FFM to provide the most accurate estimates of RMR
(LMI, 2004). Therefore the equation of Nelson (1992), which includes measured FM and
FFM as its predictors, should, in theory, provide a reliable and accurate assessment of
RMR in tandem with changes that occur in body composition (Nelson, Weinsier, Long &
Schutz, 1992).
Although the Nelson (1992) model was chosen based on its inclusion of the FM
and FFM as its predictive variables for estimating RMR (LMI, 2004) the derivation of the
Nelson (1992) model was not based on measurements made directly from the BOD POD
system or an ADP method (Nelson, Weinsier, Long & Schutz, 1992).
Estimation of Resting Metabolic Rate
RMR is the energy requirement to sustain vital functions during rest or physical
inactivity expressed as kcal/day (Hasson, Howe, Jones & Freedson, 2011). Total energy
expenditure is comprised of four components: the sleeping metabolic rate, the energy cost
of arousal, the thermic effect of food, and the energy cost of physical activity. RMR is the
summation of the sleeping metabolic rate and the energy cost of arousal which is
equivocal to the minimum rate of energy expenditure in an awake, relaxed person in a
supine position in a thermo-neutral environment (Ravussin & Bogardus, 1989). RMR
accounts for the largest proportion of total energy expenditure except for individuals that
are involved in high-energy physical activity (Li, Tereskowski, Edwards, Simpson &
Buchholz, 2010).
Because of its important role in regulating body weight and composition there is
an increased need in providing patients with their baseline metabolic requirements.
6
However, providing patients with an inaccurate RMR might have adverse consequences
(Hasson, Howe, Jones & Freedson, 2011). Inaccurate assessment of body composition
can lead to body dysmorphism and relative disorders such as anorexia and obesity in
tandem with their individual clinical implications (Fields, Wilson, Gladdens, Hunter &
Pascoe, 2001).
Concurrently, estimations of RMR based off of estimated body composition
measures might have similar consequences. Thus, the accurate prediction of RMR has
many useful applications in the weight management of both normal-weight and obese
individuals. Dieting is a common practice in the United States. Many diet regimes create
weight loss through depletion of muscle mass and body water rather than body fat as a
consequence of inappropriate nutrition (Hoffman & Hilderbrant, 2001). Clients might
find difficulty in monitoring progress in a weight management program through diet and
exercise when the weight remains constant while ratio of body fat to lean muscle mass
changes (Hoffman & Hilderbrant, 2001).
It is unfortunate that many of these individuals are unaware of their energy
requirements to maintain an optimal metabolism (Mifflin et al., 1990). Therefore it is of
the upmost importance to establishing an accurate method of predicting resting metabolic
requirements. Table one illustrates multiple RMR estimation models and their population
specifics.
Many attempts have been made over the past century to establish estimation
models to predict RMR in humans. Today, predictive equations are most often utilized in
clinical practice to predict a subject’s RMR for prescribing daily dietary-energy
7
requirements due to their ease of use and low cost (Frakenfield, Rowe, Smith & Cooney,
2003).
Table 1: RMR Estimation Models and Derivation Specifics
N1
Reference
Equation
Cunningham (1980)
223
502 + 21.6(FFM)
Dore et al. (1982)
140 712+8.24(wt)+0.02(FFM)-3.25(age)
Bernstein et al. (1983)
185 251 + 22(FFM) + 6.4(FM) - 2.1(age)
Garrow & Weber (1985) 104
310 + 24.2(FFM) + 5.8(%fat)
Ravussin et al. (1986)
249
392 + 21.8(FFM)
Owen (1988)
104
186 + 23.6(FFM)
Kashiwazaki (1988)
134
304 + 24.5(FFM)
Mifflin et al. (1989)
483
413 + 19.7(FFM)
Nelson (1992)
213
25.8(FFM) + 4.04(FM)
1
Sample size used for derivation
2
Sex: M = Male; F = Female
3
Body Type: L = Lean; O = Obese
Sex2
M/F
F
M/F
F
M/F
M/F
M/F
M/F
M/F
Body3
L/O
L/O
O
L/O
L/O
L/O
L/O
L/O
L/O
In addition to use in practice, many body composition analysis devices utilize
these predictive equations to provide an RMR estimation feature. Many predictive
models are built upon measures from a general sample thus the applicability of the model
to subjects that fall outside of the norm might not be appropriate. Frakenfield, Rowe,
Smith & Cooney (2003) explored the agreement of predictive models to estimate RMR.
Here, models based on general samples had a tendency to over-predict RMR in obese
subjects when compared to a weight adjustment method (Frakenfield, Rowe, Smith &
Cooney, 2003).
Multiple factors can influence RMR including: time of day, anxiety, diurnal
variation, the thermic effect of food, elevated post-exercise oxygen consumption,
stimulants, and pharmaceuticals. Therefore standard conditions for establishing a RMR
are necessary including: 8-12hr fasting for food and ergometric agents and a 12hr
8
abstinence from exercise (Haugen, Melanson, Tran, Kearney & Hill, 2003). Haugen et al.
(2003) found within-subject variances for repeated RMR measures between morning
measurements of 4.5%, afternoon measurements of 2.8%, and 4.6% between morning
and afternoon measurements (Haugen, Melanson, Tran, Kearney & Hill, 2003).
Many of the predictive models used to estimate RMR are not derived using body
composition measurements from ADP (Nelson, Weinsier, Long & Schutz, 1992).
Hasson, Howe, Jones & Freedson, (2011) compared the accuracy of four resting
metabolic rate regression models compared to a measured RMR when stratified by sex,
ethnicity, and BMI (Hasson, Howe, Jones & Freedson, 2011). Here, the accuracy of each
model compared to the measured RMR greatly varied when stratified by each parameter.
When employing a regression based model, clinicians and practitioners need to consider
what sample that model was derived on to assess whether its estimation is appropriate
(Hasson, Howe, Jones & Freedson, 2011).
Ravussin and Bogardus (1989) found a strong positive correlation between FFM
and RMR. However, the obese person required less energy per unit body mass than there
lean controls. This suggests that energy preservation efficiency increases with an increase
in fat mass. Thus RMR requirements decrease with increased FM (Ravussin & Bogardus,
1989).
Indirect Calorimetery
Accurate assessment of energy requirements is a necessary component in the
formulation and evaluation of a nutrition focused plan. The metabolic rate can be
measured via IC or estimated by predictive equations. Predictive equations are more
often used due to their ease of use and low cost in many cases using surrogate variable
9
acquired for other anthropometric analysis which might result in significant variance
from the actual metabolic rate. Portable indirect calorimeters that measure gas exchange
provide accurate readouts within 5% of direct calorimetery. It is important for
practitioners to understand the limitations of the predictive methods and to consider their
applicability to their patients/clients (Frankenfield, Roth-Yousey & Compher, 2005).
The statistical laws of model derivation from multivariate regression work best
when applied to specific subsets of people rather than a general population. However,
when applied to individual cases, large errors might occur when the subject does not
match the sample that the model was derived upon including age, sex, and body
composition. Frankenfield, Roth-Yousey & Compher (2005) identified an acceptable
estimation of RMR within ±10% of the direct or indirect measure (Frankenfield, RothYousey & Compher, 2005).
The Parvomedics TrueOne 2400 system® (CART) is a gas exchange measurement
IC system that reliably measures minute ventilation, oxygen consumption, carbon dioxide
production (Crouter, Antczak, Hudak, Valle & Haas, 2006). Crouter et al. (2006)
explored the accuracy and reliability of the CART system when compared to Douglas
Bag method which was considered the gold standard measure of gas exchange
calorimetery. At rest, the CART system was not statistically different than the Douglas
Bag method (p≥0.05; CV 4.7-5.7%) (Crouter, Antczak, Hudak, Valle & Haas, 2006).
Therefore the interchangeability of CART is equivocal to the Douglas Bag Method.
Agreement between Estimation Methods
In clinical practice, professionals aim to have data based on direct measurement
without adverse effect or influence which might be cumbersome, not practical, or
10
impossible. Thus these true values remain unknown. In lieu of direct measurement,
indirect approaches are used where a new method has to be evaluated via comparison to a
well established technique rather than a true quantity (i.e. IC compared to a predictive
equation). If the new measurement technique agrees with the well established technique,
the new method can be utilized interchangeably based on its practicality (Bland &
Altman, 1986).
Many scientific studies use the product-moment correlation coefficient (r)
between measures as an indicator of the degree of agreement. The correlation statistic has
a null hypothesis where two measures are not linearly related. However, the presence of a
linear relationship between methods is not necessarily agreement between the two
measures. Rather, it is the strength of their relationship along a straight line. Here, perfect
correlation occurs where the points of measure are relative upon a straight line not
necessarily a unit-to-unit relationship. Correlation does not take into account variations
outside of a unit-to-unit relationship or scale. For example a situation where two units of
change for the first measure are observed for every one unit change of the second
measure can have a perfect correlation if this relationship remains constant upon a
straight line. Furthermore, larger values of each measure carry greater weight than
smaller values. Since most investigations look at comparisons of measures over
large/whole ranges, a high or inflated correlation coefficient is likely (Bland & Altman,
1986).
It is unlikely that two methods of measurement agree exactly. Therefore, the
agreement method developed by Bland & Altman (1986) can be utilized to determine the
amount of difference between each method. Prior to using this analysis, the maximum
11
allowable/practical difference between each RMR estimation method needs to be
considered (Bland & Altman, 1986).
Frakenfield, Rowe, Smith & Cooney (2003), found the practical difference
between RMR estimation methods where the main outcomes of each subjects estimated
RMR was less or equal to ±10% from the measured value (Frakenfield, Rowe, Smith &
Cooney, 2003). Therefore the level agreement limit of RMR estimation measures is set at
ten percent above and below the CART measure.
The lack of agreement using this method is summarized by calculating the bias
estimated via the mean difference and its sample standard deviation. In the case where a
consistent bias is present, the mean difference can be subtracted from the new
measure/method. Assuming a Normal (Gaussian) distribution, 95% of the averages will
lie between two standard deviations above and below the mean difference (Bland &
Altman, 1986).
It is important to note that this measure of agreement is a point estimate for that
sample. However, the agreement/bias of a second sample might be different. Albeit, the
95% confidence interval and mean difference with its standard errors can be used for
applications to a whole population of similar characteristics where with 95% certainty the
difference between measures will fall within the lower and upper limits represented as the
following with mean difference (d) and sample standard deviation(S): Lower Limits of
Agreement: d – 2S and Upper Limits of Agreement: d + 2S (Bland & Altman, 1986).
12
CHAPTER III
METHODOLOGY
Hypothesis and Statement of Objectives
The current methodology of estimating resting metabolic rate using the
measurements obtained from ADP via BOD POD will result in large variances from
measures obtained from the gas exchange IC via CART. The main objective of this paper
is to compare the agreement between multiple RMR estimation models using body
composition measures obtained from an ADP against IC via CART.
Subjects and Inclusion Criteria
This is a cross-validation research study that requires all subjects to be tested by
the Cos Med BOD POD System® and the Parvomedics TrueOne 2400 Indirect
Calorimetery System®. The study population is comprised of a convenience sample of
healthy college-aged adult men and women (18-30 years old) recruited from a
Midwestern university and local surrounding community. Informed consent was provided
by each subject and participation was voluntary with each subject able to terminate
testing at any time (See appendix A for sample letter of informed consent). A total of 66
subjects (41 female and 25 male) participated in and completed the study with no
dropouts. The current study was approved by The University of Akron’s Institutional
Review Board (appendix B).
13
Testing Procedure
Measurements collected for each participant from the BOD POD included RMR,
FM, FFM, body fat percentage, and body volume and RMR from the CART (RMR-C).
Prior to testing, subjects were required to: 1) Fast 12 hours prior to the test 2) Avoid
strenuous exercise 24 hours prior to the test 3) Wear tight fitting clothing (including the
provided skull cap) and 4) remove all jewelry.
The testing protocol was broken into three phases. The first included collecting
anthropometric measurements and subject specifics (height, weight, age, and declaration
of fasting protocol). Anthropometric measurements, including height and weight, were
measured via Detecto Digital Stadiometer®. The metabolic phase employs the use the
ParvoMedics TrueOne 2400 Indirect Calorimetery System® calibrated to manufacturer
specifications. The subject is required to lie in a supine position in a quiet room for a total
of 60 minutes while attached to the CART via nasal/oral mask. The first 30 minutes of
the test is discarded in order for the subject to reach steady state. RMR is represented as
the average of last 15 minutes of cart measure extrapolated out to 24 hrs. The final phase
employs the use of the Cos Med BOD POD System® calibrated to manufactures
specifications using a vessel of a known volume (50L). The subject entered the chamber
wearing tight fitting clothing and a Lycra cap. The door is closed sealing the chamber for
testing to begin. Body fat percentage is estimated using the SIRI 1961 equation (LMI,
2004). A maximum of a ten minute period was allotted between CART and BOD POD
measurements.
14
Statistical Analysis
A repeated-measures one-way ANOVA with a Greenhouse-Geisser correction
was performed comparing RMR estimation models accompanied by a Fisher’s Least
Significant Difference (LSD) multiple comparisons test and 95% confidence intervals of
models differences against RMR-C. Inter-quartile range box plots used to visually
illustrate differences between methods and highlight outliers. Pearson correlations were
performed between each parameter method compared to RMR-C. Tabulation was
performed to sum the total proportion of subjects that varied greater than ±10% of the
RMR-C value. Measures were performed for the total sample and split by sex. Sample
characteristic expressed as mean ± standard deviation with model estimates expressed as
mean ± standard error of the mean (S.E.M.) with significance set at p<0.05.
Bland Altman measures of agreement were performed between RMR-C and
Nelson (1992) and Dore et al. (1982). Values expressed as mean difference ± (S.E.M.),
LLA (lower limits of agreement = mean difference - 2SD), and ULA (Upper limits of
agreement = mean difference + 2SD). Paired sample t-tests were performed between the
Nelson (1992) and Dore et al. (1982) estimation models and the RMR-C values.
Significance was set at p<0.05. All statistical analysis was performed using Minitab
Statistical software version 16 and SPSS version 20.
15
CHAPTER IV
RESULTS
Sample characteristics are illustrated in table 2 below. Based on the sample
characteristics, the study sample was comprised of young men and women in their early
twenties, who were within a normal BMI range and sex appropriate body fat percentages.
Table 2: Sample Characteristics
Parameter
Total
Male
n = 66
n = 25
Age (yr)
23.0 ± 4.7
22.7 ± 4.4
Height (cm)
170.2 ± 9.4
178.6 ± 7.0
Weight (kg)
68.9 ± 4.1
82.4 ± 12.9
2)
BMI (kg/m
23.8 ± 3.3
26.3 ± 3.3
1
FM (kg)
14.7 ± 6.0
14.6 ± 8.4
1
FFM (kg)
54.2 ± 12.6
67.8 ± 8.0
Body Fat %1
22.0 ± 7.7
17.9 ± 8.3
1
Density (kg/L)
1.0480 ± 0.0185 1.0580 ± 0.0199
Parameters expressed as Mean ± SD
1
Measured via BOD POD body composition estimates
Female
n = 41
23.2 ± 5.0
165.0 ± 6.6
61.1 ± 7.4
22.3 ± 2.3
14.8 ± 4.2
46.3 ± 6.7
24.5 ± 6.2
1.0419 ± 0.0156
Tabulation of RMR estimates that fell outside of ±10% of the cart measure are
illustrated in table 3 below. Here, the Dore et al. 1982 model was that highest performing
model with 41%, 44%, and 39% within range of the RMR-C estimates for total, males,
and females, respectively. The Nelson (1992) model performed with 14%, 8%, and 17%
within range of the RMR-C estimates for total, males, and females, respectively.
Concurrently, no other model performed with greater than 10% of subjects within 10% of
CART.
16
Table 3: Agreement between Estimation Models and RMR-C
Model
Total
Male
Female
Cunningham (1990)
3%
4%
2%
Dore et al. (1982)
41%
44%
39%
Bernstein et al. (1983)
3%
4%
2%
Garrow & Weber (1985)
2%
4%
0%
Ravussin & Bogardus (1989)
3%
4%
2%
Owen (1988)
3%
4%
2%
Kashiwazaki (1988)
3%
4%
2%
Mifflin et al. (1989)
5%
8%
2%
Nelson (1992)
14%
8%
17%
The above is represented as the percentage of subjects whose agreement between
estimation model and RMR-C is less than ±10%.
Box plots of the total sample and split by sex of RMR by model is represented in
figure 1. It is notable that for the female sample, there we numerous outliers identified
using the inter-quartile range method. The repeated measures one-way analysis of
variance of estimated RMR by model reached significance for the total sample and when
split by sex (total: F = 398.9, female: F = 210.0, male: F = 223.0; p <0.001). The multiple
comparisons test revealed that the Dore et al. (1982) model did not significantly differ
from the RMR-C measure (p = 0.907). However, when split by sex Dore et al. (1982)
significantly over-predicted for females by 150±43 kcals (p < 0.001) and under-predicted
for males 231±87 kcals (p = 0.014) when compared to RMR-C. Results of the repeated
measures ANOVA is represented in table 4.
17
Table 4: Results of Repeated Measures ANOVA
Model
Dore et al. (1982)
Sample
Total
Male
Female
d ± S.E.M.
6±48
-231±87
150±43
p-value
0.907
0.014
0.001
95% C.I.
(-90, 101)
(-411, -52)
(63, 237)
Nelson (1992)
Total
Male
Female
-436±44
-576±82
-351±46
<0.001
<0.001
<0.001
(-525, -348)
(-746, -406)
(-445, -258)
Cunningham (1980)
Total
Male
Female
1268±70
1216±97
1299±97
<0.001
<0.001
<0.001
(1128, 1407)
(1015, 1416)
(1104, 1495)
Bernstein et al. (1983)
Total
Male
Female
1253±73
1189±93
1291±103
<0.001
<0.001
<0.001
(1107, 1398)
(997, 1381)
(1083, 1499)
Garrow & Weber (1985)
Total
Male
Female
1524±76
1501±102
1538±106
<0.001
<0.001
<0.001
(1373, 1675)
(1291, 1712)
(1324, 1752)
Ravussin & Bogardus
(1989)
Total
Male
Female
1182±71
1134±98
1212±97
<0.001
<0.001
<0.001
(1041, 1323)
(933, 1336)
(1015, 1409)
Owen (1988)
Total
Male
Female
1198±75
1187±102
1205±104
<0.001
<0.001
<0.001
(1049, 1348)
(977, 1397)
(995, 1415)
Kashiwazaki (1988)
Total
Male
Female
1427±77
1434±102
1423±107
<0.001
<0.001
<0.001
(1273, 1581)
(1219, 1650)
(1206, 1640)
Mifflin et al. (1989)
Total
Male
Female
944±66
853±93
1000±90
<0.001
<0.001
<0.001
(812, 1077)
(661, 1045)
(818, 1182)
1
d ± S.E.M represented
Fisher’s LSD Multiple Comparisons Test, Significance (p <0.05)
n = 66 (male 25; female 41)
2
18
Estimated RMR by Model (Total)
RMR (kcal/day)
6000
5000
4000
3000
2000
1000
R
RM
C
els
N
on
m
ha
ing
n
n
Cu
re
Do
s
in
ter
du
te
bs
ar
ns
e
r
g
r
W
Be
d
Bo
an
nd
a
w
sin
r ro
us
Ga
v
Ra
O
w
en
K
hiw
as
i
ak
az
lin
iff
M
i
ak
az
lin
iff
M
i
ak
az
lin
iff
M
Estimated RMR by Model (Female)
RMR (kcal/day)
6000
5000
4000
3000
2000
1000
R
RM
C
els
N
on
am
gh
n
i
nn
Cu
re
Do
r
s
in
du
s te
s te
ar
eb
rn
g
r
e
W
B
d
Bo
d
an
an
w
in
r ro
ss
Ga
vu
a
R
O
w
en
K
a
iw
sh
Estimated RMR by Model (Male)
RMR (kcal/day)
5000
4000
3000
2000
1000
R
RM
C
els
N
on
m
ha
ing
n
n
Cu
re
Do
s
in
ter
du
te
bs
ar
ns
e
r
g
r
W
Be
d
Bo
d
an
n
a
w
sin
r ro
us
Ga
v
Ra
Figure 1: Box Plots of Estimated RMR by Model
19
O
w
en
K
hiw
as
Table 5 illustrates the results of sample paired t-tests and Bland Altman Limits of
Agreement of Nelson (1992) and Dore et al. (1982) compared to RMR-C values. As a
whole the Nelson (1992) model significantly under-predicted RMR when compared to
the RMR-C. The Dore et al. (1982) model was the best performing pre-existing model
when considering a total sample. However when split by sex, a significance difference
was detected with an over-prediction for female subjects and an under-prediction for
male subjects. Considering the Bland Altman Limits of Agreement, each model
performed outside of the ±10% acceptable limit when compared to mean RMR-C values.
The Dore et al. (1982) model performed best for females and the Nelson (1992) model
performed best for the male population.
Table 5: Paired Sample t-test and Limits of Agreement
d±S.E.M.1
LLA2
ULA3
p-value4
Model
t-Statistic
Nelson (1992)
436±44
-297
1139
0.001
9.88
Female
351±46
-230
933
0.001
7.58
Male
578±86
-245
1401
0.001
6.74
Dore et al. (1982)
-6±48
-767
756
0.907
-0.12
Female
-150±43
-688
338
0.001
-3.50
Male
229±91
-641
1100
0.019
2.53
Analysis based on Bland & Altman, 1986 with model comparison to RMR-C
d±S.E.M, LLA, ULA represented as kcal/day
1
d = mean difference; expressed as RMR-C – Estimation Model
2
LLA = Bland Altman Lower Limits of Agreement d – 2S
3
ULA = Bland Altman Upper Limits of Agreement d + 2S
4
p-value: null hypothesis d ≠ 0
20
CHAPTER V
DISCUSSION
The aim of the current study was to investigate which RMR estimation model,
using anthropometrics derived from ADP, had the best agreement when compared RMR
estimated via gas exchange IC. There has been limited research concerning the
applicability of predictive models in estimating RMR in young, healthy adults. Most
studies have focused on critically ill, obese, and/or mental health patients (Li,
Tereskowski, Edwards, Simpson & Buchholz, 2010). Dixon et al. (2005) compared ADP
estimates to hydrostatic weighing estimates and found no statistical or practical
difference in methods when using lean, young adult subjects (Dixon, Deitrick, Pierce,
Cutrufello & Drapeau, 2005).
IC measured via gas exchange has proven to be a powerful RMR measurement
method in vivo. However, the applicability of this technique might not be cost effective or
technically feasible for use by clinicians in everyday practice (Li, Tereskowski, Edwards,
Simpson & Buchholz, 2010). Therefore in practice, many anthropometric measurement
tools, including the Cos Med BOD POD System® use their body composition
measurements in tandem with predictive models to estimate RMR without regard to
which method was used to derive that model.
21
The BOD POD uses the Nelson (1992) model due to its inclusion of FFM (LMI,
2004). The Nelson (1992) model was formulated on a sample of obese and non-obese
men and women (n=213; 86 male, 127 female) based upon the contribution of FM and
FFM to RMR. The single best indicator of RMR in this model was FFM (r² =0.53-0.88 or
proportion of variation in RMR accounted for by FFM) (Nelson, Weinsier, Long &
Schutz, 1992). Similarly, Mifflin et al., (1990) found FFM as the best single predictor of
RMR (r² = 0.64) when considering age, sex, height, weight, FM, and FFM. (Mifflin et al.,
1990) However, the Nelson (1992) model was not derived using ADP but rather tradition
densitometry and anthropometry via hydro-densitometry (Nelson, Weinsier, Long &
Schutz, 1992). The previous statement might support the current study’s findings of low
prediction of RMR-C by FFM (r² = 0.30) in tandem with the under-prediction of RMR-C.
The ability to accurately assess body composition has a multitude of health
consequences associated with level of fatness. Barreira et al. (2012) explored the
anthropometric correlates of total and central body fat in relation to the development of
cardiovascular disease risk factors in male and female adults. Here an increase in total
body fatness was strongly correlated with increased cardiovascular risk (dyslipidemia and
elevated blood pressure) in addition to elevated fasting plasma glucose (Barreira, Staiano,
Harrington, Heymsfield & Smith, 2012).
The results of the current study identify the Dore et al. (1982) model as having the
best agreement with the RMR-C measurements. Dore et al. (1982) explored the metabolic
demands of women with different degrees of obesity. Here, RMR was 4% higher than
predicted in severely obese women. Following significant weight loss with a mean of
30kg, same subject RMR decreased to 1% higher than predicted. Here, a hyper-metabolic
22
state was not observed with increased weight loss. However the agreement of the
predictive model to gas exchange IC, improved as BMI decreased to a normal range. The
Dore et al. (1982) model was derived a sample of 140 women with varying degrees of
obesity. Similar to the Nelson (1992) model, Dore et al. (1982) includes FFM as a
predictor of RMR. This model did not employ ADP measurements in its derivation but
rather total body potassium to estimate skeletal muscle mass used interchangeably as
FFM (Doré, Hesp, Wilkins & Garrow, 1982). It is interesting that this model out
performed a dual sex model considering it is based on primarily obese female subjects.
Therefore based on the paired sample t-test results between the Nelson (1992) and
Dore et al. (1982) models compared to RMR-C, the Dore et al. (1982) model is the best
overall model when considering RMR estimation using ADP measurements. However,
there was lack of agreement of the Dore et al. (1982) model compared to RMR-C when
the sample was split by sex. The Bland Altman Limits of Agreement suggest that both
Dore et al. (1982) and Nelson (1992) models performed similar for the overall population
while Nelson (1992) best predicts for males and Dore et al. (1982) best predicts for
females. This supports the notion that when employing a regression based model,
clinicians/practitioners need to consider what sample the model was derived on to assess
whether its estimation is appropriate (Hasson, Howe, Jones & Freedson, 2011).
This study has highlighted that the Nelson (1992) model as a whole is
inappropriate for RMR estimation using ADP measurements. The Dore et al. (1982)
model might be a better option for use with this technique considering the study sample.
RMR estimation models should be chosen based on the origins of measurements they
employ.
23
Limitations
The current study is designed around a convenience sample of young, physically
active, lean adults. Therefore no true statistical consideration was given to sample size
requirements for estimating model agreement and model derivation. Nor, was any
consideration made for outliers. Additionally, this study does not consider the impact of
lean versus obese or ethnic variations in metabolism. Further studies are warranted in
validation of the derived model as well as studying the impact of increased body fatness
or ethnicity when using any of the studied or derived models in the current study.
Regression techniques were not appropriate for this study given sample size
limitations, but should be explored in future studies. Furthermore, no control to indicate
steady state RMR had been achieved was utilized beyond allowing 30 minutes to reach
steady state before measurement. Future studies should investigate the threshold of the
respiratory quotient (RQ) as an indicator of true RMR.
24
CHAPTER VI
CONCLUSION
The aim of the current study was to investigate the agreement of multiple preexisting RMR estimation models with gas exchange IC RMR measurements using body
composition measures obtained from ADP. The Nelson (1992) model used for RMR
estimation with ADP measures under-predicts total caloric needs for a total sample and
when split by sex. Based on analysis of variance, paired sample t-test, and Bland Altman
techniques, the Dore et al. (1982) model more accurately predicted RMR in the entire
sample but significantly varied when separated by sex. Based on limits of agreement the
total sample and the sample split by sex each had a different preferable model. It is
imperative for practitioners to consider the sample by which an estimation model is
derived and how and what anthropometric measures are used as its predictors to
appropriately estimate RMR. The recommendations of this study suggest that the Dore et
al. (1982) model might be more appropriate for estimation of RMR for young healthy
adult men and women.
25
REFERENCES
Barreira, T., Staiano, A., Harrington, D., Heymsfield, S., & Smith, S. (2012).
Anthropometric correlates of total body fat, abdominal adiposity, and
cardiovascular disease risk factors in a biracial sample of men and women. Mayo
Clin Proc, 87(5), 452-460.
Bland , J., & Altman, D. (1986). Statistical methods for assessing agreement between two
methods of clinical measurement. Lancet, (i), 307-310.
Crouter, S., Antczak, A., Hudak, J., Valle, D., & Haas, J. (2006). Accuracy and reliability
of the Parvomedics TrueOne 2400 and Medgraphics VO2000 metabolic systems.
Eur J Appl Pyhsiol, (98), 139-151.
Cooper, J., Watras, A., OBrien, M., Luke, A., Dobratz, J., Earthman, C., & Schoeller, D.
(2009). Assessing validity and reliability of resting metabolic rate in six gas
analysis systems. JADA, (109), 128-132.
Cunningham, J. (1991). Body composition as a determinant of energy expenditure: a
synthetic review and a proposed general prediction equation. Am J Clin Nutr,
(54), 963-969.
Dixon, C., Deitrick, R., Pierce, J., Cutrufello, P., & Drapeau, L. (2005). Evaluation of the
bod pod and leg-to-leg bioelectrical impedance analysis for estimating percent
body fat in national collegiate athletic association division iii collegiate athletes. J
Strength Cond Res,19(1), 85-91.
Doré, C., Hesp, R., Wilkins, D., & Garrow, J. (1982). Prediction of energy requirements
of obese patients after massive weight loss. Human Nutrition. Clinical
Nutrition, 36C(1), 41-48.
26
Frankenfield, D., Roth-Yousey, L., & Compher, C. (2005). Comparison of predictive
equations for resting metabolic rate in healthy nonobese and obese adults: A
systematic review. JADA, 105(5), 775-789.
Frakenfield, D., Rowe, W., Smith, S., & Cooney, R. (2003). Validation of several
established equations for resting metabolic rate in obese and nonobese people.
JADA 103(9), 1152-1159.
Fields, D., Wilson, D., Gladdens, B., Hunter, G., & Pascoe, D. (2001). Comparison of the
bod pod with the four-compartment model in adult females. Med Sci Sports
Exerc, 33(9), 1605-1610.
Fields, D., Goran, M., & McCroy, M. (2002). Body-composition assessment via airdisplacement plethysmography in adults and children: a review. Am J Clin Nutr,
(75), 453-467.
Hasson, R., Howe, C., Jones, B., & Freedson, P. (2011). Accuracy of four resting
metabolic rate prediction equations: Effects of sex, body mass index, age, and
race/ethnicity. J Sci Med Sport, (14), 344-351.
Haugen, H., Melanson, E., Tran, Z., Kearney, J., & Hill, J. (2003). Variability of
measured resting metabolic rate. Am J Clin Nutr, (78), 1141-1144.
Hoffman, C., & Hilderbrant, L. (2001). Use of the air displacement plethysmography to
monitor body composition: A beneficial tool for dietitians. JADA,101(9), 986988.
Li, A., Tereskowski, C., Edwards, M., Simpson, J., & Buchholz, A. (2010). Published
predictive equations overestimate measured resting metabolic rate in young,
healthy females. J Am Col Nutr, 29(3), 222-227.
Life Measurement Inc. (2004). Customer training: body composition tracking system.
LMI Part # 2102948 Rev B.
Maxwell, S., Kelley, K., & Rausch, J. (2008). Sample size planning for statistical power
and accuracy in parameter estimation. Annu Rev Pyschol, (59), 537-563.
Mifflin, M., St Jeor, S., Hill, L., Scott, B., Daugherty, S., & Koh, Y. (1990). A new
predictive equation for resting energy expenditure in healthy individuals. Am J
Clin Nutr, 51, 241-247.
Nelson, K., Weinsier, R., Long, C., & Schutz, Y. (1992). Prediction of resting energy
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27
Ravussin, E., & Bogardus, C. (1989). Relationship of genetics, age, and physical fitness
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28
APPENDICES
29
APPENDIX A
LETTER OF INFORMED CONSENT
The Validity of Air Displacement Plethysmography in Measuring Resting Metabolic Rate
in Young Adults
DESCRIPTION:
You are invited to participate in a research study investigating the
accuracy of air displacement plethysmography (BOD POD) in
measuring resting metabolic rate (RMR). From the information
gathered in this study we hope to learn more about BOD POD,
including its accuracy in measuring RMR.
PROCEDURES:
With your consent, we would like to perform a body composition
test using the BOD POD and also measure your RMR using a
metabolic cart. You will be required to fast for twelve hours
including abstaining from taking ergometric substances
(i.e. caffeine or smoking) and to wear tight-fitting clothing (i.e.
spandex or lyca material) or swim apparel. The first measurement
of your RMR will be measured by the metabolic cart. This will
require you to lay down for 1 hr while the metabolic cart measures
your RMR by collecting expired air using a face mask. Ten
minutes after that measurement is taken, we will measure your
RMR via the BOD POD. The BOD POD procedure measures your
height, weight, body fat percentage, and resting metabolic rate.
This is a non-invasive test which requires you to sit in an egg-like
chamber for two or three 50 second cycles.
RISKS:
We do not expect any risks associated with the testing procedure.
Since the BOD POD is an enclosed chamber you might have a
feeling of claustrophobia. There is a window for you to view out of
and we will stop the test if any discomfort is felt.
BENEFITS:
At the end of the testing you will receive a printed copy of the
results from the BOD POD and from the metabolic cart. The
information gathered can be used to get a better idea of where you
stand with your body composition and resting metabolic rate.
30
I understand that if I have any questions about the research being conducted I may
contact the chief researcher, Brian Miller, whose email address is listed below:
Brian Miller
[email protected]
I also understand that participation is voluntary and may opt out of the research at any
point without penalty.
__________________________________________________________________
Print Name
__________________________________________________________________
Signature
Date
31
APPENDIX B
IRB LETTER OF APPROVAL
32