Ability of Prediction Equations
to Estimate Basal Energy Expenditure
CJ Hines, PhD,
RD and LD Branford, PhD, RD
University of Invalidity
Obesity, or an excess in body
fatness, is a common risk factor for many chronic health problems
such as coronary heart disease, stoke, noninsulin-dependent diabetes
Currently in the United States, approximately 35% of adult women
and 31% of adult men aged 20 years and older are considered obese
(1); furthermore, prevalence of obesity has increased dramatically
over the last decade (2).
Obesity results when energy
consumed is greater than energy expended over a period of time.
The majority (60-70%) of total energy expenditure (TEE) is used
for basal energy expenditure (BEE) (3). Thus, an accurate BEE is
necessary to determine an individual’s caloric requirements for
energy balance. This is especially important in the obese population
since excess adiposity results from an energy imbalance.
Because of the alarming prevalence
of obesity in the United States, it has become necessary to assess
the accuracy of currently used methods for the estimation of BEE
for this group of individuals. The Harris-Benedict equation (HBE)
is a regression equation frequently used by health practitioners
to estimate BEE (4,5). There are separate equations for males and
females, and age, height, and ideal body weight are independent
variables in the HBE.
In addition to the HBE, the
Ireton-Jones equation (IJE) is used to estimate BEE in the obese
(6). The IJE is a regression equation developed specifically
to estimate the BEE of obese individuals. However, Ringwald et al.
reported that prediction equations, including IJE and HBE were not
accurate for determining BEE in obese adolescent cancer patients
It is important to evaluate
the ability of available prediction equations to determine BEE in
the healthy obese population. The purpose of this study is to compare
measured basal energy expenditure (MBEE) with calculated HBE and
the IJE in obese females.
Subjects were recruited from
Small Town, USA. Ten obese premenopausal Caucasian females between
the ages of 19 and 38 years completed the study. Due to time constraints,
subjects were not screened for medical conditions. Obesity was defined
as >100% ideal body weight. Approval for this study by Human
Subjects Review Board was not applied for since the study protocol
did not require blood draws.
Measured basal energy expenditure
(MBEE) was defined as energy expenditure measured on an outpatient
basis at rest. Subjects acclimated to mouthpieces and nose clips,
then rested in the supine position on a mattress for approximately
30-40 minutes, wearing light clothing without shoes. Heart rate
was monitored by chest electrodes with a digital wrist receiver
display (Polar USA, Stamford, Conn). Measurements did not commence
until the subjects’ resting heart rates did not decrease for 5 minutes.
BEE was determined using an
open circuit indirect calorimeter. Standard quality control protocol
was used, and one of three technicians measured the BEE.
Weight was measured in street
clothes/shoes to the nearest pound on a beam balance scale, and
height was measured with the head position in the Frankfort plane
(eye and ear level) to the nearest 1/10 centimeter with a measuring
board. Body mass index (BMI) was calculated. Percent body fat was
determined by sum of skinfolds. Fat and lean weight were calculated
from body weight and percent body fat.
Predicted BEEs (kilocalories
for 24 hours) were calculated using the HBE and the IJE. The HBE
for adult females is as follows (4):
BEE = 655.0955 + 9.5634 (W)
+ 1.8496 (H) – 4.6756 (A)
where W is ideal weight in
kg, H is height in cm, and A is age in years.
The IBW was calculated using
the HAMWI formula (8).
The Ireton-Jones equation
is as follows (6):
BEE (v) = 294(S) + 11(ABW)
+ 791 (R2=0.52)
where S=sex (0=female, 1=male)
and ABW=actual body weight.
Descriptive statistics included
means, standard deviations, and ranges. Paired t-tests were used
to compare measured BEE and predicted BEE using HBE and IJE. Pearson’s
correlation was used to determine the relationships between measured
BEE and BEE predicted by HBE and the IJE. Data were analyzed using
the Statistical Package for the Social Sciences (version 6.01, 1994,
SPSS Inc, Chicago, Ill), and significance was set at p< 0.05.
All subjects (n=10) were Caucasian.
The average age of participants was 29.5+5.4 years with a
range of 19 to 38 years. All subjects were over 100% of IBW as determined
by the HAMWI equation (8).
of all subjects are shown in Table 1. Average BEE for the entire
sample was 1359+252 kilocalories/d.
Mean + SD measured
and predicted metabolic rates with ranges are shown in Table 1.
There was no significant difference between MBEE and BEE calculated
with. BEE calculated with IJE was significantly higher than measured
BEE by 37% (p < 0.0001). Measured BEE was not significantly correlated
with BEE predicted by IJE or HBE (r= .15, p=.60 and r=.61, p=.21,
respectively). not HBE.
Since most widely available
methods of predicting energy expenditure were not developed for
obese individuals, using them may not be accurate or appropriate
when applied to the modern population (7,9). The HBE was chosen
for comparison to measured BEE in obese females in the present study
because it is one of the most commonly used equations to predict
BEE in the United States (4,5).
There are many reasons why
results from studies comparing measured to predicted BEE might differ.
Variability in environmental temperatures, inpatient or outpatient
status of subjects, the degree of familiarity of subjects with the
calorimetry measurement procedure, and/or the subsequent comfort
of the measurement equipment/apparatus can all influence measured
BEE values. BEE is known to rise in individuals subjected to less
than thermoneutral temperatures, in an attempt to achieve homeostatic
control of core body temperature (10). Berke et al. (11) reported
an 8% increase in BEE measured under outpatient conditions.
Another possible explanation
for the differences between measured and calculated BEE in this
study may be due to dietary intake and the nutritional status of
the subjects. Subjects in the Harris and Benedict were 12 hours
postabsorptive, in which case the thermic affect of food is thought
to be minimal. However, Dauncey reported that the thermic affect
of food could last 15 hours after the last meal (12).
Predictive equations remain
the most commonly utilized method to estimate BEE. The HBE was developed
in part to provide a normal standard to which BEE in disease states
can be compared (4,5). The HBE is still considered to be the most
accurate method to predict BEE, and the present study confirms its
accuracy. Prediction equations used to estimate BEE are regression
equations and may not be well suited for predicting an individual’s
BEE; however, when calorimetry equipment is not available, use of
the HBE is warranted.
Measurement of BEE using indirect
calorimetry is the most accurate method to determine BEE, but is
not always possible in a clinical or outpatient setting. This study
proves that the HBE should be used to estimate BEE in obese subjects
- National Center for Health
Statisitics: NHANES III reference manuals and reports. Centersfor
Disease Control and Prevention: Hyattesville, MD, 1996.
- Wolinski I, Klimis-Travis
D. Nutritional Concerns of Women. New York, NY: CRC Press, Inc;
- Williams SR. Nutrition
and Diet Therapy. St Louis, MO: Times Mirror/Mosby College Publishing;
- Harris JA, Benedict FG.
A Biometric Study of Basal Metabolism in Man. Washington, DC:
Carnegie Institute of Washington ; 1919. Publication No. 279.
5. Zeman FJ, Ney DM. Applications
of Clinical Nutrition. Englewood Cliffs, NJ: Prentice Hall; 1988.
6. Ireton-Jones CS, Turner
WW. Actual or ideal body weight: Which should be used to predict
energy expenditure? J Am Diet Assoc. 1991;91:193-195.
7. Ringwald-Smith, K, Williams,
R, Mackert, P, Stricklin, L, Sargent, T, and Bowman, L. Comparison
of energy estimation equations with measured energy expenditure
in obese adolescent patients with cancer. J Am Diet Assoc 99:
8. Hamwi, G. Changing dietary
concepts. In: Danowski TS, ed. Diabetes Mellitus: Diagnosis and
Treatment. New York, NY: American Diabetes Association, 1964.
9. Soares JM, Shetty PS.
Basal metabolic rate and metabolic economy in chronic undernutrition.
Eur J Clin Nutr. 1993;47:389-394.
10. Blaxter K. Energy
Metabolism in Animals and Man. New York, NY: Cambridge University
11. Berke EM, Gardner AW,
Goran MI, Poehlman ET. Effect of pre-testing environment on resting
metabolic rate measurements. Am J Clin Nutr. 1992;55:626-629.
12. Dauncy MJ. Metabolic
effects of altering the 24-hour energy intake in man using direct
and indirect calorimetry. Br J Nutr. 1980;43:257-269.
measured and predicted BEE of subjects
Characteristic Mean +SD
Height (cm) 166.2 +
4.6 158.5 - 175.9
Weight (kg) 105.7 +21.72
86.4 - 159.1
Body Mass Index (BMI)a
38.3 + 8.1 30.8 - 59.1
% Body fat 43.4
+ 3.9 35.6 - 51.4
Actual BEE (kcal/d) 1416.8
+ 238.3 997.0 – 1775.0
HBE c 1427.1 +
51.3* 1349.0 – 1514.0
IJEf 1953.6 +
247.9** 1741.0 – 2541.0
Mass Index (kg/m2).
Free Mass (kg).
calculated with Harris-Benedict equation using ideal body weight
*Not significantly different
from measured BEE.
from measured BEE at P<0.0001.
Please evaluate this article
in the space below.
When you submit your evaluation, you will be able to compare yours
with the experts.