“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Note: This article will be published in a forthcoming issue of
the International Journal of Sport Nutrition and Exercise
Metabolism. This article appears here in its accepted, peerreviewed form; it has not been copyedited, proofed, or
formatted by the publisher.
Section: Original Research
Article Title: Comparison of Measured and Predictive Values of Basal Metabolic Rate in
Brazilian Paralympic Track & Field Athletes
Authors: Claudia Ridel Juzwiak1, Ciro Winckler1,2,3, Daniel Paduan Joaquim2, Andressa da
Silva3,4, and Marco Tulio de Mello3,4
Affiliations: 1Universidade Federal de São Paulo, Brazil. 2Brazilian Paralympic Committee,
Brazil. 3Brazilian Paralympic Academy, Brazil. 4Universidade Federal de Minas Gerais,
Brazil.
Running Head: Basal metabolic rate of Paralympic athletes
Journal: International Journal of Sport Nutrition and Exercise
Acceptance Date: November 30, 2015
©2015 Human Kinetics, Inc.
DOI: http://dx.doi.org/10.1123/ijsnem.2015-0015
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
COMPARISON OF MEASURED AND PREDICTIVE VALUES OF BASAL
METABOLIC RATE IN BRAZILIAN PARALYMPIC TRACK & FIELD ATHLETES
Basal Metabolic Rate of Paralympic Athletes
Authors: Claudia Ridel Juzwiak1, Ciro Winckler1,2,3, Daniel Paduan Joaquim2, Andressa Silva
de Mello3,4, Marco Tulio de Mello3,4
1
Universidade Federal de São Paulo, Brazil
2
Brazilian Paralympic Committee, Brazil
3
Brazilian Paralympic Academy, Brazil
4
Universidade Federal de Minas Gerais, Brazil
Addresses of the authors:
Ciro Winckler: [email protected]
Universidade Federal de São Paulo - Departamento de Ciências do Movimento Humano/ Rua
Silva Jardim, 136 - Santos – 11015-020, Brazil
Daniel Paduan Joaquim: [email protected]
Street: Alexandre Fleming, 619/53, Aparecida – Santos, SP – 11040-010, Brazil
Andressa Silva de Mello: [email protected]
Street: Antonio Augusto de Carvalho, 97/501, Ouro Preto - Belo Horizonte, MG - 31340-020,
Brazil.
Marco Túlio de Mello: [email protected]
Universidade Federal de Minas Gerais - Escola de Educação Física. Avenida Presidente
Carlos Luz - 3003/3004 - Belo Horizonte, MG – Brazil
Corresponding author:
Claudia Ridel Juzwiak: [email protected]
Universidade Federal de São Paulo - Departamento de Ciências do Movimento Humano/ Rua
Silva Jardim, 136 - Santos – 11015-020, Brazil
Phone number: 55 13 38783763
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
ABSTRACT
To compare Basal Metabolic Rate (BMR) predicted by different equations with measured
BMR of the Brazilian paralympic track & field team aiming to verify which of these
equations is best suited for use in this group. Method: 19 male and 11 female athletes
grouped according to functional classification (vision impairment-VI, limb deficiency-LD,
and cerebral palsy-CP) had their BMR measured by indirect calorimetry and compared to
values predicted by different equations: Cunningham, Owen, Harris-Benedict, FAO/OMS,
Dietary Reference Intakes, and Mifflin. Body composition data were obtained by skinfold
measurements. Results were reported as mean and standard deviation and analyzed using the
Wilcoxon test and Pearson´s Correlation Coefficient. The Root Mean Squared Prediction
Error (RMSPE) was calculated to identify the similarity between the estimated and predicted
BMR. Results: Mean measured BMR was 254.2, 262.4, and 262.7 kcal/kg of fat free
mass/day for VI, LD, and CP, respectively. Owen´s equation had the best predictive
performance in comparison to measured BMR for LD and CP athletes, within 104 and 125
kcal/day, while Mifflin’s equation predicted within 146 kcal/day for VI athletes. Conclusion:
for this specific group of athletes the Owen and Mifflin equations provided the best
predictions of BMR.
Key-words: energy requirement, athletic performance, persons with impairment
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Introduction
Basal metabolic rate (BMR) expresses the amount of energy required to maintain
metabolic activity and vital functions in the waking condition (Institute of Medicine, 2005)
and accounts for 60-75% of daily energy expenditure (Compher et al., 2006; Wang et al.,
2000). The main difference between BMR and resting metabolic rate (RMR) lies in the
resting and fasting time before measurements. Many variables such as age, height, body
mass, ethnicity, and body surface area, body composition, diet-induced thermogenesis and
recent physical activity may influence the prediction of RMR (Buchholtz et al., 2001; Volp et
al., 2011).
The equation to estimate BMR which resulted from the pioneer study of Harris and
Benedict in 1919 has been widely adopted for clinical and nutritional assessment. Since then,
several researchers have attempted to compare and develop other equations based on different
populations, anthropometric and physiological data (Frankenfield et al., 1998).
A practical and common way to estimate energy needs is to predict the basal or
resting energy rate (BMR or RMR) through equations and to multiply the result by an activity
factor, which represents the athletes’ daily activities (American College of Sport Medicine –
ACSM et al., 2009; Thompson & Manore, 1996). Furthermore, the use of methods such as
doubly labeled water (DLW), direct and indirect calorimetry, which can provide more
accurate information on energy requirements (Burke, 2001) are seldom available in the
everyday setting and routine of sports nutritionists.
An imbalance between energy intake and expenditure resulting in energy deficit can
result in muscle mass loss, a greater risk of muscle injury and fatigue, increased time for
recovery between bouts of exercise, and menstrual cycle disturbances (ACSM et al., 2009;
Carbone et al., 2012; Thompson & Manore, 1996).
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Although sports for athletes with impairment have been increasingly gaining space in
the global scenario, there are still few studies investigating the nutritional issues of this
population (Crosland & Broad, 2011). There are a number of equations which were
developed for sedentary or moderately active subjects which have been used to estimate
energy requirements of athletes (ACSM et al., 2009; Thompson & Manore, 1996); however,
the accuracy of these equations in estimating the BMR of athletes with impairment is
unknown. Athletes with vision impairment, intellectual impairment, cerebral palsy, short
stature, limb deficiency and spinal cord injuries can participate in different track and field
events (Winckler, 2012), grouped according to their functional capabilities, which minimizes
the effect of different impairments on the outcome of competition (Tweedy &
Vanlandewijck, 2011).
The characteristic energy system contribution and energy cost of the various
disciplines in track & field (Carbone et al., 2012), as well as the varied impairments presented
by those athletes renders a very heterogeneous group and imposes a great challenge in
determining their energy requirements (Crosland & Broad, 2011).
It is assumed that persons with vision impairment present physiological and
functional capacities similar to individuals without impairment; however, studies on resting
energy expenditure or the energy expenditure with physical activities in this population are
scant (Crosland & Broad, 2011). On the other hand, persons with other impairment can have
their energy requirements affected by their functional limitation. For instance, persons with
spinal cord injuries can have 14 to 27% lower RMR than non-injured subjects (Buchholz &
Pencharz, 2004); a study with achondroplastic dwarves found varied values of BMR
measured by densitometry and a greater RMR/kg body weight than in normal height
individuals (Owen et al., 1990). The presence of muscle spasms (athetosis) in adults with
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
cerebral palsy increases RMR (Johnson et al., 1995) and the presence of an imbalance in gait
affected energy expenditure of limb deficiency and le autres (Crosland & Broad, 2011).
The objective of this study was to compare the BMR predicted by different equations
with BMR measured by indirect calorimetry in Paralympic athletes of the Brazilian national
track & field team in order to verify which of these equations is more appropriate for use in
this population, so as to establish the best protocol to estimate the track & field team energy
requirements and to assist the athletes in their nutritional care.
Method
The Ethics Committee of the Federal University of São Paulo approved this study
under appraisal #0294/11, and an informed and written consent was obtained from all
participants.
Subjects
Thirty Paralympic track & field athletes (19 male and 11 female) participated in this
cross-sectional study. These athletes participated in the London Paralympic games (2012), as
well as in the Toronto ParaPan Games (2015). Athletes were grouped according to their
functional classification in: vision impairment (VI), limb deficient (LD), and ambulatory
cerebral palsy athletes (CP). The LD group (n=11) was composed by eight limb deficient
athletes and three impaired muscle power (two had one impaired arm each and one athlete,
both legs); of the eight CP athletes, five were hypertonic, one ataxic and two athetotic. The
characteristics of the study participants are described in Table 1.
Procedures
Data were collected in the first ‘Week of Evaluation and Testing’, held by the
Brazilian Paralympic Committee six times a year, in which the Athletic Paralympic Team is
evaluated by a multidisciplinary health professional group.
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Basal Metabolic Rate
BMR assessments occurred between 05:00 and 07:00 AM. Fifteen athletes were
assessed each morning and the BMR device was recalibrated after every 5 athletes. Athletes
slept for at least eight hours and did not leave their beds before the evaluation. Athletes were
instructed to not go to the bathroom during the night. Fasting was maintained for at least 12
hours prior to testing, with no intake of coffee or other caffeine-containing products, as well
as drugs that affect the BMR. For the tests, subjects were evaluated lying in a supine position
and in a thermoneutral environment, with temperature between 22° to 26°C. Tests were
conducted using COSMED Fitmate® PRO® device, validated by Nieman et al., (2006) and
Vandarakis et al., (2013), calibrated using the ATS/ERS guidelines COSMED 3L calibration
syringe. The athletes used the equipment’s full face mask, allowing the collection of exhaled
air in the respirometer. The test protocol consisted of 15 minutes of collection. The first five
minutes were disregarded; afterwards, samples were analyzed every 30 seconds. The average
values were applied to Weir’s (1949) equation to calculate the BMR, expressed in
kcal/kg/day and kcal/kg fat-free mass/day.
Anthropometric and Body Composition Assessment
In a fasting state for at least two hours and wearing bathing suits, athletes had their
body mass (kg) (electronic scale, 0.1 kg accuracy) and height (cm) (stadiometer Sanny®, 0.1
cm accuracy) measured.
All athletes were assessed standing, with both feet together and the head placed in the
Frankfort plane. One unilateral lower limb deficient athlete was measured wearing his
prosthesis and the other bi-lateral lower limb athlete had his body length assessed in a supine
position, from the top of the head to the bottom of the stumps, using a retractable measuring
tape
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Body composition was estimated by skinfolds measurements, using a Sanny® skinfold
calliper, (0.1cm accuracy, constant 10g/mm² pressure on contact). The skinfolds (triceps,
subscapular, biceps, iliac crest, abdominal, thigh and calf) were measured according to the
procedures proposed by the International Society for the Advancement of Kinanthropometry
(ISAK), USA (2001), while midaxillary skinfold according to Heyward and Stolarczyk
(1996). Athletes with cerebral palsy were measured on the dominant side, so as not to affect
assessment. Body density was estimated by Jackson and Pollock (1978) and Jackson and
Pollock (1980) equations, for men and women, respectively, and all converted to body fat
percentage by Siri’s (1961) equation.
BMR was estimated using the equations presented in Table 2.
Statistical Analysis
Data were analyzed using SPSS version 15.0. The normality of the groups was tested
by Kolmogorov-Smirnov. The Wilcoxon matched-pair signed-rank test was used to compare
non-parametric data between groups according to sex and functional classification. Pearson
Correlation Coefficient was used to verify if fat-free mass correlated with measured BMR.
Statistical significance was set at p ≤0.05.
The Root Mean Squared Prediction Error (RMSPE) was used to evaluate how BMR
estimated from each equation is close to the actual measured BMR. Smaller values show that
the predicted value is closer to the measured RMR (Thompson & Manore, 1996).
𝑅𝑀𝑆𝑃𝐸 =
√Σ measured BMR−predicted BMR)2
𝑛
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Results
The studied population was very heterogeneous, both regarding the sports discipline,
as well as the athletes’ impairment. The Kolmogorov-Smirnov analysis showed normality of
the distribution for both body composition and BMR; however, the LD group had three
outlier athletes, whose condition was associated with the amputation level, as they presented
the greatest amputation area among the group (more than 10% of their body mass)
(Osterkamp, 1995). When the BMR results (kcal/day) of these athletes were excluded from
the analysis, the distribution was normal. However, when analysis was conducted with BMR
results in kcal/kgFFM/day, this adjustment enabled the inclusion of the three outlier athletes.
Table 3 shows athletes’ characteristics according to their impairment and sex.
Table 4 shows the comparison between measured and predicted BMR in kcal/kg/day
and kcal/kgFFM/day using different equations.
Fat-free mass estimated by skinfolds correlated with measured BMR (r=0.758)
Mean predicted BMR values obtained from all equations were higher than mean
measured values (Table 4).
No statistical difference (p=0.68) was found for BMR values between men and
women.
For the VI athletes, Owen, DRI, FAO/OMS and Mifflin equations provided results
with no difference from measured BMR values. The RMSPE analyses (Table 5) showed that
of the four, Mifflin’s equation presented the best performance for VI, overestimating energy
requirements within 146 kcal/day.
For the LD and CP athletes all the equations produced results statistically different
from measured BMR values. With the exclusion of outliers from the LD group, Owen’s
(1986/1987) equations showed no significant difference from estimated BMR (p=0.093).
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Despite the lack of statistical significance between measure and predicted BMR, the
RMSPE analysis indicate that both Owen’s and Mifflin’s equation had a good performance in
estimating BMR for LD (within 104 and 117 kcal/day, respectively) and CP athletes (within
125 and 130 kcal/day, respectively). These lower values of RMSPE ranged from 8 to 10%
overestimation.
The Cunningham’s (1980) equation overestimated energy requirements by 24%, 23%,
and 22% for VI, LD, and CP, respectively.
When RMSPE values were analyzed grouping athletes by functional classification
and sex, values for male VI athletes using Owen’s (1986/1987) equations resulted in 106
kcal. A smaller RMSPE difference was also found using Mifflin’s (1990) equation for male
VI (64 kcal/day). The highest RMSPE values were found for female VI athletes for all
equations.
Discussion
Anthropometric and body composition parameters are of paramount importance in
determining energy requirements (Osterkamp, 1995; Portal et al., 2010) and can be
considered as an indirect indicator of nutritional status (Ribeiro et al., 2011). However, there
are still few studies describing body composition characteristics of athletes with varied
impairment and from different sports. The difficulty in collecting data is due to the different
classes and degrees of impairment, which prevent the establishment of a representative
sample; furthermore, there is no "gold-standard" evaluation for this population, although
some studies suggest Dual-energy X-ray Absorptiometry (DXA) as the reference method
(Hildreth et al., 1997; Ribeiro et al., 2011).
Due to the difficulties in estimating body composition in the physically impaired
population and the lack of validation of the current methods, Van de Vliet et al. (2011)
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
suggest skinfolds sum as a good strategy to follow up body composition alterations in
athletes. Male CP athletes had skinfolds sum close to the values found (56.5±6.36 mm) for
male runner athletes in a Brazilian study (Santos & Guimarães, 2002). Female throwers in
this study (1 VI and 2 CP) had mean skinfolds sum (212±25.92 mm), below values found for
female throwers (277.03±33.01 mm) studied by Santos and Guimarães (2002).
The amount of fat-free mass and its influence on BMR/RMR has been widely
investigated and demonstrates a linear relationship between those two variables (Johnstone et
al., 2005; Müller et al., 2002; Wang et al., 2000), corroborating the relationship found in this
study. This relationship exists because fat-free mass is more metabolically active than
adipose tissue’, which may contribute to the energy required and RMR (Cunningham, 1980;
Hill et al., 1995; Mifflin et al., 1990). Studies have shown that fat-free body mass, regardless
of sex is one of the physiological variables that correlated most closely with RMR, so it was
expected that equations using this parameter would be the most accurate (Buchholz et al.,
2001; Owen et al., 1986; Thompson & Manore, 1996). However, caution should be taken
when using fat-free mass values estimated from skinfolds, especially when applying nonspecific equations and in case of subjects with high fat-mass.
In our study there was no significant difference in BMR measured between groups of
athletes with different impairments. However it should be considered that due to the small
size of the sample, male and female athletes were grouped together. Also, due to the athletics
characteristics, athletes participate in different sports disciplines.
Despite the lack of statistical results when comparing measured and predictive BMR,
it was possible to identify that some equations could provide a surplus energy. If a surplus
energy intake is maintained constant, depending on the periodization (i.e. transition phase
when there is a decrease in the energy expenditure) and time span, it can lead to increased
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
body mass and body fat, with deleterious impact on athletic performance (Miller & Braun,
2009; Stellingwerff et al., 2011).
Some equations may overestimate BMR of women due to the large difference in body
composition between sexes, as was demonstrated in the study by Buchholz et al., (2001).
Owen’s study (1986/1987), showed that the equations proposed by Harris and Benedict
(1919) and Cunningham (1991) overestimate measured RMR. Owen’s (1987) equation for
females considers total body mass as a variable, instead of fat-free mass; notwithstanding,
mean predicted values were closer to measured values than those obtained from other
equations. As the VI group was composed by both male and female athletes, this can explain
the higher RMSPE values found in the VI group. However, when the RMSPE were analyzed
for male and female VI athletes separately, the values for male VI were smaller than those
found for women.
Furthermore, all the equations used in this study to predict the BMR are non-specific
for athletes, especially for athletes with impairment. Owen’s (1986/1987) equations, which
had the best performance for LD and CP athletes, was developed using data from 60 lean and
obese men and from 44 healthy and obese women. In addition, the groups studied by
Cunningham (1980) and Harris and Benedict (1919) were not of athletes, but of relatively
thinner subjects when compared to the group studied by Owen (1986/1987) and Mifflin
(1990).
In a study conducted by Thompson and Manore (1996) with male and female
endurance athletes, RMR predicted by Owen’s (1986/1987) equation was significantly lower
than measured RMR by 229 kcal/day and 335 kcal/da for men and women, respectively.
Also, Thompson and Manore (1996) found that Cunningham’s (1980) equation best predicted
RMR for both male and female athletes (within 158 kcal/day and 106 kcal/day, respectively).
In our study Cunningham’s (1980) equation had the worst performance, overestimating daily
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
kcal needs from 314 kcal to 357 kcal, depending on the group, with the exception of female
VI athletes, when analyzed separately.
Owen’s (1986/1987) and Mifflin’s (1990) equation showed the lowest kcal/day
difference in relation to measured BMR for LD, respectively 104 and 117 kcal. Mifflin’s
(1990) equation was obtained from a population of nonathletic subjects, consisting of 247
woman and 251 men with normal body mass and between 19 and 78 years old, and yielded a
significant difference in the study of Thompson and Manore (1996), as opposed to our
findings.
Considering the characteristics of the populations from which the predictive equations
were developed it is not clear why Owen’s (1986/1987) and Mifflin’s (1990) equations had
the best performance in predicting BMR in this group of athletes with impairment. It is
possible that it is because our group of athletes had a greater variability in percentage body
fat due to the nature of their sport disciplines than the endurance group studied by Manore
and Thompson (1996), and a more similar range of body fatness to Owen’s study compared
to those used in developing the Cunningham equation.
Predicting BMR of athletes with impairment poses a double challenge as equations
were developed for non-athletic and populations without impairment. Therefore, the best
option for this group would be to measure their actual BMR or use methods such as DLW;
however, such facilities are costly and not easily available.
Both RMR and the energy cost of exercise are important data to understand energy
expenditure of paralympic athletes, as their impairments may affect one or both
measurements (i.e. involuntary muscle spasms and higher RMR in PC; mode of ambulation,
extension of amputation, type and adjustment of prostheses and energy expenditure in
exercise). This knowledge will be useful in the task of nutritionally advising these athletes.
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Although our aim was to find an equation which would yield a more accurate result
for a group composed of heterogeneous athletes (as regards functional classification and
sports discipline), it can be suggested from our results that some existing predictive equations
could align better with different disability groups, depending on the characteristics of the
subjects assessed in the studies which proposed those equations. Studies testing those
equations with a greater number of paralympic athletes could help clarify this issue.
Notwithstanding, this study’s finding suggests that Mifflin’s (1990) and Owen’s (1986/1987)
equations could be used to predict BMR of this specific group.
In the case of the athletes whose conditions proved to deviate the results from a
normal distribution of the population, the adjustment of BMR in kcal/kg/day and especially in
kcal/kgFFM/day seems to be more adequate than using absolute kcal/day values, because it
allows adjusting to their condition regarding muscle atrophy or lack of missing limb´s muscle
mas.
Our results should be considered with caution until new studies with athletes with
different impairments, grouped not only according to impairment, but by sex and sport
discipline, produce results describing the energy expenditure (both BMR and energy
expenditure of exercise) so that more comparative data is available to better understand this
issue, while allowing the development of specific equations. We also suggest that further
studies should focus on the energy expenditure on physical activity of athletes with
impairment and the estimation of their energy availability.
Furthermore, in addition to the BMR, the other components of the energy
expenditure, including
exercise and other (in)voluntary activities (i.e. muscle spasm in
athetosis), have to be assessed carefully to determine daily energy needs. Frequent follow up
of the athletes as regards body composition and performance evaluation is important to assure
that energy intake is being optimal.
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
This study brings additional information on athletes with varied impairments and their
energy requirement. The identification of the equation which best predicts the BMR will
provide a more accurate estimation of the energy requirements of this group of athletes.
Conclusion
This study’s finding suggests that Owen´s and Mifflin’s equation can be used to predict BMR
of this very heterogeneous group of track & field paralympic athletes.
Acknowledgements
All of the authors are grateful to the Comitê Paralímpico Brasileiro (CPB)/Academia
Paralímpica Brasileira (APB), Conselho Nacional de Desenvolvimento Cientifico e
Tecnológico (CNPq), Associação Fundo de Incentivo a Psicofarmacologia (AFIP), Centro
Multidisciplinar em Sonolência e Acidentes (CEMSA), Centros de Pesquisa, Expansão e
Difusão do Instituto do Sono (CEPID/SONO), Fundação de Amparo a Pesquisa do Estado de
Sao Paulo (FAPESP) and the Centro de Estudos em Psicobiologia e Exercício (CEPE).
Declaration of interests
The authors report no conflicts of interest. The authors alone are responsible for the content
and writing of this article.
Authorship
The study was designed by CRJ and CW. Data were collected by CRJ, MTM and ASM. Data
were analyzed by DPJ and CW. All authors carried out the interpretation of the results,
preparation and revision of the manuscript and approved its final version.
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
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“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Table 1. Distribution of athletes according to functional classification, sex and discipline
FUNCTIONAL
MALE
FEMALE
CLASSIFICATION
n=19
n=11
VI
T11, T12
n=05
n=03
LD
T43, T45, T46
n=04
n=02**
CP
T35,T36,T37, T38
n=02*
n=03
Middle and long
VI
T11, T12
n=02
-
distance runners
LD
T46
n=03
-
CP
-
-
-
VI
T11
-
n=01
LD
F44, F57
n=02
-
CP
F35, F36, F37
n=01
n=02
VI
-
-
-
LD
T46
-
n=01**
CP
T36, T37, T38
n=02*
n=02
Sprinters
Throwers
Jumpers
LD = limb deficiency; VI= vision impairment; CP= cerebral palsy.
*Two male athlete of the CP group participates in jumping and sprinting events.
**One of the female athletes of the LD group participates in jumping and sprinting events.

Two female athletes of the CP group participate in jumping and sprinting events.
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Table 2 – BMR predictive equations
Author (year)
Predictive equations
1. Cunningham (1991)
BMR = 370 + 21.6 x (FFM)
2. Cunningham (1980)
BMR = 500 + 22 x (FFM)
3. Owen (1987)
Male
BMR = 290 + 22.3 x (FFM)
4. Owen (1986)
Female
BMR = 50.4 + 21.1 x (BM)
5. Harris Benedict (1919)
Male
BMR = 66.47 + 13.75 x (BM) + 5 x (H) - 6.76 x (A)
Female
BMR = 655.1 + 9.56 x (BM) + 1.85 x (H) - 4.68 x (A)
6. DRI 2005 (adults)
Male
BMR = 293 - 3.8 x (A) + 456.4 x (H*) + 10.12 x (BM)
Female
BMR = 255 - 2.35 x (A) + 361.6 x (H*) + 9.39 x (BM)
7. FAO/OMS (1985)
Male
18 to 30 y
BMR = 15.3 x (BM) + 679
30 to 60 y
BMR = 11.6 x (BM) + 879
Female
18 to 30 y
BMR = 14.7 x (BM) + 496
30 to 60 y
BMR = 8.7 x (BM) + 829
8. Mifflin et al (1990)
Male
BMR = 9.99 x (BM) + 6.25 x (H) – 4.92 x (A) + 166 x (1) - 161
Female
BMR = 9.99 x (BM) + 6.25 x (H) – 4.92 x (A) + 166 x (0) – 161
BRM= Basal Metabolic Rate (kcal/day); BM= Body Mass (kg); H=Height (cm); H*=Height (m); FFM= Fatfree mass (kg); A=age (years).
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes” by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Table 3. Anthropometric, body composition and Basal Metabolic Rate of the athletes according to sex and impairment
MALE
FEMALE
n=19
n=11
VI
LD
CP
VI
LD
CP
n=7
n=9
n=3
n=4
n=2
n=5
Body Mass (kg)
68.2±11
64.6±5.6
66.7±2.9
65.3±10.5
47.6±6.8
60.8±7.4
Height (m)*
1.71±0.1
1.74±0.05
1.77±0.04
1.69±0.1
1.59±0.09
1.63±0.06
Age (years)
26±4.1
29±9.6
23±2.9
26±4.4
32±0
23±4.1
Training (h/day)
5.1±1.4
4.8±1.1
3.7±0.5
4.7±1.2
5±0
4.3±0.9
 Skinfolds (mm)** Φ
93±51.1
82±36.5
61±7.3
98±54
99.5±21.9
156±43.1
1426±401
1447±144
1557±93
1387±390
1086
1206±151
BMR (kcal/kg/day)
22±3.6
22±2
23±0.8
21±5.2
22±0.3
18±2.1
BMR (kcal/kgFFM/day)
25±3.7
25±2.3
25±0.7
24±4.2
28±1.4
25±3.5
%BF
14±9.3
10±5.8
6±1.4
15±10
18±5.3
25±4.8
58.5±11.4
57.7±5.7
62.2±1.4
54.9±11
38.7±8.11
45.3±2.9
BMR (kcal/day)
FFM (kg)
LD = limb deficiency; VI= vision impairment; CP= cerebral palsy.
BMR=Basal Metabolic Rate (kcal) measured by calorimetry; FFM= Fat free mass (kg); %BF= body fat percentage.
*Height (m) of one bi-lateral lower limb athlete was not considered (value=1.48m, without prosthesis, supine position)
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes” by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Table 4 – Comparison of measured BMR and predicted BMR by different equations according to the impairment (mean and standard-deviation).
kcal/kg/d
p
VI
LD
CP
n=11
n=11
n=08
kcal/kgFFM/d
p
25±4.2
kcal/kg/d
p
22±1.8
kcal/kgFFM/d
p
26±2.4
kcal/kg/d
p
21±2.8
kcal/kgFFM/d
p
Measured BMR
21±4.6
25±2.7
Cunningham (1991)
24±2.1
0.026
28±1.6
0.033
25±1.3
0.003
28±1.6
0.003
23±2.4
0.028
29±1.1
0.028
Cunningham (1980)
26±2.3
0.003
31±2.1
0.003
27±1.5
0.003
31±2.1
0.003
26±2.5
0.018
31±1.5
0.018
Owen (1986/1987)
23±1.5
0.155
28±2.1
0.155
24±1.6
0.041
27±0.8
0.041
23±1.8
0.018
28±2.0
0.018
(1919)
24±1.9
0.041
29±2.6
0.041
26±3.2
0.008
29±5.3
0.008
24±1.9
0.018
29±2.1
0.018
DRI (2002)
23±1.9
0.091
28±2.2
0.091
25±1.3
0.004
29±2.4
0.004
23±2.1
0.018
29±1.7
0.018
FAO/OMS (1985)
24±2.5
0.068
29±3.3
0.075
26±1.3
0.004
29±2.8
0.004
24±1.9
0.018
29±1.7
0.018
Mifflin (1990)
24±2.5
0.068
27±2.5
0.182
26±1.3
0.004
28±2.0
0.016
24±1.9
0.018
28±1.8
0.018
Harris & Benedict
LD = limb deficiency; VI= vision impairment; CP= cerebral palsy.
p≤0.05 for statistical significance.
“Comparison of Measured and Predictive Values of Basal Metabolic Rate in Brazilian Paralympic Track & Field Athletes”
by Juzwiak CR et al.
International Journal of Sport Nutrition and Exercise Metabolism
© 2015 Human Kinetics, Inc.
Table 5 - Root Mean Squared Prediction Error (RMSPE) (kcal) of predicted BMR in
comparison to measured BMR, according to disability
Equations
VI
LD
CP
n=11
n=11
n=8
Cunningham (1991)
188
160
148
Cunningham (1980)
341
312
299
Owen (1986/1987)
169
104
125
Harris & Benedict (1919)
204
160
198
DRI (2002)
164
168
145
FAO/OMS (1985)
206
196
159
Mifflin (1990)
146
117
130
VI= visually impaired; LD= limb deficient; CP= cerebral palsy