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1 Exercise Physiology Laboratory, ABSTRACT Smith, D. A., J. Dollman, R. T. Withers, M. Brinkman, J. P. Keeves, and D. G. Clark. Relationship between maximum aerobic power and resting metabolic rate in young adult women.
J. Appl. Physiol. 82(1): 156-163, 1997.
fitness
INTRODUCTION THE RESTING METABOLIC RATE (RMR) is the energy required
by the rested and postabsorptive body to maintain physiological
processes. It normally comprises 60-75% of daily
energy expenditure, and most of the interindividual variability is
explained by such factors as age, sex, genetics, body composition, body
temperature, and energy balance (27). Factors that potentiate RMR will,
therefore, impact on energy balance and reduce the risk of health
disorders associated with excess fat stores.
Recent studies examining the effect of aerobic fitness on RMR have been
equivocal in their outcomes. Some investigations report a positive
relationship between aerobic fitness and RMR (cross-sectional studies:
3, 4, 31, 33, 38, 47; longitudinal studies: 20, 23, 30, 47), whereas
others do not (cross-sectional studies: 1, 7, 14, 19, 34, 51;
longitudinal studies: 5, 8, 49). These disparate results can be
attributed to various methodological factors, including design of
longitudinal and cross-sectional studies; sample sizes and statistical
power; measurement errors for RMR and body composition; statistical
treatment of the data to account for age, gender, body composition, and
other covariates known to influence RMR; short-term energy balance;
pretesting conditions of subjects; time span between the previous bout
of exercise and RMR measurement; and criteria for selecting and
classifying subjects into trained and untrained groups. Furthermore,
few studies have investigated the influence of aerobic fitness on RMR
in premenopausal women, presumably because of the periodicity of RMR
with the menstrual cycle (6, 26). These issues were acknowledged in the
design of the present cross-sectional study that investigated the
influence of aerobic fitness and body composition on the RMR of young
healthy women.
METHODS
The literature is inconclusive as to the chronic effect of
aerobic exercise on resting metabolic rate (RMR), and furthermore there
is a scarcity of data on young women. Thirty-four young women
exhibiting a wide range of aerobic fitness [maximum aerobic
power (
O2 max) = 32.3-64.8
ml · kg
1 · min1]
were accordingly measured for RMR by the Douglas bag method, treadmill
O2 max, and fat-free
mass (FFM) by using Siri's three-compartment model. The interclass
correlation (n = 34) between RMR
(kJ/h) and O2 max
(ml · kg1 · min1)
was significant (r = 0.39, P < 0.05). However, this
relationship lost statistical significance when RMR was indexed to FFM
and when partial correlation analysis was used to control for FFM differences. Furthermore, multiple linear-regression analysis indicated that only FFM emerged as a significant predictor of RMR
(kJ/h). When high- (n = 12) and
low-fitness (n = 12) groups were
extracted from the cohort on the basis of
O2 max scores, independent t-tests revealed
significant between-group differences (P < 0.05) for RMR
(kJ · kg1 · h1)
and
O2 max
(ml · kg1 · min1)
but not for RMR (kJ/h), RMR (kJ · kg
FFM1 · h1),
and FFM. Analysis of covariance of RMR (kJ/h) with FFM as the covariate
also showed no significant difference
(P = 0.56) between high- and
low-fitness groups. Thus the results suggest that
1) FFM accounts for most of the
differences in RMR between subjects of varying
O2 max values and
2) the RMR per unit of FFM in young healthy women is unrelated to
O2 max.
O2 max) = 32.3-64.8
ml · kg1 · min1]
were recruited from the local community. Subjects were young (19.3-31.4 yr), nonobese [Quetelet's index = 18.4-27.3
kg/m2, body fat (BF) = 16.5-35.0%], self-reported mass stable (±2 kg) during
the preceding year, nonsmokers, and not suffering from diseases or
taking any medications that are known to affect energy metabolism or
restrict exercise testing, and none had a history of any clinical
eating disorders.
O2 max tests, each
subject's heart rate (HR) response to running helped identify the
optimal running speed for her
O2 max test.
RMR.
RMR was determined by open-circuit indirect calorimetry on 2 days
during the middle-to-late follicular phase of the menstrual cycle
(days 1-7 after menstruation).
The lower of these two measurements was used in further calculations.
Subjects were 12-h fasted, having consumed a standardized meal before 8 P.M. of the preceding evening; were
euhydrated; and had refrained from exercise for at least 36 h. After
being transported to the laboratory for arrival between 7:00 and 8:30
A.M. in a relaxed state, subjects
voided and then donned a preweighed hospital gown before having their
nude body mass measured to the nearest 25 g. After supine
rest for 50 min in a thermoneutral environment (24 ± 0.5°C),
oxygen consumption (O2) was
measured by the collection of expirate through an R2600 Hans Rudolph
respiratory valve (Kansas City, MO) and into a 150-liter Douglas bag
(Plysu, Buckinghamshire, UK) that had been previously flushed with the
subject's expirate and evacuated. Collections were made for two 10-min
periods separated by 15 min during which time the mouthpiece and
noseclip were removed while the subject remained relaxed in the supine
position. A third 10-min collection was made if the
O2 of the first two trials
differed by >5%. This occurred on only 4 of the 68 days of RMR
testing, and on these occasions the two closest values were averaged.
The CO2 (model LB-2, Sensormedics,
Yorba Linda, CA) and O2 (model
S-3A, Ametek, Pittsburgh, PA) concentrations of the dry mixed expirate were determined by gas analyzers that were calibrated before each expirate collection by using Lloyd-Haldane-verified gases that spanned
the physiological range of measurement. Gas volumes were measured by
a Parkinson Cowan CD-4 dry gas meter; the accuracy of
this instrument was checked daily against a 350-liter
Tissot gasometer (Warren E. Collins, Braintree, MA). The
resultant O2 and
respiratory exchange ratio (RER) values were then converted to
kilojoules per hour (18), and the average of the two trials was
regarded as the RMR for that day. Our intraday
(subject 1: 5 RMR trials on the same
day) and interday (subject 2: 5 RMR
trials on alternate days) coefficients of variation were 2.2 and 2.4%, respectively.
HR was monitored continuously during the RMR trials by using an
electrocardiogram (ECG; B-D Electrodyne model ST-219, Becton Dickinson,
Sharon, MA), and oral temperature
(Toral) was measured by a
calibrated digital clinical thermometer on three occasions during each
RMR trial.
Fat-free mass.
All tests were conducted in the morning when subjects were 12-h fasted,
were euhydrated, and had refrained from exercise for 36 h. To minimize
inter- and intrasubject biological variability, all tests of body
composition were conducted on the same day and within 3 days of the RMR
measurements. Fast-free mass (FFM) was determined by using Siri's
three-compartment body composition model (45) as follows.
1) Body density (BD) was determined
by underwater weighing at residual volume (RV). The ventilated RV was
measured by helium dilution before and after the underwater mass trials
with the subject immersed to neck level. The average of the three
heaviest immersed masses, body mass in air, the mean RV, and a
correction for water density were used to calculate BD. The intraclass
correlation for test-retest reliability for the BD measurement of 6 women (19-34 yr, 20.1-33.9% BF) was 0.98, and the mean of
absolute differences was 0.0018 g/cm3.
2) Total body water (TBW) was
measured by isotopic dilution using a deuterium
(2H) dose of 40 mg
2H2O/kg.
A saliva sample was taken from the subjects on arrival at the
laboratory to determine the background enrichment of
2H2O.
Subjects then ingested the
2H2O
dose (~100 g), which was followed by three distilled water rinsings
of ~30 g each. Subjects were then required to remain relaxed until a
second saliva sample was taken 3.5 h later. The 2H concentrations in the doses and
saliva samples were measured on an isotope-ratio mass spectrometer
(model 602D, V. G. Micromass, Manchester, UK), which was calibrated
against Vienna Standard Mean Ocean Water and International Atomic
Energy Agency enriched standards 302A and 302B. In the calculation of
the isotope dilution space, corrections were made for the volume of
urine passed during the equilibration period and the 4% exchange of
2H with nonaqueous hydrogen (42).
The intraclass reliability for repeated measurements of TBW in five
women (20-28 yr, TBW = 23.22-35.79 kg, 20.1- 37.0% BF)
was 0.995, and the mean of absolute differences was 0.38 kg.
O2 max.
O2 max was measured
by using a model 18-60 Quinton treadmill (Seattle, WA). Minute
ventilation was monitored by a calibrated volume tranducer (P. K. Morgan, Kent, UK) that was connected to the inspiratory port of an
R2700 Hans Rudolph respiratory valve. HR was measured during the
O2 max test as
previously described for RMR, and a medically qualified doctor
monitored the ECG display for abnormalities.
After a warm-up period of horizontal running at 7.5 km/h for 3-5
min, the treadmill speed was increased to either 10 (untrained) or 12 km/h (trained) for 2 min and then was held constant while the elevation
was augmented by 2%/min until the subjects were exhausted. The
criterion for the attainment of
O2 max was a change
of <2
ml · kg1 · min1
between successive workloads. Test-retest reliability for
O2 max (l/min) of 6 subjects (23-41 yr,
O2 max = 2.12-5.61 l/min) yielded an intraclass correlation of 0.997 and a
mean of absolute differences of 0.07 l/min.
Estimated energy intake and expenditure.
Daily energy intake and macronutrient intake were estimated before
testing by using a self-reporting 7-day food diary (Wednesday to
Tuesday inclusive). Each subject was supplied with a 0-2 kg portable digital scale with a taring function and taught how to accurately complete the food diary. Subjects were also informed of the
importance of maintaining usual eating habits during the study period.
Body mass was measured daily, and records were reviewed with the
subjects at the completion of the 7 days. The CSIRO Australia, Division
of Human Nutrition computer program, which is based on both the
Australian (15) and British (29) food tables, was used to calculate
total daily energy intake (kJ/day). Daily energy expenditure (kJ/day)
was also recorded for the same 7-day period by using an activity diary.
Each day was divided into 1-h periods, and subjects were instructed to
record to the nearest 5 min how long they spent sleeping, sitting
relaxed, sitting erect, standing, strolling, walking, jogging, running,
or sprinting. All other activities that could not be listed under one
of the nine major headings (e.g., swimming, cycling, and ironing) were
itemized separately, together with an assessment of the intensity at
which they were performed. We also tabulated aerobic-type activities that were designed to either maintain or improve fitness. This category
included activities with an energy expenditure greater than normal pace
walking (25). Energy expenditure (kJ/day) was estimated by using the
subject's body mass on that day and the appropriate energy expenditure
values compiled by McArdle et al. (25).
Statistical analyses.
All variables were tested for normality, and variables being compared
were tested for homogeneity of variance by using SPSS (28). The RMR and
O2 max values, which
were indexed for mass and FFM by simple division, were also corrected
for mathematical bias (40). Interclass correlations
(n = 34) indicated the association between RMR and other measured variables. Partial correlation analyses
were used to measure the association between RMR (kJ/h) and
O2 max (l/min) while
controlling for interindividual variation in body composition and
anthropometric variables, Toral,
RER, and resting HR (HRrest).
Similarly, the partial coefficients for the regression of RMR (kJ/h) on
FFM and O2 max were
tested for significance. This technique is a better measure of
association if the independent variables (FFM and
O2 max) are
correlated because only the component of each independent variable that
is unique to that variable is regressed against the dependent variable (RMR, kJ/h; Ref. 12). A power analysis (16) indicated that 12 subjects
per group were required to detect a 10% potentiation of RMR
(kJ · kg
FFM1 · h1)
with a power of 0.80 at P = 0.05 (2-tailed test), assuming a coefficient of variation of 8.5%. The data
from the 12 subjects with the highest and lowest fitness on the basis
of O2 max
(ml · kg1 · min1)
were, therefore, extracted from the cohort. Independent
t-tests were used to compare these
groups for RMR,
O2 max, and FFM. After confirmation that the data did not violate the assumptions of
linearity and homogeneity of regression, RMR (kJ/h) was also analyzed
for between-group differences by using analysis of covariance (ANCOVA)
with FFM as the covariate. The 0.05 probability level was used for all
two-tailed tests of statistical significance.
RESULTS
1 · h1)
and FFM (kJ · kg
FFM1 · h1)
are presented in both their corrected (40) and uncorrected forms.
Average energy intake estimated from the 7-day diet diary was 2,381 kJ/day less (P = 0.0001) than energy
expenditure derived from the 7-day activity diary (9,511 ± 2,589 vs. 11,892 ± 2,277 kJ/day). Table 3
contains the bivariate correlations between RMR (kJ/h,
kJ · kg1 · h1,
and kJ · kg
FFM1 · h1)
and indicators of aerobic fitness, body composition, and other independent variables. Whereas absolute RMR (kJ/h) was
significantly correlated (P < 0.05)
with many variables, including
O2 max (ml · kg1 · min1),
only Toral was significantly
correlated with RMR (kJ · kg
FFM1 · h1;
r = 0.52, P < 0.05). Table
4 shows the partial correlations between
RMR (kJ/h; dependent variable) and
O2 max (l/min;
independent variable), when controlled for age,
Toral, and body composition parameters. Significant correlations between RMR and
O2 max are maintained
when the influences of percent BF, fat mass (FM), age,
Toral, height, RER, and
HRrest are controlled for, but
significance is lost when FFM is partialed out
(r = 0.16, P > 0.05). Multiple linear
regression of RMR (kJ/h) on FFM (kg) and corrected
O2 max (ml · kg1 · min1;
Ref. 40) resulted in the following equation
|
O2 max explain a
significant percentage of the RMR variance (adjusted
R2 = 41.4%, P = 0.0001), but only the
partial regression coefficient for FFM (3.39) was significant
(P = 0.0003). This
indicates that an increase in FFM results in a significant rise in
predicted RMR. In contrast, the partial regression coefficient for
O2 max of 0.45 (P = 0.45) indicates that an increase
in O2 max has a
nonsignificant effect on RMR. A similar conclusion was reached when
O2 max was expressed
absolutely (l/min) or on a relative (ml · kg1 · min1)
but uncorrected basis.
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O2 max (l/min and
ml · kg1 · min1;
P = 0.0001). A difference was also
present for percent BF (P = 0.0002),
and the high-fitness group had an average of 3.3 kg more FFM than did
the low-fitness group (P = 0.09). The
RMR, energy intake, energy expenditure,
Toral, and
HRrest data are presented in Table
2. On average, the high-fitness group had a lower
HRrest (P = 0.014) but expended more energy
at rest (P 
0.05 for
kJ · kg1 · h1)
than did the low-fitness group, and they had 16.1 and 24.3% greater
daily energy requirements as assessed by activity (1,749 kJ/day) and
diet diaries (2,097 kJ/day), respectively. The high-fitness group was
also involved in significantly more aerobic activity than was the
low-fitness group (3,332 ± 2,418 vs. 745 ± 839 kJ/day; P = 0.002), and this may partially
explain the differences in their energy requirements. Student's
t-tests revealed no group differences
for absolute RMR (kJ/h; P = 0.14) and
RMR corrected for FFM differences (kJ · kg
FFM1 · h1;
P = 0.64). Additionally, an
ANCOVA demonstrated no significant between-group difference
in RMR (kJ/h) controlled for individual variation in FFM
(P = 0.56;
F ratio for group main effect = 0.36).
DISCUSSION
This study investigated the relationship between RMR and aerobic
fitness in adult women (n = 34, 19-31 yr) exhibiting a wide range of
O2 max values
(32.3-64.8
ml · kg1 · min1).
There was no relationship between
O2 max
(ml · kg1 · min1)
and RMR controlled for interindividual FFM differences by using the
three following statistical methods: the corrected ratio of RMR to FFM
(kJ · kg
FFM1 · h1;
Ref. 40), partial correlation analysis, and significance testing of the
partial regression coefficients from the regression of RMR (kJ/h) on
FFM and O2 max
(ml · kg1 · min1,
l/min; Ref. 12). When our subjects were categorized into groups of high
and low fitness (each n = 12)
according to their
O2 max (ml · kg1 · min1),
independent t-tests indicated no
significant between-group differences for absolute RMR (kJ/h) and
corrected RMR (kJ · kg FFM1 · h1).
Furthermore, ANCOVA revealed no significant between-group difference in
RMR (kJ/h) with FFM as the covariate. Our findings, therefore, indicate
that there is no relationship between aerobic fitness and RMR per
kilogram of FFM.
The literature is equivocal as to the relationship between aerobic
fitness and RMR. This situation remains unchanged when attention is
focused on studies that have been conducted on nonobese premenopausal
women. Approximately equal numbers of investigations have reported that
aerobic fitness is associated with an increase (cross-sectional
studies: 3, 11, 38; longitudinal studies: 23, 47) or no change
(cross-sectional studies: 1, 51, present investigation; longitudinal
study: 49) in RMR per kilogram of FFM. Whereas three of the four latter
groups demonstrated that trained women have significantly
(P < 0.05) greater RMRs than do
their untrained counterparts when expressed absolutely (1) or per unit
of body mass (51; present investigation), this was presumably because the untrained subjects have more FM, which has a much lower metabolic rate than does the FFM. Also, whereas Tremblay et al. (47) found an 8%
elevation in RMR per kilogram of FFM consequent to an 11-wk training
program for eight subjects they classified as moderately obese, neither
O2 max nor submaximal
work test data were reported. It is, therefore, impossible to relate
the increase in RMR to changes in aerobic fitness even though there
were statistically significant decreases in both mass and FM. The lack
of agreement in the literature regarding the relationship between
aerobic fitness and RMR (kJ · kg
FFM1 · min1)
may be related to the following, which will be discussed individually: measurement error, statistical power, and sample size; subject selection criteria; methodologies for RMR and FFM determinations; experimental design; and strategies for statistical analyses of the
data.
of 0.80 and 0.05, respectively (16).
Subject selection criteria.
The importance of selecting subjects free of conditions that can affect
valid measurements cannot be overstated, yet many studies (14, 21, 23,
49) fail to acknowledge the confounding influence of some of the
following variables on energy metabolism: energy imbalance over the
preceding 6 mo, diabetes and other diseases, medications, smoking
status, caffeine ingestion, and obesity. The present study screened
volunteers for such factors.
RMR methodology.
Pretesting protocols for RMR determinations vary between studies and
may account for some of the variance in results. While recent studies
(7, 8, 11, 31-38, 41) have tended to apply more stringent
conditions regarding the physical activity, menstrual status, prior
sleeping arrangements, medications, and food intake, other
investigations (5, 14, 19, 23, 49, 51) have failed to control for some
of these known confounding variables. Because an acute elevation of
O2 has been reported for up
to 12-24 h postexercise (2), our study prohibited exercise
training on the previous day. This 36-h restriction of exercise before RMR testing is consistent with the protocols of Ballor and Poehlman (3), Poehlman and colleagues (30, 31, 33, 38), Schulz et al. (43), and
Broeder et al. (7, 8).
The timing of the RMR measurement in relation to the menstrual cycle
may also be an important consideration. As indicated by Bisdee et al.
(6), resting energy metabolism is lower in the late follicular phase
compared with the late luteal phase, and while some studies involving
women have acknowledged this source of variation (1, 3, 11, 38), others
failed to consider this problem (51). Our study restricted RMR
determinations to the mid-to-late follicular phase of the menstrual
cycle.
Because RMR is assessed in the morning, the preceding night's sleeping
arrangements have been the focus of some investigations (4, 10, 48).
Although Berke et al. (4) found a significantly lower RMR (7-8%;
P < 0.01) for inpatient compared
with outpatient conditions for elderly subjects, two other research
groups (10, 48) reported no difference for previously habituated young
adults. The inpatient procedure is more expensive, time consuming, and inconvenient for both subjects and researchers. Furthermore, it may
upset the subjects because of a lack of quality sleep due to an
unfamiliar bed and foreign surroundings. We, therefore, chose to let
our subjects sleep at home, and they were then driven to the laboratory
by one of the researchers.
Being a participant in a scientific investigation is a unique
experience for most people; this is associated with the increased likelihood of anxiety, which may elevate RMR. However, few studies (7,
14, 21, 33) report habituating their subjects to the laboratory
environment, technical staff, and the testing procedures before data
collection. Subjects in this study were habituated to all testing
procedures during a preliminary visit to the laboratory. Previous
trials in this laboratory (unpublished observations) demonstrated a
further reduction in the RMR of some subjects between the second and
third RMR test. This prompted us to measure the RMR twice
after the habituation trial with the subjects' true RMR being the
lower of these two testing sessions.
The method of gas collection for indirect calorimetric determination of
RMR has varied between studies. Segal (44) concluded that there were no
statistically significant differences
(P > 0.05) among the RMRs of
habituated subjects when expirate was collected via a mouthpiece with
noseclip, ventilated hood, or face mask. Our study, using mouthpiece
and noseclip, involved two 10-min collection periods that were
separated by 15 min when the mouthpiece and noseclip were removed and
the subject remained at rest in a supine position. This contrasts with
those studies that incorporated longer continuous collection periods of
30 (1, 7, 11, 35, 36), 45 (20, 30, 31, 38), 60 (5), or 90 min (23). Our
protocol was based on the assumption that subjects become increasingly
restless in a motionless supine position and that coughing, swallowing,
and minor body movements are more likely to contribute to an elevated
RMR over a protracted collection period.
To maintain energy balance, the athlete in heavy training must increase
energy intake to match the requirements of the exercise plus recovery.
While the trained subjects in most RMR studies are restricted from
exercise for a period >24 h before RMR measurement, it is presently
unclear whether energy intake during this period of inactivity matches
a lower energy expenditure or remains elevated, thereby sustaining a
state of positive energy balance before the RMR determination. The
impact of a changing energy intake in the days before RMR testing was
first investigated in the 1920s by Kleitman (cited in Ref. 22) and more
recently by several investigators (13, 22, 27, 52). Dauncey (13) found
a significant increase in RMR (measured at least 14 h after the
previous meal) of 12 ± 3% when subjects consumed an additional
~4,000 kJ during the previous 24 h. It was also evident that the
individual response of RMR to overeating was large (range 0-25%;
14). Woo et al. (52) reported that the postprandial RMR of six
normal-weight young men was elevated by both an overfeeding-induced
positive energy balance and increases in physical activity. When
subjects were in positive energy balance from overeating 1,177 kcal
during the previous day, RMR was increased relative to a control day
(248 ± 6 vs. 235 ± 4 ml
O2/min). Furthermore, when
subjects consumed this additional intake (1,177 kcal/day) but
maintained energy balance by increasing energy expenditure, RMR was
further elevated (259 ± 9 ml
O2/min). It is interesting to note
that the mean rise in RMR due to the overeating-induced positive energy
balance of 1,177 kcal/day (13 ml
O2/min) was almost equivalent to
that when physical activity was augmented by a further ~1,177
kcal/day (11 ml O2/min). The
larger SDs for the two experimental conditions indicate a greater
variability for the individual response to changes in energy input and
energy expenditure. To date, only the study by Schulz et al. (43)
restricted the energy intake of trained subjects to their sedentary
requirements before RMR measurement. They found no RMR difference
between trained and untrained subjects. Undereating is unlikely in most
groups, but the influence of this may not be dire because the lowering
of RMR due to underfeeding appears to take a few days to a week (27). Furthermore, if subjects are aware that they will miss the following day's breakfast and perhaps lunch, then the possibility of
overfeeding, particularly on the preceding night's meal, is high. With
the possibility of a hypermetabolic state that varies in magnitude between people, it would appear prudent to control pretesting energy
intake (22, 27).
FFM determination.
The two-compartment underwater-weighing model involves the
measurement of BD, which is then fed into either the Brozek et al. (9)
or Siri (45) equation to estimate the percent BF. The body mass can
therefore be partitioned into the FM and FFM; most studies of resting
energy metabolism have used this model to estimate the FFM (1, 3, 7, 8,
19, 30-38, 43). However, both the Brozek et al. (9) and Siri (45)
equations assume that the overall density of the four FFM components
(water, protein, bone mineral, nonbone mineral) is invariant at 1.1000 g/cm3. We estimated the FFM via
the three-compartment model of Siri, which uses measurements of BD and
TBW to partition the body mass into FM, water, and fat-free dry tissue.
This model is more valid than the traditional two-compartment
underwater-weighing model because it controls for biological
variability in TBW, which possesses both the largest percentage
(73.7%) and lowest density (0.9937 g/cm3) of the four FFM
components. The error of ±2% body mass for estimating FM by using
this three-compartment model is considerably smaller than that of
±4% for the two-compartment underwater-weighing method (45).
Hence, our methodology enables the RMR to be indexed against a more
valid measure of the FFM.
Experimental design.
Failure to agree about the relationship between exercise training and
RMR has emerged from both cross-sectional and longitudinal study
designs. In cross-sectional studies, a comparison of RMR is made
between individuals or groups differing in training status or
O2 max. To reduce the
effect of confounding factors on the relationship between aerobic
fitness and RMR, investigators usually try to recruit subjects who are
homogeneous for these confounding factors; if this is not possible then
statistical manipulations are used to remove or "partial" out
their influence. Cross-sectional investigations of metabolism are
expedient and cost effective, but their results may be compromised by
the fact that there is no control for the genetic influence on both RMR
and O2 max; genotype
can represent up to 45% of the variance in RMR remaining after
adjustment for FFM, age, and gender (40).
Statistical treatment of the data.
A statistical bias is introduced when absolute RMR is simply divided by
body mass or FFM. This is of concern when a comparision is made of
individuals or groups who differ in these variables (40, 46) because
the nonzero intercept of the relationship between RMR and body mass or
FFM results in an underestimate of the indexed RMR of the larger
compared with the smaller person (40). This bias is also present for
the comparison of
O2 max indexed to body
mass
(ml · kg1 · min1)
and FFM (ml · kg
FFM1 · min1);
hence, the present study corrected both RMR and
O2 max for statistical
bias.
The statistical techniques of partial correlation and ANCOVA have also
been used to compare RMR between groups or individuals differing in FFM
(7, 33, 43). These techniques are not without some limitations (12, 17,
24). The partial correlation is considered less meaningful than
regression techniques, especially if the primary assumption of
bivariate normality is not maintained (12). This study also used the
method of partial regression coefficients to test for the influence
of O2 max on
RMR independent of FFM. The coefficients from the regression of RMR
(kJ/h) on the independent variables (FFM and
O2 max in
ml · kg1 · min1
or l/min) are significance tested for their contribution to the variance in RMR (12). This technique is preferred to the use of partial
correlations (24). Regardless of the use of any of the aforementioned
statistical methods to remove the influence of FFM on RMR and
O2 max, there was
neither a significant relationship between RMR and
O2 max nor a
significant difference for RMR between our high- and low-fitness
groups.
Relationship between RMR and other variables.
The previously reported (7, 32) positive correlation between FFM and
absolute RMR was confirmed in this study
(r = 0.66, P = 0.0001). Furthermore, absolute RMR
was also related to other variables indicative of body size such as
height and mass (Table 3). Whereas a number of variables were
associated with RMR
(kJ · kg1 · h1),
only Toral was significantly
related to RMR (kJ · kg
FFM1 · h1;
r = 0.52, P < 0.01). This finding is in
agreement with those of Rising et al. (41). However,
Toral is considered a poor
indicator of average body temperature if indeed there is such a
measure, and it can be quantified, because it is normally lower than
other sites thought to reflect core temperature (e.g., tympanic,
esophageal, and rectal) and is quite sensitive to external conditions
(39). Further research is, therefore, needed to quantify better whole body temperature to confirm this suspected correlation with RMR.
Conclusion.
This study investigated the relationship between RMR,
,
and FFM in young women with a wide range of
O2 max. While a
high-fitness group expended more energy at rest
(kJ · kg1 · h1)
than did a low-fitness group, there was no evidence to suggest the
existence of a positive relationship between RMR and
O2 max when
statistical control was exerted for the influence of FFM.
ACKNOWLEDGEMENTS
We are grateful to Prof. Charles M. Tipton and two anonymous reviewers for their valuable assistance in revising this manuscript.
FOOTNOTES
Address for reprint requests: R. T. Withers, Exercise Physiology Laboratory, School of Education, The Flinders University of South Australia, GPO Box 2100, Adelaide 5001, South Australia.
Received 26 April 1995; accepted in final form 26 August 1996.
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ABSTRACT
0.001;
0.25 (24.86)