Key words
hydration - fat mass - fat-free mass - relative reliability - absolute reliability
Introduction
Body composition is generally considered as a determinant of performance in many sports,
and as such is included in most of the testing procedures routinely used in the physiological
assessment of athletes [2]. Among possible implications, it allows monitoring training-induced changes in order
to formulate adapted nutritional and hydration recommendations, or to adjust the choice
of training methods and the training load. It also allows monitoring the growth of
children and adolescents by identifying those at risk of under- or over-fatness, and
implementing a nutritional and/or a psychological follow-up when required [10].
Body composition covers different components that are not easy to assess simultaneously,
including fat mass, fat-free mass, bone minerals and water content. Reference methods
such as dual energy X-ray absorptiometry, hydrodensitometry or air-displacement plethysmography
are expensive and often impractical in the follow-up of athletes when they are numerous.
Anthropometric methods such as the sum of skinfolds, the body mass index and different
circumference measurements are more accessible, but suffer important limitations.
For instance, the sum of skinfolds is both operator-dependent and skinfold calliper-dependent,
and relies on some questionable assumptions, such as a similar distribution of subcutaneous
and internal fat in all individuals of the same sex [15]. The body mass index, which was developed to standardize weight by height, and other
anthropometric measurements generally poorly reflect body composition [5]
[16] and are particularly inadapted to sports where both body mass and muscular mass
are important, such as rugby union.
Bioelectrical impedance is considered a good compromise between reference methods
and anthropometric methods because it requires limited operator training, is not operator-dependent,
does not expose subjects to ionizing radiation, is financially affordable, and is
fast and easy to implement [14]. With this method, a low-level electrical current is passed through the body, and
the resulting signal of impedance (or opposition to the flow of current) is used to
assess the different components of body composition. In fact, considering that electrolytes
in the body’s water are excellent conductors of electrical current, the signal of
impedance has the potential to provide a very good measure of total body water and
fat-free mass (since water represents around 73% of this component). On the other
hand, if we consider that adipose tissue has a low water content, it is a poor conductor
of electrical current and can also be estimated from the signal of impedance. The
main limitation with this method is the difficulty in converting resistance and reactance,
the two components of the signal of impedance, into body composition [18]. It requires a regression equation developed from the comparison between this signal
and body composition measured with a reference method. Considering that the signal
of impedance may differ according to the analyzer [8]
[19] or individual characteristics such as ethnic group, sex, age and physical fitness
[14], these equations are complex to develop. They also represent the real added value
of each commercial brand since they cannot be used interchangeably with other analyzers.
Consequently, they require a specific examination of their reliability in order to
determine the limits beyond which the changes observed during athlete follow-up were
induced by the training intervention and not just due to measurement noise.
The aim of this study was therefore to examine the intra- and inter-day reliability
of body composition measurements provided by a multifrequency bioelectrical impedance
meter, the Z-Metrix (BioparHom, Bourget du Lac, France).
Methods
Participants
Eighteen healthy, well-trained students in physical education (13 males and 5 females)
from the same ethnic group (white) gave their written informed consent to participate
in the study. The usual training load of their academic program was 8 to 12 h of sport
per week. Their mean±SD age, body mass and body height were 21.6±3.5 years, 68.2±9.9 kg
and 175.7±6.7 cm, respectively (22.1±3.9 years, 72.2±7.8 kg and 178.8±4.8 cm for males
and 20.4±2.1 years, 57.8±6.8 kg and 167.4±2.3 cm for females). The protocol was conducted
in accordance with recognized ethical standards and national/international laws [9]
Experimental design
Once included, participants came to a laboratory room of constant temperature (21°C)
on four consecutive days, both in the morning (between 10 and 12 AM) and in the evening
(between 4 and 6 PM). This design was chosen because despite the care taken by staff
to standardize tests and measurements used in the follow-up of athletes, it is not
always easy to exactly control the time of day. Body mass was measured to the nearest
0.1 kg and body height to the nearest 0.5 cm at the beginning of each visit, with
participants barefoot and wearing light clothes. Participants were asked to refrain
from strenuous training, to eat similarly and to abstain from alcoholic beverages
24 h before and throughout the 4-day experimental period. They were also asked to
arrive to the laboratory fully hydrated at least three hours after their last meal
(i. e. breakfast or lunch) and to empty their bladder before each session.
Body composition
Skin was cleaned with alcohol before each measurement. Current injector electrodes
with a surface size of 9 cm2 were placed just below the metacarpophalangeal joint in the middle of the dorsal
side of the right hand, and just above the external malleolus of the right leg. Detector
electrodes were placed on the dorsal side of the right wrist, midline with the radial
styloid process and the ulnar styloid process, and on the right leg, 5 cm vertically
above the upper side of the current injector electrode placed above the external malleolus.
According to the specifications of the manufacturer, participants adopted a standing
position, with arms separated from trunk by about 30° and legs separated by about
45°. Body composition was assessed with a Z-Metrix (BioparHom, Bourget du Lac, France).
Z Metrix is a multifrequency bioelectrical impedance meter that injects a low-intensity
current (77 μA) from the ankle to the wrist. Total body resistance, reactance and
impedance are computed by using the frequency range of 1 kHz to 1 MHz. Undisclosed
equations validated by the manufacturer and equations available in the literature
were used to estimate hydric, tissular, metabolic and ionic indicators presented in
[Table 1].
Table 1 Origin of the equations used by the Z-Metrix to estimate body composition compartments.
|
Parameter
|
Origin of equations
|
|
Hydric indexes
|
|
Total water (L)
|
Undisclosed equation from BioparHom
|
|
Extracellular water (L)
|
Undisclosed equation from BioparHom
|
|
Intracellular water (L)
|
Undisclosed equation from BioparHom
|
|
Extracellular/intracellular water ratio (%)
|
Ratio
|
|
Lean body mass hydration (%)
|
Undisclosed equation from BioparHom
|
|
Metabolic indexes
|
|
Metabolic activity (a.u.)
|
Undisclosed equation from BioparHom
|
|
Quantity of active cells (%)
|
Kotler et al. (1996)
|
|
Availability of active cellular mass (a.u.)
|
Barbosa et al. (2003); Wells et al. (2004)
|
|
Protein content of active cellular mass (%)
|
Wang (2004)
|
|
Tissular indexes
a
|
|
Fat mass (%)
|
Undisclosed equation from BioparHom
|
|
Fat free mass (%)
|
Undisclosed equation from BioparHom
|
|
Bone mineral content (%)
|
Undisclosed equation from BioparHom
|
|
Ionic indexes
|
|
Sodium (mmol)
|
Moore (1963)
|
|
Potassium (mmol)
|
Moore (1963)
|
|
Extracellular sodium (mmol.l−1)
|
Moore (1963)
|
|
Extracellular potassium (mmol.l−1)
|
Undisclosed equation from BioparHom
|
|
Intracellular potassium (mmol.l−1)
|
Undisclosed equation from BioparHom
|
|
Creatinine (g.day−1)
|
Wang et al. (2003a and 2003b); Wang (2004)
|
|
Nitrogen (g)
|
James (1981)
|
a.u.: arbitrary units
a: these indexes are independent of each other
Statistical analysis
Standard statistical methods were used to calculate means and standard deviations.
Normal Gaussian distribution of the data was verified by the Shapiro-Wilk test, and
homoscedasticity by a modified Levene’s Test.
Systematic bias, which refers to a general trend for measurements to differ in a particular
direction between repeated tests [1], was assessed with a general linear model with repeated measures. The sphericity
was checked by Mauchley’s test. When the assumption of sphericity was not met, the
significance of F-ratios was adjusted according to the Greenhouse-Geisser procedure
when the epsilon correction factor was <0.75, or according to the Huynh-Feldt procedure
when the epsilon correction factor was >0.75. Multiple comparisons were made with
the Newman-Keuls post hoc test.
Relative reliability, which represents the degree to which individuals maintain their
position in a sample with repeated measurements [1], was assessed with the intraclass correlation coefficient (ICC model 2,1). Absolute
reliability, which is the degree to which repeated measurements vary for individuals,
was assessed with the standard error of measurement (SEM). Both the ICC and the SEM
were computed from the breakdown of a two-way ANOVA (trials x subjects) with repeated
measures, as presented elsewhere [3]. We considered an ICC over 0.90 as very high, between 0.70 and 0.89 as high and
between 0.50 and 0.69 as moderate [17]. Currier [4] has suggested that an ICC value higher than 0.80 was acceptable for clinical work.
SEM can also be used to determine the minimum difference to be considered real (MD),
which represents the limit under which the observed difference is within what we might
expect to see in repeated testing just due to measurement noise [21]. It is calculated as follows:
Statistical significance was set at p<0.05 level for all analyses. All calculations
were made with Statistica 6.0 (Statsofts, Tulsa, OK).
Results
Information regarding intra- and inter-day reliability of tissular, metabolic, hydric
and ionic indexes is presented in [Table 2]–[5], respectively. Whatever the measurement, we did not find any systematic bias within
or between days.
Table 2 Intra- and inter-day reliability of tissular indexes.
|
Parameter
|
Day 1 mean±SD
|
Day 2 mean±SD
|
Day 3 mean±SD
|
Day 4 mean±SD
|
ICC
|
SEM %
|
MD %
|
|
Morning
|
|
FM (%)
|
14.3±5.6
|
13.7±5.7
|
13.8±5.7
|
13.1±6.1
|
0.86
|
15.0
|
41.5
|
|
FFM (%)
|
44.2±7.3
|
44.0±7.1
|
44.2±7.4
|
44.3±7.1
|
0.99
|
1.5
|
4.2
|
|
BMC (%)
|
3.3±0.4
|
3.4±0.4
|
3.4±0.4
|
3.4±0.4
|
0.93
|
3.0
|
8.2
|
|
Afternoon
|
|
FM (%)
|
13.4±5.4
|
13.5±6.4
|
13.3±5.5
|
13.1±5.2
|
0.95
|
10.0
|
27.7
|
|
FFM (%)
|
45.0±6.7
|
44.7±6.5
|
44.8±6.8
|
44.8±6.8
|
0.99
|
1.8
|
5.0
|
|
BMC (%)
|
3.4±0.5
|
3.3±0.6
|
3.4±0.5
|
3.4±0.4
|
0.77
|
6.8
|
18.8
|
FM: fat mass; FFM: fat-free mass; BMC: bone mineral content; SD: standard deviation;
ICC: intraclass correlation coefficient; SEM: standard error of measurement; MD: minimum
difference to be considered real
Table 3 Intra- and inter-day reliability of metabolic indexes.
|
Parameter
|
Day 1 mean±SD
|
Day 2 mean±SD
|
Day 3 mean±SD
|
Day 4 mean±SD
|
ICC
|
SEM %
|
MD %
|
|
Morning
|
|
MA (a.u.)
|
7.2±0.6
|
7.2±0.7
|
7.1±0.7
|
7.2±0.7
|
0.78
|
3.9
|
10.8
|
|
QCA (%)
|
49.4±4.7
|
48.5±6.4
|
49.2±5.2
|
49.2±4.8
|
0.79
|
4.9
|
13.6
|
|
ACM AV (a.u.)
|
0.8±0.1
|
0.8±0.1
|
0.8±0.1
|
0.8±0.1
|
0.85
|
4.0
|
10.9
|
|
ACM PC (%)
|
13.4±1,8
|
13.2±1.8
|
13.4±1.8
|
13.4±1.7
|
0.72
|
7.0
|
19.3
|
|
Afternoon
|
|
MA (a.u.)
|
7.4±0.6
|
7.2±0.5
|
7.1±0.7
|
7.1±0.7
|
0.74
|
4.2
|
10.8
|
|
QCA (%)
|
50.2±4.4
|
49.3±4.8
|
49.1±4.9
|
49.1±5.3
|
0.91
|
2.7
|
7.6
|
|
ACM DI (a.u.)
|
0.7±0.1
|
0.8±0.1
|
0.8±0.1
|
0.8±0.1
|
0.71
|
5.3
|
14.7
|
|
ACM PC (%)
|
13.5±1.7
|
13.5±1.7
|
13.3±1.7
|
13.4±1.7
|
0.95
|
2.9
|
8.1
|
MA: metabolic activity; QCA: quantity of active cells; ACM AV: active cellular mass availability index; ACM PC: active cellular mass protein content; a.u.: arbitrary units; SD: standard deviation;
ICC: intraclass correlation coefficient; SEM: standard error of measurement; MD: minimum
difference to be considered real
Table 4 Intra- and inter-day reliability of hydric indexes.
|
Parameter
|
Day 1 mean±SD
|
Day 2 mean±SD
|
Day 3 mean±SD
|
Day 4 mean±SD
|
ICC
|
SEM %
|
MD %
|
|
Morning
|
|
TW (L)
|
44.9±8.4
|
44.4±7.5
|
45.3±7.9
|
44.8±7.6
|
0.98
|
1.9
|
5.3
|
|
EW (L)
|
19.1±3.5
|
18.9± 3.1
|
19.4±3.2
|
19.1±3.0
|
0.97
|
2.4
|
6.5
|
|
IW (L)
|
26.9±5.1
|
26.7±4.9
|
26.9±5.3
|
27.0±5.2
|
0.98
|
2.4
|
6.6
|
|
EW/IW (%)
|
71.5±4.6
|
71.4±4.8
|
72.5±5.2
|
71.7±5.1
|
0.79
|
3.0
|
8.4
|
|
LBMH (%)
|
68.5±6.3
|
67.5±6.1
|
68.8±5.7
|
68.2±5.1
|
0.92
|
2.2
|
6.2
|
|
Afternoon
|
|
TW (L)
|
45.5±8.2
|
45.7±7.5
|
46.5±7.5
|
46.1±7.5
|
0.97
|
2.9
|
8.1
|
|
EW (L)
|
19.2±3.4
|
19.4±3.1
|
19.8±3.1
|
19.6±3.0
|
0.95
|
3.6
|
10.0
|
|
IW (L)
|
28.0±5.0
|
27.7±4.8
|
27.8±4.8
|
27.7±5.0
|
0.61
|
2.8
|
7.8
|
|
EW/IW (%)
|
68.6±4.9
|
70.2±4.7
|
71.6±6.0
|
71.2±5.1
|
0.67
|
4.0
|
11.2
|
|
LBMH (%)
|
67.1±5.9
|
68.1±6.4
|
68.1±5.1
|
68.3±6.1
|
0.86
|
3.3
|
9.1
|
TW: total water; EW: extracellular water; IW: intracellular water; LBMH: lean body
mass hydration; SD: standard deviation; ICC: intraclass correlation coefficient; SEM:
standard error of measurement; MD: minimum difference to be considered real
Table 5 Intra- and inter-day reliability of ionic indexes.
|
Parameter
|
Day 1 mean±SD
|
Day 2 mean±SD
|
Day 3 mean±SD
|
Day 4 mean±SD
|
ICC
|
SEM %
|
MD %
|
|
Morning
|
|
Sodium (mmol)
|
3 058±572
|
3 027±513
|
3 086±540
|
3 056±519
|
0.98
|
1.9
|
5.3
|
|
Potassium (mmol)
|
3 959±817
|
3 916±735
|
4 000±773
|
3 957±742
|
0.98
|
2.1
|
5.9
|
|
Sodium EC (mmol.l−1)
|
173±4
|
173±4
|
173±3
|
173±3
|
0.75
|
1.0
|
2.7
|
|
Potassium EC (mmol.l−1)
|
9.4±1.1
|
9.5±1.2
|
9.3±1.1
|
9.4±1.1
|
0.95
|
2.4
|
6.8
|
|
Potassium IC (mmol.l−1)
|
128±14
|
129±13
|
127±13
|
129±15
|
0.94
|
2.3
|
6.5
|
|
Creatinine (g.day−1)
|
1.4±0.4
|
1.4±0.4
|
1.4±0.4
|
1.4±0.4
|
0.99
|
1.5
|
4.0
|
|
Nitrogen (g)
|
1 361±226
|
1 373±222
|
1 380±230
|
1 387±221
|
0.97
|
2.5
|
6.9
|
|
Afternoon
|
|
Sodium (mmol)
|
3 100±556
|
30 578±548
|
31 66±519
|
3 140±511
|
0.93
|
4.5
|
12.5
|
|
Potassium (mmol)
|
4 020±796
|
4 038±735
|
4 126±722
|
4 138±816
|
0.94
|
4.4
|
12.2
|
|
Sodium EC (mmol.l−1)
|
175±4
|
174±4
|
173±4
|
173±3
|
0.63
|
1.2
|
3.4
|
|
Potassium EC (mmol.l−1)
|
9.7±1.0
|
9.6±1.0
|
9.4±1.0
|
9.5±1.1
|
0.91
|
3.0
|
8.4
|
|
Potassium IC (mmol.l−1)
|
127±14
|
128±14
|
129±16
|
128±14
|
0.93
|
3.0
|
8.4
|
|
Creatinine (g.day−1)
|
1.4±0.4
|
1.4±0.4
|
1.4±0.4
|
1.4±0.4
|
0.99
|
2.1
|
5.9
|
|
Nitrogen (g)
|
1 426±197
|
1 429±211
|
1 424±217
|
1 428±211
|
0.94
|
3.6
|
10.0
|
SD: standard deviation; ICC: intraclass correlation coefficient; SEM: standard error
of measurement; MD: minimum difference to be considered real
The relative reliability of tissular indexes was high to very high (0.77<ICC<0.99).
However, we found substantial differences in the absolute reliability, since the SEM
of fat-free mass was less than 1.8%, while that of fat mass was higher than 10% ([Table 2]). This difference inevitably impacted the MD, since the observed difference required
to exceed the noise in the measurement during repeated testing was less than 5% for
fat-free mass, whereas it ranged from 27.7 to 41.5% for fat mass.
The relative reliability of metabolic indexes was high to very high (0.71<ICC<0.95),
whereas absolute reliability ranged from 2.7 to 7%. With the exception of the active
cellular mass protein content, whose morning SEM and MD were more than twice that
of the afternoon ([Table 3]), absolute reliability could be considered as homogeneous across indexes or time
of the day.
With the exception of intracellular water and the ratio between extracellular and
intracellular water during the afternoon (ICC<0.67), all hydric indexes were associated
with a high to very high relative reliability (0.79<ICC<0.98; [Table 4]). The absolute reliability of all measures, including intracellular water and the
ratio between extracellular and intracellular water, could be considered as very reasonable
(SEM<4% and MD<11%).
Information regarding ionic indexes is presented in [Table 5]. With the exception of extracellular sodium, particularly in the afternoon, all
ionic indexes could be considered as very highly reliable because their ICC ranged
from 0.91 to 0.99. Absolute reliability appeared to be better in the morning (SEM<2.5%
and MD<6.9%) in comparison with the afternoon (SEM<4.5% and MD<12.5%)
Discussion
The purpose of this study was to examine the intra- and inter-day reliability of body
composition measurements provided by a multifrequency bioelectrical impedance meter,
the Z-Metrix (BioparHom, Bourget du Lac, France). Our three main results were 1) the
absence of systematic bias within and between days; 2) a high to very high relative
reliability of measurements; and 3) an absolute reliability that vary substantially
according to the measurements or the time of day.
Whatever the dimension of physical fitness or performance, the cornerstone of an athlete’s
follow-up is the appreciation of change in order to guide the decision-making of coaches. Change can be defined as a valid
confirmation of improvement or worsening in a measurement over a given time span due
to intervention [6]. Reliability is a key feature in the tracking of change, because small changes are
easier to detect when the reliability is high. The assessment of reliability relies
on a specific assessment of three components: systematic bias, relative reliability
and absolute reliability [1]. The absence of systematic bias and the high to very high relative reliability of
the body composition measurements provided by bioelectrical impedance meters have
been consistently reported in the literature [7]
[12]
[20]. Results obtained with the Z-Metrix are in agreement with the literature and confirm
all the interest in bioelectrical impedancemetry to assess body composition. It does
not mean, however, that these measurements would be useful in the follow-up of athletes,
because bias and relative reliability do not give information about changes at the
individual level. The key statistic for this purpose is absolute reliability, and
more precisely, the minimum difference considered as real, which represents the limit
under which the observed difference is within what we might expect to see in repeated
testing just due to measurement noise [21]. This noise, or error of measurement, can be related to the device itself (mechanical
or electrical drift), to the operator, to the athlete, to the protocol, and to environmental
conditions during the test. In our study, some measurements like body fat were associated
with poor absolute reliability because the cut-off value was >27%, whereas other measurements,
like most of the hydric and ionic measurements, had a decreased absolute reliability
(i. e. an increased cut-off value) during the afternoon when compared with the morning.
The poor absolute reliability of fat mass has already been underscored in the meta-analysis
by Talma et al. [20].
Several explanations can be put forward to understand these observations. At the device
level, bioelectrical impedance is generally considered as precise, because the standard
error of measurement for resistance and reactance is around 1% [7]. It also requires limited operator training, which is one of its strengths. As previously
underscored, the main difficulty resides in converting the raw impedance signal into
body composition. This conversion relies on two assumptions: 1) the human body is
shaped like a perfect cylinder with a uniform length and cross-sectional area, and
2) the relationship between body composition and electrical impedance is uniform within
and between individuals [13]. Although these assumptions are only partially true [10]
[13], one may consider this error to be constant, and as such to affect the accuracy
of the measurements but not their reliability. The conversion of the signal of impedance
also requires population-specific equations that account at least for body height,
body mass, age, and resistance. If the three first parameters are easy to control,
resistance has been shown to be very sensitive to a number of factors, one of the
most important being hydration [11]. Therefore, all the factors that potentially modify total body water, such as eating,
drinking or exercising, also potentially alter total body resistance and the estimation
of fat-free mass [10]. Body position, limb position, electrode position and surface size, as well as skin
condition are also known to affect the signal of impedance, thereby influencing the
estimation of body composition compartments [14]. The list of factors to reduce measurement noise and improve absolute reliability
is long. In our study, hydration, and particularly the water content of the food absorbed
during the last meal, was the single factor that was difficult to control within a
day and that may have contributed to the lower absolute reliability of hydric and
ionic indicators in the afternoon. Afternoon visits were scheduled between 4 and 6
PM, at least three hours after lunch. Participants were also asked to empty their
bladder before the session. Although it is indispensable, it has to be recognized
that this procedure does not warrant precise control of hydration level. Apart from
this point, our protocol fulfilled most of the current recommendations [14]. In spite of this high standardization, hydric and ionic indicators were associated
with a low absolute reliability. It does not mean that these measurements should not
be used in the follow-up of athletes, but simply that the cut-off value to signify
that a real change has occurred should be adapted. Data presented in [Table 4], [5] can be used for this purpose.
Practical implications
Bioelectrical impedance combines several characteristics that are particularly interesting
in the follow-up of athletes because it requires limited operator training, is not
operator-dependent, is financially affordable, and is fast and easy to implement.
The question for sport scientists and coaches is to determine cut-off values to identify
when a real change has occurred and whether it justifies decision-making by the coach.
Reliability, and more particularly absolute reliability, is particularly important
for this purpose. Our data suggest that some indicators like fat mass or other hydric
and ionic indicators are associated with an important error of measurement. It requires
the staff highly standardize the protocol, with a preference for morning measurements.
It also implies the use of specific cut-off values to support a valid interpretation
of changes over a time span.
Conclusion
The purpose of this study was to examine the intra- and inter-day reliability of body
composition measurements provided by a multifrequency bioelectrical impedance meter,
the Z-Metrix (BioparHom, Bourget du Lac, France). Our three main results were 1) the
absence of systematic bias within and between days; 2) a high to very high relative
reliability of measurements; and 3) an absolute reliability that varies substantially
according to the measurements or the time of day. These data underscore the importance
of high standardization and also the need to adjust the cut-off value for each parameter
in order to make sure that a real change has occurred. Future studies should focus
on implementing these measures in real-life conditions with different sports or training
loads in order to select the most sensitive ones in the follow-up of athletes.
