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Introduction: One repetition maximum test (1RM) is often used to evaluate muscle strength and to prescribe the in-tensity of strength training. However, the determination of the initial test load, and duration of the test make difficult to use the same in non-individualised environments. Objective: To determine coefficients to estimate the maximum strength (1RM), based on the relationship between muscular strength, lean body mass and total body mass. Methods: Twenty-eight strength-trained and non-strength-trained men participated in this study. Muscle strength was determined using the 1 RM test in the bench press, supported barbell row, 45° leg press and squat exercise, while body composition was measured using the skinfolds method. After verifying the correlations between muscular strength and body mass and composition, the coefficients to predict the maximal strength were calculated by dividing the value of the 1 RM by the total body mass and lean body mass (kg) and by linear regression equation based in these parameters. Results: Significant correlations were found between body mass and lean body mass with muscular strength in all the exercises (r = 0.47 -0.76, P < 0.05). The greatest correlations were observed between the muscular strength values and lean body mass. There was a significant difference between the coefficients obtained from trained and non-trained subjects in all the tested exercises (P < 0.05). Conclusions: The results suggest that the coefficients of prediction of the 1RM should take into account the body composition and the training status of the individuals.
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PREDICTION OF ONE REPETITION MAXIMUM LOAD
BY TOTAL AND LEAN BODY MASS IN TRAINED AND
UNTRAINED MEN
Eduardo Lusa Cadore(A,B,C,D,E,F), Ronei Silveira Pinto(C,D,E,F), Michel Arias Brentano(A,B,C,D,E,F), Rodrigo Fer-
rari Silva(A,B,C,F), Eduardo Marczwski da Silva(A,B,C,F), Rafael Spinelli(A,B,C,F), Cleiton Silva Correa(C,D,E,F), Luiz
Fernando Martins Kruel(A,B,C,D,E,F)
Exercise Research Laboratory, Physical Education School, Federal University of Rio Grande do Sul, Porto
Alegre, Brazil
Abstract
Introduction: One repetition maximum test (1RM) is often used to evaluate muscle strength and to prescribe the in-
tensity of strength training. However, the determination of the initial test load, and duration of the test make difficult to use
the same in non-individualised environments.
Objective: To determine coefficients to estimate the maximum strength (1RM), based on the relationship between
muscular strength, lean body mass and total body mass.
Methods: Twenty-eight strength-trained and non-strength-trained men participated in this study. Muscle strength was
determined using the 1 RM test in the bench press, supported barbell row, 45° leg press and squat exercise, while body
composition was measured using the skinfolds method. After verifying the correlations between muscular strength and body
mass and composition, the coefficients to predict the maximal strength were calculated by dividing the value of the 1 RM
by the total body mass and lean body mass (kg) and by linear regression equation based in these parameters.
Results: Significant correlations were found between body mass and lean body mass with muscular strength in all the
exercises (r = 0.47 - 0.76, P < 0.05). The greatest correlations were observed between the muscular strength values and
lean body mass. There was a significant difference between the coefficients obtained from trained and non-trained subjects
in all the tested exercises (P < 0.05).
Conclusions: The results suggest that the coefficients of prediction of the 1RM should take into account the body
composition and the training status of the individuals.
Key words: strength training, fat-free mass, force development, body composition
Introduction
Strength training promotes neuromuscular adap-
tations which are dependent of the intensity relative
to maximum load (% of 1-RM) at which the training
is performed. Low to moderate intensity of training
(40-60% of 1-RM) result in an increase in the local
muscular endurance, while moderate to high intensity
training (65 to 90% of 1-RM) result in greater increases
in the maximum strength and of the cross-sectional
area – CSA [1]. The prescription of the overload in
each exercise in the training routine can be determined
using apercentage relative to the 1-RM (i.e. 50, 70,
80%) [2], using arange of maximum repetitions –
RMs (i.e. 8-10 RMs, 12-15 RMs) [3], or using scales
of perceived exertion (i.e. Borg, OMNI) [4]. Different
overloads result in distinct physiological responses,
and it may be modulated accordingly with the aims
of the training program [5,6].
The 1-RM test is amethod widely used for the
determination of the intensity of strength training.
Nevertheless, the 1-RM test is difficult to apply in
health gyms, physical training centres and rehabilita-
tion clinics. Among the factors that limit its utilisation
in these environments are the lack of valid criteria
for establishing the initial overload (i.e., first trial),
and the total time spent in its performance [7]. Given
this situation, some investigators have proposed the
prediction of the 1-RM values in strength exercises
using linear regression equations, based on the inverse
relationship observed between the overload lifted with
the number of maximum repetitions performed with
this overload (RMs) [8-16]. With the same purpose,
some investigators have proposed the use of coef-
ficients to predict the maximum strength, calculated
using the total body mass (TBM) values [17-18]. In
study of Baechle and Groves [18], specific coefficients
calculated from the quotient between 1RM and the
TBM values were proposed for each exercise, based
on the relationship between TBM and the strength
development. Hence, these authors suggested that
each exercise presents aspecific coefficient, and the
1RM value in each exercise may be obtained by the
product between these generic coefficient calculated
and the TBM. However, in this purpose, the investiga- However, in this purpose, the investiga-However, in this purpose, the investiga-
tors did not take into consideration the subjects’ body
composition, particularly the lean body mass (LBM),
Medicina Sportiva
Med Sport 16 (3): 111-117, 2012
DOI: 10.5604/17342260.1011391
ICID: 1011391
Copyright © 2012 Medicina Sportiva
ORIGINAL RESEARCH
111
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Sportiva 16 (3): 111-117, 2012
which is adeterminant variable in the force produc-
tion. Another unconsidered factor in this method was
the physical fitness status of the subjects (i.e. trained,
untrained), which could change the relationship be-
tween TBM and LBM with maximum strength, due to
the possible neural adaptations resulting from strength
training in trained individuals [19]. Therefore, this
methodological approach may have conditioned the
results, since the strength development appears to be
greatly affected by the body composition or the quality
of the neuromuscular function of the subjects.
Due to the greater relationship between the LBM,
even assessed using indirect methods, with the mus-
cle strength, when compared with the relationship
between TBM with muscular strength [8,20-22], the
determination of specific coefficients for each exercise,
based on the LBM, may improve the 1RM prediction.
Furthermore, to take into consideration the subjects
training status (i.e. strength-trained and untrained)
could better explain the relationship between maxi-
mum strength with the LBM, and consequently pro-
vide more accurate coefficients of strength prediction.
Therefore, the aim of this study was to calculate
coefficients to predict the maximum strength (1RM)
of strength-trained and untrained men, based on the
relationships between the muscle strength with the
LBM, as well as the relationship between the mus-
cle strength with the TBM. Our hypothesis is that
the LBM, even when measured indirectly, will have
agreater correlation with the maximal strength than
the TBM. In addition, our second hypothesis is that
the coefficients of 1RM prediction will be different
between strength-trained and untrained subjects.
Methods
Experimental Design
The subjects attended the laboratory on two differ-
ent days. Firstly, their anthropometric characteristics
were measured. On asecond test day, the subjects per- On asecond test day, the subjects per-On asecond test day, the subjects per-
formed the maximum dynamic strength tests (1RM) in
four free-weight exercises. After that, the relationship
between maximum strength (1RM values) with total
body mass and lean body mass were evaluated in both
groups (i.e. trained and untrained). Based on these
relationships, the coefficients were calculated using
the quotient between the individual maximal strength
values and the corresponding total body mass and lean
body mass. After that, the coefficients obtained from
the different groups were compared.
Participants
Twenty-eight healthy (40 ± 4 years) strength-
trained and untrained men volunteered for this study
after completing an ethical consent form. Subjects were
carefully informed about the design of the study with
special information given regarding the possible risks
and discomfort related to the procedures. The study
was conducted according to Declaration of Helsinki
and was approved by Ethics Committee of Federal
University of Rio Grande do Sul, Brazil. According
with their characteristics, subjects were divided into
two groups: the strength-trained group (TG, n = 13)
and the untrained group (UG n = 15). Those subjects
that were able to perform asquat exercise with 130%
of their body weight and the bench press with 100% of
their body weight were considered trained. The sub- The sub-he sub-
jects of TG used to practiced non-competitive strength
training composed by multiple sets of 6-12 maximum
repetitions (RM), 4 to 6 weekly training sessions with
the aim of hypertrophy for at least three years. The UG
included sedentary individuals who had not engaged
in any regular or systematic training program during
the year prior to the study. None of the subjects were
using anabolic steroids or any medication that might
have influenced the muscle-skeletal metabolism.
Moreover, none of the subjects were on acontrolled
diet or any other kind of dietary restriction. The an- The an-The an-
thropometric characteristics and the strength values
(1 RM) of both groups are shown in table 1. There
were no significant differences between the groups
Table 1. Physical characteristics and muscular strength
Trained Untrained
Age (years) 40.3 ± 4.7 39.9 ± 3.7
Height (cm) 173.2 ± 5.3 173.7 ± 7.3
Body mass (kg) 79.3 ± 9.7 79.7 ± 10.9
% Fat mass 19.4 ± 5.6 25 ± 4.8†
% Lean mass 80.6 ± 5.6 74.9 ± 4.8†
Bench Press (kg) 87.2 ± 19.2 56.6 ± 11.9†
Supported Row (kg) 84.7 ± 18.4 61.5 ± 9.7†
Squat (kg) 139 ± 50.2 90.3 ± 15.4†
Leg Press (kg) 385.8 ± 84.8 231.2 ± 37.3†
Values are mean ± SD; Strength-trained group (n = 13); and untrained group (n = 15), respectively. Significant difference between groups: †P < 0.01
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Sportiva 16 (3): 111-117, 2012
in relation to age, total body mass and height. The
subjects in the TG had significantly greater 1RM
values (P<0.001) in all the assessed exercises, as well
as greater lean body mass (P<0.001).
Anthropometric measurements
Body mass and height were measured using an
Asimed analog scale (resolution of 0.1 kg) and an
Asimed stadiometer (resolution of 1 mm), respec-
tively.. Body density was estimated using the skinfold
protocols proposed by Jackson and Pollock [23]. In
order to measure the skinfolds, aplicometer (CES-
CORF) with aresolution of 0.1 mm was used. Body
composition was assessed using the skinfold tech-
nique. Aseven-site skinfold equation was used to esti-
mate body density [23] and body fat was subsequently
calculated using the Siri equation [24].
Dynamic muscular strength
The 1RM test was performed in the bench press,
supported barbel row, 45° leg press and squat exer-45° leg press and squat exer-
cises, using free weights with 0.5 kg of resolution. One
week prior to the test day, subjects were familiarized
with all procedures in two sessions. On the test day,
the subjects warmed up for five minutes on acycle
ergometer, stretched all major muscle groups, and
performed specific movements for the exercise test.
To avoid the influence of fatigue in the 1RM values,
each subject’s maximal load was determined with no
more than five attempts with afour-minute recovery
between attempts and 5 minutes between exercises.
Performance time for each contraction (concentric
and eccentric) was two seconds, controlled by an
electronic metronome (Quartz, CA, USA). The test-
retest reliability coefficients (ICC) were over 0.95 in
all strength tests.
Calculation of the coefficients
In order to calculate the coefficients, the data from
the 1 RM in each exercise were normalized by the body
mass (formula 1) and by the lean body mass (formula
2), individually for each exercise, and then the mean
for each group was calculated. Basically, the formulas
used were:
Coefficientbody mass = 1 RM/TBM
Coefficientlean body mass= 1 RM/LBM
Which 1 RM is the dynamic maximum strength
(kg), TBM is the body mass (kg) and LBM is the lean
body mass (kg).
Statistical analysis
The data are expressed as means ± standard devia-
tion. After determining normality and homogeneity,
by means of the Shapiro-Wilk and Levene tests respec-
tively, the differences in the muscle strength between
groups was tested using independent t tests. Pe a r s on’s
product-moment correlation tests were employed to
investigate the possible correlations between the mus-
cle strength in the different exercises with the lean body
mass and the body mass values in the different groups.
To establish equations to estimate maximal strength
based on total and lean body mass, linear regression
equation was employed. Because of the differences
between the muscle strength values between groups,
different coefficients to estimate maximal strength
were determined in each group. Differences between
coefficients obtained in each groups were tested using
independent t tests. Furthermore, ANOVA one-way
was used to compare the muscle strength tested and
those predicted by the coefficients and linear regres-
sion equations. The level of significance adopted was
P < 0.05, the statistical power was over 0.90 and all the
tests were done in SPSS 15.0 software.
Results
The correlations between individual values of
1RM in the different exercises with the correspond-
ing values of TBM and LBM are shown in the figures
1 to 8. There were significant correlations between
the individual 1RM values and the corresponding
values of TBM and LBM in the bench press and r
= 0.78; P = 0.0001, respectively); squat (r = 0.4; P =
0.035 and r = 0.65; P = 0.0001, respectively), and only
between the between the 1 RM values and the LBM
in the supported barbel row (r = 0.64, P = 0.001) and
Fig. 1. Relationship between bench press 1 RM values (kg) and total body mass (kg)
Fig. 2. Relationship between supported barbell row 1 RM values (kg) and
total body mass (kg)
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Sportiva 16 (3): 111-117, 2012
Fig. 3. Relationship between squat 1 RM values (kg) and total body mass (kg)
Fig. 4. Relationship between leg press 1 RM values (kg) and total body
mass (kg)
Fig. 5. Relationship between bench press (kg) 1 RM values and lean body
mass (kg)
Fig. 6. Relationship between supported row 1 RM values (kg) and lean body
mass (kg)
Fig. 7. Relationship between squat 1 RM values (kg) and lean body mass (kg)
Fig. 8. Relationship between leg press 1 RM values (kg) and lean body mass (kg)
Table 2. Coefficients to prediction 1 repetition maximum (1 RM) values by total body mass and lean body mass
Trained subjects
TBM
Untrained subjects
TBM
Trained subjects LBM Untrained subjects
LBM
Bench press 1.1 ± 0.7* 0.7 ± 0.12 1.4 ± 0.17* 1.0 ± 0.13
Squat 1.7 ± 0.5* 1.2 ± 0.2 1.3 ± 0.6* 1.1 ± 0.2
Supported row 1.1 ± 0.2* 0.8 ± 0.12 2.1 ± 0.2* 1.6 ± 0.13
Leg press 4.9 ± 0.8* 3.0 ± 0.5 6.0 ± 0.9* 4.0 ± 0.7
TBM, total body mass; LBM, lean body mass. *P < 0.0001, significant differences between groups. Strength-trained subjects (n=13) and untrained
subjects (n=15).
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Sportiva 16 (3): 111-117, 2012
leg press exercises (r = 0.59; P = 0.001). There were
no significant correlations between the 1 RM and the
TBM in the 1RM values of supported barbel row (r =
0.35; P = 0.071) and 45° leg press exercises (r = 0.33;
P = 0.083).
The coefficients determined by the relationship
between 1 RM and TBM and 1 RM and LBM in the
different exercises are shown in table 2; and, those pre-
dicted by linear regression equations are shown in table
3. There were significant differences between trained
and untrained subjects in all coefficients calculated (P
< 0.01). There were no significant differences between
the assessed strength values and the strength values
predicted by the total and lean body mass (Table 4).
Discussion
The primary findings of the present study were
the greater correlation values found between 1RM
values with LBM, even when determined indirectly
(skinfolds measures), when compared with the correla-
tions between 1RM values with TBM in the different
strength exercises tested. Moreover, the present study
has shown that the coefficients to estimate the 1 RM,
based in the TBM and LBM were different between
groups with different training status (i.e. strength
trained, untrained). Thus, our results suggest that
the quality of the neuromuscular function interferes
in the estimation of the maximum strength by these
coefficients. Our results confirm our hypotheses that
LBM would obtain greater correlation values with
muscle strength than TBM, and the coefficients would
be different between strength-trained and untrained
subjects.
Regarding the correlations between the 1 RM
values with the anthropometric variables, the greater
r values obtained between the 1 RM and the LBM are
in accordance with observations reported in previ-
ous studies [8,15,27-29], in which higher correlation
indices were noted between the 1 RM and the LBM
(predicted by the skinfolds method), than between
the 1 RM and the TBM. In study assessing the bench
press exercise in agroup of male strength-trained
students, Mayhew et al. [28], found asignificant
correlation between the 1 RM and the TBM (r =
0.68, P < 0.01), and a greater correlation between
the 1 RM and the LBM (r = 0.73, P < 0.01). Similarly,
in another investigation by Mayhew et al. [15], in
which school-aged strength-trained soccer players
were evaluated, greater correlations were reported
between the 1 RM with the LBM, when compared
with those observed between the 1 RM with the TBM
in several strength exercises: bench press (r = 0.68 vs.
r = 0.53; both P < 0.05), squat (r = 0.60 vs. r = 0.50;
both P < 0.05) and dead lift (r = 0.64 vs. r = 0.50;
both P < 0.05). Similar results were found in astudy
by Bale et al. [9] in which correlations were found
Table 3. Linear regression equations based on total and lean body mass in trained and untrained subjects
Trained subjects by
body mass
Untrained subjects by
body mass
Trained subjects by
Lean body mass
Untrained subjects by
lean body mass
Bench press y=1,243x – 11,4 y=0,628x – 6,49 y=2,22x – 54,3 y=1,152x – 12,05
Squat y=2,825x – 84,6 y=1,6938x – 37,6 y=5,036x – 181,6 y=2,3953x – 45,2
Supported row y=0,705x – 28,7 y=0,6923x – 7,9 y=1,563x – 14,93 y=0,9874x – 4,3
Leg press y=4,586x – 22,1 y=3,2224x – 12,7 y=7,878x – 116,4 y=3,9978x – 6,12
Table 4. Dynamic strength values (1 RM) based on test, calculated TBM and LBM coefficients, and calculated by linear
regression equation based on TBM and LBM in strength-trained and untrained subjects
1 RM
(kg)
1 RMTBM
(kg)
1 RMLBM
(kg)
1 RMTBMEQ
(kg)
1RMLBMEQ
(kg)
Bench pressTG 87 ± 19† 87 ± 10† 86 ± 10† 87 ± 12† 87 ± 16†
SquatTG 139 ± 50† 138 ± 17† 137 ± 15† 139 ± 27† 139 ± 37†
Supported rowTG 84 ± 18† 84 ± 10† 84 ± 9† 84 ± 6† 84 ± 11†
Leg pressTG 385 ± 84† 386 ± 47† 384 ± 44† 385 ± 44† 385 ± 57†
Bench pressUG 56 ± 11 56 ± 7 56 ± 7 56 ± 6 56 ± 8
SquatUG 97 ± 31 97 ± 13 97 ± 12 97 ± 18 97 ± 18
Supported rowUG 63 ± 11 63 ± 8 63 ± 8 63 ± 7 63 ± 7
Leg pressUG 244 ± 62 244 ± 33 244 ± 31 244 ± 35 244 ± 30
TG, Strength-trained subjects (n = 13); UG, untrained subjects (n = 15); TBM, total body mass; LBM, lean body mass; TBMEQ, linear regression equ-
ation based on TBM; LBMEQ, linear regression equation based on LBM. †Significant differences between groups: P<0.001
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Sportiva 16 (3): 111-117, 2012
between the 1 RM value in the bench press with the
TBM (r = 0.54; P < 0.05), and with the LBM (r = 0.68;
P < 0.05), as well as between 1 RM in the dead lift
with the TBM (r = 0.50) and with LBM (r = 0.54) in
college-aged players. The greater correlation values
between 1 RM with the LBM, though predicted by
indirect methods (i.e. skinfolds measurements), such
as in the abovementioned studies, as well as in the
present investigation, could be explained by the fact
that LBM is amore representative value of the muscle
mass, and consequently, of the tissue responsible to
the strength development. Indeed, it has been shown
that the fat mass was related with lower strength
performance in the bench press [15,28] and dead lift
exercises [15], as well as in the isometric strength of
the knee extensors muscles [27]. Moreover, athletes
with alow percentage of fat mass used to have greater
strength values [30].
In the present study, free weight exercises were used
to calculate the prediction coefficients of the maxi-
mum strength (1 RM), based on anthropometric vari-
ables (TBM and LBM). The utilisation of free-weight
exercises have an important practical application since
these coefficients may have great reproducibility in any
other location when the same free-weight exercises are
employed. The 1 RM values may be greatly affected by
the mechanical characteristics of the external load of
the apparatus used in strength training (i.e. variable-
radius pulleys – CAMS and invariables, position of
the pulleys, different external moment).
The results of the present study demonstrated that
the LBM, calculated indirectly using a2-component
model, presented greater correlation coefficients than
those obtained between muscle strength and TBM.
Therefore, the LBM should be considered when elab-
orating maximum strength prediction coefficients.
Nevertheless, the 1 RM values predicted by coef-
ficients based on TBM, as well as by linear equation
regression calculated by the TBM were not different
from the real 1RM values. Thus, given the impos-
sibility of measuring the lean body mass, the use of
coefficients derived from the TBM may represent
apractical and easy way to estimate the 1 RM, or at
least the first test load during the 1RM test. Likewise,
differences were observed in the coefficients between
strength-trained and untrained subjects, with the
strength-trained participants having greater strength
values in all exercises. These differences appear to be
associated with the neural adaptations resulting from
strength training [2,19], such as the greater number
of motor units recruited and the greater firing rate
in this recruitment [2,19].
Based on the results reported here, it can be in-
ferred that strength-trained would present greater
coefficients of strength prediction. In the present
investigation, the 1 RM prediction coefficients elabo-
rated from the LBM, together with the consideration
of the subjects’ physical fitness levels, made possible
to define the predicted 1 RM values, which did not
differ significantly from the evaluated 1 RM values
(Table 4). This methodological strategy for predicting
the 1 RM based on specific coefficients in strength
exercises appears to be both efficient and appropri-
ate and its use reduces the time spent in maximum
strength evaluation tests (1 RM). The coefficients
calculated in the present study can be applied in any
place, since the exercises used were performed with
free weights.
In summary, the present study showed greater
correlation values between maximum muscle strength
(1 RM) with LBM, when compared with TBM. Fur-
thermore, the coefficients to predict 1RM values were
different between strength-trained and untrained
subjects. The present results have aimportant practi-
cal application, since the 1RM test is widely used to
investigate the neuromuscular adaptations to strength
training, as well as to prescribe this type of physical
training. Thus the coefficients calculated in the present
study may be an useful method to reduce the time
spent during this test, as well as to facilitate the same
by providing the initial test overload, which can be
very much closer of the real strength value. Further
studies are required in order to establish coefficients
of strength prediction in others populations, such as
children and the elderly.
Acknowledgements
This study was partially supported by the National
Council of Technological and Scientific Development
(CNPq), Coordination of Improvement of Higher
Education Personnel (CAPES).
Declaration of interest
The authors report no conflicts of interest.
References
1. Campos GER, Luecke TJ, Wendeln HK, et al. Muscular
adaptations in response to three different resistance-training
regimens: specificity of repetition maximum training zones.
Eur J Appl Physiol 2002; 88: 50–60.
2. Häkkinen K, Pakarinen A, Kraemer WJ, et al. Selective
muscle hypertrophy, changes in EMG and force, and serum
hormones during strength training in older women. J Appl
Physiol 2001; 91: 569-80.
3. Marx JO, Ratamess NA, Nindl BC, et al. Low-volume circuit
versus high-volume periodized resistance training in women.
Med Sci Sports Exerc 2001; 33(4): 635–43.
4. Gearhart RF, Goss FL, Lagally KM, et al. Ratings of perceive
exertion in active muscle during high-intensity and low-inten-
sity resistance exercise. J Strength Cond Res 2002; 16(1): 87-91.
5. Kraemer WJ, Ratamess NA. Fundamentals of resistance train-Fundamentals of resistance train-
ing: progression and exercise prescription. Med Sci Sports
Exerc 2004; 36(4): 674-88.
6. Knutgen HG, Kraemer WJ. Terminology and measurement
in exercise performance. J Appl Sport Sci Res 1987; 1(1): 1-10.
7. Brown LE, Weir JP. ASEP procedures recommendation I:
accurate assessment of muscular strength and power. J Exerc
Physiol 2001; 4(3): 1-21.
117
Cadore E.L., Pinto R.S., Brentano M.A., Silva R.F., Marczwski da Silva E., Spinelli R., Correa C.S., Kruel L.F.M./ Medicina
Sportiva 16 (3): 111-117, 2012
Authors’ contribution
A – Study Design
B – Data Collection
C – Statistical Analysis
D – Data Interpretation
E – Manuscript Preparation
F – Literature Search
G – Funds Collection
8. Baker D, Wilson Carlyon R. Periodization: The effect on
strength of manipuling volume and intensity. J Strength Cond
Res 1994; 8: 235-42.
9. Bale P, Colley E, Mayhew JL, et al. Anthropometric and so-
matotype variables related to strength in American football
player. J Sports Med Phys Fit 1994; 34: 383-9.
10. Braith RW, Graves JE, Legget SH, et al. Effect of training on
the relationship between maximal and submaximal strength.
Med Sci Sports Exerc 1993; 25(1): 132-8.
11. Cummings B, Finn KJ. Estimation of aone repetition maxi-
mum bench press for untrained women. J Strength Cond Res
1998; 12(4): 262-5.
12. Jaric, S. Muscle Strength Testing: Use of Normalisation for
Body Size. Sports Med 2002; 32: 615-31.
13. Lesuer DA, Mccormick JH, Mayhew JL, et al. The accuracy
of prediction equations for estimating 1-RM performance
in the bench press, squat and deadlift. J Strength Cond Res
1997; 11(4): 211-3.
14. Mannion AF, Adams MA, Cooper RG, et al. Prediction of
maximal back muscle strength from indices of body mass and
fat-free body mass. Rheumatology (Oxford) 1999; 38(7): 652-5.
15. Mayhew JL, Ware JS, Bemben MG, et al. The NFL-225 test as
ameasure of bench press strength in college footballplayers.
J Strength Cond Res 1999; 13: 130-4.
16. Simpson SR, Rozenek R, Garhammer J, et al. Comparison of
one repetition maximums between free weight and universal
machine exercises. J Strength Cond Res 1997; 11(2): 103-6.
17. Ballman KL, Scanlan JM, Mayhew JL, et al. Prediction of
bench press strength in untrained females from anthropo-
metric dimensions and psychological indicators of activity
involvement. J Human Mov Studies 1999; 37: 217-33.
18. Baechle TR, Groves BR. Weight training: steps to success.
Champaign, IL: Human Kinetics, 1998.
19. Häkkinen K, Alen M, Kraemer WJ, et al. Neuromuscular
adaptations during concurrent strength and endurance tra-
ining versus strength training. J Appl Physiol 2003; 89: 42-52.
20. Hickner RC, Mehta PM, Dyck DP, et al. Relationship between
fat-to-fat-free mass ratio and decrements in leg strength after
downhill running. J Appl Physiol 2001; 90: 1334-41.
21. Pereira MIR, Gomes PSC. Testes de força e resistência mus-Testes de força e resistência mus-
cular: confiabilidade e predição de uma repetição máxima –
Revisão e novas evidências. Rev Bras Med Esp 2003; 9: 325-35.
22. Scanlan JM, Ballman KL, Mayhew JL, et al. Anthropometrics
dimensions to predict 1-RM bench press in untrained females
J Sports Med Phys Fit 1999; 39: 54-60.
23. Jackson AS, Pollock ML. Generalized equations for predicting
body density of men. Brit J Nutr 1978; 40: 497-504.
24. Heyward VH. Advanced fitness assessment & exercise prescrip-
tion. Champaign, IL: Human Kinetics, 1997; 3th ed: 121-141.
25. Ploutz-Snyder L, Giamis EL. Orientation and familiarization
to 1 RM strength testing in old and young women. J Strength
Cond Res 2001; 15: 519-23.
26. Lombardi VP. Beginning weight training: the safe and effective
way. Dubuque: Brown & Benchmark, 1989.
27. Mayhew JL, Ball, TE, Ward TE, et al. Relationships of struc-
tural dimensions to bench press strength in college males. J
Sports Med Phys Fit 1991; 31(2): 135-41.
28. Mayhew JL, Piper FC, Ware JS. Anthropometrics correlates
with strength performance among resistance trained athletes.
J Sports Med Phys Fit 1993; 33: 159-65.
29. Mayhew JL, Ware JS, Cannon K, et al. Validation of NFL-225
test for predicting 1 RM bench press performance in college
football players. J Sports Med Phys Fit 2004; 42: 304-8.
30. Roemmich JN, Sinning WE. Weight loss and wrestling
training: effects on nutrition, growth, maturation, body
composition, and strength. J Appl Physiol 1998; 85: 1349-56.
Accepted: September 15, 2012
Published: September 25., 2012
Address for correspondence:
Eduardo Lusa Cadore
LAPEX, Escola de Educação Física, UFRGS
Felizardo St, 750
Jardim Botânico
CEP: 90690-200
Porto Alegre/ RS,
Brazil
Phone: +5551 3308-5820
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Email:edcadore@yahoo.com.br
Ronei Silveira Pinto: ronei.pinto@ufrgs.br
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Cleiton Silva Correa: cleitonesef@yahoo.com.br
Michel Arias Brentano: michel.brentano@bol.com.br
Eduardo Marczwski da Silva: eduardomarczwski@yahoo.com.br
Rafael Spinell: larrys@terra.com.br
Luiz Fernando Martins Kruel: kruel@esef.ufrgs.br
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