Relationship between Handgrip Strength, Anthropometric and Body
Composition Variables in Different Athletes
Olivia Di Vincenzo
1
, Maurizio Marra
1
, Delia Morlino
1
, Enza Speranza
1
, Rosa Sammarco
1
,
Iolanda Cioffi
1
and Luca Scalfi
2
1
Department of Clinical Medicine and Surgery, Federico II University of Naples,
Via S. Pansini 5, 80131, Naples, Italy
2
Department of Public Health, Federico II University of Naples, Via S. Pansini 5, 80131, Naples, Italy
Keywords: Athletes, Handgrip Strength, Body Composition, Bioimpedance Analysis, Phase Angle.
Abstract: Handgrip strength (HGS) is a relatively inexpensive, portable and simple functional capacity test which
provides information about muscle function. In the field of sport, HGS is largely used as one of the main
indicators for testing and monitoring progress in muscle power. This study aimed to evaluate the relation of
HGS with both anthropometric and body composition variables in a group of male athletes compared to a
control group matched for age, body weight and body mass index. Male athletes aged 17-40 years who train
for a minimum of 16/18 hours per week were recruited. Anthropometry, measures of HGS and bioimpedance
analysis were performed. HGS and FFM were similar between the two groups, whereas FM in both absolute
and percentage values was higher (p<0.05) in controls than in athletes. On the other hand, phase angle (PhA)
values clearly increased in athletes by 6.1% (p=0.008) compared to controls. In athletes FFM showed a very
strong correlation with HGS (r=0.918, p=0.000), whereas in controls body weight gave the best correlation
(r=0.509). Additionally, multiple regression analysis showed that the main predictor of HGS was FFM in
athletes and body weight in controls. Our data suggest that FFM was the main determinant of muscular
function in athletes, but not in control subjects.
1 INTRODUCTION
The handgrip strength (HGS) measurement, using a
dynamometer, is a relatively inexpensive, portable
and simple functional capacity test which provides
information about overall muscle function.
It has been widely used for evaluating muscle
strength in the general population as well as in
subjects with illnesses. Additionally, in sport, it is
largely used as one of the main indicators for testing
and monitoring progress in muscle power (Cronin
2017).
Strength is important for several sports such as
baseball, climbing, boxe, hockey, paddling,
swimming, tennis and weightlifting, which require
high values of HGS for optimizing performance and
potentially preventing injury (Cronin 2017).
The sex- and age-specific reference curves for
HGS are well-established in some studies for general
populations (Lunaheredia 2005; Schlüssel 2008;
Wang 2018), but, to the best of our knowledge, there
are no studies that developed reference values for
athletes.
So far, few studies have reported strong
correlations between HGS and some anthropometric
characteristics (Drinkwater 2008; Torres-Unda 2013;
Pizzigalli 2017). Surprisingly, the relationship
between HGS and both anthropometric and body
composition parameters has not been deeply
considered.
Bioelectrical impedance analysis (BIA) is a
widely used, non-invasive tool for assessing body
composition in athletes. Additionally, raw BIA
variables, such as phase angle (PhA), have been
shown to be significantly associated with muscle
strength and physical activity (Moon 2013;
Mundstock 2019) and to be increased in athletes
compared to general population (Di Vincenzo 2019;
Di Vincenzo 2020).
From a sports performance perspective, it may be
interesting to understand how HGS relates to
anthropometric as well as body composition
parameters in different athletes.
Therefore, the aim of this study was to evaluate
the relation of HGS with both anthropometric and
body composition variables in a group of male
athletes compared to a control group.
148
Di Vincenzo, O., Marra, M., Morlino, D., Speranza, E., Sammarco, R., Cioffi, I. and Scalfi, L.
Relationship between Handgrip Strength, Anthropometric and Body Composition Variables in Different Athletes.
DOI: 10.5220/0010110401480151
In Proceedings of the 8th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2020), pages 148-151
ISBN: 978-989-758-481-7
Copyright
c
2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
2 METHODS
Inclusion criteria of the present study were: male
athletes aged 17-40 years who train for a minimum of
16/18 hours per week. Subjects affected by overt
metabolic and/or endocrine diseases and/or regularly
taking any medications, were excluded. Athletes were
compared to healthy subjects matched for age, body
weight and Body Mass Index (BMI) who served as
control group.
Participants were studied in the morning (8 a.m.),
after an overnight fast according to standardized
conditions, abstaining from vigorous physical activity
for 24 hours before the assessment.
Body weight and stature were measured to the
nearest 0.1 kg and 0.5 cm, respectively, using a
platform beam scale with a built-in stadiometer (Seca
709; Seca, Hamburg Germany). BMI (kg/m²) was
calculated as body weight (kg) divided by squared
stature (m).
BIA was performed at 50 kHz (Human Im Plus II,
DS Medica). Measurements were carried out on the
nondominant side of the body, in the post-absorptive
state, after voiding and with the subject in the supine
position for 20 minutes, with a leg opening of 45°
from the median line of the body and the upper limbs,
30° apart from the trunk (Kyle 2004). The BIA
variables considered were resistance (R), reactance
(Xc), and PhA. FFM was estimated using the Sun
equation (Sun et al. 2003). Fat mass (FM) was
calculated as the difference between body weight and
FFM.
Isometric strength of upper limbs was assessed by
HGS in both dominant and non-dominant hands with
a Jamar dynamometer (JAMAR, Roylan, UK).
Subjects performed the test standing with upper limbs
by their sides, and they were instructed to squeeze a
dynamometer at maximal voluntary isometric
contraction. The measurement was repeated three
times alternately on both sides (dominant and non-
dominant arm) 1 min apart to avoid fatigue. The
dominant hand was determined by asking subjects if
they were right or left-handed. The mean value was
recorded in kilograms (Fess 1992).
Statistical Analysis
Results are reported as mean±standard deviations
(SD), unless otherwise specified. Significance was
defined as p <0.05. The Student’s unpaired t-test was
used to analyse differences between groups. Linear
correlation was applied for evaluating associations
between variables. Multivariate linear regression
analysis was performed to assess the main predictors
of HGS. The model used the following variables: age,
body weight, stature, BMI, FFM, FM. Statistical
analyses were performed using IBM SPSS (version
20).
3 RESULTS
Fifty-three male athletes practicing different sport
specialties were selected for this study and compared
to sixty-three age-, sex- and weight-matched healthy
male controls. Subject’s characteristics are
summarized in Table 1.
Table 1: Subject’s characteristics.
Athletes
(n =53)
Controls
(n =63)
Age (years)
Weight (kg)
Stature (cm)
BMI (kg/m
2
)
26.7±9.7 24.6±5.8
71.8±12.7 73.1±10.6
176±7 175±6
23.1±3.0 23.8±2.8
Data are reported as mean±standard deviation;
BMI=body mass index.
In Table 2 are reported body composition and PhA
data of the two groups. HGS was slightly higher in
athletes than in controls but the difference was not
statistically significant. According to BIA analysis,
FFM was similar between the two groups, whereas
controls showed higher values for both absolute and
percentage FM (p<0.05) compared to athletes. While,
PhA values were higher by 6.1% (p=0.008) in athletes
than in controls.
Table 2: Handgrip strength, body composition and
bioelectrical impedance phase angle variables.
Athletes
(n =53)
Controls
(n =63)
HGS (kg) 37.1±7.0 35.7±9.0
FFM (kg)
FM (kg)
FM (%)
PhA (degrees)
61.4±9.0 60.4±7.5
10.6±4.5 13.0±5.5*
14.1±4.2 17.1±5.5*
7.70±0.74 7.26±0.91*
Data are reported as mean±standard deviation;
HGS=handgrip strength; FFM=fat-free mass; FM=fat
mass; PhA=phase angle.
* p>0.05.
Pearson’s correlation coefficients assessed the
association of HGS with both anthropometric and
body composition variables, and they are shown in
Table 3. We found that in athletes both
anthropometric and body composition variables were
Relationship between Handgrip Strength, Anthropometric and Body Composition Variables in Different Athletes
149
significantly associated with HGS, except for PhA.
The strongest correlation was found between HGS
and FFM (r=0.918, p=0.000) as shown in Figure 1. In
controls, HGS was correlated with all anthropometric
variables (p<0.05). While, among body composition
variables, HGS was directly associated with FFM and
FM, but not with FM% and PhA.
Table 3: Pearson’s correlation for the association of
handgrip strength with both anthropometric and body
composition variables.
Athletes
Controls
r p r p
Age 0.171 0.222 0.294 0.020
Weight 0.910 0.000 0.509 0.000
Stature 0.660 0.000 0.372 0.003
BMI 0.832 0.000 0.423 0.001
FFM 0.918 0.000 0.304 0.016
FM 0.712 0.000 0.224 0.080
FM% 0.528 0.000 0.176 0.171
PhA 0.214 0.123 0.061 0.636
BMI=body mass index; HGS=handgrip strength; FFM=fat-
free mass; FM=fat mass; PhA=phase angle.
Figure 1: Linear correlation between handgrip strength and
fat-free mass in male athletes.
Finally, multiple regression analysis was performed
to assess the main determinant of HGS for both
groups. The only predictors of HGS were FFM
(β=0.910) and body weight (β=0.509) for athletes and
controls, respectively.
4 DISCUSSION
This study aimed to evaluate the relationship between
HGS, anthropometric and body composition
variables in a group of male athletes compared to a
control group.
Our results showed that HGS was higher in
athletes than in controls, but the difference was not
statistically significant. Additionally, we found that
PhA, a BIA parameter considered as promising
marker of muscle quality, was higher in athletes than
in control subject, in accordance with literature
results (Marra 2018a; Marra 2018b; Di Vincenzo
2019; Di Vincenzo 2019; Di Vincenzo 2020).
Overall most of parameters considered were
positively related to HGS in both groups. However,
multiple regression analysis showed that the only
predictors of HGS were body weight for controls and
FFM for athletes. The latter might be related to a
different quality of muscle mass.
In conclusion our study showed that FFM was the
main determinant of muscular function in athletes,
but not in control subjects. Further evaluations are
needed to verify the relation between HGS and body
composition variables in athletes.
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