Relationship between Handgrip Strength, Anthropometric and Body

Composition Variables in Different Athletes

Olivia Di Vincenzo

, Maurizio Marra

, Delia Morlino

, Enza Speranza

, Rosa Sammarco

Iolanda Cioffi

and Luca Scalfi

Department of Clinical Medicine and Surgery, Federico II University of Naples,

Via S. Pansini 5, 80131, Naples, Italy

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

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

)

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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