Raw Bioelectrical Impedance Analysis (BIA) Variables and Physical

Fitness in Semi-Professional Basketball Players

Giada Ballarin

, Fabiana Monfrecola

, Paola Alicante

, Ada Di Gregorio

, Maurizio Marra

Anna Maria Sacco

and Luca Scalfi

Department of Public Health, Federico II University of Naples, Via S. Pansini 5, Naples, Italy

Department of Clinical Medicine and Surgery, Federico II University of Naples, Via S. Pansini 5, Naples, Italy

Keywords: Basketball, Physical Fitness, Body Composition, BIA, Phase Angle, Impedance Ratio.

Abstract: Body composition (BC) and physical performance are routinely assessed in athletes. Incomplete data are

available on BC and its relationships with physical fitness in basketball players. Our study aimed to evaluate

differences in raw BIA variables such as impedance ratio=IR and phase angle=PhA, and their relationships

with physical fitness in semi-professional male basketball players compared to controls. Fourteen basketball

players (age 21.9±5.3 years, body weight 84.6±11.3 kg, body mass index=BMI 24.6±2.0 kg/m²) and fifty-

seven control men (age 22.6±2.0 years, body weight 75.1±9.4 kg, BMI 24.1±2.3 kg/m²) participated in the

study. BC was assessed using bioelectrical impedance analysis (BIA) and physical fitness using handgrip

strength (HGS), long jump (L-J), squat jump (SQ-J) and counter-movement jump (CM-J). PhAs were higher

and IRs were lower in basketball players for the whole body and limbs. Differences in HGS between groups

did not persist after adjusting for body weight, whereas L-J (+32.6%), SQ-J (+35.4%) and CM-J (+28.7%)

were clearly higher in the basketball group. HGS, L-J, SQ-J and CM-J significantly correlated with both IRs

and PhAs (whole body and limbs). In conclusion, this study shows that raw BIA variables were significantly

different in semi-professional basketball players compared to controls and also exhibit significant

relationships with physical fitness.

1 INTRODUCTION

The evaluation of physical fitness and body

composition (BC) is crucial in assessing athletes’

performance. In basketball players VO

max, long

jump and shuttle-run test have been evaluated with

the aim to identify talents or design training programs

(Tsunawake et al. 2003; Calleja González et al. 2018).

For instance, a difference has been observed in BC,

aerobic fitness, anaerobic power, and vertical jump

among positional roles (Ostojic et al. 2006; Ponce-

González et al. 2015). Indeed, there is some

conflicting evidence (Mancha-Triguero et al. 2019):

vertical jump height was around or greater than 60 cm

in the study by Ostojic et al. (2006), but much lower

in other studies (Ponce-González et al. 2015).

As far as BC is concerned, previous papers have

shown high fat-free mass (FFM) and low fat mass

(FM) in both male and female basketball players

(Fields et al. 2018a). FM was similar in high school

and elite female players (Tsunawake et al. 2003),

whereas female adolescents practicing basketball or

other team sports had more lean body mass and less

FM than controls (Ubago-Guisado et al. 2017). No

significant seasonal differences were observed, but

male players showed an increase in FFM across years

(Fields et al. 2018b) and female players a reduced

body fat (Stanforth et al. 2014). A low percentage of

body fat was found at higher playing levels and a wide

variability of skinfold thickness was observed among

different playing positions (Vaquera et al. 2015).

BC has been evaluated in basketball players with

different techniques such as DXA, air pletismo-

graphy and hydrostatic weighing. (Hoare 2000;

Vaquera et al. 2015; Fields et al. 2018b; Raymond-

Pope et al. 2020). Skinfold thickness measurement

and bioelectrical impedance analysis (BIA) are the

most commonly technique used in the field . On the

other hand, only few studies evaluated BC in

basketball players using bioimpedance analysis=BIA

(Nescolarde et al. 2011; Gerodimos et al. 2017). Our

interest was further stimulated by the fact that raw

BIA variables such as impedance ratio (IR as ratio

between impedance=Z at high frequency and Z at low

Ballarin, G., Monfrecola, F., Alicante, P., Di Gregorio, A., Marra, M., Sacco, A. and Scalﬁ, L.

Raw Bioelectrical Impedance Analysis (BIA) Variables and Physical Fitness in Semi-Professional Basketball Players.

DOI: 10.5220/0010066101330138

In Proceedings of the 8th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2020), pages 133-138

ISBN: 978-989-758-481-7

133

frequency) or phase angle (PhA) may be considered

as promising markers of muscle quality. As a matter

of fact, these raw BIA variables have been related to

muscle structure (body cell mass=BCM) and the ratio

between extracellular water=ECW and intracellular

water=ICW in different pathophysiological

conditions (Lukaski et al. 2017) and also in athletes

(Di Vincenzo et al. 2019). In addition, IR and PhA

have been associated with muscle function in various

diseases (de Blasio et al. 2019; Mundstock et al.

2019), but not in athletes (Di Vincenzo et al. 2019).

Finally, the relationship between physical fitness

and BC has been evaluated in few previous studies in

male athletes: no or a weak correlation emerged

between vertical jump or standing broad jump

performance and stature (Davis et al. 2003; Sidhu

2018), whereas there was a stronger relationship

between vertical jump and lower percentage of body

fat (Davis et al. 2003; Aouadi et al. 2012). No data are

available on the association between physical fitness

and raw BIA variables.

Based on previous literature, basketball training

might be expected to influence muscle quality. It

might be supposed that raw BIA variables differ

between semiprofessional basketball players and

controls and that there are significant relationships

between these variables and physical fitness.

Facing this background, we aimed to evaluate in

semi-professional basketball players vs. controls: 1)

differences in raw BIA variables (whole body and

limbs); 2) differences in physical fitness; 3)

relationships between physical fitness and raw BIA

variables.

2 METHODS

This cross-sectional study included fourteen male

semi-professional basketball athletes (age 21.9±5.3

years, stature 185.1±7.1 cm, body weight 84.6±11.3

kg, BMI 24.6±2.0 kg/m²) and fifty-seven control men

(age 22.6±2.0 years, stature 176.1±7.7 cm, body

weight 75.1±9.4 kg, BMI 24.1±2.3 kg/m²). Basketball

players were semi-professional athletes (A.S.D.

Folgore Nocera basketball team), who competed in

the Italian fifth division championship (C-silver).

Athletes trained at least six hours a week in three

sessions and played a match every week. Every

training session lasted at least 120 minutes and can be

considered as a moderately hard training program.

Controls were recruited among the students attending

the Federico II University of Naples. Controls did not

practice sport and did less than 100 minutes of

moderate-vigorous activity. All subjects were

otherwise healthy.

The participants avoided physical exercise for 24

hours before the measurement session, being studied

by the same operator following standard procedures.

Body weight was measured to the nearest 0.1 kg using

a platform beam scale and stature to the nearest 0.5

cm using a stadiometer (Seca, Hamburg, Germany).

Body mass index=BMI was then calculated as body

weight (kg)/stature² (m²).

Concerning BIA, Z and PhA were measured at

frequencies between 5 and 300 kHz (HUMAN IM

TOUCH analyser, DS MEDICA, Milano), in

standardized conditions: ambient temperature

between 23-25 °C, fast >3 h, empty bladder, and

supine position for 10 min. BIA data for the whole

body and separately for upper limbs and lower limbs

were taken into consideration with respect to: 1)

bioimpedance index=BI index, calculated as stature²

divided by Z at 5 or 300 kHz, as marker of ECW and

total body water=TBW or FFM, respectively; 2) IR

between Z at high frequency and Z at low frequency

(three ratios: Z 50 kHz/Z 5 kHz, Z 100 kHz/Z 5 kHz,

and Z 300 kHz/Z 5 kHz); 3) PhA measured at 50 kHz.

In all cases, mean values for right and left body sides

were considered for statistical analysis.

FFM was estimated using the Sun equation, which

included stature, body weight and resistance (very

close to Z) as predictors (Sun et al. 2003). Fat mass

(FM) was calculated as body weight minus FFM.

2.1 Fitness Tests

Selected physical fitness tests were performed

according to standard procedures with respect to:

1) Handgrip strength (HGS) to measure isometric

strength of upper limbs (Dynex dynamometer, MD

systems Inc., Ohio USA). Maximum values of six

attempts on preferred and non-preferred sides of the

body were used for statistical analysis.

2) Long jump (L-J) to assess lower body muscle

strength. Participants performed a two foot take-off

and landing. The swinging of the arms and flexing of

the knees are permitted to provide forward drive. The

subject attempts to jump as far as possible, landing on

both feet without falling backwards. Length was

measured to the nearest point of contact on the

landing. Two attempts were performed and the best

value was used for analysis.

3) Squat jump (SQ-J) and countermovement jump

(CM-J) (OptoJump device, MicroGate, Italy) to

assess the explosive power of lower limbs. Subjects

performed SQ-J with hands placed on the hips, while

CM-J has been performed with arm swinging. In both

icSPORTS 2020 - 8th International Conference on Sport Sciences Research and Technology Support

134

cases, the highest of three jumps was used for

statistical analysis.

2.2 Statistical Analysis

Results are expressed as mean±standard deviation.

Statistical significance was pre-determined as

p<0.05. All statistical analyses were performed using

the Statistical Package for Social Sciences (SPSS Inc,

Chicago, IL, USA) version 24.

Shapiro-Wilk test was applied to assess normality

of the sample. The general linear model was used to

assess differences after controlling for body weight.

One-way ANOVA was performed to assess

differences between the two groups. Partial

correlations were employed to evaluate association

between variables after controlling for group and age.

3 RESULTS

The general characteristics of the study groups are

reported in Table 1. As expected, basketball players

were taller and heavier than controls, with no

statistical difference for age and BMI. They also

exhibited higher FFM (from BIA, p<0.01), but there

was no difference after adjusting for body weight.

Table 1: Individual characteristics and body composition in

14 basketball players and 57 controls.

Basketball

players

Controls

Age yrs 21.9 ±5.3 22.6±2.0

Body weight kg 84.6 ±11.3

75.1±9.4

Stature cm 185.1 ±7.1

176.1±7.7

Body mass index kg/m

24.6 ±2.0 24.1±2.3

Fat-free mass kg 66.8 ±6.3 60.5±6.7

Fat mass kg 17.8 ±6.5 14.8±5.0

Fat mass % 20.5 ±5.5

19.4±4.9

mean±standard deviation.

a=p<0.05 between groups.

As far as raw BIA variables are concerned (Table

2), BI indexes at 5, 50, 100 and 300 kHz were

significantly higher in the basketball group than in the

control group (+7.6%, +9.2%, +9.9% and +10.6%,

respectively); no differences persisted after

adjustment for body weight.

On the other hand, in basketball players PhAs were

higher for the whole body, upper limbs and lower

limbs (+9.8%, +8.8% and +9.2% respectively) (Table

3).

Table 2: Bioimpedance indexes calculated for the whole

body in 14 basketball players and 57 controls.

Bioimpedance (BI) index

(cm

/kHz)

Basketball

players

Controls

5 kHz 59.2±6.0

55.0±6.8

50 kHz 70.3±6.6

64.4±8.3

100 kHz 75.5±7.0

68.7±9.0

300 kHz 83.7±7.4 75.7±9.9

mean±standard deviation.

a=p<0.05 between groups.

In the opposite direction, significant lower IRs

were observed in the basketball group compared to

the control group for the whole body and lower limbs

(p<0.10 for upper limbs). This was true for each of

the three ratios considered (data shown for IR Z 300

kHz/Z 5 kHz).

Focusing on physical fitness, HGS was well

correlated (p<0.05) with L-J (r=0.505) SQ-J

(r=0.613) and CM-J (r=0.641). HGS was

significantly higher in the basketball group (Table 3),

but the difference was reduced after adjusting for

body weight (adjusted means 48.7 kg vs 46.4 kg). On

the contrary, a clear difference emerged between

groups in L-J (+32.6% in basketball players), SQ-J

(+35.4%) and CM-J (+28.7%).

Table 3: Phase angle at 50 kHz and impedance ratio Z 300

kHz/Z 5 kHz measured for the whole body and limbs in 14

basketball players and 57 controls.

Basketball

players

Controls

Impedance ratio

Whole body 0.707±0.025 0.727±0.022

Upper limbs 0.713±0.028 0.727±0.025

Lower limbs 0.713±0.025

0.736±0.024

Phase angle (degrees)

Whole body 7.52±0.82

6.85±0.67

Upper limbs 6.58±1.00

6.05±0.74

Lower limbs 8.46±0.76

7.75±0.70

mean±standard deviation. PhA=phase angle.

a=p<0.05 between groups.

The relationships of physical fitness with selected

variables of interest were first evaluated by partial

correlation (after adjusting for group and age). Table

5 shows that HGS, L-J, SQ-J and CM-J substantially

correlated with IRs (each of the three ratios) and PhA

measured on the whole body; similar results also

come out in most cases for upper or lower limbs (data

Raw Bioelectrical Impedance Analysis (BIA) Variables and Physical Fitness in Semi-Professional Basketball Players

135

not shown). Actually, physical fitness variables were

more strictly correlated with raw BIA variables than

with stature, body weight or BMI.

4 DISCUSSION

This study shows that raw BIA variables such as IR

and PhA were significantly different in semi-

professional basketball players compared to controls

(suggesting improved muscle structure) and also

exhibit significant relationships with physical fitness.

Table 4: Physical fitness tests performed in 14 basketball

players and 57 controls.

Performance Tests

Basketball

players

Controls

Handgrip strength kg 51.4±10.4

45.9±8.2

Long jump cm 221.8±23.6

167.3±29.6

Squat jump cm 32.9±8.2

24.3±5.7

Countermovement jump cm 30.5±8.7

23.7±4.8

mean±standard deviation.

a=p<0.05 between groups.

There have been a number of studies on BC in

basketball players, showing high FFM, low FM,

differences depending on playing levels or playing

position, etc. (Tsunawake et al. 2003; Stanforth et al.

2014; Vaquera et al. 2015; Ubago-Guisado et al.

2017; Fields et al. 2018a; Fields et al. 2018b).

BIA is a widely used, non-invasive and portable

technique to assess body composition, which has

been used only by few studies in basketball players

(Nescolarde et al. 2011; Gerodimos et al. 2017).

In this study BIA was performed in semi-

professional basketball players and controls to

evaluate body composition compartments and raw

BIA variables.

First, FFM was estimated by means of predictive

equations that include BIA variables, age, stature and

body weight (Sun et al. 2003). After adjusting for

confounders, no major impact of training on FFM or

FM emerged, possibly because of the characteristics

of the training program.

Then, raw BIA variables such as BI index, IR and

PhA were considered. BI index is not widely

mentioned in the literature, although its relationships

with ECW (at low frequencies) and TBW or FFM (at

high frequencies) are well known. In this study

whole-body BI indexes were higher in the basketball

players, with a larger difference between groups at

300 kHz than 5 kHz, suggesting significant

differences in body water compartments.

Table 5: Partial correlation of physical fitness with

impedance ratio (IR=Z 300 kHz/Z 5 kHz) and phase angle.

Physical fitness tests IR* Phase angle

r p r p

Handgrip strength -0.375 0.002

0.418 <0.001

Long jump -0.349 0.005

0.335 0.008

Squat jump -0.522 <0.001

0.530 <0.001

Countermovement jump -0.467 <0.001

0.480 <0.001

Results for the whole body (adjusted for age and group)

*Similar findings for IR 50 kHz/5 kHz or 100 kHz/5 kHz

As primary aim of the study, the attention was

focused on those raw BIA variables (IR and PhA) that

are markers of cell integrity and quite possibly of

muscle structure and quality (Lukaski et al. 2017a; Di

Vincenzo et al. 2019), being also associated with

muscle strength and physical activity (de Blasio et al.

2019; Mundstock et al. 2019). Overall, evidence on

IR and PhA is still lacking in athletes (Di Vincenzo et

al. 2019).

PhA is believed to be a proxy of BCM (and

inversely related to the ratio between ECW and ICW).

PhA was significantly higher in basketball players

than controls (+9.8% for the whole body), with a

similar difference between groups for upper limbs

and lower limbs. These observations were reinforced

by data on IR, which is a proxy of the ratio between

ECW and ICW (and inversely related to BCM). To

the best of our knowledge, there is no consistent

information on the IR of athletes. There is also no

agreement on the frequencies to be used for

calculating IR. For calculating IRs we selected three

high frequencies (50, 100 and 300 kHz) and one low

frequency (5 kHz). Our results were similar for the

three ratios considered, indicating that IRs were

significantly lower in the basketball players for the

whole body and lower limbs (less clearly for upper

limbs). At first glance, the differences in IRs appear

negligible in percentage terms; actually, they should

be considered in view of the very small standard

deviations observed for these variables. For instance,

the difference in IR Z 300 kHz/Z 5 kHz for the whole

body was 0.020, which was close to the pooled SD of

0.024.

Overall, evidence on IR and PhA suggests

different effects of training on distinct limb muscles

with a more marked improvement in raw BIA

variables for the lower limbs.

As further point, we evaluated physical fitness

focusing on the domain of strength. Previous studies

have already assessed physical performance, for

instance VO

max, in basketball players (Mancha-

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Triguero et al. 2019). We select physical fitness

variables that can be measured in both basketball

players and controls. Unlike HGS, L-J, SQ-J and CM-

J, which are all markers of lower limb strength, were

clearly higher in the basketball players, with a similar

relative difference between groups.

Finally, another aim of the study was to

demonstrate a relationship between function and

body composition as explored by raw BIA variables.

As far as we know, in athletes there are no

consistent data on this topic (Di Vincenzo et al. 2019).

Based on our results, the variables of physical fitness

considered were all significantly correlated with IRs

and PhA. It should be noted that the associations with

L-J, SQ-J and CM-J were stronger for lower-limb

than upper-limb IRs or PhA, while the opposite was

seen for HGS. Thus, raw BIA variables might be used

for a more effective evaluation of muscle quality in

terms of both muscle structure and strength.

In conclusion, this preliminary study gives some

information about the use of raw BIA variables in

assessing the athletes’ nutritional status. Actually,

raw BIA variables such as IR and PhA were

significantly different in semi-professional basketball

players, suggesting higher BCM, and also exhibit

significant relationships with physical fitness. More

information may be given by segmental BIA of upper

and lower limbs, which can be further useful for a

better evaluation of the relationships between

physical fitness and BC.

Large cross-sectional studies and, possibly,

longitudinal studies are needed to confirm that the

concurrent use of BIA and physical fitness tests is

valuable in assessing muscle quality, and to assess

differences due to gender, training volume, playing

position, playing levels, etc.

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