Raw Bioelectrical Impedance Analysis Variables (Impedance Ratio

and Phase Angle) and Physical Fitness in Cross-Fit® Athletes

Giada Ballarin

, Fabiana Monfrecola

, Paola Alicante

, Rossella Chierchia

, 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: High Intensity Functional Training (HIFT), Physical Fitness, Body Composition, BIA, Phase Angle,

Impedance Ratio.

Abstract: Few data are available on body composition and its relationships with physical fitness in Cross-Fit® athletes.

Our study aimed to evaluate changes in raw bioelectrical impedance analysis (BIA) variables and their

relationships with physical fitness in male Cross-Fit® athletes. Fifteen male Cross-Fit® athletes (age 19-35

years, weight 83.8±5.6 kg, body mass index-BMI 26.0±1.9 kg/m²) and fifty-one control men, (age 20-30

years, weight 76.5±10.8 kg, BMI 24.6±3.2 kg/m²) participated in the study. Body composition was evaluated

by using BIA and physical fitness was assessed by measuring handgrip strength (HGS), long jump (L-J), squat

jump (SQ-J) and counter-movement jump (CM-J). Phase angles were higher and impedance ratios were lower

in Cross-Fit® athletes for the whole body and limbs (both these directly-measured raw BIA variables are

promising markers of muscle quality). HGS was only slightly higher in the Cross-Fit® group, whereas a clear

difference emerged between groups in L-J (+16.2% in Cross-Fit® athletes), SQ-J (+21.5%) and CM-J

(+21.5%). HGS, L-J, SQ-J and CM-J significantly correlated with both impedance ratios and phase angles

(for whole body and limbs). In conclusion, raw BIA variables such as impedance ratio and phase angle

significantly change in Cross-Fit® athletes compared to controls and also exhibit significant relationships

with physical fitness.

1 INTRODUCTION

Cross-Fit® is a type of high intensity functional

training (HIFT) that emphasizes functional, multi-

joint movements to improve physical fitness

(PhysFit) in terms of strength, power, flexibility and

cardiovascular endurance. It results in greater muscle

recruitment than other exercise programmes and can

be adjusted to any ﬁtness level (Feito et al. 2018).

Cross-Fit® exercises are based on elements of

gymnastics, weightlifting and cardiovascular fitness,

and are included in combinations known as workouts

of the day (WODs) (Fisker et al. 2017), which are

executed quickly, repetitively, and with little or no

recovery time between sets.

The strength and power indexes of squat test

(Martínez-Gómez et al. 2019) and the sum of

different one-repetition maximum loads (Butcher et

al. 2015) have been used as indicators of Cross-Fit®

performance, while counter-movement jump test was

applied to assess muscular fatigue before, during and

after different WODs (Maté-Muñoz et al. 2017).

CrossFit training may be useful for enhancing health-

related physical ﬁtness parameters in physically

inactive adults (Brisebois et al. 2018) and for

improving VO

max (Feito et al. 2019), standing long

jump and shuttle run (Eather et al. 2016). More

generally, an eight-week HIFT resulted in signiﬁcant

enhancements of muscular strength for back squat

and deadlift (Banaszek et al. 2019).

As far as handgrip strength (HGS) is concerned,

no specifical data are available in Cross-Fit® athletes

(Claudino et al. 2018); indeed prevoius researches

have shown that physically active individuals had

higher HGS when compared to those inactive (de

Lima et al 2016).

To the best of our knowledge, the effect of Cross-

Fit® training on body composition have been

evaluated in few studies only. Cross-Fit® did not

significantly affect body mass index (BMI) or body

composition in sedentary men and women (Heinrich

et al. 2014) whereas in both boys and girls there were

Ballarin, G., Monfrecola, F., Alicante, P., Chierchia, R., Marra, M., Sacco, A. and Scalﬁ, L.

Raw Bioelectrical Impedance Analysis Variables (Impedance Ratio and Phase Angle) and Physical Fitness in Cross-Fit

 Athletes.

DOI: 10.5220/0010066001030108

In Proceedings of the 8th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2020), pages 103-108

ISBN: 978-989-758-481-7

103

improvements of body composition parameters such

as BMI and waist circumference (Eather et al. 2016)

and lean body mass further increased in already

previously active young adults (Murawska-Cialowicz

et al. 2015). Among obese adults the only effect was

a rise in lower-limb lean body mass (Feito et al.

2019). Positive effects on body composition have

been also reported in cancer survivors (Heinrich et al.

2015).

Bioelectrical impedance analysis (BIA) is a

widely used, non-invasive field method for assessing

body composition, which measures the electrical

characteristics of human body (i.e impedance-Z and

phase angle-PhA) either at 50 kHz (single-frequency

BIA) or at several frequencies in the range 1-1000

kHz (multifrequency BIA or spectroscopy).

Our interest was motivated by the fact that to the

best of our knowledge, there were no data available

on raw BIA variables in Cross-Fit® athletes.

Impedance ratio (IR=the ratio between Z at higher

frequencies and Z at lower frequencies) and PhA may

be considered as promising markers of muscle quality

and therefore of value in athletes. Actually, these

variables have been associated with muscle structure

in terms of body cell mass (BCM) and the ratio

between extracellular water-ECW and intracellular

water-ICW (Lukaski et al. 2017). In addition, IR ans

PhA have also been specifically related to muscle

strength and physical activity (de Blasio et al. 2019;

Mundstock et al. 2019).

Facing this background, the general aim of our

study was to evaluate the usefulness of raw BIA

variables in assessing muscle structure/quality in

athletes. Specific aims were to study were to study

raw BIA variables, such as IR and PhA and selected

variables of physical fitness in Cross-Fit® athletes

compared to control subjects.

2 METHODS

2.1 Participants

This cross-sectional study included fifteen male

Cross-Fit® athletes, age 19-35 years, and fifty-one

control men, age 20-30. Cross-Fit® athletes were

recruited from a gym located in Naples. They trained

at least five hours a week in three different sessions

and had practiced at least 18 months of specific

training. Other inclusion criteria were being healthy

and having a body mass index (BMI) below 28 kg/m².

Eighty-three per cent of the potential participants

agreed to be included in the study. Controls were

students attending the Federico II University of

Naples who did not practice sport and did less than

100 minutes of moderate-vigorous activity per week.

Subjects were studied in the morning, after an

overnight fasting, by the same operator and 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). BMI was then calculated

as body weight (kg)/stature² (m²).

2.2 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.

Subjects were asked to lie down with their upper

limbs and lower limbs slightly abducted to avoid any

contact between body segments. The measuring

electrodes were placed on the anterior surface of the

wrist and ankle, and the injecting electrodes placed on

the dorsal surface of the hand and the foot,

respectively. Whole body and segmental BIA have

been performed using a six-electrode technique

according to Organ et al. (1994). We considered data

for the whole body and separately for upper and lower

limbs with respect to the following BIA raw

variables: 1) bioimpedance (BI) indexes at 5 or at 50-

100-300 kHz (stature²/Z), as markers of ECW and fat-

free mass (FFM) respectively; 2) IR between Z at

high frequency (300 kHz) and Z at low frequency (5

kHz); 3) PhA measured at 50 kHz. The means of

measures for right and left sides of body were

considered.

FFM was estimated using the Sun equation (Sun

et al. 2003). Fat mass (FM) was calculated as the

difference between body weight and FFM.

2.3 Fitness Tests

The selected physical fitness tests were performed

according to standardized procedures. Handgrip

strength (HGS) was measured with a Dynex

dynamometer (MD systems, Ohio USA) to assess

isometric strength of upper limbs as described by

Beaudart et al. (2019). Maximum values on three

attempts on the dominant and three attempts on the

non-dominant body side was used for analysis; long

jump (L-J) was used to assess lower body muscle

power. 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

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

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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. Squat jump (SQ-J) and

countermovement jump (CM-J) were measured with

the OptoJump® device (MicroGate, Italy) to assess

the explosive power of lower limbs (Markovic et al.

2004). In both cases, the highest of three jumps was

used for analysis.

2.4 Statistical Analysis

Results are reported 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 the

normality of data. The general linear model was used

to assess differences after controlling for body

weight.

Differences between groups were assessed using one-

way ANOVA or general linear model (when data

were adjusted for weight). Association between

variables was evaluated using partial correlations to

control for group and age, while multiple regression

was employed to identify the predictors of physical

fitness.

3 RESULTS

The general characteristics of the study groups are

reported in Table 1. Cross-Fit® athletes were slightly

heavier than controls, with no statistical difference for

stature and BMI.

Table 1: General characteristics and body composition in

Cross-Fit® athletes and controls.

Cross-Fit®

(n=15)

Controls

(n=51)

Age yrs 27.6±6.3 25.4±3.7

Weight kg 83.8±5.6

76.5±10.8

Stature cm 179.4±4.1 176.4±6.8

Body mass index kg/m

26.0±1.9 24.6±3.2

Fat-free mass kg 66.9±3.3 61.7±7.2

Fat mass kg 16.9±4.6 15.0±5.6

Fat mass % 20.0±4.3

19.2±5.2

mean±standard deviation. a=p<0.05 between groups.

According to BIA, FFM was significantly higher in

the Cross-Fit® athletes (p<0.05), with no difference

after controlling for body weight. Similarly, as far as

raw BIA variables were concerned (Table 2), BI

indexes at 5 and 300 kHz were higher in the Cross-Fit®

group compared to control group (+7.5% and +9.8%,

respectively). Indeed those differences di not persist

after controlling for body weight.

Table 2: Bioimpedance indexes of the whole body in Cross-

Fit® athletes and controls.

Bioimpedance index

(cm

/kHz) at the frequency:

Cross-Fit®

(n=15)

Controls

(n=51)

5 kHz 60.3±4.3

56.1±7.1

50 kHz 72.1±5.0

65.9±8.9

100 kHz 77.3±5.5

70.4±9.7

300 kHz 85.3±6.2 77.7±10.8

mean±standard deviation.

a=p<0.05 between groups.

On the other hand, as reported in Table 3, PhAs

were clearly higher in Cross-Fit® athletes by 6.6%

for the whole body, 5.8% for upper limbs and 5.6%

for lower limbs. In the opposite direction, significant

lower IRs were observed in the Cross-Fit® group

compared to the control group. Selected physical

fitness tests were performed, focusing on the domain

of strength.

As summarized in Table 4, HGS was only slightly

higher in the Cross-Fit® group, the difference being

further reduced after adjusting for body weight. On

the contrary, higher values emerged in Cross-Fit®

athletes regarding L-J (+16.2%), SQ-J (+21.5%) and

CM-J (+21.5%).

Table 3: Impedance ratio (IR=Z 300 kHz/Z 5 kHz) and

phase angle (at 50 kHz) measured on the whole body and

limbs in Cross-Fit® athletes and controls.

Cross-Fit®

(n=15)

Controls

(n=51)

Impedance ratio

Whole body 0.707±0.023 0.724±0.022

Upper-limbs 0.701±0.026 0.722±0.022

Lower-limbs 0.719±0.026

0.734±0.026

Phase angle (degrees)

Whole body 7.46±0.70

7.00±0.66

Upper-limbs 6.72±0.71

6.26±0.71

Lower-limbs 8.26±0.82

7.82±0.72

mean±standard deviation. PhA= phase angle

a=p<0.05 between Cross-Fit® athletes and controls

Raw Bioelectrical Impedance Analysis Variables (Impedance Ratio and Phase Angle) and Physical Fitness in Cross-Fit

 Athletes

105

Then, the association of PhysFit with selected

variables of interest was evaluated by partial

correlation (Table 5). HGS, L-J, SQ-J and CM-J

showed a significant association with whole body IR

and PhA.

Table 4: Physical fitness in Cross-Fit® athletes and controls

as assessed by different tests.

Performance Tests

Cross-Fit®

(n=15)

Controls

(n=51)

Handgrip strength kg 52.0±6.2 47.7±8.3

Long jump cm 193.6±25.7

166.7±36.7

Squat jump cm 29.9±10.2

24.6±6.0

Countermovement

ump

cm 28.9±10.6

23.8±5.3

mean±standard deviation.

a=p<0.05 between groups.

Similar results were also obtained in most cases

for the association with upper-limb and lower-limb

IRs and PhA (results not shown). Multiple regression

analysis (data on the whole body) showed that BI

index at 300 kHz was the most important predictor of

HGS, whereas IR or PhA were the most significant

predictors of L-J, SQ-J and CM-J.

4 DISCUSSION

This study shows that raw BIA variables such as IR

and PhA significantly differed (suggesting improved

muscle structure) in Cross-Fit® athletes compared to

controls and also exhibit significant relationships

with PhysFit.

We performed BIA in Cross-Fit® athletes and

controls. First, FFM was estimated by means of

predictive equations that include BIA variables, age,

stature and body weight (Sun et al. 2003).

Table 5: Partial correlation of physical fitness with

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

Performance Tests

IR Phase angle

r p r p

Handgrip strength -0.412 <0.001 0.461 <0.001

Long jump -0.308 0.018 0.273 0.036

Squat jump -0.536 <0.001 0.504 <0.001

Countermovement jump -0.361 0.004 0.337 0.008

Results for the whole body (after adjustment for age).

No major impact of Cross-Fit® training emerged

from our data with respect to FFM or FM. Then, as

major aim, we focused our attention on those raw BIA

variables (IR and PhA) that are related to ECW/ICW

ratio, body cell mass (BCM), and cellular integrity

(Lukaski et al. 2017). PhA and IR have also been

shown to be significantly associated with muscle

strength and physical activity (de Blasio et al. 2019;

Mundstock et al. 2019) and to vary between genders

and with aging (Barbosa-Silva et al. 2018; Bosy-

Westphal et al. 2008).

PhA describes the angular shift (phase difference)

between voltage and current sinusoidal waveforms,

which in humans is likely due to cell membranes and

tissue interface (Lukaski et al. 2017; Norman et al.

2012). As reported in a recent systematic review of

our group (Di Vincenzo et al. 2019), it is still to be

defined to what extent PhA changes between different

sports and with training/un-training. Only few studies

have shown that mean whole-body PhA is higher in

athletes vs. controls, while scarce data are available

on the segmental evaluation of upper and lower limbs

(Di Vincenzo et al. 2019).

We observed that PhA was significantly higher in

Cross-Fit® athletes, with a relatively small difference

between groups (+6.6% for the whole body, +5.8%

for upper limbs and +5.6% for lower limbs). Said

differently, the variation of whole-body PhA (0.46

degrees) was close to the pooled SD of 0.70 degrees.

An increase of this magnitude (or slightly higher) has

been already observed by us in female ballet dancers,

cyclists and male marathon runners (Di Vincenzo et

al. 2019).

The Z of human tissues is frequency-dependent

since alternate current at low frequencies passes

through the extracellular fluid, whereas at higher

frequencies (i.e. ≥50 kHz) also penetrates cell

membranes. Thus, the IR is similar to a phase shift

(Mundstock et al. 2019), being inversely correlated

with PhA when calculated with the approach used in

the present study.

To the best of our knowledge, there are no data on

the IR of subjects practicing sports. Actually, we

found that IRs were clearly lower in the Cross-Fit®

group than in the control group. A 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 those

variables. For instance, the difference in IR for the

whole body was 0.017, which was close to the pooled

SD of 0.025.

A few previous studies have shown that Cross-

Fit® training may be useful for improving health-

related PhysFit (Brisebois et al. 2018; Feito et al.

2019; Eather et al. 2016; Banaszek et al. 2019). As

further point, we evaluated a certain number of

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PhysFit tests, concentrating on the domain of

strength. These tests were selected according to the

fact that they can be applied to subjects practicing

different sports, as well as in young controls.

Interestingly, no increase in HGS was observed in

Cross-Fit® athletes, in agreement with previous study

on Judo (Sterkowicz et al. 2016). On the contrary,

higher mean values were observed for L-J, SQ-J and

CM-J, demonstrating an improvement in the

explosive strength of lower limbs.

Finally, an interesting issue was to explore

whether and to what extent PhysFit was related to

those raw BIA variables that are promising markers

of muscle structure. As far as we know, no consistent

data are available in the literature on the topic (Di

Vincenzo et al. 2019).

Based on our results (partial correlation), the

fitness variables considered were all significantly

associated, although differently, with IR 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 IR or PhAs, while the opposite was observed for

HGS. While our results are pretty consistent, a small

sample of Cross-Fit® athletes has been evaluated and

gender differences were not analysed because only

young men were measured. Moreover, further studies

are needed to confirm that the concurrent use of BIA

and physical fitness tests is a valuable approach for

assessing muscle quality in athletes in terms of both

muscle structure and strength.

In conclusion, raw BIA variables such as IR and

PhA significantly change in male Cross-Fit® athletes

compared to controls, suggesting higher BCM, and

also exhibit significant relationships with PhysFit.

More information on body composition are given by

segmental BIA of upper and lower limbs, which can

be useful for a better evaluation of the relationships

between body composition and PhysFit.

REFERENCES

Banaszek, Amy et al. 2019. ‘The Effects of Whey vs. Pea

Protein on Physical Adaptations Following 8-Weeks of

High-Intensity Functional Training (HIFT): A Pilot

Study’. Sports (Basel, Switzerland) 7(1).

Barbosa-Silva, Maria Cristina G. et al. 2018. ‘Bioelectrical

Impedance Analysis: Population Reference Values for

Phase Angle by Age and Sex’. The American Journal

of Clinical Nutrition 82(1): 49–52.

Beaudart C., Rolland Y., Cruz-Jentoft, et al. 2019

‘Assessment of Muscle Function and Physical

Performance in Daily Clinical Practice: A position

paper endorsed by the European Society for Clinical

and Economic Aspects of Osteoporosis, Osteoarthritis

and Musculoskeletal Diseases (ESCEO) ’. Calcif

Tissue Int.;105(1):1-14

de Blasio, Francesca et al. 2019. ‘Raw Bioelectrical

Impedance Analysis Variables Are Independent

Predictors of Early All-Cause Mortality in Patients

With COPD’. Chest 155(6): 1148–57.

Bosy-Westphal, Anja et al. 2008. ‘Phase Angle From

Bioelectrical Impedance Analysis: Population

Reference Values by Age, Sex, and Body Mass Index’.

Journal of Parenteral and Enteral Nutrition 30(4):

309–16.

Brisebois, Matthew F., Brandon R. Rigby, and David L.

Nichols. 2018. ‘Physiological and Fitness Adaptations

after Eight Weeks of High-Intensity Functional

Training in Physically Inactive Adults’. Sports (Basel,

Switzerland) 6(4).

Butcher, Scotty, Tyler Neyedly, Karla Horvey, and Chad

Benko. 2015. ‘Do Physiological Measures Predict

Selected CrossFit® Benchmark Performance?’

Open Access Journal of Sports Medicine: 241.

Claudino, João G. et al. 2018. ‘CrossFit Overview:

Systematic Review and Meta-analysis’. Sports

Medicine 4:11.

Di Vincenzo, Olivia, Maurizio Marra, and Luca Scalfi.

2019. ‘Bioelectrical Impedance Phase Angle in Sport:

A Systematic Review’. Journal of the International

Society of Sports Nutrition 16(1): 49.

Eather, Narelle, Philip James Morgan, and David Revalds

Lubans. 2016. ‘Improving Health-Related Fitness in

Adolescents: The CrossFit Teens

Randomised

Controlled Trial’. Journal of Sports Sciences 34(3):

209–23.

Feito, Yuri, Michael J. Giardina, Scotty Butcher, and

Gerald T. Mangine. 2019. ‘Repeated Anaerobic Tests

Predict Performance among a Group of Advanced

CrossFit-Trained Athletes’. Applied Physiology,

Nutrition, and Metabolism = Physiologie Appliquee,

Nutrition et Metabolisme 44(7): 727–35.

Feito, Yuri, Katie Heinrich, Scotty Butcher, and Walker

Poston. 2018. ‘High-Intensity Functional Training

(HIFT): Definition and Research Implications for

Improved Fitness’. Sports 6(3): 76.

Feito, Yuri, Pratik Patel, Andrea Sal Redondo, and Katie

Heinrich. 2019. ‘Effects of Eight Weeks of High

Intensity Functional Training on Glucose Control and

Body Composition among Overweight and Obese

Adults’. Sports 7(2): 51.

Fisker, F. Y. et al. 2017. ‘Acute Tendon Changes in Intense

CrossFit Workout: An Observational Cohort Study’.

Scandinavian Journal of Medicine & Science in Sports

27(11): 1258–62.

Heinrich, K. M. et al. 2015. ‘High-Intensity Functional

Training Improves Functional Movement and Body

Composition among Cancer Survivors: A Pilot Study’.

European Journal of Cancer Care 24(6): 812–17.

Heinrich, Katie M, Pratik M Patel, Joshua L O’Neal, and

Bryan S Heinrich. 2014. ‘High-Intensity Compared to

Moderate-Intensity Training for Exercise Initiation,

Enjoyment, Adherence, and Intentions: An Intervention

Study’. BMC Public Health 14(1): 789.

Raw Bioelectrical Impedance Analysis Variables (Impedance Ratio and Phase Angle) and Physical Fitness in Cross-Fit

 Athletes

107

de Lima, Tiago R., Silva, Diego A.S., de Castro, João A.C.

and Christofaro, Diego G.D., 2016. ‘Handgrip strength

and sociodemographic and lifestyle factors: A

systematic review of the adult population’. Journal of

Bodywork & Movement Therapies: 401-413.

Lukaski, Henry C., Ursula G. Kyle, and Jens Kondrup.

2017. ‘Assessment of Adult Malnutrition and Prognosis

with Bioelectrical Impedance Analysis: Phase Angle

and Impedance Ratio’. Current Opinion in Clinical

Nutrition and Metabolic Care 20(5): 330–39.

Markovic, Goran, Drazan Dizdar, Igor Jukic, and Marco

Cardinale. 2004. ‘Reliability and Factorial Validity of

Squat and Countermovement Jump Tests’. Journal of

Strength and Conditioning Research 18(3): 551–55.

Martínez-Gómez, Rafael et al. 2019. ‘Full-Squat as a

Determinant of Performance in CrossFit’. International

Journal of Sports Medicine 40(09): 592–96.

Maté-Muñoz, José Luis et al. 2017. ‘Muscular Fatigue in

Response to Different Modalities of CrossFit Sessions’

ed. Pedro Tauler. PLOS ONE 12(7): e0181855.

Mundstock, Eduardo et al. 2019. ‘Association between

Phase Angle from Bioelectrical Impedance Analysis

and Level of Physical Activity: Systematic Review and

Meta-Analysis’. Clinical Nutrition (Edinburgh,

Scotland) 38(4): 1504–10.

Murawska-Cialowicz, E., J. Wojna, and J. Zuwala-Jagiello.

2015. ‘Crossfit Training Changes Brain-Derived

Neurotrophic Factor and Irisin Levels at Rest, after

Wingate and Progressive Tests, and Improves Aerobic

Capacity and Body Composition of Young Physically

Active Men and Women’. Journal of Physiology and

Pharmacology: An Official Journal of the Polish

Physiological Society 66(6): 811–21.

Norman, Kristina, Nicole Stobäus, Matthias Pirlich, and

Anja Bosy-Westphal. 2012. ‘Bioelectrical Phase Angle

and Impedance Vector Analysis--Clinical Relevance

and Applicability of Impedance Parameters’. Clinical

Nutrition (Edinburgh, Scotland) 31(6): 854–61.

Organ LW, Bradham GB, Gore DT, Lozier SL. 1994.

‘Segmental bioelectrical impedance analysis: theory

and application of a new technique’. J Appl Physiol

Bethesda Md 1985.;77:98–112.

Sterkowicz, Stanisław et al. 2016. ‘Effect of Acute Effort

on Isometric Strength and Body Balance: Trained vs.

Untrained Paradigm’ ed. Stephen E Alway. PLOS ONE

11(5): e0155985.

Sun, Shumei S. et al. 2003. ‘Development of Bioelectrical

Impedance Analysis Prediction Equations for Body

Composition with the Use of a Multicomponent Model

for Use in Epidemiologic Surveys’. The American

Journal of Clinical Nutrition 77(2): 331–40.

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