RESEARCH PAPER
Anthropometric, body composition and behavioural predictors of bioelectrical impedance phase angle in Polish young adults – preliminary results
 
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1
Department of Clinical Dietetics, Faculty of Health Sciences, Medical University, Lublin, Poland
2
Department of Medical Informatics and Statistics with E-learning Laboratory, Faculty of Health Sciences, Medical University, Lublin, Poland
3
Institute of Rural Health, Lublin, Poland
CORRESPONDING AUTHOR
Michał Skrzypek   

Department of Clinical Dietetics, Faculty of Health Sciences, Medical University of Lublin, ul. Chodźki 1, room 161, 20-093, Lublin, Poland
 
Ann Agric Environ Med. 2020;27(1):91–98
 
KEYWORDS
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ABSTRACT
Introduction and objective:
Bioimpedance analysis (BIA) phase angle (PhA) is an index of the integrity of cells and cellular membranes. The aim of the study was identification of behavioural and anthropometric predictors of PhA in a group of young adults.

Material and methods:
A cross-sectional observational study of health behaviours, anthropometric indicators and body composition assessed by the BIA method was conducted in a group of Polish young adults (n=92) aged 18 – 24 (mean – 19.33, STD – 0.915). Behavioural variables included: level of physical activity, eating behaviours and nutritional knowledge. Body composition was analysed by means of BIA phase-sensitive 8-electrode medical SECA mBCA 525 device.

Results:
The mean PhA value in the examined cohort was 6.38±0.75 (males – 7.22±0.72; females – 6.13±0.57). Males also showed higher statistically significant other body composition indices, excluding fat mass. The multiple regression model, including anthropometric variables and gender, which explained the effect of these variables on PhA, occurred to be significant (p<0.0000) and allowed explanation of the 82.49% of PhA variability. PhA was significantly predicted from body mass index (BMI), absolute fat mass, visceral adipose tissue value, skeletal muscle mass value and gender. The regression model, including behavioural predictors and gender, allowed explanation of the lower percentage of PhA variability (42.75%; p<0.0000) and included general intensity of health behaviours, level of nutritional knowledge, and gender. A regression model which would consider simultaneously anthropometric and behavioural variables could not be constructed.

Conclusions:
In the examined cohort, anthropometric and body composition variables showed a stronger predictive value with respect to PhA, compared to behavioural variables.

ABBREVIATIONS
(in the order in which they appear in the text): BIA – bioelectrical impedance analysis; Z – impedance; R – resistance; Xc – reactance; PhA – phase angle; BIVA – bioelectrical impedance vector analysis; ECW – extracellular body water; TBW – total body water; FFM – fat free mass; FM – fat mass; BCM – body cell mass; ECM – extracellular matrix; PA – physical activity; BMI – body mass index; SMM – skeletal muscle mass; IPAQ – International Physical Activity Questionnaire; MET – metabolic equivalent; KomPAN – questionnaire for investigating nutritional attitudes and habits; pHDI-10 – pro-Healthy Diet Index-10; nHDI-14 non-Healthy Diet Index-14; HBI – Healthy Behaviour Inventory; VAT – visceral adipose tissue; ICW – intercellular body water; FFQ – food frequency questionnaire
 
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