Lower Percentage of Fat Mass among Tai Chi Chuan Practitioners.

Author: Stagi S1, Doneddu A2, Mulliri G2, Ghiani G2, Succa V1, Crisafulli A2, Marini E1
Author Information:
1Department of Life and Environmental Sciences, University of Cagliari, Cittadella Universitaria, Monserrato, 09042 Cagliari, Italy.
2Department of Medical Sciences and Public Health, University of Cagliari, 09124 Cagliari, Italy.
Conference/Journal: Int J Environ Res Public Health.
Date published: 2020 Feb 14
Other: Volume ID: 17 , Issue ID: 4 , Special Notes: doi: 10.3390/ijerph17041232. , Word Count: 214


The aim of the study was to analyze total and regional body composition in Tai Chi Chuan (TCC) middle-aged and elderly practitioners. A cross-sectional study on 139 Italian subjects was realized: 34 TCC practitioners (14 men, 20 women; 62.8 ± 7.4 years) and 105 sedentary volunteers (49 men, 56 women; 62.8 ± 6.4 years). Anthropometric measurements (height, weight, arm, waist, and calf circumferences), hand-grip strength, and physical capacity values were collected. Total and regional (arm, leg, and trunk) body composition was analyzed by means of specific bioelectrical impedance vector analysis (specific BIVA). TCC practitioners of both sexes were characterized by a normal nutritional status, normal levels of physical capacity, and normal values of hand-grip strength. Compared to controls, they showed lower percentages of fat mass (lower specific resistance) in the total body, the arm, and the trunk, and higher muscle mass (higher phase angle) in the trunk, but lower muscle mass in the arm. Sexual dimorphism was characterized by higher muscle mass (total body, arm, and trunk) and lower %FM (arm) in men; sex differences were less accentuated among TCC practitioners than in the control. TCC middle-aged and elderly practitioners appear to be less affected by the process of physiological aging and the associated fat mass changes, compared to sedentary people.

KEYWORDS: Tai Chi Chuan; ageing; body composition; specific bioelectrical impedance vector analysis (BIVA)

PMID: 32075041 DOI: 10.3390/ijerph17041232

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