Effect of Physical Training on Body Composition in Moscow Adolescents

Transcription

1 Effect of Physical Training on Body Composition in Moscow Adolescents Elena Godina, Irena Khomyakova, Arsen Purundzhan, Anna Tretyak and Ludmila Zadorozhnaya Institute and Museum of Anthropology, Moscow State University, Russia Abstract The influence of physical activity on body mass components has been studied using a sample of Moscow children. 195 girls and 259 boys of Russian ethnicity from 12 to 17 years old were investigated cross-sectionally in 2005 in different Moscow schools. According to the level of physical activity they were divided into three groups: 1 those who did not take part in regular physical exercise (44 boys and 50 girls); 2 those who took part in special sports programs in general education schools (82 boys and 82 girls); 3 students of special sports schools with a high sports ranking (133 boys and 63 girls). The program included anthropometric measurements, evaluation of sexual maturation indices, somatotypes, and functional traits (diastolic and systolic blood pressure, pulse rate, hand grip, etc). For the study of body composition, bioelectrical impedance analysis (BIA) was used. The estimates of body mass components were also calculated using the anthropometric measurements. For the fat component, the estimates obtained by BIA and the anthropometric methods were highly correlated: r Age changes of BIA measurements and body components were analysed. With multiple regression analysis it was shown that BIA measurements are dependent on a great number of morphological and functional traits, with the most informative sets of traits being selected. The degree of physical activity has a strong effect on body components: the contents of fat-free mass (FFM) and total body water (TBW) significantly increase, and the fat mass (FM) in girls decreases. J Physiol Anthropol 26(2): , browse/jpa2 [DOI: /jpa ] Keywords: auxology, body composition, physical training, Moscow schoolchildren Introduction The study of body composition brings more insight to classical research into age changes during growth and development, and promotes further understanding for assessing nutritional differences, the role of physical activity, and other factors influencing growth. The first equations for predicting the size of body mass components on the basis of anthropometric measurements were introduced at the beginning of the 20 th century (Matiegka, 1921). Later they were developed and elaborated by many researchers. In the second half of the 20 th century, other experimental methods appeared: densitometric, isotope dilution, absorptiometric, bioelectrical, and others (for a review of the methods, see Zemel and Barden, 2004). Among several bioelectrical methods, one that is widely used is bioelectrical impedance analysis (BIA). It is based on the differences in human body tissue resistance (impedance) to a small electrical current. Thus, adipose tissue has much higher resistance as compared to those tissues that contain a lot of water (extracellular fluid, blood, muscle, etc.) and comprise the main part of the fat-free mass (Baumgartner, 1996). The present study was aimed at the assessment of body composition differences in groups of children according to the level of their physical activity using anthropometric and BIA methods. Materials and Methods The data for the present study were collected in 2005 in different Moscow schools. 195 girls and 259 boys from 12 to 17 years old were investigated cross-sectionally and divided into three groups according to their physical activity status: 1 those who did not take part in regular physical exercise (44 boys and 50 girls); 2 those who took part in special sports programs in general education schools (82 boys and 82 girls); 3 students of special sports schools with a high sports ranking (133 boys and 63 girls). All the children were of the same ethnicity (Russians). The anthropometric program included about 30 measurements taken according to the standard techniques (Bounak, 1941; Weiner and Lourie, 1969). Subjects were measured bare-footed, wearing only underwear. Weight was taken using a battery-operated digital weighing scale (precision 100 g). Height, arm, and leg length (sum of the heights of the iliospinale and symphysion points divided by two) were measured using an anthropometer (1 mm precision). Body diameters (biiliac, bicristal, chest width, and chest depth) were

2 230 Physical Training and Body Composition measured using a spreading caliper (1 mm precision). Chest, waist, hips, arm, forearm, thigh, and calf circumferences were measured using a plastic measuring tape (5 mm precision). Chest circumference was measured at the union of the 3 rd and 4 th sternebrae. Subcutaneous skinfolds (subscapular, biceps, triceps, forearm, abdomen, suprailiac, thigh, and calf) were measured using a skinfold Harpenden caliper (1 mm precision). Elbow, wrist, knee, and ankle widths were measured with a special sliding caliper (1 mm precision). The body mass index (BMI) of the studied subjects was calculated as weight (kg) divided by height 2 (m). Total fat (fat mass) was calculated by Matiegka s equations (1921): FM d S k, where FM is fat mass (kg), d is average thickness of subcutaneous skinfolds (mm), S is body surface (cm 2 ), and k Subcutaneous fat mass (SFM) was calculated according to the following formula (Smirnova and Shagurina, 1981): SFM S (d/2 k) 0.9, where SFM is subcutaneous fat mass (kg), S is body surface (cm 2 ), d is average thickness of subcutaneous skinfolds (mm), and k is skin thickness (0.7 mm for children). Fat-free mass (FFM) was calculated as Body mass minus Fat mass (FM). For the bioelectric impedance analysis, a single-frequency system, ABC-01 Medass (Nikolaev, 2001), was used. It operates with a frequency of 50 khz and is analogous to other such systems. It uses four electrodes attached to the hand and ipsilateral foot in the standard fashion (Baumgartner, 1996). The BIA technique gives two indices of tissue impedance active resistance (R Z ) and reactive resistance, or reactance (Z C ). The analyzer also measures phase angle f. The analyzer is equipped with original software, which gives estimates of body components (Nikolaev, 2001). Active resistance (R Z ) is positively connected with fat mass (FM) and negatively with total body water (TBW) and fat-free mass (FFM): the bigger the R Z value, the more the fat mass; reactance (Z C ) is positively connected with active cell mass in FFM (Nikolaev, 2001). It may be assumed that these two measurements are the most objective, while further estimates of body components are derivatives of these measurements. Statistical analysis included calculations of the basic statistical parameters for the group of children studied. The procedure of standardizing with standard scores (Z-scores) was calculated for each child and each measurement. Later it allowed the summarizing of the results according to the different categories of the level of physical activity, irrespective of the age of the children. To reveal the relationships between characteristics of tissue resistance and morphological traits, a number of methods of multivariate statistical analysis were used. To evaluate the intergroup differences according to physical activity level, one-way ANOVA was applied. All calculations were performed with the STATISTICA 6.0. software package. Results and Discussion The age changes of some measurements are shown in Table 1. Due to the large number of traits investigated, it is hardly possible to present the corresponding figures for all of the them, so only a selection of the parameters is given: stature, weight, BMI, BIA measurements, and the calculated estimates of body mass components. If we consider gender differences, in stature boys are ahead of girls at all ages, apart from age 12, where the values are slightly in favour of girls. Thus, the second crossing of growth curves for the investigated groups takes place between 12 and 13 years. In body mass boys surpass girls in all groups with considerable differences of 6 9 kg starting from age 13. In BMI the prevalence of boys stays until the age of 15, and at 16 and 17 there are practically equal values. It should be noticed that the estimates of body fat calculated by anthropometric methods are systematically lower in girls than those calculated by BIA methods. In boys, the difference between two values is much smaller, and at age 16 the values received by anthropometric estimates are higher. This corresponds with the findings of Goss et al. (2003). FM in girls increases and remains almost stable in the age period between 15 and 17 years, while in boys there is a slight decrease in this parameter. FFM shows a much stronger increase in boys, then in girls, thus emphasizing the importance of fat-free component development in pubertal boys. For greater reliability of the results, further analysis was conducted with standardized estimates (Z-scores). The relationships of bioimpedance characteristics with the set of anthropometric and functional characteristics were analyzed with multiple regression analysis, where R Z and Z C were the dependent variables and morphofunctional traits the independent ones (Table 2). In Table 2, the estimates of R and p are given for all regression equations for boys and girls. It can be seen that the correlation between all independent variables included in the equation is higher for R Z than for Z C. Evidently, the estimates are higher in boys, with the exception of skinfolds, which have higher values in girls. This supports the assumption about the role of the fat component in the development of somatic traits in girls. The highest estimates of the multiple correlation coefficients were obtained for the whole set of independent variables, which is not surprising taking into consideration the large number of variables in the analysis 29 traits. One of the goals of the present study was to compare the body component estimates obtained by different methods. In Table 3, the correlation coefficients for the estimates of different body components calculated by two methods are given. As could be assumed, the highest correlations are seen between different values of FM. Anthropometric estimates of

3 Godina, E et al. J Physiol Anthropol, 26: , Table 1 Means and standard deviations of some measurements in Moscow children Boys Girls X SD X SD 12 years n 16 n 18 Stature, cm Body mass, kg Body mass index (BMI) Active resistance (R Z ), ohms Reactive resistance, or reactance (Z C ), ohms Fat mass (FM-BIA), kg Fat-free mass (FFM-BIA), kg Total] body fat (FM-anthro), kg years n 33 n 24 Stature, cm Body mass, kg Body mass index (BMI) Active resistance (R Z ), ohms Reactive resistance, or reactance (Z C ), ohms Fat mass (FM-BIA), kg Fat-free mass (FFM-BIA), kg Total body fat (FM-anthro), kg years n 60 n 20 Stature, cm Body mass, kg Body mass index (BMI) Active resistance (R Z ), ohms Reactive resistance, or reactance (Z C ), ohms Fat mass (FM-BIA), kg Fat-free mass (FFM-BIA), kg Total body fat (FM-anthro), kg years n 37 n 22 Stature, cm Body mass, kg Body mass index (BMI) Active resistance (R Z ), ohms Reactive resistance, or reactance (Z C ), ohms Fat mass (FM-BIA), kg Fat-free mass (FFM-BIA), kg Total body fat (FM-anthro), kg years n 52 n 47 Stature, cm Body mass, kg Body mass index (BMI) Active resistance (R Z ), ohms Reactive resistance, or reactance (Z C ), ohms Fat mass (FM-BIA), kg Fat-free mass (FFM-BIA), kg Total body fat (FM-anthro), kg years n 21 n 22 Stature, cm Body mass, kg Body mass index (BMI) Active resistance (R Z ), ohms Reactive resistance, or reactance (Z C ), ohms Fat mass (FM-BIA), kg Fat-free mass (FFM-BIA), kg Total body fat (FM-anthro), kg

4 232 Physical Training and Body Composition Table 2 Results of multiple regression analysis of the BIA characteristics and anthropometric traits, sexual maturation indices, and functional characteristics in Moscow adolescents Boys Girls R Z Z C R Z Z C R p R p R p R p Body circumferences Skinfolds Skeletal traits* Stature and weight < BMI <0.000 Functional characteristics** All the above-listed traits *Skeletal traits: 4 body diameters, 4 bone width, arm, leg and corpus length. ** Functional characteristics: diastolic and systolic blood pressure, pulse rate, body temperature and dynamometry, or hand grip (both hands). Table 3 Correlation coefficients between different estimates of body components in boys and girls* Table 4 Influence of physical activity level on the BIA measurements and body mass components in boys and girls (one-way ANOVA) Boys Girls 1. FM-BIA FFM-BIA SFM-anthro FM-anthro * Correlation coefficients for boys are above the diagonal, and for girls below the diagonal. FM and SFM are connected with functional correlation, which can be explained by the presence of the same traits in the equations. The problem of the influence of physical training on body composition has been studied by many researchers (Pařizková, 1974, 1977; Malina and Bouchard, 1991; Eisenmann and Malina, 2002). It has been stated that the range of changes in body composition depends on the type, intensity, and duration of physical exercise (Malina and Bouchard, 1991). In our study we followed the changes in three groups of children with different levels of physical activity, from those whose physical training practically did not exist (group 1) to those who participated in special sports programs in general education schools (group 2), and finally to young sportsmen from special sports schools (group 3). The results of one-way ANOVA (Table 4) show that there are clear sex differences in the influence of physical activity on the structure of body mass components. In boys there are significant differences in R Z and FFM-BIA estimates. This demonstrates the role of muscles in the first place in the formation of male body shape. In girls practically all estimates show the significant effect of physical exercise. Only the FM calculated by the BIA method remained independent from this factor. At the same time R Z, which is supposed to be in direct proportion to the fat mass, has a highly significant connection with the level of physical activity. Such inconsistency of results may be explained by the F p F p SFM-anthro FM-anthro R Z Z C FM-BIA FFM-BIA non-adequacy of the prediction model calculated for children, and also by the fact that both impedance estimates are connected with all of the body components. It should be also kept in mind that the sample size was rather limited, all the more so since the children were divided into three categories. Figure 1 represents differences in FM evaluated by anthropometric equations. In girls of the third group there is a distinctive decrease of FM-anthro and SFM-anthro estimates with the increasing level of physical activity. In boys such a tendency, though much less expressed, is revealed only for SFM. More evident and highly significant differences between the three groups of children studied are revealed for BIA estimates (three-component model). Among the adolescents with a high degree of physical activity and sports ranking (the 3rd group) the contents of FFM and TBW significantly increase, and the FM decreases (Figure 2), particularly in girls. Thus, we may state clear gender differences in the reaction of body mass components to physical training. While there is a significant gain in FFM both for boys and for girls, the decline of fat content (FM) was more expressed in females from the 2nd to the 3rd group. It is worth noting that in girls of the 2nd group FM was even bigger by both anthropometric and BIA estimates (Figures 1 and 2). It may be assumed that in general education schools those girls who want to lose weight enter a

5 Godina, E et al. J Physiol Anthropol, 26: , Fig. 1 Changes of body mass components estimated by an anthropometric method in groups of children with different levels of physical activity. Fig. 2 Changes of body mass components estimated by BIA in groups of children with different levels of physical activity. special sports program. However the differences between the 2nd and 3rd groups are highly significant in girls. In boys the picture of decline in FM is steadier and the differences are bigger from the 1st group to the 2nd one, with a much smaller decline from the 2nd to the 3rd group (Figure 2). This may be connected with the particular sports programs that the boys were entering at schools, but to answer this question one needs more data and further analysis. Conclusions In summary, the results of the present study show that the estimates of fat components obtained by BIA and anthropometric methods are highly correlated both in boys and in girls ( ), but for girls the BIA technique overestimates the fat component. The degree of physical activity has a strong effect on body composition: the contents of fat-free mass (FFM) and total body water (TBW) significantly increase, and the fat mass (FM) in girls decreases. Acknowledgements This work was supported by the Russian Foundation of Basic Research, grants _o _o a, a; and the Russian Foundation of Humanities Research, grant _o a. References Baumgartner RN (1996) Electrical impedance and total body electrical conductivity. In Roche AF, Heymsfield SB, Lohman TG eds Human Body Composition. Human Kinetics Bounak VV(1941) Anthropometry. Moscow. [In Russian] Eisenmann JC, Malina RM (2002) Age-related changes in subcutaneous adipose tissue of adolescent distance runners and association with blood lipoproteins. Ann Hum Biol, 29,

6 234 Physical Training and Body Composition Goss F, Robertson R, Williams A, Sward K, Abt K, Ladewig M, Timmer J, Dixon C (2003) A comparison of skinfolds and leg-to-leg bioelectrical impedance for the assessment of body composition in children//dynamic Medicine,2,5 Malina RM, Bouchard C (1991) Growth, Maturation and Physical Activity. Human Kinetics Books, Champaign, Illinois Matiegka J (1921) The testing of physical efficiency. Am J Phys Anthrop, 4, Nikolaev DV (2001) Bioelectrical Impedance Analyzer ABC- 01 Medass : Application Guide. Moscow. [In Russian] Pařizková J (1974) Particularities of lean body mass and fat development in growing boys as related to their motor activity. Acta Paediat Belgica, 28 (Suppl.), Pařizková J (1977) Body Fat and Physical Fitness. The Hague: Martinus Nijhoff Smirnova NS, Shagurina TP (1981) Methods of Anthropometric Investigation. Moscow State Univ. Publishers. [In Russian] Weiner JS, Lourie JA (1969) Human Biology. A Guide to Field Methods. Blackwell Scientific Publ., Oxford, Edinburgh Zemel B, Barden E (2004) Measuring Body Composition. In Hauspie RC, Cameron N and Molinari L eds Methods in Human Growth Research. Cambridge University Press This article was presented at the 8th International Congress of Physiological Anthropology, 2006 (ICPA 2006), in Kamakura, Japan. Received: September 30, 2006 Accepted: December 11, 2006 Correspondence to: Elena Godina, Ph.D., D.Sc., Professor, Leading Researcher, Institute and Museum of Anthropology, Moscow State University, Mokhovaya str., 11 Moscow RUSSIA Phone: (495) godina@antropos.msu.ru