3. Results

### 3.1 Results of descriptive statistics among young females

Table 1 provides the results of descriptive statistics of body composition. Figure 1. provides the distribution of results of the correspondents' responses in relation to physical activities habits.


Body composition score BCS ð Þ¼�64:554 þ ð Þ 0:092 � BW þ �ð 0:107 � BMIÞþ �ð 1:001 � FMIÞ þ ð Þ 1:353 � SMM% þ �ð 0:626 � BFM%Þþ �ð 0:079 � VFAÞ þ ð Þ 4:894 � FFMI :

Relation between Lifestyle and Body Composition among Young Females in Serbia of 18–29…

Table 2 presents the results of Pearson's coefficient of correlation between

Statistically, most prominent correlations were found between lifestyle score and body composition score (r = 0.505, p < 0.01), while the highest negative statistically relevant correlation was between the body fat mass percentage and

Figure 2 presents the relation between body composition score and lifestyle score, which is explained by applying the method of mathematical modeling. The change of trend of relation between body composition score and lifestyle score has

In relation to the model body composition score, we could claim that the intercept stood at 28.99, while the trend of change (curve inclination) was defined by the coefficient of regressive constant of 2.26. In other words, this means that the increase of lifestyle score by 1 point led to the increase in the value of body

composition score by 2.26 points on average, that is, to its rise for as much as 2.3%. On the basis of the value of determination coefficient (R2 = 0.255), we conclude that 25.5% of the overall variability of body composition score results was determined by lifestyle score, that is, by the variability of an independent variable. The rest of variability of 74.5% has not been explained by the regression model, that is, it

BW �0.131\* �0.121 �0.071 �0.046 �0.117 �0.139\* BMI �0.159\* �0.151\* �0.091 �0.036 �0.208\*\* �0.184\*\* BFM% �0.311\*\* �0.322\*\* �0.209\*\* �0.204\*\* �0.395\*\* �0.408\*\* SMM% 0.353\*\* 0.378\*\* 0.252\*\* 0.188\*\* 0.451\*\* 0.461\*\* VFA �0.222\*\* �0.243\*\* �0.136\* �0.082 �0.306\*\* �0.281\*\* FFMI 0.144\* 0.207\*\* 0.163\* 0.164\*\* 0.174\*\* 0.242\*\* FMI �0.264\*\* �0.277\*\* �0.180\*\* �0.115 �0.324\*\* �0.331\*\* BCS 0.371\*\* 0.416\*\* 0.281\*\* 0.235\*\* 0.474\*\* 0.505\*\* BW—body weight; BMI—body mass index; BFM%—body fat mass percentage; SMM%—skeletal muscle mass percentage; VFA—visceral fat area; FFMI—fat-free mass index; FMI—fat-mass index; BCS—body composition

How much time do you spend behind a computer?

How would you describe your daily lifestyle?

Lifestyle score

3.3 Results of correlation analysis among young females

lifestyle score (r = 0.408\*\*).

• y = 2.26x + 28.99

is influenced by other factors.

Do you exercise?

score. \*p < 0.05. \*\*p < 0.01.

Table 2.

69

How often? How do you spend your free time?

Correlation between lifestyle and body composition among young females.

been defined by the following ratio:

DOI: http://dx.doi.org/10.5772/intechopen.83586

lifestyle in relation to body composition among young females.

(1)

BH—body height; BW—body weight; BMI—body mass index; BFM—body fat mass; BFM%—body fat mass percentage; SMM—skeletal muscle mass; SMM%—skeletal muscle mass percentage; VFA—visceral fat area; FFMI —fat-free mass index; FMI—fat-mass index; LSS—lifestyle score; BCS—body composition score.

### Table 1.

Basic descriptive indicators of body composition among young females.

Figure 1. Percentage of the responses given to each question from the questionnaire.

### 3.2 Results of regressive analysis among young females

On the basis of results gathered via multidimensional modeling and regressive analysis, we selected a mathematical model with the highest degree of prediction of the optimal model of our correspondents' body composition. Within this model, body composition score (BCS) presents a criterion variable, while body composition variables (BW, BMI, FMI, SMM%, BFM%, VFA, FFMI) present the most discriminative variable of body composition that makes up a predictive part of the defined model.

Body composition score has been given quantitatively, that is, by a numerical value calculated in the following way: all body composition variables applied here were subjected to the factorial analysis, and on that basis a reduced set of singular variables that statistically best describe correspondents' body composition has been selected. For each correspondent, BCS has been selected on the basis of specification equation that has the following form:

Relation between Lifestyle and Body Composition among Young Females in Serbia of 18–29… DOI: http://dx.doi.org/10.5772/intechopen.83586

$$\begin{aligned} \text{Body composition score } (\text{BCS}) &= -64.554 + (0.092 \times \text{BW}) \\ &+ (-0.107 \times \text{BMI}) + (-1.001 \times \text{FMI}) + (1.353 \times \text{SMM96}) \\ &+ (-0.626 \times \text{BFM96}) + (-0.079 \times \text{VFA}) + (4.894 \times \text{FFMI}). \end{aligned} \tag{1}$$

### 3.3 Results of correlation analysis among young females

Table 2 presents the results of Pearson's coefficient of correlation between lifestyle in relation to body composition among young females.

Statistically, most prominent correlations were found between lifestyle score and body composition score (r = 0.505, p < 0.01), while the highest negative statistically relevant correlation was between the body fat mass percentage and lifestyle score (r = 0.408\*\*).

Figure 2 presents the relation between body composition score and lifestyle score, which is explained by applying the method of mathematical modeling. The change of trend of relation between body composition score and lifestyle score has been defined by the following ratio:

• y = 2.26x + 28.99

In relation to the model body composition score, we could claim that the intercept stood at 28.99, while the trend of change (curve inclination) was defined by the coefficient of regressive constant of 2.26. In other words, this means that the increase of lifestyle score by 1 point led to the increase in the value of body composition score by 2.26 points on average, that is, to its rise for as much as 2.3%.

On the basis of the value of determination coefficient (R2 = 0.255), we conclude that 25.5% of the overall variability of body composition score results was determined by lifestyle score, that is, by the variability of an independent variable. The rest of variability of 74.5% has not been explained by the regression model, that is, it is influenced by other factors.


BW—body weight; BMI—body mass index; BFM%—body fat mass percentage; SMM%—skeletal muscle mass percentage; VFA—visceral fat area; FFMI—fat-free mass index; FMI—fat-mass index; BCS—body composition score. \*p < 0.05.

\*\*p < 0.01.

3.2 Results of regressive analysis among young females

Percentage of the responses given to each question from the questionnaire.

equation that has the following form:

model.

68

Figure 1.

Table 1.

Cardiorespiratory Fitness

On the basis of results gathered via multidimensional modeling and regressive analysis, we selected a mathematical model with the highest degree of prediction of the optimal model of our correspondents' body composition. Within this model, body composition score (BCS) presents a criterion variable, while body composition variables (BW, BMI, FMI, SMM%, BFM%, VFA, FFMI) present the most discriminative variable of body composition that makes up a predictive part of the defined

Mean SD %cV Min. Max. 95% confidence interval for mean

BH 169.89 6.95 4.09 151.70 193.70 169.020 170.759 BW 64.44 11.74 18.23 44.60 143.70 62.969 65.906 BMI 22.42 4.00 17.85 17.40 48.63 21.922 22.924 BFM% 24.14 8.69 36.00 5.90 55.28 23.057 25.231 SMM% 41.11 4.58 11.13 24.80 50.70 40.555 41.700 VFA 51.83 32.09 61.91 11.80 254.50 47.817 55.844 FFMI 16.45 1.35 8.24 12.63 22.23 16.276 16.615 FMI 5.87 3.28 55.91 1.93 26.40 5.463 6.284 LSS 9.29 3.72 40.05 0 15 8.821 9.751 BCS 50.00 16.67 33.33 5.10 84.30 47.914 52.083 BH—body height; BW—body weight; BMI—body mass index; BFM—body fat mass; BFM%—body fat mass percentage; SMM—skeletal muscle mass; SMM%—skeletal muscle mass percentage; VFA—visceral fat area; FFMI

—fat-free mass index; FMI—fat-mass index; LSS—lifestyle score; BCS—body composition score.

Basic descriptive indicators of body composition among young females.

Lower bound Upper bound

Body composition score has been given quantitatively, that is, by a numerical value calculated in the following way: all body composition variables applied here were subjected to the factorial analysis, and on that basis a reduced set of singular variables that statistically best describe correspondents' body composition has been selected. For each correspondent, BCS has been selected on the basis of specification

Table 2.

Correlation between lifestyle and body composition among young females.

visceral adipose tissue [54]. There is a strong relation among visceral fats, metabolic syndrome, and the most common chronic non-infectious diseases of contemporary humans [55]. Enzi et al. [56] found that among lean or obese young women, subcutaneous abdominal fat dominates in relation to abdominal visceral fat, both measured by CT at the upper renal pole. There are no clearly defined standards for the visceral fat content. According to the measuring technique applied here, a VFA of 100 cm<sup>2</sup> [45] is considered as a risk, but this needs to be taken with caution

Relation between Lifestyle and Body Composition among Young Females in Serbia of 18–29…

as further studies are needed. Among our correspondents, the average value

healthy, but also ill persons [57]. FFMI retains stable values among young and middle-aged women and men, and then drops after 74 years of age. Average FFMI

the category of normal and high values. According to the these authors, BMI increase that comes with aging has been complemented with the increase in BF and

Fat-mass index (FMI) is significantly higher among sedentary than physically active men than women, and the difference rises with age [57]. Average FMI value

Out of the maximum score of 15 for physical activity, our correspondents had 9.29 3.72, which can be considered to be a moderate activity. Close to 50% of them said to be active over 4 h per week, which could be said to correspond to the recommendation of 30 min of exercising each day [58]. Still, only a third has been continuously active throughout the year, which is near to the percentage of those who described their lifestyle as very active. Half of the correspondents spend their free time in sedentary activities (using the phone, computer, watching TV, reading

With regard to the mathematical model, the results of the combined influences of individual variables of body composition showed that FFMI and SMM% (4.894 vs. 1.353) had the greatest influence on the point score of body composition, while BW = 0.092 had the lowest influence (please see the formula). Apparently, taken overall variability of the point score, the most influential to the optimal model of body composition were those variables that define fat-free muscle mass dependent on longitudinality (FFMI), or the percentage of muscle tissue in the organism independent of voluminosity (SMM%). As both cases apply to contractile components, that is, body components most responsible for motorical quality, that is, movement, the results clearly showed that fat-free body structure was the most sensitive body component for defining the optimal body composition within the defined optimal body model among young females from the investigated sample. In addition, the two abovementioned most sensitive variables of the model are body components that are directly developed by physical activity, regardless of that activity being endurance training or resistance or weight training. Thereby, it has been directly shown that young females were no different in relation to the occurrence of body fat in the body—as a variable directly linked to diet and sedentary lifestyle, but dominantly differed by the occurrence of contractile component—as a

variable directly dependent of the amount of physical activity or exercising.

value of our correspondents (16.45 1.35 kg/m<sup>2</sup>

DOI: http://dx.doi.org/10.5772/intechopen.83586

obtained by the research of Kyle et al. [57] (14.6 and 16.8 kg/m<sup>2</sup>

Fat-free mass index is the indicator indexed according to the body height (FFMI

) and is considered as not only a good indicator of the body composition of

, while it was lover by 10 cm<sup>2</sup> in the study of Gába and

) corresponded to the values

) corresponded to the values obtained by

) and belong to the category of normal.

) and belonged to

was 51.83 32.09 cm2

FFM, and thereby with FFMI.

of our correspondents (5.87 3.28 kg/m<sup>2</sup>

the research of Kyle et al. [57] (3.9–8.1 kg/m2

Přidalová [53].

4.1 Lifestyle

and the like).

71

—kg/m<sup>2</sup>

Figure 2. Linear regression of body composition and lifestyle scores.
