3.1. Variable Reduction
We grouped the twenty-five anthropometric kinanthropometric variables five 5 factors that contained all of them (
Table 2):
F-Fat mass: It reflected 21.7% of explained variability.
F-Muscle mass: It reflected 20.5% of explained variability.
F-Bone mass: It reflected 12.6% of explained variability.
F-Skinfolds: It reflected 11.9% of explained variability.
F-Robustness: It reflected 11.9% of explained variability.
The results obtained through the EFA validate the use of these five factors instead of the twenty-five anthropometric and kinanthropometric variables, as these imply 78.6% of all the variables. From the assignment of coefficients, we also calculated the factorial and standardized scores of the participants in our sample (
Table 3).
3.2. Relationship of Explanatory Variables Based on Lifestyle and Type of Sport Practiced
Using the univariate ANOVA test, we analyzed if there were statistically significant differences based on lifestyle and the type of sport practiced (
Table 4):
Fat mass: The statistical significance (p < 0.10) had a small effect (1.6%). Using Tukey’s post hoc test, a statistical significance was detected (p < 0.05), which reflected a higher value in the control group compared to the rest of the groups.
Muscle mass: Highly significant differences were observed (p < 0.001) with a moderate–high effect size (9.4%). Using Tukey’s post hoc test, it was detected that the upper and lower body group (p < 0.01) presented higher values than the rest of the groups. Furthermore, no differences were observed between the control group and the group that mainly used the lower body (p > 0.05).
Bone mass: No statistically significant results were observed when comparing the groups with each other (p > 0.05), despite the mean values being generally higher among people in the control group.
Skinfolds: Statistically significant differences were observed (p < 0.05) with a slight effect size (2.1%). Using Tukey’s post hoc test, it was detected that the control group presented the highest values in the population, being statistically significant when compared with the upper and lower body group (p < 0.05) and the group that mainly used the lower body (p < 0.05). No statistically significant differences were observed between these last two groups (p > 0.05). It should be noted that this analysis could not be carried out based on sex, since the pectoral fold was not collected in women, so they were discarded when doing the analysis.
Robustness: Statistically significant differences were observed (p < 0.01) with a moderate–mild effect size (2.8%). Using Tukey’s post hoc test, it was detected that those in the upper and lower body group had values above the average, while those in the control group and the group that mainly used the lower body had values below the average (p < 0.05). It should be noted that the members of this last group presented a certain tendency toward statistical significance (p < 0.10), which reflects that they would tend to be less corpulent than the rest of the population.
Cormic Index: Highly significant statistical differences were observed (p < 0.001) with a moderate–mild effect size (3.7%). Using Tukey’s post hoc test, it was detected that the subjects in the group that mainly used the lower body had lower values (p < 0.01) than the rest of the individuals in the upper and lower body group and in the group control. While between these last two groups no differences were observed between them (p > 0.05).
Lower Relative Index of the Lower Limbs: Highly significant statistical differences were observed (p < 0.001) with a moderate–mild effect size (3.4%), with the lower body group being the one that differs from the other two (in this case with a higher index to be inverse to the Cormic Index).
The existence of highly significant global differences (
p < 0.001) between groups was proven through the results of the Multivariate Analysis of Variance test (M-ANOVA), since the joint effect of the linear combination of all the variables when behaving differently in the groups is moderate–high (9.7%) (
Table 4).
To confirm the veracity of the profiles detected, we performed a Discriminant Analysis to find out which variables are most associated with each of the three groups studied. We tried to minimize the errors that could be made when we classified the subjects into one of these three categories.
The variables that we considered are muscle mass, robustness, and the Cormic Index, since all of them present a high statistical significance (
p < 0.001). Other ones, such as fat mass and skinfolds were excluded in this model, because their discriminatory capacities to (
p > 0.05) are not enough (
Table 5).
The efficiency of this model to classify people can be evaluated by comparing the predicted group of each individual with their finally assigned group, thus establishing the percentage of success. In this case, 245 of the 375 people were correctly cataloged, which represents a 65.3% correctness, which is practically two-thirds of the population.
The degree of partial efficacy is much higher within the upper and lower body group, since 184 of the 216 members were correctly catalogued, which represents 85.2%, while in the mainly lower body group, it was 61 of 135, which represents 45.2%. In the case of the members of the control group, 16 were classified within the category of upper and lower body and 8 in the category of mainly lower body, which represents 66.7% and 33.3%, respectively (
Figure 2).
Although technically they were erroneously classified in all cases, since the sedentary individuals were not amateur athletes, it is true that if they had been amateur athletes, they would have been classified that way (
Figure 2).
We can appreciate how the variability of the cases of the upper and lower body group is much higher than in the other groups. Most of the cases are located to the right of the central axis of the discriminant function, while those that mainly use lower body tend to be rather to the left of the same axis (
Figure 3).
3.3. Relationship of Explanatory Variables According to the Practiced Sport
Analyzing the five EFA factors in each of the sports disciplines, we observed no statistically significant differences in American football, gym, capoeira, and swimming, but we have observed them in
Table 6.
The results showed that the sedentary group has high values in fat mass and skinfolds (p < 0.020 and a large effect of 20.6%). Among the sports, those with high values in fat mass were as follows: walking, which also had with low values in bone mass and robustness (p < 0.001 and a very large effect of 34.8%); rugby, which had high values in fat mass, skinfolds, and robustness. (p < 0.001 and a large effect of 20.8%); volleyball, which also had low values in muscle mass, in addition to bone mass and skinfolds (p < 0.001 and a very large effect of 33.2%); and basketball, which showed high values in fat mass, bone mass, and robustness and low values in fat mass (p < 0.001 and a large effect of 19%).
On the other hand, football had low values in fat mass and skinfolds (p < 0.030 and a moderate effect of 5.5%) and cycling also had low values in fat mass and muscle mass (p < 0.001 and very large effect of 41.2%).
Airsoft had high values in muscle mass and skinfolds and low values in fat mass (p < 0.01 and a large effect of 20.8%). Canoe showed high values in muscle mass and low values in fat mass and skinfolds (p < 0.001 and a very large effect of 38.9%).
CrossFit also showed high values in muscle mass and low values in fat mass, bone mass, skinfolds, and robustness. (p < 0.001 and a very large effect of 30.1%).
The sports that showed differences in robustness were running, which had low values in muscle mass and robustness. (p < 0.001 and a large effect of 23.7%) and handball, which had high values in robustness and low values in skinfolds (p < 0.001 and a large effect of 15.6%).