Geometrically, a circle area is computed as: Ac=?��?r2 (5) Where Ac is the circle area [m2], �� a constant value of 3.14 and r is selleck the radius [m].The area of an oval or ellipse is found: Ao=w?l?0.8 (6) Where Ao is the oval area [m2], w is the width [m] and l the length [m]. So, transferring the geometrical knowledge to anthropometrics, it seems that the breaths are the exogenous variables that might be able to predict more powerful TTSA estimation equations. Added to this we had approximately 75 male and 55 female subjects to compute and additional ones to validate the estimation equations using forward step-by-step multiple regression models. When computing multiple regression models it is stated that it is necessary to consider at least 15 subjects for each exogenous variables inserted in the model (i.

e. K > 15). Therefore, our decision was to insert 5 exogenous variables (i.e. body mass, height, BCD, CSD and CP) trying to maintain some data consistence. Body mass and height were inserted because they are the variables used in equation 2. The BCD, CSD and CP were added because geometrically they seem to be the variables that allow a higher TTSA estimation. Analyzing the descriptive data presented in Table 1, mean values are similar or slightly lower than other papers reporting anthropometrical data (Mazza et al., 1994; Strza?a et al., 2005; 2007; Knechtke et al., 2010) and TTSA (Nicolas et al. 2007; Nicolas and Bideau, 2009; Caspersen et al., 2010). This research presents a higher dispersion data, as the age range is also higher.

Remaining papers focused on stricter chronological age frames or even made separate groups analysis for children and adults. In this sense, it can be speculated that data is in accordance with the main literature. The development of biomechanical models, in this case a statistical one estimating the TTSA based on selected anthropometrical variables, can be a feasible way to promote hydrodynamic evaluation (i.e. drag force) with relevant information for swimmers and coaches (Barbosa et al., 2010a). So, being descriptive statistics similar to main literature and presenting moderate dispersions it allowed to compute and validate the biomechanical models (Barbosa et al., 2010b), as in this case the TTSA estimation equations, based on these data.

Computation of trunk transverse surface area prediction models For both male and female gender the final model for the TTSA estimation equations included the CP and the CSD. The equations were significant and with a prediction level qualitatively considered as moderate. Anacetrapib This means that some other variables not considered for the prediction can have some impact on the TTSA estimation. Forcing new variables entering the model could increase slightly the coefficient of determination but, would also increase the error of estimation. In this sense, it was decided to maintain the true nature of the model developed and not forcing other variables to be included on it.