Jorge E. Morais

Morphometric study for estimation and validation of trunk transverse surface area to assess human drag force on water.

Morphometric Study for Estimation and Validation of Trunk Transverse Surface Area To Assess Human Drag Force on Water The aim of this study was to compute and validate estimation equations for the trunk transverse surface area (TTSA) to be used in assessing the swimmers drag force in both genders. One group of 133 swimmers (56 females, 77 males) was used to compute the estimation equations and another group of 131 swimmers (56 females, 75 males) was used for its validations. Swimmers were photographed in the transverse plane from above, on land, in the upright and hydrodynamic position. The TTSA was measured from the swimmers photo with specific software. Also measured was the height, body mass, biacromial diameter, chest sagital diameter (CSD) and the chest perimeter (CP). With the first group of swimmers, it was computed the TTSA estimation equations based on stepwise multiple regression models from the selected anthropometrical variables. For males TTSA=6.662*CP+17.019*CSD-210.708 (R2=0.32; Ra2=0.30; P<0.01) and for females TTSA=7.002*CP+15.382*CSD-255.70 (R2=0.34; Ra2=0.31; P<0.01). For both genders there were no significant differences between assessed and estimated mean TTSA. Coefficients of determination for the linear regression models between assessed and estimated TTSA were R2=0.39 for males and R2=0.55 for females. More than 80% of the plots were within the 95% interval confidence for the Bland-Altman analysis in both genders.

The Influence of Anthropometric, Kinematic and Energetic Variables and Gender on Swimming Performance in Youth Athletes

Abstract The aim of this study was to assess the: (i) gender; (ii) performance and; (iii) gender versus performance interactions in young swimmers’ anthropometric, kinematic and energetic variables. One hundred and thirty six young swimmers (62 boys: 12.76 ± 0.72 years old at Tanner stages 1-2 by self-evaluation; and 64 girls: 11.89 ± 0.93 years old at Tanner stages 1-2 by self-evaluation) were evaluated. Performance, anthropometrics, kinematics and energetic variables were selected. There was a non-significant gender effect on performance, body mass, height, arm span, trunk transverse surface area, stroke length, speed fluctuation, swimming velocity, propulsive efficiency, stroke index and critical velocity. A significant gender effect was found for foot surface area, hand surface area and stroke frequency. A significant sports level effect was verified for all variables, except for stroke frequency, speed fluctuation and propulsive efficiency. Overall, swimmers in quartile 1 (the ones with highest sports level) had higher anthropometric dimensions, better stroke mechanics and energetics. These traits decrease consistently throughout following quartiles up to the fourth one (i.e. swimmers with the lowest sports level). There was a non-significant interaction between gender and sports level for all variables. Our main conclusions were as follows: (i) there are non-significant differences in performance, anthropometrics, kinematics and energetics between boys and girls; (ii) swimmers with best performance are taller, have higher surface areas and better stroke mechanics; (iii) there are non-significant interactions between sports level and gender for anthropometrics, kinematics and energetics.

The Power Output and Sprinting Performance of Young Swimmers

Abstract Barbosa, TM, Morais, JE, Marques, MC, Costa, MJ, and Marinho, DA. The power output and sprinting performance of young swimmers. J Strength Cond Res 29(2): 440–450, 2015—The aim of this article was to compare swimming power output between boys and girls and to model the relationship between swimming power output and sprinting performance in young swimmers. One hundred young swimmers (49 boys and 51 girls, aged between 11 and 13 years) underwent a test battery including anthropometrics (body mass, height, arm span [AS], and trunk transverse surface area), kinematic and efficiency (velocity, stroke frequency, stroke length, speed fluctuation, normalized speed fluctuation, stroke index, and Froude efficiency), hydrodynamics (active drag and active drag coefficient), and power output (power to overcome drag, power to transfer kinetic energy to water, and external power) assessments and sprinting performance (official 100 freestyle race). All variables but the trunk transverse surface area, stroke length normalize to AS, speed fluctuation, active drag coefficient, and Froude efficiency were significantly higher in boys than in girls with moderate-strong effects. Comparing both sexes but controlling the effect of the sprinting performance, most variables presented a no-significant variation. There was a significant and strong relationship between power output and sprinting performance: y = 24.179x 2.9869 (R 2 = 0.426; standard error of estimation = 0.485; p < 0.001). As a conclusion, boys presented better performances than girls because of their higher power output. There is a cubed relationship between power output and sprinting performance in young swimmers.

Characterization of speed fluctuation and drag force in young swimmers: a gender comparison.

The aim of this study was to compare the speed fluctuation and the drag force in young swimmers between genders. Twenty-three young pubertal swimmers (12 boys and 11 girls) volunteered as subjects. Speed fluctuation was measured using a kinematical mechanical method (i.e., speedo-meter) during a maximal 25-m front crawl bout. Active drag, active drag coefficient and power needed to overcome drag were measured with the velocity perturbation method for another two maximal 25m front crawl bouts with and without the perturbation device. Passive drag and the passive drag coefficient were estimated using the gliding decay velocity method after a maximal push-off from the wall while being fully immersed. The technique drag index was also assessed as a ratio between active and passive drag. Boys presented meaningfully higher speed fluctuation, active drag, power needed to overcome drag and technique drag index than the girls. There were no significant gender differences for active drag coefficient, passive drag and passive drag coefficient. There were positive and moderate-strong associations between active drag and speed fluctuation when controlling the effects of swim velocity. So, increasing speed fluctuation leads to higher drag force values and those are even higher for boys than for girls.

Modelling the relationship between biomechanics and performance of young sprinting swimmers

Abstract The aim of this study was to compute a swimming performance confirmatory model based on biomechanical parameters. The sample included 100 young swimmers (overall: 12.3 ± 0.74 years; 49 boys: 12.5 ± 0.76 years; 51 girls: 12.2 ± 0.71 years; both genders in Tanner stages 1–2 by self-report) participating on a regular basis in regional and national-level events. The 100 m freestyle event was chosen as the performance indicator. Anthropometric (arm span), strength (throwing velocity), power output (power to overcome drag), kinematic (swimming velocity) and efficiency (propelling efficiency) parameters were measured and included in the model. The path-flow analysis procedure was used to design and compute the model. The anthropometric parameter (arm span) was excluded in the final model, increasing its goodness-of-fit. The final model included the throw velocity, power output, swimming velocity and propelling efficiency. All links were significant between the parameters included, but the throw velocity–power output. The final model was explained by 69% presenting a reasonable adjustment (models goodness-of-fit; x2/df = 3.89). This model shows that strength and power output parameters do play a mediator and meaningful role in the young swimmers’ performance.

A Comparison of Experimental and Analytical Procedures to Measure Passive Drag in Human Swimming.

The aim of this study was to compare the swimming hydrodynamics assessed with experimental and analytical procedures, as well as, to learn about the relative contributions of the friction drag and pressure drag to total passive drag. Sixty young talented swimmers (30 boys and 30 girls with 13.59±0.77 and 12.61±0.07 years-old, respectively) were assessed. Passive drag was assessed with inverse dynamics of the gliding decay speed. The theoretical modeling included a set of analytical procedures based on naval architecture adapted to human swimming. Linear regression models between experimental and analytical procedures showed a high correlation for both passive drag (Dp = 0.777*Df+pr; R2 = 0.90; R2 a = 0.90; SEE = 8.528; P<0.001) and passive drag coefficient (CDp = 1.918*CDf+pr; R2 = 0.96; R2 a = 0.96; SEE = 0.029; P<0.001). On average the difference between methods was -7.002N (95%CI: -40.480; 26.475) for the passive drag and 0.127 (95%CI: 0.007; 0.247) for the passive drag coefficient. The partial contribution of friction drag and pressure drag to total passive drag was 14.12±9.33% and 85.88±9.33%, respectively. As a conclusion, there is a strong relationship between the passive drag and passive drag coefficient assessed with experimental and analytical procedures. The analytical method is a novel, feasible and valid way to gather insight about one’s passive drag during training and competition. Analytical methods can be selected not only to perform race analysis during official competitions but also to monitor the swimmer’s status on regular basis during training sessions without disrupting or time-consuming procedures.

Growth influences biomechanical profile of talented swimmers during the summer break

This study aimed to analyse the effect of growth during a summer break on biomechanical profile of talented swimmers. Twenty-five young swimmers (12 boys and 13 girls) undertook several anthropometric and biomechanical tests at the end of the 2011–2012 season (pre-test) and 10 weeks later at the beginning of the 2012–2013 season (post-test). Height, arm span, hand surface area, and foot surface area were collected as anthropometric parameters, while stroke frequency, stroke length, stroke index, propelling efficiency, active drag, and active drag coefficient were considered as biomechanical variables. The mean swimming velocity during an all-out 25 m front crawl effort was used as the performance outcome. After the 10-week break, the swimmers were taller with an increased arm span, hand, and foot areas. Increases in stroke length, stroke index, propelling efficiency, and performance were also observed. Conversely, the stroke frequency, active drag, and drag coefficient remained unchanged. When controlling the effect of growth, no significant variation was determined on the biomechanical variables. The performance presented high associations with biomechanical and anthropometric parameters at pre-test and post-test, respectively. The results show that young talented swimmers still present biomechanical improvements after a 10-week break, which are mainly explained by their normal growth.

Variation of Linear and Nonlinear Parameters in the Swim Strokes According to the Level of Expertise

The aim was to examine the variation of linear and nonlinear proprieties of the behavior in participants with different levels of swimming expertise among the four swim strokes. Seventy-five swimmers were split into three groups (highly qualified experts, experts and nonexperts) and performed a maximal 25m trial for each of the four competitive swim strokes. A speed-meter cable was attached to the swimmers hip to measure hip speed; from which speed fluctuation (dv), approximate entropy (ApEn) and fractal dimension (D) variables were derived. Although simple main effects of expertise and swim stroke were obtained for dv and D, no significant interaction of expertise and stroke were found except in ApEn. The ApEn and D were prone to decrease with increasing expertise. As a conclusion, swimming does exhibit nonlinear properties but its magnitude differs according to the swim stroke and level of expertise of the performer.

Comparison of Classical Kinematics, Entropy, and Fractal Properties As Measures of Complexity of the Motor System in Swimming

The aim of this study was to compare the non-linear properties of the four competitive swim strokes. Sixty-eight swimmers performed a set of maximal 4 × 25 m using the four competitive swim strokes. The hips speed-data as a function of time was collected with a speedo-meter. The speed fluctuation (dv), approximate entropy (ApEn) and the fractal dimension by Higuchis method (D) were computed. Swimming data exhibited non-linear properties that were different among the four strokes (14.048 ≤ dv ≤ 39.722; 0.682 ≤ ApEn ≤ 1.025; 1.823 ≤ D ≤ 1.919). The ApEn showed the lowest value for front-crawl, followed by breaststroke, butterfly, and backstroke (P < 0.001). Fractal dimension and dv had the lowest values for front-crawl and backstroke, followed by butterfly and breaststroke (P < 0.001). It can be concluded that swimming data exhibits non-linear properties, which are different among the four competitive swimming strokes.

Estimating the Trunk Transverse Surface Area to Assess Swimmer's Drag Force Based on their Competitive Level

Estimating the Trunk Transverse Surface Area to Assess Swimmers Drag Force Based on their Competitive Level The aim of this study was to compute and validate trunk transverse surface area (TTSA) estimation equations to be used assessing the swimmers drag force according to competitive level by gender. One group of 130 swimmers (54 females and 76 males) was used to compute the TTSA estimation equations and another group of 132 swimmers (56 females and 76 males) were used for its validations. Swimmers were photographed in the transverse plane from above, on land, in the upright and hydrodynamic position. The TTSA was measured from the swimmers photo with specific software. It was also measured the height, body mass, biacromial diameter, chest sagital diameter (CSD) and the chest perimeter (CP). With the first group of swimmers it was computed the TTSA estimation equations based on stepwise multiple regression models from the selected anthropometrical variables. The TTSA prediction equations were significant and with a prediction level qualitatively considered as moderate. All equations included only the CP and the CSD in the final models. In all prediction models there were no significant differences between assessed and estimated mean TTSA. Coefficients of determination for the linear regression models between assessed and estimated TTSA were moderate and significant. More than 80% of the plots were within the 95% interval confidence for the Bland-Altman analysis in both genders. So, TTSA estimation equations that are easy to be computed by coached and researchers were developed. All equations accomplished the validation criteria adopted.