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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.  相似文献   
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The aim was to compare the passive drag-gliding underwater by a numerical simulation and an analytical procedure. An Olympic swimmer was scanned by computer tomography and modelled gliding at a 0.75-m depth in the streamlined position. Steady-state computer fluid dynamics (CFD) analyses were performed on Fluent. A set of analytical procedures was selected concurrently. Friction drag (Df), pressure drag (Dpr), total passive drag force (Df+pr) and drag coefficient (CD) were computed between 1.3 and 2.5 m · s?1 by both techniques. Df+pr ranged from 45.44 to 144.06 N with CFD, from 46.03 to 167.06 N with the analytical procedure (differences: from 1.28% to 13.77%). CD ranged between 0.698 and 0.622 by CFD, 0.657 and 0.644 by analytical procedures (differences: 0.40–6.30%). Linear regression models showed a very high association for Df+pr plotted in absolute values (R2 = 0.98) and after log–log transformation (R2 = 0.99). The CD also obtained a very high adjustment for both absolute (R2 = 0.97) and log–log plots (R2 = 0.97). The bias for the Df+pr was 8.37 N and 0.076 N after logarithmic transformation. Df represented between 15.97% and 18.82% of the Df+pr by the CFD, 14.66% and 16.21% by the analytical procedures. Therefore, despite the bias, analytical procedures offer a feasible way of gathering insight on one’s hydrodynamics characteristics.  相似文献   
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The aim of this study was to examine the performance characteristics of male and female finalists in the 100-m distance at the 2016 European Championships in swimming (long-course-metre). The performances of all 64 (32-males and 32-females) were analysed (8 swimmers per event; Freestyle, Backstroke, Breaststroke and Butterfly). A set of start and turn parameters were analysed. In the start main outcome, male swimmers were faster in Butterfly (5.71 ± 0.14s) and females in Freestyle (6.68 ± 0.28s). In the turn main outcome, male and female swimmers were faster in Freestyle (males: 9.55 ± 0.13s; females: 10.78 ± 0.28s). A significant and strong stroke effect was noted in the start and turn main outcome, in both sexes. In the start plus the turn combined, males and females were faster in Freestyle (males: 15.40 ± 0.20s; females: 17.45 ± 0.54s). The start and the turn combined accounted almost one-third of the total race time in all events, and non-significant differences (p > 0.05) were noted across the four swim strokes. Once this research made evident the high relevance of start and turns, it is suggested that coaches and swimmers should dedicate an expressive portion of the training perfecting these actions.  相似文献   
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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 (model's 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.  相似文献   
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The purpose of this study was to learn the interplay between dry-land strength and conditioning, and stroke biomechanics in young swimmers, during a 34-week training programme. Twenty-seven swimmers (overall: 13.33?±?0.85 years old; 11 boys: 13.5?±?0.75 years old; 16 girls: 13.2?±?0.92 years old) competing at regional- and national-level competitions were evaluated. The swimmers were submitted to a specific in-water and dry-land strength training over 34 weeks (and evaluated at three time points: pre-, mid-, and post-test; M1, M2, and M3, respectively). The 100-m freestyle performance was chosen as the main outcome (i.e. dependent variable). The arm span (AS; anthropometrics), throwing velocity (TV; strength), stroke length (SL), and stroke frequency (SF; kinematics) were selected as independent variables. There was a performance enhancement over time (M1 vs. M3: 68.72?±?5.57?s, 66.23?±?5.23?s; Δ?=??3.77%; 95% CI: ?3.98;?3.56) and an overall improvement of the remaining variables. At M1 and M2, all links between variables presented significant effects (p?p?≤?.05). Between M1 and M3, the direct effect of the TV to the stroke biomechanics parameters (SL and SF) increased. The model predicted 89%, 88%, and 92% of the performance at M1, M2, and M3, respectively, with a reasonable adjustment (i.e. goodness-of-fit M1: χ2/df?=?3.82; M2: χ2/df?=?3.08; M3: χ2/df?=?4.94). These findings show that strength and conditioning parameters have a direct effect on the stroke biomechanics, and the latter one on the swimming performance.  相似文献   
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