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      Physiological and Biomechanical Evaluation of a Training Macrocycle in Children Swimmers

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          Abstract

          Physiological responses related to 400-m front crawl performance were examined in a 11-week training macrocycle in children 11.6 ± 1.2 years old. Fourteen girls and twenty-nine boys completed a maximum intensity 400-m test, at the beginning (Τ1) and at the end of four weeks of general preparation (Τ2), four weeks of specific preparation (Τ3), and three weeks of the competitive period (Τ4). Blood lactate (La), blood glucose (Glu) and heart rate were measured post effort. Stroke rate (SR), stroke length (SL) and stroke index (SI) were measured during the test. The 400-m time was decreased at T2, T3, and T4 compared to T1 by 4.2 ± 4.9, 7.5 ± 7.0, and 8.6 ± 7.3% ( p < 0.05) and at T3 and T4 compared to T2 by 3.1 ± 4.3 and 4.2 ± 4.6%, respectively ( p < 0.05). La was not different between tests ( p > 0.05) and Glu was decreased at T3 compared to other testing moments ( p < 0.05). SR, SL, and SI were higher at T3 and T4 compared to T1 ( p < 0.05). SL and SI were also increased at T4 compared to T2 ( p < 0.05). Performance changes from T1 to T2 were related to SL and SI changes (r = 0.45 and 0.83, p < 0.05), and subsequent changes between T2 to T3 were related to SR, SI, La, and Glu changes (r = 0.48, 0.68, 0.34, and 0.42, p < 0.05). Performance change from T3 to T4 was related to SL, SI, and La modifications (r = 0.34, 0.70, and 0.53, p < 0.05). Performance gains may be related to various biomechanical or physiological changes according to training macrocycle structure.

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          A framework for understanding the training process leading to elite performance.

          The development of performance in competition is achieved through a training process that is designed to induce automation of motor skills and enhance structural and metabolic functions. Training also promotes self-confidence and a tolerance for higher training levels and competition. In general, there are two broad categories of athletes that perform at the highest level: (i) the genetically talented (the thoroughbred); and (ii) those with a highly developed work ethic (the workhorse) with a system of training guiding their effort. The dynamics of training involve the manipulation of the training load through the variables: intensity, duration and frequency. In addition, sport activities are a combination of strength, speed and endurance executed in a coordinated and efficient manner with the development of sport-specific characteristics. Short- and long-term planning (periodisation) requires alternating periods of training load with recovery for avoiding excessive fatigue that may lead to overtraining. Overtraining is long-lasting performance incompetence due to an imbalance of training load, competition, non-training stressors and recovery. Furthermore, annual plans are normally constructed in macro-, meso- and microcycles around the competitive phases with the objective of improving performance for a peak at a predetermined time. Finally, at competition time, optimal performance requires a healthy body, and integration of not only the physiological elements but also the psychological, technical and tactical components.
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            High-intensity interval training improves VO2peak, maximal lactate accumulation, time trial and competition performance in 9–11-year-old swimmers

            Training volume in swimming is usually very high when compared to the relatively short competition time. High-intensity interval training (HIIT) has been demonstrated to improve performance in a relatively short training period. The main purpose of the present study was to examine the effects of a 5-week HIIT versus high-volume training (HVT) in 9–11-year-old swimmers on competition performance, 100 and 2,000 m time (T 100 m and T 2,000 m), VO2peak and rate of maximal lactate accumulation (Lacmax). In a 5-week crossover study, 26 competitive swimmers with a mean (SD) age of 11.5 ± 1.4 years performed a training period of HIIT and HVT. Competition (P < 0.01; effect size = 0.48) and T 2,000 m (P = 0.04; effect size = 0.21) performance increased following HIIT. No changes were found in T 100 m (P = 0.20). Lacmax increased following HIIT (P < 0.01; effect size = 0.43) and decreased after HVT (P < 0.01; effect size = 0.51). VO2peak increased following both interventions (P < 0.05; effect sizes = 0.46–0.57). The increases in competition performance, T 2,000 m, Lacmax and VO2peak following HIIT were achieved in significantly less training time (~2 h/week).
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              Stability of elite freestyle performance from childhood to adulthood

              Stability of athletic performance is important for practitioners and coaches, since it allows the selection of appropriate training methods and prediction of ages for best results. We performed a longitudinal study of 1694 season-best performances of 242 elite-standard swimmers throughout their careers, from 12 to 18 years of age. Mean stability (descriptive statistics and one-way repeated-measures ANOVA, followed by a Bonferroni post-hoc test) and normative stability (Cohen's kappa tracking index and the Pearson correlation coefficient) were determined for seven consecutive seasons. Performance improvements in all events were observed (14.36-18.97%). Bonferroni post-hoc tests verified changes in almost all events assessed. Cohen's kappa demonstrated low stability (0.17-0.27) in relative performance. Pearson correlations only became high from 15 to 16 years in the 50-m and 100-m events, and from 16 to 17 years in the 200-m, 400-m, and 1500-m events. Our results show that: (a) swimmers should display a substantial improvement (14-19%) to become elite standard as adults, such as at 18 years; (b) 16 is the age at which the ability to predict adult performance increases markedly.
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                Author and article information

                Journal
                Sports (Basel)
                Sports (Basel)
                sports
                Sports
                MDPI
                2075-4663
                04 March 2019
                March 2019
                : 7
                : 3
                : 57
                Affiliations
                [1 ]Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal; sara_ferreira_1120@ 123456hotmail.com (S.F.); diogoduarte_03@ 123456hotmail.com (D.C.); a.sofia.monteiro@ 123456gmail.com (A.S.M.); jpvb@ 123456fade.up.pt (J.P.V.-B.); ricfer@ 123456fade.up.pt (R.F.)
                [2 ]Porto Biomechanics Laboratory, University of Porto, 4200-450 Porto, Portugal
                [3 ]Faculty of Sport Science, University of Murcia, 30720 San Javier, Spain; abraldes@ 123456um.es
                [4 ]School of P.E. & Sport Science, National and Kapodistrian University of Athens, 17237 Athens, Greece
                Author notes
                [* ]Correspondence: atoubekis@ 123456phed.uoa.gr
                Author information
                https://orcid.org/0000-0002-4109-2939
                https://orcid.org/0000-0002-2040-354X
                https://orcid.org/0000-0002-5811-0443
                Article
                sports-07-00057
                10.3390/sports7030057
                6473474
                30836622
                d30e8af4-c077-43dd-99d4-c40dd9855231
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 19 January 2019
                : 25 February 2019
                Categories
                Article

                performance,lactate,stroke rate,stroke length,stroke index,macrocycle

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