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Federico Y. Fontana, Alessandro Colosio, Gabriela F. De Roia, Giorgio Da Lozzo and Silvia Pogliaghi

Anthropometric evaluation of athletes is necessary to optimize talent identification and player development.

Objectives:

To provide a specific anthropometric reference database of senior male rugby players competing at different levels in the southern European region.

Design:

Cross-sectional.

Methods:

In 362 professional players (25 ± 4 y; 138 Italian national team, 97 first-division, and 127 second-division national championships) the authors measured mass, stature, and percentage body fat (plicometry). Mean, SD, and coefficient of variation were calculated for forwards and backs and for positional subgroups. Binomial logistic regression and receiver-operating-characteristic curve were performed to assess which variables best predicted level assignment (international vs national level).

Results:

For all competitive levels forwards were significantly heavier and taller and had a larger percentage body fat and fat-free mass than backs. The lower the competitive level, the higher the within-role variability observed; furthermore, players in a specific positional subgroup were lighter, shorter, and fatter and had less fat-free mass. Fat-free mass is the variable that best predicts the likelihood of being classified as an international or national player (cutoff value 79.54 kg).

Conclusions:

The data confirm the specificity in the physical requirements of rugby in individual playing positions at all competitive levels and document significant differences among elite and 1st- and 2nd-division players in the same positional role. These differences may reflect the variable technical abilities, selection, training practices, and requirements of the game among these categories.

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Sabrina Skorski, Oliver Faude, Chris R. Abbiss, Seraina Caviezel, Nina Wengert and Tim Meyer

Purpose:

To date, there has been limited research examining the influence of pacing pattern (PP) on middle-distance swimming performance. As such, the purpose of the current study was to examine the influence of PP manipulation on 400-m freestyle swimming performance.

Methods:

15 front-crawl swimmers (5 female, 10 male; age 18 ± 2 y) performed 3 simulated 400-m swimming events. The initial trial was self-selected pacing (PPSS). The following 2 trials were performed in a counterbalanced order and required participants to complete the first 100 m more slowly (PPSLOW: 4.5% ± 2.2%) or quickly (PPFAST: 2.4% ± 1.6%) than the PPSS trial. 50-m split times were recorded during each trial.

Results:

Overall performance time was faster in PPSS (275.0 ± 15.9 s) than in PPFAST (278.5 ± 16.4 s, P = .05) but not significantly different from PPSLOW (277.5 ± 16.2 s, P = .22). However, analysis for practical relevance revealed that pacing manipulation resulted in a “likely” (>88.2%) decrease in performance compared with PPSS.

Conclusion:

Moderate manipulation of the starting speed during simulated 400-m freestyle races seems to affect overall performance. The observed results indicate that PPSS is optimal in most individuals, yet it seems to fail in some swimmers. Future research should focus on the identification of athletes possibly profiting from manipulations.

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Jorge E. Morais, António J. Silva, Daniel A. Marinho, Vítor P. Lopes and Tiago M. Barbosa

Purpose:

To develop a performance predictor model based on swimmers’ biomechanical profile, relate the partial contribution of the main predictors with the training program, and analyze the time effect, sex effect, and time × sex interaction.

Methods:

91 swimmers (44 boys, 12.04 ± 0.81 y; 47 girls, 11.22 ± 0.98 y) evaluated during a 3-y period. The decimal age and anthropometric, kinematic, and efficiency features were collected 10 different times over 3 seasons (ie, longitudinal research). Hierarchical linear modeling was the procedure used to estimate the performance predictors.

Results:

Performance improved between season 1 early and season 3 late for both sexes (boys 26.9% [20.88;32.96], girls 16.1% [10.34;22.54]). Decimal age (estimate [EST] –2.05, P < .001), arm span (EST –0.59, P < .001), stroke length (EST 3.82; P = .002), and propelling efficiency (EST –0.17, P = .001) were entered in the final model.

Conclusion:

Over 3 consecutive seasons young swimmers’ performance improved. Performance is a multifactorial phenomenon where anthropometrics, kinematics, and efficiency were the main determinants. The change of these factors over time was coupled with the training plans of this talent identification and development program.

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Sergio Lara-Bercial and Clifford J. Mallett

In 2011, the Innovation Group of Leading Agencies of the International Council for Coaching Excellence initiated a project aimed at supporting the identification and development of the next generation of high performance coaches. The project, entitled Serial Winning Coaches, studied the personalities, practices and developmental pathways of professional and Olympic coaches who had repeatedly achieved success at the highest level of sport. This paper is the third publication originating from this unique project. In the first paper, Mallett and Coulter (2016) focused on the development and testing of a novel multilayered methodology in understanding a person through a single case study of a successful Olympic coach. In the second, Mallett and Lara-Bercial (2016) applied this methodology to a large sample of Serial Winning Coaches and offered a composite account of their personality. In this third instalment, we turn the focus onto the actual practices and developmental pathways of these coaches. The composite profile of their practice emerging from the analysis revolves around four major themes: Philosophy, Vision, People and Environment. In addition, a summary of the developmental activities accessed by these coaches and their journey to success is also offered. Finally, we consider the overall findings of the project and propose the concept of Driven Benevolence as the overarching operational principle guiding the actions and behaviours of this group of Serial Winning Coaches.

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Katherine A. Beals and Melinda M. Manore

The purpose of this study was to delineate and further define the behavioral, psychological, and physical characteristics of female athletes with subclinical eating disorders. Subjects consisted of 24 athletes with subclinical eating disorders (SCED) and 24 control athletes. Group classification was determined by scores on the Eating Disorder Inventory (EDI), the Body Shape Questionnaire (BSQ), and a symptom checklist for eating disorders (EDI-SC). Characteristics representative of the female athletes with subclinical eating disorders were derived from an extensive health and dieting history questionnaire and an in-depth interview (the Eating Disorder Examination). Energy intake and expenditure (kcal/d) were estimated using 7-day weighed food records and activity logs. The characteristics most common in the female athletes with subclinical eating disorders included: (a) preoccupation with food, energy intake, and body weight; (b) distorted body image and body weight dissatisfaction; (c) undue influence of body weight on self-evaluation; (d) intense fear of gaining weight even though at or slightly below (-5%) normal weight; (e) attempts to lose weight using one or more pathogenic weight control methods; (g) food intake governed by strict dietary rules, accompanied by extreme feelings of guilt and self-hatred upon breaking a rule; (h) absence of medical disorder to explain energy restriction, weight loss, or maintenance of low body weight; and (i) menstrual dysfunction. Awareness of these characteristics may aid in more timely identification and treatment of female athletes with disordered eating patterns and, perhaps, prevent the development of more serious, clinical eating disorders.

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Alison Keogh, Barry Smyth, Brian Caulfield, Aonghus Lawlor, Jakim Berndsen and Cailbhe Doherty

Purpose: Despite the volume of available literature focusing on marathon running and the prediction of performance, no single prediction equations exists that is accurate for all runners of varying experiences and abilities. Indeed the relative merits and utility of the existing equations remain unclear. Thus, the aim of this study was to collate, characterize, compare, and contrast all available marathon prediction equations. Methods: A systematic review was conducted to identify observational research studies outlining any kind of prediction algorithm for marathon performance. Results: Thirty-six studies with 114 equations were identified. Sixty-one equations were based on training and anthropometric variables, whereas 53 equations included variables that required laboratory tests and equipment. The accuracy of these equations was denoted via a variety of metrics; r 2 values were provided for 68 equations (r 2 = .10–.99), and an SEE was provided for 19 equations (SEE 0.27–27.4 min). Conclusion: Heterogeneity of the data precludes the identification of a single “best” equation. Important variables such as course gradient, sex, and expected weather conditions were often not included, and some widely used equations did not report the r 2 value. Runners should therefore be wary of relying on a single equation to predict their performance.

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Jorge E. Morais, António J. Silva, Daniel A. Marinho, Ludovic Seifert and Tiago M. Barbosa

Purpose:

To apply a new method to identify, classify, and follow up young swimmers based on their performance and its determinant factors over a season and analyze the swimmers’ stability over a competitive season with that method.

Methods:

Fifteen boys and 18 girls (11.8 ± 0.7 y) part of a national talent-identification scheme were evaluated at 3 different moments of a competitive season. Performance (ie, official 100-m freestyle race time), arm span, chest perimeter, stroke length, swimming velocity, speed fluctuation, coefficient of active drag, propelling efficiency, and stroke index were selected as variables. Hierarchical and k-means cluster analysis were computed.

Results:

Data suggested a 3-cluster solution, splitting the swimmers according to their performance in all 3 moments. Cluster 1 was related to better performances (talented swimmers), cluster 2 to poor performances (nonproficient swimmers), and cluster 3 to average performance (proficient swimmers) in all moments. Stepwise discriminant analysis revealed that 100%, 94%, and 85% of original groups were correctly classified for the 1st, 2nd, and 3rd evaluation moments, respectively (0.11 ≤ Λ ≤ 0.80; 5.64 ≤ χ2 ≤ 63.40; 0.001 < P ≤ .68). Membership of clusters was moderately stable over the season (stability range 46.1–75% for the 2 clusters with most subjects).

Conclusion:

Cluster stability is a feasible, comprehensive, and informative method to gain insight into changes in performance and its determinant factors in young swimmers. Talented swimmers were characterized by anthropometrics and kinematic features.

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Helen T. Douda, Argyris G. Toubekis, Alexandra A. Avloniti and Savvas P. Tokmakidis

Purpose:

To identify the physiological and anthropometric predictors of rhythmic gymnastics performance, which was defined from the total ranking score of each athlete in a national competition.

Methods:

Thirty-four rhythmic gymnasts were divided into 2 groups, elite (n = 15) and nonelite (n = 19), and they underwent a battery of anthropometric, physical fitness, and physiological measurements. The principal-components analysis extracted 6 components: anthropometric, flexibility, explosive strength, aerobic capacity, body dimensions, and anaerobic metabolism. These were used in a simultaneous multiple-regression procedure to determine which best explain the variance in rhythmic gymnastics performance.

Results:

Based on the principal-component analysis, the anthropometric component explained 45% of the total variance, flexibility 12.1%, explosive strength 9.2%, aerobic capacity 7.4%, body dimensions 6.8%, and anaerobic metabolism 4.6%. Components of anthropometric (r = .50) and aerobic capacity (r = .49) were significantly correlated with performance (P < .01). When the multiple-regression model—y = 10.708 + (0.0005121 × VO2 max) + (0.157 × arm span) + (0.814 × midthigh circumference) - (0.293 × body mass)—was applied to elite gymnasts, 92.5% of the variation was explained by VO2max (58.9%), arm span (12%), midthigh circumference (13.1%), and body mass (8.5%).

Conclusion:

Selected anthropometric characteristics, aerobic power, flexibility, and explosive strength are important determinants of successful performance. These findings might have practical implications for both training and talent identification in rhythmic gymnastics.

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Sarah Kölling, Rob Duffield, Daniel Erlacher, Ranel Venter and Shona L. Halson

The body of research that reports the relevance of sleep in high-performance sports is growing steadily. While the identification of sleep cycles and diagnosis of sleep disorders are limited to lab-based assessment via polysomnography, the development of activity-based devices estimating sleep patterns provides greater insight into the sleep behavior of athletes in ecological settings. Generally, small sleep quantity and/or poor quality appears to exist in many athletic populations, although this may be related to training and competition context. Typical sleep-affecting factors are the scheduling of training sessions and competitions, as well as impaired sleep onset as a result of increased arousal prior to competition or due to the use of electronic devices before bedtime. Further challenges are travel demands, which may be accompanied by jet-lag symptoms and disruption of sleep habits. Promotion of sleep may be approached via behavioral strategies such as sleep hygiene, extending nighttime sleep, or daytime napping. Pharmacological interventions should be limited to clinically induced treatments, as evidence among healthy and athletic populations is lacking. To optimize and manage sleep in athletes, it is recommended to implement routine sleep monitoring on an individual basis.

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Nicola Furlan, Mark Waldron, Kathleen Shorter, Tim J. Gabbett, John Mitchell, Edward Fitzgerald, Mark A. Osborne and Adrian J. Gray

Purpose:

To investigate temporal variation in running intensity across and within halves and evaluate the agreement between match-analysis indices used to identify fluctuations in running intensity in rugby sevens.

Methods:

Data from a 15-Hz global positioning system (GPS) were collected from 12 elite rugby sevens players during the IRB World Sevens Series (N = 21 full games). Kinematic (eg, relative distance [RD]) and energetic (eg, metabolic power [MP]) match-analysis indices were determined from velocity–time curves and used to investigate between-halves variations. Mean MP and RD were used to identify peak 2-minute periods of play. Adjacent 2-minute periods (prepeak and postpeak) were compared with peak periods to identify changes in intensity. MP and RD were expressed relative to maximal oxygen uptake (V̇O2max) and speed at V̇O2max, respectively, and compared in their ability to describe the intensity of peak periods and their temporal occurrence.

Results:

Small to moderate reductions were present for kinematic (RD; 8.9%) and energetic (MP; 6%) indices between halves. Peak periods (RD = 130 m/min, MP =13 W/kg) were higher (P < .001) than the match average (RD = 94 m/min, MP = 9.5 W/kg) and the prepeak and postpeak periods (P < .001). RD underestimated the intensity of peak periods compared with MP (bias 16%, limits of agreement [LoA] ± 6%). Peak periods identified by RD and MP were temporally dissociated (bias 21 s, LoA ± 212 s).

Conclusions:

The findings suggest that running intensity varies between and within halves; however, the index used will influence both the magnitude and the temporal identification of peak periods.