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The aims of this study were to: (1) assess the stability (mean and normative) of the lap performance, and a set of clean swim and turn variables of junior male swimmers in the 200 m freestyle, and; (2) verify the relationship between the start, clean swim, turn, and finish phases in the 200 m freestyle. Seventy-six individual races in the 200 m freestyle at the 2019 long-course LEN European Junior Championships were analysed. Start, clean swim, turn, and finish variables were assessed. The lap performance showed a significant variance. The highest variation was verified between the first and third lap (Coefficient of Variation = 7.37%). The clean swim and the total turn also presented a significant variance. Normative stability indicated a moderate to very-high stability for all variables. All phases of the race had significant correlations with the final race time (p < 0.001). The total turn (i.e., the total time spent to perform the turn), specifically turn #3, showed the largest correlation with the total race performance. The significant correlation between all phases of the race and the final race time indicates that coaches and swimmers should customise the swimmers' preparation and race strategy at major international competitions, based on the individual characteristics of each swimmer.

The aim of this study was to analyse the effect of swimming goggles on swimming hydrodynamics by numerical simulations. An elite swimmer volunteered for this research. The swimmer's head was scanned both without goggles, and while wearing 3 different types of goggles (Nikko, Ankor and Swedish). Numerical simulations were conducted at 2 m/s with the Fluent code. The condition without goggles showed the highest viscous drag (1.65 N), followed by the Ankor (1.64 N), Swedish (1.63 N) and Nikko (1.62 N) goggles, respectively. The highest pressure drag was found in the situation without goggles (11.34 N), followed by the Ankor (10.87 N), Nikko (10.78 N) and Swedish (10.20 N) goggles. The condition without goggles presented the highest total drag (12.99 N), followed by the Ankor (12.52 N), Nikko (12.40 N) and Swedish (11.83 N) goggles. Thus, Swedish goggles yields the best hydrodynamics, followed by the Nikko and Ankor goggles and lastly without goggles. Thus, goggles minimise the swimmer's drag comparing to not wearing any. The design of the goggles may impose varying drag forces and therefore it is advised to use goggles at least in competition.

The purpose of this study was to compare the in-water force of young competitive swimmers using tethered swimming and differential pressure sensors. Thirty-one swimmers (16 girls and 15 boys) were randomly assigned to perform two in-water tests. Swimmers completed two maximum bouts of 25 m front crawl with a differential pressure system and a 30 s maximum bout with an attached load cell (tethered-swimming). The peak force (F PEAK , in N) of dominant and non-dominant upper limbs was retrieved for further analysis. Comparison between methods revealed significant differences in all force variables (p ≤ 0.05) and the biases (mean differences) were large in girls (F PEAK dominant, 45.89 N; F PEAK non-dominant, 43.79 N) and boys (F PEAK dominant, 67.26 N; F PEAK non-dominant, 61.78 N). Despite that, simple linear regression models between the two methods showed significant relationships with a moderate effect in all variables for girls, whereas in boys a high and moderate effect was verified for F PEAK of dominant and non-dominant limbs (respectively). It seems that using pressure sensors and tethered swimming leads to different F PEAK values in young competitive, where correction factors are needed to compare data between both methods., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Purpose: The aims of this study were to classify, identify and follow-up young swimmers' performance and its biomechanical determinants during two competitive seasons (in seven different moments of assessment-M), and analyze the individual variations of each swimmer. Method: Thirty young swimmers (14 boys: 12.70 ± 0.63 years-old; 16 girls: 11.72 ± 0.71 years-old) were recruited. A set of anthropometric, kinematic, efficiency, hydrodynamic and mechanical power variables were assessed. Results: The cluster solution (i.e., number of ideal clusters for this sample) resulted in three clusters, which were named as: cluster 1 ("talented"), cluster 2 ("proficient"), and cluster 3 ("non-proficient"). The performance improved between moments of assessment in all clusters (cluster 1-M1: 68.07 ± 6.62s vs M7: 61.46 ± 3.43s; cluster 2-M1: 73.14 ± 4.87s vs M7: 65.33 ± 2.97s; cluster 3-M1: 82.60 ± 4.18s vs M7: 70.09 ± 3.48s). Anthropometric features also increased between moments of assessment, and remaining biomechanical variables (kinematic, efficiency, hydrodynamic and mechanical power) also increased between M1 and M7, in all clusters. Cluster 1 increased their swimmer's membership between M1 and M7 (4 to 11), cluster 2 decreased (12 to 5), and cluster 3 maintained (14). Conclusion: It can be concluded that the cluster formation depends on different determinant factors during two competitive seasons, and young swimmers are prone to change from one cluster to another over this period of time.

Short-distance swimmers may exhibit imbalances in their upper-limbs' thrust (differences between the thrust produced by each upper-limb). At maximal speed, higher imbalances are related to poorer performances. Additionally, little is known about the relationship between thrust and swim speed, and whether hypothetical imbalances exist in the speed achieved while performing each upper-limb arm-pull. This could be a major issue at least while swimming at maximal speed. This study aimed to: (1) verify a hypothetical inter-upper limb difference in the determinants related to front-crawl at maximal swim speed, and; (2) identify the main predictors responsible for the swim speed achieved during each upper-limb arm-pull. Twenty-two male swimmers of a national junior swim team (15.92 ± 0.75 years) were recruited. A set of anthropometric, dry-land strength, thrust and speed variables were assessed. Anthropometrics identified a significant difference between dominant and non-dominant upper-limbs (except for the hand surface area). Dry-land strength presented non-significant difference (p < 0.05) between the dominant and non-dominant upper-limbs. Overall, thrust and speed variables revealed a significant difference (p < 0.05) between dominant and non-dominant upper-limbs over a 25 m time-trial in a short-course pool. Swimmers were not prone to maintaining the thrust and speed along the trial where a significant variation was noted (p < 0.05). Using multilevel regression, the speed achieved by each upper-limb identified a set of variables, with the peak speed being the strongest predictor (dominant: estimate = 0.522, p < 0.001; non-dominant: estimate = 0.756, p < 0.001). Overall, swimmers exhibit significant differences between upper-limbs determinants. The upper-limb noting a higher dry-land strength also presented a higher thrust, and consequently higher speed. Coaches should be aware that sprint swimmers produce significant differences in the speed achieved by each one of their upper-limbs arm-pull.

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; R2a = 0.90; SEE = 8.528; P<0.001) and passive drag coefficient (CDp = 1.918*CDf+pr; R2 = 0.96; R2a = 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.

Background: Triathletes are pushing their limits in multi-stage Ironman-distance triathlons. In the present case study, we investigated the pacing during 20, 40, and 60 Ironman-distance triathlons in 20, 40, and 60 days, respectively, of one professional IRONMAN® triathlete. Case study: Event 1 (20 Ironman-distance triathlons in 20 days), Event 2 (40 Ironman-distance triathlons in 40 days), and Event 3 (60 Ironman-distance triathlons in 60 days) were analyzed by discipline (swimming, cycling, running, and overall event time), by Deca intervals (10 days of consecutive Ironman-distance triathlons) and additional data (sleep duration, body mass, heart rate in cycling and running). To test differences between Events and Deca intervals within the same discipline, T-tests (2 groups) or one-way ANOVAs (3 or more groups) were used. Results: Swimming splits were fastest in Event 1, (ii) cycling and running splits were fastest in both Event 2 and 3, (iii) overall speed was fastest in Event 3, (iv) sleep duration increased during Event 2 but decreased in Event 3, (v) body mass decreased in Event 2, but increased in Event 3 and (vi) heart rate during cycling was similar in both Event 2 and 3. In contrast, heart rate during running was greater in Event 3. Conclusion: In a professional IRONMAN® triathlete finishing 20, 40, and 60 Ironman-distance triathlons in 20, 40, and 60 days, respectively, split performances and both anthropometrical and physiological changes such as body mass and heart rate differed depending upon the duration of the events. [ABSTRACT FROM AUTHOR]

Purpose. The IRONMAN® triathlon is an endurance multisport discipline of high popularity. Professional IRONMAN® triathletes need to qualify for the IRONMAN® World Championships and, therefore, would benefit from identifying the fastest race courses. Our purpose was to identify the fastest races held for professional IRONMAN® triathletes competing from 2002 to 2022. Methods. This was an observational study, sampling 7,078 race records (380 different events in 55 different event locations) of professional IRONMAN® triathletes of both sexes (4,235 males and 2,843 females). We downloaded information about sex, nationality, both split (swimming, cycling, and running) and overall race times, the event location, and the year. Information about race course characteristics, water temperatures, and air temperatures was obtained. Descriptive statistics were calculated for each event location, and factorial ANOVA tests were used to explore the statistical significance of the results. A confidence interval of 95% was adopted. Results. The fastest overall IRONMAN® average race times were achieved during the IRONMAN® Tallinn, IRONMAN® Switzerland held in Thun, and IRONMAN® Des Moines. A lake for the swim split characterized the first five courses, while the bike split featured rolling or hilly terrain. For the run split, most of the courses were characterized as flat. For environmental characteristics, lower water and air temperatures also added time to the average finish time. Conclusions. Understanding the fastest race courses and their characteristics would assist professional IRONMAN® triathletes in selecting races that offer the optimal conditions for their competition. Fast IRONMAN® race courses typically feature a lakeside swim course, a rolling or hilly bike course, and a flat run course. [ABSTRACT FROM AUTHOR]

This study aimed to analyse the stability of elite male long-distance swimmers (1500 m), and to identify the main predictors related to the pace. The performance of 16 elite male swimmers (22.59 ± 2.10 years-old) participating in the 1500 m event at the 2016 (London) and 2018 (Glasgow) LEN European Aquatic Championships were analysed. The lap performance, clean swim performance, turn performance, and a set of stroke mechanics variables were assessed. The lap performance presented a significant and moderate variation with all laps included (p < 0.001) and deleting the first and last lap (p = 0.002). Swimmers were significantly faster in the first half in comparison of the second. The total turn also presented a significant and moderate variation. The hierarchical linear modelling retained the time (estimate = 0.0019, p = 0.007), stroke frequency (estimate = −27.49, p < 0.001) and stroke length (estimate = −6.55, p < 0.001) as the main predictors of the clean swim performance. By contrast to the analysis based on the lap performance, clean swim performance presented a non-significant variation. Coaches should be aware that stroke length maintenance could negatively affect the clean swim performance, whereas a small increase of stroke frequency may present a meaningful enhancement of the total race time. [ABSTRACT FROM AUTHOR]

Muscular weakness and strength imbalance between the thigh muscles are considered risk factors for knee injuries. Hormonal changes, characteristic of puberty, strongly affect muscle strength; however, it is unknown whether they affect muscular strength balance. The present study aimed to compare knee flexor strength, knee extensor strength, and strength balance ratio, called the conventional ratio (CR), between prepubertal and postpubertal swimmers of both sexes. A total of 56 boys and 22 girls aged between 10 and 20 years participated in the study. Peak torque, CR, and body composition were measured using an isokinetic dynamometer and dual-energy X-ray absorptiometry, respectively. The postpubertal boys group presented significantly higher fat-free mass (p < 0.001) and lower fat mass (p = 0.001) than the prepubertal group. There were no significant differences among the female swimmers. Peak torque for both flexor and extensor muscles was significantly greater in postpubertal male (p < 0.001, both) and female swimmers (p < 0.001 and p = 0.001, respectively) than in prepubertal swimmers. The CR did not differ between the pre- and postpubertal groups. However, the mean CR values were lower than the literature recommendations, which brings attention to a higher risk of knee injuries. [ABSTRACT FROM AUTHOR]

Introduction: In youth swimming, researchers are interested in understanding how anthropometry and parameters related to swimming technique (biomechanics, energetics, and efficiency) influence the performance. However, there is not any review in the literature that consolidates the body of knowledge of this topic. The objective of this study was to review systematically the current body of work on the influence of determinant factors related to swimming technique (biomechanics, energetics, and efficiency) and anthropometry in the young performance of swimmers. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to identify relevant studies. Results: After screening, 240 studies were analyzed and 59 related to swimming performance, and its determinant factors were retained for synthesis. Studies revealed a high-quality index by PEDro scale (mean score was 7.17 ± 1.40). Twenty-five studies were longitudinal designs and the remaining 34 cross-sectional designs. Most of the studies (N = 39, 66.1%) reported concurrently two or more determinant factors (anthropometrics, biomechanics, energetics, and efficiency). Conclusion: Youth swimming research relies on a multifactorial assessment. From the synthesis, it is possible to conclude that the performance of young swimmers is characterized by a multifactorial, holistic, and dynamic phenomenon. Better performance has always been related to better swimming technique and higher anthropometrics. This suggests that both anthropometrics (i.e., nature) and training (i.e., nurture) play key roles in the swimming performance of young swimmers. [ABSTRACT FROM AUTHOR]