Effect of torso morphology on maximum hydrodynamic resistance in front crawl swimming

Augmented feedback can change body shape to improve glide efficiency in swimming

Curvatures of the body can disrupt fluid flow and affect hydrodynamic resistance. The purpose of this study was to evaluate the effect of a feedback intervention on glide performance and torso morphology. Eleven male and female national swimmers performed glides before and after augmented feedback. Feedback consisted of self-modelling visual feedback and verbal cuing, to manipulate body curvatures that affect hydrodynamic resistance. Two-dimensional landmark position data (knee, hip and shoulder) were used to enable computation of glide factor and glide coefficient as indicators of glide efficiency; posture (trunk incline and hip angle); and performance (horizontal velocity). Underwater images of the swimmers were manually traced to derive transverse and sagittal diameters, cross-sectional areas, and continuous form outlines (anterior and posterior) of the torso. Maximum rate of change in cross-sectional area and form gradient progressing caudally, were calculated for torso segments: shoulder-chest, chest-waist, waist-hip. Mean velocity, glide factor and glide coefficient values significantly (p< 0.001) improved due to the intervention, with large effect size (d) changes 0.880 (p= 0.015), 2.297 and 1.605, respectively. Significant changes to form gradients were related to reductions in lumbar lordosis and chest convexity. The study provides practical cuing phrases for coaches and swimmers to improve glide efficiency and performance.

Understanding the effects of training on underwater undulatory swimming performance and kinematics

In swimming, the underwater phase after the start and turn comprises gliding and dolphin kicking, with the latter also known as underwater undulatory swimming (UUS). Swimming performance is highly dependent on the underwater phase; therefore, understanding the training effects in UUS and underwater gliding can be critical for swimmers and coaches. Further, the development of technique in young swimmers can lead to exponential benefits in an athlete's career. This study aimed to evaluate the effects of a training protocol on UUS and underwater gliding performance and kinematics in young swimmers. Seventeen age group swimmers (boys = 10, girls = 7) performed maximal UUS and underwater gliding efforts before and after a seven-week training protocol. Time to reach 10 m; intra-cyclic mean, peak, and minimum velocities; and gliding performance improved significantly after the training protocol. The UUS performance improvement was mostly produced by an improvement of the upbeat execution, together with a likely reduction of swimmers' hydrodynamic drag. Despite the changes in UUS and gliding, performance was also likely influenced by growth. The findings from this study highlight kinematic variables that can be used to understand and quantify changes in UUS and gliding performance.

Does a jammer-type racing swimsuit improve sprint performance during maximal front-crawl swimming?

We investigated the effects of jammer-type racing swimsuits (RS) on swimming performance during arm-stroke-only (pull) and whole-body stroke (swim) in 25-m front-crawl with maximal effort. Twelve well-trained male collegiate swimmers wore RS and a conventional swimsuit (CS) and performed three tests: pull, swim, and pull using the system to measure active drag (MAD pull). Swimming velocity and intra-abdominal pressure (IAP) were determined in all tests. Stroke indices during pull and swim and drag-swimming velocity relationship and maximum propulsive power during MAD pull were also determined. Swimming velocities during pull and swim while wearing an RS (1.59 ± 0.13 and 1.77 ± 0.09 m·s-1, respectively) were significantly higher than those wearing a CS (1.57 ± 0.14 and 1.74 ± 0.08 m·s-1, respectively). Stroke length during pull and swim was significantly greater while wearing an RS (1.68 ± 0.12 and 1.83 ± 0.13 m, respectively) than wearing a CS (1.63 ± 0.10 and 1.81 ± 0.13 m, respectively). However, no significant differences were confirmed between the other variables in all tests. In conclusion, swimming performance is improved when wearing an RS compared with a CS.

Load-Velocity Profile and Active Drag in Young Female Swimmers: An Age-Group Comparison

PURPOSE

The present study aimed to establish differences in load-velocity profiling, active drag (AD), and drag coefficient (Cd) between 3 age groups of female swimmers.

METHODS

Thirty-three swimmers (11, 13, or 16 y old) were recruited. The individual load-velocity profile was determined for the 4 competitive swimming strokes. The maximal velocity (V0), maximal load (L0), L0 normalized to the body mass, AD, and Cd were compared between the groups. A 2-way analysis of variance and correlation analysis were conducted.

RESULTS

Compared with their younger counterparts, 16-year-old swimmers generally had larger V0, L0, and AD, which was particularly evident when comparing them with 11-year-old swimmers (P ≤ .052). The exception was breaststroke, where no differences were observed in L0 and AD and Cd was smaller in the 16-year-old group than the 11-year-old group (P = .03). There was a negative correlation between Cd and V0 for all groups in backstroke (P ≤ .038) and for the 11-year-old group and 13-year-old group in breaststroke (P ≤ .022) and front crawl (P ≤ .010). For the 16-year-old group, large correlations with V0 were observed for L0, L0 normalized to the body mass, and AD (P ≤ .010) in breaststroke and for L0 and AD with V0 in front crawl (P ≤ .042). In butterfly, large negative correlations with V0 were observed in the 13-year-old group for all parameters (P ≤ .027).

CONCLUSIONS

Greater propulsive force is likely the factor that differentiates the oldest age group from the younger groups, except for breaststroke, where a lower Cd (implying a better technique) is evident in the oldest group.

Identification of key somatic features that are common and the ones that differ between swim strokes through allometric modeling

Introduction

The aim of this study was to explore which key somatic features are common to four swim strokes and medley, and specifically to identify which characteristics benefit only specific strokes.

Methods

The sample was composed of 130 swimmers (95 males aged 19.5 ± 2.9 years and 35 females aged 18.4 ± 2.8 years). A set of anthropometric variables was used to predict swimming speed in the four swimming strokes and medley.

Results

A multiplicative model with allometric body size components was used to identify the demographic and anthropometric predictors of swimming speed. Trunk height and waist circumference were the only variables significantly different among swimming strokes (p < 0.05). Associations between swimming speed and arm length were similar in breaststroke and medley, and in freestyle, backstroke and butterfly (R2 = 60.9%). The model retained as swimming speed predictors the age2, upper body circumference, hand breadth, waist circumference, and subscapular skinfold thickness (these last two had negative associations).

Conclusion

All these predictors were common to all four swim strokes and medley. Arm length was also retained as a significant predictor, but this one varied significantly between the four different swim strokes and medley. These findings highlight the importance of having a "V-shape" trunk, longer upper limbs, and large hands as predictors of swimming performance.

The role of the biomechanics analyst in swimming training and competition analysis

Swimming analysts aid coaches and athletes in the decision-making by providing evidence-based recommendations. The aim of this narrative review was to report the best practices of swimming analysts that have been supporting high-performance athletes. It also aims to share how swimming analysts can translate applied research into practice. The role of the swimming analyst, as part of a holistic team supporting high-performance athletes, has been expanding and is needed to be distinguished from the job scope of a swimming researcher. As testing can be time-consuming, analysts must decide what to test and when to conduct the evaluation sessions. Swimming analysts engage in the modelling and forecast of the performance, that in short- and mid-term can help set races target-times, and in the long-term provide insights on talent and career development. Races can be analysed by manual, semi-automatic or fully automatic video analysis with single or multi-cameras set-ups. The qualitative and quantitative analyses of the swim strokes, start, turns, and finish are also part of the analyst job scope and associated with race performance goals. Land-based training is another task that can be assigned to analysts and aims to enhance the performance, prevent musculoskeletal injuries and monitor its risk factors.

Physiological Features of Olympic-Distance Amateur Triathletes, as Well as Their Associations with Performance in Women and Men: A Cross-Sectional Study

The purpose of this study was to verify the physiological and anthropometric determinants of triathlon performance in female and male athletes. This study included 40 triathletes (20 male and 20 female). Dual-energy X-ray absorptiometry (DEXA) was used to assess body composition, and an incremental cardiopulmonary test was used to assess physiological variables. A questionnaire about physical training habits was also completed by the athletes. Athletes competed in the Olympic-distance triathlon race. For the female group, the total race time can be predicted by V̇O₂max (β = -131, t = -6.61, p < 0.001), lean mass (β = -61.4, t = -2.66, p = 0.018), and triathlon experience (β = -886.1, t = -3.01, p = 0.009) (r² = 0.825, p < 0.05). For the male group, the total race time can be predicted by maximal aerobic speed (β = -294.1, t = -2.89, p = 0.010) and percentage of body fat (β = 53.6, t = 2.20, p = 0.042) (r² = 0.578, p < 0.05). The variables that can predict the performance of men are not the same as those that can predict the triathlon performance of women. These data can help athletes and coaches develop performance-enhancing strategies.

Construction of an anthropometric discriminant model for identification of elite swimmers: an adaptive lasso approach

Background

Anthropometric characteristics are important factors that affect swimming performance. The aim of this study is to build a discriminant model using anthropometric factors to identify elite short-to-medium-distance freestyle swimmers through an adaptive Lasso approach.

Methods

The study recruited 254 swimmers (145 males and 109 females) who were divided them into elite (aged 17.9 ± 2.2 years, FINA points 793.8 ± 73.8) and non-elite (aged 17.1 ± 1.3 years, FINA points 560.6 ± 78.7) groups. Data for 73 variables were obtained, including basic information, anthropometric and derivative indicators. After filtering out highly correlated variables, 24 candidate variables were retained to be used in adaptive Lasso to select variables for prediction of elite swimmers. Deviance and area under the curve (AUC) were applied to assess the goodness of fit and prediction accuracy of the model, respectively.

Results

The adaptive Lasso selected 12 variables using the whole sample, with an AUC being 0.926 (95% CI [0.895-0.956]; P = 2.42 × 10^-29). In stratified analysis by gender, nine variables were selected for male swimmers with an AUC of 0.921 (95% CI [0.880-0.963]; P = 8.82 × 10^-17), and eight variables were for female swimmers with an AUC of 0.941 (95% CI [0.898-0.984]; P = 7.67 × 10^-15).

Conclusion

The adaptive Lasso showed satisfactory performance in selecting anthropometric characteristics to identify elite swimmers. Additional studies with longitudinal data or data from other ethnicities are needed to validate our findings.

Numerical and experimental methods used to evaluate active drag in swimming: A systematic narrative review

Introduction: In swimming, it is necessary to understand and identify the main factors that are important to reduce active drag and, consequently, improve the performance of swimmers. However, there is no up-to-date review in the literature clarifying this topic. Thus, a systematic narrative review was performed to update the body of knowledge on active drag in swimming through numerical and experimental methods.

Methods: To determine and identify the most relevant studies for this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach was used.

Results: 75 studies related to active drag in swimming and the methodologies applied to study them were analyzed and kept for synthesis. The included studies showed a high-quality score by the Delphi scale (mean score was 5.85 ± 0.38). Active drag was included in seven studies through numerical methods and 68 through experimental methods. In both methods used by the authors to determine the drag, it can be concluded that the frontal surface area plays a fundamental role. Additionally, the technique seems to be a determining factor in reducing the drag force and increasing the propulsive force. Drag tends to increase with speed and frontal surface area, being greater in adults than in children due to body density factors and high levels of speed. However, the coefficient of drag decreases as the technical efficiency of swimming increases (i.e., the best swimmers (the fastest or most efficient) are those with the best drag and swimming hydrodynamics efficiency).

Conclusion: Active drag was studied through numerical and experimental methods. There are significantly fewer numerical studies than experimental ones. This is because active drag, as a dynamical phenomenon, is too complex to be studied numerically. Drag is greater in adults than in children and greater in men than in women across all age groups. The study of drag is increasingly essential to collaborate with coaches in the process of understanding the fundamental patterns of movement biomechanics to achieve the best performance in swimming. Although most agree with these findings, there is disagreement in some studies, especially when it is difficult to define competitive level and age. The disagreement concerns three main aspects: 1) period of the studies and improvement of methodologies; 2) discrimination of methodologies between factors observed in numerical vs. experimental methods; 3) evidence that drag tends to be non-linear and depends on personal, technical, and stylistic factors. Based on the complexity of active drag, the study of this phenomenon must continue to improve swimming performance.

Turn Performance Variation in European Elite Short-Course Swimmers

Turn performances are important success factors for short-course races, and more consistent turn times may distinguish between higher and lower-ranked swimmers. Therefore, this study aimed to determine coefficients of variation (CV) and performance progressions (∆%) of turn performances. The eight finalists and eight fastest swimmers from the heats that did not qualify for the semi-finals, i.e., from 17th to 24th place, of the 100, 200, 400, and 800 (females only)/1500 m (males only) freestyle events at the 2019 European Short Course Championships were included, resulting in a total of 64 male (finalists: age: 22.3 ± 2.6, FINA points: 914 ± 31 vs. heats: age: 21.5 ± 3.1, FINA points: 838 ± 74.9) and 64 female swimmers (finalists: age: 22.9 ± 4.8, FINA points: 904 ± 24.5 vs. heats: age: 20.1 ± 3.6, FINA points: 800 ± 48). A linear mixed model was used to compare inter- and intra-individual performance variation. Interactions between CVs, ∆%, and mean values were analyzed using a two-way analysis of variance (ANOVA). The results showed impaired turn performances as the races progressed. Finalists showed faster turn section times than the eight fastest non-qualified swimmers from the heats (p < 0.001). Additionally, turn section times were faster for short-, i.e., 100 and 200 m, than middle- and long-distance races, i.e., 400 to 1500 m races (p < 0.001). Regarding variation in turn performance, finalists showed lower CVs and ∆% for all turn section times (0.74% and 1.49%) compared to non-qualified swimmers (0.91% and 1.90%, respectively). Similarly, long-distance events, i.e., 800/1500 m, showed lower mean CVs and higher mean ∆% (0.69% and 1.93%) than short-distance, i.e., 100 m events (0.93% and 1.39%, respectively). Regarding turn sections, the largest CV and ∆% were found 5 m before wall contact (0.70% and 1.45%) with lower CV and more consistent turn section times 5 m after wall contact (0.42% and 0.54%). Non-qualified swimmers should aim to match the superior turn performances and faster times of finalists in all turn sections. Both finalists and non-qualified swimmers should pay particular attention to maintaining high velocities when approaching the wall as the race progresses.