Effect of intensity training block on anxiety state and performance in competitive swimmers.

Background: An increase in training intensity could create changes in psychological and physiological variables in competitive athletes. For this reason, it is very relevant to know how an intensive training block could influence psychological variables in competitive swimmers. This study examined the effect of an intensive training block (HIT) for 2 weeks on the anxiety state and swimming performance compared to standard aerobic training. Methods: Twenty-two male competition swimmers were randomly assigned to two groups: HIT group (n = 11; age = 16.5 ± 0.29 years) and control group following the standard training program (n = 11; age = 16.1 ± 0.33 years). Psychological status variables (cognitive anxiety, somatic anxiety and self-confidence) and swimming performance (100-m front crawl) were measured pre-and post-test. Results: A significant effect of time was found for all psychological variables and swimming performance (F ≥ 17.6; p < 0.001; d ≥ 0.97). Furthermore, a significant group × time interaction effect was found in cognitive (F = 14.9; p < 0.001; d = 0.62) and somatic anxiety (F = 5.37; p = 0.031; d = 0.55) were found. Only a significant group effect was found in somatic anxiety (F = 27.1; p < 0.001; d = 1.2). Post hoc comparison revealed that both groups increased their cognitive anxiety and swimming performance, and decreased their self-confidence from pre to post test. However, cognitive anxiety increase significantly more in the HIT group compared to the control group. Furthermore, only the HIT training group significantly increased somatic anxiety over time, while somatic anxiety did not change significantly over time in the control group. Conclusion: Our findings indicated that a sudden increase in training intensity increased state anxiety more than standard training, but both conditions similarly enhanced swimming performance. Although the current level of psychological state is not affecting swimming performance negatively over this period, it should be regularly monitored by psychologists as it over a longer training period perhaps could have a negative influence on swimming performance.

Analyzing Key Factors on Training Days within a Standard Microcycle for Young Sub-Elite Football Players: A Principal Component Approach.

Utilizing techniques for reducing multivariate data is essential for comprehensively understanding the variations and relationships within both biomechanical and physiological datasets in the context of youth football training. Therefore, the objective of this study was to identify the primary factors influencing training sessions within a standard microcycle among young sub-elite football players. A total of 60 male Portuguese youth sub-elite footballers (15.19 ± 1.75 years) were continuous monitored across six weeks during the 2019-2020 in-season, comprising the training days from match day minus (MD-) 3, MD-2, and MD-1. The weekly training load was collected by an 18 Hz global positioning system (GPS), 1 Hz heart rate (HR) monitors, the perceived exertion (RPE) and the total quality recovery (TQR). A principal component approach (PCA) coupled with a Monte Carlo parallel analysis was applied to the training datasets. The training datasets were condensed into three to five principal components, explaining between 37.0% and 83.5% of the explained variance (proportion and cumulative) according to the training day (p < 0.001). Notably, the eigenvalue for this study ranged from 1.20% to 5.21% within the overall training data. The PCA analysis of the standard microcycle in youth sub-elite football identified that, across MD-3, MD-2, and MD-1, the first was dominated by the covered distances and sprinting variables, while the second component focused on HR measures and training impulse (TRIMP). For the weekly microcycle, the first component continued to emphasize distance and intensity variables, with the ACC and DEC being particularly influential, whereas the second and subsequent components included HR measures and perceived exertion. On the three training days analyzed, the first component primarily consisted of variables related to the distance covered, running speed, high metabolic load, sprinting, dynamic stress load, accelerations, and decelerations. The high intensity demands have a high relative weight throughout the standard microcycle, which means that the training load needs to be carefully monitored and managed.

Data Mining Paths for Standard Weekly Training Load in Sub-Elite Young Football Players: A Machine Learning Approach.

The aim of this study was to test a machine learning (ML) model to predict high-intensity actions and body impacts during youth football training. Sixty under-15, -17, and -19 sub-elite Portuguese football players were monitored over a 6-week period. External training load data were collected from the target variables of accelerations (ACCs), decelerations (DECs), and dynamic stress load (DSL) using an 18 Hz global positioning system (GPS). Additionally, we monitored the perceived exertion and biological characteristics using total quality recovery (TQR), rating of perceived exertion (RPE), session RPE (sRPE), chronological age, maturation offset (MO), and age at peak height velocity (APHV). The ML model was computed by a feature selection process with a linear regression forecast and bootstrap method. The predictive analysis revealed that the players' MO demonstrated varying degrees of effectiveness in predicting their DEC and ACC across different ranges of IQR. After predictive analysis, the following performance values were observed: DEC (x¯predicted = 41, ß = 3.24, intercept = 37.0), lower IQR (IQRpredicted = 36.6, ß = 3.24, intercept = 37.0), and upper IQR (IQRpredicted = 46 decelerations, ß = 3.24, intercept = 37.0). The player's MO also demonstrated the ability to predict their upper IQR (IQRpredicted = 51, ß = 3.8, intercept = 40.62), lower IQR (IQRpredicted = 40, ß = 3.8, intercept = 40.62), and ACC (x¯predicted = 46 accelerations, ß = 3.8, intercept = 40.62). The ML model showed poor performance in predicting the players' ACC and DEC using MO (MSE = 2.47-4.76; RMSE = 1.57-2.18: R2 = -0.78-0.02). Maturational concerns are prevalent in football performance and should be regularly checked, as the current ML model treated MO as the sole variable for ACC, DEC, and DSL. Applying ML models to assess automated tracking data can be an effective strategy, particularly in the context of forecasting peak ACC, DEC, and bodily effects in sub-elite youth football training.

Match-to-Match Variation on High-Intensity Demands in a Portuguese Professional Football Team.

The aim of this study was to analyze the match-to-match variation in high-intensity demands from one Portuguese professional football team according to playing positions. Twenty-three male outfield professional football players were observed during eighteen matches of the Portuguese Second League. Time-motion data were collected using Global Positioning System (GPS) technology. Match running performance was analyzed based on the following three playing positions: defenders (DF), midfielders (MF), and forwards (FW). Repeated measures ANOVA was utilized to compare match running performance within each position role, and seasonal running variation. Practical differences were assessed using the smallest worthwhile change (SWC), coefficient of variation (CV), and twice the coefficient of variation (2CV). Significant differences were found among playing positions in total distance covered (F = 15.45, p < 0.001, η2 = 0.33), average speed (F = 12.79, p < 0.001, η2 = 0.29), high-speed running (F = 16.93, p < 0.001, η2 = 0.36), sprinting (F = 13.49, p < 0.001, η2 = 0.31), accelerations (F = 4.69, p = 0.001, η2 = 0.132), and decelerations (F = 12.21, p < 0.001, η2 = 0.284). The match-to-match running performance encompassed TD (6.59%), AvS (8.67%), HSRr (37.83%), SPR (34.82%), ACC (26.92%), and DEC (27.85%). CV values for total distance covered ranged from 4.87-6.82%, with forwards and midfielders exhibiting the greatest and smallest variation, respectively. Midfielders demonstrated the highest match-to-match variation for all other analyzed variables (8.12-69.17%). All playing positions showed significant variation in high-demanding variables (26.94-37.83%). This study presents the initial analysis of match-to-match variation in high-intensity demands within a Portuguese professional football team. Thus, the position's specificity and context can provide a helpful strategy for evaluating match-to-match running performance, and for recommending individualized training exercises based on the peak and high-intensity demands for each player's role within the game.

Exploring Injury Prevention Strategies for Futsal Players: A Systematic Review.

Futsal carries a high risk of injury for players. This systematic review aimed to assess the existing literature on injury prevention strategies for futsal players. The literature was searched using PubMed, Web of Science, and Scopus databases from inception to 20 March 2024. Relevant articles were searched using the terms "futsal" AND "injury" AND "prevention". Fourteen studies were included in the review. The review identified several injury prevention strategies with potential benefits for futsal players. Structured warm-up routines were shown to improve balance and eccentric strength and to reduce total, acute, and lower limb injuries. Proprioceptive training methods were suggested to improve joint stability and landing mechanics, which may reduce the risk of injury. Furthermore, multicomponent methods that include components such as core stability and flexibility have shown potential for reducing injury rates in futsal players. Finally, by reducing fatigue and improving movement control, strength training procedures designed to correct muscular imbalances may improve performance, which may ultimately minimize the risk of injury. This systematic review demonstrates the potential benefits of different injury prevention strategies for futsal players. The combination of several strategies, such as proprioceptive training, multicomponent programs, warm-up routines, and strength training specifically designed to address muscular imbalances, appears promising.

Race analysis in swimming: understanding the evolution of publications, citations and networks through a bibliometric review.

The aim of this study was to conduct a scoping and bibliometric review of swimming articles related to race analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to identify relevant studies. Articles on race analysis in swimming published between 1984 and December 31, 2023 were retrieved from the Web of Science database. 366 records were screened and a total of 74 articles were retained for analysis. Until 2012, there were some time intervals with no or few publications. From 2012, there was a clear upward trend in publications and citations. This theme was led by the United States of America, Australia, and Spain. Australia and Spain maintain their status as the countries with the most publications. The analysis of author collaborations revealed two clusters with Spanish authors, and the remaining clusters are composed of Portuguese, Swiss, and Australian authors. With this bibliometric review, it has been possible to understand the evolution of the articles published on race analysis in swimming, the countries and the authors that have contributed most to this topic over the years. The prediction model shows that the number of articles and citations on this topic will continue to increase over the next 10 years (until 2034).

Understanding the Importance of Drag Coefficient Assessment for a Deeper Insight into the Hydrodynamic Profile of Swimmers.

The main objective of this study was to confirm that the passive drag coefficient is less dependent on swimming speed than the passive drag, Froude, and Reynolds numbers, even as swimming speed increases. The sample consisted of 12 young proficient non-competitive swimmers (seven males and five females: 20.4 ± 1.9 years). Passive drag was measured with a low-voltage isokinetic engine at 1.2, 1.4, 1.6 and 1.8 m/s. The frontal surface area was measured using digital photogrammetry. Passive drag showed significant differences with a strong effect size over the four towing speeds measured (F = 116.84, p < 0.001, η2 = 0.91) with a quadratic relationship with speed. The Froude and Reynolds numbers had similar trends, but with linear relationships. Conversely, the passive drag coefficient showed non-significant differences across the four towing speeds (F = 3.50, p = 0.062, η2 = 0.33). This strongly suggests that the passive drag coefficient should be the variable of choice for monitoring the hydrodynamic profile of swimmers rather than the absolute value of passive drag.

A Single Dose of Beetroot Juice not Enhance Performance during Intervallic Swimming Efforts.

Despite the numerous scientific evidence on the topic, there is no clear and consistent answer that clarifies the true effects of beetroot juice (BJ) supplementation on different types of physical performance. This study examined whether an acute intake of BJ improves swimming performance, physiological variables of anaerobic metabolism, or subjective measures during high-intensity interval exercise with incomplete rest in competitive swimmers. Eighteen competitive swimmers (nine females and nine males) participated in this cross-over randomized, placebo-controlled, double-blind and counterbalanced study. In two trials, swimmers ingested BJ (70 mL, 6.4 mmol/400 mg NO3-) or placebo (PLA) (70 mL, 0.04 mmol/3 mg NO3-) three hours before a 2×6×100 m maximal effort with 40 seconds rest between repetitions and three minutes between blocks. The 100 m times showed no differences between groups (p > 0.05), but there was an interaction between block×repetition×condition (F5 = 3.10; p = 0.046; ηp2 = 0.54), indicating that the BJ group decreased the time of the sixth repetition of block2 compared to block1 (p = 0.01). Lactate concentration showed no differences between conditions (p > 0.05), but there was a main effect of block (ηp2 = 0.60) and a block×repetition interaction (ηp2 = 0.70), indicating higher values in block2 and increasing values between repetitions in block1. The subjective scales, perception of exertion (RPE) and Total Quality Recovery (TQR), showed no effects of condition (p > 0.05), but BJ swimmers had a greater TQR in the last repetitions of each block. In conclusion, a single dose of BJ did not enhance intermittent swimming performance or modified the physiological (lactate and heart rate) or subjective (RPE and TQR) variables; although there was a possible positive effect on the exercise tolerance at the end of effort.

Measurement of the active drag coefficient in front-crawl: A stroke-by-stroke analysis.

The purpose of this study was to understand the change in active drag coefficient (CDA) over successive stroke cycles in front-crawl and the relationship between swimming speed and CDA. Eighteen national competitive swimmers (nine girls and nine boys with a mean age of 14.91 ± 0.59 years) were recruited. Swimming speed, propulsion (Ftotal) and frontal surface area were measured to calculate the CDA. Swimming speed (F = 1.790, p = 0.182, η2 = 0.07) and CDA (F = 0.907, p = 0.413, η2 = 0.06) did not change significantly over time, but swimming speed showed a decrease between the second and third stroke cycle. On the other hand, the Ftotal changed significantly over time (F = 4.437, p = 0.019, η2 = 0.21). Swimming speed and CDA showed a linear and strong relationship (R2 = 63.8 %). A stroke-by-stroke analysis showed that national level swimmers were able to maintain their hydrodynamic profile during a front-crawl maximal trial. Thus, it can be argued that a decrease in swimming speed can be related to a decrease in Ftotal. Swimming speed and CDA showed an inverse and significant relationship, with lower values of CDA resulting in faster swimming speeds.

Optimizing performance and mood state in competitive swimmers through tapering strategies.

Tapering is a concept that is of great importance in relation to performance, due of its great effect on the psychological and physical condition of the swimmer. Therefore, the present study aims to investigate the effect of two-week of tapering characterized by a progressive training volume reduction on mood state and swimming performance in competitive swimmers. Twenty-four competitive male swimmers were randomly assigned into two groups. Experimental group (age = 16.9 ± 0.5 years) and control group (16.1 ± 0.4 years). The mood subscales (tension, depression, anger, fatigue, confusion and vigor), total mood disturbance and swimming performance (50-m front crawl) were measured in pre and posttest. Our findings revealed a significant improvement in mood subscales (20.8 to 47.8%), total mood disturbance (14.4%) and in swimming performance (3.5%) after 2 weeks of tapering training. A significant correlation was observed between the total mood disturbance and the 50 m front crawl (r = -0.63) only in the experimental group. It was concluded that a progressive reduction in training volume with a maintain of intensity could improve mood state and swimming performance. In addition, a change in total mood disturbance could affect swimming performance. Swimming coaches are advised to include tapering period according to the standards we mentioned earlier before competitive swimming to improve mental state, which helps the swimmers to overcome the negative influences of overtraining and therefore they can promote sprint-swimming performance.

Effects of anthropometrics, thrust, and drag on stroke kinematics and 100 m performance of young swimmers using path-analysis modeling.

The aim of this study was to understand the interactions between anthropometric, kinetic, and kinematic variables and how they determine the 100 m freestyle performance in young swimmers. Twenty-five adolescent swimmers (15 male and 10 female, aged 15.75 ± 1.01 years) who regularly participated in regional and national competitions were recruited. The 100 m freestyle performance was chosen as the variable to be predicted. A series of anthropometric (hand surface area-HSA), kinetic (thrust and active drag coefficient (CDA )), and kinematic (stroke length (SL); stroke frequency (SF), and swimming speed) variables were measured. Structural equation modeling (via path analysis) was used to develop and test the model. The initial model predicted performance with 90.1% accuracy. All paths were significant (p < 0.05) except the thrust-SL. After deleting this non-significant path (thrust-SL) and recalculating, the model goodness-of-fit improved and all paths were significant (p < 0.05). The predicted performance was 90.2%. Anthropometrics had significant effects on kinetics, which had significant effects on kinematics, and consequently on the 100 m freestyle performance. The cascade of interactions based on this path-flow model allowed for a meaningful prediction of the 100 m freestyle performance. Based on these results, coaches and swimmers should be aware that the swimming predictors can first meaningfully interact with each other to ultimately predict the 100 m freestyle performance.

Analysis of upper limb propulsion in young swimmers in front-crawl through Statistical Parametric Mapping.

This study aimed to: (i) verify the within-subject effect of the dominant and non-dominant upper limb propulsion during consecutive arm-pulls through discrete (average) and continuous analysis (SPM), and; (ii) compare young swimmers' propulsion between both upper limbs through discrete (average) and continuous analysis (Statistical Parametric Mapping - SPM). The sample consisted of 17 young male swimmers (age = 16.02 ± 0.61-years) who regularly participate in national and international level competitions. A set of kinematic and propulsion variables were measured during a 25-m maximal trial in front-crawl. Statistical analysis of propulsion was performed using discrete variables and through SPM. Swimming velocity showed a significant decrease over time. A significant interaction between the "time" (consecutive arm-pulls) and "side" (dominant vs. non-dominant) effects was observed in both statistical analyzes. Only the dominant upper limb demonstrated a significant "time" effect with a significant difference (p < 0.05) between the first and third arm-pulls. SPM indicated that the "time" effect was observed between the âˆ¼ 34% and âˆ¼ 42% of the arm-pull. The differences between the first and third arm-pull were verified between the âˆ¼ 32% and âˆ¼ 43% of the arm-pull. A non-significant "side" effect was verified in both analyzes. Therefore, SPM analysis provided more sensitive and accurate outputs than discrete analysis. This will allow coaches to design specific training drills focused on specific moments of the arm-pull.

Resultant equations for training load monitoring during a standard microcycle in sub-elite youth football: a principal components approach.

Applying data-reduction techniques to extract meaningful information from electronic performance and tracking systems (EPTS) has become a hot topic in football training load (TL) monitoring. The aim of this study was to reduce the dimensionality of the internal and external load measures, by a principal component approach, to describe and explain the resultant equations for TL monitoring during a standard in-season microcycle in sub-elite youth football. Additionally, it is intended to identify the most representative measure for each principal component. A principal component analysis (PCA) was conducted with a Monte Carlo parallel analysis and VariMax rotation to extract baseline characteristics, external TL, heart rate (HR)-based measures and perceived exertion. Training data were collected from sixty sub-elite young football players during a 6-week training period using 18 Hz global positioning system (GPS) with inertial sensors, 1 Hz short-range telemetry system, total quality recovery (TQR) and rating of perceived exertion (RPE). Five principal components accounted for 68.7% of the total variance explained in the training data. Resultant equations from PCA was subdivided into: (1) explosiveness, accelerations and impacts (27.4%); (2) high-speed running (16.2%); (3) HR-based measures (10.0%); (4) baseline characteristics (8.3%); and (5) average running velocity (6.7%). Considering the highest factor in each principal component, decelerations (PCA 1), sprint distance (PCA 2), average HR (PCA 3), chronological age (PCA 4) and maximal speed (PCA 5) are the conditional dimension to be considered in TL monitoring during a standard microcycle in sub-elite youth football players. Current research provides the first composite equations to extract the most representative components during a standard in-season microcycle in sub-elite youth football players. Futures research should expand the resultant equations within training days, by considering other well-being measures, technical-tactical skills and match-related contextual factors.

Comparison of swimming velocity between age-group swimmers through discrete variables and continuous variables by Statistical Parametric Mapping.

The aim of this study was to compare the swimming velocity in front-crawl between age-group swimmers using discrete variables against Statistical Parametric Mapping (SPM). The sample consisted of 30 young male swimmers divided into three groups (each with 10 swimmers) based on their age (group #1: 13.60 ± 0.84 years; group #2: 15.40 ± 0.32 years; group 3: 16.39 ± 0.69 years). Swimmers performed three maximal trials in front-crawl. The best performance was used for analysis. Comparison of swimming velocity between groups was analysed using discrete variables and as a continuous variable (SPM). As a discrete variable, the mean swimming velocity showed a significant difference between groups (p < 0.05). Moreover, when analysed by SPM, swimming velocity showed a significant difference (p = 0.021) between the ~ 44% and ~ 51% of the stroke cycle (transition of the propulsion phases between sides). Post-hoc comparison revealed a significant difference between group #1 and group #3 only in SPM analysis. Researchers, coaches, and practitioners should know that both measurement approaches can be used simultaneously. However, SPM offers more sensitive and accurate results about the swimmers' stroke cycle.

Using Statistical Parametric Mapping as a statistical method for more detailed insights in swimming: a systematic review.

Swimming is a time-based sport and hence strongly dependent from velocity. Most studies about swimming refer to velocity as discrete variable, i.e., 0-D (no time dimension). However, it was argued that using swimming velocity as a continuous variable (1-D, with time dimension) with Statistical Parametric Mapping (SPM) can bring deeper and detailed insights about swimming performance. Therefore, the aim of this study was to perform a systematic review about the current body of knowledge of using Statistical Parametric Mapping in a swimming context. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to identify relevant articles. After screening, nine articles related to Statistical Parametric Mapping (SPM) analysis in swimming were retained for synthesis. Results showed that four articles (44.4%) aimed to understand the kinematics, isokinetic joint torque or electromyographic (EMG) pattern of the swimmer's shoulder either on land or during front crawl trials. Two articles (22.2%) focused on understanding the swimming velocity while performing the breaststroke stroke. One article (11.1%) analyzed the swimmers' propulsion at front-crawl stroke, another one (11.1%) compared swimming velocity during a complete stroke cycle in young swimmers of both sexes as a discrete variable and as a continuous variable. Also, one article (11.1%) analyzed the underwater undulatory velocity. In an EMG context, some findings verified in SPM are not possible to be discovered with traditional 0-D statistical methods. Studies about swimming velocity (breaststroke, freestyle, and underwater undulatory velocity) and propulsion (front-crawl) also highlighted the SPM advantages in comparison to traditional statistical methods. By using SPM, researchers were able to verify specifically where within the stroke cycle significant differences were found. Therefore, coaches can get more detailed information to design specific training drills to overcome hypothetical handicaps.

Association between the Upper Quarter Dynamic Balance, Anthropometrics, Kinematics, and Swimming Speed.

Besides recurrently assessed water-based parameters, there are also some individual characteristics that affect swimming performance that are not water related. In the past few years, dynamic balance has been associated with land sports performance. Conversely, evidence on this topic in swimming is scarce. The purpose of this study was to assess the association between on-land dynamic balance and swimming performance. Sixteen young adults and recreational swimmers were recruited for the present study (8 males 20.8 ± 2.0 years, and 8 females 20.1 ± 1.9 years). A set of anthropometric features were measured. The upper quarter Y-balance test was selected as a dynamic balance outcome, and swimming speed as the swimming performance indicator. The results showed a moderate and positive correlation between dynamic balance and swimming performance (p < 0.05). Speed fluctuation was highly and negatively related to swimming speed (p < 0.001), i.e., swimmers who had higher scores in the dynamic balance were more likely to deliver better performances. This suggests that in recreational swimmers, the stability and mobility of the upper extremity had a greater influence on swimming performance. Therefore, swimming instructors are advised to include dynamic balance exercises in their land-based training sessions to improve their swimmers' performance.

Anthropometric Measures, Muscle Resistance, and Balance in Physically Active, Aged Adults.

OBJECTIVES: This study aimed to examine the relationship between age, body mass index, muscle strength, and balance in physically active, aged adults. METHODS: Eighty-five participants were recruited for this study, having an average age of 70.31 years (SD = 9.90), ranging from 50 to 92 years. Twenty-six (30.6%) participants were male and fifty-nine (69.4%) were female. The participants had an average body mass index of 27.30 kg/m2 (SD = 3.62), ranging from 20.32 to 38.58 kg/m2. Participants undertook the Timed-Up and Go to test balance, and the chair-stand test to assess lower body strength. Hierarchical regression analyses were conducted. Three models (Model 1, 2, and 3) were tested to assess their relationships with balance: M1-Lower body muscle strength; M2-Lower body muscle strength and body mass index; M3-Lower body muscle strength, body mass index, and age. RESULTS: All hierarchical models displayed significant variance. The third model explained 50.9% of the variance in dynamic balance, [F(3, 81) = 27.94, p < 0.001, R = 0.71, Ra2 = 0.51]. The difference in Ra2 between the first, second, and third models was statistically significant (p < 0.05). Age, body mass index, and lower body muscle strength had significant (p < 0.05) correlations with balance. In terms of the significant impact of each predictor, age had the strongest association with balance (p < 0.05). CONCLUSIONS: The results are useful to understand mechanisms or diagnose people at risk of fall.

Identifying Differences in Swimming Speed Fluctuation in Age-Group Swimmers by Statistical Parametric Mapping: A Biomechanical Assessment for Performance Development.

The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 ± 0.86 years; 60 girls: age = 12.46 ± 0.94 years) were analysed. The dataset for each sex was divided into three tiers: (i) tier #1 - best-performing swimmers; (ii) tier #2: intermediate-performing swimmers, and; (iii) tier #3 - poorest-performing swimmers. As a discrete variable, swimming speed showed significant sex and tier effects, and a significant sex*tier interaction (p < 0.001). Speed fluctuation showed a non-significant sex effect (p > 0.05), a significant tier effect (p < 0.001), and a non-significant sex*tier interaction (p > 0.05). As a continuous variable, the swimming speed time-curve presented significant sex and tier effects (p < 0.001) throughout the stroke cycle, and a significant sex*tier interaction (p < 0.05) in some moments of the stroke cycle. Swimming speed fluctuation analysed as a discrete variable and as a continuous variable can be used in a complementary way. Nonetheless, SPM can provide deeper insight into differences within the stroke cycle. Thus, coaches and practitioners should be aware that different knowledge about the swimmers' stroke cycle can be learned by assessing swimming speed using both methods.

Interaction of Kinematic, Kinetic, and Energetic Predictors of Young Swimmers' Speed.

PURPOSE: The aim of this study was to assess the interaction of kinematic, kinetic, and energetic variables as speed predictors in adolescent swimmers in the front-crawl stroke. DESIGN: Ten boys (mean age [SD] = 16.4 [0.7] y) and 13 girls (mean age [SD] = 14.9 [0.9] y) were assessed. METHODS: The swimming performance indicator was a 25-m sprint. A set of kinematic, kinetic (hydrodynamic and propulsion), and energetic variables was established as a key predictor of swimming performance. Multilevel software was used to model the maximum swimming speed. RESULTS: The final model identified time (estimate = -0.008, P = .044), stroke frequency (estimate = 0.718, P < .001), active drag coefficient (estimate = -0.330, P = .004), lactate concentration (estimate = 0.019, P < .001), and critical speed (estimate = -0.150, P = .035) as significant predictors. Therefore, the interaction of kinematic, hydrodynamic, and energetic variables seems to be the main predictor of speed in adolescent swimmers. CONCLUSIONS: Coaches and practitioners should be aware that improvements in isolated variables may not translate into faster swimming speed. A multilevel evaluation may be required for a more effective assessment of the prediction of swimming speed based on several key variables rather than a single analysis.

Agreement between Different Methods to Measure the Active Drag Coefficient in Front-Crawl Swimming.

The aim of this study was to analyze the agreement of the active drag coefficient measured through drag and propulsion methods. The sample was composed of 18 swimmers (nine boys: 15.9 ± 0.9 years; nine girls: 15.3 ± 1.2 years) recruited from a national swimming team. The velocity perturbation method was used as the drag measurement system and the Aquanex system as the propulsion system. For both sexes combined, the frontal surface area was 0.1128 ± 0.016 m2, swim velocity 1.54 ± 0.13 m.s-1, active drag 62.81 ± 11.37 N, propulsion 68.81 ± 12.41 N. The level of the active drag coefficient agreement was calculated based on the mean values comparison, simple linear regression, and Bland Altman plots. The mean data comparison revealed non-significant differences (p > 0.05) between methods to measure the active drag coefficient. Both the linear regression (R2 = 0.82, p < 0.001) and Bland Altman plots revealed a very high agreement. The active drag coefficient should be the main outcome used in the interpretation of the swimmers' hydrodynamic profile, because it is less sensitive to swimming velocity. Coaches and researchers should be aware that the active drag coefficient can also be calculated based on propulsion methods and not just based on drag methods. Thus, the swimming community can now use different equipment to measure the hydrodynamics of their swimmers.