Although performance and biomechanical evaluations are becoming more swimming-specific, dryland testing permits monitoring of a larger number of performance-related variables. However, as the degree of comparability of measurements conducted in-water and on land conditions is unclear, we aimed to assess the differences between force production in these two different conditions. Twelve elite swimmers performed a 30 s tethered swimming test and four isokinetic tests (shoulder and knee extension at 90 and 300°/s) to assess peak force, peak and average torque, and power symmetry index. We observed contralateral symmetry in all the tests performed, e.g., for 30 s tethered swimming and peak torque shoulder extension at 90°/s: 178 ± 50 vs. 183 ± 56 N (p = 0.38) and 95 ± 37 vs. 94 ± 35 N × m (p = 0.52). Moderate to very large direct relationships were evident between dryland testing and swimming force production (r = 0.62 to 0.96; p < 0.05). Swimmers maintained similar symmetry index values independently of the testing conditions (r = −0.06 to −0.41 and 0.04 to 0.44; p = 0.18–0.88). Asymmetries in water seems to be more related to technical constraints than muscular imbalances, but swimmers that displayed higher propulsive forces were the ones with greater force values on land. Thus, tethered swimming and isokinetic evaluations are useful for assessing muscular imbalances regarding propulsive force production and technical asymmetries.

Purpose: to compare different methods to assess the arm stroke efficiency ( ${\eta }_F$ ), when swimming front crawl using the arms only on the Measurement of Active Drag System (MAD System) and in a free-swimming condition, and to identify biophysical adaptations to swimming on the MAD System and the main biophysical predictors of maximal swimming speed in the 200 m front crawl using the arms only ( $v_{200m}$ ). Methods: fourteen swimmers performed twice a 5 × 200 m incremental trial swimming the front crawl stroke using the arms only, once swimming freely, and once swimming on the MAD System. The total metabolic power was assessed in both conditions. The biomechanical parameters were obtained from video analysis and force data recorded on the MAD System. The ${\eta }_F$ was calculated using: (i) direct measures of mechanical and metabolic power (power-based method); (ii) forward speed/hand speed ratio (speed-based method), and (iii) the simplified paddle-wheel model. Results: both methods to assess ${\eta }_F$ on the MAD System differed (p < 0.001) from the expected values for this condition ( ${\eta }_F$ = 1), with the speed-based method providing the closest values ( ${\eta }_F$ ~0.96). In the free-swimming condition, the power-based ( ${\eta }_F$ ~0.75), speed-based ( ${\eta }_F$ ~0.62), and paddle-wheel ( ${\eta }_F$ ~0.39) efficiencies were significantly different (p < 0.001). Although all methods provided values within the limits of agreement, the speed-based method provided the closest values to the “actual efficiency”. The main biophysical predictors of $v_{200m}$ were included in two models: biomechanical (R² = 0.98) and physiological (R² = 0.98). Conclusions: our results suggest that the speed-based method provides the closest values to the “actual ${\eta }_F$ ” and confirm that swimming performance depends on the balance of biomechanical and bioenergetic parameters

The current study aimed to longitudinally evaluate anthropometric, physiological, and biomechanical variables related to middle-distance performance during a 45-week swimming training season. Thirty-four swimmers (age: 12.07 ± 1.14 years) performed a maximum of 400 m front crawl at the beginning (T1) and finish of the first macrocycle (T2, 15 weeks) and the finish of the second (T3, 18 weeks) and third macrocycles (T4, 12 weeks). Time-related variables, stroke rate (SR), stroke length (SL), and stroke index (SI) were recorded during the test, and blood lactate ([La]) and glucose ([Glu]) concentrations were measured post-exercise. The time of the 400 m effort decreased after each macrocycle (T2 vs. T1, 7.8 ± 5.6%; T3 vs. T2, 3.7 ± 3.1%; T4 vs. T3, 3.8 ± 3.4%; p < 0.01). Four hundred meter speed changes between T1 and T2 were positively related to variations in [La], [Glu], SL, and SI (r = 0.36–0.60, p < 0.05). Changes between T2 and T3 were related to SI only (r = 0.5, p < 0.05), and modifications between T3 and T4 were associated with SL and SI variations (r = 0.34 and 0.65, p < 0.05). These results indicate that a well-structured year plan including three macrocycles leads to a significant age-group swimming performance improvement, mostly connected with an increase in technical proficiency.

Context: It is essential to determine swimmers’ anaerobic potential and better plan training, understanding physiological effects of the fatigue.Purpose: To study changes in the characteristics of the intracyclic velocity variation during an all-out 50-m swim and to observe differences in speed and stroking parameters between these changes.Methods: 28 competitive swimmers performed a 50-m front-crawl all-out test while attached to a speedometer. The velocity–time (v[t]) curve off all stroke cycles was analyzed per individual using a routine that included a wavelet procedure, allowing the determination of the fatigue thresholds that divide effort in time intervals.Results: One or 2 fatigue thresholds were observed at individual level on the v(t) curve. In males, when 1 fatigue threshold was identified, the mean velocity and the stroke index dropped (P < .05) in the second time interval (1.7 ± 0.0 vs 1.6 ± 0.0 m/s and 3.0 ± 0.2 vs 2.8 ± 0.3 m/s, respectively). When 2 fatigue thresholds were identified, the mean velocity of the first time interval was higher than that of the third time interval (P < .05), for both male (1.7 ± 0.0 vs 1.6 ± 0.1 m/s) and female (1.5 ± 0.1 vs 1.3 ± 0.1 m/s) swimmers.Conclusion: One or 2 fatigue thresholds were found in the intracyclic velocity-variation patterns. Concurrently, changes in velocity and stroke parameters were also observed between time intervals. This information could allow coaches to obtain new insights into delaying the degenerative effects of fatigue and maintain stable stroke-cycle characteristics over a 50-m event.

The current study aimed to evidence the strengths and weaknesses of two indirect methods for assessing the anaerobic alactic contribution to a specific CrossFit^® workout. Thirty experienced crossfitters performed the Fran workout at maximal intensity, and ventilatory data were collected during the recovery period using a telemetric portable gas analyser to assess the oxygen uptake (VO₂) of the off-kinetics fast component (Ana_recovery). The kinetics of maximal phosphocreatine splitting (Ana_PCr) were determined based on the literature. No differences between the two methods were observed (31.4 ± 4.0 vs. 30.4 ± 4.1 kJ for Ana_recovery and Ana_PCr, respectively). Despite the existence of some caveats (e.g., errors derived from a delay at the onset of VO₂ recovery and the assumption of given values in the concentration of phosphocreatine per kilogram of wet muscle, respectively) in both methods, the data indicate that they yield similar results and allow for estimations of alactic energy contribution from a short-duration and high intensity CrossFit^® routine. The current data contributes to CrossFit^® workout evaluations and training strategies, helping researchers to evaluate crossfitters more accurately. The advantage of the two methods used in the current study is that they are non-invasive, which differs greatly from muscle biopsies.

The speedometer is widely used to evaluate swimming velocity but has some constraints. With the constant development of inertial units (IMUs), it is expected that they will become a good alternative to the speedometer. This study aimed to compare the data retrieved by an IMU and a speedometer when breaststroke is performed at maximum speed. Sixteen swimmers, nine males and seven females (20.3 ± 3.3 vs. 18.7 ± 1.1 years old, 65.8 ± 11.2 vs. 57.7 ± 9.1 kg of body mass and 1.75 ± 0.07 vs. 1.61 ± 0.10 m of height, respectively), performed 4 × 25 m of breaststroke sprint. They were equipped with an IMU fixed to the sacrum and with the line of an electromechanical speedometer (acquisition frequency of 50 Hz) fixed at the central point in the lumbar region. Statistical parametric mapping was used to compare the velocity curves, IBM SPSS was used for descriptive statistics and Bland–Altman plots were used for agreement of measurements. The results show that the IMU and speedometer do not show similar patterns, and the velocity values measured by the IMU are lower (p < 0.001). Bland–Altman plots presented a larger bias in terms of coefficient of variation and intracycle velocity variation. It can be concluded that IMUs and speedometers are not substitutes for each other as methods for evaluating intracycle velocity variations.