This study aimed to analyze the effects of a swimming training mesocycle in master swimmers’ performance and active drag. Twenty-two 39.87 ± 6.10 year-old master swimmers performed a 25 m front crawl at maximal intensity before and after a typical four-week training mesocycle. Maximum, mean and minimum speeds, speed decrease and hip horizontal intra-cyclic velocity variation were assessed using an electromechanical speedometer, and the active drag and power to overcome drag were determined using the measuring active drag system. Maximum, mean and minimum front crawl speeds improved from pre- to post-training (mean ± 95% CI: 3.1 ± 2.8%, p = 0.04; 2.9 ± 1.6%, p = 0.01; and 4.6 ± 3.1%, p = 0.01; respectively) and the speed decrease along the 25 m test lowered after the training period (82.5 ± 76.3%, p = 0.01). The training mesocycle caused a reduction in the active drag at speeds corresponding to 70% (5.0 ± 3.9%), 80% (5.6 ± 4.0%), and 90% (5.9 ± 4.0%), but not at 100% (5.9 ± 6.7%), of the swimmers’ maximal exertions in the 25 m test. These results showed that four weeks of predominantly aerobic training could improve master swimmers’ performance and reduce their hydrodynamic drag while swimming mainly at submaximal speeds.

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.

Empirical observations supports that the addition of a plastic strip - also known as Randall foils - on the top edge of a rowing blade improve rowing efficiency during the cycle propulsive phase. The aim of the current study was to analyze the effect of using Big blades with and without Randall foils on rowing performance. Twenty experienced rowers performed two 90 s tethered rowing bouts (with and without Randall foils) to assess its impact on force production and physiologic variables. All tests were randomized and a repeated measure design was used to compare experimental conditions. Higher values of peak and mean peak forces (479.4 ± 134.7 vs 423.2 ± 153.0, d=0.83 and 376.5 ± 101.4 vs 337.1 ± 113.3 N, d=0.68), peak oxygen uptake (47.9 ± 7.5 vs 45.3 ± 7.3 mL∙kg-1∙min-1, d=0.19), peak blood lactate concentration (7.9 ± 1.6 vs 6.9 ± 1.7 mmol∙L-1, d=0.16), blood lactate increasing speed (0.08 ± 0.01 vs 0.07 ± 0.06 [(mmol·L-1)·s-1], d=0.27) and lactic anaerobic energy (27.4 ±7.9 vs 23.4 ± 8.1 kJ, d=0.23) were found for Big blades with vs without Randall foils, p<0.05. The current data suggest that the Randall foils can positivly affect rowing performance.

We characterized the physical and physiological profiles of high-level female Portuguese handball players and examined the relationships between their anthropometric characteristics, general motor performance and cardiopulmonary fitness. Twenty-four high-level female handball players with an average age of 23.6 ± 5.5 years, height of 173.6 ± 5.1 cm and body mass of 72.6 ± 9.1 kg volunteered to participate. A Pearson correlation test was used to assess the relationship between variables. Direct relationships were observed between the players’ height and arm span (r = 0.741), as well as between their squat jump and countermovement jump performances with regard to body mass (r = 0.448 and 0.496, respectively). The 9 m jump shot has a large relationship with the 7 m standing throw (r = 0.786) and between left hand dynamometry and body mass index (r = 0.595). The 30 m sprint has a relationship with the 7 m standing throw (r = −0.526) and the 9 m jump throw (r = −0.551). Oxygen uptake has a relationship with the players’ height (r = −0.482) and time limit (r = 0.513), while the fitness index has a relation to the players’ height (r = −0.488) and arm span (r = −0.422). Our results should be considered when using physical testing to plan optimal physical training regimens in elite team handball.

Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and variables’ contributions to determine their real-life applicability. This work aims to evaluate the performance variations of a previously developed four-level physical fatigue model when alternating its inputs to have a comprehensive view of the impact of each physiological variable on the model’s functioning. Data from heart rate, breathing rate, core temperature and personal characteristics from 24 firefighters during an incremental running protocol were used to develop the physical fatigue model based on an XGBoosted tree classifier. The model was trained 11 times with different input combinations resulting from alternating four groups of features. Performance measures from each case showed that heart rate is the most relevant signal for estimating physical fatigue. Breathing rate and core temperature enhanced the model when combined with heart rate but showed poor performance individually. Overall, this study highlights the advantage of using more than one physiological measure for improving physical fatigue modelling. The findings can contribute to variables and sensor selection in occupational applications and as the foundation for further field research.