Search Results

Purpose : Assessing the relationship between external load (EL) and internal load (IL) in youth male beach handball players. Methods : A total of 11 field players from the Lithuanian U17 beach handball team were monitored across 14 training sessions and 7 matches. The following EL variables were assessed by means of inertial movement units: PlayerLoad™, accelerations, decelerations, changes of direction, and jumps and total of inertial movements. IL was assessed objectively and subjectively using the summated heart rate zones and training load calculated via session rating of perceived exertion, respectively. Spearman correlations (ρ) were used to assess the relationship between EL and IL. The interindividual variability was investigated using linear mixed models with random intercepts with IL as dependent variable, PlayerLoad^™ as the independent variable, and players as random effect. Results : The lowest significant (P < .05) relationship was for high jumps with objective (ρ = .56) and subjective (ρ = .49) IL. The strongest relationship was for PlayerLoad^™ with objective (ρ = .9) and subjective (ρ = .84) IL. From the linear mixed model, the estimated SD of the random intercepts was 19.78 arbitrary units (95% confidence interval, 11.75–33.31); SE = 5.26, and R ² = .47 for the objective IL and 6.03 arbitrary units (95% confidence interval, 0.00–7330.6); SE = 21.87; and R ² = .71 for the subjective IL. Conclusions : Objective and subjective IL measures can be used as a monitoring tool when EL monitoring is not possible. Coaches can predict IL based on a given EL by using the equations proposed in this study.

Purpose: Biological differences between men and women are well known; however, literature addressing knowledge about the influence of sex on specific and general performance in team handball is almost nonexistent. Consequently, the aim of the study was to assess and compare specific and general physical performance in male and female elite team-handball players, to determine if the differences are consequential for general compared with specific physical performance characteristics and the relationship between general and specific physical performance. Methods: Twelve male and 10 female elite team-handball players performed a game-based performance test, upper- and lower-body strength and power tests, a sprinting test, and an incremental treadmill running test. Results: Significant differences (P < .05) between male and female players were found for peak oxygen uptake and total running time during the treadmill test, 30-m sprinting time, leg-extension strength, trunk- and shoulder-rotation torque, and countermovement-jump height, as well as offense and defense time, ball velocity, and jump height in the game-based performance test. An interaction (sex × test) was found for time and oxygen uptake, and except shoulder-rotation torque and ball velocity in women, the authors found only a low relationship between specific and general physical performance. Conclusion: The results of the study revealed that male players are heavier, taller, faster, and stronger; jump higher; and have better aerobic performance. However, female players performed relatively better in the team-handball-specific tests than in the general tests. The findings also suggest that female players should focus more on strength training.

Purpose: To determine the effect of in-season differential training on volleyball spike-jump technique and performance in elite-level female players. Methods: During the season, spike jumps of 12 elite female players (Austrian Volleyball League Women) were recorded by 13 Qualisys Oqus cameras (250 Hz) and an AMTI force plate (1000 Hz). First measurement was made at the beginning of the investigation. Two identical measurements were repeated after a first and a second interval. The first interval served as control phase. The second interval was comparable in length and regular program but included differential training (6 wk, 8 sessions of 15–20 min) as a modified warm-up. It addressed specific performance determinants. Analyses of variances were calculated for the 3 measurements and for the development during control and intervention phase. Results: Initial jump height (0.44 [0.09] m) changed by −4.5% during the control phase and +11.9% during the intervention (P < .001, ${\eta }_{\mathrm{p}}^2=.70$ ). All approach variables, arm backswing, and velocity-conversion strategy improved compared with the control phase (Δ%: 6.1–51.2%, P < .05, ${\eta }_{\mathrm{p}}^2=.40-.80$ ). Joint angles, countermovement depth, maximal angular velocities, and torso incline were not affected (Δ%: −2.9–9.1%, P = .066–.969, ${\eta }_{\mathrm{p}}^2=.00-.27$ ). Conclusions: In-season differential training led to technical adaptations and increased spike-jump height in elite female players. The differential training program allowed players to experience a range of adaptability and to adjust toward an individual optimum in technical components of performance determinants. Coaches are encouraged to apply technical differential training to elite athletes and to target biomechanical performance factors specifically.

Purpose: Although the session rating of perceived exertion (sRPE) is primarily a marker of internal training load (TL), it may be sensitive to external TL determining factors, such as duration and volume. Thus, sRPE could provide further information on accumulated fatigue not available from markers of internal TL. Therefore, the purpose of this study was to investigate sRPE during heavy training bouts at relatively constant intensity. Methods: Eleven university swimmers performed a high-volume training session consisting of 4 × 10 × 100-yd (4 × 10 × 91.4 m). Repetition lap time and heart rate were measured for each repetition and averaged for each set. Blood lactate concentration was measured after each set. At the end of each set, a 10-minute rest period was allowed, during which sRPE values were obtained, as if the training bout had ended. Results: There were no differences between sets for lap time (P = .096), heart rate (P = .717), and blood lactate concentration (P = .466), suggesting that the subjects were working at the same external and internal intensity. There was an increase (P = .0002) in sRPE between sets (first 4 [1.2], second 5 [1.3], third 7 [1.3], and fourth 8 [1.5]), suggesting that even when maintaining the same intensity, the perception of the entire workload increased with duration. Conclusions: Increases in duration, although performed with a consistent internal and external intensity, influences sRPE. These findings support the concept that sRPE may provide additional information on accumulated fatigue not available from other markers of TL.

Training intensity distribution is important to training program design. The zones 1 to 2 boundary can be defined by the Talk Test and the rating of perceived exertion. The zones 2 to 3 boundary can be defined by respiratory gas exchange, maximal lactate steady state, or, more simply, by critical speed (CS). The upper boundary of zone 3 is potential defined by the velocity at maximum oxygen uptake (vVO₂max), although no clear strategy has emerged to categorize this intensity. This is not normally definable outside the laboratory. Purpose: This study predicts vVO₂max from CS, determined from 1 (1.61 km) and 2 (3.22 km) citizen races in well-trained runners. Methods: A heterogeneous group of well-trained runners (N = 22) performed 1- and 2-mile races and were studied during submaximal and maximal treadmill running to measure oxygen uptake, allowing computation of vVO₂max. This vVO₂max was compared with CS. Results: vVO₂max (4.82 [0.53] m·s⁻¹) was strongly correlated with CS (4.37 [0.49] m·s⁻¹; r = .84, standard error of estimate [SEE] = 0.132 m·s⁻¹), 1-mile speed (5.09 [0.51] m·s⁻¹; r = .84, SEE = 0.130 m·s⁻¹), and 2-mile speed (4.68 [0.49] m·s⁻¹; r = .86, SEE = 0.120 m·s⁻¹). Conclusions: CS, calculated from 2 citizen races (or even training time trials), can be used to make reasonable estimates of vVO₂max, which can be used in the design of running training programs.

Introduction: The relationship between the percentage of a fatiguing ambulatory task completed and rating of perceived exertion (RPE) appears to be linear and scalar, with a relatively narrow “window.” Recent evidence has suggested that a similar relationship may exist for muscularly demanding tasks. Methods: To determine whether muscularly demanding tasks fit within this “ambulatory window,” we tested resistance-trained athletes performing bench press and leg press with different loadings predicted to allow 5, 10, 20, and 30 repetitions and measured RPE (category ratio scale) at the end of the concentric action for each repetition. Results: There was a regular, and strongly linear, pattern of growth of RPE for both bench press (r = .89) and leg press (r = .90) during the tasks that allowed 5.2 (1.2), 11.6 (1.9), 22.7 (2.0), and 30.8 (3.2) repetitions for bench press and 5.5 (1.5), 11.4 (1.6), 20.2 (3.0), and 32.4 (4.2) repetitions for leg press, respectively. Conclusions: The path of the RPE growth versus percentage task fit within the window evident for ambulatory tasks. The results suggest that the RPE versus percentage task completed relationship is scalar, relatively linear, and apparently independent of exercise mode.

Purpose: The session rating of perceived exertion (sRPE) is a well-accepted method of monitoring training load in athletes in many different sports. It is based on the category-ratio (0–10) RPE scale (BORG-CR10) developed by Borg. There is no evidence how substitution of the Borg 6–20 RPE scale (BORG-RPE) might influence the sRPE in athletes. Methods: Systematically training, recreational-level athletes from a number of sport disciplines performed 6 randomly ordered, 30-min interval-training sessions, at intensities based on peak power output (PPO) and designed to be easy (50% PPO), moderate (75% PPO), or hard (85% PPO). Ratings of sRPE were obtained 30 min postexercise using either the BORG-CR10 or BORG-RPE and compared for matched exercise conditions. Results: The average percentage of heart-rate reserve was well correlated with sRPE from both BORG-CR10 (r = .76) and BORG-RPE (r = .69). The sRPE ratings from BORG-CR10 and BORG-RPE were very strongly correlated (r = .90) at matched times. Conclusions: Although producing different absolute numbers, sRPE derived from either the BORG-CR10 or BORG-RPE provides essentially interchangeable estimates of perceived exercise training intensity.