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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.

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.

The session rating of perceived exertion (sRPE) method was developed 25 years ago as a modification of the Borg concept of rating of perceived exertion (RPE), designed to estimate the intensity of an entire training session. It appears to be well accepted as a marker of the internal training load. Early studies demonstrated that sRPE correlated well with objective measures of internal training load, such as the percentage of heart rate reserve and blood lactate concentration. It has been shown to be useful in a wide variety of exercise activities ranging from aerobic to resistance to games. It has also been shown to be useful in populations ranging from patients to elite athletes. The sRPE is a reasonable measure of the average RPE acquired across an exercise session. Originally designed to be acquired ∼30 minutes after a training bout to prevent the terminal elements of an exercise session from unduly influencing the rating, sRPE has been shown to be temporally robust across periods ranging from 1 minute to 14 days following an exercise session. Within the training impulse concept, sRPE, or other indices derived from sRPE, has been shown to be able to account for both positive and negative training outcomes and has contributed to our understanding of how training is periodized to optimize training outcomes and to understand maladaptations such as overtraining syndrome. The sRPE as a method of monitoring training has the advantage of extreme simplicity. While it is not ideal for the precise recording of the details of the external training load, it has large advantages relative to evaluating the internal training load.