Keller, S, Koob, A, Corak, D, von Schöning, V, and Born, DP. How to improve change-of-direction speed in junior team sport athletes—Horizontal, vertical, maximal, or explosive strength training? J Strength Cond Res 34(2): 473–482, 2020—The purpose of the study was to compare the effects of 4 different training methods on change-of-direction (COD) speed in junior team sport athletes. Specifically, we investigated whether horizontal load training incorporating lateral acceleration and deceleration would induce superior performance adaptations with respect to COD speed, compared with common vertically oriented maximal strength (squats and deadlifts), explosive strength (power clean and high pull), and vertical jumping exercises. Male U15 team sport athletes (n = 45) were assigned to 1 of 4 groups and performed 2 intervention training sessions per week for 4 weeks, in addition to their usual sport-specific training. Before and after the training period, COD speed, countermovement and drop jump heights, 1-legged lateral jump, and standing long jump performance were assessed. All 4 training groups improved COD speed (p ≤ 0.01, effect size [ES] ≥1.35). Countermovement and 1-legged lateral jump performance improved with the horizontal load (p < 0.01, ES = 0.81 and p < 0.01, ES = 1.36), maximal (p = 0.01, ES = 0.56 and p < 0.01, ES = 1.14), and explosive strength training (p < 0.01, ES = 0.95 and p < 0.01, ES = 1.60, respectively). The standing long jump improved with the maximal (p < 0.01, ES = 1.14) and explosive strength training (p < 0.01, ES = 0.60). In conclusion, all 4 training methods improved the COD speed in junior U15 team sport athletes. These findings emphasize the importance of well-developed lower-body strength and power, which contribute to fast COD speed. From a practical perspective, conditioning programs for junior athletes can incorporate horizontally and vertically oriented exercises with similar effectiveness on COD speed.

This study investigated biomechanical characteristics of Speed Rope Skipping (RS) and estimated the contribution of the lower and upper limbs to overall performance. Lower (jumping), upper (turning), and whole-body (skipping) performance were examined in 23 rope skippers. All tests were recorded by 2 D video and nine skipping tests were performed in a 3 D motion capture system. Similar movement patterns were observed for the lower limbs in all participants, while handle trajectories differed in shape and symmetry according to performance. In general, turning unlike jumping performance was close to and significantly correlated with skipping performance. Therefore, it appears that lower extremity movement may be adapted to the limiting capacity of the upper extremity to maintain movement stability.

Purpose: Understanding physiological determinants of lactate threshold 2 (LT2) is crucial for tracking adaptations and deriving individualized training recommendations in cycling. Therefore, the study investigated: 1. the accuracy of modeling power output at LT2 in young athletes of both sexes using maximal oxygen uptake ( $${\dot{V}}\textrm{O}_{2_\textrm{peak}}$$ V ˙ O 2 peak ), fractional utilization of $${\dot{V}}\textrm{O}_{2_\textrm{peak}}$$ V ˙ O 2 peak (% $${\dot{V}}\textrm{O}_{2_\textrm{peak}}$$ V ˙ O 2 peak ), and oxygen cost of cycling (Cc); 2. the influence of Cc determination on the model accuracy; 3. the influence of the model predictors and inclusion of maximal lactate accumulation rate ( $${\dot{c}}La_\textrm{max}$$ c ˙ L a max ) on power at LT2 depending on sex. Methods: Eighty-three cyclists and triathletes (22 females, 61 males; age [median and IQR]: 14.6 [13.8–17.6] years, $${\dot{V}}\textrm{O}_{2_\textrm{peak}}$$ V ˙ O 2 peak [mean ± SD]: 59.2 ± 6.5 mL⋅kg^–1⋅min^–1) performed an incremental test to determine power at LT2, $${\dot{V}}\textrm{O}_{2_\textrm{peak}},$$ V ˙ O 2 peak , % $${\dot{V}}\textrm{O}_{2_\textrm{peak}}$$ V ˙ O 2 peak at LT2, and Cc (assessed at 3 W⋅kg^–1, 75% $${\dot{V}}\textrm{O}_{2_\textrm{peak}},$$ V ˙ O 2 peak , and 90% LT2). Results: Modeled and experimentally determined power at LT2 demonstrated excellent agreement for all, male and female athletes (ICC $$\ge$$ ≥ 0.961), with Cc at 90% LT2 providing the highest accuracy (ICC $$\ge$$ ≥ 0.986). The three physiological determinants explained $$\ge$$ ≥ 98% of the variance in power at LT2, with the largest unique contribution from $${\dot{V}}\textrm{O}_{2_\textrm{peak}}$$ V ˙ O 2 peak (62 and 67% of total $$R^2$$ R 2 ), followed by Cc (8 and 34%) and % $${\dot{V}}\textrm{O}_{2_\textrm{peak}}$$ V ˙ O 2 peak at LT2 (5 and 12%) in males and females, respectively, while $${\dot{c}}La_\textrm{max}$$ c ˙ L a max did not improve the regression. Conclusion: $${\dot{V}}\textrm{O}_{2_\textrm{peak}}, $$ V ˙ O 2 peak , % $${\dot{V}}\textrm{O}_{2_\textrm{peak}}$$ V ˙ O 2 peak at LT2 and Cc accurately predict power at LT2 in young cycling athletes independent of sex, with determination of Cc at 90% LT2 providing the highest accuracy. While $${\dot{V}}\textrm{O}_{2_\textrm{peak}}$$ V ˙ O 2 peak contributes most to LT2 in both sexes, Cc appears more important in young females.

Purpose: This study aimed to investigate: 1. The influence of sex and age on the accuracy of the classical model of endurance performance, including maximal oxygen uptake ( $$\dot{V}\mathrm{O}_{2}\mathrm{peak}$$V ˙ O 2 peak ), its fraction (LT2_%), and cost of running (C_R), for calculating running speed at lactate threshold 2 (vLT2) in young athletes. 2. The impact of different C_R determination methods on the accuracy of the model. 3. The contributions of $$\dot{V}\mathrm{O}_{2}\mathrm{peak}$$V ˙ O 2 peak , LT2_%, and C_R to vLT2 in different sexes. Methods: 45 male and 55 female young squad athletes from different sports (age: 15.4 ± 1.3 years; $$\dot{V}\mathrm{O}_{2}\mathrm{peak}$$V ˙ O 2 peak : 51.4 ± 6.8 $$\hbox {mL} \cdot \hbox {kg}^{-1} \cdot \hbox {min}^{-1}$$mL · kg - 1 · min - 1 ) performed an incremental treadmill test to determine $$\dot{V}\mathrm{O}_{2}\mathrm{peak}$$V ˙ O 2 peak , LT2_%, C_R, and vLT2. C_R was assessed at a fixed running speed (2.8 $$ \hbox {m} \cdot \hbox {s}^{-1} $$m · s - 1 ), at lactate threshold 1 (LT1), and at 80% of $$\dot{V}\mathrm{O}_{2}\mathrm{peak}$$V ˙ O 2 peak , respectively. Results: Experimentally determined and modeled vLT2 were highly consistent independent of sex and age (ICC $$\ge$$≥ 0.959). The accuracy of vLT2 modeling was improved by reducing random variation using individualized C_R at 80% $$\dot{V}\mathrm{O}_{2}\mathrm{peak}$$V ˙ O 2 peak (± 4%) compared to C_R at LT1 (± 7%) and at a fixed speed (± 8%). 97% of the total variance of vLT2 was explained by $$\dot{V}\mathrm{O}_{2}\mathrm{peak}$$V ˙ O 2 peak , LT2_%, and C_R. While $$\dot{V}\mathrm{O}_{2}\mathrm{peak}$$V ˙ O 2 peak and C_R showed the highest unique (96.5% and 31.9% of total $$R^2$$R 2 , respectively) and common (– 31.6%) contributions to the regression model, LT2_% made the smallest contribution (7.5%). Conclusion: Our findings indicate: 1. High accuracy of the classical model of endurance performance in calculating vLT2 in young athletes independent of age and sex. 2. The importance of work rate selection in determining C_R to accurately predict vLT2. 3. The largest contribution of $$\dot{V}\mathrm{O}_{2}\mathrm{peak}$$V ˙ O 2 peak and C_R to vLT2, the latter being more important in female athletes than in males, and the least contribution of LT2_%.

The study investigated associations of metabolic, anthropometric, and neuromuscular parameters with 50 to 400 m front crawl performance. Competition performances of 24 female swimmers (14.9 ± 1.3 years) were recorded and metabolic determinants (maximal oxygen uptake and lactate accumulation [ċLa_max], cost of swimming [C], and lactate threshold 1 [LT1] using 200 m all-out, 20 s sprint, 500 m submaximal, and 3 min incremental test, respectively), anthropometry and dryland strength (squat and bench press 1 repetition maximum [1RM_SQ/1RM_BP] and mean propulsive power [MPP_SQ/MPP_BP]) were assessed. 1RM_SQ (61.9 ± 13.3 kg) and MPP_BP (207 ± 45 W) correlated significantly with 50 (1.84 ± 0.07 m∙s⁻¹) and 100 m performance (1.68 ± 0.06 m∙s⁻¹) (r ≥ 0.45) and ċLamax (0.35 ± 0.12 mmol·L⁻¹·s⁻¹) and body mass (60.1 ± 7.0 kg) with 50 and 100 m, respectively (r ≥ 0.44). Only LT1 (1.23 ± 0.04 m∙s⁻¹) correlated significantly with 200 (1.52 ± 0.05 m∙s⁻¹) and 400 m performance (1.43 ± 0.06 m∙s⁻¹) (r ≥ 0.56). Multiple regression explained 33–35% and 61–86% of the variance in short- and middle-distance performance based on 1RM_SQ and arm span and LT1, C, and fat percentage, respectively. Based on the analyses, mechanical determinants are more predictive of short- and metabolic determinants of middle-distance performance.