Search Results

Purpose: The primary aim of this study was to evaluate whether the internal (session rating of perceived exertion [sRPE] and Edwards heart-rate-based method) and external training load (jumps) affect the presession well-being perception on the day after (ie, +22 h), according to age and tactical position, in elite (ie, Serie A2) female volleyball training. Methods: Ten female elite volleyball players (age = 23 [4] y, height = 1.82 [0.04] m, body mass = 73.2 [4.9] kg) had their heart rate monitored during 13 team (115 individual) training sessions (duration: 101 [8] min). Mixed-effect models were applied to evaluate whether sRPE, Edwards method, and jumps were correlated (P ≤ .05) to Hooper index factors (ie, perceived sleep quality/disorders, stress level, fatigue, and delayed-onset muscle soreness) in relation to age and tactical position (ie, hitters, central blockers, opposites, and setters). Results: The results showed a direct relationship between sRPE (P < .001) and presession well-being perception 22 hours apart, whereas the relationship was the inverse for Edwards method internal training load. Age, as well as the performed jumps, did not affect the well-being perception of the day after. Finally, central blockers experienced a higher delayed-onset muscle soreness than hitters (P = .003). Conclusions: Findings indicated that female volleyball players’ internal training load influences the pretraining well-being status on the day after (+ 22 h). Therefore, coaches can benefit from this information to accurately implement periodization in a short-term perspective and to properly adopt recovery strategies in relation to the players’ well-being status.

Purpose : This study aimed to describe the career performance progression of elite early- and later-success international swimmers competing in sprint events (ie, 50 and 100 m). Methods: The career performance trajectories of 6003 swimmers (50.9% females; 58,760 unique records) competing in the 4 swimming strokes were evaluated. Swimmers with early and later success were identified. The authors identified the top 50 all-time swimmers competing in junior career who did not reach the top 50 rankings in their senior career, and vice versa, and successful swimmers in both junior and senior career. Results: Early-success swimmers mainly achieved their peak performance before the age of 20 years and approximately 5–6 years before successful senior swimmers or approximately 3–4 years before successful swimmers both in junior and senior careers. The annual performance improvements of later-success swimmers were higher (about 1%–2%) until the age of 20 to 24 years, whereas early-success swimmers showed a performance stagnation at about 16 to 18 years in females and 19 to 20 years in males. Conclusions: Early-success swimmers who achieved peak performance at a young age were unable to maintain the same level of competitiveness in adulthood as they experienced a plateau in performance from the age of 20 years. The procedure of considering early performances solely for talent identification (and not the current rate of progression) might represent a limited approach for selecting future elite swimmers. Our results indicate that performance progression in the transition toward adult careers might be a strong indicator of performance potential.

Purpose: To monitor elite youth female basketball training to verify whether players’ and coaches’ (3 technical coaches and 1 physical trainer) session rating of perceived exertion (s-RPE) has a relationship with Edwards’ method. Methods: Heart rate of 15 elite youth female basketball players (age 16.7 [0.5] y, height 178 [9] cm, body mass 72 [9] kg, body mass index 22.9 [2.2] kg·m⁻²) was monitored during 19 team (268 individual) training sessions (102 [15] min). Mixed effect models were applied to evaluate whether s-RPE values were significantly (P ≤ .05) related to Edwards’ data, total session duration, maximal intensity (session duration at 90–100% HR_max), type of training (ie, strength, conditioning, and technique), and whether differences emerged between players’ and coaches’ s-RPE values. Results: The results showed that there is a relationship between s-RPE and Edwards’ methods for the players’ RPE scores (P = .019) but not for those of the trainers. In addition, as expected, both players’ (P = .014) and coaches’ (P = .002) s-RPE scores were influenced by total session duration but not by maximal intensity and type of training. In addition, players’ and coaches’ s-RPE values differed (P < .001)—post hoc differences emerged for conditioning (P = .01) and technique (P < .001) sessions. Conclusions: Elite youth female basketball players are better able to quantify the internal training load of their sessions than their coaches, strengthening the validity of s-RPE as a tool to monitor training in team sports.

Purpose: The present study aimed to verify if practicing tackles during rugby union training sessions would affect the players’ internal training load and acute strength loss. Method: A total of 9 male Italian Serie A rugby union players (age: 21 [2] y) were monitored by means of an integrated approach across 17 sessions, 6 with tackles (WT) and 11 with no tackles (NT). Edwards training load was quantified using heart-rate monitoring. Global positioning system devices were used to quantify the total distance and time at >20 W. Work-to-rest ratio was quantified by means of a video analysis. Before (PRE) and after (POST) the session, the players’ well-being and rating of perceived exertion were measured, respectively. The countermovement jump and plyometric push-up jump tests were performed on a force plate to record the players’ PRE–POST concentric peak force. Linear mixed models were applied to quantify the differences between WT and NT in terms of training load and PRE–POST force deltas, even controlling for other training factors. Results: The Edwards training load (estimated mean [EM]; standard error [SE]; WT: EM = 214, SE = 11.8; NT: EM = 194, SE = 11.1; P = .01) and session rating of perceived exertion (WT: EM = 379, SE = 21.9; NT: EM = 277, SE = 16.4; P < .001) were higher in WT than in NT. Conversely, no difference between the sessions emerged in the countermovement jump and plyometric push-up concentric peak force deltas. Conclusions: Although elite rugby union players’ external and internal training load can be influenced by practicing tackles, upper- and lower-limb strength seem to not be affected.

Purpose: To evaluate if the internal training load (ITL; Edwards heart rate [HR]-based and session-rating of perceived exertion [RPE] methods) is affected by the presession well-being perception, age, and position in elite (ie, Serie A2) female volleyball training. Methods: Twelve female elite volleyball players (age: 22 [4] y, height: 1.80 [0.06] m, body mass: 74.1 [4.3] kg) were monitored using an HR monitor during 32 team training sessions (duration: 1:36:12 [0:22:24], in h:min:s). Linear mixed-effects models were applied to evaluate if well-being perception (ie, perceived sleep quality/disorders, stress level, fatigue, and delayed-onset muscle soreness) may affect ITL depending on age and tactical position. Results: Presession perceived fatigue influenced ITL according to the session-RPE (P = .032) but not according to the Edwards method. Age was inversely correlated to the Edwards method (P < .001) and directly correlated to the session-RPE (P = .027). Finally, central blockers experienced a higher training load than hitters (P < .001) and liberos (P < .001) for the Edwards method, as well as higher than hitters (P < .001), liberos (P = .003), and setters (P = .008) for  session-RPE. Conclusions: Findings indicated that female volleyball players’ perceived ITL is influenced by presession well-being status, age, and position. Therefore, coaches can benefit from this information to specifically predict players’ ITL in relation to their individual characteristics.

The evaluation of change-of-direction (COD) performance is strongly focused on the time spent to perform the test trials, while much less is known about the technical execution adopted during the COD movements. Thus, the purposes of this study were to evaluate (1) the relationship between straight- and COD-sprint tests and (2) the technical execution of COD movements in relation to different age categories of young rugby players. Young rugby players (N = 157, age range 8–19 y) completed a test battery composed of a 15-m straight-sprint test (15SS) and a 15-m sprint test performed with 2 changes of direction (15COD). Significant differences were detected between age categories for both tests. Significant correlations were found between 15SS and 15COD. The analysis of the technical execution of the 15COD test showed differences between age categories, with a prevalence of rounded turns up to the U15 category. These findings confirmed the relationship between straight and COD abilities in young male rugby players. Moreover, the new approach introduced by this study, based on the analysis of COD technical execution, revealed that this performance could be conditioned by the age and mastery level of the players.