The purpose of this study was to test the relationships between training workload (WL) parameters with variations in anaerobic power and change of direction (COD) in under-16 soccer players. Twenty-three elite players under 16 years were daily monitored for their WL across 20 weeks during the competition soccer season. Additionally, players were assessed three times for anthropometric, body composition, COD, and anaerobic power. A correlational analysis between the mean differences between assessments and accumulated WL parameters were conducted. Moreover, a regression analysis was executed to explain the variations in the percentage of change in fitness levels considering the accumulated WL parameters and peak height velocity. The accumulated daily loads during one week showed a large and a moderate correlation with peak power and COD at different periods of the season. Regression analysis showed no significant predictions for COD (F(12, 10) = 1.2, p = 0.41) prediction, acute load (F(12, 10) = 0.63, p = 0.78), or chronic load (F(12, 10) = 0.59, p = 0.81). In conclusion, it may be assumed that the values of the chronic workload and the accumulated training monotony can be used to better explain the physical capacities of young soccer players, suggesting the importance of psychophysiological instruments to identify the effects of the training process in this population.

The purposes of this study were (i) to analyze the variations in maximal oxygen consumption (VO2max), maximal heart rate (HRmax), heart rate at rest, acceleration, maximal speed, agility, anaerobic sprint test (RAST) of peak power (RPP), RAST of minimum power, RAST of average power (RAP), and RAST of fatigue index (RFI) during the competitive season, using maturation status and accumulated training load as covariates, and (ii) to describe the differences between responders and non-responders in relation to baseline levels. Twenty-three elite players from the same team competing in the national under-16 competitions were evaluated for 20 weeks in period 1 (before league), middle (mid league), and period 2 (after league). The VO2max (p = 0.009), maximal speed (p = 0.001), RPP (p < 0.001), RAP (p < 0.001), and RFI (p < 0.001) significantly changed across the assessment periods. Interestingly, using accumulated training load and maturation status as covariates revealed no statistical significance (p > 0.05). When analyzing responders and non-responders, only HRmax (between periods 1 and 2) showed no differences between the groups. As a conclusion, it can be seen that accumulated training load and maturation status play an important role in the differences observed across the season. Thus, coaches should consider the importance of these two factors to carefully interpret fitness changes in their players and possibly adjust training decisions according to the maturation level of the players.

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Purpose: The purposes of this study were to (1) analyze the variations of acute and chronic training load and well-being measures during 3 periods of the season (early, mid, and end) and (2) test the associations between weekly training load and well-being measures during different periods of the season. Methods: Thirteen professional volleyball players from a team competing in the Portuguese Volleyball First Division (age 31.0 [5.0] y) were monitored during an entire season. Weekly acute (wAL) and chronic load (wCL), acute to chronic workload ratio (wACWL), and training monotony (wTM) were calculated during all weeks of the season. The weekly values of muscle soreness (wDOMS), stress (wStress), fatigue (wFatigue), sleep (wSleep), and Hooper index (wHI) were also obtained across the season. Results: The midseason had meaningfully low values of wAL (−26.9%; effect size [ES]: −1.12) and wCL (−28.0%; ES: −2.81), and greater values of wACWL (+38.9%; ES: 2.81) compared with early season. The wCL (+10.6%; ES: 0.99), wStress (44.6%; ES: 0.87), and wHI (29.0%; ES: 0.62) were meaningfully greater during the end of season than in midseason. Overall, wAL presented very large correlations with wDOMS (r = .80), wSleep (r = .72), and wFatigue (r = .82). Conclusions: The results of this study suggest that the load was meaningfully higher during early season; however, stress was higher during the final stages of the season. Overall, it was also found that the acute load is more highly correlated with well-being status and its variations than chronic load or training monotony.

Volleyball is considered a very explosive and fast-paced sport in which plyometric training is widely used. Our purpose was to review the effects of plyometric training on volleyball players’ performance. A systematic search was conducted according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines using PubMed, SciELO, SPORTDiscus, Medline, Scopus, Academic Search Complete, CINAHL and Web Science for articles published no later than December 2018. Any criteria were imposed for the included sample. The search focus was on interventional studies in which athletes underwent a plyometric program. To the 1831 articles found, another five were added, identified through other sources. Duplicated files were removed, titles and abstracts were screened, which left 21 remaining studies for extensive analysis. Results showed that the vertical jump (15 studies) was the major ability studied in plyometric training interventions, followed by strength (four studies), horizontal jump (four studies), flexibility (four studies) and agility/speed (three studies). In addition, it was observed that young (under 18 years old) female athletes were the most studied. The included studies indicated that plyometric training seems to increase vertical jump performance, strength, horizontal jump performance, flexibility and agility/speed in volleyball players. However, more studies are needed to better understand the benefits of plyometric training in volleyball players’ performance.

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The purpose of this study was twofold: (i) characterize the external and internal training load of professional volleyball players with a focus on intra-week changes and (ii) test the relationships between internal and external load measures. Eight male professional players (age: 23.0±5.22 yo; body mass: 84.5 ± 7.58 kg; height: 193.0±9.71 cm; BMI: 22.0±0.02 kg/m2) were monitored daily over 15 weeks. The monitoring process included both internal (rate of perceived exertion [RPE] and session-RPE [s-RPE]) and external load variables, which were measured by an inertial measurement unit. Results revealed that, within-week variations revealed that RPE was significantly higher during MD-2 (d=0.59) and MD-3 (d=0.56) than MD-1. A significantly higher number of jumps was observed on MD-2 than MD-1 (d=0.69). Considering the relationships between internal and external load measures, small positive correlations were found between RPE and the number of jumps (r=0.17) and between s-RPE and the number of jumps (r=0.49). In conclusion, a tapering strategy was observed on the day before a match, as internal and external loads decreased. Both internal and external load measures are necessary to provide an accurate perception of the impact of training stimuli on players.