Effects of preseason training on body composition, running performance, biochemical markers and workload variation in professional rugby union players

Few studies have examined the impact of a preseason training intervention through systematic measures in Pro D2 rugby union (RU). Therefore, this study aimed to describe the effects of 12 weeks of preseason training (three blocks) on body composition, running performance, biochemical markers, and workload (WL) variation in professional RU players. Physiological (physical and biochemical) responses to preseason WL were analyzed by examining changes in anthropometric characteristics, Yo-Yo intermittent recovery level 1 (Yo-Yo IR1) test, blood samples (BS), Hooper index (1-7), the 10-Hz global positioning system (GPS), and session rating of perceived exertion (s-RPE) in nineteen elite male players. Changes throughout the preseason were analyzed using the one-way and mixed-model analysis of variance. Significant (p < 0.01) improvements occurred in anthropometry and Yo-Yo IR1 running performance in forwards and backs. Total distance (p < 0.01) and impact (p < 0.05) during the second block were meaningfully higher than the other two blocks, with backs showing higher values than forwards. As expected, WL decreased significantly (p < 0.01) during the last training block. The WL variations were correlated with changes in biochemical markers over the preseason period. The collected data can be used for i) profiling French Pro D2 rugby championships players, ii) establishing effective training strategies, and iii) setting preseason WL expectations.

A Systematic Review of the Relationship between Workload and Injury Risk of Professional Male Soccer Players

The number of studies on the relationship between training and competition load and injury has increased exponentially in recent years, and it is also widely studied by researchers in the field of professional soccer. In order to provide practical guidance for workload management and injury prevention in professional athletes, this study provides a review of the literature on the effect of load on injury risk, injury prediction, and interpretation mechanisms. The results of the research show that: (1) It appears that short-term fixture congestion may increase the match injury incidence, while long-term fixture congestion may have no effect on both the overall injury incidence and the match injury incidence. (2) It is impossible to determine conclusively whether any global positioning system (GPS)-derived metrics (total distance, high-speed running distance, and acceleration) are associated with an increased risk of injury. (3) The acute:chronic workload ratio (ACWR) of the session rating of perceived exertion (s-RPE) may be significantly associated with the risk of non-contact injuries, but an ACWR threshold with a minimum risk of injury could not be obtained. (4) Based on the workload and fatigue recovery factors, artificial intelligence technology may possess good predictive power regarding injury risk.

Will Next Match Location Influence External and Internal Training Load of a Top-Class Elite Professional European Soccer Team?

Background: The purpose of this study is to compare training load (TL) preceding a home versus away match in a top-class elite European team during the 2015–2016 season. Methods: Twenty elite outfield soccer players with a mean ± SD age, height and body mass of 25.9 ± 4.6 years, 183.1 ± 6.6 cm and 78.6 ± 6.6 kg, respectively, participated in this study. Total distance covered, high-speed running distance (HSRD), average speed (AvS), rating of perceived exertion (RPE) multiplied by training duration (s-RPE) and Hooper index (HI) were collected. Data from 24 weeks were analyzed through match-day minus/plus approach (MD-5, -4, -3, -2, -1, MD + 1). Results: All external TL variables indicated a decrease from MD-5 until MD-1 and then an increase to MD + 1 (p < 0.01). HI decreased from MD-5 to MD-1, but s-RPE increased until MD-3 and then decreased until MD + 1. When comparing TL data that preceded home matches versus away matches, for MD-5, HSRD and muscle soreness exhibited higher values when away match neared (p < 0.05). For MD-4 and MD-3, total distance, HSRD and AvS exhibited higher values closer to an away match than a home match (p < 0.05). For MD-1, total distances covered were higher closer to a home match than an away match (p < 0.01). For MD + 1, all HI items and AvS were higher when an away match was played (p < 0.05). Conclusions: This study confirms and provides evidence regarding the influence on internal and external TL data preceding home and away matches from a team that played in European competitions.

Training Periodization Over an Elite Rugby Sevens Season: From Theory to Practice

PURPOSE

To describe the training periodization in rugby sevens players competing in the World Rugby Sevens Series during a non-Olympic season.

METHODS

Workload data were collected over a 33-wk period in 12 male players participating in a full competitive season. Workload was quantified using session rating of perceived exertion and global positioning system-derived data during training and competition. Self-reported well-being was assessed using a questionnaire. Each variable was analyzed weekly and through 5 mesocycles (preseason, in-season 1-4), each of which ended with competition blocks.

RESULTS

The perceived load decreased throughout the season for the full squad (-68% [26%] between preseason and final competitive block, large effect) and when unavailable players were removed from the analysis (-38% [42%], moderate). Weekly perceived load was highly variable, with a typical periodization in 4 phases during each mesocycle (regeneration, training overload, taper, and competition). During the preseason, the workload was higher during the overload training phase than during the competitive period (range: +23% to +59%, large to very large, for the distance covered above individual maximal aerobic speed and the number of accelerations). This observation no longer persisted during the season. The well-being score decreased almost certainly from in-season 3 (moderate).

CONCLUSIONS

These results highlighted the apparent difficulty in maintaining high-load training periods throughout the season in players engaged on the World Rugby Sevens Series despite ∼4-7 training weeks separating each competitive block. This observation was likely explained by the difficulties inherent to the World Rugby Sevens Series (risk of contact injury, calendar, and multiple long-haul travel episodes) and potentially by limited squad-rotation policies.

Training load quantification in elite swimmers using a modified version of the training impulse method

Prior reports have described the limitations of quantifying internal training loads using hear rate (HR)-based objective methods such as the training impulse (TRIMP) method, especially when high-intensity interval exercises are performed. A weakness of the TRIMP method is that it does not discriminate between exercise and rest periods, expressing both states into a single mean intensity value that could lead to an underestimate of training loads. This study was designed to compare Banister's original TRIMP method (1991) and a modified calculation procedure (TRIMPc) based on the cumulative sum of partial TRIMP, and to determine how each model relates to the session rating of perceived exertion (s-RPE), a HR-independent training load indicator. Over four weeks, 17 elite swimmers completed 328 pool training sessions. Mean HR for the full duration of a session and partial values for each 50 m of swimming distance and rest period were recorded to calculate the classic TRIMP and the proposed variant (TRIMPc). The s-RPE questionnaire was self-administered 30 minutes after each training session. Both TRIMPc and TRIMP measures strongly correlated with s-RPE scores (r = 0.724 and 0.702, respectively; P < 0.001). However, TRIMPc was ∼ 9% higher on average than TRIMP (117 ± 53 vs. 107 ± 47; P < 0.001), with proportionally greater inter-method difference with increasing workload intensity. Therefore, TRIMPc appears to be a more accurate and appropriate procedure for quantifying training load, particularly when monitoring interval training sessions, since it allows weighting both exercise and recovery intervals separately for the corresponding HR-derived intensity.