Are non-starters accumulating enough load compared with starters? Examining load, wellness, and training/match ratios of a European professional soccer team.

BACKGROUND: The aims of the study were to: (i) compare accumulated load and wellness between starters and non-starters of a European professional soccer team; (ii) analyze the relationships between wellness and load measures and; (iii) compare training/match ratio (TMr) of external and internal load between starters and non-starters. METHODS: Ten players were considered starters while seven were classified as non-starters over a 16-week period in which six training sessions and match day (MD) were considered in each weekly micro-cycle. The following measures were used: wellness (fatigue, quality of sleep, muscle soreness, stress, and mood); load (rated of perceived exertion (RPE), session-RPE (s-RPE), high-speed running (HSR), sprinting, accelerations (ACC) and decelerations (DEC)). Accumulated wellness/load were calculated by summing all training and match sessions, while TMr was calculated by dividing accumulated training load by match data for all load measures and each player. Mann-Whitney U test was used for wellness variables, while independent T-test was used for the remaining variables to compare groups. Moreover, relationships among variables were explored using the Spearman's Rho correlation coefficient. RESULTS: The main results showed that non-starters presented higher significant values for fatigue (p < 0.019; g = 0.24) and lower significant values for duration (p < 0.006; ES = 1.81) and s-RPE (p < 0.001; ES = 2.69) when compared to starters. Moreover, positive and very large correlation was found between quality of sleep and RPE, while negative and very large correlation were found between stress and deceleration, and mood and deceleration (all, p < 0.05). Finally, non-starters presented higher values in all TMr than starters, namely, RPE (p = 0.001; g = 1.96), s-RPE (p = 0.002; g = 1.77), HSR (p = 0.001; g = 2.02), sprinting (p = 0.002; g = 4.23), accelerations (p = 0.001; g = 2.72), decelerations (p < 0.001; g = 3.44), and duration (p = 0.003; g = 2.27). CONCLUSIONS: In conclusion, this study showed that non-starters produced higher TMr in all examined variables despite the lower match and training durations when compared with starters, suggesting that physical load was adjusted appropriately. Additionally, higher RPE was associated with improved sleep while higher number of decelerations were associated with decreased wellness, namely, stress and mood for non-starters.

The relationship between wellness and training and match load in professional male soccer players.

The aims of this study were to: (i) analyse the within-microcycle variations in professional soccer players; (ii) analyse the relationships between wellness and training and match load demands; (iii) analyse the relationships between match-day (MD) demands and wellness during the following day (MD+1); and (iv) analyse the relationships between MD and wellness during the day before match-play (MD-1). Thirteen professional soccer players (age: 24.85±3.13 years) were monitored daily over 16-weeks for wellness and training and match-play intensity. The daily wellness measures included fatigue, quality of sleep, muscle soreness, mood and stress using a 1-5 scale. Internal intensity was subjectively measured daily using the rating of perceived exertion (RPE) and the multiplication of RPE by session duration (s-RPE). While external intensity was quantified utilising high-speed running, sprinting, and acceleration and deceleration metrics. Data was analysed from each training session before (i.e., MD-5) or after the match (i.e., MD+1). Repeated measures ANOVA or Friedman ANOVA was used to analyse the aims (i) where Spearman correlation was applied to analyse the relationships between the aims (ii) and (iii) between sleep quality and training intensity. The main results for aim (i) showed that MD+1 presented the lowest values for wellness variables (p < 0.05). While MD-1 presented the lowest internal and external load values (for all variables), with MD presenting the highest values (p < 0.05). Regarding aim (ii), the main result showed significant large negative correlations between fatigue and s-RPE (r = -0.593; p = 0.033). Considering aim (iii), significant small to very large negative correlations were found for sleep quality, fatigue and muscle soreness with all internal and external variables (p < 0.05). Lastly, the main results for aim (iv) showed large negative correlations for fatigue and session duration; fatigue and s-RPE; muscle soreness and session duration; muscle soreness and s-RPE; and muscle soreness and decelerations (p < 0.05, for all). The main conclusions were that MD had an influence on wellness and internal and external training intensity, notably MD-1 and MD+1 were most affected. In this regard, a tendency of higher internal and external intensity on MD was associated with lower wellness measures of sleep quality, muscle soreness and fatigue on MD+1.

Changes in Muscle Thickness after 8 Weeks of Strength Training, Electromyostimulation, and Both Combined in Healthy Young Adults.

The aim of this study was to verify and compare the effects of electromyostimulation training (EMS), strength training (ST), and both combined (STEMS), through the analysis of the elbow flexors muscle thickness. Forty subjects (24.45 ± 3.53 years), were randomly divided equally in 4 groups: 3 experimental groups and 1 control group. Each experimental group was submitted to one of three interventions, either an ST protocol, an EMS protocol, or a STEMS protocol. The control group (CG) did not perform any type of physical activity. Ultrasonography (US) was used to measure muscle thickness (MT) at 50 and 60% of the distance between the acromion and the olecranon. The results showed a significant difference in the elbow flexors muscle thickness after 8 weeks, both in the STG, EMSG, and STEMSG, but not in the CG. However, no significant differences were observed between the intervention protocols. It seems that an increase in MT can be obtained using either with ST, EMS, or both combined, however, the results doesn't support the overlap of one method in relation to the others. EMS can be another interesting tool to induce muscle hypertrophy, but not necessarily better.

A Novel Approach to Training Monotony and Acute-Chronic Workload Index: A Comparative Study in Soccer.

Load is a multifactorial construct, but usually reduced to parameters of volume and intensity. In the last decades, other constructs have been proposed for assessing load, but also relying on relationships between volume and intensity. For example, Foster's Training Monotony has been used in athletes' load management simply by computing mean weekly load divided by its standard deviation, often multiplied by session rate of perceived exertion. Meanwhile, the Acute to Chronic Workload Ratio (ACWR) has been debated by the sport scientists as a useful monitoring metric and related to so-called injury prevention. None of these models includes parameters that are representative of training specificity, namely load orientation. The aim of this study is to present broader conceptual approaches translated by new indices for assessing Intraweek Training Monotony (ITM) and Acute to Chronic Workload Index (ACWI) while incorporating load orientation, session duration and weekly density (frequency normalized) in addition to parameters related to proxies of external and/or internal load. Our ITM and Foster's Training Monotony were similar in terms of average values, but very different for individualized analysis, illustrating how average values may be deceiving. While Foster's model provided clusters of values, ITM provided more scattered, individualized data. ACWI and ACWR provided very distinct qualitative information, and the two models were uncorrelated. Therefore, the models incorporating training load orientation presented in this study provide distinct and not redundant information when compared to previous models. More importantly, ITM and ACWI are metrics that are compatible to each other and might fit to coaches' monitoring targets in the short and medium terms, respectively. Because our models include several parameters, including load orientation, we contend that might provide a more complete monitoring tool. However, we suggest they are used for intraindividual comparisons and not so strongly for interindividual comparisons.

Are Heart Rate and Rating of Perceived Exertion Effective to Control Indoor Cycling Intensity?

Indoor cycling's popularity is related to the combination of music and exercise leading to higher levels of exercise intensity. It was our objective to determine the efficacy of heart rate and rating of perceived exertion in controlling the intensity of indoor cycling classes and to quantify their association with oxygen uptake. Twelve experienced males performed three indoor cycling sessions of 45 min that differed in the way the intensity was controlled: (i) oxygen uptake; (ii) heart rate; and (iii) rating of perceived exertion using the OMNI-Cycling. The oxygen uptake levels were significantly higher (p = 0.007; µp2 = 0.254) in oxygen uptake than heart rate sessions. Oxygen uptake related to body mass was significantly higher (p < 0.005) in the oxygen uptake sessions compared with other sessions. Strong correlations were observed between oxygen uptake mean in the oxygen uptake and rating of perceived exertion sessions (r =0.986, p < 0.0001) and between oxygen uptake mean in the oxygen uptake and heart rate sessions (r = 0.977, p < 0.0001). Both heart rate and rating of perceived exertion are effective in controlling the intensity of indoor cycling classes in experienced subjects. However, the use of rating of perceived exertion is easier to use and does not require special instrumentation.