scholarly journals Blood Biomarkers Variations across the Pre-Season and Interactions with Training Load: A Study in Professional Soccer Players

2021 ◽  
Vol 10 (23) ◽  
pp. 5576
Author(s):  
Filipe Manuel Clemente ◽  
Francisco Tomás González-Fernández ◽  
Halil Ibrahim Ceylan ◽  
Rui Silva ◽  
Saeid Younesi ◽  
...  

Background: Pre-season training in soccer can induce changes in biological markers in the circulation. However, relationships between chosen hematological and biochemical blood parameters and training load have not been measured. Objective: Analyze the blood measures changes and their relationships with training loads changes after pre-season training. Methodology: Twenty-five professional soccer players were assessed by training load measures (derived from rate of perceived exertion- known as RPE) during the pre-season period. Additionally, blood samples were collected for hematological and biochemical analyses. Results: For hematological parameters, significant increases were found for platelets (PLT) (dif: 6.42; p = 0.006; d = −0.36), while significant decreases were found for absolute neutrophils count (ANC) (dif: −3.98; p = 0.006; d = 0.11), and absolute monocytes count (AMC) (dif: −16.98; p = 0.001; d = 0.78) after the pre-season period. For biochemical parameters, there were significant increases in creatinine (dif: 5.15; p = 0.001; d = −0.46), alkaline phosphatase (ALP) (dif: 12.55; p = 0.001; d = −0.84), C-reactive protein (CRP) (dif: 15.15; p = 0.001; d = −0.67), cortisol (dif: 2.85; p = 0.001; d = −0.28), and testosterone (dif: 5.38; p = 0.001; d = −0.52), whereas there were significant decreases in calcium (dif: −1.31; p = 0.007; d =0.49) and calcium corrected (dif: −2.18; p = 0.015; d = 0.82) after the pre-season period. Moreover, the Hooper Index (dif: 13.22; p = 0.01; d = 0.78), and all derived RPE measures increased after pre-season period. Moderate-to-very large positive and negative correlations (r range: 0.50–0.73) were found between the training load and hematological measures percentage of changes. Moderate-to-large positive and negative correlations (r range: 0.50–0.60) were found between training load and biochemical measures percentage of changes. Conclusions: The results indicated heavy physical loads during the pre-season, leading to a decrease in immune functions. Given the significant relationships between blood and training load measures, monitoring hematological and biochemical measures allow coaches to minimize injury risk, overreaching, and overtraining.

Kinesiology ◽  
2021 ◽  
Vol 53 (1) ◽  
pp. 71-77
Author(s):  
Alireza Rabbani ◽  
Del P. Wong ◽  
Filipe Manuel Clemente ◽  
Mehdi Kargarfard

The aim of the present study was to compare the fitness profiles and internal training loads between senior team and academy team soccer players during an in-season phase. Twenty-two professional soccer players from the senior team (n=12; 28.3<img width="12" alt="" height="20"> 2.0 years) and under 19 (U19) team (n=10; 18.0<img width="12" alt="" height="20"> 0.4 years) of the same club participated in the present study. High-intensity running performance, acceleration, maximal sprint, and change of direction (COD) ability were all tested during the mid-season break of a competitive season. Session rating of perceived exertion (sRPE) reflecting the internal training load during the entire first half of the season was being documented daily. Senior players showed small to moderate superiority in COD (1.8%, 90% confidence intervals [CI, -3.2; 7.1], ES: 0.24 [-0.44; 0.92]), maximal sprint (2.3%, [0.0; 4.7], ES: 0.81 [0.00; 1.63]) and acceleration (3%, [0.2; 5.8], ES: 0.96 [0.06; 1.85]). The U19 showed small better high-intensity intermittent running fitness (2.5%, [-1.2; 6.3], ES: 0.39 [-0.20; 0.97]). When analyzing internal training loads (from M-3 to M+3), the U19 showed small to very large higher sRPE values for all days (range; 8.2%; 229.3%, [-8.1; 328.3], ES range; 0.25; 2.70, [-0.26; 3.3]), except for match days (M), on which unclear trivial difference was observed (-1.5%, [-9.6; 7.5], ES -0.09 [-0.65; 0.46]). Our results showed that senior players and youth players had different fitness profiles and internal training loads during the first half of a competitive season; this should be taken into consideration when designing specific and individualized recovery and training sessions.


2014 ◽  
Vol 9 (3) ◽  
pp. 497-502 ◽  
Author(s):  
Michel S. Brink ◽  
Wouter G.P. Frencken ◽  
Geir Jordet ◽  
Koen A.P.M. Lemmink

Purpose:The aim of the current study was to investigate and compare coaches’ and players’ perceptions of training dose for a full competitive season.Methods:Session rating of perceived exertion (RPE), duration, and training load (session RPE × duration) of 33 professional soccer players (height 178.2 ± 6.6 cm, weight 70.5 ± 6.4 kg, percentage body fat 12.2 ± 1.6) from an under-19 and under-17 (U17) squad were compared with the planned periodization of their professional coaches. Before training, coaches filled in the session rating of intended exertion (RIE) and duration (min) for each player. Players rated session RPE and training duration after each training session.Results:Players perceived their intensity and training load (2446 sessions in total) as significantly harder than what was intended by their coaches (P < .0001). The correlations between coaches’ and players’ intensity (r = .24), duration (r = .49), and load (r = .41) were weak (P < .0001). Furthermore, for coach-intended easy and intermediate training days, players reported higher intensity and training load (P < .0001). For hard days as intended by the coach, players reported lower intensity, duration, and training load (P < .0001). Finally, first-year players from the U17 squad perceived training sessions as harder than second-year players (P < .0001).Conclusion:The results indicate that young elite soccer players perceive training as harder than what was intended by the coach. These differences could lead to maladaptation to training. Monitoring of the planned and perceived training load of coaches and players may optimize performance and prevent players from overtraining.


Author(s):  
Sullivan Coppalle ◽  
Guillaume Ravé ◽  
Jason Moran ◽  
Iyed Salhi ◽  
Abderraouf Ben Abderrahman ◽  
...  

This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players’ RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg’s 0–10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12–15.9 km/h; 16–19.9 km/h; 20–24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p =0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p =0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.


2019 ◽  
Vol 9 (23) ◽  
pp. 5174
Author(s):  
Alessio Rossi ◽  
Enrico Perri ◽  
Luca Pappalardo ◽  
Paolo Cintia ◽  
F. Iaia

The use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and S-RPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and S-RPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.


Author(s):  
Lillian Gonçalves ◽  
Filipe Manuel Clemente ◽  
Joel Ignacio Barrera ◽  
Hugo Sarmento ◽  
Gibson Moreira Praça ◽  
...  

This study aimed to analyze the variations of fitness status, as well as test the relationships between accumulated training load and fitness changes in women soccer players. This study followed an observational analytic cohort design. Observations were conducted over 23 consecutive weeks (from the preseason to the midseason). Twenty-two women soccer players from the same first Portuguese league team (22.7 ± 5.21 years old) took part in the study. The fitness assessment included anthropometry, hip adductor and abductor strength, vertical jump, change of direction, linear speed, repeated sprint ability, and the Yo-Yo intermittent recovery test. The training load was monitored daily using session rating of perceived exertion (s-RPE). A one-way repeated ANOVA revealed no significant differences for any of the variables analyzed across the three moments of fitness assessments (p > 0.05). The t-test also revealed no differences in the training load across the moments of the season (t = 1.216; p = 0.235). No significant correlations were found between fitness levels and accumulated training load (range: r = 0.023 to −0.447; p > 0.05). This study revealed no differences in the fitness status during the analyzed season, and the fitness status had no significant relationship with accumulated training load.


Author(s):  
Filipe Manuel Clemente ◽  
Rui Silva ◽  
Daniel Castillo ◽  
Asier Los Arcos ◽  
Bruno Mendes ◽  
...  

The aim of this study was two-fold: (1) to analyze the variations of acute load, training monotony, and training strain among early (pre-season), mid (first half of season), and end season (second half of season) periods; (2) to compare these training indicators for playing positions in different moments of the season. Nineteen professional players (age: 26.5 ± 4.3 years; experience as professional: 7.5 ± 4.3 years) from a European First League team participated in this study. The players were monitored daily over a 45-week period for the total distance (TD), distance covered (DC) at 14 km/h−1 or above (DC > 14 km/h), high-speed running above 19.8 km/h−1 (HSR) distance, and number of sprints above 25.2 km/h−1. The acute load (sum of load during a week), training monotony (mean of training load during the seven days of the week divided by the standard deviation of the training load of the seven days), and training strain (sum of the training load for all training sessions and matches during a week multiplied by training monotony) workload indices were calculated weekly for each measure and per player. Results revealed that training monotony and training strain for HSR were meaningfully greater in pre-season than in the first half of the in-season (p ≤ 0.001; d = 0.883 and p ≤ 0.001; d = 0.712, respectively) and greater than the second half of the in-season (p ≤ 0.001; d = 0.718 and p ≤ 0.001; d = 0.717). The training monotony for the sprints was meaningfully greater in pre-season than in the first half of in-season (p < 0.001; d = 0.953) and greater than the second half of in-season (p ≤ 0.001; d = 0.916). Comparisons between playing positions revealed that small-to-moderate effect sizes differences mainly for the number of sprints in acute load, training monotony, and training strain. In conclusion, the study revealed that greater acute load, training monotony, and training strain occurred in the pre-season and progressively decreased across the season. Moreover, external defenders and wingers were subjected to meaningfully greater acute load and training strain for HSR and number of sprints during the season compared to the remaining positions.


Author(s):  
Luiz Guilherme Cruz Gonçalves ◽  
Carlos Augusto Kalva-Filho ◽  
Fábio Yuzo Nakamura ◽  
Vincenzo Rago ◽  
José Afonso ◽  
...  

This study aimed to quantify the weekly training load distributions according to match location, opponent standard, and match outcome in professional soccer players. Rate-of-perceived-exertion-based training load (sRPE) and distance- and accelerometry-based measures were monitored daily during 52 training sessions and 11 matches performed by 23 players. Athletes who played ≥ 60 min during non-congested weeks were considered for data analysis. The training days close to away matches (e.g., one day before the match = MD-1) presented greater sRPE, distance-based volume measures, and mechanical work (player load) compared to the training days close to home matches (p = 0.001–0.002; effect size (ES) = medium−large). The most distant days of the home matches (e.g., five days before the match = MD-5) presented higher internal and external loads than before away matches (p = 0.002–0.003, ES = medium). Higher sRPE, distance-based volume measures, and mechanical work were found during the middle of the week (e.g., three days before the match, MD-3) before playing against bottom vs. medium-ranking teams (p = 0.001–0.01, ES = small−medium). These metrics were lower in MD-5 before matches against bottom vs. medium-ranking opponents (p = 0.001, ES = medium). Higher values of all external load measures were observed during the training session before winning matches (MD-1) compared to a draw or loss (p < 0.001–0.001, ES = medium−large). In conclusion, the training load distribution throughout the week varied considerably according to match-contextual factors.


Kinesiology ◽  
2017 ◽  
Vol 49 (2) ◽  
pp. 153-160 ◽  
Author(s):  
Asier Los Arcos ◽  
Javier Yanci

The aim of this study was to examine the association of perceived respiratory and muscular exertions and associated training load (TL) for monitoring changes in several aerobic fitness and neuromuscular performance parameters during 32 weeks of soccer training in young professional players. Twenty male soccer players (age=20.6±1.8 years, body height=1.80±.06 m, body mass=73.6±6.7 kg) belonging to the same reserve team of a Spanish La Liga Club participated in this study. Countermovement jump (CMJ), CMJ with arm swing, linear sprint running (over 5 m and 15 m) and an aerobic fitness running test were performed at the start of the pre-season (Test 1) and 32 weeks later (Test 2). During these eight months, after each training session and match, players rated their perceived exertion (sRPE) separately for respiratory (sRPEres) and leg musculature (sRPEmus) effort. Training load was calculated by multiplying the sRPE value by the duration of each training session or match. Accumulated training and match volume (i.e., time) and associated respiratory and muscular training loads were negatively correlated with the changes in aerobic&nbsp;fitness performance after 32 weeks of training (r=-.53/-.62). In addition, accumulated perceived respiratory load was negatively correlated with the changes in 15 m sprint performance (r=-.51/-.53). A high practice volume (time) and associated respiratory and leg muscular TL can impair the long-term improvement of aerobic fitness and sprint performance in professional soccer players.


2013 ◽  
Vol 8 (2) ◽  
pp. 195-202 ◽  
Author(s):  
Brendan R. Scott ◽  
Robert G. Lockie ◽  
Timothy J. Knight ◽  
Andrew C. Clark ◽  
Xanne A.K. Janse de Jonge

Purpose:To compare various measures of training load (TL) derived from physiological (heart rate [HR]), perceptual (rating of perceived exertion [RPE]), and physical (global positioning system [GPS] and accelerometer) data during in-season field-based training for professional soccer.Methods:Fifteen professional male soccer players (age 24.9 ± 5.4 y, body mass 77.6 ± 7.5 kg, height 181.1 ± 6.9 cm) were assessed in-season across 97 individual training sessions. Measures of external TL (total distance [TD], the volume of low-speed activity [LSA; <14.4 km/h], high-speed running [HSR; >14.4 km/h], very high-speed running [VHSR; >19.8 km/h], and player load), HR and session-RPE (sRPE) scores were recorded. Internal TL scores (HR-based and sRPE-based) were calculated, and their relationships with measures of external TL were quantified using Pearson product–moment correlations.Results:Physical measures of TD, LSA volume, and player load provided large, significant (r = .71−.84; P < .01) correlations with the HR-based and sRPE-based methods. Volume of HSR and VHSR provided moderate to large, significant (r = .40−.67; P < .01) correlations with measures of internal TL.Conclusions:While the volume of HSR and VHSR provided significant relationships with internal TL, physical-performance measures of TD, LSA volume, and player load appear to be more acceptable indicators of external TL, due to the greater magnitude of their correlations with measures of internal TL.


Author(s):  
Hadi Nobari ◽  
Gibson Moreira Praça ◽  
Filipe Manuel Clemente ◽  
Jorge Pérez-Gómez ◽  
Jorge Carlos Vivas ◽  
...  

The aim of this study was to compare the weekly average training monotony new body load (wTMNBL) and strain (wTSNBL), as well as the weekly average training monotony metabolic power average (wTMMPA) and strain (wTSMPA) between four periods of a season (preseason, early-season, mid-season, and end-season), considering starters and non-starters. Twenty-one professional soccer players (age: 28.27 ± 3.78 years) were monitored throughout a season in the highest level of professional football Premier League in Iran. Data were captured by Global Positioning System (GPS) devices. Independent samples T-tests were applied to analyze the between-group differences for all dependent derived-GPS variables for the full season and its different periods (preseason, early-season, mid-season, and end-season). Based on the amount of time attending in match and training, players were divided into two groups (starters and non-starters) each week. The magnitude of the between-group difference revealed a very large significant greater weekly average TMNBL ( p<0.001, d = −2.42), TSNBL ( p<0.001; d = −2.74), TMMPA ( p<0.001; d =–2.79) and TSMPA ( p<0.001; d = −3.27) for starters when compared to non-starters during the early-season. The findings also revealed a very large significant difference when starters were compared to non-starters during the mid-season (TMNBL: p<0.001, d = −2.89; TSNBL: p<0.001, d = −2.99; TMMPA: p<0.001, d = −3.28; and TSMPA: p<0.001, d = −3.25) and end-season (TMNBL: p<0.001, d = −2.89; TSNBL: p<0.001, d = −3.07; TMMPA: p<0.001, d = −3.16; and TSMPA: p<0.001, d = −3.58). In summary, the results of this study revealed that starters present regularly higher values of NBL, MPA-based weekly training monotony, and training strain than non-starters. This result must be taken into account when planning weekly workloads for these groups. Specifically, starters might experience high values of external workloads because of match-related demands. Therefore, weekly adjustments in their training workload are required to reduce injury risk.


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