Physical fitness changes among amateur soccer players: effects of the pre-season period

Effects of mid-season camp period loading on sports injury anxiety and physical performance of professional male soccer players

OBJECTIVE

This study aimed to investigate the effects of 3-week mid-season camp period loading on sports injury anxiety level and physical performances of professional male soccer players.

METHODS

Thirty-four male soccer players [mean age 21.76 (3.42) y] were included in the study. Sports injury anxiety level with the Sports Injury Anxiety Scale, quadriceps muscle strength was assessed with the digital hand dynamometer, flexibility was assessed with the sit-and-reach test, dynamic balance was assessed with the Y balance test, power was assessed with the single-leg jump test, agility was assessed with the t-test, and sprint was assessed with the 20-m sprint test.

RESULTS

There was no significant difference between the pretest and posttest values of Sport Injury Anxiety Scale scores (p = 0.657). A significant difference was found between the pretest and posttest values of quadriceps muscle strength and dynamic balance (p = 0.040, p = 0.048, respectively). There was no significant difference between the pretest and posttest values of flexibility, single-leg jump, agility, and sprint (p = 0.289, p = 0.128, p = 0.064, p = 0.471, respectively).

CONCLUSION

The 3-week mid-season camp period loading improved quadriceps muscle strength and dynamic balance, but did not affect the sports injury anxiety level, flexibility, jumping performance, agility, and sprint. The results should be considered by sports professionals. It is recommended that further studies be conducted on the effects of different camp period contents, durations and loads in regions with different physical conditions (sea level, high altitude, etc.).

Effects of Game Weekly Frequency on Subjective Training Load, Wellness, and Injury Rate in Male Elite Soccer Players

To compare the effects of playing one or two games per week on subjective perceived exertion (RPE) and (RPE-based) training load, monotony index, sleep, stress, fatigue, and muscle soreness (Hooper index), total mood disturbance, and injury rate in elite soccer players. Fourteen males from a first-division soccer club (age: 24.42 ± 4.80 years) competed in two games per week for six weeks and one game per week for twelve weeks (a total of 24 games). Paired t-tests and non-parametric Wilcoxon signed ranks evaluated the significance of the differences (p < 0.05). The main findings were that RPE was significantly larger when playing two games per week compared with one game. However, subject total and mean training load, mood disturbance, monotony, and subjective perception of sleep, stress, fatigue, muscle soreness monitoring (Hooper index), and the number of injuries were not different. The findings suggested that competing in two matches per week does not negatively influence injury rate and players’ perceptions of training load or wellness, even though players perceive two games per week as more physically demanding compared with one game per week.

Does maturity estimation, 2D:4D and training load measures explain physical fitness changes of youth football players?

OBJECTIVES

The purpose of the present study was two-fold: (1) To analyse physical fitness changes of youth football players after a full-season; and (2) to examine whether physical fitness changes are explainable by estimated maturity status, 2digit:4digit ratio (2D:4D) from each hand and training load (TL) measures.

METHODS

Twenty-seven youth elite Under-15 football players were daily monitored for training load measures during 38 weeks. At the beginning and at the end of the season, all players were assessed for physical fitness. Also, the maturity status estimation and the length of the second and fourth digits of both hands were collected at the beginning of the season.

RESULTS

Significant differences were found for all physical fitness measures after the season. The second and fourth digits of left and right hands had negative moderate correlations with change of direction (COD) changes (r=-.39 to - 0.45 | p = .05 to 0.02). Also, the maturity offset measure had negative moderate correlations with COD changes (r=-.40 | p = .04). From the reported significant correlations, the maturity offset, Left 4D, Right 2D and Right 4D significantly predicted the Mod.505 COD test changes (β = 0.41, p = .04; β = -0.41, p = .04; β = -0.45, p = .02; and β = -0.44, p = .03, respectively).

CONCLUSION

The maturity offset and the 2D:4D measures have the potential to predict COD performance changes over-time in youth football players. Given the lack of associations between the maturity estimation, 2D:4D and training load measures, with the overall physical fitness measures, coaches should rely only at COD changes.

Effects of High-Intensity Resistance Training on Physical Fitness, Hormonal and Antioxidant Factors: A Randomized Controlled Study Conducted on Young Adult Male Soccer Players

Purpose: The aim of this study was to test the effects of high-intensity resistance training (HIRT) intervention on the physical fitness, hormonal and antioxidant factors of adult male soccer players. Methods: A randomized controlled study design was implemented. Eighteen soccer players (age: 20.3 ± 0.66 years; stature: 174.0 ± 6.01 cm; body mass: 69.1 ± 6.4 kg; body mass index: 22.8 ± 1.6 kg/m2) voluntarily participated in this study. Players were assessed before and after an intervention lasting 8 weeks, with three training sessions a week. Assessments of physical fitness included the Yo-Yo intermittent recovery test level 1 (YYIRT1), 10-, 20-, and 30 m sprint time (ST), running-based anaerobic sprint test (RAST) and change-of-direction time (COD). Hormonal tests included cortisol, testosterone and growth hormone (GH), whereas the antioxidant assessment included superoxide dismutase (SOD), malondialdehyde (MDA) and glutathione (GSH). Results: Between-group analysis revealed no significant differences at baseline, whereas it revealed that HIRT presented significant better results than the control group on YYIRT (p = 0.032), 10 m ST (p = 0.041), 20 m ST (p = 0.040), 30 m ST (p = 0.044), RAST (p = 0.013), and COD (p = 0.031) after the intervention period. The within group analysis revealed that the HIRT group significantly improved the YYIRT1 (p < 0.001), VO2max (p < 0.001), 10 m ST (p < 0.001), 20 m ST (p = 0.006), 30 m ST (p < 0.001), RAST (p < 0.001) and COD (p < 0.001). Moreover, HIRT group significantly reduced the cortisol (p < 0.001) and MDA (p = 0.021), whereas it significantly increased the GH (p < 0.001), testosterone (p < 0.001), SOD (p = 0.009) and GSH (p = 0.005). Conclusions: The HIRT is effective for improving physical fitness, revealing significant better adaptations than controls. Moreover, hormonal and antioxidant adaptations are also confirmed after HIRT intervention.

Relationships between Fitness Status and Blood Biomarkers in Professional Soccer Players

Background

Physical conditions are recognized to be optimal after the pre-season (PS) phase in professional sports. Given that blood measures may also reveal variations, which in turn, may present associations with fitness changes.

Objective

The aim of this study is to test the changes of blood markers and physical fitness outcomes at the beginning and following the PS phase. Additionally, we aimed also to analyze the associations of training adaptations between blood markers and the physical fitness measures. Methodology. 25 professional male soccer players (28.1 ± 4.6 years old, 2.0 ± 7.8 kg, and 176.7 ± 4.9 cm) were assessed for hematological and biochemical parameters, and physical fitness measures in the baseline and after the phase of PS.

Results

Increases in platelets were observed after the PS phase (p = 0.001, η2 = 0.39). Regarding the biochemical parameters, significant increases between PS were found for creatinine (Cre) (p = 0.001, η2 = 0.66), alkaline phosphatase (ALP) (p = 0.001, η2 = 0.79), C-Reactive Protein (CRP) (p = 0.001, η2 = 0.74), cortisol (C) (p = 0.001, η2 = 0.63), and testosterone (T) (p = 0.001, η2 = 0.76), whereas significant decreases were found for albumin (Alb) (p = 0.004, η2 = 0.29), and calcium corrected (Ca Corr.) (p = 0.002, η2 = 0.32). Moderate correlations were found between albumin and the 5-meter linear sprint split (r = -0.44 (95%CI: -0.71; -0.05)) and CRP (r = -0.48 (95%CI: -0.74; -0.10)). Moderate correlations were found between VAMEVAL and hemoglobin (r = 0.44 (95%CI: 0.05; 0.71)).

Conclusions

The overall physical fitness measures improved after the PS phase. Also, significant variations (decreases/increases) were observed for the case of biomchemical and hematological outcomes. Coaches should carefully consider the adaptative changes observed in blood parameters as the changes in whole organism and metabolism after specific critical phases as the PS in professional players. Thus, optimal management of stimulus/recovery can be warranted to minimize illness and injury rate and to follow the direction and dynamics of adaptative changes.

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

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.

Associations between Physical Status and Training Load in Women Soccer Players

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.