Variations of workload and well-being measures across a professional basketball season

Weekly Fluctuations in, and Associations Between, Salivary Hormone Responses, Load, and Well-Being During the Pre-season in Professional Male Basketball Players

ABSTRACT

Kamarauskas, P, Scanlan, A, Ferioli, D, and Conte, D. Weekly fluctuations in, and associations between, salivary hormone responses, load, and well-being during the pre-season in professional male basketball players. J Strength Cond Res 38(1): 128-135, 2024-This study aimed to (a) quantify weekly fluctuations in hormonal responses (testosterone [T], cortisol [C], and their ratio [T:C]), external (PlayerLoad [PL] and PL·minute-1) and internal (session rating of perceived exertion load [sRPE-load], summated heart rate zones [SHRZs], and percentage of maximal heart rate [%HRmax]) load measures, and well-being measured using a self-reported questionnaire and (b) determine the associations between weekly changes in hormonal responses and load measures with weekly changes in well-being during the pre-season phase in basketball players. Twenty-one professional male basketball players (age: 26.2 ± 4.9 years) were monitored during a 5-week pre-season phase. Linear mixed models were used to determine weekly differences in each variable and associations between weekly changes in hormonal and load variables with weekly changes in well-being. Findings revealed that T (p < 0.001) and T:C (p = 0.002) increased toward the end of the pre-season phase. Moreover, higher (p < 0.05) external (PL·minute-1) and internal (%HRmax) load intensities were evident during the first 3 weeks of the pre-season, with no significant fluctuations in other load variables. Weekly changes in PL and sRPE-load were negatively associated (p < 0.05) with weekly changes in well-being, albeit weak in magnitudes (R2 = 0.061-0.105). These results highlight that a periodized approach was undertaken across the pre-season predominantly predicated on altering weekly load intensities, which coincided with positive hormonal responses toward the end of the pre-season. In addition, weak relationships were evident between weekly changes in hormonal responses and load measures with well-being, emphasizing that a low commonality may be present between these constructs.

Relationships between training load, peak height velocity, muscle soreness and fatigue status in elite-level young soccer players: a competition season study

BACKGROUND

This study aimed to compare training load parameters, delayed onset muscle soreness (DOMS), and fatigue status between season periods (1^st and 2^nd halves) in U14 soccer players and to analyze the relationships between training load parameters based on season periods (1^st and 2^nd halves) with peak height velocity (PHV), DOMS, and fatigue status in under-14 (U14) young elite soccer players. Additionally, it was intended to analyze if fatigue, DOMS and PHV could explain training load parameters across the season.

METHODS

Twenty U14 players that competed in the national league participated in this study. The players were monitored during the whole season (26 weeks), and evaluations were carried out at the end of the in-season. Anthropometric and body composition parameters and the maturity offset of each player were utilized to compute each player's age at PHV. Players reported their levels of DOMS and fatigue status using Hooper index questionnaires. The internal load was monitored using the rating of perceived exertion (RPE). Acute weekly internal load (AW), chronic weekly internal load (CW), acute: chronic workload ratio (ACWR), training monotony (TM), and training strain (TS) were also obtained.

RESULTS

The main results showed that TM was higher in the 2^nd half, while CW, AW and DOMS were higher in the 1^st half of the season. Moreover, the main correlations showed a positive correlation between PHV and TS (2^nd half of the season) and between fatigue and TM (1^st half of the season).

CONCLUSION

In conclusion, variations in well-being status and PHV cannot explain the variations in internal training loads in elite U14 soccer players. In addition, internal training load indices during the first half of the competitive season can promote a fundamental base for progression loads during the second period of the competitive season.

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.