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Using Testosterone and Cortisol as Biomarker for Training Individualization in Elite Basketball: A 4-Year Follow-up Study

Schelling, Xavi1; Calleja-González, Julio2; Torres-Ronda, Lorena3; Terrados, Nicolás4

Journal of Strength and Conditioning Research: February 2015 - Volume 29 - Issue 2 - p 368–378
doi: 10.1519/JSC.0000000000000642
Original Research
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Schelling, X, Calleja-González, J, Torres-Ronda, L, and Terrados, N. Using testosterone and cortisol as biomarker for training individualization in elite basketball. A 4-year follow-up study. J Strength Cond Res 29(2): 368–378, 2015—The purpose of this study was to determine the responses of testosterone and cortisol, with special reference to playing positions, playing time (PT), and phase of the season. We performed a follow-up study during 4 consecutive seasons to investigate the effects of PT, positional role, and phase of the season on anabolic-catabolic biomarkers (plasma total testosterone -TT- and cortisol -C-) on 20 professional male basketball players (27.0 ± 4.2 years; 24.4 ± 1.2 kg·m−2). First blood samples were collected right after the off-season period and considered as baseline. Samples were taken periodically every 4–6 weeks, always after a 24- to 36-hour break after the last game played. Statistical procedures were nonparametric mainly. Hormonal status was playing position-dependent, power forward (PF) showed the lowest TT values (median ± interquartile range [IQR]; PF: 18.1 ± 4.9; nmol·L−1), and small forwards showed the highest ones of cortisol (0.55 ± 0.118 μmol·L−1). Players who played between 13 and 25 minutes per game showed the highest values of TT (22.8 ± 6.9 nmol·L−1) and TT/C (47.1 ± 21.2). March and April showed the most catabolic or stressed hormonal state (low TT/C values and high ones of cortisol) and that is necessary to take into account according to PT (>25-minute per game) and specific playing position. Monitoring plasma TT and cortisol is recommended to prevent excessive stress caused by professional basketball season requirements.

1Bàsquet Manresa SAD, Manresa, Spain;

2University of Basque Country, Vitoria, Spain;

3National Institute of Physical Education of Catalonia (INEFC), Lleida, Spain; and

4University of Oviedo, Oviedo, Spain.

Address correspondence to Xavi Schelling, ender80@hotmail.com.

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Introduction

One of the main goals of top-level sport training coaches is to prescribe training loads (TL) to maximize athletes' performance during competitive season. Successful training not only must involve overload but also must avoid the combination of excessive overload plus inadequate recovery because it may result in performance degradation and even poor health states (24). In this regard, there is strong evidence for considerable heterogeneity in the responsiveness to regular physical activity (responders and nonresponders) (6). Therefore, controlling the internal load induced by training and competition is a key factor to efficiently monitor the training process.

Modern-day basketball is a contact and dynamic sport in which athletes require a combination of power, speed, agility, endurance, and sport-specific skills (4). A top-level calendar involves competition frequencies from every 2–3 days to weekly (8). In this context, a precise knowledge of training and competition effects on the players is paramount for appropriate training periodization. Furthermore, in a basketball team, the individualization principle of training should also consider the specific roles linked to each playing position (5) and the competition playing time (PT) (7). In particular, these 2 factors are essential to prescribe the most appropriate intervention(s) to mitigate the possible negative effects of training and competition: adjusting workouts to improve the recovery processes or to compensate for the lack of stimuli.

Fatigue-related mechanisms are still debated in the literature (34), but the importance of the hypothalamic-pituitary-adrenal/gonadal axis in the regulation of metabolism and homeostasis is well documented (11,24). Testosterone (T), cortisol (C), and testosterone-to-cortisol ratio (T/C) have been proposed as indicators of the balance between anabolic and catabolic processes (1,29,35). Repeated exercise bouts without a sufficient period of recovery can cause a persistent disturbance in this balance (1,19). Higher levels of testosterone have been previously linked to performance and to optimal recovery processes, whereas diminished levels of testosterone and increased levels of cortisol have been linked to overtraining and reduced performance (1,9,26). A better understanding of the effects of a competitive season on these biomarkers may provide some opportunities for enhanced programming strategies at an individual player level (2). However, despite the possible advantages of the aforementioned variables, only few publications have dealt with hormonal responses in basketball (13,16,21). Their main results suggest that professional basketball players can maintain a good anabolic-catabolic balance along the season (21). They also reported positive correlations between sessions rating of perceived exertion and cortisol changes (28), as well as increases in secretion rates and absolute concentrations of salivary cortisol after intense training and competition (15). However, no disturbances in hormonal markers after a 28-day summer training camp were also observed (16). Finally, from a psychophysiological point of view, some studies concluded that in a highly competitive situation, testosterone changes are not directly linked to the game outcome (victory/defeat), but instead to the personal contribution of the individual to the game outcome and to the causes, he/she attributes to this outcome (13).

The purpose of this study was therefore (a) to investigate changes in plasma total testosterone (TT) and cortisol (C) during 4 consecutive seasons in professional male basketball players according to playing position, PT, and phase of the season; and (b) to examine the usefulness of hormonal balanceas monitor of training responses. It was hypothesized that TT, cortisol, and TT/C would show their lowest values of TT and TT/C and the highest ones of cortisol at the end of the regular season, and on players with the greatest PTs along the season.

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Methods

Experimental Approach to the Problem

A descriptive study with nonrandomized sampling was performed. Total testosterone and cortisol from blood plasma and TT/C were analyzed as indicators of the balance between anabolic and catabolic processes (1,29,35) and of the recovery state (1,19). Despite the practical interest of these indicators of individual response to TL (internal load), no study verified their specific sensibility according to playing position, PT, and phase of the season on top-level basketball players. Indeed, the only one similar article that was published (21) assessed 1 team along 1 season with 4 blood samples regardless the PT nor playing position. Therefore, the association of hormonal indicators and the specific demands of elite basketball is yet to be investigated.

Players undertook the first blood sample evaluation at the beginning of the season, on their first training day (August), to serve as a baseline. Criteria for the baseline blood test required a week without training and not have any travel that changed time zones (chronobiology) (14). Included from August to May, samples were drawn every 4–6 weeks, in a resting state, after a 24- to 36-hour break after the last game or workout (Figure 1). Between 7 and 9 blood samples were obtained per player per season, depending on team's season calendar and weekly schedule (Table 1).

Figure 1

Figure 1

Table 1

Table 1

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Subjects

Twenty male professional basketball players (27.0 ± 4.2 years; 93.7 ± 10.1 kg; 195.7 ± 7.4 cm; 24.4 ± 1.2 kg·m−2; % fat: 14.0 ± 3.3%) those competed in the Spanish First Division (ACB) during 4 consecutive seasons (2007–2011) participated in the study. Players were classified according to their playing position: point guard (PG, n = 5), shooting guard (SG, n = 6), small forward (SF, n = 3), power forward (PF, n = 3), and center (CE, n = 3) (Table 2). All participants performed their usual team training program. The weekly training during the regular season generally involved 2 strength sessions (90 min·wk−1), 1 “high intensity interval training” session (30–45 min·wk−1), 1–2 shooting sessions (45–90 min·wk−1), 5–6 technical-tactical team sessions (525–625 min·wk−1), 1 game, and 1 recovery session the day after the game (consisting of water-based contrast therapy, stretching, and physiotherapy). The study was approved by the Ethics Committee for Clinical Research of Sports Administration of Catalonia (00998/11722/2011), performed in accordance with the Declaration of Helsinki (Fortaleza, 2013). All players, no one under the age of 18, and coaches, gave their written informed consent before participation.

Table 2

Table 2

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Procedures

Blood Sampling

Subjects came to the laboratory between 8:00 and 9:00 AM, in a fasting state. The schedule was always the same to avoid hormonal variations because of circadian rhythms and food intake. Blood samples were obtained from the antecubital vein and collected in dry test tubes, with no anticoagulants, for immediate analysis. Total testosterone concentration was determined by electrochemiluminescence immunoassay (ECLIA, Testosterone II, 05200067 190, Cobas, Mannheim, Germany) with a measuring range of 0.087–52.0 nmol·L−1, and a coefficient of variation between measurements of 1.2–4.7%. cortisol concentration was also determined by ECLIA (Cortisol, 11875116122, Cobas) with a measuring range of 0.0005–1.750 μmol·L−1, and a coefficient of variation between measurements of 1.1–1.7%. Immunoassays were analyzed using a Modular Analytics E170 (Roche Diagnostics Ltd., West Sussex, United Kingdom). All procedures were performed in a specialized laboratory (Nogueras Laboratories, Manresa, Spain). Total testosterone-to-cortisol ratio was estimated from total molar concentrations: TT in nmol·L−1 and cortisol in μmol·L−1.

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Playing Time

Playing time was obtained from the official site www.acb.com (visited: 01/09/2011). From these values, the average PT per game over each season was calculated, and results were then analyzed according to 2 different classifications to determine the best clustering without missing information: “PT-6” (consisting of 6 ranges of 5 minutes each, i.e., 0–5 minutes, 6–10 minutes, 11–15 minutes, 16–20 minutes, 21–25 minutes, and 26–30 minutes), and “PT-3” (consisting in 3 ranges of 12–15 minutes, i.e., 0–12 minutes, 13–25 minutes, and 25–40 minutes).

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Phase of the Season

The hormonal data from the 4 seasons were grouped by month, except for January and February, and March and April, which were analyzed together because not all seasons had blood samples of these months. Consequently, there were 8 monthly categories: August, September, October, November, December, January-February, and March-April. The preseason lasted from August to September, and the regular season was from October to May.

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Statistical Analyses

Analyses were performed using SPSS v20.0 (Chicago, IL, USA), Cliff's Delta Calculator (University of Salvador, Salvador, Argentina) and Effect Calculator (Hong Kong Polytechnic University, Hong Kong, China) softwares. Parametric data are presented as mean ± SD and nonparametric data as median ± IQR. Both concentration values of each hormone (TT and cortisol) or ratio (TT/C) and percentage of variation (3) were considered (varTT, varC, and varTT/C), with the first sampling (August) representing 0%. The distribution of each variable was examined with Shapiro-Wilk (n < 50) and Kolmogorov-Smirnov (n > 50) normality tests. Homogeneity of variance was verified by a Levene test and sphericity by Mauchly test. The non-normal groups were logarithmically transformed (Log10) and rechecked. The only parametric data were TT and TT/C when were analyzed in relation to the factor labeled “Month.” These data thus required an analysis of variance with repeated measures with the Tukey post hoc test to identify which pairs differ significantly. All other variables were analyzed with nonparametric tests. To compare differences between independent groups, the Kruskal-Wallis test was performed, and the Mann-Whitney U-test was used to identify which pairs showed significant differences. To analyze differences between related groups (“Month” factor), the Friedman test was performed, and the Wilcoxon test was used to identify which pairs showed significant differences. For all these analyses, significance was set at p ≤ 0.05. Data were assessed for clinical significance using an approach based on the effect size (ES) (18): Cliff's Delta (Δ) for non-normal data and Cohen's d (d) for normal data. Threshold values for Cliff's Δ statistics were ≤0.1 (trivial), >0.1–0.3 (small), >0.3–0.45 (moderate), >0.45 (large), and for Cohen's d statistics were >0.2–0.5 (small), >0.5–0.8 (moderate), >0.8 (large). Linear regressions with Spearman's Rho (rs) were also used to establish the respective relationships between hormonal variables. The following criteria were adopted for interpreting the magnitude of correlation: ≤0.1 (trivial), >0.1–0.3 (small), >0.3–0.5 (moderate), >0.5–0.7 (large), >0.7–0.9 (very large), >0.9–1.0 (almost perfect) (18).

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Results

In the correlational analysis between the hormonal variables, the relationships (Spearman's Rho, rs) and percentages of common variance (CV) were: TT vs. TT/C (CV: 46.6%; rs: 0.683; p < 0.001), cortisol vs. TT/C (CV: 53.7%; rs: −0.733; p < 0.001) and varC vs. varTT/C (CV: 57.8%; rs: −0.760; p < 0.001).

Hormonal parameters according to playing positions and its significance and ES are presented in Figure 2. All the hormonal variables, except for varC and varTT/C, showed significant differences between individual playing positions. Analyzing all the data together, PF showed the lowest values of TT and TT/C, and SF the highest ones of cortisol.

Figure 2

Figure 2

The main results obtained of the hormonal values in relation to 6 categories of PT per game averaged over the season (PT-6) showed that players who played 11–15 minutes and 16–20 minutes showed the highest TT and TT/C values: 11–15 minutes (median ± IQR; TT: 25.1 ± 9.0 nmol·L−1, TT/C: 48.1 ± 24.9) and 16–20 minutes (median ± IQR; TT: 22.7 ± 5.1 nmol·L−1, TT/C: 49.0 ± 18.5). Figure 3 shows the results of grouping the PT into 3 ranges (PT-3) and its significance and ES. Players who played between 13 and 25 minutes showed the highest values of TT and TT/C and of its percentage variations (varTT and varTT/C). The players who played more than 25 minutes showed the largest decreases in varTT and in varTT/C. No player averaged more than 30 minutes per game.

Figure 3

Figure 3

The monthly hormonal analysis, significance, and ES are displayed in Figure 4, representing all the participants together. Three months in particular showed significant differences compared with the others: September, October, and March-April, being the last third of the regular season (March-April), which showed the lowest TT/C and varTT/C. March-April showed, as well, the highest levels of cortisol and varC.

Figure 4

Figure 4

Figures 5 and 6 show the monthly results analyzed according to playing position (i.e., PG, SG, SF, PF, CE) and PT (i.e., 0–12 minutes, 13–25 minutes, and 25–40 minutes), respectively. Significance and ES among ranges of each variable are displayed for every month. Data form March-April revealed that, according to playing position, PF showed the lowest TT/C and TT values, CE the highest ones, and SF the highest cortisol values. When PT was analyzed, no remarkable significant differences were obtained.

Figure 5

Figure 5

Figure 6

Figure 6

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Discussion

To the best of our knowledge, this is the first study that analyzed monthly hormonal responses in relation to playing position and PT in elite basketball players along 4 consecutive seasons. The 3 main findings of this study suggest the following summaries. The first conclusion is that a players' hormonal status is playing position specific, with SFs and PFs presenting the most catabolic state. Second, players who played more than 25 minutes or less than 13 minutes need specific training interventions to improve recovery or to compensate for the lack of exercise stimulus, respectively. Finally, the last third of the regular season showed the most catabolic and/or stressed state with all players.

As previously reported (21), TT and cortisol correlate significantly with TT/C, with cortisol best explaining this index, with a CV of 53.7% between these factors (vs. 46.6% between TT and TT/C). However, cortisol is a hormone that responds to emotional stress (30), and thus it may be altered by psychological factors. Hence, it could be suggested that TT/C could depend on these factors and may not reliably reflect the anabolic-catabolic balance (10). To properly assess and monitor the 2 biomarkers studied, cortisol and TT, additional measurements of emotional/psychological status are suggested (14). We recommend, to perform psychological tests also to verify the mood state and/or emotional stress level (i.e., Profile Of Mood States, Recovery-Stress Questionnaire for Athletes or State-Trait Anxiety Inventory) to identify possible interaction of athlete psychology. Interestingly, there were low significant correlations between hormone concentrations or ratio values and percentage of variation values: TT vs. varTT (rs: 0.33), cortisol vs. varC (rs: 0.43), TT/C vs. varTT/C (rs: 0.42). In this regard, some authors postulated that the percentage of variation values is more representative of the physiological responses than the absolute value of the hormone concentration (3,14). Hormonal data showed a large variability, both interindividual (among individuals) and intraindividual (sample-to-sample variability across time). These results are in agreement with those reported by Sebastian-Gambaro et al. (32) and suggest that hormonal values require individual interpretation for each player and estimating the overall team state using an average value should be avoided (12).

Hormonal responses according to playing position reveal significant differences of SFs and PFs compared with all other positions (point guards, shooting guards, and centers). Both positions shared lower TT and higher cortisol values (increased catabolic or stressed profile), especially compared with centers, who had the most anabolic profile (Figures 2 and 5). Our results extend previous findings on hormone-specific adaptations by types of sport and playing positions (5,13). This information can be used to develop position-specific training adjustments, taking into account not only between general player sizes but also among all traditional 5 playing positions. Our findings suggest that if TT/C is considered as a valid marker to determine metabolic status, the lowest values showed by SFs and PFs could indicate a high physical demand placed on these players. According to this hypothesis, PFs are the playing position that has changed the most recently, to become more fast and athletic than before. Conversely, the high TT/C values found in centers could reflect the emphasis on strength development for this playing position and where muscle hypertrophy is a training consequence.

In this study, analyses were undertaken according to PT, which is more precise than previous investigations that only differentiated between “starters” and “non-starters.” When divided into 5-minute intervals, with a total of 6 ranges, our results showed that players who played less than 5 minutes had the lowest TT and TT/C values and the highest ones of cortisol. In contrast, players who played between 11–15 minutes and 16–20 minutes showed the highest TT and TT/C values. When PT was grouped into 12-minute intervals, with a total of 3 ranges, results were similar. Players who played between 13 and 25 minutes showed the highest values of TT and TT/C, significantly higher than those who played less than 13 minutes (Figure 3). Although we are not aware of any comparable data in basketball literature, a possible explanation for these results could be that since TT increases in response to intense exercise sessions (23), players who played less than 13 minutes would not experience enough stimuli during official games, considered as the most intense sessions. For these players, the high cortisol concentrations could be explained by the emotional stress induced by their smaller perceived personal contribution to the team achievements (13,30). Conversely, players who played more than 25 minutes showed the largest decreases in TT concentration and in TT/C, which may reflect the cumulated fatigue (22). The results of this study suggest that players who play less than 13 minutes in official games should compensate for this lack of stimuli to adjust their hormonal profile to those who play between 13 and 25 minutes. In contrast, players with over 25 minutes of PT should require specific interventions to recover from the game overload.

Focusing on possible differences in hormonal values per month, our results showed significant differences between September, October, and March-April, coinciding with specific periods of the season: (a) the preseason, September and October, (b) the first two-thirds of regular season, November to February, and (c) the last third March and April (Figures 4–6). We would like to highlight mainly the preseason responses and the players' state at the end of the regular season. The preseason effects, typically characterized by high TL (25), are reflected in the September and October results. In September, cortisol showed significantly lower values than January-February and March-April. The increase in cortisol concentrations from September is possibly related to the physiological stress associated with the physical nature of the competitive basketball season and the psychological stress associated with official competition, which nonexistent in the preseason (17). These results agree with previous studies conducted in cyclists and football players (17), despite differences in the demand for these sports. In September, we also obtained significantly higher TT/C values compared with that of in March-April. The increase, as a metabolic status marker (3,20,31), could reflect the anabolic processes predominance in the organism (20,31,34), probably induced because of the maximal strength and high-intensity endurance training prioritized in this phase (25), as well as the appropriate player recovery process.

In the last third of the regular season (March-April), the players showed an increased catabolic and/or stressed profile hormonally. During the last phase of the season, the higher cortisol values were significantly different from the beginning of the season (September-October). The increment in cortisol values after a training and competition period has been reported by several authors (33). In Basketball, previous studies attribute this increased cortisol to the accumulated training and to the competition effect (15,16). However, there are studies that do not report a clear pattern of cortisol, such as Martínez et al. (21), who obtained an irregular pattern of cortisol throughout the season. The conflicting results and discrepancies of study conducted by Martínez et al. (21) could be explained by differences in periodization. Martínez’s participants played an international competition during the week and the play-offs in June, experienced a tapering phase (27), thus explaining the differences in cortisol. The subjects of this study, however, ended their season in mid-May and usually played 1 game per week only, a probable influence in the cortisol pattern. Another explanation for the lack of similar results with the aforementioned study can be related to the study design, as they performed the first blood sample in October, when the season had already begun. Furthermore, if we consider that cortisol has immunosuppressive effects in response to exercise (15), the large increase observed at the end of the season could lead increase risk to an athlete's injury or illness. Regarding TT/C, the behavior is the opposite of cortisol, obtaining the lowest values of the season in this phase. Our results do not match the pattern with those reported by Hoffman et al. (16) and Martinez et al. (21). Hoffman et al. (16) speculated that their seasonal hormonal pattern could be due to altered endocrine response by an incomplete recovery state by subjects. The discrepancies with the results of Martínez et al. with our results could be explained by the research design or teams' periodization differences. Our results and hypothesis would be supported by those presented by Argus et al. (2), who postulated that their results, obtained along rugby competition, may reflect the accumulated fatigue throughout the season or indicate lack of recovery (2,3), whose effects could lead to an alteration of the hypothalamic-pituitary-adrenal axis (34).

Finally, as future research continues to investigate endocrine changes in basketball, the studied biomarkers should be analyzed and compared with the external load from practices and games (e.g., accelerometers, GPS, or time motion). Additional relationships such as the psychological state (e.g., POMS, RESTQ-Sport, STAI), nutritional parameters and sleep quality, can provide a deeper understanding of seasonal fatigue.

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Practical Applications

A good understanding of the internal effects of a competitive season on each player may provide opportunity for enhanced programming strategies individually. Monitoring hormonal state (through plasma TT and cortisol) is recommended to prevent excessive stress caused by professional basketball season requirements. Hormonal status is professional basketball playing position-dependent, being SFs and PFs who present the most catabolic/stressed hormonal profile. Basketball players who play more than 25 minutes or less than 13 minutes need specific-training interventions to improve the recovery processes or to compensate the lack of stimuli, respectively. These individual interventions will be crucial at the last third of the season.

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Acknowledgments

The authors of this article would like to thank Dr. Ramón Serra, team's Chief of Medical Services and Mútua Intercomarcal and Laboratorios Nogueras for their cooperation and timeliness in collecting samples. We would also like to thank Bàsquet Manresa organization, technical staff and players for their collaboration and Dr. Anne Delextrat and Mr. Carl Valle for help with preparation of this article.

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References

1. Adlercreutz H, Harkonen M, Kuoppasalmi K, Naveri H, Huhtamieni H, Tikkanen H, Remes K, Dessypris A, Karvonen J. Effect training on plasma anabolic and catabolic steroid hormones and their responses during physical exercise. Int J Sports Med 7: 27–28, 1986.
2. Argus C, Gill N, Keogh J, Hopkins W, Beaven C. Changes in strength, power, and steroid hormones during a professional rugby union competition. J Strength Cond Res 23: 1583–1592, 2009.
3. Banfi G, Dolci A. Free testosterone/cortisol ratio in soccer: Usefulness of a categorization of values. J Sports Med Phys Fitness 46: 611–616, 2006.
4. Ben Abdelkrim N, Castagna C, Jabri I, Battikh T, El Fazaa S, El Ati J. Activity profile and physiological requirements of junior elite basketball players in relation to aerobic–anaerobic fitness. J Strength Cond Res 24: 2330–2342, 2010.
5. Ben Abdelkrim N, Chaouachi A, Chamari K, Chtara M, Castagna C. Positional role and competitive-level differences in elite-level men's basketball players. J Strength Cond Res 24: 1346–1355, 2010.
6. Bouchard C, Rankinen T. Individual differences in response to regular physical activity. Med Sci Sports Exerc 33: S446–S451, 2001. discussion S452–S453.
7. Caterisano A, Patrick BT, Edenfield WL, Bastón MJ. The effects of a basketball season on aerobic and strength parameters among college men: Starters vs reserves. J Strenght Cond Res 11: 21–24, 1997.
8. Cormery B, Marcil M, Bouvard M. Rule change incidence on physiological characteristics of elite basketball players: A 10-year-period investigation. Br J Sport Med 42: 25–30, 2008.
9. Coutts A, Reaburn P, Piva TJ, Murphy A. Changes in selected biochemical, muscular strength, power, and endurance measures during deliberate overreaching and tapering in rugby league players. Int J Sports Med 28: 116–124, 2007.
10. Crowley M, Matt K. Hormonal regulation of skeletal muscle hypertrophy in rats: The testosterone to cortisol ratio. Eur J Appl Physiol Occup Physiol 73: 66–72, 1996.
11. Engelmann M, Landgraf R, Wotjak CT. The hypothalamic-neurohypophysial system regulates the hypothalamic-pituitary-adrenal axis under stress: An old concept revisited. Front Neuroendocrinology 25: 132–149, 2004.
12. Filaire E, Bernain X, Sagnol M, Lac G. Preliminary results on mood state, salivary testosterone:cortisol ratio and team performance in a professional soccer team. Eur J Appl Physiol 86: 179–184, 2001.
13. González-Bono E, Salvador A, Serrano MA, Ricarte J. Testosterone, cortisol, and mood in a sports team competition. Horm Behav 35: 55–62, 1999.
14. Hackney A, Viru A. Research methodology: Endocrinologic measurements in exercise science and sports medicine. J Athl Train 43: 631–639, 2008.
15. He C, Tsai ML, Ko MH, Chang CK, Fang SH. Relationships among salivary immunoglobulin A, lactoferrin and cortisol in basketball players during a basketball season. Eur J Appl Physiol 110: 989–995, 2010.
16. Hoffman JR, Epstein S, Yarom Y, Zigel L, Einbinder M. Hormonal and biochemical changes in elite basketball players during a 4-week training camp. J Strength Cond Res 13: 280–285, 1999.
17. Hoffman JR, Kang J, Ratamess N, Faigenbaum A. Biochemical and hormonal responses during an intercollegiate football season. Med Sci Sports Exerc 37: 1237–1241, 2005.
18. Hopkins W, Marshall S, Batterham A, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 41: 3–13, 2009.
19. Hough JP, Papacosta E, Wraith E, Gleeson M. Plasma and salivary steroid hormone responses of men to high-intensity cycling and resistance exercise. J Strength Cond Res 25: 23–31, 2011.
20. Kilinc F. Effects of vitamin C and E combination on hormonal, enzymatic and hematological values in blood of forced training basketball players. Biol Sport 27: 29–33, 2010.
21. Martínez AC, Seco J, Tur JA, Abecia LC, Orella EE, Biescas AP. Testosterone and cortisol changes in professional basketball players through a season competition. J Strength Cond Res 24: 1102–1108, 2010.
22. Maso F, Lac G, Filaire E, Michaux O, Robert A. Salivary testosterone and cortisol in rugby players: Correlation with psychological overtraining items. Br J Sport Med 38: 260–263, 2004.
23. Meckel Y, Nemet D, Bar-Sela S, Radom-Aizik S, Cooper DM, Sagiv M, Eliakim A. Hormonal and inflammatory responses to different types of sprint interval training. J Strenghth Cond Res 25: 2161–2169, 2011.
24. Meeusen R, Duclos M, Foster C, Fry A, Gleeson M, Nieman D, Raglin J, Rietjens G, Steinacker J, Urhausen A. Prevention, diagnosis, and treatment of the overtraining syndrome: Joint consensus statement of the European College of Sport Science and the American College of Sports Medicine. Med Sci Sports Exerc 45: 186–205, 2013.
25. Metaxas T, Koutlianos N, Sendelides T, Mandroukas A. Preseason physiological profile of soccer and basketball players in different divisions. J Strength Cond Res 23: 1704–1713, 2009.
26. Mujika I, Chatard J, Padilla S, Guezennec C, Geyssant A. Hormonal responses to training and it's tapering off in competitive swimmers: Relationships with performance. Eur J Appl Physiol Occup Physiol 74: 361–366, 1996.
27. Mujika I, Padilla S, Pyne D, Busso T. Physiological changes associated with the pre-event taper in athletes. Sports Med 34: 891–927, 2004.
28. Nunes J, Crewther B, Ugrinowitsch C, Tricoli V, Viveiros L, de Rose D, Aoki M. Salivary hormone and immune responses to three resistance exercise schemes in elite female athletes. J Strength Cond Res 25: 2322–2327, 2011.
29. Phillips BE, Hill DS, Atherton PJ. Regulation of muscle protein synthesis in humans. Curr Opin Clin Nutr Metab Care 15: 58–63, 2012.
30. Salvador A, Costa R. Coping with competition: Neuroendocrine responses and cognitive variables. Neurosci Biobehav Rev 33: 160–170, 2009.
31. Schröder H, Navarro E, Mora J, Galiano D, Tramullas A. Effects of a-tocopherol, ß-carotene and ascorbic acid on oxidative, hormonal and enzymatic exercise stress markers in habitual training activity of professional basketball players. Eur J Nutr 40: 178–184, 2001.
32. Sebastian-Gambaro MA, Lirón-Hernández J, Fuentes-Arderiu X. Intra- and Inter-individual biological variability data bank. Eur J Clin Chem Clin Biochem 35: 845–852, 1997.
33. Seidman D, Dolev E, Deuster P, Burstein R, Arnon R, Epstein Y. Androgenic response to long-term physical training in male subjects. Int J Sports Med 11: 421–424, 1990.
34. Urhausen A, Gabriel H, Kindermann W. Blood hormones as markers of training stress and overtraining. Sports Med 20: 251–276, 1995.
35. Viru A, Viru M. Cortisol—Essential adaptation hormone in exercise. Int J Sports Med 25: 461–464, 2004.
Keywords:

hormones; physiology; overtraining; periodization; team sports

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