Competitive team-sports, such as handball, basketball, and futsal are high-intensity intermittent team-sports that place heavy emphasis on aerobic fitness. Although team sports are not endurance sports per se, a minimum level of aerobic fitness is crucial in the ability to maintain an elevated intensity work to play at top-level professional leagues (1,10,31,39). Aerobic improvement increases the number of sprints and the distance covered during a match and promotes more ball involvement in soccer (15). Reduction of the time spent at high-intensity during the games observed in handball, basketball, and futsal (1,2,31) also indicates the potential benefit of aerobic conditioning in indoor team-sports. Therefore, it seems of paramount importance to frequently assess changes in aerobic capacity in professional team-sport players throughout the season.
The speed at the “maximal lactate steady state” (MLSS) is generally considered the gold standard for determination of aerobic capacity. However, MLSS determination is tedious and time-consuming (24). In field testing of team-sports, fixed lactate thresholds, such as running speeds associated with 3 mmol·L−1 (S3mM) and 4 mmol·L−1 (S4mM), which is also termed OBLA (34), are often preferred to MLSS (9,12,22,25) because they reduce the time and cost of the assessment procedure, are easy to measure in several athletes at the same time in field settings, and have been shown to reflect the speed at the MLSS as appropriate as other lactate thresholds (3,13). Unfortunately, the determination of the fixed lactate thresholds requires qualified personnel and involves blood sampling, which is an invasive technique that can be aversive to some participants. Moreover, when performing field testing of teams, typically composed of 10–25 players, the cost of blood sampling is high and requires the participation of several qualified professionals. These issues often hinder the appropriate monitoring of endurance capacity in team-sports.
In an attempt to monitor players and predict aerobic performance more regularly, submaximal noninvasive low-cost tests have been of general interest to sport teams and to the sport scientist's community. Based on previous studies (8,20,25–27) and personal observations from years of professional experience in the assessment of team-sports' endurance (9,11,12), we have noticed that the S3mM and S4mM determined during a progressive maximal field test usually occur at a mean intensity close to 90% of maximal heart rate (HRmax). It also seems that this relationship is maintained despite alterations in the intensity of the individual or fixed lactate thresholds due to training, detraining, or hypoxia (6,7,15,18,23,25,26). Nevertheless, whether the intensity at 90%HRmax could be used as a simple variable to assess S3mM and S4mM has never been investigated. Being able to assess aerobic capacity by means of an easy and noninvasive estimation of the fixed lactate thresholds would certainly cheapen and facilitate the monitoring of aerobic performance. This would be of particular interest to teams and coaches with limited resources. Accordingly, the primary aim of this study was to investigate the relationships between the running speeds associated with the broadly used 90% of HRmax (S90%HRmax) and the S3mM and S4mM (or OBLA). It was hypothesized that S90%HRmax would be related to fixed lactate thresholds in team-sport players.
Experimental Approach to the Problem
A cross-sectional study was carried out to investigate the relationships between S90%HRmax and fixed blood lactate thresholds (S3mM and S4mM). Professional team-sport players performed a 4-stage discontinuous progressive running test followed, if exhaustion was not previously achieved, by an additional maximal continuous incremental running test to attain HRmax. The individual S3mM, S4mM, and S90%HRmax were determined by linear interpolation and relationships examined.
Players from 3 professional teams of futsal (n = 10), handball (n = 16), and basketball (n = 10) participated in this study. The teams belonged to the Spanish First Divisions of futsal and handball and the Spanish National Second Division of basketball. Adult (19–36 years) male players from three professional teams of futsal (n = 10), handball (n = 16) and basketball (n = 10) participated in this study. All participants were free of known cardiovascular, respiratory, and circulatory dysfunctions, and they were not taking any drug or medication known to influence physical performance. Table 1 shows participant characteristics.
The study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures were approved by the local institutional review board. Athletes and coaches were carefully informed about the possible risks and benefits of the project, and written informed consent was obtained from every volunteer.
The study was conducted during the first 2–3 days of the preseason training period, that is, 4–6 weeks before the start of official competitions. Testing was integrated into the training schedule. Participants refrained from vigorous exercise during the previous 24 hours and were instructed to fast for at least 2 hours before the exercise test. They were also instructed to abstain from caffeinated and alcoholic beverages during the testing day. The pre-exercise meal for each participant was the same in an effort to standardize nutritional intake. All testing sessions were carried out in the same indoor court and at the same time of the day to lessen circadian variability.
After a nonstandardized 15-min warm-up period, participants performed a 4-stage discontinuous progressive running test around the indoor court (40 × 20 m) (9,10). Each stage was 5 minutes long, with a 3-minute resting period between stages. The running speeds at each stage were 10, 12, 14, and 16 km·h−1. This incremental test protocol was the test protocol routinely used for regular testing by all these 3 sport teams. The choice of the running speeds was made to assure blood lactate concentration ([La−]) values lower and similar or slightly higher than 4 mmol·L−1, based on previous endurance assessments performed for exercise prescription purposes with these teams (9,10). A 5-minute-long stage duration protocol was used because it is the minimum stage duration needed to reach a lactate equilibrium between muscle and blood at exercise intensities approaching S4mM (32), and because S4mM approximates MLSS better with 5-minute stage duration protocols than with lower-stage duration protocols (21). To ensure constant speed during each stage, participants were instructed to even pace their running after an audio signal connected to a preprogrammed laptop (Balise Temporelle, Bauman, Switzerland). Heart rate was recorded every 15 seconds (Sport Tester; Polar Electro Oy, Kempele, Finland) and averaged for the last 3 minutes of every completed stage. Immediately after each stage, and until participants attained a [La−] above 5 mmol·L−1, capillary blood samples from an hyperemic earlobe were taken and [La−] amperometrically determined (Lactate Pro LT-1710; Arkray KDK Corp., Shiga, Japan).
The participants (3 futsal players) who did not reach volitional exhaustion during the discontinuous running test were required to rest for 5–8 minutes after the completion of the final stage and then began a maximal continuous incremental test to obtain their HRmax. Starting speed in this additional test was 13.6 km·h−1, and it was increased by 0.8 km·h−1 every minute until players could no longer maintain the required speed. Either in the discontinuous or continuous protocol, volunteers were vigorously encouraged to complete exhaustion. Maximal heart rate was considered to be the highest HR value recorded, and it coincided in most cases with the voluntary termination of exercise (29).
S3mM and S4mM were determined by linear interpolation. S90%HRmax was calculated following identical procedures. The test-retest intraclass correlation coefficients of S3mM, S4mM, and HRmax have been shown to range between 0.94 and 0.99, and coefficients of variation (CVs) range between 1.2 and 1.7% (5,30,37).
Statistical analyses were performed using SPSS 17.0 (SPSS Inc., Chicago, USA). Gaussian distribution was verified by Shapiro-Wilk's test when appropriate. Multiple comparisons between teams were evaluated using the Kruskal-Wallis test. Linear regression analyses were performed to determine the relationships between the variables of interest. Assumptions of linear regressions were checked and met. Evaluation of Cook's distance revealed minimal influence of the individual data points on the regression models. Pearson product-moment correlation coefficients (r) were used to indicate the magnitude and direction of each linear relationship. The magnitudes of the correlations were interpreted as follows: 0.1–0.3, small; 0.3–0.5, moderate; 0.5–0.7, large; 0.7–0.9, very large; and >0.9, extremely large (17). The accuracy of each linear regression was evaluated using the standard error of the estimates (SEE), the 95% confidence intervals (CIs) for the slope, and the 90% CI for the correlation coefficients. The slopes of the regression lines were compared using analysis of covariance. Post hoc power calculation for the linear regressions, assuming type I error of 0.05, indicated a power of above 99%. Statistical significance was set at p ≤ 0.05. Data in the text, tables, and figures are reported as mean and SD.
Figure 1 illustrates [La−] and %HRmax at the 10, 12, and 14 km·h−1 exercise stages. Absolute HR values did not differ among the teams at any of the exercise stages (p > 0.05). Heart rate values for all the teams as a whole were 156 ± 10, 171 ± 10, and 182 ± 9 b·min−1 at completion of the 10, 12, and 14 km·h−1 stages, respectively. Basketball players reached significantly lower HRmax values (184 ± 9 b·min−1) compared with futsal (192 ± 5 b·min−1; p = 0.038; 95% CI −14.60 to −0.33) and handball (192 ± 6 b·min−1; p = 0.046; 95% CI −16.71 to −0.10) players. Every participant fulfilled the criterion of HRmax above 90% of age-predicted HRmax (29).
Descriptive features of S3mM, S4mM, and S90%HRmax are summarized in Table 2. Mean S3mM significantly differed from mean S4mM (p = 0.027; 95% CI −1.75 to −0.08). There were no significant differences between mean S90%HRmax and mean S3mM (p = 0.51; 95% CI −1.20 to 0.38). Mean S90%HRmax neither differed from mean S4mM (p = 0.32; 95% CI −0.28 to 1.29).
Figure 2 shows the linear relationships between S90%HRmax and S3mM (Figure 2A), as well as between S90%HRmax and S4mM (Figure 2B). The rate of increase did not differ between the basketball, handball, and futsal teams (p > 0.05). Regression equations of Figure 2 are reported in Table 3.
Unplanned regression analyses revealed significant linear relationships between %HRmax at 10 and 12 km·h−1 and S3mM and S4mM (Figure 3). There were no differences in the rate of decline between the teams (p > 0.05). Very large and significant (p < 0.001) correlations between S3mM and %HRmax at S3mM (r = 0.67; SEE = 1.14 km·h−1; 95% CI 0.16–0.39; 90% CI ±0.16), as well as between S4mM and %HRmax at S4mM (r = 0.68; SEE = 1.09 km·h−1; 95% CI 0.14–0.34; 90% CI ±0.16), were also found.
The main finding of this study was that S90%HRmax accurately predicted S3mM and S4mM in professional handball, basketball, and futsal players. This is the first study reporting the potential of S90%HRmax to be used as a simple, low-cost, and noninvasive performance variable to frequently assess and monitor aerobic capacity in professional team-sport players.
A careful analysis of the existing literature revealed that S3mM and S4mM, as well as MLSS, usually occur at a mean intensity close to 90% HRmax (8,19,20,25–27). Although it has been suggested that there might be a steady-state relationship between the intensity at a fixed [La−] and %HRmax (6,26), the intensity at a given %HRmax as a simple performance variable to predict S3mM and S4mM had never been investigated before. In this study, S90%HRmax was close to S3mM and S4mM (Table 2), and it was found to be a good predictor of both fixed lactate thresholds in homogeneous groups of professional team-sport players, particularly in futsal and handball (Figure 2). Likewise, %HRmax at 10 and 12 km·h−1 were observed to accurately estimate both fixed lactate thresholds (Figure 3). The magnitudes of these correlations are similar to those observed between S4mM and MLSS (19,36) and similar or even higher than those observed between individual or fixed lactate thresholds and HR deflection points, which, unlike S90%HRmax, are not always possible to determine (4,16,35). These results indicate that S90%HRmax can be used as a noninvasive and easy method to estimate S3mM and S4mM during a progressive running test in professional indoor team-sport players.
The estimations of S3mM and S4mM from S90%HRmax were less accurate in basketball than in futsal and handball (Figure 2). Similar results were found when %HRmax at 10 and 12 km·h−1 were taken as predictor variables (Figure 3). It is well known that the correlation coefficients are influenced by the range in the predictor and responding variables; the greater the range or the heterogeneity of a group, the greater the magnitude of the correlation coefficient. In this study, grouped CVs (a normalized measure of dispersion) for S3mM, S4mM, and S90%HRmax ranged between 10.3 and 12.6% (Table 2). However, when we examined each teams' linear relationships separately, therefore narrowing the range in S3mM, S4mM, and S90%HRmax (e.g., futsal CVs ranged between 5.2 and 5.5% in S3mM, S4mM, and S90%HRmax; Table 2), it can be observed that correlation magnitudes augmented in futsal and handball but not in basketball (Figure 2). The nonattainment of a true HRmax in some basketball players is suggested to be the main factor explaining these less accurate estimations. Mean differences between HRmax and age-predicted HRmax (Age-predicted HRmax = 211 − 0.64·age) were well within ±10.8 b·min−1 SEE reported for the HRmax vs. age relationship (29) in every sport team including basketball. Nevertheless, mean absolute HRmax was 8 b·min−1 lower (p ≤ 0.05) in basketball than in futsal and handball, despite athletes being of similar age. Furthermore, basketball players' mean absolute HRmax only corresponded to 95% of their age-predicted HRmax, which is lower than the 98 and 99% observed in our futsal and handball players, as well as lower than the 97–101% values previously reported in soccer players (15), long-distance runners (7), cross-country skiers (20), amateur or professional cyclists (27), and basketball players (28). Lack of motivation and participants' lack of familiarity with this kind of running tests could have also partly hampered the achievement of a real HRmax (38). Although most of the futsal (80%) and handball (77%) players were well accustomed to the exercise protocol, because they were previously tested using the same testing procedures, 70% of the basketball players were not familiar with this test. Other factors such as the effect of the nonstandardized warm-up (each team performed their own warm-up routine supervised by their respective physical trainer) or anthropometric, speed, and strength characteristics, are thought to be less plausible factors to explain these differences.
The observed relationships between S90%HRmax and both fixed lactate thresholds (Figure 2) and the nonsignificant differences between S90%HRmax and S3mM and S4mM (Table 2) do not necessarily imply that exercise intensity at either of the fixed lactate thresholds must coincide with the intensity at 90%HRmax. In fact, the large correlations between S3mM or S4mM and %HRmax, at which S3mM or S4mM occurred, suggest that team-sport players with higher fixed lactate thresholds achieve the thresholds at higher %HRmax compared with those with lower fixed lactate thresholds. This is in agreement with previous studies showing that endurance-trained individuals achieve their fixed lactate thresholds at a higher relative load (expressed either as percentage of maximal oxygen uptake or HRmax) than less fit individuals (8,18). Nonetheless, because of the fact that S3mM and S4mM explained <50% of the variance in %HRmax at their respective fixed lactate thresholds, other factors such as age (33) could influence these relationships.
This study is limited in some aspects. The use of maximal secondary criteria (e.g., perceived exertion or peak [La−] measures) or a maximal confirmation test could have helped to verify nonattainment of HRmax, particularly in basketball. Besides, because steady-state [La−] can vary among athletes, the assessment of the MLSS may have led to a greater accuracy in the determination of endurance capacities. Nevertheless, the intensity at a given submaximal [La−] accurately predicts endurance capacity (5,14,20) and lessens the testing burden on the participant and researcher associated with the MLSS (24). The type of exercise protocol used is unlikely to have influenced S3mM or S4mM vs. %HRmax relationships (38), although further corroboration is recommended. Finally, this investigation was conducted on team-sport players with an S4mM between 9.6 and 14.8 km·h−1 during a specific time of the season that limited the applicability of the results. Because of the fact that individual or fixed lactate thresholds, as well as the MLSS, usually occur at a mean intensity close to 90% HRmax regardless of the level of aerobic capacity of the individuals (8,19,20,25–27), and that this relationship is maintained despite alterations in the intensity of the individual or fixed lactate thresholds due to training, detraining, or hypoxia (6,7,15,18,23,25,26), S90%HRmax might be a useful variable to predict and monitor fixed lactate thresholds during an entire competitive season and in other populations with an S4mM lower than 9.6 km·h−1 or higher than 14.8 km·h−1. However, whether a steady-state relationship between fixed lactate thresholds and %HRmax is maintained throughout an entire competitive season, in athletes of other sports, in athletes with different level of aerobic capacity or under other conditions (e.g., glycogen-depleted state or hypoxia) remains unclear, is beyond the scope of this study, and deserves further research.
This study supports that aerobic capacity can be assessed and monitored through S90%HRmax in basketball, handball, and futsal players with an S4mM between 9.6 and 14.8 km·h−1. The use of S90%HRmax as an endurance performance variable could facilitate the assessment and monitoring of aerobic capacity in team-sports and coaches with limited resources. Indeed, this variable is a simple, low-cost, and noninvasive variable that allows investigation of several players at the same time without the need of expensive equipment or technical expertise to administer the test. Further research to confirm these results in other sports composed of athletes with an S4mM lower than 9.6 km·h−1 or higher than 14.8 km·h−1 and to explore the possible physiological mechanisms underpinning the fixed lactate thresholds and S90%HRmax relationships is warranted.
None of the authors declare any relationships with companies or manufactures that would benefit from the results of this study. The results of this study do not constitute endorsement of the product by the authors or the National Strength and Conditioning Association. No funding was received for this work from any of the following organizations or any other institution: National Institutes of Health (NIH), Wellcome Trust, Howard Hughes Medical Institute (HHMI).
1. Alvarez JC, D'Ottavio S, Vera JG, Castagna C. Aerobic fitness in futsal players of different competitive level. J Strength Cond Res 23: 2163–2166, 2009.
2. Ben Abdelkrim N, El Fazaa S, El Ati J. Time-motion analysis and physiological data of elite under-19-year-old basketball players during competition. Br J Sports Med 41: 69–75, 2007.
3. Beneke R. Anaerobic threshold, individual anaerobic threshold, and maximal lactate steady state
in rowing. Med Sci Sports Exerc 27: 863–867, 1995.
4. Bodner ME, Rhodes EC. A review of the concept of the heart rate deflection point. Sports Med 30: 31–46, 2000.
5. Borch KW, Ingjer F, Larsen S, Tomten SE. Rate of accumulation of blood lactate during graded exercise as a predictor of “anaerobic threshold”. J Sports Sci 11: 49–55, 1993.
6. Foster C, Fitzgerald DJ, Spatz P. Stability of the blood lactate-heart rate relationship in competitive athletes. Med Sci Sports Exerc 31: 578–582, 1999.
7. Friedmann B, Bauer T, Menold E, Bartsch P. Exercise with the intensity of the individual anaerobic threshold in acute hypoxia. Med Sci Sports Exerc 36: 1737–1742, 2004.
8. Garcia-Tabar I, Sánchez-Medina L, Aramendi J, Ruesta M, Ibañez J, Gorostiaga E. Heart rate variability thresholds predict lactate thresholds in professional world-class road cyclists. J Exerc Physiol Online 16: 38–50, 2013.
9. Gorostiaga EM, Granados C, Ibanez J, Gonzalez-Badillo JJ, Izquierdo M. Effects of an entire season on physical fitness changes in elite male handball players. Med Sci Sports Exerc 38: 357–366, 2006.
10. Gorostiaga EM, Granados C, Ibanez J, Izquierdo M. Differences in physical fitness and throwing velocity among elite and amateur male handball players. Int J Sports Med 26: 225–232, 2005.
11. Gorostiaga EM, Llodio I, Ibanez J, Granados C, Navarro I, Ruesta M, Bonnabau H, Izquierdo M. Differences in physical fitness among indoor and outdoor elite male soccer players. Eur J Appl Physiol 106: 483–491, 2009.
12. Granados C, Izquierdo M, Ibanez J, Ruesta M, Gorostiaga EM. Effects of an entire season on physical fitness in elite female handball players. Med Sci Sports Exerc 40: 351–361, 2008.
13. Hauser T, Adam J, Schulz H. Comparison of selected lactate threshold parameters with maximal lactate steady state
in cycling. Int J Sports Med 35: 517–521, 2014.
14. Heck H, Mader A, Hess G, Mucke S, Muller R, Hollmann W. Justification of the 4-mmol/l lactate threshold. Int J Sports Med 6: 117–130, 1985.
15. Helgerud J, Engen LC, Wisloff U, Hoff J. Aerobic endurance training improves soccer performance. Med Sci Sports Exerc 33: 1925–1931, 2001.
16. Hofmann P, Bunc V, Leitner H, Pokan R, Gaisl G. Heart rate threshold related to lactate turn point and steady-state exercise on a cycle ergometer. Eur J Appl Physiol Occup Physiol 69: 132–139, 1994.
17. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 41: 3–13, 2009.
18. Hurley BF, Hagberg JM, Allen WK, Seals DR, Young JC, Cuddihee RW, Holloszy JO. Effect of training on blood lactate levels during submaximal exercise. J Appl Physiol Respir Environ Exerc Physiol 56: 1260–1264, 1984.
19. Jones AM, Doust JH. The validity of the lactate minimum test for determination of the maximal lactate steady state
. Med Sci Sports Exerc 30: 1304–1313, 1998.
20. Kindermann W, Simon G, Keul J. The significance of the aerobic-anaerobic transition for the determination of work load intensities during endurance training. Eur J Appl Physiol Occup Physiol 42: 25–34, 1979.
21. Kuipers H, Rietjens G, Verstappen F, Schoenmakers H, Hofman G. Effects of stage duration in incremental running tests on physiological variables. Int J Sports Med 24: 486–491, 2003.
22. Loures JP, Chamari K, Ferreira EC, Campos EZ, Zagatto AM, Milioni F, da Silva AS, Papoti M. Specific determination of maximal lactate steady state
in soccer players. J Strength Cond Res 29: 101–106, 2015.
23. Lucia A, Hoyos J, Perez M, Chicharro JL. Heart rate and performance parameters in elite cyclists: A longitudinal study. Med Sci Sports Exerc 32: 1777–1782, 2000.
24. Mann T, Lamberts RP, Lambert MI. Methods of prescribing relative exercise intensity: Physiological and practical considerations. Sports Med 43: 613–625, 2013.
25. McMillan K, Helgerud J, Grant SJ, Newell J, Wilson J, Macdonald R, Hoff J. Lactate threshold responses to a season of professional British youth soccer. Br J Sports Med 39: 432–436, 2005.
26. Mujika I. The cycling physiology of Miguel Indurain 14 years after retirement. Int J Sports Physiol Perform 7: 397–400, 2012.
27. Mujika I, Padilla S. Physiological and performance characteristics of male professional road cyclists. Sports Med 31: 479–487, 2001.
28. Narazaki K, Berg K, Stergiou N, Chen B. Physiological demands of competitive basketball. Scand J Med Sci Sports 19: 425–432, 2009.
29. Nes BM, Janszky I, Wisloff U, Stoylen A, Karlsen T. Age-predicted maximal heart rate in healthy subjects: The HUNT fitness study. Scand J Med Sci Sports 23: 697–704, 2013.
30. Pfitzinger P, Freedson PS. The reliability of lactate measurements during exercise. Int J Sports Med 19: 349–357, 1998.
31. Povoas SC, Seabra AF, Ascensao AA, Magalhaes J, Soares JM, Rebelo AN. Physical and physiological demands of elite team handball. J Strength Cond Res 26: 3365–3375, 2012.
32. Rusko H, Luhtanen P, Rahkila P, Viitasalo J, Rehunen S, Harkonen M. Muscle metabolism, blood lactate and oxygen uptake in steady state exercise at aerobic and anaerobic thresholds. Eur J Appl Physiol Occup Physiol 55: 181–186, 1986.
33. Rusko H, Rahkila P, Karvinen E. Anaerobic threshold, skeletal muscle enzymes and fiber composition in young female cross-country skiers. Acta Physiol Scand 108: 263–268, 1980.
34. Sjödin B, Jacobs I. Onset of blood lactate accumulation and marathon running performance. Int J Sports Med 2: 23–26, 1981.
35. Vachon JA, Bassett DR Jr, Clarke S. Validity of the heart rate deflection point as a predictor of lactate threshold during running. J Appl Physiol (1985) 87: 452–459, 1999.
36. Vobejda C, Fromme K, Samson W, Zimmermann E. Maximal constant heart rate—a heart rate based method to estimate maximal lactate steady state
in running. Int J Sports Med 27: 368–372, 2006.
37. Weltman A, Snead D, Stein P, Seip R, Schurrer R, Rutt R, Weltman J. Reliability and validity of a continuous incremental treadmill protocol for the determination of lactate threshold, fixed blood lactate concentrations, and VO2max. Int J Sports Med 11: 26–32, 1990.
38. Whipp BJ, Davis JA, Torres F, Wasserman K. A test to determine parameters of aerobic function during exercise. J Appl Physiol Respir Environ Exerc Physiol 50: 217–221, 1981.
39. Ziv G, Lidor R. Physical attributes, physiological characteristics, on-court performances and nutritional strategies of female and male basketball players. Sports Med 39: 547–568, 2009.
Keywords:Copyright © 2015 by the National Strength & Conditioning Association.
OBLA; maximal lactate steady state; exercise testing; elite athletes; heart rate monitor