Some easy-to-apply field tests have been developed over the years to assess swimmers' aerobic and anaerobic capacity, aiming to provide a more accurate monitoring of the training process and respective prescription (19,28). From these, critical swimming speed (CSS), the maximal swimming speed that could be maintained continuously for a long time without exhaustion (32), has been frequently used. Critical swimming speed has been suggested to strongly correlate with some well-known aerobic performance indexes, such as the averaged 400-m speed (32), the speed at 4 mmol·L−1 blood lactate concentration (7,8,32), the speed of the 30-minute test (6,8) and the maximal lactate steady-state speed (12). Complementary, increased CSS values after an aerobic training program have been previously reported for national level (12), well-trained (13) and age group swimmers (15,21,29).
Furthermore, the y-intercept of the distance-time relationship (anaerobic distance capacity [ADC]) has been assessed as an index of anaerobic capacity, corresponding to the maximal distance covered supported by anaerobic energy sources (6,17) and it evidence changes after a high-intensity swimming training period (13). However, the validity of ADC has not been confirmed (6,18,31). In addition, it is known that the calculation of ADC and CSS is mainly dependent on the number and distance of trials (29) and the mathematical model selection (35). In fact, the use of very short distance trials (10–50 m) to calculate CSS may help in the evaluation of anaerobic potential and in the establishment of anaerobic pacing (14,16), conversely to the CSS calculated from longer distances (200–800 m) (8). Whatever the case, anaerobic potential (power or capacity) evaluation is a challenging task for land sports and even more in swimming.
Speed reserve (SRes) has also been proposed as a performance index in cyclic sports (e.g., running), expressing the difference between the maximum speeds attained during a short and a long race (2). This parameter has been previously used to express exercise intensity and explain the individual variability of the time to exhaustion at several running speeds (1). Furthermore, SRes was also used to describe the difference between the maximum running speed and the speed at maximal oxygen uptake in trained athletes, being observed that decrements in all-out speed were directly proportional with this difference (in this case, it was designated as anaerobic SRes) (3).
Despite its simplicity (being calculated with only 2 distances), SRes has not been used in swimming, and there is no report about its behavior after a training period. The purpose of this study was to propose the use of SRes as a new tool for coaches and scientists to evaluate swimmers' conditioning status. In addition, we aimed to determine whether SRes presents a strong relationship with CSS and ADC and to examine its behavior after an 8-week training program focused at developing maximal aerobic swimming speed. It was hypothesized that SRes would have a practical application in swimming and present high inverse and direct relationships with CSS and ADC, respectively. Moreover, it was expected that SRes and ADC would decrease and CSS would increase as a response to an aerobic training program.
Experimental Approach to the Problem
During an 8-week training period, and through a controlled repeated measures design, the changes in SRes, CSS and ADC were examined in a group of nonactive swimmers with previous national level competitive experience (experimental: E group, n = 15). The weekly training distance was used as an independent variable and was increased by 14% during the second (weeks 3–6) and 12% in the third part (weeks 6–8) of the swimming training program.
To examine the effect of the training program, changes in SRes, CSS, and ADC (dependent variables) were evaluated before (pre) and after the 8-week period (post). The training intensity was adjusted according to the maximum aerobic speed of each subject (estimated during a 400 m maximum test). A control group (C, n = 6) that was not engaged on the 8-week training program was also tested for changes on SRes, CSS, and ADC during the same period.
Twenty-one male University students (age range: 21.2 to 24.8 years) volunteered for this study (mean ± SD age for the E and C groups, respectively: 22.3 ± 0.9 vs. 22.2 ± 2.2 years; previous training experience: 11.6 ± 3.8 vs. 11.0 ± 3.4 years; height: 1.78 ± 0.03 vs. 1.81 ± 0.05 m; body weight: 77.5 ± 6.6 vs. 85.4 ± 6.1 kg). The inclusion criterion was a performance >60% of the world record in the 400-m front-crawl event. All subjects were nonactive athletes for a minimum time of 6 months and were matched for age and training experience. Their maximum test performances (50, 100, and 400 m) corresponded to 345 ± 35 FINA points for a 50-m pool. In addition, subjects were instructed to abstain from any strenuous exercise, followed their usual diet pattern before testing and during the 8-week training period, and signed an informed consent before participating in the study. This consent provided information about the procedures of the experiment, indicating the right to withdraw and to have access to the data at any time. The investigation was approved by the University Institutional Review Board for use of human subjects, in accordance with the ethical standards and the Declaration of Helsinki.
Speed reserve was calculated as the difference between the speed of the 50-m test and the average speed of the 400-m test, all conducted at maximal intensity front-crawl swimming. Critical swimming speed was calculated through the slope of the regression line between swimming distances and corresponding times (32) and the y-intercept of the regression line was used as an indicator of ADC (13), using 2 (50 and 400 m: CSS2 and ADC2) and 3 front-crawl maximum time trials (50, 100, and 400 m: CSS3, ADC3) (30) performed with a push start. The 50- and 400-m time trials took place in a single session (with a 30-minutes passive recovery in between) (13), and performance was recorded using a digital stopwatch (Seiko S141).
Maximal aerobic speed was estimated during the 400-m maximum test as the average speed measured between 50 and 350 m (5,10). All training sessions and data collections were conducted at the same period of the year (April to June), time of the day (10.00–13.00 hours), and under the same water temperature conditions (26–27° C).
The training program was held 3 times a week over a period of 8 weeks. Each training session started with a standard warm-up of 600 m on a low to moderate self-paced swim, and included 6 × 100 m front crawl at the maximal aerobic speed with 30-second rest (11). Taking into consideration that the adaptations after a training program are greater for untrained compared with trained swimmers (13), the weekly training distance in the present study was increased twice during the 8-week period, changing totally by 26% (1800 vs. 2100 vs. 2400 m for weeks 1–3, 3–6, and 6–8, respectively). The training process was supervised by the research personnel and the main set was timed individually. Swimmers were able to comply with the required training pace following instructions and feedback by a member of the research personnel walking along the side of the pool.
Homogeneity of variance was tested using the Levene's test and sphericity was verified by the Mauchly's test. Two-way analysis of variance for repeated measures was performed to compare pre- and posttraining data between C and E groups (2 groups × 2 testing periods). Comparisons between means were made using the Tukey's honest significant difference post hoc test. The effect size (ES), 95% confidence intervals (CI) and power of analysis (P) were also calculated (22). Pearson's product-moment correlation coefficient was applied to test the correlation between variables. Analysis was computed using the SPSS v.19 statistical package (IBM Corp., Armonk, New York). Data are presented as mean ± SD and statistical significance level was set at p ≤ 0.05.
Speed Reserve, Critical Swimming Speed, and Anaerobic Distance Capacity Values
The mean ± SD values regarding SRes, CSS2, CSS3, ADC2, and ADC3 of the E and C groups at pre- and posttraining are shown in Table 1. Speed reserve values were higher in the E compared with C group at pretraining (p = 0.04) but not different at posttraining (p = 0.99). In contrast, CSS2 and CSS3, as well as ADC2 and ADC3, values were not different between E and C groups both before and after intervention (p ≤ 0.05). CSS3 was greater compared with CSS2, and ADC3 was greater compared with ADC2 before and after the training period in both groups (p ≤ 0.05).
Regarding the E group, it was observed that SRes decreased 8.1 ± 8.4% after 8 weeks of training (95% CI: −12.7 to −3.4%, ES = −0.6, P = 0.62, p = 0.03). Conversely, CSS2 increased 5.5 ± 3.2% corresponding to 93.5 ± 0.2 and 93.9 ± 0.1% of the 400-m velocity at pre- and posttraining, respectively (95% CI: 3.7–7.2%, ES = 0.6, P = 0.99, p < 0.01). The same behavior was observed for CSS3, as it increased 6.0 ± 3.2%, corresponding to 95.0 ± 0.1 and 95.4 ± 0.1% of the 400-m speed at pre- and posttraining, respectively (95% CI: 4.3–7.8%, ES = 0.7, P = 0.99, p < 0.01). ADC2 and ADC3 decreased by 12.0 ± 9.4% (95% CI: −17.2 to −6.8%, ES = −0.7, P = 0.63, p = 0.01) and 9.0 ± 11.2% (95% CI: −15.2 to −2.8%, ES = −0.5, P = 0.87, p = 0.01). All the studied parameters of the C group showed no significant changes after 8 weeks compared with pre-intervention values (Table 1; p > 0.05).
Correlations Between CSS2, CSS3, ADC2, ADC3, and Speed Reserve
For the E group, high negative correlations were observed between SRes with CSS2 and CSS3 for pre- and postvalues. ADC2 and ADC3 were significantly and positively correlated with SRes in the E and C groups (Table 2).
Maximum Tests Performance
As could be observed in Table 3, performance of the E group was significantly improved in the maximum tests of 400 and 100 m after the training program (5.6 ± 2.9%, ES = 0.6, p = 0.01 and 4.1 ± 3.3%, ES = 0.6, p = 0.01, respectively), but not for the 50 m maximum test (1.36 ± 0.2%, ES = 0.2, p = 0.43). However, swimming performance of the C group did not change significantly for all maximum tests (p > 0.05) (Table 3).
The findings of the current study support the hypothesis that SRes has a practical application in swimming and show a high inverse relationship with CSS, and a high direct relationship with ADC before and after an 8-week aerobic training program with intensity corresponding to maximal aerobic speed. Thus, lower values of SRes and ADC conclude to higher values for CSS and vice versa. In addition, SRes, ADC2, and ADC3 indices were reduced at the same extent for the E group (8.0–12.0%), suggesting that they may express similar training effects. Decreased SRes indicates that swimmers improved the 400-m speed, through improvement of aerobic capacity, while maintaining the 50-m speed. Decreased ADC may indicate a negative effect of this type of aerobic training on anaerobic characteristics (i.e., anaerobic power).
To our knowledge, this is the first attempt to propose SRes in swimming, examine its effect after an aerobic training program and the potential for correlation with CSS and ADC. Any comparison between these parameters should be made by accepting inherent limitations concerning CSS and ADC calculation, as well as their relevant physiological meaning (number and duration of distances, selected model, aerobic and anaerobic indices validity). Furthermore, the sensitivity of changes for these indexes after a training period should be evaluated.
Change of CSS and ADC are directly dependent on performance changes of 50, 100, and 400 m. Performance in the 50-m maximum test of the E group was unchanged after the training compared with the baseline values and this can be possibly attributed to the type of the training program applied. Aerobic training may not be adequate to increase performance in a short duration test (i.e., 50 m—30 seconds; 20) because aerobic energy contribution is much less compared with the other 2 maximum tests (100 and 400 m; 9,23). In contrast, performance in maximum 100- and 400-m tests was significantly improved in the E group (p ≤ 0.05), explaining the alterations in both CSS2 and CSS3.
The validity of CSS as an index of aerobic endurance has been confirmed by several studies with swimmers (8,12,30). In contrast, the ADC has not been validated as an index of anaerobic power or capacity (6,18,31). However, it has been recently suggested that the y-intercept of the force vs. time relationship is related with the force during a 30-second maximum tethered swimming test and may express anaerobic capacity (17). Similarly, this parameter is related with the power produced during a Wingate test (27). Whatever the case, the different values of ADC2 and ADC3 indicate the reliance of this parameter to several intervening factors affecting its validity. In the present study, we have not assessed the validity of the ADC as an index of anaerobic capacity or power, but we have related it with the SRes.
Apart from its practical use, CSS was chosen to be correlated with SRes and ADC also because of its sensitivity to determine the adaptations on aerobic endurance after an aerobic training period. MacLaren and Coulson (13) reported a significant increase in CSS (1.38 vs. 1.42 m·s−1 or 2.81%) after 8 weeks of an aerobic training program in competitive swimmers. In the present study, the increment was greater (for the CSS2 and CSS3 5.5 ± 3.2 and 6.0 ± 3.2%, respectively), maybe because of the fact that subjects were untrained. Similar improvements in CSS calculated from distances of 200/400 m and 50/200/400 m, are reported by Reis and Alves (21) after 9 weeks of aerobic training in 13-year-old swimmers (6.6 and 5.2%, respectively). Age, sex, training level of swimmers, and the methodology for CSS determination (i.e., the number and duration of distances used) are some crucial parameters resulting in the variation of results of the above-mentioned studies. It is likely that CSS is influenced by longer training periods with aerobic orientation. As a consequence, comparison among different studies appears to be difficult (Table 4).
Reduction in postvalues of ADC2 and ADC3 (9.0 and 12.0%) seem reasonable because it is probably an index of anaerobic capacity (13) and the intensity of the training was at the maximum aerobic speed, thus favoring aerobic adaptations. In a previous study that involved aerobic and anaerobic swimming training program of 8 and 3 weeks, respectively. in well-trained swimmers, authors reported a reduction in ADC values of 13 and 11% (13). Nevertheless, the total training volume per week applied during the aerobic training phase in the above-mentioned study was significantly higher compared with the present study (35,000–45,000 vs. 3600–4200 m·wk−1).
Assessment of the anaerobic potential is problematic not only in swimming but in land sports also. A promising alternative for this purpose could be the anaerobic critical velocity, an issue that remains to be validated (14,16). In the present study, we did not tested the swimmers in short distances (10–50 m) and thus, it is not possible to compare SRes with the anaerobic critical velocity.
The 2-trial performance model for calculation of CSS and monitoring training adaptations with the slope of the distance-time relationship, as applied in this study, is presented as less reliable (4) and it often overestimates CSS (34). However, because SRes calculation is based on the difference of 2 distances (2), we had to accept CSS2 and also compare it with CSS3, which is considered to be a more reliable calculation (4). The 400 m was adopted as the longest distance for CSS calculation because it is denoted as a race that enables V[Combining Dot Above]O2max to be reached (25). This swim trial has been reported as a reliable and noninvasive field test for the determination of maximal aerobic speed (10,25), even for less-trained swimmers (24), or for populations swimming faster than 60.5% of the world speed record (26) (>65.5% for the participants in this study). Longer distances of approximately 15 minutes suggested by previous authors (34), would not be appropriate to be used because of the long time that subjects of this study refrained from regular swimming training.
The arguments presented in the previous paragraphs support the use of CSS as a valid index of aerobic endurance with an adequate sensitivity to detect training-induced changes. Although the strength of evidence is not convincing to characterize ADC as an index of anaerobic nature (capacity or power), we have examined its relation with the SRes. In a former study, the difference between maximal speed and critical speed was defined as maximal SRes and included aerobic SRes (aerobic SRes = maximal aerobic speed − critical speed) and anaerobic SRes (anaerobic SRes = maximal speed − maximal aerobic speed). The authors suggested that running velocities may be expressed as a percentage of maximal SRes and anaerobic SRes to better explain the individual variability of the time to exhaustion (1). Additionally, Bundle et al. (3) and Weyand et al. (33) proposed a general relationship applied in running and cycling to predict maximum speed and power, respectively, for efforts with duration varying from 3 to 240 seconds including 2 maximum performance trials, one supported by the anaerobic power and the other with speed at maximal oxygen uptake. The findings of the studies cited in the last paragraph show the value of adopting SRes as a tool to explain different exhaustion times at the same relative intensity and to help in prediction of performance.
It should be possible in swimming to use various distances apart from those that are presented in this study (50 and 400 m), as long as they describe the fastest time achieved on a distance considerably shorter than the racing one, as an alternative approach for the anaerobic potential of SRes, for example, 25 and 400 m, or examining the possibility of higher correlation between SRes values obtained during longer testing distances and other indexes of aerobic or anaerobic capacity, helping to explain its physiological meaning. In the present study, the main focus was to detect training-induced changes on the SRes relative to practical and easy-to-apply tests and not to validate it. Appropriate experimental design should be planned and help to validate SRes.
The lack of available literature in this matter reveals the necessity of further investigation of SRes, performed in higher level athletes of all swimming techniques, as a noninvasive, simple and practical index of the evaluation of the training progress and the comparison among swimmers. Theoretically, an athlete that demonstrates a smaller value of SRes would have less energy cost for maintaining a certain speed. Furthermore, the adaptation of SRes concept can aid to a better comprehension of decrements in swimming speed from maximum to critical and propose equation models for predicting swimming performance.
An obvious limitation of the current study can be addressed regarding the application of the results to swimming groups of different competitive level. This study has been focused on nonactive swimmers, as the objective was that possible adaptations would be totally attributed to specific characteristics of the high-intensity short duration aerobic training program. Another possible limitation could be that any changes occurred after a training period in untrained subjects can be attributed to biomechanical (stroke cycle kinematics) and bioenergetics factors adaptations (aerobic/anaerobic components), which cannot be separated in the present experimental setting. Finally, because SRes, CSS, and ADC are presented as protocol depended, altering the testing distances used in the future can lead to different results. In the present study, we made no effort to compare the SRes vs. more time-consuming and more accepted methods for aerobic endurance (i.e., lactate threshold, maximum lactate steady state) or anaerobic power and capacity (i.e., Wingate test, calculation of oxygen deficit).
In conclusion, the present study suggests that SRes, as determined from the speed difference between the 50 and 400 m maximum swimming tests, has a practical application in swimming showing strong relationships with CSS and ADC and may be used to determine changes in performance after an 8-week aerobic swimming program in a group of previously trained swimmers. However, it remains unknown how a training program focused on developing anaerobic capacity would affect SRes.
According to this study, SRes as calculated from distances of 50 and 400 m is possibly proposed as an index of anaerobic capacity. Coaches could benefit from this 2 distance calculation easily applied in one training session, or during competitions, using only a stopwatch without invasive procedures. It also can be used as an alternative and useful tool for comparison between athletes, evaluation of the training status, and monitoring improvement. Speed reserve calculated by using different testing distances could be also applied, providing the possibility to express aerobic/anaerobic potential in swimming.
The authors thank the subjects for their participation. No outside funding was received for this work. The results of the present study do not constitute endorsement of the product by the authors or the National Strength and Conditioning Association.
1. Blondel N, Berthoin S, Billat V, Lensel G. Relationship between run times to exhaustion at 90, 100, 120, and 140% of vVO2max an velocity expressed relatively to critical velocity and maximal velocity. Int J Sports Med 22: 27–33, 2001.
2. Bompa T. Periodization: Theory and Methodology of Training (4th ed.). Champaign, IL: Human Kinetics, 1999.
3. Bundle MW, Hoyt RW, Weyand PG. High-speed running performance: A new approach to assessment and prediction. J App Physiol (1985) 95: 1955–1962, 2003.
4. Dekerle J. The use of critical velocity in swimming? A place for critical stroke rate? Portugese J Sport Sci 6(Suppl. 2): 201–205, 2006.
5. Dekerle J, Pelayo P, Clipet B, Depretz S, Lefevre T, Sidney M. Critical swimming speed does not represent the speed at maximal lactate steady state. Int J Sports Med 25: 1–7, 2004.
6. Dekerle J, Sidney M, Hespel JM, Pelayo P. Validity and reliability of critical speed, critical stroke rate, and anaerobic capacity in relation to front crawl swimming performance. Int J Sports Med 23: 93–98, 2002.
7. Denadai BS, Greco CC, Teixeira M. Blood lactate response and critical speed in swimmers aged 10-12 years of different standards. J Sports Sci 18: 779–784, 2000.
8. Fernandes RJ, Vilas-Boas JP. Critical velocity as a criterion for estimating aerobic training pace in juvenile swimmers. In: Biomechanics and Medicine in Swimming VII. Keskinen K.L., Komi P.V., Hollander A.P., eds. Jyvaskyla, Finland: Gummerus Printing, 1999. pp. 233–238.
9. Laffite LP, Vilas-Boas JP, Demarle A, Silva J, Fernandes R, Billat VL. Changes in physiological and stroke parameters during a maximal 400-m free swimming test in elite swimmers. Can J Appl Physiol 29 (Suppl): S17–S31, 2004.
10. Lavoie JM, Montpetit RR. Applied physiology of swimming. Sports Med 3: 165–189, 1986.
11. Libicz S, Roels B, Millet GP. VO2
responses to intermittent swimming sets at velocity associated with VO2
max. Can J Appl Physiol 30: 543–553, 2005.
12. Machado MV, Junior OA, Marques AC, Colantonio E, Cyrino ES, De Mello MT. Effects of 12 weeks of training on critical velocity and maximal lactate steady state in swimmers. Eur J Sport Sci 11: 165–170, 2011.
13. MacLaren DP, Coulson M. Critical swimming speed can be used to determine changes in training status. In: Biomechanics and Medicine in Swimming VII. Keskinen K.L., Komi P.V., Hollander A.P., eds. Jyvaskyla, Finland: Gummerus Printing, 1999. pp. 227–231.
14. Marinho DA, Barbosa TM, Silva AJ, Neiva HP. Applying anaerobic critical velocity in non-elite swimmers. Int J Swim Kinet 1: 33–50, 2012.
15. Marinho DA, Silva AJ, Reis VM, Costa AM, Brito JP, Ferraz R, Marques MC. Changes in critical velocity and critical stroke rate during a 12 week swimming training
period: A case study. J Hum Sport Exerc 4: 48–56, 2009.
16. Neiva HP, Fernandes RJ, Vilas-Boas JP. Anaerobic critical velocity in four swimming techniques. Int J Sports Med 32: 195–198, 2011.
17. Papoti M, da Silva ASR, Araujo GG, Santiago V, Martins LEB, Cunha SA, Gobatto CA. Aerobic and anaerobic performances in tethered swimming. Int J Sports Med 34: 712–719, 2013.
18. Papoti M, Zagatto AM, de Freitas Júnior PB, Cunha SA, Martins LEB, Gobatto CA. Use of the y-intercept in the evaluation of the anaerobic fitness and performance prediction of trained swimmers. Rev Bras Med Esporte 11: 124e–128e, 2005.
19. Pelayo P, Alberty M, Sidney M, Potdevin F, Dekerle J. Aerobic potential, stroke parameters, and coordination in swimming front-crawl performance. Int J Sports Physiol Perform 2: 347–359, 2007.
20. Peyrebrune MC, Toubekis AC, Lakomy HKA, Nevill ME. Estimating the energy contribution during single and repeated sprint swimming. Scand J Med Sci Sports 24: 369–376, 2014.
21. Reis J, Alves F. Training induced changes in critical velocity andV4 in age group swimmers. Portugese J Sport Sci 6(Suppl. 2): 311–313, 2006.
22. Rhea M. Determining the magnitude of treatment effect size in strength training research through the use of effect size. J Strength Cond Res 18: 918–920, 2004.
23. Ribeiro J, Sousa A, Figueiredo P, Clemente V, Pelarigo J, Monteiro J, Vilas-Boas JP, Fernandes RJ. Energy characterization of 100 m maximal front crawl. Abstracts of XVIIth FINA World Sports Medicine Congress. J Sports Sci Med 11: 775–791, 2012.
24. Rinehardt KF, Kraemer RR, Gormely S, Colan S. Comparison of maximal oxygen uptakes from the tethered, the 183-and 457-m unimpeded supramaximal freestyle swims. Int J Sports Med 12: 6–9, 1991.
25. Rodriguez FA. Maximal oxygen uptake and cardiorespiratory response to maximal 400-m free swimming, running and cycling tests in competitive swimming. J Sports Med Phys Fitness 40: 87–95, 2000.
26. Schnitzler C, Ernwein V, Seifert I, Chollet D. Comparison of spatio-temporal, metabolic, and psychometric responses in recreational and highly trained swimmers during and after a 400-m freestyle swim. Int J Sports Med 27: 1–8, 2006.
27. Shimoyama Y, Okita K, Baba Y, Sato D. Does the y-intercept of a regression line in the critical velocity concept represent the Index for evaluating anaerobic capacity? In: Biomechanics and Medicine in Swimming XI. Kjendlie P.L., Stallman R.K., Cabri J., eds. Oslo, Norway: Norwegian School of Sport Sciences, 2010. pp. 288–290.
28. Smith DJ, Norris SR, Hogg JM. Performance evaluation of swimmers. Scientific tools. Sports Med 32: 539–554, 2002.
29. Toubekis AG, Tsami AP, Smilios IG, Douda HT, Tokmakidis SP. Training-induced changes on blood lactate profile and critical velocity in young swimmers. J Strength Cond Res 25: 1563–1570, 2011.
30. Toubekis AG, Tsami AP, Tokmakidis SP. Critical velocity and lactate threshold in young swimmers. Int J Sports Med 26: 117–123, 2006.
31. Toussaint HM, Wakayoshi K, Hollander AP, Ogita F. Simulated front crawl swimming performance related to critical speed and critical power. Med Sci Sports Exerc 30: 144–151, 1998.
32. Wakayoshi K, Ikuta K, Yoshida T, Udo M, Moritani T, Mutoh Y, Miyashita M. Determination and validity of critical velocity as an index of swimming performance in the competitive swimmer. Eur J Appl Physiol Occup Physiol 64: 153–157, 1992.
33. Weyand PG, Lin JE, Bundle MW. Sprint performance-duration relationships are set by the fractional duration of external force application. Am J Physiol Regul Integr Comp Physiol 290: R758–R765, 2006.
34. Wright B, Smith DJ. A protocol for the determination of critical speed as an index of swimming endurance performance. In: Medicine and Science in Aquatic Sports (Medicine and Sports Science). Miyashita M., Mutoh Y., Richardson A.B., eds. Basel, Switzerland: Karger, 1994. pp. 55–59.
35. Zacca R, Wenzel BM, Piccin JS, Marcilio NR, Lopes AL, Castro FA. Critical velocity, anaerobic distance capacity, maximal instantaneous velocity and aerobic inertia in sprint and endurance swimmers. Eur J Appl Physiol 110: 121–131, 2010.
Keywords:Copyright © 2015 by the National Strength & Conditioning Association.
performance indexes; maximal aerobic speed; swimming training