Perceptual & Motor Skills: Perception 2014, 118, 3, 833-849. © Perceptual & Motor Skills 2014
INTERPRETATION AND PERCEPTION OF SLOW, MODERATE, AND FAST SWIMMING PACES IN DISTANCE AND SPRINT SWIMMERS1, 2 PIETRO LUIGI INVERNIZZI, STEFANO LONGO, RAFFAELE SCURATI, MARTINA A. MAGGIONI, AND GIOVANNI MICHIELON Department of Biomedical Sciences for Health, Università degli Studi di Milano, Italy ANDREA BOSIO Human Performance Laboratory, Mapei Sport Centre, Castellanza, Italy Summary.—This study assessed how accurately professional swimmers can interpret instructions to swim “slow,” “moderate,” and “fast.” 8 distance swimmers (6 males, 2 females; M age = 19 yr., SD = 3) and 8 sprint swimmers (7 males, 1 female; M age = 18 yr., SD = 1) performed an all-out 50-m crawl stroke and three sets of 8 × 50-m crawl stroke trials interpreting the coach's instruction to swim at slow, moderate, and fast paces. No differences were detected between groups in absolute speed. Nevertheless, distance and sprint swimmers significantly differed in speed normalized to their own 50-m all-out speed (effect sizes = 6.72, 6.20, 1.35 for slow, moderate, and fast, respectively), stroke frequency (effect sizes = 0.81, 1.12, 1.54, respectively), and blood lactate concentration (effect sizes = 0.99, 2.56, 1.70, respectively).
It is well known that the pacing strategy is critical in middle- and long-distance performances (de Koning, Bobbert, & Foster, 1999; Foster, Heimann, Esten, Brice, & Porcari, 2001; Hettinga, de Koning, Hulleman, & Foster, 2012), influencing the mean power production in relation to the energy lost in the environment (van Ingen Schenau & Cavanagh, 1990), which is essential for being successful in competitions. In swimming, three pacing approaches can be adopted: the positive, negative, and even pacing. Positive pacing can be employed in sprint events (50–100 m distance) when the first half of the race is swum 2–3 sec. (Maglischo, 2003) faster than the second half because of the contribution of diving start (Thompson, Haljand, & MacLaren, 2000), whereas the even and negative pacing (i.e., the second half is swum at the same speed or faster than the first half, respectively), can be adopted in middle distance races (more than 400 m distance) (Maglischo, 2003). It is important to mention that, as a rule, swimmers cannot use any pace-making device during the race (FINA Rules and Regulations SW Address correspondence to Prof. Pietro Luigi Invernizzi, Department of Biomedical Sciences for Health, Università degli Studi di Milano, via Kramer 4/A, 20129, Milano, Italy or email ([email protected]). 2 No sources of funding were used to assist in the preparation of the manuscript. The authors have no conflicts of interest that are directly relevant to the content of the manuscript. 1
DOI 10.2466/27.29.PMS.118k23w0
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10.16, 2011), and it is unlikely that any signal (visual or auditory) coming from the coach or people on the stage could be detected by the swimmer during the competition. Therefore, they do not receive any visual or auditory feedback that could be useful to manage their stroke pace throughout the competition. Consequently, any inaccurate self-perception of swimming speed could compromise the results of the whole race. For example, in case of positive split during a distance competition, a state of early fatigue might occur and become detrimental for the success of the race (Maglischo, 2003). Thus, the improvement of the swimmers’ ability to selfregulate the stroke pace should be an important part of the training. During a training session, coaches often administer the swimmer's pacing intensities through simple statements, such as: “swim slow,” “swim moderate,” “swim fast (hard),” “swim at race-pace.” However, the swimmers’ experience and their perception of swimming speed are crucial for finding the correct pace that must be held (Stewart & Hopkins, 1997). It seems that swimmers cannot easily fulfill the pace-training intensities requested by coaches, either when the prescription of the intensity is expressed as a percentage of their own seasonal-best pace or expressed as a given perceived effort (such as easy, moderate, hard, race pace) (Stewart & Hopkins, 1997). Moreover, it has been shown that there is a mismatch between the perceived training intensities by coaches and swimmers (Foster, et al., 2001; Wallace, Slattery, & Coutts, 2009); therefore, it is useful to further investigate the swimmers' adherence to the coaches' intensity prescriptions. Swimmers try to maintain a constant speed throughout the race, which is biomechanically determined by the frequency and length of the stroke (SF and SL, respectively) towards effective propulsion. It has been shown that swimmers can adapt the stroke parameters in relation to the race distance (Keskinen & Komi, 1993; Pelayo, Mujika, Sidney, & Chatard, 1996; Cappaert, 1999). However, with greater fatigue, an increase in SF and a decrease in SL represent a common strategy to maintain the swimming speed (Keskinen & Komi, 1993; Zamparo, Bonifazi, Faina, Milan, Sardella, Schena, et al., 2005). Previous studies have demonstrated that 50 m sprinters show higher speed and SF with lower SL at their competitive speed, compared to middle and distance swimmers (Keskinen & Komi, 1993; Pelayo, et al., 1996; Seifert, Chollet, & Bardy, 2004). Furthermore, it has been demonstrated recently that there were no differences in speed, SF, and SL between sprinters and distance swimmers when swimming at maximal speed (McCabe, Psycharakis, & Sanders, 2011). However, whether the stroke parameters remain the same in both groups when swimmers swim at different self-selected submaximal paces (e.g., slow, moderate, or fast) is not yet clear. Therefore, this aspect needs further investigation.
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In previous studies, it has been shown that sprinters are characterized by greater maximal strength, higher glycolysis, and higher ATP-PCr enzymatic activity than distance swimmers (Costill, Maglischo, & Richardson, 1992; Olbrecht, Mader, Heck, & Hollmann, 1992). Moreover, sprinters exhibited higher lactate production than long distance swimmers (Cohen's effect size: 0.92) during an incremental exercise (Seifert, Komar, Lepretre, Lemaitre, Chavallard, Alberty, et al., 2010) implying that they are more fatigable and have lower swim economy and efficiency than long-distance swimmers (Seifert, et al., 2010). However, little is known about lactate production of distance swimmers and sprinters exercising at submaximal and self-perceived paces. In addition, the relative contributions of energy metabolism differ between sprint and distance swimming, with a much higher involvement of the anaerobic metabolism for the former (about 20% ATPCP, 60% anaerobic-lactate, 20% aerobic-glucose metabolism) and mainly aerobic for the latter (about 35% anaerobic lactate and 65 aerobic-glucose metabolism) (Maglischo, 2003). Hypothesis 1. A given pace requested by the coach is consistently and reliably interpreted and reproduced by swimmers in successive bouts. Hypothesis 2. A statistically significant difference exists between sprinters and distance swimmers in interpreting the coach's pacing requests. METHOD Participants Sixteen high level front crawl swimmers were recruited for this study. The mean time in competitive swimming was 11 yr. (SD = 1), and all swimmers reached the minimum time established by the Italian Swimming Federation to participate at the National Championship. Moreover, the swimmers self-reported that they were in good health, free from any disease, and were not taking any medications. In addition, as prescribed by the Italian Swimming Federation for all the swimmers who take part in competitions, the participants were in possession of a valid medical certificate. Two groups were drawn based on their specialty: distance (n = 8; 6 men, 2 women) mainly competing in 400 m races and more rarely in 800 m races, and sprinters (n = 8; 7 men, 1 woman) competing mainly in 50 m races and more rarely in 100 m races. The anthropometric characteristics of the groups are reported in Table 1. All participants volunteered and signed the informed consent form where all the procedures were explained. The consent was in accordance with the Declaration of Helsinki. The Ethics Committee of the University of Milan approved the study.
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Study Design and Procedure Swimmers visited the swimming pool (length 50 m, water temperature 26–27 °C) in two separate occasions, abstaining from any intensive swimming training 2–3 days prior to each visit. The first session served as familiarization with the protocol and evaluation of the 50-m front crawl stroke all-out performance, which was performed after a standardized warm-up (10 laps of 50-m front crawl at low intensity) with the swimmer pushing off from the wall of the pool to avoid any influence of the dive. This test was chosen to confirm the actual competitive background of the swimmers and because the subsequent test trials were performed based on 50-m laps (see below). Each swimmer was filmed with a camera (50 Hz sampling rate, Handycam HDR-R7E, Sony, Japan) fixed on a support mounted on a rail running along the poolside. To minimize parallax error, the support was pushed at the swimmer's speed along the pool by an operator. Markers were placed along the poolside every one meter so that the distance of the swimmer with respect of the starting wall was known. To avoid the influence of the initial push off phase, the all-out speed over 50 m (S50, m·s−1), SF50 (Hz), and SL50 (m) were calculated for the middle 20 m. Three all-out trials were executed (5 min. recovery between trials), and the best performance was considered for further analysis. SF50 and SL50 were extrapolated watching the recorded video in a separate occasion by an expert video analyst from this study’s research group. Three maximal trials was a strategy chosen to be sure that the swimmers did a real all-out effort during the first trial. Five min. recovery is unlikely to be a sufficient time to recover from a maximal effort over a swimming distance of 50 m. Indeed, if a swimmer got his or her best performance during the second or third trial, the best performance was not considered and the trial should have been repeated on a different day. All the swimmers who participated in this study obtained the best performance during their first trial. For each participant, the S50 was obtained from the distance covered (20 m) divided by the time needed to cover this distance that was derived from the video frames (50 frames per second). The SF50 was calculated from the number of the strokes completed within the middle 20 m divided by the time needed to cover this distance. The SL50 was calculated dividing S50 by SF50. Swimmers were asked to give their rate of perceived exertion (RPE50) using the Borg CR–10 scale (Borg, 1998), ranging from 0 (“no exertion”) to 10 (“maximal exertion”). Swimmers were well accustomed with the use of the RPE scale. To analyze blood lactate concentration (La−50), a blood sample of 5 μl was obtained from the fingertip with a portable lactate analyzer (Lactate PRO™, Arkray KDK, Japan) 1 and 3 min. after the end of each all-out trial. Peak La−50 was used for further analysis.
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During the second visit, after a warm-up (10 laps of 50 -m front crawl at low intensity), the participants were instructed to swim three sets at three prescribed paces: slow, moderate, and fast pace in this order. Each set consisted of 8 trials of 50 m front crawl based on the swimmers' selfperception of these paces. It was clearly stated that each pace should have been kept constant throughout the 8 trials. To avoid any influence of the dive, all the trials started with the swimmer pushing off from the wall of the pool. A recovery of 3.5 min. was allowed between sets, while 2 sec., 3 sec., and 5 sec. of rest were allowed between each trial at slow, moderate, and fast paces, respectively. As suggested by Maglischo (2003), one of the modalities utilized during race pacing training for distance swimmers consisted of an 8 × 50 m interval set with 5 sec. rest in between. Similarly, very short rest periods were also given during the slower trials to maintain evenness through all the trials and to entail swimmers to focus on the swim and not on the turn. An interval work with slow and fast sets of swimming is a form of suitable training session for both distance and sprint swimmers, at different times of the competitive year (Tabata, Nishimura, Kouzaki, Hirai, Ogita, Miyachi, et al., 1996; Maglischo, 2012). Both groups were well-accustomed with this type of training. With the same method used during the all-out trial, mean speed (S), SF, and SL were calculated in the central 20 m of each trial. Along with stroking parameters, La− and RPE were determined at the end of each trial, with the same method used during the all-out trial. Moreover, S was expressed as percentage of S50 (%S50) at each pace. For each participant, stroke parameters (i.e., S, %S50, SF, and SL) were calculated as the means of the 8 trials within each set (slow, moderate, and fast pace). Statistical Analyses All results are presented as mean ± SD. Normality of data distribution was checked through the Shapiro-Wilk's test. An independent samples Student's t test was used to compare the two groups after the all-out 50-m trial in all variables. Two-way mixed model analysis of variance (ANOVA) was applied with group (distance, sprinters) as between-subjects factor and pace (slow, moderate, fast) as within-subjects factor to compare the groups after the testing protocol. The mean speed, calculated as the mean of the speeds maintained over each one of the eight sessions, represents the pace for the three different prescribed intensities (slow, moderate, fast). When a significant group factor was detected, an independent samples Student's t test was applied to check between-groups differences, whereas pairwise comparisons with Bonferroni's adjustment were applied to look at within-groups differences in pace. Moreover, a paired Student's t test was used to compare the variables at fast pace with the variables during the all-out 50 -m trial. Since RPE values were not normally dis-
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tributed, non-parametric analyses were applied. Mann-Whitney's test was employed for unpaired comparisons, whereas Friedman's (for repeatedmeasure) and Wilcoxon's (for pairwise) tests were employed for withingroups comparisons. Relationship between La − and RPE at each pace was tested with a Spearman's rho correlation coefficient. Level of significance was set at p < .05. The magnitude of the changes was assessed using effect size (ES) statistic with 95% confidence intervals (CI) and percentage change (Batterham & Hopkins, 2006; Hopkins, 2007). Effect sizes were classified as follows: < 0.2 = trivial, 0.2–0.6 = small, 0.6–1.2 = moderate, 1.2– 2.0 large, > 2.0 = very large (Hopkins, 2003). The analysis was conducted with the Statistical Package for Social Sciences 18.0 software (SPSS, Inc., Chicago, IL, USA). RESULTS As can be seen in Table 1, at baseline the all-out 50-m performances (S50) as well as the lactate concentrations (La−50) were significantly different between the two groups, whereas their anthropometric characteristics and perceived exertion (RPE50) did not differ significantly. Taken together, these data confirm the different competitive performance backgrounds of the two groups, while also confirming their similar and comparable anthropometric and perceptual backgrounds. TABLE 1 CHARACTERISTICS OF THE STUDY PARTICIPANTS Variable
Distance (n = 8) M
SD
Sprinters (n = 8) M
SD
t
p
Age, yr.
19
3.0
18
1.0
0.72
.48
Body mass, kg
67.5
3.6
70.6
6.8
− 1.14
.27
0.05
− 1.40
.18
1.4
− 0.37
.72
Height, m BMI, m2/kg
1.76 21.8
0.03 0.6
1.79 21.9
ES M
SD
S50, m·sec.−1
1.79
0.07
1.95
0.12
− 3.26
.006
1.96
1.08
SF50, Hz
0.90
0.04
0.95
0.03
− 2.60
.02
1.36
0.89
SL50, m
1.99
0.05
2.06
0.08
− 2.34
.03
0.84
0.65
La−50, mmol·l−1
7.75
0.77
12.24
1.44
− 3.50
.004
5.17
1.11
9.50 0.53 9.38 0.74 − 0.23* .81 RPE50 Note.—Anthropometric variables and baseline performance and physiological data for distance swimmers and sprinters with statistical tests of differences between the groups. BMI = body mass index; S50 = best mean speed during all-out 50 m front crawl performance; SF50 = mean stroke frequency during all-out 50 m front crawl performance; SL50 = mean stroke length during all-out 50-m front crawl performance; La−50 = mean lactate concentration during all-out 50 m front crawl performance; RPE50 = rate of perceived exertion after the all-out 50 m front crawl performance; ES = effect size with 95% confidence limits. *Z score for non-parametric Mann-Whitney U test, p < .05.
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Reliability Intraclass correlation coefficients (ICC) with 95% confidence intervals are provided in Table 2. As shown in the table, both groups’ ICC values ranged from .89 to .99 in all performance parameters. These high values support Hypothesis 1; swimmers can consistently and reliably interpret and reproduce a given pace requested by the coach throughout the trials in a set. These values also confirmed that our protocol was highly reliable and showed that the workload was feasible for both groups without altering their stroking parameters despite their different competitive background. TABLE 2 INTRACLASS CORRELATION COEFFICIENTS (95% CONFIDENCE INTERVAL) Distance
Variable
Sprinters
Slow
Moderate
Fast
Slow
Moderate
Fast
S
.90 (.73 − .98)
.98 (.96 − 1.00)
.99 (.97 − 1.00)
.95 (.85 − .99)
.94 (.83 − .99)
.98 (.94 − .99)
SF
.91 (.75 − .98)
.99 .94 (.84 − .99) (.97 − 1.00)
.98 (.94 − .99)
.89 (.72 − .98)
.89 (.72 − .98)
.92 .99 .98 .98 .95 .96 (.89 − .99) (.79 − .98) (.97 − 1.00) (.95 − 1.00) (.96 − 1.00) (.87 − .99) Note.—Intraclass correlation coefficients (ICC) with 95% confidence interval at each different pace in distance and sprinters. S = speed; SF = stroke frequency; SL = stroke length. SL
Speed The ANOVA did not show significant group effect (F1 = 0.06, p = .80) nor an interaction for S expressed in absolute terms (F2 = 1.81, p = .18). As expected, values of S increased significantly (F2 = 159.63, p = 4.91e-16; Fig. 1-A) as pace increased in both distance (1.31 ± 0.06, 1.42 ± 0.08, 1.52 ± 0.11 m·sec.−1, at slow, moderate, and fast paces, respectively; ES range, 1.28–2.65) and sprinters (1.30 ± 0.04, 1.43 ± 0.05, 1.55 ± 0.08 m·sec.−1, at slow, moderate, and fast paces, respectively; ES range = 2.46–4.94). When expressed as %S50, the speeds were also statistically different between the groups (F1 = 12.46, p = .03) at slow (73.5 ± 2.3 vs 66.8 ± 4.8 %S50, distance swimmers and sprinters, respectively; t14 = 3.56, p = .003; ES = 6.72 ± 3.46), moderate (79.5 ± 3.4 vs 73.3 ± 4.0 %S50, respectively; t14 = 3.32, p = .005; ES = 6.20 ± 3.30), and fast (84.9 ± 4.1 vs 79.6 ± 3.3 %S50, respectively; t14 = 2.85, p = .01; ES = 1.35 ± 0.83) paces as shown in Fig. 1-B. Hypothesis 2 was partially supported. When paces were expressed in absolute terms, sprinters and distance swimmers did not differ significantly; however, when the speed was normalized to their best performance (i.e., %S50), significant differences emerged between groups: the distance group swam always at higher percentage of its S50, compared to the sprinters group, at each prescribed pace.
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0.6
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e at er od M
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90
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er M od
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80
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Sl ow
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at er M od
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Sl ow
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0.8
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2.0
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FIG. 1. Changes of parameters at slow, moderate, and fast pace in distance swimmers (filled circle) and sprinters (empty circle). A. S, mean speed (m s−1); S50, mean speed during the all-out 50 m front crawl stroke trial. B. %S50, mean speed expressed as percentage of S50. C. SF, stroke frequency (Hz); SF50, mean stroke frequency during the all-out 50 m front crawl trial. D. SL, stroke length (m); SL50, mean stroke length during the all-out front crawl trial. *Significant difference between groups with p ≤ .05; **significant difference between groups with p ≤ .01; #significant difference from previous speed with p ≤ .05; ##significant difference from previous speed with p ≤ .01.
When comparing S at fast pace with S50, significant differences were found in both groups (t7 = − 10.39, p = 1.66e-05; t7 = − 14.27, p = 1.97e-6, distance and sprint swimmers, respectively; Fig. 1-A). Stroke Parameters For SF, the ANOVA revealed significant within- and between-subjects differences (F2 = 103.67, p = 1.14e-13, and F1 = 8.15, p = .013, respectively) with no significant interaction (F2 = 0.77, p = .43). Significant differences in SF were found between groups (Fig. 1-C), with the distance swimmer group showing higher values at slow (0.59 ± 0.06 vs 0.55 ± 0.04 Hz, distance
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and sprinters, respectively; t14 = 2.14, p = .025; ES = 0.81 ± 0.83), moderate (0.70 ± 0.08 vs 0.63 ± 0.05 Hz, respectively; t14 = 2.39, p = .031; ES = 1.12 ± 0.84), and fast (0.78 ± 0.04 vs 0.72 ± 0.03 Hz, respectively; t14 = 3.29, p = .005; ES = 1.54 ± 0.84) paces. Within each group, SF significantly increased as pace increased (F2 = 103.67, p = 1.38e-08; ES range = 1.09–2.54, 1.54–3.43, in distance and sprinters, respectively). When comparing SF at fast pace with SF50, significant differences were found in both groups (t7 = –8.13, p = 8.22e–05; t7 = − 23.27, p = 6.86e-08, respectively; Fig. 1-C). For SL, the ANOVA showed no significant differences between groups (F1 = 3.49, p = .08) or interaction (F2 = 1.13, p = .33). A significant decrease was detected (F2 = 26.10, p = 3.99e-07) as pace increased in both distance swimmers (2.27 ± 0.30, 2.10 ± 0.24, 1.95 ± 0.13 m, at slow, moderate, and fast paces, respectively; ES range = 0.49–1.06) and sprinters (2.39 ± 0.22, 2.31 ± 0.18, 2.16 ± 0.13 m, respectively; ES range = 0.35–1.00), as shown in Fig. 1-D. When comparing SL at fast pace with SL50, a significant difference was found only in the sprinters group (t7 = − 0.74, p = .48; t7 = 2.49, p = .04, for distance and sprint swimmers, respectively; Fig. 1-D). Lactate and RPE For La−, the ANOVA did not show a significant interaction (F2 = 1.93, p = .16); however, there were significant differences between groups (F1 = 45.00, p = 9.96e-0.6) and among paces (F2 = 161.32, p = 4.29e-16). The distance swimmer group showed significant lower lactate compared to sprinters at slow pace (2.0 ± 0.4 vs 2.6 ± 0.8 mmol·l−1, respectively; t14 = –2.14, p = .05; ES = 0.99 ± 0.84), moderate pace (3.1 ± 0.6 vs 4.6 ± 0.5 mmol·l−1, respectively; t14 = –5.43, p = 8.91e-05; ES = 2.56 ± 0.84), and fast pace (5.9 ± 0.5 vs 7.4 ± 0.1 mmol·l−1, respectively; t14 = − 3.65, p = .03; ES = 1.70 ± 0.84). Within groups, La− significantly increased as pace increased, as shown in Fig. 2-A (p ≤ .01; ES range = 1.86–6.74, 2.59–6.25, for distance and sprinters, respectively). When comparing La− at fast pace with La−50, a significant difference was found only in the sprinters group (t7 = − 0.47, p = .65; t7 = 2.40, p = .047, for distance and sprint swimmers, respectively; Fig. 2-A). The RPE values were not significantly different between groups for the three paces (Z = − 1.89, p = .06; Z = − 1.44, p = .16; Z = − 1.77, p = .83), rising significantly [χ2(2) = 30.52, p = 2.35e-07] in both distance swimmers (1.4 ± 1.1, 3.6 ± 1.3, 6.3 ± 1.5 at slow, moderate, and fast paces, respectively; ES range = 1.58–3.58) and sprinters (2.5 ± 0.9, 4.3 ± 1.3, 7.6 ± 2.2, respectively; ES range = 1.44–4.03) as pace increased (Fig. 2-B). Furthermore, no significant correlations between La− and RPE at any pace were found in both groups (ρ = .23, p = .39; ρ = − .09, p = .72; ρ = .52, p = .06; slow, moderate, and fast, respectively). When comparing RPE at fast pace with RPE50, significant differences were found in both groups (Z = − 2.53, p = .011 and
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B
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e at
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t Fa s
od er
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od er
at
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La– (mmol · l–1)
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FIG. 2. A. Changes in lactate concentration (La−) in mmol·l−1 at slow, moderate, and fast paces in distance swimmers (filled circle) and sprinters (empty circle). B. Changes in rate of perceived exertion (RPE) at slow, moderate, and fast paces in distance swimmers (filled circle) and sprinters (empty circle). **Significant difference between groups with p ≤ .01; #significant difference from previous speed with p ≤ .05; ##significant difference from previous speed with p ≤ .01.
Z = − 2.02, p = .042, for distance and sprint swimmers, respectively; Fig. 2-B). DISCUSSION The main findings of the present study were that distance and sprint swimmers had similar interpretations of prescribed paces (i.e., to swim at slow, moderate, and fast pace) when expressed in absolute terms. However, when the speed was normalized to their all-out performance (i.e., %S50), significant differences emerged between groups: the distance group swam always at higher percentage of its S50, with higher SF and lower La− compared to the sprinters group at each prescribed pace (Fig. 1-C and 2-A). The groups of the present investigation were drawn based on the swimmers' specialization; therefore, the S50, SF50, SL50, and La−50 were significantly different between groups at the all-out 50-m trial, with better performance given by sprinters. However, the absolute speed values of each prescribed pace were similar between groups. A possible explanation for this lack of difference between speeds could be related to the water environment. It has been shown that drag increases in an exponential fashion as speed increases up to 2.0 m·sec.−1 (Toussaint, Roos, & Kolmogorov, 2004); as a consequence, to obtain small increases in swimming speed at the upper speeds, high upper and lower limb strength is required. However, it is well known and accepted that the swimming technique is another key determinant of performance (Fernandes, Marinho, Barbosa, & Vilas-Boas, 2006). Due to the relevant number of years during which all
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the participants underwent both conditioning and technical training, it was assumed that the swimming technique was well consolidated. For this reason, it is likely that strength characteristics were more important determinants of the swimming speed in this group of participants. Hence, the speed range in which swimmers could change their pace from slow to fast is small (1.30–1.55 m·sec.−1 in the present study). It is likely that the lack of differences between groups at each absolute speed was due to this small range of variation in swimming speed imposed by the aquatic environment. A previous study (Stewart & Hopkins, 1997) showed that national swimmers were not so effective in judging the intensity of the workout during training when prescribed as easy, moderate, hard, and race-pace (corresponding to 77%, 78–87%, 88–97%, and 98% of the seasonal-best, respectively). Particularly, a poor correlation between the coaches' prescribed intensity and the individual swimmers' interpretation was found, highlighting the difficulty in distinguishing between hard and race-pace or between easy and moderate. The slow, moderate, and fast paces swum by the distance group in the present study when expressed as %S50 (73.5%, 79.5%, and 84.9% S50, respectively) were close to the values established by Stewart and Hopkins (1997), despite the differences in swimming protocols and the level of the swimmers. However, the sprinters group consistently swam at a slower pace (66.8%, 73.3%, 79.6% S50; slow, moderate, fast, respectively) throughout the trials. In other words, when the speed was selected based on the swimmer's experience, sprinters adopted a more conservative approach, swimming less close to their own best all-out performance than distance swimmers. From a purely physiological point of view, the different aerobic/anaerobic characteristics and the different anaerobic thresholds of the two groups could explain the present results. Distance swimmers are characterized by higher aerobic capacity than sprinters. This is due to intrinsic characteristics of the distance swimmers (genetics) rather than just training volume. In fact, even sprint swimmers undergo training that aims to increase aerobic capacity and power (training sessions of overall distances between 3000 and 6000 m). It is therefore logical to believe that swimmers with the anaerobic thresholds at a higher percentage of their VO2max (such as distance swimmers) can maintain a relative absolute speed higher than swimmers with anaerobic thresholds at a lower percentage of VO2max (such as sprinters). However, this point of view is a less adequate explanation of the results. Several interesting studies (Sgherza, Axen, Fain, Hoffman, Dunbar, & Haas, 2002; Amann & Dempsey, 2008; Marcora, Bosio, & de Morree, 2008; Blanchfield, Hardy, de Morree, Staiano, & Marcora, 2013; de Morree & Marcora, 2013) have shown that without any change in the
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aerobic characteristics of the participants (and therefore without changes in the anaerobic threshold), performance and pacing were altered when the experimental interventions affected the perception of effort. This was tested both using drugs with effects on the central nervous system (Sgherza, et al., 2002) and using fatiguing protocols that necessarily altered the central motor command (Amann & Dempsey, 2008; Marcora, et al., 2008; de Morree & Marcora, 2013). Even psychological interventions can have a positive effect on perception of effort and performance (Blanchfield, et al., 2013). Therefore, the choice of a given pace to interpret a requested intensity cannot be easily explained only by the aerobic or anaerobic capacity of a swimmer. Hence, a key role could be represented by the perception of effort, previous experience, and the distance to be covered (de Morree & Marcora, 2013). Likely, the sprinters, based on their effort awareness, perceived that a further increase in speed could have compromised the success of the given task (i.e., to complete the 8 × 50 m swimming at a constant pace), which they did accomplish successfully, based on reliability data (ICC range, .89–.99, Table 2). This explanation is supported by the lactate production during the fast pace and all-out 50 -m swim. Indeed, in distance swimmers the La− at fast pace and La−50 values were not statistically different, while in the sprinters group significant differences were found, confirming the different effort employed between the all-out trial and the fast pace. Moreover, it has been shown that when metabolite concentrations are high (Allen, Lamb, & Westerblad, 2008), as was found in in the sprinters group, a higher central motor command is required to maintain a given intensity, leading to an increase in RPE (Marcora, et al., 2008). Consequently, it could be hypothesized that, to avoid an early exercise cessation due to an excessive rise in RPE before the end of the last bout at fast pace, the sprinters kept a lower %S50 compared to the distance swimmers (Wright, 1996; Marcora, et al., 2008). However, the RPE values found at the end of each set were not statistically different between groups, which could seem in contrast with the previous considerations. On the other hand, given the fact that the sprinters swam at lower relative paces perceiving similar effort compared to distance, it could be inferred that the RPE was actually higher in sprinters. This observation could additionally support the explanation. It is well known that SL is an important determinant of swimming performance (Chollet, Pelayo, Delaplace, Tourny, & Sidney, 1997; Fernandes, et al., 2006). Moreover, with the increase of swimming speed there is an increase in SF and SL (Craig, Skehan, Pawelczyk, & Boomer, 1985; Zamparo, et al., 2005), and when a swimmer is close to his own peak speed a further increase in speed is determined only by an increase in SF (Craig, et al.,
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1985; Nomura, Takei, & Yanagida, 2003). Furthermore, a direct relationship between SF and oxygen consumption exists (Wakayoshi, D’Acquisto, Cappaert, & Troup, 1995); in other words, when the intensity increases there is an increase in SF. However, when fatigue occurs, the swimmer is no longer able to sustain the optimal SL; therefore, a compensation with increase in SF is adopted to keep the mean speed (Alberty, Potdevin, Dekerle, Pelayo, Gorce, & Sidney, 2008). In the present study, the increase in speed for the requested task (slow, moderate, fast) was obtained by an increase in SF (Craig, et al., 1985; Nomura, et al., 2003), which paralleled a significant reduction in SL throughout the trials in both groups, as shown by previous authors who applied progressive fatiguing protocols (Zamparo, et al., 2005; Barbosa, Fernandes, Keskinen, & Vilas-Boas, 2008). Interestingly, the distance group systematically swam with higher SF than the sprinters at all intensities (slow, moderate, and fast). It is known that generally sprint swimming is associated with higher mean speed and SF with lower SL compared to middle and distance swimming (Keskinen & Komi, 1993; Pelayo, et al., 1996; Seifert, et al., 2004). However, when studying the effect of swimming specialization on stroke parameters, McCabe, et al. (2011) did not find any difference between the two types of swimmers in S, SF, and SL. The latter results might seem in contrast with the present findings, though McCabe, et al. (2011) investigated the response during a set of all-out performances, whereas this study tested the ability to adapt to the coaches' instructions at three different submaximal paces. The differences found between the distance swimmers and the sprinters are probably due to the fact that the former swam at a higher intensity (i.e., speed normalized by their own all-out speed) than the latter group. From a purely physiological point of view this finding can be explained, as the distance swimmers likely swam at a higher percentage of their maximum oxygen uptake. Indeed, there is a direct relationship between SF and oxygen uptake (Wakayoshi, et al., 1995). However, swimmers do not base the choice of a swimming speed to accomplish a given task directly from the perception of their maximum oxygen uptake; rather, they utilize their own perception of effort, experience relative to previous efforts, and the distance to be covered. Therefore, the conscious decision of the distance swimmers to adopt a relative higher speed than the sprinters determined the need of swimming at a higher SF. Consequently, it seems that when a self-regulated pace is requested the swimmers' specialization influences stroke characteristics, which was not observed previously in shorter distances (McCabe, et al., 2011). A further explanation of the different SF between groups could be their different training methods. Sprinters are used to adopt very high SF during their short and very close to all-out repetitions, typical of their training sessions; therefore, they might prefer a
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low SF when a slow speed is requested, with the intention of maintaining a less fatiguing neural activation (Keskinen & Komi, 1993). Limitations and Conclusions From a practical point of view, the use of simple adjectives to define different intensities of training can be considered a valid reliable method. In particular, when a broader and general training is performed swimmers seem to comply successfully and consistently follow the coaches' requirements. However, this study would suggest that coaches should monitor the time and SF when specific and race paces are requested during training. A question that may arise is whether swimmers are still able to comply with the coaches' requirements consistently with high fatigue (e.g., after a 2–3 hr. of intense swimming). Although not included in the present study, it is likely that in high fatigue conditions the gap between distance swimmers and sprinters, in terms of relative and absolute speed, may become wider and more evident than observed in the current study, and that the reliability of the different intensities (slow, moderate, fast) might be poorer (i.e., lower ICC). Future investigations should be conducted to address these issues. The lack of a test to assess the anaerobic threshold of the two groups is a limitation of this study. Although it could have better characterized the swimmers, the goal was to assess the differences in response to a requested task between the two types of swimmers and not to assess their physiological responses to a given swimming velocity. To summarize, the results of this study show that high level distance swimmers interpret the prescribed paces “slow,” “moderate,” and “fast” swimming at a higher percentage of their own all-out performance with higher stroke frequency, and produce lower blood lactate concentration than sprint swimmers. These different interpretations of the prescribed paces can be attributed to the specificity of training and competitions, and imply the involvement of prior knowledge about the features of the test. Being accustomed to all-out performances, the group of sprinters adopted slower normalized speed and stroke frequency than distance swimmers at the prescribed paces, probably to prevent premature fatigue. On the contrary, at training intensities close to the race pace, distance swimmers spontaneously chose higher normalized speeds than sprinters when swimming at the prescribed paces, producing less blood lactate concentration. REFERENCES
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WRIGHT, R. A. (1996) Brehm's theory of motivation as a model of effort and cardiovascular response. In P. M. Gollwitzer & J. A. Bargh (Eds.), The psychology of action. New York: Guilford Press. Pp. 424-453. ZAMPARO, P., BONIFAZI, M., FAINA, M., MILAN, A., SARDELLA, F., SCHENA, F., & CAPELLI, C. (2005) Energy cost of swimming of elite long-distance swimmers. European Journal of Applied Physiology, 94, 697-704. Accepted March 5, 2014.
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INTERPRETATION AND PERCEPTION OF SLOW, MODERATE, AND FAST SWIMMING PACES IN DISTANCE AND SPRINT SWIMMERS1, 2 PIETRO LUIGI INVERNIZZI, STEFANO LONGO, RAFFAELE SCURATI, MARTINA A. MAGGIONI, AND GIOVANNI MICHIELON Department of Biomedical Sciences for Health, Università degli Studi di Milano, Italy ANDREA BOSIO Human Performance Laboratory, Mapei Sport Centre, Castellanza, Italy Summary.—This study assessed how accurately professional swimmers can interpret instructions to swim “slow,” “moderate,” and “fast.” 8 distance swimmers (6 males, 2 females; M age = 19 yr., SD = 3) and 8 sprint swimmers (7 males, 1 female; M age = 18 yr., SD = 1) performed an all-out 50-m crawl stroke and three sets of 8 × 50-m crawl stroke trials interpreting the coach's instruction to swim at slow, moderate, and fast paces. No differences were detected between groups in absolute speed. Nevertheless, distance and sprint swimmers significantly differed in speed normalized to their own 50-m all-out speed (effect sizes = 6.72, 6.20, 1.35 for slow, moderate, and fast, respectively), stroke frequency (effect sizes = 0.81, 1.12, 1.54, respectively), and blood lactate concentration (effect sizes = 0.99, 2.56, 1.70, respectively).
It is well known that the pacing strategy is critical in middle- and long-distance performances (de Koning, Bobbert, & Foster, 1999; Foster, Heimann, Esten, Brice, & Porcari, 2001; Hettinga, de Koning, Hulleman, & Foster, 2012), influencing the mean power production in relation to the energy lost in the environment (van Ingen Schenau & Cavanagh, 1990), which is essential for being successful in competitions. In swimming, three pacing approaches can be adopted: the positive, negative, and even pacing. Positive pacing can be employed in sprint events (50–100 m distance) when the first half of the race is swum 2–3 sec. (Maglischo, 2003) faster than the second half because of the contribution of diving start (Thompson, Haljand, & MacLaren, 2000), whereas the even and negative pacing (i.e., the second half is swum at the same speed or faster than the first half, respectively), can be adopted in middle distance races (more than 400 m distance) (Maglischo, 2003). It is important to mention that, as a rule, swimmers cannot use any pace-making device during the race (FINA Rules and Regulations SW Address correspondence to Prof. Pietro Luigi Invernizzi, Department of Biomedical Sciences for Health, Università degli Studi di Milano, via Kramer 4/A, 20129, Milano, Italy or email ([email protected]). 2 No sources of funding were used to assist in the preparation of the manuscript. The authors have no conflicts of interest that are directly relevant to the content of the manuscript. 1
DOI 10.2466/27.29.PMS.118k23w0
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10.16, 2011), and it is unlikely that any signal (visual or auditory) coming from the coach or people on the stage could be detected by the swimmer during the competition. Therefore, they do not receive any visual or auditory feedback that could be useful to manage their stroke pace throughout the competition. Consequently, any inaccurate self-perception of swimming speed could compromise the results of the whole race. For example, in case of positive split during a distance competition, a state of early fatigue might occur and become detrimental for the success of the race (Maglischo, 2003). Thus, the improvement of the swimmers’ ability to selfregulate the stroke pace should be an important part of the training. During a training session, coaches often administer the swimmer's pacing intensities through simple statements, such as: “swim slow,” “swim moderate,” “swim fast (hard),” “swim at race-pace.” However, the swimmers’ experience and their perception of swimming speed are crucial for finding the correct pace that must be held (Stewart & Hopkins, 1997). It seems that swimmers cannot easily fulfill the pace-training intensities requested by coaches, either when the prescription of the intensity is expressed as a percentage of their own seasonal-best pace or expressed as a given perceived effort (such as easy, moderate, hard, race pace) (Stewart & Hopkins, 1997). Moreover, it has been shown that there is a mismatch between the perceived training intensities by coaches and swimmers (Foster, et al., 2001; Wallace, Slattery, & Coutts, 2009); therefore, it is useful to further investigate the swimmers' adherence to the coaches' intensity prescriptions. Swimmers try to maintain a constant speed throughout the race, which is biomechanically determined by the frequency and length of the stroke (SF and SL, respectively) towards effective propulsion. It has been shown that swimmers can adapt the stroke parameters in relation to the race distance (Keskinen & Komi, 1993; Pelayo, Mujika, Sidney, & Chatard, 1996; Cappaert, 1999). However, with greater fatigue, an increase in SF and a decrease in SL represent a common strategy to maintain the swimming speed (Keskinen & Komi, 1993; Zamparo, Bonifazi, Faina, Milan, Sardella, Schena, et al., 2005). Previous studies have demonstrated that 50 m sprinters show higher speed and SF with lower SL at their competitive speed, compared to middle and distance swimmers (Keskinen & Komi, 1993; Pelayo, et al., 1996; Seifert, Chollet, & Bardy, 2004). Furthermore, it has been demonstrated recently that there were no differences in speed, SF, and SL between sprinters and distance swimmers when swimming at maximal speed (McCabe, Psycharakis, & Sanders, 2011). However, whether the stroke parameters remain the same in both groups when swimmers swim at different self-selected submaximal paces (e.g., slow, moderate, or fast) is not yet clear. Therefore, this aspect needs further investigation.
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In previous studies, it has been shown that sprinters are characterized by greater maximal strength, higher glycolysis, and higher ATP-PCr enzymatic activity than distance swimmers (Costill, Maglischo, & Richardson, 1992; Olbrecht, Mader, Heck, & Hollmann, 1992). Moreover, sprinters exhibited higher lactate production than long distance swimmers (Cohen's effect size: 0.92) during an incremental exercise (Seifert, Komar, Lepretre, Lemaitre, Chavallard, Alberty, et al., 2010) implying that they are more fatigable and have lower swim economy and efficiency than long-distance swimmers (Seifert, et al., 2010). However, little is known about lactate production of distance swimmers and sprinters exercising at submaximal and self-perceived paces. In addition, the relative contributions of energy metabolism differ between sprint and distance swimming, with a much higher involvement of the anaerobic metabolism for the former (about 20% ATPCP, 60% anaerobic-lactate, 20% aerobic-glucose metabolism) and mainly aerobic for the latter (about 35% anaerobic lactate and 65 aerobic-glucose metabolism) (Maglischo, 2003). Hypothesis 1. A given pace requested by the coach is consistently and reliably interpreted and reproduced by swimmers in successive bouts. Hypothesis 2. A statistically significant difference exists between sprinters and distance swimmers in interpreting the coach's pacing requests. METHOD Participants Sixteen high level front crawl swimmers were recruited for this study. The mean time in competitive swimming was 11 yr. (SD = 1), and all swimmers reached the minimum time established by the Italian Swimming Federation to participate at the National Championship. Moreover, the swimmers self-reported that they were in good health, free from any disease, and were not taking any medications. In addition, as prescribed by the Italian Swimming Federation for all the swimmers who take part in competitions, the participants were in possession of a valid medical certificate. Two groups were drawn based on their specialty: distance (n = 8; 6 men, 2 women) mainly competing in 400 m races and more rarely in 800 m races, and sprinters (n = 8; 7 men, 1 woman) competing mainly in 50 m races and more rarely in 100 m races. The anthropometric characteristics of the groups are reported in Table 1. All participants volunteered and signed the informed consent form where all the procedures were explained. The consent was in accordance with the Declaration of Helsinki. The Ethics Committee of the University of Milan approved the study.
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Study Design and Procedure Swimmers visited the swimming pool (length 50 m, water temperature 26–27 °C) in two separate occasions, abstaining from any intensive swimming training 2–3 days prior to each visit. The first session served as familiarization with the protocol and evaluation of the 50-m front crawl stroke all-out performance, which was performed after a standardized warm-up (10 laps of 50-m front crawl at low intensity) with the swimmer pushing off from the wall of the pool to avoid any influence of the dive. This test was chosen to confirm the actual competitive background of the swimmers and because the subsequent test trials were performed based on 50-m laps (see below). Each swimmer was filmed with a camera (50 Hz sampling rate, Handycam HDR-R7E, Sony, Japan) fixed on a support mounted on a rail running along the poolside. To minimize parallax error, the support was pushed at the swimmer's speed along the pool by an operator. Markers were placed along the poolside every one meter so that the distance of the swimmer with respect of the starting wall was known. To avoid the influence of the initial push off phase, the all-out speed over 50 m (S50, m·s−1), SF50 (Hz), and SL50 (m) were calculated for the middle 20 m. Three all-out trials were executed (5 min. recovery between trials), and the best performance was considered for further analysis. SF50 and SL50 were extrapolated watching the recorded video in a separate occasion by an expert video analyst from this study’s research group. Three maximal trials was a strategy chosen to be sure that the swimmers did a real all-out effort during the first trial. Five min. recovery is unlikely to be a sufficient time to recover from a maximal effort over a swimming distance of 50 m. Indeed, if a swimmer got his or her best performance during the second or third trial, the best performance was not considered and the trial should have been repeated on a different day. All the swimmers who participated in this study obtained the best performance during their first trial. For each participant, the S50 was obtained from the distance covered (20 m) divided by the time needed to cover this distance that was derived from the video frames (50 frames per second). The SF50 was calculated from the number of the strokes completed within the middle 20 m divided by the time needed to cover this distance. The SL50 was calculated dividing S50 by SF50. Swimmers were asked to give their rate of perceived exertion (RPE50) using the Borg CR–10 scale (Borg, 1998), ranging from 0 (“no exertion”) to 10 (“maximal exertion”). Swimmers were well accustomed with the use of the RPE scale. To analyze blood lactate concentration (La−50), a blood sample of 5 μl was obtained from the fingertip with a portable lactate analyzer (Lactate PRO™, Arkray KDK, Japan) 1 and 3 min. after the end of each all-out trial. Peak La−50 was used for further analysis.
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During the second visit, after a warm-up (10 laps of 50 -m front crawl at low intensity), the participants were instructed to swim three sets at three prescribed paces: slow, moderate, and fast pace in this order. Each set consisted of 8 trials of 50 m front crawl based on the swimmers' selfperception of these paces. It was clearly stated that each pace should have been kept constant throughout the 8 trials. To avoid any influence of the dive, all the trials started with the swimmer pushing off from the wall of the pool. A recovery of 3.5 min. was allowed between sets, while 2 sec., 3 sec., and 5 sec. of rest were allowed between each trial at slow, moderate, and fast paces, respectively. As suggested by Maglischo (2003), one of the modalities utilized during race pacing training for distance swimmers consisted of an 8 × 50 m interval set with 5 sec. rest in between. Similarly, very short rest periods were also given during the slower trials to maintain evenness through all the trials and to entail swimmers to focus on the swim and not on the turn. An interval work with slow and fast sets of swimming is a form of suitable training session for both distance and sprint swimmers, at different times of the competitive year (Tabata, Nishimura, Kouzaki, Hirai, Ogita, Miyachi, et al., 1996; Maglischo, 2012). Both groups were well-accustomed with this type of training. With the same method used during the all-out trial, mean speed (S), SF, and SL were calculated in the central 20 m of each trial. Along with stroking parameters, La− and RPE were determined at the end of each trial, with the same method used during the all-out trial. Moreover, S was expressed as percentage of S50 (%S50) at each pace. For each participant, stroke parameters (i.e., S, %S50, SF, and SL) were calculated as the means of the 8 trials within each set (slow, moderate, and fast pace). Statistical Analyses All results are presented as mean ± SD. Normality of data distribution was checked through the Shapiro-Wilk's test. An independent samples Student's t test was used to compare the two groups after the all-out 50-m trial in all variables. Two-way mixed model analysis of variance (ANOVA) was applied with group (distance, sprinters) as between-subjects factor and pace (slow, moderate, fast) as within-subjects factor to compare the groups after the testing protocol. The mean speed, calculated as the mean of the speeds maintained over each one of the eight sessions, represents the pace for the three different prescribed intensities (slow, moderate, fast). When a significant group factor was detected, an independent samples Student's t test was applied to check between-groups differences, whereas pairwise comparisons with Bonferroni's adjustment were applied to look at within-groups differences in pace. Moreover, a paired Student's t test was used to compare the variables at fast pace with the variables during the all-out 50 -m trial. Since RPE values were not normally dis-
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tributed, non-parametric analyses were applied. Mann-Whitney's test was employed for unpaired comparisons, whereas Friedman's (for repeatedmeasure) and Wilcoxon's (for pairwise) tests were employed for withingroups comparisons. Relationship between La − and RPE at each pace was tested with a Spearman's rho correlation coefficient. Level of significance was set at p < .05. The magnitude of the changes was assessed using effect size (ES) statistic with 95% confidence intervals (CI) and percentage change (Batterham & Hopkins, 2006; Hopkins, 2007). Effect sizes were classified as follows: < 0.2 = trivial, 0.2–0.6 = small, 0.6–1.2 = moderate, 1.2– 2.0 large, > 2.0 = very large (Hopkins, 2003). The analysis was conducted with the Statistical Package for Social Sciences 18.0 software (SPSS, Inc., Chicago, IL, USA). RESULTS As can be seen in Table 1, at baseline the all-out 50-m performances (S50) as well as the lactate concentrations (La−50) were significantly different between the two groups, whereas their anthropometric characteristics and perceived exertion (RPE50) did not differ significantly. Taken together, these data confirm the different competitive performance backgrounds of the two groups, while also confirming their similar and comparable anthropometric and perceptual backgrounds. TABLE 1 CHARACTERISTICS OF THE STUDY PARTICIPANTS Variable
Distance (n = 8) M
SD
Sprinters (n = 8) M
SD
t
p
Age, yr.
19
3.0
18
1.0
0.72
.48
Body mass, kg
67.5
3.6
70.6
6.8
− 1.14
.27
0.05
− 1.40
.18
1.4
− 0.37
.72
Height, m BMI, m2/kg
1.76 21.8
0.03 0.6
1.79 21.9
ES M
SD
S50, m·sec.−1
1.79
0.07
1.95
0.12
− 3.26
.006
1.96
1.08
SF50, Hz
0.90
0.04
0.95
0.03
− 2.60
.02
1.36
0.89
SL50, m
1.99
0.05
2.06
0.08
− 2.34
.03
0.84
0.65
La−50, mmol·l−1
7.75
0.77
12.24
1.44
− 3.50
.004
5.17
1.11
9.50 0.53 9.38 0.74 − 0.23* .81 RPE50 Note.—Anthropometric variables and baseline performance and physiological data for distance swimmers and sprinters with statistical tests of differences between the groups. BMI = body mass index; S50 = best mean speed during all-out 50 m front crawl performance; SF50 = mean stroke frequency during all-out 50 m front crawl performance; SL50 = mean stroke length during all-out 50-m front crawl performance; La−50 = mean lactate concentration during all-out 50 m front crawl performance; RPE50 = rate of perceived exertion after the all-out 50 m front crawl performance; ES = effect size with 95% confidence limits. *Z score for non-parametric Mann-Whitney U test, p < .05.
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Reliability Intraclass correlation coefficients (ICC) with 95% confidence intervals are provided in Table 2. As shown in the table, both groups’ ICC values ranged from .89 to .99 in all performance parameters. These high values support Hypothesis 1; swimmers can consistently and reliably interpret and reproduce a given pace requested by the coach throughout the trials in a set. These values also confirmed that our protocol was highly reliable and showed that the workload was feasible for both groups without altering their stroking parameters despite their different competitive background. TABLE 2 INTRACLASS CORRELATION COEFFICIENTS (95% CONFIDENCE INTERVAL) Distance
Variable
Sprinters
Slow
Moderate
Fast
Slow
Moderate
Fast
S
.90 (.73 − .98)
.98 (.96 − 1.00)
.99 (.97 − 1.00)
.95 (.85 − .99)
.94 (.83 − .99)
.98 (.94 − .99)
SF
.91 (.75 − .98)
.99 .94 (.84 − .99) (.97 − 1.00)
.98 (.94 − .99)
.89 (.72 − .98)
.89 (.72 − .98)
.92 .99 .98 .98 .95 .96 (.89 − .99) (.79 − .98) (.97 − 1.00) (.95 − 1.00) (.96 − 1.00) (.87 − .99) Note.—Intraclass correlation coefficients (ICC) with 95% confidence interval at each different pace in distance and sprinters. S = speed; SF = stroke frequency; SL = stroke length. SL
Speed The ANOVA did not show significant group effect (F1 = 0.06, p = .80) nor an interaction for S expressed in absolute terms (F2 = 1.81, p = .18). As expected, values of S increased significantly (F2 = 159.63, p = 4.91e-16; Fig. 1-A) as pace increased in both distance (1.31 ± 0.06, 1.42 ± 0.08, 1.52 ± 0.11 m·sec.−1, at slow, moderate, and fast paces, respectively; ES range, 1.28–2.65) and sprinters (1.30 ± 0.04, 1.43 ± 0.05, 1.55 ± 0.08 m·sec.−1, at slow, moderate, and fast paces, respectively; ES range = 2.46–4.94). When expressed as %S50, the speeds were also statistically different between the groups (F1 = 12.46, p = .03) at slow (73.5 ± 2.3 vs 66.8 ± 4.8 %S50, distance swimmers and sprinters, respectively; t14 = 3.56, p = .003; ES = 6.72 ± 3.46), moderate (79.5 ± 3.4 vs 73.3 ± 4.0 %S50, respectively; t14 = 3.32, p = .005; ES = 6.20 ± 3.30), and fast (84.9 ± 4.1 vs 79.6 ± 3.3 %S50, respectively; t14 = 2.85, p = .01; ES = 1.35 ± 0.83) paces as shown in Fig. 1-B. Hypothesis 2 was partially supported. When paces were expressed in absolute terms, sprinters and distance swimmers did not differ significantly; however, when the speed was normalized to their best performance (i.e., %S50), significant differences emerged between groups: the distance group swam always at higher percentage of its S50, compared to the sprinters group, at each prescribed pace.
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**
1.0
SF (Hz)
1.6 1.4
0.6
## ##
D SL (mm)
** **
70 ##
50
at
SF
## # *
2.0 ##
## 50
SL
t Fa s
e at er od M
t
ow Sl
at er od M
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2.5
1.0 e
ow Sl
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1.5
##
60
Fa s
%S50
3.0
**
90
50
er M od
##
80
e
Sl ow
t Fa s
at er M od
##
100
t
##
0.4 e
Sl ow
**
##
##
##
1.0
**
**
0.8
Fa s
1.2
B
##
##
## ##
1.8
50
S (m· s–1)
2.0
C
S
A
FIG. 1. Changes of parameters at slow, moderate, and fast pace in distance swimmers (filled circle) and sprinters (empty circle). A. S, mean speed (m s−1); S50, mean speed during the all-out 50 m front crawl stroke trial. B. %S50, mean speed expressed as percentage of S50. C. SF, stroke frequency (Hz); SF50, mean stroke frequency during the all-out 50 m front crawl trial. D. SL, stroke length (m); SL50, mean stroke length during the all-out front crawl trial. *Significant difference between groups with p ≤ .05; **significant difference between groups with p ≤ .01; #significant difference from previous speed with p ≤ .05; ##significant difference from previous speed with p ≤ .01.
When comparing S at fast pace with S50, significant differences were found in both groups (t7 = − 10.39, p = 1.66e-05; t7 = − 14.27, p = 1.97e-6, distance and sprint swimmers, respectively; Fig. 1-A). Stroke Parameters For SF, the ANOVA revealed significant within- and between-subjects differences (F2 = 103.67, p = 1.14e-13, and F1 = 8.15, p = .013, respectively) with no significant interaction (F2 = 0.77, p = .43). Significant differences in SF were found between groups (Fig. 1-C), with the distance swimmer group showing higher values at slow (0.59 ± 0.06 vs 0.55 ± 0.04 Hz, distance
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and sprinters, respectively; t14 = 2.14, p = .025; ES = 0.81 ± 0.83), moderate (0.70 ± 0.08 vs 0.63 ± 0.05 Hz, respectively; t14 = 2.39, p = .031; ES = 1.12 ± 0.84), and fast (0.78 ± 0.04 vs 0.72 ± 0.03 Hz, respectively; t14 = 3.29, p = .005; ES = 1.54 ± 0.84) paces. Within each group, SF significantly increased as pace increased (F2 = 103.67, p = 1.38e-08; ES range = 1.09–2.54, 1.54–3.43, in distance and sprinters, respectively). When comparing SF at fast pace with SF50, significant differences were found in both groups (t7 = –8.13, p = 8.22e–05; t7 = − 23.27, p = 6.86e-08, respectively; Fig. 1-C). For SL, the ANOVA showed no significant differences between groups (F1 = 3.49, p = .08) or interaction (F2 = 1.13, p = .33). A significant decrease was detected (F2 = 26.10, p = 3.99e-07) as pace increased in both distance swimmers (2.27 ± 0.30, 2.10 ± 0.24, 1.95 ± 0.13 m, at slow, moderate, and fast paces, respectively; ES range = 0.49–1.06) and sprinters (2.39 ± 0.22, 2.31 ± 0.18, 2.16 ± 0.13 m, respectively; ES range = 0.35–1.00), as shown in Fig. 1-D. When comparing SL at fast pace with SL50, a significant difference was found only in the sprinters group (t7 = − 0.74, p = .48; t7 = 2.49, p = .04, for distance and sprint swimmers, respectively; Fig. 1-D). Lactate and RPE For La−, the ANOVA did not show a significant interaction (F2 = 1.93, p = .16); however, there were significant differences between groups (F1 = 45.00, p = 9.96e-0.6) and among paces (F2 = 161.32, p = 4.29e-16). The distance swimmer group showed significant lower lactate compared to sprinters at slow pace (2.0 ± 0.4 vs 2.6 ± 0.8 mmol·l−1, respectively; t14 = –2.14, p = .05; ES = 0.99 ± 0.84), moderate pace (3.1 ± 0.6 vs 4.6 ± 0.5 mmol·l−1, respectively; t14 = –5.43, p = 8.91e-05; ES = 2.56 ± 0.84), and fast pace (5.9 ± 0.5 vs 7.4 ± 0.1 mmol·l−1, respectively; t14 = − 3.65, p = .03; ES = 1.70 ± 0.84). Within groups, La− significantly increased as pace increased, as shown in Fig. 2-A (p ≤ .01; ES range = 1.86–6.74, 2.59–6.25, for distance and sprinters, respectively). When comparing La− at fast pace with La−50, a significant difference was found only in the sprinters group (t7 = − 0.47, p = .65; t7 = 2.40, p = .047, for distance and sprint swimmers, respectively; Fig. 2-A). The RPE values were not significantly different between groups for the three paces (Z = − 1.89, p = .06; Z = − 1.44, p = .16; Z = − 1.77, p = .83), rising significantly [χ2(2) = 30.52, p = 2.35e-07] in both distance swimmers (1.4 ± 1.1, 3.6 ± 1.3, 6.3 ± 1.5 at slow, moderate, and fast paces, respectively; ES range = 1.58–3.58) and sprinters (2.5 ± 0.9, 4.3 ± 1.3, 7.6 ± 2.2, respectively; ES range = 1.44–4.03) as pace increased (Fig. 2-B). Furthermore, no significant correlations between La− and RPE at any pace were found in both groups (ρ = .23, p = .39; ρ = − .09, p = .72; ρ = .52, p = .06; slow, moderate, and fast, respectively). When comparing RPE at fast pace with RPE50, significant differences were found in both groups (Z = − 2.53, p = .011 and
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B
##
10
** **
5
**
# ##
##
e at
[L a]
50
t Fa s
od er
M
od er
at
e
Sl ow
Sl ow
0
0
50
##
R PE
##
M
5
##
t
10
#
##
RPE
La– (mmol · l–1)
##
Fa s
A
15
FIG. 2. A. Changes in lactate concentration (La−) in mmol·l−1 at slow, moderate, and fast paces in distance swimmers (filled circle) and sprinters (empty circle). B. Changes in rate of perceived exertion (RPE) at slow, moderate, and fast paces in distance swimmers (filled circle) and sprinters (empty circle). **Significant difference between groups with p ≤ .01; #significant difference from previous speed with p ≤ .05; ##significant difference from previous speed with p ≤ .01.
Z = − 2.02, p = .042, for distance and sprint swimmers, respectively; Fig. 2-B). DISCUSSION The main findings of the present study were that distance and sprint swimmers had similar interpretations of prescribed paces (i.e., to swim at slow, moderate, and fast pace) when expressed in absolute terms. However, when the speed was normalized to their all-out performance (i.e., %S50), significant differences emerged between groups: the distance group swam always at higher percentage of its S50, with higher SF and lower La− compared to the sprinters group at each prescribed pace (Fig. 1-C and 2-A). The groups of the present investigation were drawn based on the swimmers' specialization; therefore, the S50, SF50, SL50, and La−50 were significantly different between groups at the all-out 50-m trial, with better performance given by sprinters. However, the absolute speed values of each prescribed pace were similar between groups. A possible explanation for this lack of difference between speeds could be related to the water environment. It has been shown that drag increases in an exponential fashion as speed increases up to 2.0 m·sec.−1 (Toussaint, Roos, & Kolmogorov, 2004); as a consequence, to obtain small increases in swimming speed at the upper speeds, high upper and lower limb strength is required. However, it is well known and accepted that the swimming technique is another key determinant of performance (Fernandes, Marinho, Barbosa, & Vilas-Boas, 2006). Due to the relevant number of years during which all
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the participants underwent both conditioning and technical training, it was assumed that the swimming technique was well consolidated. For this reason, it is likely that strength characteristics were more important determinants of the swimming speed in this group of participants. Hence, the speed range in which swimmers could change their pace from slow to fast is small (1.30–1.55 m·sec.−1 in the present study). It is likely that the lack of differences between groups at each absolute speed was due to this small range of variation in swimming speed imposed by the aquatic environment. A previous study (Stewart & Hopkins, 1997) showed that national swimmers were not so effective in judging the intensity of the workout during training when prescribed as easy, moderate, hard, and race-pace (corresponding to 77%, 78–87%, 88–97%, and 98% of the seasonal-best, respectively). Particularly, a poor correlation between the coaches' prescribed intensity and the individual swimmers' interpretation was found, highlighting the difficulty in distinguishing between hard and race-pace or between easy and moderate. The slow, moderate, and fast paces swum by the distance group in the present study when expressed as %S50 (73.5%, 79.5%, and 84.9% S50, respectively) were close to the values established by Stewart and Hopkins (1997), despite the differences in swimming protocols and the level of the swimmers. However, the sprinters group consistently swam at a slower pace (66.8%, 73.3%, 79.6% S50; slow, moderate, fast, respectively) throughout the trials. In other words, when the speed was selected based on the swimmer's experience, sprinters adopted a more conservative approach, swimming less close to their own best all-out performance than distance swimmers. From a purely physiological point of view, the different aerobic/anaerobic characteristics and the different anaerobic thresholds of the two groups could explain the present results. Distance swimmers are characterized by higher aerobic capacity than sprinters. This is due to intrinsic characteristics of the distance swimmers (genetics) rather than just training volume. In fact, even sprint swimmers undergo training that aims to increase aerobic capacity and power (training sessions of overall distances between 3000 and 6000 m). It is therefore logical to believe that swimmers with the anaerobic thresholds at a higher percentage of their VO2max (such as distance swimmers) can maintain a relative absolute speed higher than swimmers with anaerobic thresholds at a lower percentage of VO2max (such as sprinters). However, this point of view is a less adequate explanation of the results. Several interesting studies (Sgherza, Axen, Fain, Hoffman, Dunbar, & Haas, 2002; Amann & Dempsey, 2008; Marcora, Bosio, & de Morree, 2008; Blanchfield, Hardy, de Morree, Staiano, & Marcora, 2013; de Morree & Marcora, 2013) have shown that without any change in the
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aerobic characteristics of the participants (and therefore without changes in the anaerobic threshold), performance and pacing were altered when the experimental interventions affected the perception of effort. This was tested both using drugs with effects on the central nervous system (Sgherza, et al., 2002) and using fatiguing protocols that necessarily altered the central motor command (Amann & Dempsey, 2008; Marcora, et al., 2008; de Morree & Marcora, 2013). Even psychological interventions can have a positive effect on perception of effort and performance (Blanchfield, et al., 2013). Therefore, the choice of a given pace to interpret a requested intensity cannot be easily explained only by the aerobic or anaerobic capacity of a swimmer. Hence, a key role could be represented by the perception of effort, previous experience, and the distance to be covered (de Morree & Marcora, 2013). Likely, the sprinters, based on their effort awareness, perceived that a further increase in speed could have compromised the success of the given task (i.e., to complete the 8 × 50 m swimming at a constant pace), which they did accomplish successfully, based on reliability data (ICC range, .89–.99, Table 2). This explanation is supported by the lactate production during the fast pace and all-out 50 -m swim. Indeed, in distance swimmers the La− at fast pace and La−50 values were not statistically different, while in the sprinters group significant differences were found, confirming the different effort employed between the all-out trial and the fast pace. Moreover, it has been shown that when metabolite concentrations are high (Allen, Lamb, & Westerblad, 2008), as was found in in the sprinters group, a higher central motor command is required to maintain a given intensity, leading to an increase in RPE (Marcora, et al., 2008). Consequently, it could be hypothesized that, to avoid an early exercise cessation due to an excessive rise in RPE before the end of the last bout at fast pace, the sprinters kept a lower %S50 compared to the distance swimmers (Wright, 1996; Marcora, et al., 2008). However, the RPE values found at the end of each set were not statistically different between groups, which could seem in contrast with the previous considerations. On the other hand, given the fact that the sprinters swam at lower relative paces perceiving similar effort compared to distance, it could be inferred that the RPE was actually higher in sprinters. This observation could additionally support the explanation. It is well known that SL is an important determinant of swimming performance (Chollet, Pelayo, Delaplace, Tourny, & Sidney, 1997; Fernandes, et al., 2006). Moreover, with the increase of swimming speed there is an increase in SF and SL (Craig, Skehan, Pawelczyk, & Boomer, 1985; Zamparo, et al., 2005), and when a swimmer is close to his own peak speed a further increase in speed is determined only by an increase in SF (Craig, et al.,
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1985; Nomura, Takei, & Yanagida, 2003). Furthermore, a direct relationship between SF and oxygen consumption exists (Wakayoshi, D’Acquisto, Cappaert, & Troup, 1995); in other words, when the intensity increases there is an increase in SF. However, when fatigue occurs, the swimmer is no longer able to sustain the optimal SL; therefore, a compensation with increase in SF is adopted to keep the mean speed (Alberty, Potdevin, Dekerle, Pelayo, Gorce, & Sidney, 2008). In the present study, the increase in speed for the requested task (slow, moderate, fast) was obtained by an increase in SF (Craig, et al., 1985; Nomura, et al., 2003), which paralleled a significant reduction in SL throughout the trials in both groups, as shown by previous authors who applied progressive fatiguing protocols (Zamparo, et al., 2005; Barbosa, Fernandes, Keskinen, & Vilas-Boas, 2008). Interestingly, the distance group systematically swam with higher SF than the sprinters at all intensities (slow, moderate, and fast). It is known that generally sprint swimming is associated with higher mean speed and SF with lower SL compared to middle and distance swimming (Keskinen & Komi, 1993; Pelayo, et al., 1996; Seifert, et al., 2004). However, when studying the effect of swimming specialization on stroke parameters, McCabe, et al. (2011) did not find any difference between the two types of swimmers in S, SF, and SL. The latter results might seem in contrast with the present findings, though McCabe, et al. (2011) investigated the response during a set of all-out performances, whereas this study tested the ability to adapt to the coaches' instructions at three different submaximal paces. The differences found between the distance swimmers and the sprinters are probably due to the fact that the former swam at a higher intensity (i.e., speed normalized by their own all-out speed) than the latter group. From a purely physiological point of view this finding can be explained, as the distance swimmers likely swam at a higher percentage of their maximum oxygen uptake. Indeed, there is a direct relationship between SF and oxygen uptake (Wakayoshi, et al., 1995). However, swimmers do not base the choice of a swimming speed to accomplish a given task directly from the perception of their maximum oxygen uptake; rather, they utilize their own perception of effort, experience relative to previous efforts, and the distance to be covered. Therefore, the conscious decision of the distance swimmers to adopt a relative higher speed than the sprinters determined the need of swimming at a higher SF. Consequently, it seems that when a self-regulated pace is requested the swimmers' specialization influences stroke characteristics, which was not observed previously in shorter distances (McCabe, et al., 2011). A further explanation of the different SF between groups could be their different training methods. Sprinters are used to adopt very high SF during their short and very close to all-out repetitions, typical of their training sessions; therefore, they might prefer a
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low SF when a slow speed is requested, with the intention of maintaining a less fatiguing neural activation (Keskinen & Komi, 1993). Limitations and Conclusions From a practical point of view, the use of simple adjectives to define different intensities of training can be considered a valid reliable method. In particular, when a broader and general training is performed swimmers seem to comply successfully and consistently follow the coaches' requirements. However, this study would suggest that coaches should monitor the time and SF when specific and race paces are requested during training. A question that may arise is whether swimmers are still able to comply with the coaches' requirements consistently with high fatigue (e.g., after a 2–3 hr. of intense swimming). Although not included in the present study, it is likely that in high fatigue conditions the gap between distance swimmers and sprinters, in terms of relative and absolute speed, may become wider and more evident than observed in the current study, and that the reliability of the different intensities (slow, moderate, fast) might be poorer (i.e., lower ICC). Future investigations should be conducted to address these issues. The lack of a test to assess the anaerobic threshold of the two groups is a limitation of this study. Although it could have better characterized the swimmers, the goal was to assess the differences in response to a requested task between the two types of swimmers and not to assess their physiological responses to a given swimming velocity. To summarize, the results of this study show that high level distance swimmers interpret the prescribed paces “slow,” “moderate,” and “fast” swimming at a higher percentage of their own all-out performance with higher stroke frequency, and produce lower blood lactate concentration than sprint swimmers. These different interpretations of the prescribed paces can be attributed to the specificity of training and competitions, and imply the involvement of prior knowledge about the features of the test. Being accustomed to all-out performances, the group of sprinters adopted slower normalized speed and stroke frequency than distance swimmers at the prescribed paces, probably to prevent premature fatigue. On the contrary, at training intensities close to the race pace, distance swimmers spontaneously chose higher normalized speeds than sprinters when swimming at the prescribed paces, producing less blood lactate concentration. REFERENCES
ALBERTY, M., POTDEVIN, F., DEKERLE, J., PELAYO, P., GORCE, P., & SIDNEY, M. (2008) Changes in swimming technique during time to exhaustion at freely chosen and controlled stroke rates. Journal of Sports Sciences, 26, 1191-1200.
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