Biomechanics and Medicine in Swimming XI

BiomechanicsandmedicineinswimmingXi
5cm; Arm Span: 182 ± 5cm; Competitive experience: 5 ± 1 years old)
participated in the study. All swimmers trained while performing the
testing (high volume; low to moderate intensity level). They swam six
to eight training sessions per week (six to eight km per session). Prior
to the commencement of the testing, the person in charge of the swimmers
signed informed consents, the study being approved for the Ethics
Committee of the University.
Protocol: Maximal swimming trials (front crawl) were over 6 distances
(50, 100, 200, 400, 800 and 1500-m) within 16 days with a minimum
of 48 hours between each trial. The order of the trials was randomized.
A 50-m pool of controlled temperature (28 ± 0.5 ºC) was used. All trials
were conducted at the same time of the day (8:00 am) with a prior
warm up performed in free style at moderate intensity for 2000-m. Each
swimmer was instructed to perform as best as they could during each
trial. Performance times were measured by three experienced coaches
who used a manual chronometer.
Data analysis: The average performance time collected from each
of the three measures were manually typed into a spreadsheet for the
estimative of the various CSS. CSS was computed for the following
combinations of distances: CSS 1 (50-100m), CSS 2 (50-200m), CSS 3
(50-400m), CSS 4 (50-800m), CSS 5 (50-1500m), CSS 6 (100-200m),
CSS 7 (100-400m), CSS 8 (100-800m), CSS 9 (100-1500m), CSS 10 (200-
400m), CSS 11 (200-800m), CSS 12 (200-1500m), CSS 13 (400-800m),
CSS 14 (400-1500m) and CSS 15 (800-1500m).
Statistical analysis: To address the first aim of the study, different
combinations for predicting CSS were compared. Mean and standard
deviation were selected to present the data after normality and sphericity
analysis by the Shapiro Wilk and Mauchly tests, respectively. The
t-test for pared sample was used for the comparison between CSS 10 vs
all others 14 CSS combinations and between CSS 10 vs V 1500 . Significant
results were selected when Type I error was rejected for a threshold
level of 5%. SPSS software was employed for the aforementioned tests.
The levels of agreement between the several predictions of CSS were
analyzed using Bland and Altman analysis (Bland and Altman, 1986).
The results were presented based on the differences between each CSS
predictions (i.e. CSS 10 vs the all others 14 CSS combinations) and between
CSS 10 and V 1500 .
results
Mean and standard deviation of performance time (s) in each distance
are shown in Table 1. The results of comparisons between CSS 10 with
the other predictions and with V 1500 are shown in Table 2.
Biomechanics and Medicine in Swimming XI / Chapter 4 Training Page 168
Table 1: Mean and standard deviation of performance time (s) for each
distance (50, 100, 200, 400, 800 and 1500-m).
Table 1: Mean and standard deviation of performance time (s) for each distance (50,
100, Biomechanics 200, 400, 800 and and Medicine 1500-m). in Swimming XI / Chapter 4 Training Page 168
n 50 m 100 m 200 m 400 m 800 m 1500 m
11 29.36 ± 1.67 65.64 ± 3.50 146.55 ± 7.57 305.09 ± 16.65 643.55 ± 36.75 1230.91 ± 88.58
Table 2: Comparison between CSS10 with the other predictions and V1500 by the t test
and the level of agreement expressed in absolute and percentage results.
Comparison between CSS10
(1.27 m·s
308
-1 Agreement (Bland and Altman)
T test
Limits (95%)
) and . . .
Significance Bias
- 1.96 SD + 1.96 SD
CSS1 (50-100m) (1.39 m·s -1 ) 0.0004* -0.119 (-8.9%) -0.268(-19.3%) 0.028 (1.5%)
-1
CSS2 (50-200m) (1.28 m·s ) 0.5253 -0.016 (-1.4%) -0.181 (-14.6%) 0.148 (11.9%)
CSS3 (50-400m) (1.27 m·s -1 ) 0.5928 -0.005 (-0.5%) -0.074 (-6.1%) 0.062 (5.1%)
-1
CSS4 (50-800m) (1.23 m·s ) 0.0410* 0.042 (3.3%) -0.075 (-6.2%) 0.159 (12.8%)
-1
CSS5 (50-1500m) (1.21 m·s ) 0.0229* 0.054 (4.4%) -0.077 (-6.3%) 0.186 (15.2%)
CSS6 (100-200m) (1.24 m·s -1 ) 0.4356 0.026 (2.1%) -0.186 (-15.3%) 0.240 (19.4%)
-1
CSS7 (100-400m) (1.26 m·s ) 0.3389 0.010 (0.8%) -0.060 (-5.0%) 0.082 (6.6%)
-1
CSS8 (100-800m) (1.22 m·s ) 0.0200* 0.052 (4.1%) -0.070 (-5.8%) 0.175 (14.1%)
CSS9 (100-1500m) (1.21 m·s -1 ) 0.0160* 0.059 (4.9%) -0.074 (-6.1%) 0.194 (15.9%)
-1
CSS11 (200-800m) (1.21 m·s ) 0.0096* 0.055 (4.5%) -0.057 (-4.8%) 0.169 (13.7%)
-1
CSS12 (200-1500m) (1.21 m·s ) 0.0114* 0.062 (5.1%) -0.068 (-5.6%) 0.193 (15.7%)
CSS13 (400-800m) (1.19 m·s -1 ) 0.0095* 0.080 (6.5%) -0.083 (-7.0%) 0.244 (20.0%)
-1
CSS14 (400-1500m) (1.20 m·s ) 0.0117* 0.072 (5.9%) -0.080(-6.6%) 0.224 (18.5%)
-1
CSS15 (800-1500m) (1.20 m·s ) 0.0311* 0.065 (5.5%) -0.104 (-8.7%) 0.236 (197%)
V1500 (1.22 m·s -1 ) 0.0605 0.040 (3.5%) -0.090 (-7.2%) 0.170 (14.2%)
* = p < 0.05
Comparing the proposed combinations used in this study with CSS10, CSS1 showed the
highest difference (p = 0.0004) and worst level of agreement (-0.119 m·s -1 ; -8.9%). The
lowest error on the estimation of CSS was observed from CSS3 (-0.005 m·s -1 ; -0.5%)
and CSS7 (0.010 m·s -1 Table 1: Mean and standard deviation of performance time (s) for each distance (50,
100, 200, 400, 800 and 1500-m).
Table n 2: 50 Comparison m 100 m between 200 m CSS10 400 with m the other 800 m predictions 1500 m and
11 29.36 ± 1.67 65.64 ± 3.50 146.55 ± 7.57 305.09 ± 16.65 643.55 ± 36.75 1230.91 ± 88.58
V1500 by the t test and the level of agreement expressed in absolute and
percentage Table 2: Comparison results. between CSS10 with the other predictions and V1500 by the t test
and the level of agreement expressed in absolute and percentage results.
Comparison between CSS10 (1.27 m·s
; 0.8%) both including the 400-m trial in their calculation.
Furthermore, the limits of agreement were very similar (CSS3: -6.1 to 5.1%; CSS7: -5.0
to 6.6%). CSS10 overestimated V1500 by +3.5%.
DISCUSSION
The method used to calculate CSS (two-parameter model) has many advantages as a)
ease of application, b) analysis of large numbers of athletes can be carried out during
training sessions without the use of expensive equipment or blood collection, and c)
easy to assess the evolution or that occurs with training in a given period of time. This
-1 Agreement (Bland and Altman)
T test
Limits (95%)
) and . . .
Significance Bias
- 1.96 SD + 1.96 SD
CSS1 (50-100m) (1.39 m·s -1 ) 0.0004* -0.119 (-8.9%) -0.268(-19.3%) 0.028 (1.5%)
-1
CSS2 (50-200m) (1.28 m·s ) 0.5253 -0.016 (-1.4%) -0.181 (-14.6%) 0.148 (11.9%)
CSS3 (50-400m) (1.27 m·s -1 ) 0.5928 -0.005 (-0.5%) -0.074 (-6.1%) 0.062 (5.1%)
-1
CSS4 (50-800m) (1.23 m·s ) 0.0410* 0.042 (3.3%) -0.075 (-6.2%) 0.159 (12.8%)
-1
CSS5 (50-1500m) (1.21 m·s ) 0.0229* 0.054 (4.4%) -0.077 (-6.3%) 0.186 (15.2%)
CSS6 (100-200m) (1.24 m·s -1 ) 0.4356 0.026 (2.1%) -0.186 (-15.3%) 0.240 (19.4%)
-1
CSS7 (100-400m) (1.26 m·s ) 0.3389 0.010 (0.8%) -0.060 (-5.0%) 0.082 (6.6%)
-1
CSS8 (100-800m) (1.22 m·s ) 0.0200* 0.052 (4.1%) -0.070 (-5.8%) 0.175 (14.1%)
CSS9 (100-1500m) (1.21 m·s -1 ) 0.0160* 0.059 (4.9%) -0.074 (-6.1%) 0.194 (15.9%)
-1
CSS11 (200-800m) (1.21 m·s ) 0.0096* 0.055 (4.5%) -0.057 (-4.8%) 0.169 (13.7%)
-1
CSS12 (200-1500m) (1.21 m·s ) 0.0114* 0.062 (5.1%) -0.068 (-5.6%) 0.193 (15.7%)
CSS13 (400-800m) (1.19 m·s -1 ) 0.0095* 0.080 (6.5%) -0.083 (-7.0%) 0.244 (20.0%)
-1
CSS14 (400-1500m) (1.20 m·s ) 0.0117* 0.072 (5.9%) -0.080(-6.6%) 0.224 (18.5%)
-1
CSS15 (800-1500m) (1.20 m·s ) 0.0311* 0.065 (5.5%) -0.104 (-8.7%) 0.236 (197%)
V1500 (1.22 m·s -1 ) 0.0605 0.040 (3.5%) -0.090 (-7.2%) 0.170 (14.2%)
* = p < 0.05
Comparing the proposed combinations used in this study with CSS10, CSS1 showed the
highest difference (p = 0.0004) and worst level of agreement (-0.119 m·s -1 ; -8.9%). The
lowest error on the estimation of CSS was observed from CSS3 (-0.005 m·s -1 ; -0.5%)
and CSS7 (0.010 m·s -1 * = p < 0.05
; 0.8%) both including the 400-m trial in their calculation.
Furthermore, the limits of agreement were very similar (CSS3: -6.1 to 5.1%; CSS7: -5.0
to 6.6%). CSS10 overestimated V1500 by +3.5%.
DISCUSSION
The method used to calculate CSS (two-parameter model) has many advantages as a)
Comparing the proposed combinations used in this study with CSS 10 ,
CSS 1 showed the highest difference (p = 0.0004) and worst level of
agreement (-0.119 m·s -1 ; -8.9%). The lowest error on the estimation of
CSS was observed from CSS 3 (-0.005 m·s -1 ; -0.5%) and CSS 7 (0.010
m·s -1 ; 0.8%) both including the 400-m trial in their calculation. Furthermore,
the limits of agreement were very similar (CSS 3: -6.1 to 5.1%;
CSS 7 : -5.0 to 6.6%). CSS 10 overestimated V 1500 by +3.5%.
dIscussIon
The method used to calculate CSS (two-parameter model) has many advantages
as a) ease of application, b) analysis of large numbers of athletes
can be carried out during training sessions without the use of expensive
equipment or blood collection, and c) easy to assess the evolution or that
occurs with training in a given period of time. This eases the testing,
especially for swimmer populations such as children and adolescents.
Having access to information of possible differences between CSS10 and
other combinations for predicting CSS seems important when for some
reason the coach needs to calculate the CSS with other distances.
The first result of the present study was that the highest difference
recorded in this study was that between CSS10 and CSS1 . However, a
level of agreement is a more sensitive tool for testing the accuracy of a
method to predict a single parameter. The substantial contribution of the
anaerobic metabolism in the 50 and 100-m races (Gastin, 2001) could
explain this high difference between CSS10 and CSS1 (bias: -0.119 m·s- 1 ; -8.9%).
The lowest error in the estimation of CSS10 was observed for CSS3 (-0.005 m·s-1 ; -0.5%) and CSS7 (0.010 m·s-1 ; 0.8%) both of them including
the 400-m performance and one short distance (50 or 100-m) in the
modeling. In addition, the limits of agreement were very similar (CSS3: -6.1 to 5.1%; CSS7 : -5.0 to 6.6%). Gastin (2001) used mathematical
modeling techniques and suggested that performances lower than 75-s
(under maximal intensity) need more contribution of anaerobic than
aerobic metabolism. However, there is a lower concentration of glycolytic
enzymes and an increased concentration of aerobic enzymes in adolescents
compared with adults. Adolescents have a lower concentration
of steroid hormones, which will increase with the maturation process
and therefore increase muscle mass and anaerobic capacity (Dipla et al.,
2009). Thus, probably the low anaerobic capacity of the swimmers is responsible
for the similarity between CSS10 , CSS3 and CSS7 because the
distances of 50 and 100-m do not seem to have increased the value of
CSS obtained by CSS3 and CSS7 . For females, it has been observed that
muscle power reaches an earlier steady state, i.e soon after 14-15 years
(Dipla et al., 2009). This might suggest studies to measure the level of
agreement between CSS10 and the other combinations in young female
and male swimmers (14-15 years), also to check whether this level of
agreement will be maintained until adulthood.
Third, the results showed that the level of agreement between
CSS10 and the combinations that use the distance of 800-m, 1500-m
or both, can underestimate CSS10 in young swimmers. In our study,
the underestimation of CSS10 was established between - 3.3 to 6.5%.
The greater contribution of aerobic metabolism of the total energy required
to travel distances of 800 and 1500-m (800-m: 643.55 ± 36.75
s, 1500-m: 1230.91 ± 88.58 s) seems to have been the main reason for
these findings (Gastin, 2001). In addition, although they are swimmers
with a considerable level of experience, the application of extensive tests
(800 and 1500-m) in young swimmers in the swimming training situation
rather than a competitive situation may diminish their motivation
which would affect the performance of the test. Moreover, as they are
sprint swimmers, some of them probably did not have sufficient experience
with longer events.
Finally, CSS10 (200-400m) was higher than V1500 (+3.5%) with performances
over this distance ranging from 19.5 to 22-min (1231 ± 88 s)
in young swimmers. This difference was similar to that found bewteen
CSS10 and the speed of 30-min test by Dekerle et al. (2002), which
suggested a correction of - 3.2% to establish the swimming velocity over