Biomechanics and Medicine in Swimming XI

The K value, expressing the stability throughout the overall swimmer’s
career, was moderate (K = 0.38 ± 0.05) with 0.33 < K < 0.43 for a 95 %
confidence interval. So, based on overall values of the seven consecutive
seasons, a low swimming performance stability and prediction can be
considered.
Table 1 presents the self-correlation values for pairwised ages
throughout swimmer’s career. Self-correlations were significant in
all situations (P < 0.05), except between the 16 and 17 years. Overall,
throughout swimmers career, self-correlation ranged between a moderate
and a high stability (0.30 < r < 0.60). Indeed, most of the pair wise
self-correlations were r ≥ 0.60. Stability becomes high (r = 0.644) from
14 until 18 years old.
Table 1: Pearson Correlation Coefficients from children to adult age in
the 100-m Breaststroke event.
Age 12 13 14 15 16 17
13 0.863* 1
14 0.610* 0.850* 1
15 0.459* 0.727* 0.867* 1
16 0.300 0.582* 0.741* 0.728* 1
17 0.300 0.505* 0.696* 0.678* 0.802* 1
18 0.398* 0.485* 0.644* 0.598* 0.730* 0.839*
* P < 0.05
dIscussIon
The aim of this study was to track and analyze the 100-m Breaststroke
performance stability throughout the elite swimmer’s career. Main data
suggests an obvious performance enhancement in the 100-m Breaststroke
event, from children to adult age. Analyzing the overall tracking
values, the prediction of adult swimmer’s performance level, based on
childhood performance is moderate. When more strict time frames are
used, swimming performance stability and prediction increases starting
at the age of 14. It seems that the change from 13 to 14 years can be a
milestone, were the ability to predict the swimmer’s performance level
strongly increases.
The trend for performance enhancement throughout swimmer’s career
might be associated to some scientific improvements and innovations
that along with the normal individual development process, allow
swimmers to obtain better performances. As swimming performance is
determined by different parameters, the individual development will affect
the energy cost of swimming (Kjendlie et al., 2003). Changes in anthropometric
characteristics, like body length and body mass, are identified
as causes to increase energy cost (Chatard et al., 1985). Along with
these morphological changes, development shows an increase in swimmers
muscular strength. It appears that hormonal development that occurs
during maturation has a determinant role in increasing muscle size
and strength (Matos and Winsley, 2007). Furthermore, the sequence
in which training loads or volumes are applied, as a series of training
blocks, is critical to enhance performance. The model of training load
reduction adopted before important competitions has a high effect on
the athlete’s performance (Mujika et al., 2002). This type of training
reduction is not common in younger ages.
Another explanation for this career improvement is the technological
sophistication of the swimming suits. The type of material (e.g. polyurethane),
the ways to sew the fabric pieces, suit types and sizes, the
effect of swim suits upon wobbling body mass, and body compression
might explain the major advantages of wearing these recently developed
suits leading better performances in adult ages (Marinho et al., 2009).
However, despite this performance improvement during the swimmers
career, a slight “breaking effect” starting at the age of 16 (Figure 1)
can be observed. This fact may be associated with: (i) a maximal level of
external training load reached by the swimmers, which is more difficult
chaPter4.trainingandPerformance
to overcome; (ii) a decrease in physiological functional capacity with age
or; (iii) a slowdown or stagnation in the development of anthropometric
characteristics.
The self-correlation values (Table 1) ranged between moderate and
high stability throughout the swimmer’s career. The initial sharp increase
in stability can be observed during the change from 13 to 14 years old.
This can be explained by: i) maturational process, that provides greater
availability for training process, in order to obtain more ambitious performances,
ii) consolidation of values and culture of swimming performance
acquired throughout swimmer’s career.
The K value, expressing the stability throughout the overall swimmer’s
career, was low (K = 0.38 ± 0.05). Based on such long careers, it
is clear that swimmers performance stability is difficult to maintain on
a high level. Moreover, there are several episodes that can influence the
performance stability, such as illness or an acute/chronic injury. When
more strict time frames are used, performance stability and prediction
increases.
conclusIon
The prediction of adult performance level, based on childhood performance
is low, when considering the swimmer’s overall career. When
more strict time frames are used, swimming performance stability and
prediction increases starting at the age of 14. It seems that the change
from 13 to 14 years can be a milestone, where the ability to predict the
final swimmer’s performance level strongly increases.
reFerences
Chatard, J.C., Padilla, S., Carzorla, G. & Lacour, J.R. (1985). Influence
of body height, weight, hydrostatic lift and training on the energy
cost of the front crawl. NZL Sports Med, 13, 82-84
Kjendlie, P.L., Ingjer F., Madsen, O., Stallman, R.K. & Stray-Gundersen,
J. (2003). Differences in the energy cost between children and
adults during front crawl swimming. Eur J Appl Physiol. 91, 473-480
Landis, J.R. & Koch, G.G. (1977). The measurement of observer agreement
for categorical data. Biometrics, 37, 439-446.
Malina, R.M. (2001). Adherence to physical activity from childhood to
adulthood: a perspective forma tracking studies. Quest, 53, 346-355.
Marinho, D.A., Barbosa, T.M., Kjendlie, P.L., Vilas-Boas, J.P., Alves,
F.B., Rouboa, A.I. & Silva, A.J. (2009). In: M. Peters, ed. Swimming
simulation: a new tool for swimming research and practical applications.
Lecture Notes in Computational Science and Engineering – Computational
Fluid Dynamics for Sport Simulation. Berlin: Springer.
Matos, N. & Winsley, R.L. (2007). Trainability of young athletes and
overtraining. J Sport Science, 6, 353-367
Mujika, I., Padilla, S., Pyne, D. (2002). Swimming performance changes
during the final 3 weeks of training leading to the Sydney 2000
Olympic Games. Int J Sports Med, 23(8), 582-587.
Pyne, D., Trewin, C. & Hopkins W. (2004). Progression and variability
of competitive performance of Olympic swimmers. J Sports Sci. 22(7),
613-20.
AcKnoWledGMents
Mário J. Costa would like to acknowledge to the Portuguese Science
and Technology Foundation (FCT) for the PhD grant (SFRH/
BD/62005/2009).
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