Edited by: Joao R. Vaz, University of Lisbon, Portugal
Reviewed by: Luís Silva, New University of Lisboa, Portugal; Henrique Pereira Neiva, University of Beira Interior, Portugal; Peter C. Raffalt, University of Southern Denmark, Denmark
This article was submitted to Biomechanics and Control of Human Movement, a section of the journal Frontiers in Sports and Active Living
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Understanding fluctuations and associations between swimming performance-related variables provide strategic insights into a swimmer's preparation program. Through network analysis, we verified the relationships between anthropometrics, maturation, and kinematics changes (Δ) in 25-m breaststroke (BREAST) and butterfly (FLY) swimming performance, before and after a 47-week swimming training season. Twenty age-group swimmers (
The performance of age-group swimmers improves based on the relationships among technical, physical and anthropometric factors, which are characterized by a complex adaptive system (CAS). Whereas there is body growth, drag and propulsion change, i.e., the swimming performance related factors may be deeply influenced by the anthropometric characteristics. Not conceptualizing swimming performance as a CAS phenomenon is a limitation that should be avoided (Morais et al.,
There is a scientific and practical interest in individual maturation and the ideal period to start working on individual physical skills in long-term athletic development (LTAD; Lätt et al.,
The human body consists of several interdependent systems, and multiple factors can affect the ability to swim fast. Identifying which factors are important for fast swimming and how to maximize these factors for performance improvements requires understanding the existing network relationships. Interventions and/or phenomena in a specific system can trigger responses in another apparently unrelated system (Goethel et al.,
Twenty age-group swimmers participated in this study. Age for girls (
All swimmers were evaluated during two identical testing sessions: (i) before the training season, that is, during the first week of training after the summer vacation; and (ii) after 47 weeks, at the end of the last macrocycle of the season. First, the anthropometric profile was obtained, which was consisted by height (HE), arm span (AS), total body mass (BM), and sitting height (SH). After an approximately 400-m moderate-intensity warmup, swimmers performed two 25-m all-out swim tests (T25; randomized order), one in breaststroke (BREAST), and one in butterfly (FLY), whereas kinematic variables were collected manually by one trained and experienced evaluator (Hay and Guimarães,
Then, stroke rate (SR) was multiplied by 60 to obtain SR in cycles·min−1.
Height, AS, BM, and SH were measured (Heyward and Stolarczyk,
Maturity offset equations (Mirwald et al.,
where BMO and GMO are, respectively, boys and girl's maturity offset; LL is leg length; SH is sitting height; A is age, and BM is body mass. With BMO and GMO data, any negative result is before PHV (maturity offset < 0, i.e., time left to reach the peak), and any positive results are after PHV (maturity offset = or > 0, i.e., indicating whether the participant is exactly at the beginning moment of PHV or how much this has passed). These equations are gender-specific, considering biological significance and statistics to predict maturity. Maturity offset indicates how far, in years, an age-group swimmer is approaching or moving away from PHV.
Mean, SD and 95% confidence intervals were obtained and reported for all studied variables. Shapiro–Wilk test was applied to verify the data distribution, and comparisons were performed with paired-samples
To verify the associations among anthropometric, kinematics, and maturation variables changes, for both, BREAST and FLY, a machine learning technique (Network Analysis) was used (Epskamp et al.,
(i)
(ii)
(iii)
We used the pairwise Markov random field model to improve the accuracy of the partial correlation network. The estimation algorithm used assumes the highest-order interaction of the true graph. The algorithm includes an L1 (regularized neighborhood regression) penalty. Regularization is achieved by a “less absolute contraction and selection operator” (LASSO) that controls the model's sparsity (Friedman et al.,
BEFORE and AFTER 47 weeks (47w) mean ± SD values (95% confidence intervals),
Age (years) | 10.2 ± 1.2 |
11.1 ± 1.2 |
<0.001; 0.75 (moderate) |
|||
Height (cm) | 142.3 ± 9.7 |
147.8 ± 9.5 |
<0.001; 0.57 (small) |
|||
AS (cm) | 143.6 ± 10.4 |
150.8 ± 11.3 |
<0.001; 0.41 (small) |
|||
BM (kg) | 36.7 ± 8.2 |
41.4 ± 8.5 |
<0.001; 0.56 (small) |
|||
MO (years) | −2.0 ± 0.9 |
−1.3 ± 1.0 |
<0.001; 0.73 (moderate) |
|||
T25 (s) | 32.0 ± 7.5 |
30.3 ± 7.0 |
24.5 ± 3.8 |
21.8 ± 3.6 |
<0.001; 1.26 (large) |
<0.001; 1.52 (large) |
0.71 ± 0.12 |
0.76 ± 0.18 |
0.90 ± 0.12 |
1.05 ± 0.16 |
<0.001; 1.58 (large) |
<0.001; 1.70 (large) |
|
SR (cycles·min−1) | 45.9 ± 11.9 |
37.1 ± 9.7 |
58.6 ± 8.6 |
45.6 ± 11.8 |
0.001; 1.22 (large) |
0.002; 0.78 (moderate) |
SL (m) | 0.96 ± 0.23 |
1.30 ± 0.38 |
0.93 ± 0.13 |
1.43 ± 0.25 |
0.63; 0.16 (trivial) |
0.11; 0.40 (small) |
SI (m2·s−1) | 0.69 ± 0.24 |
1.04 ± 0.41 |
0.84 ± 0.20 |
1.50 ± 0.35 |
0.005; 0.67 (moderate) |
0.001; 1.20 (large) |
For BREAST,
BREAST network of association between changes (Δ) in anthropometrics, maturation, and kinematics (using gender as a dichotomous variable);
The weight matrix for the BREAST is presented in
The weight matrix for the BREAST with the Δ% (gender as a dichotomous variable) (
Δ |
Δ |
Δ |
Δ |
Δ |
Δ |
Δ |
Δ |
Δ |
Δ |
||
---|---|---|---|---|---|---|---|---|---|---|---|
Gender | 0 | ||||||||||
ΔAge | −0.24 | 0 | |||||||||
ΔHeight | −0.19 | −0.22 | 0 | ||||||||
ΔAS | 0.10 | −0.28 | 0.79 | 0 | |||||||
ΔBM | 0.09 | −0.15 | 0.29 | 0.38 | 0 | ||||||
ΔT25 | 0.01 | −0.26 | 0.23 | 0.37 | 0.07 | 0 | |||||
Δ |
−0.03 | 0.24 | −0.10 | −0.27 | −0.02 | – |
0 | ||||
ΔSR | 0.04 | −0.08 | −0.00 | −0.07 | −0.36 | – |
0.63 | 0 | |||
ΔSL | 0.02 | 0.02 | −0.17 | −0.02 | 0.54 | 0.05 | −0.03 | – |
0 | ||
ΔSI | −0.21 | 0.23 | 0.11 | 0.18 | 0.77 | −0.04 | 0.05 | −0.38 | 0 | ||
ΔMO | −0.12 | −0.05 | −0.63 | −0.64 | −0.26 | 0.17 | −0.18 | −0.18 | 0.18 | −0.07 | 0 |
For FLY,
FLY network of association between changes (Δ) in anthropometrics, maturation, and kinematics (using gender as a dichotomous variable);
The weight matrix for the FLY is presented in
The weight matrix for the FLY with the Δ% (gender (gender as a dichotomous variable) (
Δ |
Δ |
Δ |
Δ |
Δ |
Δ |
Δ |
Δ |
Δ |
Δ |
||
---|---|---|---|---|---|---|---|---|---|---|---|
Gender | 0.00 | ||||||||||
Δ Age | −0.39 | 0.00 | |||||||||
Δ Height | −0.33 | −0.08 | 0.00 | ||||||||
ΔAS | −0.04 | −0.03 | 0.78 | 0.00 | |||||||
ΔBM | −0.07 | 0.13 | 0.36 | 0.59 | 0.00 | ||||||
ΔT25 | −0.16 | −0.07 | 0.10 | 0.08 | 0.13 | ||||||
Δ |
0.11 | 0.07 | −0.07 | −0.11 | −0.32 | 0.00 | |||||
ΔSR | −0.30 | 0.13 | 0.41 | 0.46 | 0.14 | – |
0.00 | 0.00 | |||
ΔSL | −0.08 | – |
– |
−0.34 | −0.24 | 0.30 | −0.82 | 0.00 | |||
ΔSI | 0.39 | −0.38 | 0.08 | 0.13 | 0.31 | −0.30 | 0.27 | −0.21 | 0.12 | 0.00 | |
ΔMO | −0.27 | −0.10 | −0.43 | −0.35 | −0.17 | 0.11 | −0.21 | 0.10 | −0.07 | −0.09 | 0.00 |
BREAST and FLY centrality measures (gender as dichotomous variable) (
Gender | −1.05 | 1.73 | −2.39 | 0.31 | −2.33 | −0.01 |
ΔAge | −0.25 | −0.83 | −0.88 | −1.44 | −1.10 | −1.56 |
ΔHeight | −0.25 | 0.02 | −0.18 | 0.77 | 0.46 | 0.98 |
ΔAS | 0.02 | 0.82 | 0.91 | |||
ΔBM | 0.94 | 0.02 | 0.72 | 0.47 | 0.82 | 0.08 |
ΔT25 | 0.14 | −0.83 | 0.67 | −1.09 | 0.45 | −0.53 |
Δ |
−1.05 | −0.25 | 0.02 | −0.74 | 0.08 | −0.19 |
ΔSR | 1.34 | −0.83 | 0.30 | 0.71 | 0.82 | 0.89 |
ΔSL | −1.05 | 0.14 | −0.52 | |||
ΔSI | 0.94 | −0.25 | 0.85 | −0.49 | 0.14 | −0.72 |
ΔMO | −1.05 | −0.83 | −0.46 | −0.99 | 0.05 | −1.34 |
We performed a global analysis to identify the relationships between changes in anthropometrics, maturation, and kinematics in 12-year-old and under age-group swimmers when swimming BREAST and FLY during a typical training season (47 weeks). The main finding of this study was that changes in performance and kinematics were higher than anthropometrics after 47 weeks, that is, improvements in swimming performance (T25) do not seem to be so dependent on growth, even though AS has stood out in the analysis of centrality measures.
Changes in technique (kinematics) may be related to motor coordination development in swimming (Guignard et al.,
Simultaneous swimming techniques involve more coordinative skills and are less economic than alternate ones (Zamparo et al.,
The network analyses using changes in anthropometrics, maturation, and kinematics for both, BREAST and FLY, revealed the complexity of the systems. In swimming (Guignard et al.,
Regarding centrality, the betweenness indicates which variables are closer to others and could be the easiest path for changes. In the BREAST, changes in AS and SR (both 1.34) and SI (0.94) were highlighted. Clearly, AS changes are beyond intervention possibilities. However, the focus on SR and stroke index (SI, an indirect measure swimming efficiency) to a given v (Costill et al.,
The closeness measure can indicate which variables could be more quickly affected by interventions. Regarding BREAST, changes in AS and SR (both 1.34) and SI (0.94) were highlighted. Since AS is an anthropometric variable, the focus for faster changes in performance in BREAST should be on changes in SR and SI (a variable that incorporates both SL and v) (Costill et al.,
According to Newell (
Task constraints describe the activity to be performed by the subject and whether individual objectives, rules or instructions, and possible implements are included. Task constraints can generate changes in movement patterns, and these changes trigger changes in the system, which leads individuals to a new organizational state (Newell,
Typically, beginner swimmers spend more time with the head out of water during breathing time when swimming BREAST. It has been well reported that head position influences technique (Kapus et al.,
Organism constraints refer to the characteristics of the subject (Newell,
Maturity offset can play a role in the organism constraints for stroke coordination. For BREAST and FLY, changes in MO showed a high relationship with the development process, with high values for betweenness (
The swimming athletic development process is multifactorial (Zacca et al.,
The use of network analysis to understand a phenomenon in sports and health sciences is quite new, but its basic ideas have been noted since the 1960s (Grusky,
Twelve-year-old and under age-group swimmers regularly change their technique when swimming BREAST and FLY. Maturation, HE, AS, and SL showed a great impact on BREAST development, whereas age, SR, and HE had a strong impact for FLY. The SI represents an indirect measure of swimming efficiency and should be monitored in both BREAST and FLY to connect growth with the other technique variables. The dynamic process of athletic development and the perception of complexity of changes and relationships between swimming performance-related variables were underpinned, particularly for simultaneous techniques in age-group swimmers.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
The studies involving human participants were reviewed and approved by Universidade Federal do Rio Grande do Sul. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.
JF, PB, RZ, and FC developed the original research inquiry and analyzed data, collaborated in data interpretation, writing, and reviewing the manuscript. JF and FC recruited participants and collected data. All authors approved the final version of this manuscript.
This publication was funded by Universidade Federal do Rio Grande do Sul, Brazil. RZ is funded by Research Center in Physical Activity, Health and Leisure—CIAFEL - Faculty of Sports, University of Porto—FADEUP (FCT UID/DTP/00617/2020 and Laboratory for Integrative and Translational Research in Population Health (ITR), Porto, Portugal (LA/P/0064/2020).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
We are grateful to the swimmers for their cooperation and involvement in this research project. We also acknowledge contributions from researchers involved with the data collection.