One of the major topics of interest in sports science is the identification of talented young athletes. This process is based on talent identification and development (TID) programs that aim to identify young athletes with potential for success in adult/elite sport (Blume and Wolfarth, 2019). Detecting talent at an early stage is considered a key factor in increasing a chance of a country of achieving success in sports (Vaeyens et al., 2009). Competitive swimming is one of the three main modern Olympic sports. In competitive swimming, Olympic, and World records are broken on a regular basis, challenging the limits of athletes. Practitioners and researchers are eager to predict the next top-ranked swimmer who will contribute to the superiority of their country at major international competitions.
Talent identification and development programs follow standard steps: (1) identifying the athletes with the potential to deliver the best performances in adulthood and determining the variables responsible for such performances; (2) understanding the development and changes in performance and its determinant factors, according to a training program, and; (3) following up in order to allow to understand the variation of such variables and its relationship with performance over a given time (Morais et al., 2017). To get deeper insights into how determinant factors of swimmers change over time, their interaction and their effect on performance, researchers, and coaches should focus on a long-term approach (Staub et al., 2020a; Zacca et al., 2020). Long-term athlete development (LTAD) programs focus on providing young athletes with fundamental motor skills in tandem to their maturation stage (Martindale et al., 2005; Lang and Light, 2010).
Literature reports that performance in youth swimming is highly dependent on variables related to technique (i.e., nurture) and body dimensions (i.e., nature) (Abbott et al., 2021). Thus, research on young swimmers has been largely focused on the assessment of anthropometrics (Geladas et al., 2005; Nevill et al., 2020), strength and conditioning (Garrido et al., 2010b; Amaro et al., 2017), biomechanics (Morais et al., 2012; Silva et al., 2012), energetics, and efficiency (Denadai et al., 2000; Toubekis et al., 2006), as well as interactions among some or all of them (Morais et al., 2017; Barbosa et al., 2019). Nonetheless, most of these are cross-sectional designs. Such research design does not provide substantial information on the dynamic and complex interactions among the performance determinants over time (Morais et al., 2017). Conversely, longitudinal designs can help gather information on: (1) how determinant factors interplay and affect performance; (2) the dynamic changes that take place at these early ages, and; (3) the change of the partial contribution of each determinant factor in the performance over time (Lätt et al., 2009a,b; Morais et al., 2014a). Notwithstanding, in the last decade, it has been suggested that research on sports performance should adopt a multidisciplinary approach to better understand the athlete (Phillips et al., 2010; Seifert et al., 2013). Moreover, the relationship with the environment must be taken into account, as this relationship is considered under a complex and dynamic system framework (Phillips et al., 2010; Seifert et al., 2013). If so, it will be possible to understand the partial contribution of each determinant factor or set of factors in the performance, which will most likely change over time, as aforementioned (Barbosa et al., 2014; Morais et al., 2015).
Literature reports a review study about the relationship between performance and determinant factors in master swimmers (Ferreira et al., 2016). More recently, Koopmann et al. (2020) have systematically reviewed technical skills in talented youth athletes (which included three articles about swimmers). That said, there is no review that consolidates the available evidence of how different determinant factors can affect youth swimming performance. Therefore, the aim of this study was to review the current body of work on the influence of determinant factors related to swimming technique (biomechanics, energetics, and efficiency) and anthropometrics in the performance of young swimmers.
Literature Search and Article Selection
The Web of Science, PubMed, and Scopus databases were searched to identify studies that aimed to identify, analyze, or predict the performance of young swimmers and its determinant factors (anthropometrics, biomechanics, energetics, and efficiency). These electronic search databases were chosen because they are the most used in sports science. As an initial search strategy, the title, abstract, and the studies keywords were identified and read carefully for a first scan and selection of the journal articles. To search the articles, the following fields were used: (1) Web of Science—“Topic”; (2) PubMed—“All fields”; and (3) Scopus—“Article title, abstract, keywords.” A Boolean search strategy was used with the operators AND, OR, and a combination of the keywords presented in Table 1 (whenever suitable). If one of these fields (title, abstract, and keywords) was not clear about the topic under analysis, the complete article was read and fully reviewed to ensure its inclusion or exclusion. After deleting all duplicated and unrelated articles, 59 articles were included. The final search was carried out on March 21, 2021. Table 1 presents the used PI(E)CO search strategy (P—patient, problem or population; I—intervention; E—exposure; C—comparison, control, or comparator; O—outcomes).
The inclusion criteria were the following: (1) written in English; (2) published in a peer-reviewed journal; (3) related to assessment of the performance of young swimmers (i.e., race events or swim trials/bouts) and its determinant factors (anthropometrics, biomechanics, energetics, and efficiency); (4) included healthy and able-bodied swimmers, and; (5) reported an average sample age limited to the age of 13 (it is considered that children tend to enter the puberty stage from this age onwards—Mirwald et al., 2002). The exclusion criteria were: (1) studies that included disabled swimmers or with any pathology; (2) review papers, conference papers, and books; (3) studies including animal models; (4) publications not related to the topic in question (e.g., in other scientific fields, such as nutrition, psychology, or any other topic not related to performance); (5) studies that recruited several age groups, but did not clearly report the average of at least an age group of 13 years or under.
Figure 1 depicts the PRISMA flow diagram for identifying, screening, checking eligibility, and inclusion of the articles. There were four articles (Figure 1—“Additional records identified through other sources” that were obtained by submissions reviewed and based on references from the articles retained.
The PEDro scale was used to assess the quality of the selected articles. It was observed that this scale is a suitable and valid tool to assess the methodological quality (de Morton, 2009). Two reviewers read all the included articles and scored them according to the scale items (poor quality if score ≤ 3; fair quality if the score is between 4 and 5; high quality if the score is between 6 and 10) (de Morton, 2009). Afterwards, the Cohen's Kappa (K) was computed to assess the agreement between reviewers. It was interpreted as: (1) no agreement if ≤ 0; (2) none to slight agreement if.01 < ≤ 0.20; (3) fair if.21 < ≤ 0.40; (4) moderate if.41 < ≤ 0.60; (5) substantial if.61 < ≤ 0.80, and; (6) almost perfect if.81 < ≤ 1.00. Studies were compared based on the: (1) research design (cross-sectional vs. longitudinal designs), and (2) year of publication (published before or in 2010 vs. published after 2010). In both comparisons, distribution was non-normal. Thus, the Mann–Whitney U test ( ≤ 0.05) was selected for further inferential analysis.
PEDro mean score was 7.17 ± 1.40 points (i.e., high quality). The Cohen's Kappa yielded an almost perfect agreement between reviewers ( = 0.937, < 0.001). There were non-significant differences in PEDRo scores based on research design ( = 0.651), or year of publication ( = 0.477).
Table 2 summarizes the sample demographics, including the sample size, chronological age, maturation stage, years of experience, and competitive level based on FINA points.
Table 3 presents the summary of the studies purpose, research design, type of collected data (anthropometrics, biomechanics, energetics, and efficiency), and performance. Overall, swimming performance (time or speed) was clearly reported (normative data for time or speed at a given distance) in 51 reviewed studies (86.4%) (Table 3). Out of 59 included studies, 25 (42.4%) were based on longitudinal designs, and the remaining 34 (57.6%) were cross-sectional (Table 3). Fifty-four studies (91.5%) reported anthropometric parameters, including 34 cross-sectional designs and 20 longitudinal designs. Also, 54 studies (91.5%) analyzed the biomechanics (32 cross-sectional and 22 longitudinal designs), and 42 (71.2%) the energetics and efficiency (25 cross-sectional and 17 longitudinal designs) (Table 3). Thirty-nine studies (66.1%) reported anthropometrics, biomechanics, energetics and efficiency, and performance concurrently (i.e., interdisciplinary research). Three studies (5.1%) focused exclusively on tracking the swimming performance from childhood to adulthood.
The aim of this study was to review the current body of work on the influence of determinant factors related to swimming technique and anthropometrics in the performance of young swimmers. It was recognized that the performance of young swimmers is not exclusively dependent on one or a small set of determinant factors related to swimming technique and anthropometrics. It is rather influenced by a multidisciplinary interaction of several determinant factors. Furthermore, these factors and their partial contribution to performance can change over time according to the training plan or designed periodization.
Anthropometrics and Growth
Most studies (N = 55, ~93%) included in this review assessed the anthropometrics. Body dimensions are related to nature, i.e., genetically determined (Saavedra et al., 2010; Majid et al., 2019; Tijani et al., 2019). Researchers are prone to assess the anthropometrics of young swimmers of both sexes, because these features play one of the major roles in the swimming performance, kinematics, energetics, and efficiency (Geladas et al., 2005; Jürimäe et al., 2007; Lätt et al., 2009a), in addition to hydrodynamics (Kjendlie and Stallman, 2008; Barbosa et al., 2014). Cross-sectional studies showed that variables such as height (H), arm span (AS), and hand length (HL) are strongly and positively correlated to Freestyle sprint performance (i.e., 50 or 100 m) (Geladas et al., 2005; Morais et al., 2012; Bielec and Jurak, 2019). The same trend was verified in breaststroke, in which swimmers with longer upper-limb lengths and wider girths had a significant advantage (i.e., better performance in the 100 m) (Sammoud et al., 2018). In backstroke (25- and 50-m pace), it was observed that postpubertal swimmers were significantly faster than their prepubertal counterparts (Silva et al., 2013). The significant higher body mass (BM), H, and AS shown by the postpubertal swimmers contributed to this (Silva et al., 2013). The same trend was verified in other freestyle distances (100, 200, and 400 m—Mezzaroba and Machado, 2014; 50 and 400 m—Ferraz et al., 2020), in which H, AS/H ratio (Ferraz et al., 2020) and other lengths related to upper- (TUEL) and lower-limbs (TLEL) lengths were significantly longer in mature swimmers (Mezzaroba and Machado, 2014).
Read more Your Loss Of Appetite Can Be A Sign Of Heat Stroke!
Cluster analysis identifies homogeneous subgroups of swimmers within a larger sample (Barbosa et al., 2014; Morais et al., 2015, 2020b). Cluster analysis detects swimmers within a specific cluster that shares similar characteristics but is very different from other swimmers who do not belong to that cluster (Morais et al., 2015). Faster swimmers, competing in the 100-m freestyle, were clustered as a group with larger anthropometric features such as BM, AS, H, chest perimeter (CP), hand surface area (HSA), frontal surface area (FSA), trunk transverse surface area (TTSA), and body surface area (BSA) (Morais et al., 2015, 2020b). A study that aimed to identify key somatic variables in youth swimming recognized that all swimmers benefited from having less body fat (BF), wider shoulders and hips, longer AS, and forearm girth (FG) in the 100-m breaststroke and backstroke events (Nevill et al., 2020). This review only includes data related to breaststroke and backstroke from this article (Nevill et al., 2020) because only these strokes met the inclusion criteria (i.e., under 13 years of average age). Nonetheless, the authors agreed that such characteristics were common in the whole sample (over 13 years of average age), including the freestyle and butterfly strokes (Nevill et al., 2020).
As young swimmers grow until reaching full maturity, the best way to gather deeper insights into the influence of anthropometrics on swimming performance is to design longitudinal studies (Lätt et al., 2009a,b; Abbott et al., 2021). When following up over a competitive season, swimmers who achieved better performances (in the 100-m freestyle) also had larger body sizes (Morais et al., 2020b). A similar trend was verified in the 400-m freestyle (Lätt et al., 2009a,b). Moreover, a 3-year study that recruited 91 swimmers from a TID program showed that the AS was a major cause of performance improvement (Morais et al., 2017). Nonetheless, it was argued that swimmers must “relearn” the stroke mechanics to better use the propelling limbs, whenever meaningful body changes happen, such as during growth spurts (Morais et al., 2017). This happens because, as mentioned earlier, anthropometry not only has a direct effect on the performance of swimmers but also holds a concurrent effect on other scientific domains related to swimming techniques (Tijani et al., 2019; Morais et al., 2020b). That is, longer lengths like H and AS are strongly related to longer stroke length (SL) (kinematics) (Silva et al., 2012; Morais et al., 2017); whereas, larger TTSA or BSA is strongly related to more drag (hydrodynamics) (Barbosa et al., 2014).
Young swimmers are prone to have several growth spurts within a competitive season (Abbott et al., 2021). Such spurts contribute to the improvement in several variables related to swimming technique (Morais et al., 2013b, 2015). It was shown that, even during detraining periods (i.e., training breaks) the performance impaired, but anthropometry was responsible for slowing down such impairment (Moreira et al., 2014; Morais et al., 2020a). That is, during an 11-week detraining period, the swimmers continued to grow up. Because they were taller at the end of the break, it allowed them to minimize the performance impairment (Morais et al., 2020a). This highlights the importance of a systematic and frequent assessment of the anthropometrics.
Biomechanics is related to swimming techniques, such as SL, stroke frequency (SF), stroke index (SI), and intra-cyclic variation of the swim speed (dv), which are part of the “nurture” process and the ones that better explain performance (Lätt et al., 2009a; Barbosa et al., 2010; Morais et al., 2012). Top-tier swimmers are faster, because of better SL, SF, Reynolds number (Re), Froude number (Fr), and hull speed (Vh) scores (Barbosa et al., 2019). Faster swimmers were also prone to have less dv (Barbosa et al., 2014; Figueiredo et al., 2016) and deliver more in-water mechanical power (Barbosa et al., 2015, 2019; Morais et al., 2020b). Thus, it seems that the fastest swimmers can promote smaller speed fluctuations (Barbosa et al., 2014) and produce more power concurrently (Barbosa et al., 2019; Morais et al., 2020b). It can be argued that in-water power is related to more dry-land strength. It has been shown that variables related to dry-land strength were correlated with sprint swimming (Garrido et al., 2010a; Seffrin et al., 2021) and middle-distance events (400-m freestyle—Seffrin et al., 2021). Moreover, the power to overcome drag can be explained by 94% of the dry-land strength (Morais et al., 2016). However, faster swimmers are also under more active drag (Da) and coefficient of active drag (CDa) (Barbosa et al., 2019). It should be noted that drag variables, such as Da, passive drag (Dp), CDa, and coefficient of passive drag (CDp), are highly dependent on velocity, TTSA, and BSA (Kjendlie and Stallman, 2008). Thus, bigger and faster swimmers are prone to be under more drag (Barbosa et al., 2014, 2019). Indeed, “matured” age-group swimmers performing freestyle (Silva et al., 2012) and backstroke (Silva et al., 2013) had higher stroke kinematics scores [namely swim speed (v) and SL]. Conversely, non-significant differences were found in the index of coordination (IdC) (i.e., motor control) between pre and postpubertal swimmers (Silva et al., 2012, 2013).
Longitudinal studies showed that variables related to biomechanics change significantly over time (Lätt et al., 2009a; Morais et al., 2015, 2020b). As aforementioned, young swimmers undergo growth and maturation processes that lead to changes in the swimming technique (Lätt et al., 2009a; Morais et al., 2017). They are prone to improve the kinematics and kinetics over long-term periods of time (Morais et al., 2017, 2020b). Nonetheless, in specific moments of a season, young swimmers may impair the stroke biomechanics (Morais et al., 2013b, 2014b). Despite the variations within the season, swimmers improved the stroke biomechanics when comparing the beginning and the end of the season. Longitudinal research also reported that swimmers cluster in groups with similar traits related to stroke biomechanics (Morais et al., 2015, 2020b). As far as the long term is concerned, i.e., during one or several competitive seasons, the variables that better characterize each group may change over time. Swimmers improve and impair the stroke biomechanics several times over one or more competitive seasons (Morais et al., 2015, 2020b). Notwithstanding, variations may not occur at the same time across all clusters (Morais et al., 2015, 2020b). Moreover, it has been shown that swimmers are also likely to change groups; that is, switching to another subgroup or performance level. A swimmer who is assigned to the top-tier subgroup may not remain in that subgroup. It is possible that, over the season, the swimmer may drop to a lower tier, and lower-tier swimmers can climb up to top-tier groups (Morais et al., 2020b). Performance levels are very dynamic over time, and swimmers can move to different tiers quite often. The shift is due to a concurrent change in the determinant factors underlying the performance, which, in turn, depend on the developmental training program they are under, as well as the rate of growth and maturation.
The relationship between the in-water training programs and swimming biomechanics can be better understood when internal and external training loads are monitored. However, few studies addressed this topic in developing programs for young swimmers (Garrido et al., 2010b; Saavedra et al., 2013; Morais et al., 2014a). High-training volumes during the first part of a season (with low intensity, including warm-up, recovery, and slow-pace drills) led to an improvement in performance (Morais et al., 2014a). The same authors (Morais et al., 2014a) evaluated a group of swimmers during a competitive season in four different moments. They achieved 59% of the final performance in the second evaluation moment and 99% in the third moment. Between the 3rd and 4th (final) moments, the swimmers improved by only 1%, with the SF as the main determinant (Morais et al., 2014a). Between the 3rd and 4th moments, the periodization included an increase in the aerobic power and aerobic capacity (Morais et al., 2014a). As their older counterparts, young swimmers increase SF whenever they want to reach faster speeds (Mezzaroba and Machado, 2014; Barbosa et al., 2019). The researchers noted that changes in performance are related to the type of training swimmers were undergoing at the time of each evaluation moment. Thus, coaches can use different training strategies for their periodization to reach previously outlined goals and avoid burnout.
Read more How Arterial Dissection Can Cause a Stroke
Studies also aimed to understand the effect of dry-land strength on the performance of young swimmers (Sammoud et al., 2019, 2021; Ozeker et al., 2020). During an 8-week intervention (aerobic in-water training concurrently with dry-land strength), Garrido et al. (2010b) reported a trend in sprint performance improvement (25- and 50-m freestyle) due to strength training. This was confirmed in other sprint events (50- and 100-m freestyle and backstroke) (Alshdokhi et al., 2020). Swimmers assigned to the experimental group presented a larger increase in the selected variables compared with the control group (Alshdokhi et al., 2020). It was suggested that the improvement in dry-land strength resulted in better swimming performance. Others aimed to provide deeper insights into the effect of different types of dry-land strength and conditioning programs on sprint performance (50-m freestyle) (Amaro et al., 2017). It was noted that swimmers under explosiveness training (i.e., performing the repetition quickly) presented larger improvements in swimming speed compared with performing repetition/sets training (Amaro et al., 2017). The phenomenon of post-activation potentiation performance enhancement is defined as a voluntary dynamic force production after a short and acute bout of high-intensity voluntary exercise (Blazevich and Babault, 2019). A study used three 30-s post-activation potentiation protocols (10 min before competition) to understand its effect on the performance and stroke kinematics (Abbes et al., 2018). Authors verified that all protocols presented non-significant effects on the 50-m freestyle performance, SL, and SF. A follow-up study analyzed the effect of tethered swimming as post-activation potentiation in the 50-m freestyle performance and stroke kinematics (SL), and non-significant effects were observed (Abbes et al., 2020). Therefore, both studies suggest an unclear effect of post-activation potentiation performance enhancement on young swimmers.
Energetics and Efficiency
Energetics and efficiency also play a role in the performance of young swimmers. That said, the energetic spartial contribution to the performance increases with age (Zacca et al., 2020). It has been observed that VO2 during submaximal swimming speeds is significantly lower in children than adults (Kjendlie et al., 2004a). A study that selected anthropometrics, kinematics, energetics, and efficiency as main outcomes demonstrated that the 100-m freestyle performance was predicted by anaerobic power (AnP), critical velocity (CV), and SI (as an efficiency proxy) (de Mello Vitor and Böhme, 2010).
The CV is a variable commonly used to assess the energetics of young swimmers (Denadai et al., 2000; Marinho et al., 2011; Zarzeczny et al., 2013). It is calculated based on the distance-time slope of several events or swimming distances (Dekerle et al., 2002). It is highly correlated with aerobic performance and, hence, used to control training intensities (Zarzeczny et al., 2013; Figueiredo et al., 2016). However, CV may underestimate swimming intensity corresponding to speed at a blood lactate concentration of 4 mmo·l−1 in swimmers aged 10 to 12 years old (Denadai et al., 2000). It was suggested that it relates, instead, to the intensity corresponding to the maximum steady state of lactate concentration (Denadai et al., 2000). The CV has a significantly direct effect on the 200-m freestyle (Barbosa et al., 2010) and can also provide a strong explanation in the shorter events performances, such as the 100-m freestyle (de Mello Vitor and Böhme, 2010). Swimmers with faster CV also delivered better performances in the 100-m freestyle (Morais et al., 2013a) and 25-m freestyle time trials (Figueiredo et al., 2016).
Besides the SI, researchers also selected the Froude efficiency (ηF) as another energetic proxy (e.g., de Mello Vitor and Böhme, 2010; Morais et al., 2014a). The SI measures the ability of the swimmer to complete a given distance with a particular speed in the fewest possible number of strokes (Costill et al., 1985). The ηF estimates the amount of work or power used to translate the body in water (Zamparo et al., 2020). Both variables are straightforward and less time-consuming to compute compared with a direct measurement of other energetics variables (Figueiredo et al., 2016; Barbosa et al., 2019; Morais et al., 2020b). Larger SI and ηF are associated with better performance in short distances, as the 100-m freestyle and 25-m freestyle time trial. Indeed, the fastest swimmers distinguish themselves from others because they have a better CV, SI, and ηF (Morais et al., 2013a; Figueiredo et al., 2016; Barbosa et al., 2019). Moreover, it should be highlighted that the increase in SI and ηF is related to the technical training that young swimmers undergo (Morais et al., 2017).
For longer events, such as the 400-m freestyle, the VO2max (Duché et al., 1993; Poujade et al., 2003) and the VO2peak (Jürimäe et al., 2007) were the best predictors of swimming performance within a set of energetic variables. Hue et al. (2013) showed that the fastest swimmers in the 400-m freestyle event also had better VO2max than their slower counterparts. When tested by the 5 × 300-m protocol, young swimmers improved their swimming economy as they got older based on lower heart rate (HR) variability (Tsalis et al., 2012). In mid-distance events, another variable monitored very frequently was the energy cost of swimming (Cs), which increases with swimming speed (Poujade et al., 2002; Kjendlie et al., 2004a,b). Nonetheless, one study pointed out that kinematics (SL and SF), anthropometrics (body length—BL, BM, and BSA), and HL did not explain the CS in young swimmers (Poujade et al., 2002). The authors suggested that underwater torque, technical ability, and maturation could be strong predictors. Another study reported that passive torque presented a significant linear relationship with absolute Cs in young swimmers (Kjendlie et al., 2004b). Overall, there is solid evidence that, for similar swimming speeds, young swimmers have more Cs than their older counterparts (Zamparo et al., 2000; Kjendlie et al., 2004a). Thus, the differences between young swimmers and their older counterparts in the economy are due to the less-technical ability of the former ones.
Longitudinal studies showed that an improvement in energetics (VO2 and ΔLa) allowed an enhancement in performance (Lätt et al., 2009a,b). These studies were mostly focused on the 400-m freestyle (i.e., middle distance) (Lätt et al., 2009a,b; Ferreira et al., 2019). A research group followed boys (Lätt et al., 2009a) and girls (Lätt et al., 2009b) during two competitive seasons. It was observed that the VO2 was among the best predictors of performances of both sexes. Others noted significant correlations between a set of energetic variables (i.e., Bl and Bg) in the 400-m freestyle performance (Ferreira et al., 2019). Nevertheless, SI (efficiency) was the best predictor of all the variables assessed (Lätt et al., 2009a,b), or the one that presented the highest correlation with performance (Ferreira et al., 2019). Additionally, it was suggested that the 400-m freestyle enhancement during a season was highly related to an increase in the SI, suggesting that, when swimmers are in this age group, coaches should prioritize technical development of the swimmers (Ferreira et al., 2021). That said, the authors indicated that, concurrently, with the technical enhancement, physiological variables are as important to optimize swimming performance in such middle-distance events (Ferreira et al., 2021). Thus, at early ages, training should focus on learning the proper swimming techniques (i.e., technical training).
Nonetheless, the same reasoning (i.e., importance of energetics/efficiency) can be claimed in shorter race events, at least based on research carried out in the 100-m freestyle (Morais et al., 2013b, 2014b). The ηF increased or at least was maintained over time (Morais et al., 2020b). Additionally, high skillful swimmers yielded larger efficiency over time compared with their slower counterparts (Morais et al., 2014b, 2015). The HR (as an energetic indicator) may also present an association with the energetics of swimmers in the 50-m, 100-m, (Alshdokhi et al., 2020), and 400-m freestyle (Ferreira et al., 2019). Both studies reported that training has a positive effect on HR of young swimmers. That is, swimmers decreased the HR, suggesting that, for the same task (50-m and 100-m—Alshdokhi et al., 2020; or 400-m freestyle—Ferreira et al., 2019), they required less effort, with improved performance. Therefore, it can be implied that, besides the middle-distance events (i.e., 400-m freestyle), energetics/efficiency also presents a strong contribution in shorter events (like the 50 and 100-m freestyle).
Performance in a Long-Term Athlete Development (LTAD) Perspective
Longitudinal studies can also help to understand the evolution of swimming performance from childhood to adulthood (Costa et al., 2011; Staub et al., 2020a,b). This research is paramount to better explain how the growth pace of each swimmer affects the performance and its determinant factors (Durand-Bush and Salmela, 2002). As previously noted, the performance level is highly dynamic and depends upon growth and maturation spurts, as well as the development program the swimmer is under. Stability assessment allows the prediction of the future success of young swimmers by the estimation of the performance progression. Based on the analysis of 242 young swimmers (from 12 to 18 years old), a study observed that swimmers should display a 14–19% improvement from childhood to adulthood in all freestyle events to become part of an elite group (Costa et al., 2011). The same authors also pointed out that the age of 16 is when the ability to predict the adult competitive level increases considerably. Thus, one cannot “neglect” a swimmer who, at a given moment, is slower than his/her peers, because, the following year, he/she can become one of the best in his/her age group (Morais et al., 2015, 2020b).
A study explored how consistent career pathways are among age-group swimmers (Staub et al., 2020b). Swimmers with better FINA points at 11 years old (including events, strokes, and distances) were more likely to be ranked during more years over the analyzed time frame (8 years), but the correlation showed a weak effect (Staub et al., 2020b). The authors argued that young swimmers should get the chance to yield from LTAD programs and should not be selected only by their age-group performance level (Staub et al., 2020b). It was claimed that LTAD programs should bring awareness about this phenomenon, which requires advanced understanding from coaches and other practitioners (Lang and Light, 2010).
Read more CD163 as a Potential Biomarker of Monocyte Activation in Ischemic Stroke Patients
It has been recently reported that both nature (i.e., anthropometrics) and nurture (i.e., training—namely sports technique) are important to excel in youth swimming (Barbosa et al., 2019). The best performers among three subgroups of swimmers (subgroup #1: age-group national champions, national record holders or enrolled in talent ID programs) scored very well in variables related to both nature and nurture parameters. Conversely, swimmers in the subgroup #3 (racing at local competitions) were weaker in both dimensions, and swimmers in the subgroup #2 (racing at national competitions) showed weaknesses in nature-related factors (i.e., anthropometrics) but were reasonably good in nurture factors (i.e., training). The subgroup #2 profile shows the potential of swimmers who may be seen as less genetically predisposed, as a result of an effective developmental program (Barbosa et al., 2019; Marinho et al., 2020).
As far as LTAD is concerned, there is also an ongoing dialog about the potential negative effects of large volumes of training in young swimmers (Nugent et al., 2017). Many coaches combine assumptions based on their experience with evidence-based practice. Recently, Marinho et al. (2020) have reported that an improvement in academic degree, coaching level, and coaching experience of the coaches presented a positive and significant contribution to swimming efficiency and performance of young athletes. Swimmers under the guidance of a coach with a higher academic degree, coaching level, or more years of coaching experience were more efficient and, concurrently, delivered better performances (Marinho et al., 2020). As youth swimming training should be focused on technical training (Morais et al., 2012), coaches should be able to provide their athletes with training in key skills and abilities based on such technique determinants. Therefore, age-group coaches are advised to design training programs that are underpinned on high-level and cutting-edge evidence.
Another major topic within LTAD is early specialization (Larson et al., 2019; Staub et al., 2020a). Early specialization refers to young athletes who limit their childhood to a single sport, deliberating their training and development on a singular sport (Baker, 2003). It was claimed that early specialization might promote far more risks than benefits (Wiersma, 2000). Youth athletes can suffer from social isolation, overdependence, burnout, manipulation, injury, and compromise their growth and maturation (Malina, 2010). Conversely, an athlete who practices a set of skills with increased frequency and duration becomes more proficient in those skills than one who practices them periodically (Wiersma, 2000). In competitive swimming, there are four competitive swim strokes and one event combining all (medley), as well as several race distances. Thus, in swimming, a within specialization may occur whenever a swimmer chooses and develops at an early age a single stroke or distance (or a combination of more than one stroke or distance, or both combined) (Staub et al., 2020a). A study showed that greater diversification within the same sport is positively correlated with success at the age of 18 (Staub et al., 2020a). Thus, the younger a swimmer enters the top 100, more likely he/she is to reach a top-tier at the age of 18 (Staub et al., 2020a). This suggests that early specialization may not be the best pathway to ensure higher performance in adulthood. Additionally, Larson et al. (2019) showed that a set of markers related to early specialization was related to burnout or a dropout in youth swimming. However, it was suggested that early specialization in one event, stroke or distance could be a way for coaches to accomplish qualification times and promote rapid adolescent success at the expense of long-term elite success as adults (Staub et al., 2020a). As such, developmental programs should expose young swimmers to a broad range of events (distances and swimming strokes) and even, at early stages, to other aquatic and non-aquatic sports.
Performance of young swimmers is characterized by a multifactorial, holistic, and dynamic phenomenon relying on several features from different scientific domains. Better performance has always been related to better swimming techniques. Concurrently, anthropometry (e.g., higher AS, H, and upper limbs) also plays an important role in performance. Swimmers with larger body dimensions are the fastest. This suggests that anthropometry (i.e., nature) and training (i.e., nurture) play key roles. The contribution of energetics and efficiency becomes more important as the swimmer gets older or whenever the swimming event becomes longer. Performance enhancement of young swimmers should rely on LTAD programs, always taking into consideration the growth spurt and the external training load of the swimmer. Coaches are advised to monitor the rate of growth of their athletes, since this can affect their performance. They should put more focus on improving swimming technique and less on the external training load.
Data Availability Statement
The datasets presented in this article are not readily available because none. Requests to access the datasets should be directed to Jorge E. Morais, [email protected].
JM, TB, PF, AS, and DM conceived and designed the study. JM, TB, and AS performed the search and data analysis. PF and DM performed the quality assessment. JM carried out the drafting of the manuscript. All authors reviewed the manuscript and approved the submitted version.
This work is supported by national funds (FCT—Portuguese Foundation for Science and Technology) under the project UIBD/DTP/04045/2020.
Conflict of Interest
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.
The reviewer NDG declared a shared affiliation, with no collaboration, with one of the authors, AS, to the handling editor at the time of the review.
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.
AFG—arm flexed girth
APHV—age at peak height velocity
ARG—arm relaxed girth
AS/H—arm span / height index
Biacr B—biacromial breadth
Biacr B/Biiliac B—biacromial breadth/biiliac breadth index
Biiliac B—biiliac breadth
Bitroch B—bitrochanteric breadth
Biacr B/H—biacromial breadth/height index
BMM—bone mineral mass
BMI—body mass index
BSA—body surface area
Calf G—calf girth
CG/H—chest girth/height index
FM—whole body fat
FFM—fat free mass
FSA—frontal surface area
GG/H—gluteal girth/height index
HSA—hand surface area
LAL—lower arm length
LBM—lean body mass
LLL—lower limb length
SBMD—spine bone mineral density
SSS—sum of six skinfolds
S9—sum of nine skinfolds
TBMD—total bone mineral density
TLEL—total lower extremity length
TSA—thoracic section area
TTSA—trunk transverse surface area
TUEL—total upper extremity length
ULL—upper limb length
UAL—upper arm length
AvgPext—average power extension
AvgPflex—average power flexion
AvgPer—average power external shoulder rotation
AvgPir—average power internal shoulder rotation
CDA—coefficient of active drag
CDp—coefficient of passive drag
dv—intra-cyclic variation of the swim speed
dv/v—intra-cyclic variation of the swim speed/swim speed index
Etot—total power input
IdC—index of coordination
LFext—left forearm external rotation
LFint—left forearm internal rotation
MMI—mean mechanical impulse
Pd—power to overcome drag
Pk—mechanical power to transfer kinetic energy to water
Pext—external mechanical power
PText—peak torque extension
PTflex—peak torque flexion
PTer—peak torque external shoulder rotation
PTir—peak torque internal shoulder rotation
RFext—right forearm external rotation
RFint—right forearm internal rotation
SL·pSL–1—stroke length normalized for anatomical potential stroke length
SLJ—standing long jump
SL/AS—stroke length/arm span index
TDI—technique drag index
25-m KWP—a 25-m kick without a push
25-m free WP—25-m freestyle without a push
ΔCM-CV—distance between the center of mass and the center of volume
α63—body angle with the water line
AnCV—anaerobic critical velocity
Cs—energy cost of swimming
Cs/SA—energy cost of swimming calculated per unit of surface area
Cs/SA.HL—energy cost of swimming calculated per unit of surface area and hydrostatic lift
CSR—critical stroke rate
FAH—flexed arms hang
eVO2max—estimated aerobic power
HR rest—resting heart rate
MP30—mean power in 30 s
MAV—maximal aerobic velocity
RR sys—resting systolic blood pressure
RR diast—resting diastolic blood pressure
RPE—rate of perceived exertion
SandR—sit and reach
SRE—shuttle run endurance
V4—velocity corresponding to a blood lactate concentration of 4 mmol·l–1
VO2max—maximal oxygen uptake
VO2peak—peak oxygen uptake
ΔLa—net increase of blood lactate