100-Meter Breaststroke Swimming Performance in Youth Swimmers: the Predictive Value of 2 Anthropometrics

1 100-meter Breaststroke swimming performance in youth swimmers: the predictive value of 2 anthropometrics

4Running title: Allometric model and Breaststroke performance.

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

33 34Abstract:

35This study aimed to estimate the optimal body size, limb-segment length, and girth or breadth 36ratios of 100-m Breaststroke performance in youth swimmers. Fifty-nine swimmers (male 37[n=39; age: 11.5 ± 1.3 years]; female [n=20; age: 12.0 ± 1.0 years]) participated to this study. 38To identify size/shape characteristics associated with 100-m Breaststroke swimming 39performance, we computed a multiplicative allometric log-linear regression model, which was 40refined using backward elimination. Results showed that the 100-m Breaststroke performance 41revealed a significant negative association with fat-mass and a significant positive association 42with the segment length ratio [arm-ratio= (hand-length)/(forearm-length)] and limb girth-ratio 43[girth-ratio=(forearm-girth)/(wrist-girth)]. Additionally, leg length, biacromial and 44biiliocristal breadths revealed significant positive associations with the 100-m Breaststroke 45performance. However, height and body mass did not contribute to the model, suggesting that 46the advantage of longer levers was limb-specific rather than a general whole-body advantage. 47In fact, it is only by adopting multiplicative allometric models that the abovementioned ratios 48could have been derived. These results highlighted the importance of considering 49anthropometric characteristics of youth Breaststroke swimmers for talent identification and/or 50athlete monitoring purposes. In addition, these findings may assist orienting swimmers to the 51appropriate stroke based on their anthropometric characteristics.

52 53 54Key Words: Allometric model; Breaststroke swimming; maturity; limb lengths; girths and 55breadths. 56

64 65INTRODUCTION:

66 Competitive swimming is a type of cyclic sports activity performed with the aim of 67covering any given distance as fast as possible (1). In this context, many researchers are 68constantly trying to establish and classify factors associated with optimal swimming 69performance. Therefore, it remains important to recognize that various factors are important 70in determining swimming performance success (1,19). These factors include the aerobic- 71anaerobic capacity (17, 49), technical (e.g., stroke technique, coordination, starts and turns), 72physical fitness level (e.g., flexibility, strength, and power), psychological traits (e.g., stress 73control, motivation), and the anthropometrical characteristics (e.g., height, body mass, and 74body mass index), among others (29). 75 76 The relationship between human physical characteristics and sports performance has 77been a source of unceasing interest among scientists (29, 33, 36, 38). Notably, the association 78between anthropometric characteristics and sports performance constitutes a worthwhile 79marker for talent identification to engage children in a long-term athlete development process. 80A recent review by Morais et al. (34) suggested that anthropometric characteristics are among 81the critical factors used as precursor for an early recognition of talented athletes. It is worth 82noting that anthropometrics are more genetically controlled compared with physical fitness 83attributes (27). Therefore, anthropometrics are less susceptible to training than physical fitness 84attributes. For instance, it has been established that anthropometrics such as body length (e.g., 85height, limbs) are strongly determined by genetics (level of inherence of 70%) (6, 46). These 86facts confirm the importance of anthropometric characteristics to the early detection of 87talented athletes. In swimming, talent identification and development process play a crucial 88role in the pursuit of excellence across a long-term career. In this regards, anthropometric 89characteristics have been argued to be one of the most important factors that enable swimmers 90to achieve a high-performance level in their careers (21, 29). 91 It is worth noting that most of the previously published investigations centered their 92attention in Freestyle swimming (2, 5, 21, 29), while the Breaststroke remains understudied 93(18, 28). For instance, Lätt et al. (29) indicated that anthropometrical factors explained 45.8% 94of 100-meter Front crawl swimming performance in male swimmers aged 15 years. Likewise, 95Morais et al. (33) reported the contribution that anthropometric characteristics made on 96swimming performance and found that the arm span was significantly correlated with 100- 97meter Freestyle swimming performance in 12-year-old swimmers of both genders (r= -0.35). 98Additionally, Bond et al. (5) suggested that anthropometric variables accounted for 63.8% of 99100-meter Freestyle swimming’s total variance in 13 years old male and female swimmers. 100Further, Geladas et al. (21) revealed that upper extremity length was significantly associated 101with 100-meter Freestyle swimming performance in boys aged between 12 and 14 years. The 102same authors revealed that other anthropometric factors such as upper extremity length, 103stature, and hand length, significantly influenced girls’ swimming performance of the same 104age group. 105 Breaststroke is well known as a very challenging stroke because of the discontinuous 106propulsive action of the arms and legs and its complex time synchronization (44). During 107Breaststroke, stroke cycle consists of an arm stroke and a leg kick that occur in sequence and 108determine the stroke cycle from the start and throughout the race, rules that do not apply to 109any other swimming style (according to the Féderation Internationale de Natation (FINA, 1102014)). 111According to Barbosa et al. (2), differences between Freestyle and Breaststroke do exist in 112terms of technique, energy cost, and stroke length. Additionally, previous findings showed 113that Freestyle is the most economic stroke, followed by Backstroke, Butterfly, and 114Breaststroke (2). In the same context, by studying the temporal and velocity changes during 115the stroke cycle for a range of stroke rates, Craig et al. (13) demonstrated that Breaststroke 116swimming’s mechanics was more critical compared with the other stroke styles. Indeed, 117swimming velocity can be characterized by its independent variables: stroke length (SL) and 118stroke rate (SR) (13). Thus, increases or decreases in swimming velocity are mainly due to a 119combined increase or decrease in stroke rate (SR) and stroke length (SL) (13, 48). In addition, 120there is general agreement that stroke length (SL) in Breaststroke is the lowest compared with 121the other strokes (i.e., Freestyle, Butterfly, and Backstroke) (13). Likewise, several studies 122showed a large intra-cyclic velocity variation of the body’s center of mass during Breaststroke 123swimming (40, 30, 47). This variation makes this swimming stroke the slowest among the 124four competitive strokes (13). 125 126Until recently, numerous studies (21, 28, 37, 41) investigated the association between 127anthropometirc characteristics and swimming performance in various age group. However, 128knowledge on the effect of anthropometric characteristics on swimming performance in 129young swimmers still limited. Moreover, few studies have investigated the contribution of 130segment lengths to swimming performance. As stated in previous studies (9, 22, 41), limb 131segment lengths better predict athletic performance in sports athletes and sedentary population 132in comparison with whole-limb lengths. The allometric modeling approach is currently 133considered a relevant mode for solving this issue given its sound theoretical basis and its 134biologically driven, as well as its versatile statistical methodology (37). This approach often 135provides a dimensionless expression of data in the form of ratios [e.g, crural index, upper- 136arm-to-lower-arm ratio, and reciprocal ponderal index (stature-to-body-mass 0.333 ratio)]. 137Furthermore, its modeling techniques perfectly address the effects of age and sex differences 138on growth and biological maturation in motor performance interpretation (41). Recently, 139Nevill et al. (37) applied an allometric modeling approach to identify the optimal body size 140and limb-length segment associated with 100-meter Front crawl speed performance in youth 141swimmers (11 to 16 years). These authors revealed that lean body mass was the singularly 142most important whole-body characteristic and that having greater limb segment length ratios 143[i.e., arm ratio = (lower arm)/ (upper arm); foot-to-leg ratio = (foot)/ (lower-leg)] was key to 144personal best Front crawl swim speeds. Likewise, Sammoud et al. (41) applied the same 145approach to estimate the optimal anthropometric factors associated with 100-m Butterfly 146speed performance in children and adolescent male and female swimmers aged ~13 years. 147The same authors revealed that body fat was the singularly whole-body characteristic 148associated with Butterfly performance. Moreover, Sammoud et al. (41) demonstrated that 149limb segment length-ratio [arm-ratio = (arm-span)/(lower-arm)] and limb girth-ratio [girth- 150ratio = (calf-girth)/(ankle-girth)] were key to personal best Butterfly swim speed in children 151and adolescent male and female swimmers. 152

153 Based on the considerations described above, there is still a need to carry out further 154research about the influence of anthropometric characteristics on different swimming styles, 155particularly, Breaststroke. We believe that our allometric approach to identifying the key 156anthropometric dimensions and their ratios in youth Breaststroke swimmers is a novel 157contribution to the literature especially for the purpose of talent identification. Therefore, this 158study aimed to use allometric models to estimate the optimal body size, limb segment length, 159girth and breadth ratios associated with 100-meter Breaststroke speed performance in youth

160swimmer athletes. Based on previous findings (37, 33), we hypothesized that 100-m 161Breaststroke performance is dependent on limb segment length rather than the whole body

162size in youth swimmers of both gender.

163 164 165METHODS

166Experimental approach to the problem: 167To determine if anthropometric characteristics are important to the progression of swimmers, 168several body measurements were taken including height, body-mass, arm-span, sitting-height, 169skinfold thicknesses, limb lengths, girths, and breadths. The body composition was then 170calculated using various formulas (43). The percentage of body fat (%BF) of children with 171triceps and subscapular skinfolds <35 mm was calculated as follow: Boys= 1.21 (sum of 2 172skinfolds)-0.008 (sum of 2 skinfolds2)-1-7 and for girls= 1.33 (sum of 2 skinfolds)-0.013 (sum 173of 2 skinfolds2)-2.5. The %BF for children with triceps and subscapular skinfolds >35 mm 174was calculated as follow: Boys= 0.783 (sum of 2 skinfolds) - 1.7 and for girls= 0,546 (sum of 1752 skinfolds) +9.7. Fat-mass was calculated as follow: fat-mass = (body mass * % BF)/100; 176fat-free mass (kg) = body mass - fat mass. 177In addition, maturity offset was assessed by predicting age at peak-height-velocity (PHV) 178based on age, body mass, height, leg-length, and sitting-height using the predictive equation 179established by Mirwald et al. (32). In girls, maturity offset = -9.376 + 0.0001882·leg length 180and sitting height interaction +0.0022·age and leg length interaction +0.005841·age and 181sitting height interaction -0.002658·age and weight interaction +0.07693·weight by height 182ratio*100 (32). In boys, maturity offset= - 9.236 + 0.0002708·leg length and sitting height 183interaction -0.001663·age and leg length interaction + 0.007216·age and sitting height 184interaction+ 0.02292·weight by height ratio*100 (32). The time of 100-m Breaststroke 185swimming performance was recorded during competitions, as it represents the peak of the 186performance of the Tunisian Championship established according to the official measurement 187rules (FINA, 2004) using a high technology electronic timing (Omega, Switzerland, the 188presence of officials)”. 189 190Participants

191In total, 59 Breaststroke specialist swimmers (male [n=39; age: 11.50 ± 1.26 years]; female 192[n=20; age: 12.05 ± 0.99 years]) participated in the study. All participants were involved in 193five to six training sessions per week (4000 ± 1000 m per session; 8 ± 1 hour per-week). In 194addition, training session included the four-stroke. Written informed parental consent and 195participant assent was obtained prior to the start of the study. All youth athletes and their 196parents / legal representatives were informed about the experimental protocol and its potential 197risks and benefits before the commencement of the research project. The study was approved 198by the local Ethics Institutional Review Committee for the ethical use of human subjects at 199Ksar Saïd University, Tunisia.

200 201Performance time and average swmming speed (m.s-1) 202The swimming times and/or speeds (speed based on the race time) expressed in seconds and 203meters per second (m.s-1), respectively, were adopted as our measures of swimming 204performance. Swimming performance was recorded in a 25-m swimming pool. The 205Breaststroke average speed was calculated as the ratio between distances swam and the total 206time recorded in this distance (m.s-1). Performance (s) was measured with a high technology 207electronic timing (Omega, Switzerland) and was extracted for all subjects from the official 208results published by the Tunisian swimming Federation during the Winter National 209Championships. Water temperature was kept between 25 and 28 degrees, as determined by 210Fédération Internationale De Natation (FINA, 2014). 211 212Anthropometric measurements 213All the anthropometrical measurements were taken by one trained anthropometrist assisted by 214a recorder in accordance with standardized procedures of the international society for the 215advancement of kinanthropometry (ISAK) (45) (Table 1). Testing was carried out in a 216standardized order after a proper calibration of the measuring instruments. Each swimmer’s 217height (m) and body-mass (kg) were assessed to the nearest 0.1 cm and 0.1 kg, using a SECA 218stadiometer and a SECA weighing scale (SECA Instruments Ltd, Hamburg, Germany), 219respectively. Skinfolds measurements (in millimeters) were taken on the right-hand side of the 220body at two sites (the triceps and the subscapular) using Harpenden skinfold calipers 221(Harpenden Instruments, Cambridge, UK). Skinfold data, alongside the skinfold equation of 222Slaughter et al.(43), were used to estimate the body-fat mass and fat-free mass. The following 223limb-lengths, girths and breadths were assessed using a large sliding caliper and a non- 224stretchable tape measure via direct measures using landmarks techniques: arm span, upper- 225limb length, upper-arm length, lower-arm length, hand lengths, lower-limb length, thigh 226length, leg length, foot length, arm-relaxed girth, forearm girth, wrist girth, thigh girth, calf 227girth, ankle girth, biacromial and biiliocristal-breadths. 228Upper arm length was measured from landmarks placed to acromiale and dactylion while 229athletes stood in the erect position. Upper arm length was determined as the distance between 230the marked acromiale and radiale landmarks. The lower-arm length was measured by 231calculating the distance between the radiale and stylion landmarks. For the hand length, the 232measure was taken as the shortest distance from the marked midstylion line to the Dactylion. 233Lower limb length was determined by subtracting sitting height from standing height. Thigh 234length was determined as the distance between the marked trochanterion and tibiale lateral 235landmarks. Leg length was measured as the distance from the height of the tibiale lateral to 236the top of the box (or the floor). Foot length was determined as the distance from the 237Akropodion (i.e., the tip of the longest toe which may be the first or second phalanx) to the 238Pternion (i.e., most posterior point on the calcaneus of the foot). Arm-relaxed girth was 239measured at the marked level of the mid-acromiale- radiale. The tape should be positioned 240perpendicular to the long axis of the arm. 241Forearm girth was taken at the maximum girth of the forearm distal to the humeral 242epicondyles. Wrist girth measure is taken distal to the styloid processes. It is the minimum 243girth in this region. Thigh girth measure was taken at the marked mid-trochanterion-tibiale- 244lateral site. Calf girth was defined as the maximum girth of the calf taken at the marked 245medial calf skinfold site. Ankle girth was defined as the minimum girth of the ankle taken at 246the narrowest point superior to the Sphyrion tibiale. Biacromial breadths were determined as 247the distance between the most lateral points of the acromion processes. Biiliocristal breath 248was defined as the distance between the most lateral points on the iliac crests. 249The Intraclass correlation coefficients (ICCs) for test-retest reliability for all anthropometric 250and skinfolds measures ranged from 0.96 to 0.99. 251 **Table 1 near here** 252Statistical analysis 253Descriptive statistics were computed and expressed as means and standard deviations. To 254identify the most suitable anthropometric characteristics [i.e., body-mass (M), fat-free mass 255(FFM), fat mass (FM), stature (S), limb-lengths, girths or breadths (L)] that are associated 256with 100-meter Breaststroke speed performance, we adopted the proportional multiplicative 257model with allometric body size components, similar to the 100-meter personal best swim 258Front crawl model used to measure speeds in children and adolescents (36) and to the 100- 259meter Butterfly speed model used among swimmers of the same age group (41). The benefits 260of this model are to have proportional body size components. Additionally, this multiplicative 261allometric model was extensively adopted in physical anthropology (15), and biology (3,7), 262and a recent call has been made for consideration within molecular biology and gene 263expression investigations because human size, and what it involves, will always be of concern 264(28). Additionally, this model has been systematically used to partition out differences in size 265in efficient ways, such as differences in VO2 peak (3) and in a variety of motor tests (37). 266 267The multiplicative model:

.-1 k k k 268Breaststroke average speed (m.s ) = a · (M) 1 ·(S) 2 · (Li) i · exp( b biological age+ c·FM) 269·ε. (1)

k 270where ‘a’ is a constant and  (Li) i (i=3, 4,…, n) signifies the product of all limb segment-

271lengths, girths or breadths measurements raised to the power of ki; with i=3 to i=n 272representing the full range of limb lengths, girths, and breadths recorded for the swimmers 273(for the full list, see anthropometric measurements paragraph). 274The benefits of this model are to have proportional body size components. Note that “ε”, the 275multiplicative error ratio, also assumes the error will increase in proportion to the athlete’s 276swimming performance. The model (Equation 1) can be linearized with a log transformation. 277A linear regression analysis on log (Breaststroke average speed [m.s.-1]) can then be used to 278estimate the unknown parameters of the log-transformed model:

.-1 279Ln (Breaststroke average speed (ms ) = k1.log (M)+ k2.log(S) +ki·ln (Li)+ a + b.biological 280age +c.FM + log (ε) (2) 281Having fitted the saturated model with all available body size variables, an appropriate 282‘‘parsimonious’’ model can be obtained using ‘‘backward elimination’’ (37), in which the 283least important body size and limb segment length, girth, and breadth variables at each step 284are eliminated from the model. A parsimonious model is a model that achieves a desired level 285of explanation or prediction with as few predictor variables as possible. Further categorical or 286group differences within the population (e.g., sex ) can be explored by allowing the constant 287intercept parameter [e.g. ln(a) refers to natural logarithms in equation 2] to vary for each 288group. 289 290RESULTS 291Table 1 shows anthropometric characteristics and swimming performance data of participants. 292Boys and girls age at peak height velocity was 13.8 ± 0.6 and 12.0 ± 0.5, respectively. No 293significant difference was detected for all the anthropometric characteristics and the 294swimming performance between boys and girls (p>0.05) except for the arm span, thigh 295length, lower limb length, upper arm length, and sitting height (all p<0.05). Table 2 indicates 296the parsimonious solution to the backward elimination regression analysis of ln(Breaststroke 297average speed [m.s-1]). The multiplicative allometric model exploring the association between 298100-meter average Breaststroke speed performance (m.s-1) and the different anthropometric 299characteristics estimated that fat-mass (as a whole body size dimension), two upper-limb 300lengths (forearm and hand length), leg length for the lower limbs, two breadths (biacromial 301and biiliocristal breadths) and two girths (forearm and wrist girth) are the main significant 302predictors of log-transformed swim performance. Our allometric model detected that 303Breaststroke speed performance increases by 2.5% every additional year in youth swimmers. 304The adjusted coefficient of determination (R2) was 76.6% with the log-transformed error ratio 305being 0.06 or 6%, having taken antilogs. The constant ‘a’ did not vary significantly with sex, 306suggesting that the model can be regarded as common for children of either sex (Tables 2).

307 **Table 2 near here**

308

309DISCUSSION 310To the authors’ knowledge, this is the first study that aimed to identify the most appropriate 311body-size characteristics related to 100-m Breaststroke performance in youth swimmers. The 312outcomes of this study support previous investigations, indicating that anthropometrics are 313highly related to youth swimmers’ performance (21, 28, 37, 39, 41). Specifically, in 314agreement with previous findings (37, 41), the present results demonstrated that fat-mass was 315the single most important whole body-size characteristic negatively associated with 316Breaststroke performance. Particularly, Sammoud et al. (41) revealed that fat-mass was the 317only whole body-size characteristic negatively associated with Butterfly speed performance in 318children and adolescent male and female swimmers. Similarly, Nevill et al. (37) demonstrated 319that fat-free-mass was the single most important whole-body size characteristic positively 320associated with 100-m Freestyle performance in male and female adolescent swimmers. 321Jürimäe et al. (21) reported a significant correlation between fat-free mass and 400-m Front 322crawl performance in young swimmers. Likewise, Nasirzade et al. (35) revealed a significant 323negative correlation between muscle thickness (r range from 0.42 to 0.56) and 200-m Front 324crawl performance in young swimmers (14.5 ± 0.8 years). In addition, Helmuth (25) found 325that fat-free mass significantly correlated (r = 0.73) with 100-m Front crawl performance in 8- 326to 16-year-old male swimmers. 327The disadvantage of having higher fat-mass suggests that swimmers require greater fat-free 328mass, implying that they require greater muscularity to propel themselves faster through the 329water (40). Based on the consideration mentioned above, the positive association between fat- 330free mass and swimming performance may indicate that this anthropometric variable 331contributed significantly to the prediction of propulsion force. This propulsion force may 332translate to improve Breaststroke performance in youth boy and girl swimmers. In addition, 333inertial properties of the limbs may influence stroke rate, particularly, mass and mass 334distribution (37). Overall, greater fat-free mass is likely to be associated with greater fat-free 335mass in the limbs, translating into greater stroke rate and subsequent propulsion (37) 336Our study showed that height and body mass did not contribute significantly to the model. 337These findings extended previously reported results (21, 37, 40), suggesting that the 338advantage of longer levers was either limb-segment length, girth or breadth specific rather 339than having a more general whole body size advantage. Likewise, our allometric model 340revealed that swimming performance average speed increases by 2.5% every additional year 341of the swimmer's biological age (see Table 2). Based on this result, coaches should take into 342consideration the biological age rather than the chronological one as a key factor in 343determining performance. 344In addition, the key indicator from the allometric model reported in Table 2 is the advantage 345of having greater limb segment length ratios [i.e., arm length ratio = (hand length)/(forearm 346length)] and greater limb segment girth ratios [ i.e., arm girth ratio = (forearm girth)/(wrist 347girth)] are associated with swimming faster Breaststroke speeds. Our results illustrated that 348forearm length made a negative contribution whereas the hand length made a positive 349contribution to the 100-m Breaststroke performance. These results are in agreement with 350previous investigations (21, 23). For instance, Perez et al. (38) revealed a significant 351relationship between hand length and the average of swimming speed (r=0.51). In the same 352context, Geladas et al. (21) demonstrated a significant relationship between 100-m Freestyle 353performance time and hand length measures (r= -0.6, -0.3 in boys and girls young swimmers, 354respectively). The same authors, also, reported a significant relationship between the foot 355length and the 100-m Freestyle performance time (r=-0.49, -0.16, in boys and girls, 356respectively). Likewise, Helmuth, (25) reported a significant correlation between hand size 357and swimming performance in young male and female swimmers (8-16 years). It is 358noteworthy that the significant relationship between 100-m Breaststroke performance and 359hand length could be mainly due to the fact that propulsive force and hence swimming 360performance is positively affected by the large upper extremity length (26). Zamparo et al. 361(49) argued that having a greater hand length will also act to increase surface area, thus 362leading to a greater propelling economy. Finally, the finding of the positive contribution of 363the hand length and Breaststroke performance could be attributed to the fact that propulsive 364force and hence swimming performance is positively affected by the large upper extremity 365length. 366As with hand length, our results revealed a significant contribution by the leg length in the 367100-m Breaststroke performance. In addition, our findings demonstrated the importance of 368having shorter forearm length for better 100-m Breaststroke swimming performance. These 369observations are consistent with those established by Nevill et al. (37), who illustrated that 370longer lever length (upper-arm and lower leg) is potentially mechanically disadvantageous in 371some ways in 100-m Front crawl performance in adolescent swimmers of both sex. The same 372authors indicated that the involved muscles have to exert greater force and, hence, use greater 373energy. Likewise, the importance of having shorter forearm length for better 100-m Butterfly 374speed performance in swimmers has been recently shown (40). The same authors (40) 375indicated that swimmers with a shorter arm length have naturally a better swimming 376technique with respect to those with longer upper limbs. In the same context, Grimston and 377Hay (23) reported that the dimensions of body segments, such as the upper limbs or lower 378limbs lengths, influence the mechanics of swimming technique and muscle activity. 379Consequently, it seems crucial to focus on teaching the correct swimming technique starting 380from the early years of training (31, 40). 381In addition, our findings revealed that an increase in a forearm-girth or volume would 382improve the 100-m Breaststroke swimming performance. This is in agreement with findings 383recently established by Sammoud et al. (40) who revealed that an increase in a calf-girth or 384volume would increase the 100-m Butterfly speed performance in adolescent swimmer 385athletes. Santos et al. (42) revealed a significant association between the arm muscle area and 386the propulsive force of the arm in young male swimmers (14 ± 1.28 years). The same authors 387(41) demonstrated that the increase of the arm muscle area contributes to a greater capacity 388for strength. This significant association could be explained by the fact that swimmers having 389greater limb volume or greater muscularity seem to generate higher propulsive force and 390propel themselves faster through water (36,40). 391The current findings revealed that having a greater wrist-girth impairs performance. This is in 392agreement with those established by Sammoud et al. (41) who detected a negative 393contribution of the ankle girth in the 100-m Butterfly speed performance. A large wrist-girth 394would increase resistance through water, therefore increasing the energy cost of swimming 395(11, 12, 49). This may at least partially explain the disadvantage of having a larger wrist-girth. 396Our results also detected a positive association between the biacromial and biiliocristal 397breadths with the 100-m Breaststroke speed performance (Table 2). Recently, Sammoud et al. 398(41) revealed that having greater biacromial and biiliocristal breadths are key positive 399anthropometric indicators associated with better 100-m Butterfly speed performance. 400Likewise, Geladas et al. (21) showed that swimming sprint time was significantly correlated 401with biacromial (r= - 0.61) and biiliocristal-breadths (r= -0.46) in male swimmers. All 402together, these findings may be related to the fact that swimmers with broad shoulders are 403better suited for high power output in the water (8). In addition, the positive association 404between the body breadths and 100-m Breaststroke performance in our study suggest that a 405larger body cross-section area in swimmers may be related to sprint performance time (26). 406According to the effect of cross-sectional area on the pressure drag, several studies (4, 10) 407have shown that some anthropometric parameters like the chest girth, depth and breadth are 408significantly correlated with drag values. In addition to the anthropometric parameters, the 409shape and the contour of the body are important factors too affecting the pressure drag 410because they determine how the flow moves over the body (14). 411 412The main limitations of this research may be summarised as follows: (1) not included in the 413model were variables related to functional fitness (e.g., muscular strength or flexibility) that 414might influence stroke mechanics (2) variables from other domains that may also play an 415important role in youth swimmers’ performance (e.g. motor control, hydrodynamics, genetics) 416were not taken into consideration, (3) a direct measure of the propulsive efficiency was not 417adopted, and (4) biomechanical testing methods should be implemented in future studies to 418obtain an in-depths knowledge regarding the allometric associations between biomechanical, 419shape, and 100-m Breaststroke speed performance. 420Practical Applications 421The current study has several practical applications. First of all, the present results highlighted 422the importance of considering anthropometric characteristics of youth swimmers for talent 423identification and/or athlete monitoring purposes by coaches and sports scientists. 424Additionally, it is only by adopting multiplicative allometric models that the abovementioned 425ratios could have been derived. Furthermore, results of the present study illustrated that (1) fat 426mass was the singularly most important whole-body size characteristic, and that height and 427body mass did not contribute significantly to the allometric model, (2) 100-m Breaststroke 428average speed performance was strongly positively associated with the segment length ratio 429(arm-ratio and girth-ratio), and (3) leg length, biacromial and biiliocristal breadths were 430positively associated with the 100-m Breaststroke performance in youth swimmers.

431 432Acknowledgments 433The authors are pleased to thankfully acknowledge the athletes and their trainers who 434willingly and patiently contributed to this study.

435Disclosure statement 436No potential conflict of interest was reported by the authors.

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460 461References 462 1. Barbosa TM, Costa M, Marinho JD, Coelho M, Moreira M, Silva AJ. Modeling the 463 links between young swimmer’s performance: energetic and biomechanical profiles. 464 Pediatr Exerc Sci. 2010; 22:379–391. 465 2. Barbosa TM, Fernandes RJ, Keskinen KL, Vilas-Boas JP. The influence of stroke 466 mechanics into energy cost of elite swimmers. Eur J Appl Physiol. 2008; 103:139– 467 149. 468 3. Batterham A, George K, Mullineaux D. Allometric scaling of left ventricular mass by 469 body dimensions in males and females. Med Sci Sports Exerc. 1997; 29: 181–186. 470 PMID: 9044220.

471 4. Benjanuvatra N, Blanksby BA, Elliott BC. Morphology and hydrodynamic resistance 472 in young swimmers. Pediatr Exerc Sci. 2001;13(3):246–55. 473 5. Bond D, Goodson L, Oxford SW, Nevill AM, Duncan MJ. The Association between 474 Anthropometric Variables, Functional Movement Screen Scores and 100 m Freestyle 475 Swimming Performance in Youth Swimmers. Sports. 2015; 3: 1-11. 476 6. Bouchard C, Malina RM, Perusse L. Genetics of fitness and physical performance. 477 Champaign: Hum Kinet. 1997. 478 7. Bown J, West G. Scaling in Biology. New York: Oxford University Press.2000.

479 8. Carter JEL, Ackland TR. Kinanthropometry in aquatic sports: A studyof world class 480 athletes. Champaign, IL: Human Kinetics. 1994. 481 9. Caruso JF, Daily JS, Mason ML, Shepherd CM, McLagan JR, Marshall MR, Walker 482 RH, West JO. Anthropometry as a predictor of vertical jump heights derived from an 483 instrumented platform. J Strength Cond Res. 2012; 26:284–92. 484 10. Chatard JC, Lavoie JM, Bourgoin B, et al. The contribution of passive drag as a 485 determinant of swimming performance. Int J Sports Med. 1990;11(5):367–72. 13.

486 11. Chatard JC, Padilla S, Cazorla G, Lacour JR. Influence of body height, weight, 487 hydrostatic lift and training on the energy cost of the front crawl. N Z J Sports Med. 488 1985;13:82–4. 489 12. Chatard, JC, Lavoie, JM, Lacour, JR. Analysis of determinants of swimming economy 490 in front crawl. Eur J Appl Physiol. 1991; 61:88–92. 491 13. Craig ABJr, Skehan PL, Pawelczyk JA, Boomer WL. Velocity, stroke rate, and 492 distance per stroke during elite swimming competition. Med Sci Sport Exercise. 1985; 493 17(6):625-34. 494 14. Fish FE, Hui CA. Dolphin swimming—a review. Mammal Rev. 1991; B(21):181–95.

495 15. Fleagle J. Size and Scaling in Primate biology. In: Jungers W, editor. Size and 496 adaptation in primates. New York: Plenum Press. 1985.

497 16. Deprez D, Buchheit, M, Fransen, J, Pion, J, Lenoir, M, Philippaerts, RM, Vaeyens, R. 498 A longitudinal study investigating the stability of anthropometry and soccer-specific 499 endurance in pubertal high-level youth soccer players. J Sports Sci Med. 2015; 14(2): 500 418-26. 501 17. Di Prampero P. The energy cost of human locomotion on land and in water. Int J 502 Sport Med. 1986; 7: 55-72. 503 18. Duchè G, Falgairette G, Bedu M, Lac G. Analysis of performance of prepubertal 504 swimmers assessed from anthropometric and bio-energetic characteristics. Eur J Appl 505 Physiol Occup Physiol. 1993; Vol. 66(5): 467–471. 506 19. Figueiredo R, Pendergast DR, Vilas-Boas JP, Fernandes RJ. Interplay of 507 biomechanical, energetic, coordinative, and muscular factors in a 200 m front crawl 508 swim. Biomed Res Int. 2013; 1-12. 509 20. Federation Internationale De Natation (FINA). Talent identification programmes. 510 2014 [cited 18 September 2014]. Availablefrom: http://www.fina.org/. 511 21. Geladas N, Nassis GP, Pavlicevic S. Somatic And Physical Traits Affecting Sprint 512 Swimming Performance In Young Swimmers. Int J Sports Med. 2005;26: 139-144. 513 22. Green L, Gabriel DA. Anthropometrics and electromyography as predictors for 514 maximal voluntary isometric arm strength. J Sport Health Sci. 2012; 1:107–13. 515 23. Grimston SK, Hay JG. Relationships among anthropometric and stroking 516 characteristics of college swimmers. Med Sci Sport Exercise. 1986: 18;60-8. 517 24. Hahn, A. Identification and selection of talent in Australian rowing. Excel. 1990: 518 6(3):5–1. 519 25. Helmuth, HS. Anthropometric survey of young swimmers. AnthropolAnz. 1980;38: 520 17-34. 521 26. Huijing P, Toussaint H, Mackay R, Vervoorn K, Clarys JP, de Groot G, Hollander 522 AP. Active drag related to body dimensions. In : Ungerechts B, Reischle K, Wilke K 523 (eds). Swimming Science v.Champaign, IL : HU-man Kinetics. 1988: 31-38. 524 27. Issurin VB. Evidence-Based Prerequisites and Precursors of Athletic Talent: A 525 Review. Sport Med. 2017:11. 526 28. Jurimae J, Haljaste K, Cicchella A, Latt E, Purge P, Leppik A, Jurimae T. Analysis of 527 Swimming Performance From Physical, Physiological, and Biomechanical Parameters 528 in Young Swimmers. Pediatr Exerc Sci. 2007; 19: 70-81. 529 29. Lätt E, Jürimäe J, Mäestu J, Purge P, Rämson R, Haljaste K, Keskinen KL, Rodriguez 530 FA, Jürimäe T. Physiological, biomechanical and anthropometrical predictors of 531 sprint swimming performance in adolescent swimmers. J Sports Sci Med. 2010; 532 9:398–404.

533 30. Leblanc H, Seifert L, Tourny-Chollet C, Chollet D. Velocity variations in breaststroke 534 swimmers of different competitive levels. Int J Sports Med. 2007; 28: 140–147.

535 31. Marinho DA, Barbosa TM, Reis VM, Kjendlie PL, Alves FB, Vilas-Boas JP, 536 Machado L, Silva AJ, Rouboa AI. Swimming propulsion forces are enhanced by a 537 small finger spread. J Appl Biomech. 2010; 26(1):87-92.

538 32. Mirwald RL, Baxter-Jones AD, Bailey DA, Beunen GP. An assessment of maturity 539 from anthropometric measurements. Med Sci Sports Exerc. 2002;34(4):689-694.

540 33. Morais JE, Jesus S, Lopes V, Garrido N, Silva A, Marinho D, Barbosa TM. Linking 541 selected kinematic,anthropometric and hydrodynamic variables to young swimmer 542 performance. Pediatr Exerc Sci. 2012; 24:649–64. 543 34. Morais JE, Silva AJ, Marinho DA, Lopes VP, Barbosa TM. Determinant factors of 544 long-term performance development in young swimmers. Int J Sports Physiol Perf. 545 2017; 12: 198-205 546 35. Nasirzade A, Sadeghi H, Sobhkhiz A, Mohammadian K, Nikouei A, Baghaiyan 547 M, Fattahi A. Multivariate analysis of 200-m front crawl swimming performance in 548 young male swimmers. Acta Bioeng Biomech. 2015; 17(3):137-43. 549 36. Negra Y, Chaabene H, Hammami M, Khlifa R, Gabett T, Hachana Y. Allometric 550 scaling and age related differences in change of direction speed performances of 551 young soccer players. Sci sports. 2015.

552 37. Nevill AM, Oxford SW, Duncan MJ. Optimal Body Size and Limb Length Ratios 553 Associated with 100-m Personal-Best Swim Speeds. Med Sci Sport Exercise. 2015; 554 47, 1714-8. 555 38. Perez AJ, Bassini CF, Pereira Bmf, Sarro Kj. Correlation Between Anthropometric 556 Variables And Stroke Lenght And Frequency. In Swimmers Of Espirito Santo. 2011; 557 10: 19-27. 558 39. Reis VM, Barbosa TM, Marinho DA, Policarpo F, Reis AM, Silva AJ, Baldari C. 559 Physiological determinants of performance in breaststroke swimming events. Int J 560 Sports Med. 2010; 11: 324-335.

561 40. Schnitzler C, Seifert L, Ernwein V, Chollet D. Arm coordination adaptations 562 assessment in swimming. Int J Sports Med. 2008; 29(6):480-6. Epub 2007 Nov 16.

563 41. Sammoud S, Nevill AM, Negra Y, Bouguezzi R, Chaabene H, Hachana Y. Allometric 564 associations between body size, shape, and 100-m butterfly speed performance. J 565 Sports Med Physi Fitness. 2017.

566 42. Santos MA, Barbosa JrML, Melo WV, Veronese da Costa A, Costa MC. Estimate of 567 propulsive force in frontcrawl swimming in young athletes. Open Access J Sports 568 Med. 2012; 3: 115-120.

569 43. Slaughter MH, Lohman TG, Boileau RA, Horswill CA, Stillman RJ, VanLoan MD, 570 Bemben DA. Skinfold equations for estimation of body fatness in children and youth. 571 Hum Biol. 1988; 60: 709-23. 572 44. Soares PM, Sousa F, Vilas-Boas JP.Differences in breaststroke synchronisation 573 induced by different race velocities. In K. L. Keskinen, P. V. Komi, and A. P. 574 Hollander (eds.), Biomechanics and Medicine in Swimming VIII (pp. 53-57). 575 Jyväskylä: Gummerus Printing. 1999.

576 45. Stewart A, Marfell-Jones, M Olds T, de Ridder H. International Standards for 577 Anthropometric Assessment. Lower Hutt, New Zealand: ISAK. 2011; pp. 57–72.

578 46. Szopa J, Mleczko E, Zychowska, M, et al. Possibilities of determination of genetic 579 conditionings of somatic and functional traits on the backgrounds of family studies. J 580 Hum Kinet. 1999;2:21–36.

581 47. Takagi H, Sugimoto S, Nishijima N, Wilson B. Differences in stroke phases, arm-leg 582 coordination and velocity fluctuation due to event, gender and performance level in 583 breaststroke. Sports Biomech. 2004; 3(1):15-27. 584 48. Toussaint H, Carol A, Kranenborg H, Truijens M. Effect of fatigue on stroking 585 characteristics in an arms-only 100-m front-crawl race. Med Sci Sport Exercise. 2006; 586 38, pp. 1635-1642.

587 49. Zamparo P, Pendergast D, Mollendorf J, Termin A, Minetti A. An energy balance of 588 front crawl. Eur J Appl Physiol. 2005; 94: 134-144. 589

590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615

616Table 1: Participants’ anthropometrical characteristics and 100-m Breaststroke swimming 617performance. (Data are presented as mean ± SD)

618

619 Variables Males (n=39) Females (n=20) 620 Age (years) 11.5 ± 1.3 12.0 ± 1.0 621 Maturity offset (years) -2.3 ± 1.10 0.04 ± 1** 622 Age at peak height velocity (years) 13.8 ± 0.6 12.0 ± 0.5** Body-Mass (kg) 41.5 ± 9.5 46.0 ± 8.6 623 Height (cm) 149.9 ± 10.4 155.9 ± 8.0 624 Sitting Height (cm) 74.4 ± 5.8 76.4 ± 7.9* 625 Body-Fat % 16.8 ± 5.5 19.0 ± 4.3 Fat-Mass (kg) 7.2 ± 3.8 8.8 ± 3.1 626 Fat-Free-Mass (kg) 34.3 ± 6.8 37.1 ± 6.4 627 Body-Mass Index ( kg.m-2) 18.3 ± 2.4 18.8 ± 2.5 Upper-Limb-Length (cm) 69.3 ± 5.3 72.0 ± 4.4 628 Upper-Arm-Length (cm) 29.0 ± 2.2 30.4 ± 2.0* 629 Foream-Length (cm) 23.2 ± 1.9 23.7 ± 1.7 630 Hand-Length (cm) 18.0 ± 1.6 18.7 ± 1.0 Lower-Limb-Length (cm) 81.6 ± 6.1 85.0 ± 4.9* 631 Thigh-Length (cm) 38.9 ± 5.8 42.0 ± 2.2* 632 Leg-Length (cm) 42.0 ± 3.4 43.4 ± 2.5 633 Foot-Length (cm) 25.1 ± 2.2 25.5 ± 1.1 Arm-Relaxed-Girth (cm) 23.0 ± 3.0 23.8 ± 2.3 634 Forearm-Girth (cm) 21.1 ± 1.9 21.5 ± 1.5 635 Wrist-Girth (cm) 14.7 ± 1.1 15.4 ± 2.3 636 Thigh-Girth (cm) 43.9 ± 4.5 46.1 ± 5.6 Calf-Girth (cm) 30.3 ± 3.1 30.7 ± 3.0 637 Ankle-Girth (cm) 20.5 ± 1.8 20.5 ± 1.5 638 Biacromial-Breadth (cm) 41.5 ± 3.9 43.3 ± 2.3 Biiliocristal-Breadth (cm) 24.4 ± 2.4 25.7 ± 2.4 639 Arm-Span (cm) 150.4 ± 13.5 158.3 ± 9.7* 640 Swimming performance (s) 97.7 ± 13.4 95.4 ± 9.5 641 Breaststroke average speed (m.s-1) 1.0 ± 0.1 1.1 ± 0.1 * denotes 642 ** denotes 643 643 644 645 646 647TABLE 2. Estimated body size and limb segment parameter (B) obtained from regression 648analysis predicting log-transformed 100-m Breaststroke average speed performance. 649 Variables in the 95% Confidence B Std. Error p Model Interval for B Constant -6.813 0.561 <0.000 -7.942 to -5.685 Age at peak height 0.025 0.009 0.008 0.007 to 0.042 velocity lnForearm-girth 0.690 0.240 0.006 0.207 to 1.173 lnWrist-girth -0.348 0.128 0.009 -0.604 to -0.091 lnBiacromial- 0.565 0.221 0.014 0.121 to 1.009 Breadth lnBiiliocristal- 0.403 0.109 0.001 0.185 to 0.622 Breath lnLeg-length 0.673 0.264 0.014 0.143 to 1.203 lnHand-length 0.309 0.195 0.120 -0.083 to 0.702 LnForearm-length -0.418 0.220 0.064 -0.861 to 0.025

Fat-Mass -0.018 0.004 <0.001 -0.026 to -0.010

Ln: Natural Log; Std: Standard 650 651 652 653 654