Participation and competition in swimming often begins at an early age and is popular among boys and girls. Many children and adolescents who participate in competitive swimming continue to swim competitively or recreationally as adults (15,41). Previous studies (1,12,33,44) have described physical characteristics that are common among swimmers and differentiate between swimming events, and early identification of anatomical and physiological characteristics common to swimmers may assist coaches with athlete selection and event specialization. For example, Zuniga et al. (44) and Richardson et al. (33) described the height (HT), percent body fat (%BF), and fat-free mass (FFM) of young swimmers. In a sample of 12-year-old male swimmers, Avlonitou (1) reported that there were differences between swimming events for HT, arm length, and leg length and that these variables were associated with swimming performance. In addition, Geldas et al. (12) reported that HT, body mass (BM), and upper extremity length were correlated with sprint swimming performance (r = 0.45–0.65) in young male swimmers. Thus, the physical characteristics of young swimmers vary by events, and these characteristics influence swimming performance.
Previous studies (10,12,22,27,29,43) have also assessed the strength and speed characteristics of swimmers and their influences on performance. These studies indicated that strength is influenced by age (10,22,27) and physical characteristics (HT, arm muscle area [AMA], body segment, and FFM) (10,11,22,28,29). For example, Avolonitou (1) reported that taller swimmers with longer arms had significantly faster 50-m front crawl times than swimmers of shorter stature and arm lengths. In addition, young male swimmers with the fastest front crawl times also exhibited the greatest hand (11) and arm forces (16,27). Furthermore, Girold et al. (13) found that training for forearm flexion (FLX) and extension (EXT) strength resulted in improvements for 100-m swimming performance.
Propulsive force is highly related to strength and swimming performance (9,11–13,29,35,39). For example, Toussaint et al. (39) reported that young swimmers increased their maximal swimming velocity and performance with a better force-generating capacity because of age-related increases in muscle size. Sharp et al. (35) found that propulsive force was highly (r = 0.90) associated with swimming performance, and training-related increases in force were accompanied by improvements in sprint swimming speed. Similarly, in a group of competitive swimmers, training-related increases in stroke rate and propulsive forces resulted in improvements in swim velocity (13). In a group of adolescent male swimmers, Papoti et al. (30) reported a correlation of r = 0.86 between the propulsive force generated at the arm and swimming speed during the front crawl. Furthermore, Moura dos Santos et al. (29) found a significant relationship (r = 0.69) between AMA and propulsive force in young male swimmers and used this relationship to derive an equation to estimate estimated propulsive force (EPF) when direct measurement is not possible (29). Thus, strength and size of the arm influences propulsive forces, and increases in propulsive forces lead to faster swimming times.
Although previous studies (6,17,27,29,35,43,44) have reported relationships for anthropometric and strength characteristics of swimmers, few studies (9,12,28) have investigated the relative contributions of these factors to predicting swimming ability. In addition, no previous studies have investigated the relative contributions of FLX peak torque (PT) at 180°·s−1, forearm EXT PT at 180°·s−1, HT, %BF, and FFM to the prediction of a swimming performance-related variable such as EPF. Thus, the purpose of this study was to determine the relative contributions of FLX, EXT, HT, %BF, and FFM to the prediction of EPF and which of these variables should be a focus of training in young male swimmers.
Experimental Approach to the Problem
Young male swimmers were measured for isokinetic strength (FLX and EXT) values and for anthropometric characteristics (BM, HT, %BF, FFM, arm circumference [AC], and triceps skinfold [SKF]). Arm circumference and triceps SKF were used to determine AMA. Arm muscle area was then used to estimate propulsive force. Zero-order correlations and stepwise multiple regression analyses were used to examine the relative contributions of the variables to EPF. The variables most related to EPF should be a focus of dry-land training programs for young male swimmers.
Thirty young male swimmers (Table 1) volunteered as subjects for this study. All of the subjects competed using the front crawl swimming stroke (<200 m) in age group and/or high school swimming in the Midwest and were tested 1–2 weeks before the competitive swimming season. The subjects trained year-round and competed in local area competitions and events. Coaches self-reported that all swimmers were high performers for their age groups. All subjects were tested at the same time of day and were asked to maintain a normal diet and hydration state throughout the testing period. The study was approved by the institutional review board for human subjects, and a written informed consent was obtained from both the subjects and their parents before testing.
Eight variables were measured on each swimmer (FLX, EXT, BM, HT, %BF, FFM, AC, and triceps SKF) (Table 1). In addition, AMA and EPF (Table 1) were calculated using previously established equations (14,29).
Isokinetic Strength Testing
Concentric isokinetic FLX and EXT PT of the dominant arm (based on throwing preference) was measured using a calibrated Cybex II dynamometer (Medway, MA, USA). Positioning and stabilization were accomplished with the procedures described by the manufacturer (Cybex 1997). The participants were tested at an angular velocity of 180°·s−1. The angular velocity of 180°·s−1 was selected because it has been reported to be a representative of a swimmer's movement speed (13,32). Each participant performed 3–4 submaximal practice trials followed by three consecutive maximal efforts with the highest PT value selected as the representative score. Previous test-retest reliability from our laboratory for isokinetic PT of the forearm flexors and extensors indicated that for participants measured 2–5 days apart, the intraclass correlation coefficients (ICC) ranged from R = 0.88 to 0.99 with no significant differences between mean values for test vs. retest at velocities ranging from 0 to 300°·s−1 and with SEM values that ranged from 0.42 to 0.64 N·m (40,41).
Height, Body Mass, and Underwater Weighing
Height and BM were measured using a wall scale with a Broca plane (Seco, North Bend, WA, USA) and a calibrated physician's scale (Detecto, Webb City, MO, USA), respectively. Underwater weighing (UWW) was performed in a 4,250-L hydrostatic tank in which a swing seat was suspended from a Chatillon 9-kg scale. Each subject was submerged while seated in the swing seat and instructed to voluntarily expel as much air as possible from the lungs. Each subject's underwater weight was calculated by averaging the highest 3 values from 6–10 trials. Residual volume was determined on land using the oxygen dilution method of Wilmore (42) with each subject seated in a position similar to that during UWW. Percent body fat was calculated from body density using the age-specific conversion constants of Lohman (24). Previous test-retest reliability data for UWW from our laboratory indicated that for young male subjects (n = 16) measured 24–72 hours apart, the ICC (R) was 0.98 with a SEM of 0.9% fat. There was no significant difference from test to retest for UWW reliability. Fat-free mass was mathematically derived from the resultant %BF score. These values were comparable with those reported by Thomas and Cook (38) and Jackson et al. (19).
Skinfold and Circumference Measurements
Triceps SKF measurement was taken with a Lange Caliper (Vital Signs, Gays Mills, WI, USA) on the right side of the body according to the landmarks of Behnke and Wilmore (2). Triceps SKF was taken midway between the acromion and olecranon processes on the posterior aspect of the arm, with the arm held vertically and the fold running parallel to the length of the arm. Triceps SKF was recorded to the nearest 0.1 mm. The SKF measurement was taken in duplicate with the average of repeat trials within 0.5 mm of each other used as the representative score. The triceps SKF was converted into centimeters for the calculation of AMA (14,29). Relaxed AC was measured with a Lufkin metal tape (Lufkin, Houston, TX, USA) fitted with a Gullick handle (2) and recorded to the nearest 0.1 cm. Triceps SKF and AC were measured by the same investigator who had previously demonstrated a test-retest reliability ICC (R = 0.95–0.99)(40)AU.
Arm Muscle Area and Estimated Propulsion Force
Arm muscle area was calculated using the following equation (14,29):
Estimated propulsive force was calculated using AMA using the following equation of Moura dos Santos et al. (29):
Mean values, SDs, and ranges were calculated for all variables. Pearson product-moment correlations were used to describe the zero-order relationships and assess multicollinearity among variables. In addition, stepwise multiple regression analyses (31) were used to determine the relationships among selected predictor variables (FLX, EXT, HT, %BF, and FFM) and EPF, as well as which variables best predicted EPF. Body mass was not included in the regression analyses because it was correlated with FFM at r = 0.99. All assumptions of linear statistics were met for the zero-order and stepwise analyses. In addition, AMA was not included in the regression analyses because it was used in the estimation of EPF and, therefore, correlated at r = 1.00. An alpha level of p ≤ 0.05 was considered significant for all relationships. SPSS version 22.0 (SPSS, Inc., Chicago, IL, USA) was used for all statistical analyses.
Table 1 includes the descriptive characteristics of the subjects and the FLX, EXT, AMA, and EPF values. The zero-order correlation matrix (Table 2) indicated that BM, HT, FFM, FLX, EXT, AMA, and EPF were significantly intercorrelated (r = 0.83–1.00). In addition, AMA and EPF, FFM, and BM, as well as FLX and EXT were correlated at r = 0.95–1.00. Percent body fat was not significantly related to any of the other predictor variables.
The stepwise regression analyses (Table 3) indicated that 4 of 5 variables contributed significantly to the prediction of EPF (standardized regression coefficients = FFM [1.00], FLX [0.92], EXT [−0.62], and HT [−0.35]). Percent body fat did not significantly contribute to any of the models.
Anthropometric and body composition variables from previous studies have been provided (Table 4) for comparison with the current sample swimmers.
In this study, the BM and HT (Table 1) of the swimmers were between the 25th and 50th percentiles of those from national samples of 12-year-old boys (26) (Table 4). The BM of the current sample of swimmers, however, was greater than those previously reported for 12-year-old swimmers (3,4,17). The HT for the swimmers was within the range of those previously reported (3,4,17). In addition, the current sample of swimmers had lower %BF but similar FFM when compared with those previously reported for 9- to 14-year-old swimmers (1,12,17,33). Furthermore, the AC and triceps SKF of the swimmers were between the 12th and 15th percentiles of those from a national sample of 12-year-old boys (26) and less than 13- to 17-year-old (29), 12-year-old (33), and 13-year-old swimmers (Table 4) (4). The AMA in this study was less than those reported for 13- to 17-year-old male swimmers (29), 13- to 14-year-old male swimmers (4), and a national sample of boys (26). Thus on average, the swimmers in this study were shorter and lighter, with smaller AC and triceps SKF values than boys from national samples. The swimmers, however, were similar in HT and FFM but heavier with less %BF and smaller AC and triceps SKF values than swimmers of similar ages from previous studies (Table 4).
Swimming speed is a function of propulsive force and the ability of the segmental actions of the swimmer's arms to oppose the drag force created by water. Faster swimmers are able to generate greater propulsive forces than slower swimmers, allowing them to more effectively overcome the hydrodynamic drag force (21,29,34,37). The development of strength and optimal positioning of the forearms are required to increase propulsion during the front crawl stroke, especially at higher speeds (34). In addition, at high-skill levels, Hohmann et al. (18) found that strength was a more important contributor to performance than was the technique (18). In this study, 4 variables (FFM, FLX, EXT, and HT) contributed significantly to the stepwise models predicting EPF, and the most potent predictors were FFM and FLX (Table 3). Previous studies (7,27,34) have also reported strong relationships among strength, body composition, and swimming performance. For example, Miyashita and Kanashisa (27), Costill et al. (7), and Girold et al. (13) reported high correlations (r = 0.64–0.98) between concentric isokinetic FLX PT and front crawl sprint swimming performance. In addition, Helmuth (17) found that FFM correlated (r = 0.73) with 100-m front crawl performance in 8- to 16-year-old male swimmers. Thus, the ability of FFM and FLX to predict EPF in this study supported previous findings (7,17,18,27,34) and indicated that lean mass and strength of the forearm flexors contributed significantly to the prediction of propulsion force, which may translate to improved performance in the front crawl stroke in young male swimmers.
Previous studies (5,13,32,34,36) have also examined the importance of EXT and HT to swimming performance. For example, Girold et al. (13) and Rushall et al. (34) found that forearm EXT PT was positively correlated (r = 0.66) with 100-m front crawl swimming performance. Furthermore, previous studies reported that there is greater reliance on the triceps brachii during the EXT phase of the front crawl in skilled vs. unskilled swimmers (5,8,23). In a sample of young (12.8 ± 0.7 years) boys, HT had a positive effect on stroke length and swimming performance (28). In addition, Jurimae et al. (20) reported that HT and arm span, which were closely related to stroke efficiency (r = 0.72–0.76), were potent predictors of performance during the front crawl (r = −0.66 and −0.68). Thus, the contributions of EXT and HT to the stepwise prediction models of EPF (Table 3) supported previous studies (5,8,20,28) and indicated that both variables were added independently to the prediction of EPF (in addition to FFM and FLX) in the current sample of young male swimmers.
Four variables (FLX, FFM, EXT, and HT) contributed significantly to stepwise prediction models for EPF (Table 3), but %BF did not (Table 3). These findings supported those of Helmuth (17) and Blanksby et al. (6) who reported that %BF was not significantly related to 100-m front crawl performance in young male swimmers. Thus, the current findings were consistent with those of previous studies (6,8,17,20,25,44) that have found that %BF was not significantly related to front crawl performance in young male swimmers.
In this study, propulsive force was not directly measured but estimated from AMA (29). Although this represents a potential limitation of this study, Moura dos Santos et al. (29) reported no mean difference between EPF (21.5 ± 5.6 kilograms of force [kgf]) and measured propulsive force (22.6 ± 8.1 kgf) in their sample of 13- to 17-year-old male swimmers. Furthermore, the mean EPF value in this study (Table 1) was similar to that of Moura dos Santos et al. (Table 4) (29).
The results of this study indicated that in the current sample of young male swimmers, FFM, FLX, EXT, and HT each contributed independently to the stepwise prediction model for EPF and, of those, FFM and FLX were the most potent predictors. Percent body fat, however, was not related to EPF. Coaches of young male swimmers should include exercises specifically designed to increase FLX and EXT strength in resistance training programs. In addition, FFM and HT typically increase during normal growth in young male athletes and may contribute to the age-related improvement in swimming performance that occurs in this age group. The current findings also suggested that it is not necessary to design programs to modify %BF in young male swimmers.
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