In high-volume sports, such as long-distance running, overuse injuries can keep an athlete out of play for long periods of time or be season ending (15,32). Incidences of lower-extremity injuries in distance runners and track athletes have been reported to range from 3.4 to 39.3% with injuries of the lower leg to be the most common and injuries of the ankle and hip/pelvis less common (29). Stress fractures (SFx) in this population are serious overuse injuries that typically occur in the foot and lower leg. Early detection of risk factors by coaches, athletic trainers, and practitioners may enable quicker treatment, return to play, and prevention of more serious musculoskeletal injuries through strength training in athletes (23). Previously identified risk factors for musculoskeletal injuries have included: previous injuries, disproportionate increases in training volume, nutritional deficiencies, and hormonal irregularities. Several of these risk factors may be difficult to identify for professionals working with athletes (3,29). Expanding on the physiological norms for high-volume athletes may help identify more common sport-specific considerations for musculoskeletal injury.
Measuring muscle characteristics with an ultrasound (US) may assist in identifying athletes at risk for injury and for tracking changes during rehabilitation. These measurements are also low cost, have no health risks, and are easy to apply in clinical assessments (24). In comparison with other muscle characteristic measurements, US devices are portable and are commonly found in clinical and training facilities. To evaluate muscle quality, muscle cross-sectional area (mCSA) and echo intensity (EI) can be measured by US imaging, where mCSA is a direct measure of muscle thickness (24). Echo intensity is measured using a brightness scale of an US image and may indicate muscle quality through a grayscale analysis estimating intramuscular fat and connective tissue (9). An increase in intramuscular fat and connective tissue and a decrease in muscle fiber size and number may increase the risk of injury and decrease functionality (6). Determining muscle size and quality may help professionals working with athletes identify an appropriate resistance training program to help improve athletic performance and injury prevention by improving muscle size and quality. Ultrasound measurements of mCSA and EI have been demonstrated as reliable and effective methods for analyzing muscle characteristics (7,21). Previous research has also shown that mCSA and EI are correlated with muscle strength and power and are a measure of muscle quality (1,6,25). To date, no research has examined these muscle characteristics in cross-country athletes.
Dual-energy x-ray absorptiometry (DEXA) is a commonly used 3-compartment model to evaluate body composition and is highly correlated with a more sophisticated 6-compartment criterion (30). Using DEXA to obtain an accurate measure of body composition may be beneficial for identifying athletes at higher risks for injury and for tracking changes of lean mass (LM) during rehabilitation. Previous studies have used DEXA to evaluate the relationship between body composition and SFx in runners (4) but have not identified strong predictors because of varied body composition results. Kelsey et al. (11) demonstrated that lower LM and bone density were associated with an increased rate of SFx in female runners. In contrast, no significant differences in bone mass or body composition were linked to increased incidence of SFxs in males (4).
Body composition measurements and quantifying muscle characteristics may be useful tools for professionals working with athletes. To date, there is little research examining the relationship of muscle characteristics and body composition in cross-country runners, and no studies to the best of our knowledge that further evaluate those with or without a history of SFxs. Therefore, the primary purpose of this study was to identify the relationships between mCSA, EI, and body composition in male and female Division I cross-country runners. The secondary purpose was to examine differences in these variables in athletes stratified based on SFx history.
Experimental Approach to the Problem
Each subject participated in 1 testing session lasting 30 minutes before training camp beginning in early August. At the time of testing, all athletes were not training with the team for the 2 months prior in the summer off-season. Subjects reported to the laboratory fasted 2 hours and did not participate in exercise for a minimum of 2 hours before testing. Upon arrival, height was measured using a stadiometer (Perspective Enterprises, Portage, MI, USA), weight was measured using a digital scale (Health-o-meter, McCook, IL, USA), and a questionnaire was given about exercise, diet status, and injuries to ensure the following of pre-assessment guidelines. Muscle characteristics were measured using a GE Logiq-e B-mode US (GE Healthcare, Wauwatosa, WI, USA) from a panoramic scan of the vastus lateralis (VL) to determine mCSA and EI. Body composition was measured with a whole-body DEXA scan to determine bone mineral content (BMC), bone mineral density (BMD), fat mass (FM), LM, segmental LM, and body fat percentage (%fat).
Twenty-one male (mean ± SD; age: 19.7 ± 1.2 years; height: 178.7 ± 5.2 cm; weight: 67.7 ± 4.9 kg) and 15 female (age: 19.3 ± 1.3 years; height: 165.3 ± 7.0 cm; weight: 53.6 ± 5.2 kg) Subjects were between the ages of 18 and 22. No subjects were under the age of 18. Division I cross-country athletes participated in this study. Of the females, there were 5 freshman, 2 sophomores, 6 juniors, and 2 seniors. For the males, there were 3 freshman, 5 sophomores, 7 juniors, and 6 seniors. Before testing, all subjects signed an informed consent approved by the University's Biomedical Institutional Review Board for the protection of human subjects. Subjects were stratified first by sex and second by SFx history; previous SFxs were diagnosed by a physician using an x-ray or magnetic resonance imaging (MRI) and were then reported by their athletic trainer (SFx; female n = 9; male n = 4) and no previous SFx (female n = 6; male n = 17). Stress-fracture sites included the foot, tibia, femur, and sacrum. Descriptive statistics on subject groups are presented in Table 1. Personal best race times were recorded for the season after testing. For the men, 5-km times averaged 15:22.34 ± 27.14 seconds and 10-km times averaged 31:32.17 ± 82.96 seconds, whereas for the women, 5- and 6-km times averaged 18:13.14 ± 37.84 and 21:58.58 ± 58.16 seconds, respectively.
Muscle cross-sectional area of the VL was determined using a GE Logiq-e B-mode ultrasound (GE Healthcare) from a panoramic scan of the VL. The US settings (frequency: 26 Hz, gain: 68, depth: 4.5 cm) were kept consistent for each scan. Before the scan, the subjects laid supine for 5 minutes. During the measurement, the right leg was extended and relaxed on the examination table with a foam pad strapped to the midpoint of the thigh to standardize measurements. The US probe (GE: 12L-RS) was held perpendicular to the tissue and swept across the skin at equal pressure from the lateral VL border to the medial fascia separation. The same technician performed each scan. Echo intensity was determined from the panoramic scan using a grayscale imaging software (version 1.37; Image J, National Institutes of Health, Bethesda, MD, USA) in the standard histogram function of pixels ranging from 0 to 255 (Figure 1). Before measurement of EI, each image was calibrated by measuring the number pixels within a known distance of 1 cm. To measure EI, the same technician traced the outline of each subjects' VL along the fascia border to only capture the muscle as seen in Figure 1. Muscle characteristics test-retest reliability in our laboratory for EI were ICC = 0.74 and SEM = 4.58 and for mCSA were ICC = 0.87 and SEM = 2.12.
Dual-Energy X-ray Absorptiometry
Each subject had a full-body DEXA scan (Apex Software version 3.3; Hologic Discovery W, Bedford, MA, USA) performed by the same trained DEXA technician. Before testing, subjects were asked to remove all metal, thick clothing, and heavy plastic to reduce interference with the scan. Age, height, weight, and ethnicity were entered into the computer, and subjects were placed supine in the center of the scanning table. Bone mineral content, BMD, FM, LM, leg lean mass (leg LM), and body fat percentage (%fat) were determined using the DEXA. According to the World Health Organization (2), low BMD was identified by Z scores of ≥1 SD below healthy individuals of the same age. Dual-energy x-ray absorptiometry test-retest reliability in our laboratory was ICC = 0.98 and SEM = 0.85 for FM, ICC = 0.99 and SEM = 1.07 for LM, and ICC = 0.98 and SEM = 1.06 for %fat.
A Pearson product correlation was used to determine relationships between BMC, BMD, FM, LM, leg LM, %fat, mCSA, EI, and performance times. Separate 1-way analyses of variance were used to analyze variables between previous SFx and no previous SFx for these variables in all subjects. All analyses were run using SPSS (version 20; IBM, Armonk, NY, USA) with an alpha level of p ≤ 0.05.
Body Composition and Muscle Characteristics Relationships
For the male cross-country athletes, weight was significantly correlated with leg LM (R = 0.89, p ≤ 0.05), right leg LM (0.87, p ≤ 0.05), left leg LM (R = 0.09, p ≤ 0.05), mCSA (R = 0.55, p = 0.01), BMD (R = 0.53, p = 0.01), and BMC (R = 0.77, p < 0.01). Muscle CSA was significantly correlated with leg LM (R = 0.66, p = 0.001), right leg LM (R = 0.65, p = 0.001), left leg LM (R = 0.65, p = 0.002), BMD (R = 0.50, p = 0.02), and BMC (R = 0.54, p = 0.01). Leg LM was significantly correlated with BMD (R = 0.53, p = 0.01) and BMC (R = 0.77, p < 0.01; Table 2). Higher personal best racing times for males in the 10-km race were significantly positively correlated with FM (R = 0.489, p = 0.042) and 5-km race times were also significantly correlated with %fat (R = 0.556, p = 0.02).
For female athletes, weight was significantly positively correlated with FM (R = 0.76, p = 0.001), BMC (R = 0.80, p ≤ 0.05), leg LM (R = 0.75, p = 0.001), right leg LM (R = 0.71, p = 0.003), and left leg LM (R = 0.77, p = 0.001). Muscle CSA was significantly positively correlated with left leg LM (R = 0.54, p = 0.03) and negatively correlated with EI (R = −0.57, p = 0.02). Lean mass was significantly correlated with BMD (R = 0.58, p = 0.23) and BMC (R = 0.56, p = 0.32), whereas BMC was also correlated with leg LM (R = 0.72, p = 0.002), right leg LM (R = 0.68, p = 0.006), and left leg LM (R = 0.74, p = 0.001; Table 3). Racing times for females were not significantly correlated with any variables.
Male athletes with a history of SFx were not significantly different compared with athletes with no history in any variables (Table 3). In males with no SFx history, mCSA was significantly correlated with weight (R = 0.57, p = 0.18), leg LM (R = 0.68, p = 0.003), right leg LM (R = 0.69, p = 0.002), and left leg LM (0.654, p = 0.004). Weight was significantly correlated with leg LM (R = 0.88, p ≤ 0.05), right let LM (R = 0.87, p ≤ 0.05), and left leg LM (R = 0.87, p ≤ 0.05) in males without SFx but was not significantly correlated in males with a history of SFx. In males with SFx history, mCSA was significantly correlated with leg LM (R = 0.98, p = 0.02), right leg LM (R = 0.98, p = 0.019), and left leg LM (0.97, p = 0.03).
Female athletes with a history of SFx were not significantly different compared with athletes with no history in any variables (Table 4). In females with no SFx history, mCSA was significantly correlated with LM (R = 0.92, p = 0.01) but was not significantly correlated in females with SFx. When comparing personal best racing times females with no SFx, 6-km times were positively correlated with EI (R = 0.978, p = 0.022). Females with previous SFx, 6-km times were negatively correlated with mCSA (R = −0.783, p = 0.037), and 5-km times were significantly correlated with %fat (R = 0.807, p = 0.28).
High-volume repetitive-impact athletes have reported high rates of damage to the anatomical structures, resulting in musculoskeletal and bone injuries (11,32). In this study of Division I cross-country athletes, there was a strong relationship between LM, muscle size, and bone integrity (BMC and BMD). For men, mCSA was moderately correlated with body mass, leg LM, right and left leg LM, BMD, and BMC. Performance times for men demonstrated slower times correlated with a higher FM and %fat. For women, LM was moderately correlated with BMD and BMC, whereas mCSA had a moderate relationship with left leg LM and was inversely correlated with EI. Performance times for women had no significant correlations with muscle characteristics and body composition; however, there were significances when stratified by SFx history. Overall, this study demonstrates a potential support for improving muscle size and quality for improving athletic performance and injury prevention (22). As a result, measuring muscle characteristics, in addition to body composition, may be an important tool to use for identifying injury risk in high-volume athletes.
Use of US to evaluate muscle characteristics has recently emerged as a valid and useful tool for characterizing athletes (19). Previous data suggest that muscle characteristics, measured by the US, may be valuable in determining physiological differences among strength and size of the muscle (20). A higher EI and a smaller muscle size have previously been associated with greater intramuscular fat and connective tissue (25). Previous research supports the trend reported in this study, demonstrating a negative relationship between EI and muscle thickness in females (R = −0.57), which may suggest both muscle size and quality both factor into muscle strength (9). Although not significant, mCSA and EI also had a negative relationship in males (R = −0.25, p = 0.27). Data support a benefit from increasing muscle mass, specifically in the lower body, because of the ability to absorb forces transmitted to bone during running (31). In a study examining male endurance runners and sprinters, muscle strength was significantly correlated with lean body mass and mCSA (17). Similarly, in this study, mCSA was significantly correlated with leg LM in males (R = 0.66) but only significantly with left leg LM in females (R = 0.54). Left leg LM may have had a stronger association because of consistent track work with the inside leg (left leg) taking more stress on corner turns of the track.
Quantifying body composition of athletes has been shown to be a beneficial determinant for health and performance (16). In this study, slower male 5- and 10-km race times were significantly correlated with higher %fat and FM values. Bone mineral content was significantly correlated with LM and leg LM in females and leg LM in males. Previous research in female distance runners also demonstrated a high correlation between BMC and lean body mass (11). Low BMC and BMD are risk factors for lower-extremity overuse injuries in athletes, as well as the ratio of FM to BMC (26). To help increase BMD, BMC, LM, and lower FM, a plyometrics and resistance training program should be included (12). High-impact exercise has been shown to increase bone formation, whereas running on a treadmill was unable to augment bone growth (10). Fat mass alone in this study was only strongly correlated with weight in females (R = 0.76), whereas body fat percentage had no significant associations for females or males. Agreeably, Kelsey et al. (11) also demonstrated little association between body fat percentage and other body composition variables in female runners. Body composition has been shown to be a potential predictor for injury risk in other types of athletes (19), with a few investigations in runners (4). Previous data suggest that lower body weight may be a significant risk factor for injury in male track and field athletes (23), whereas in females lower total LM was significantly associated with risk of injury (11). In this study, LM in the legs had the strongest association with bone integrity. Additionally, looking at males and weekly mileage, runners at 60–75 miles per week were significantly lower in weight compared with lower mileage runners, while still having similar BMD (14). In agreement, weight in this study was strongly correlated with BMC and BMD for both males and females.
The secondary purpose of this study was to evaluate differences in measureable physiological variables between athletes with and without stress-fracture history. Stress fractures are a common injury in high-volume athletes, specifically in distance runners (18). A variety of factors have been established for identifying high-risk individuals for SFxs, such as low BMD, nutritional deficiencies, menstrual irregularity in females, and previous SFxs (11). However, several of these risk factors are not easily identifiable by a practitioner working with large numbers of collegiate athletes. In this study, males who had previous SFx demonstrated lower weight (mean difference [MD] = −2.7 kg), FM (MD = −0.6 kg), EI (MD = −2.3 a.u.), and mCSA (MD = −1.4 cm2) values compared with males with no history of a SFx. Agreeably, lower-body weight was reported as a significant risk factor for SFxs in male track and field athletes (23). In contrast, other previously reported risk factors were not significantly different between the 2 groups in this study, including body mass index, BMD, leg LM, and %fat. Bennell et al. (4) also demonstrated that differences in bone mass or body composition were not linked to increased incidence of SFxs (4). In females with previous SFx in this study, FM and %fat were higher (MD = 0.8 kg; MD = 0.6 kg) and mCSA was lower (MD = −1.1 cm2) compared with no SFx history. In contrast, previous data suggest a lower %fat in athletes, as well as a higher fat to BMC ratio are reported risk factors for SFx (5,23). In previous research, less LM in the lower limb was a significant factor for increased occurrence of SFxs in females (4). Conversely, both males and females with previous SFx reported no significant difference in LM values when compared with the no SFx history group. However, US values of mCSA detected a potential muscle size difference between groups, with previous SFx group yielding a smaller mCSA. Additionally, slower running times were associated with a smaller mCSA and higher %fat in females with previous SFx, and a lower muscle quality in females with no SFx history.
According to Magness et al. (15), there are 2 major types of SFx: fatigue fractures caused primarily from overstress, and insufficiency fractures caused primarily by low BMD. In this study, there were no significant differences in BMD for males or females. Females with SFx history tended to have lower BMD and Z scores (Table 4). A full-body DEXA scan was used to determine whole-body BMD in this study, whereas many studies have used regional BMD such as foot, hip, or spine. Kelsey et al. (11) measured BMD at the proximal femur, spine, and whole body, with all sites being highly correlated with risk of SFxs. These results suggest that site-specific measurements could be beneficial to runners, but whole-body BMD may also have utility for identifying SFx risk and LM, within the same scan. Additionally, BMD and BMC values are often found to be normal in comparison with age-matched device norms (8,16,23,27). Including a year-round resistance training program in addition to a running program may be beneficial for increasing BMD. Previous data suggest that an 8-week resistance training program combined with aerobic training resulted in increases BMD of the distal tibia (13). Using additional physiological variables such as body composition and muscle size may be helpful to characterize these athletes according to other sport-specific norms.
The results from this study expand on previous literature identifying relationships between muscle characteristics and body composition in runners. In this study of Division I cross-country athletes, there was a strong relationship between LM, muscle size, BMC, and BMD. For men, mCSA was correlated with body mass, leg LM, right and left leg LM, BMD, and BMC. For women, mCSA had a moderate relationship with left leg LM and was inversely correlated with EI. Although once stratified for sex and SFx history, there were no significant differences, there were trends for males with previous SFx to have lower weight and mCSA, and females with previous SFx tended to have lower mCSA and higher FM and %fat. These relationships demonstrate a potential support for improving muscle size and quality for improving athletic performance and injury prevention for the reason that increases in intramuscular fat and connective tissue, and decreases in muscle fiber size and number may increase risk of injury and decrease functionality (6). As a result, measuring muscle characteristics with an US, in addition to body composition, may be an important tool for practitioners working with large number of collegiate athletes for identifying injury risk in high-volume athletes.
Results from this study indicate improving muscle size and quality may help prevent injury prevention and improve athletic performance. Muscle characteristics, measured by the US, may be valuable in determining physiological differences among strength and size of muscle (20). Ultrasound measurements have no health hazards, are portable, and are applicable to athletic trainers, strength coaches, and practitioners working with athletes. Most facilities already have the device in house, so these measurements are low cost, quick to perform and analyze, and may be helpful to identify athletes at risk for musculoskeletal injury. Whole-body DEXA is also valuable because of the ability to identify segmental differences in body composition. Combining US measurements with body composition results may be beneficial for coaches, athletic trainers, and practitioners to determine specific athlete training programs. In athletics, improving muscle quality and quantity can improve athletic performance and help prevent injury through a year-round, sport-specific resistance training program (22). Modifications in training strategies to include resistance training or plyometric training during the off-season and in-season, as well as ensuring appropriate nutritional and recovery strategies, may be advantageous for influencing risk factors associated with overuse injury occurrence. Future research evaluating the effects of a resistance training intervention, specifically targeting the lower body, coupled with athlete nutrition profiles would be beneficial to identify the potential reduction in these risk factors for distance runners.
The authors acknowledge Rachel Stratton, RC, CSSD, for her involvement in the study. The project described was supported by the National Center for Advancing Translational Science, National Institutes of Health, through Grant 1KL2TR001109. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The results of this study do no constitute endorsement by the authors or the NSCA.
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