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PURPOSE: Longitudinal assessment of football player body composition would identify developmental and structural changes in respect to position demands, however no study has examined changes by season and age. The purpose of this study was to examine longitudinal body composition changes by position, categorized by season and age, using dual X-ray absorptiometry (DXA) in NCAA Division I football players. METHODS: Seven hundred and forty-two collegiate male football athletes aged 17-24 years (X̅age= 19.9±1.3 yrs) participated in this study. Following height and body mass measurement, each athlete completed a DXA scan to assess percent body fat (%BF), fat mass (FM), lean soft tissue mass (LM), bone mineral density (BMD), and visceral adipose tissue mass (VAT) using a GE Lunar iDXA (General Electric Medical Systems, Madison, Wisconsin, USA). DXA scans were analyzed by the same technician using enCoreTM software (platform version 16.2, General Electric Medical Systems, Madison, WI, USA), and body mass distribution ratios of total upper mass to lean leg mass ratio (TULLR), lean upper mass to lean leg mass ratio (LULLR), upper total mass to legs total mass ratio (ULR), and gynoid lean mass to leg lean mass ratio (GLR) were calculated. Athletes were categorized into Linemen (offensive and defensive linemen), Big Skill (quarterbacks, linebackers, and tight ends), Skill (running backs, defensive backs, and wide receivers), or Special Teams (punters, kickers, and long snappers). One scan per athlete was used in Pre-Season (June-September), In-Season (October-November), Post-Season (December-February), and Spring Season (March-May). Separate repeated one-way analysis of variance (ANOVA) with linear mixed-effects models assessed total and regional body composition differences across age, position groups, and seasons. TukeyHSD post hoc tests were used to determine significant differences among position groups, while adjusting for multiple comparisons (p

ABSTRACT Objective: We sought to determine if an acute bout of running accounts for measurable changes in total and regional body fat using Dual X-ray Absorptiometry (DXA). There is limited research about the change in body fat after an acute bout of physical activity. Knowing how body composition changes as a result of exercise is important for populations with symptoms of metabolic syndrome, such as overweight/obesity, increased waist circumference, high triglycerides, low levels of high density lipoprotein (HDL), increased total cholesterol, increased systolic and diastolic blood pressure, increased blood glucose and physical inactivity that can eventually lead to conditions like heart disease and diabetes (1). As the amount of visceral fat a subject has increases, so does their risk for metabolic syndrome. In addition, knowledge of body composition changes is useful in athletic populations for injury prevention and performance in athletes (2). Methods: Subjects that were between the age of 18 – 40, that reported a history of regular running exercise (approximately 45 minutes, 5 times a week), were otherwise healthy and could commit to running 90 minutes on a treadmill were recruited. All subjects completed a 90-minute run on a treadmill at 60% heart rate reserve (HRR). Body composition was measured before and immediately after the run to determine if DXA was sensitive enough to measure potential changes in body fat following acute exercise. For the present study, total and regional body fat was compared to show if particular regions had a greater change in fat during endurance exercise. The difference in pre-run to post run total and regional body fat was compared using paired t tests. Comparisons between normal weight, overweight/obese, and overall groups were made using nonpaired t tests. Statistical significance was accepted using P-value > 0.05 Changes to total and regional body fat were also compared to the least significant change (LSC) for DXA found in the literature for athletic populations. Results: A total of 16 lean (female = 7; male = 9; age = 28.1 ± 5.6 yrs; BMI = 22.0 ± 1.6 kg/m2) and 11 overweight or obese (female = 7; male = 4; age = 32.0 ± 5.2 yrs; BMI = 30.5 ± 4.8 kg/m2) were recruited and completed both study visits. Weight, VO2max, Body Mass Index (BMI), and baseline percent body fat were significantly different between the normal and overweight/obese groups. Height and age were not significantly different between the groups. No significant differences were found in the absolute change in normal and overweight/obese group. Absolute fat mass decreased slightly overall (-184.5 ± 443.6 g). There was no significant change in BF from arms or legs within the overall group. A significant loss of fat came from the trunk (-195.1 ± 488.3 g). Android/gynoid ratio change shows significant decrease (-0.02 ± 0.04), in the overall group. Although significant absolute fat loss from the trunk and the android/gynoid ratio, the difference in fat for the regions was not more than LSC using test-retest technique found in the literature. Conclusion: Significant differences in fat mass were observed from the trunk and the android/gynoid ratio regions after a 90-minute run at 60% HRR. However, when comparing these changes to the published research on least significant difference of DXA, the change in fat mass observed in this study is less than least significant difference of DXA. This means that the changes in fat measured by DXA in this study may not be accurate.

Background: Trabecular bone score (TBS), which has been proposed to be used in complementary with bone mineral density (BMD) to improve the assessment of fracture risk, is negatively associated with body mass index (BMI). The effect of soft tissue, which is expected to be thicker in subjects with high BMI, on TBS was studied using three scan types: Hologic with fast array mode (Hfa), Hologic with high definition mode (Hhd), and GE-Lunar iDXA. Methods: A spine phantom provided by Hologic for routine quality control procedure was scanned using three scan types: Hfa, Hhd, and iDXA. The phantom was scanned with an overlying soft tissue equivalent material (bolus used in radiotherapy) of 0 (without), 1, 2.5, 3.5, 5 and 7.5 cm thick. For each setting, 30 acquisitions were performed in the same way as for the quality control procedure. TBS was calculated using TBS iNsight® software version 2.1 on the same regions of interest as those used for lumbar spine BMD. Results: Mean ± SD TBS of the phantom (without overlying soft tissue) were 1.379 ± 0.018, 1.430 ± 0.009, and 1.423 ± 0.005 using Hfa, Hhd, and iDXA, respectively. A one-way repeated measures ANOVA showed that there were statistically differences in TBS due to different thicknesses of soft tissue equivalent materials for all three scan types (p < 0.001). A Tukey post-hoc test revealed that the decrease in TBS was statistically significant (p < 0.001) when the soft tissue thickness was 1 cm (−0.0246 ± 0.0044, −0.0319 ± 0.0036, and −0.0552 ± 0.0015 for Hfa, Hhd, and iDXA, respectively). Although to a lesser degree, the effects were also statistically significant for BMD (p < 0.05): an increase for Hfa and Hhd but a decrease for iDXA. However, these changes did not exceed the least significant change (LSC) derived from patients. Conclusions: Increased soft tissue thickness results in lower TBS value. Although BMD is also affected, it is unlikely to pose a clinical problem because the change is unlikely to exceed the patient-derived LSC.