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TRACKING BODY COMPOSITION CHANGES IN COLLEGIATE BASKETBALL PLAYERS: COMPARISON OF METHODS

Abstract

Amanda N. Gordon, Hannah E. Cabre, Taylor E.A Morrison, Sam R. Moore, Maggie E. Hostetter, Noah D. Patterson, Abbie E. Smith-Ryan, FACSM. University of North Carolina at Chapel Hill, Chapel Hill, NC.

BACKGROUND: Evaluating the sensitivity of various technologies to detect body composition changes over time is helpful when assessing a strength training program’s efficacy. The purpose of this study was to compare estimates of tracking lean mass (LM) and fat mass (FM) using dual-energy x-ray absorptiometry (DXA), three-dimensional body scan (3D), and multi-frequency bioelectrical impedance analysis (MF-BIA) in Division I collegiate male basketball athletes. METHODS: Body composition was measured in male basketball players (n=14; Mean ± standard deviation [SD], Age: 19.9±1.2 years, Height: 193.9±7.7 cm, Weight: 91.9±9.7 kg) before (May 2021) and after (July 2021) an 8-week training block. FM and LM were measured in the morning, following a minimum of a 2hr fast, via DXA (criterion method), 3D, and MF-BIA. Total error (TE) and standard error of estimate (SEE) were used to compare the accuracy of 3D and MF-BIA to DXA. Results were classified using Heyward and Wagner standards for evaluating prediction errors. Paired samples t-tests were used to evaluate the change in FM and LM between devices. RESULTS: 3D in comparison to DXA resulted in poor TE and fairly good SEE for LM (TE: 5.1 kg; SEE: 3.7 kg) and poor TE and very good SEE for FM (TE: 6.4 kg; SEE: 2.8). MF-BIA in comparison to DXA resulted in poor TE and ideal SEE for LM (TE: 9.1 kg; SEE: 2.2 kg) and fairly good TE and ideal SEE for FM (TE: 4.2 kg; SEE: 1.8 kg). LM changes between 3D and DXA were significantly different (Mean Difference ± Standard Error, 1.0±2.6 kg, p=0.02). No significant differences were seen in LM changes between MF-BIA and DXA (-2.8±3.0kg, p= 0.21) or between FM changes in 3D and DXA (-0.2±1.0 kg, p=0.42). FM changes between MF-BIA and DXA were significantly different (1.1±1.5 kg, p=0.01). CONCLUSIONS: The use of 3D to detect and track LM and FM changes may not be appropriate for this population due to poor TE. Depending on frequency of measurement, accessibility and time constraints, MF-BIA may be more practical for tracking changes in LM. If feasible, DXA may be the most appropriate device for tracking LM and FM changes in this population due to better TE and SEE.

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