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Abstract

While numerous methods for assessing body fat percentage have been validated, it is important to understand that using results from one modality does not guarantee the same on another. It is also important, when weighing the pros and cons of each modality, that the reliability of each approach should also be taken into consideration. PURPOSE: This study compared body fat percentages (PBF) derived from dual-energy X-ray absorptiometry (DXA), bioelectrical impedance (BIA), and air displacement plethysmography (ADP). Reliability was also determined for each modality. METHODS: 47 adults, aged 18+, were assessed for total body composition with a Lunar iDXA (GE Healthcare, Madison WI), InBody 770 (InBody, Seoul, South Korea), and BodPod GS-X (COSMED, Rome, Italy). Participants were instructed to arrive hydrated, with manufacturer suggested clothing, for testing after a minimum 4-hour fast and abstain from exercise for the previous 12 hours. Hydration was assessed before testing using a urine refractometer with an acceptable range between 1.004-1.029. Tests were repeated on a separate day, within a two-week period. Additionally, a four-compartment measure of PBF (4C) was calculated using bone mineral content from DXA, total body water from BIA, and ADP. One-way ANOVA compared an average of the two testing days for the four measures of PBF with a Bonferroni correction used breakdown of significant main effects. Reliability was determined using coefficient of variation percent (CV) and root mean square standard deviation at a 95% confidence interval (RMS). All statistical analyses were conducted using SPSS v28 (IBM Corporation, New York, USA) and alpha was set at 0.05. RESULTS: One-way ANOVA showed significant differences between the four measures of PBF F(1,46)= 417.2, pCONCLUSION: As mentioned previously, values derived from the different methods can yield different results and it is important to choose a single method when tracking individual or group changes across time. Additionally, choosing a method with lower variation can better ensure that any changes seen in PBF are due to interventions, not machine or tester error.

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