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Body fat prediction equations for skinfold and bioelectrical impedance analysis using dual-energy x-ray absorptiometry data as the criterion.
Jared Grove, You-Jou Hung
Background and purpose: Body composition evaluation during health screenings and physical examination is important for health professionals (including physical therapists) to categorize health risk and prescribe appropriate exercise interventions. In addition to measuring bone mineral density, dual energy x-ray absorptiometry (DXA) can provide precise measurement of body fat percentage with minimal radiation exposure. However, having access to DXA is very costly and other common body fat measurements are no very accurate. The purpose of this study was to develop body fat prediction equations for bioelectrical impedance analysis (BIA) and skinfold measurement, using DXA data as the criterion. Methods: This was a within-group study with repeated measures. Sixty three college age students participated in the study. Subject’s body fat percentage was examined with DXA, BIA, and the 3-site skinfold measurements. Results: Body fat percentage measured with DXA (26.27% is significantly higher than those measured with skinfold (17.64% and BIA (20.70%). The DXA criterion regression equations were created for skinfold and BIA: DXA% BF=4.65+0.43 × S3SF (sum of 3 site skinfold in mm); DXA% BF=3.79+1.09 × BIA% BF. The new regression equations were further validated using 75% -25% subject crossvalidation. Discussion: Body fat percentage varies greatly among different measurements. Adjustments are necessary to accurately predict body fat percentage when using skinfold or BIA techniques at a clinical setting. The limitation of the study is that it is unclear if the results can be generalized to subjects of a different age group and ethnicities. Implication for physiotherapy practice: Obtaining an accurate body fat percentage measurement is important for health promotion and cardiovascular risk screening. Although skin-fold and BIA equipment are more readily available, their results underestimate the body fat percentage and may mislead clinicians on the wellbeing of the patient.