Browsing by Author "Nickerson, Brett S."

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    Agreement Between A 2-Dimensional Digital Image-Based 3-Compartment Body Composition Model and Dual Energy X-Ray Absorptiometry for The Estimation of Relative Adiposity
    (Elsevier, 2022) Sullivan, Katherine; Metoyer, Casey J.; Hornikel, Bjoern; Holmes, Clifton J.; Nickerson, Brett S.; Esco, Michael R.; Fedewa, Michael, V; University of Alabama Tuscaloosa; Washington University (WUSTL); Texas A&M International University
    The purpose of this study was to compare relative adiposity (%Fat) derived from a 2-dimensional image-based 3-component (3C) model (%Fat(3C-IMAGE)) and dual-energy X-ray absorptiometry (DXA) (%Fat(DXA)) against a 5-component (5C) laboratory criterion (%Fat(5C)). 57 participants were included (63.2% male, 84.2% White/Caucasian, 22.5 +/- 4.7 yrs., 23.9 +/- 2.8 kg/m(2)). For each participant, body mass and standing height were measured to the nearest 0.1 kg and 0.1 cm, respectively. A digital image of each participant was taken using a 9.7 inch, 16g iPad Air 2 and analyzed using a commercially available application (version 1.1.2, made Health and Fitness, USA) for the estimation of body volume (BV) and inclusion in %Fat(3C-IMAGE). %Fat(3C-IMAGE) and %Fat(5C) included measures of total body water derived from bioimpedance spectroscopy. The criterion %Fat(5C) included BV estimates derived from underwater weighing and bone mineral content measures via DXA. %Fat(DXA) estimates were calculated from a whole-body DXA scan. A standardized mean effect size (ES) assessed the magnitude of differences between models with values of 0.2, 0.5, and 0.8 for small, moderate, and large differences, respectively. Data are presented as mean +/- standard deviation. A strong correlation (r = 0.94, p <.001) and small mean difference (ES = 0.24, p <.001) was observed between %Fat(3C-IMAGE) (19.20 +/- 5.80) and %Fat(5C) (17.69 +/- 6.20) whereas a strong correlation (r = 0.87, p <.001) and moderate-large mean difference (ES = 0.70, p <.001) was observed between %Fat DXA (22.01 +/- 6.81) and %Fat(5C). Furthermore, %Fat(3C-IMAGE )(SEE = 2.20 %Fat, TE= 2.6) exhibited smaller SEE and TE than %Fat(DXA) (SEE = 3.14 %Fat, TE = 5.5). The 3C image-based model performed slightly better in our sample of young adults than the DXA 3C model. Thus, the 2D image analysis program provides an accurate and non-invasive estimate of %Fat within a 3C model in young adults. Compared to DXA, the 3C image-based model allows for a more cost-effective and portable method of body composition assessment, potentially increasing accessibility to multi-component methods.
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    Generalized Equations for Predicting Percent Body Fat from Anthropometric Measures Using a Criterion Five-Compartment Model
    (Lippincott Williams & Wilkins, 2021) Cicone, Zackary S.; Nickerson, Brett S.; Choi, Youn-Jeng; Holmes, Clifton J.; Hornikel, Bjoern; Fedewa, Michael, V; Esco, Michael R.; Shenandoah University; University of Alabama Tuscaloosa; Texas A&M International University; Ewha Womans University; Washington University (WUSTL)
    Introduction: Anthropometric-based equations are used to estimate percent body fat (%BF) when laboratory methods are impractical or not available. However, because these equations are often derived from two-compartment models, they are prone to error because of the assumptions regarding fat-freemass composition. The purpose of this study was to develop a new anthropometric-based equation for the prediction of%BF, using a five-compartment (5C) model as the criterion measure. Methods: A sample of healthy adults (52.2% female; age, 18 to 69 yr; body mass index, 15.7 to 49.5 kg.m(-2)) completed hydrostatic weighing, dual-energy x-ray absorptiometry, and bioimpedance spectroscopy measurements for calculation of 5C%BF (%BF5C), as well as skinfolds and circumferences.%BF5C was regressed on anthropometric measures using hierarchical variable selection in a random sample of subjects (n = 279). The resulting equation was cross-validated in the remaining participants (n = 78). New model performance was also comparedwith several common anthropometric-based equations. Results: The new equation [%BFNew = 6.083 + (0.143 x SSnew) - (12.058 x sex) - (0.150 x age) - (0.233 x body mass index) + (0.256 x waist) + (0.162 x sex x age)] explained a significant proportion of variance in %BF5C (R-2 = 0.775, SEE = 4.0%). Predictors included sum of skinfolds (SSnew, midaxillary, triceps, and thigh) and waist circumference. The new equation cross-validated well against %BF5C when compared with other existing equations, producing a large intraclass correlation coefficient (0.90), small mean bias and limits of agreement (0.4% +/- 8.6%), and small measures of error (SEE = 2.5%). Conclusions: %BFNew improved on previous anthropometric-based equations, providing better overall agreement and less error in %BF estimation. The equation described in this study may provide an accurate estimate of %BF5C in healthy adults when measurement is not practical.