Body composition estimates using a 2D image analysis system across different environmental conditions: An agreement study.

PURPOSE: This study examined the agreement between %Fat measurements using a smartphone-based application (IMAGE) across different environmental conditions. METHODS: A single reference image was obtained using an 8 MP smartphone camera under Ambient Light in front of a white background. Additional photos were obtained using a 0.7 MP, 5 MP, and 12 MP smartphone cameras; low-, moderate-, and bright-lighting conditions; and various color backgrounds including black, green, orange, and gray. RESULTS: %Fat measured using the 0.7 MP camera (27.8 ± 6.2 %Fat) was higher than the reference (26.8 ± 6.1 %Fat) (p < 0.001). The black (32.0 ± 12.0 %Fat), green (27.5 ± 6.3 %Fat), and gray (27.8 ± 6.3 %Fat) backgrounds yielded higher %Fat than the white (p = 0.03, 0.01, and 0.001). All camera, lighting, and background conditions were strongly correlated with the reference (all intraclass correlation coefficient [ICC] >0.98, all standard error of the estimate [SEE] <1.5 %Fat, all p < 0.001), except the black background which yielded poorer agreement with the white background (ICC = 0.69, SEE = 4.5%, p < 0.001). CONCLUSION: %Fat from IMAGE were strongly correlated across various environmental conditions.

Agreement between ultrasound protocols for the estimation of body fat percentage: Comparison to a four-compartment model.

The purpose of this study was to examine the agreement between body fat percentage (%Fat) estimates derived from a standardized ultrasound protocol (%FatIASMS ), a commonly used skinfold (SKF)-site-based ultrasound protocol (%FatJP ), and a criterion four-compartment (4C) model (%Fat4C ). For the ultrasound protocols, all measurement sites were marked, measured and analyzed by the same evaluator. Subcutaneous adipose tissue (SAT) thickness was measured manually at the region where the muscle fascia was parallel to the skin and the average value per measurement site was used to calculate body density and subsequently %Fat. A repeated-measures analysis of variance with a priori planned contrasts was used to compare %Fat values between the 4C criterion and both ultrasound methods. Small nonsignificant mean differences were observed between %FatIASMS (18.82 ± 14.21%Fat, effect size [ES] = 0.25, p = 0.178), %FatJP (18.23 ± 13.32%Fat, ES = 0.32, p = 0.050) and the %Fat4C criterion (21.70 ± 7.57%Fat); however, %FatIASMS did not yield a smaller mean difference than the %FatJP (p = 0.287). Additionally, %FatIASMS (r = 0.90, p < 0.001, standard error of the estimate [SEE] = 3.29%) and %FatJP (r = 0.88, p < 0.001, SEE = 3.60%) were strongly correlated with the 4C criterion, however, %FatIASMS did not yield better agreement than %FatJP (p = 0.257). Despite slightly underestimating %Fat, both ultrasound techniques demonstrated Good-Very Good agreement with the 4C criterion, with comparable mean differences, correlations, and SEE. The International Association of Sciences in Medicine and Sports (IASMS) standardized protocol using manual calculations of SAT was comparable to the SKF-site-based ultrasound protocol when compared to the 4C criterion. These results indicate that the IASMS (with manually measured SAT) and SKF-site-based ultrasound protocols may be of practical use to clinicians.

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.

The purpose of this study was to compare relative adiposity (%Fat) derived from a 2-dimensional image-based 3-component (3C) model (%Fat3C-IMAGE) and dual-energy X-ray absorptiometry (DXA) (%FatDXA) against a 5-component (5C) laboratory criterion (%Fat5C). 57 participants were included (63.2% male, 84.2% White/Caucasian, 22.5±4.7 yrs., 23.9±2.8 kg/m2). 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 %Fat3C-IMAGE . %Fat3C-IMAGE and %Fat5C included measures of total body water derived from bioimpedance spectroscopy. The criterion %Fat5C included BV estimates derived from underwater weighing and bone mineral content measures via DXA. %FatDXA 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 %Fat3C-IMAGE (19.20±5.80) and %Fat5C (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 %FatDXA (22.01±6.81) and %Fat5C. Furthermore, %Fat3C-IMAGE (SEE = 2.20 %Fat, TE= 2.6) exhibited smaller SEE and TE than %FatDXA (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.

Accuracy of a Mobile 2D Imaging System for Body Volume and Subsequent Composition Estimates in a Three-Compartment Model.

PURPOSE: The purpose of the study was to compare a single two-dimensional image processing system (IMAGE) to underwater weighing (UWW) for measuring body volume (BV) and subsequently estimating body fat percentage (%Fat), fat mass (FM), and fat-free mass (FFM) via a 3-compartment (3C) model. METHODS: A sample of participants age 18-39 yr was recruited for this study (n = 67, 47.8% female). BV was measured with UWW and predicted via the IMAGE software. The BV estimates from UWW (3CUWW) and IMAGE (3CIMAGE) were separately combined with constant total body water and body mass values for 3C model calculation of %Fat, FM, and FFM. RESULTS: BV obtained from the IMAGE was 67.76 ± 12.19 and 67.72 ± 12.04 L from UWW, which was not significantly different (P = 0.578) and very largely correlated (r = 0.99, P < 0.001). When converted to %Fat (3CUWW = 21.01% ± 7.30%, 3CIMAGE = 21.08% ± 7.04%, P = 0.775), FM (3CUWW = 14.68 ± 5.15 kg, 3CIMAGE = 14.78 ± 5.08 kg, P = 0.578), and FFM (3CUWW = 57.00 ± 13.20 kg, 3CIMAGE = 56.90 ± 12.84 kg, P = 0.578) with the 3C model, no significant mean differences and very large correlations (r values ranged from 0.96 to 0.99) were observed. In addition, the standard error of estimate, total error, and 95% limits of agreement for all three metrics were small and considered acceptable. CONCLUSIONS: An IMAGE system provides valid estimates of BV that accurately estimates body composition in a 3C model.

Comparison of Body Composition Variables Across a Large Sample of National Collegiate Athletic Association Women Athletes From 6 Competitive Sports.

Fields, JB, Metoyer, CJ, Casey, JC, Esco, MR, Jagim, AR, and Jones, MT. Comparison of body composition variables across a large sample of National Collegiate Athletic Association women athletes from 6 competitive sports. J Strength Cond Res 32(9): 2452-2457, 2018-Body composition (BC) plays a critical role in sport performance and athlete health. Body size and BC have been widely studied in men's sports, with reported changes observed over time. However, a paucity of current data exists in women athletes. The purpose of this descriptive study was to measure and compare BC data for collegiate women athletes from 6 competitive sports. A total of 524 athletes from 2 National Collegiate Athletic Association institutions participated: basketball (BB; n = 95), gymnastics (GYM; n = 42), lacrosse (LAX; n = 81), rowing (ROW; n = 57), soccer (SOC; n = 188), and volleyball (VB; n = 61). Body height (BH) and body mass (BM) were measured using a stadiometer and calibrated digital scale, respectively. Body fat percentage (BF%), fat mass (FM), and fat-free mass (FFM) were assessed using air displacement plethysmography. One-way analysis of variance was used to assess differences across sports. Least squares difference post hoc analyses were performed when a significant finding (p ≤ 0.05) was identified. ROW had the highest BF% (29.9 ± 6.1%) and BB the greatest FFM (57.2 ± 6.1 kg). GYM had the lowest BM (58.9 ± 5.3 kg), FM (11.6 ± 2.6 kg), and BH (158.73 ± 2.13 cm). LAX, SOC, and VB had similar BF%. Body height was greatest for BB and VB (177.92 ± 7.55 cm, 176.79 ± 7.36 cm, respectively). These data may assist in the establishment of descriptive values for use in goal setting and exercise programming. The current data demonstrate a trend toward increased body size and BC from previous research.