Personal assessments of body phenotype can enhance success in weight management but are limited by the lack of availability of practical methods. We describe a novel smart phone application of digital photography (DP) and determine its validity to estimate fat mass (FM). This approach utilizes the percent (%) occupancy of an individual lateral whole-body digital image and regions indicative of adipose accumulation associated with increased risk of cardio-metabolic disease. We measured 117 healthy adults (63 females and 54 males aged 19 to 65 years) with DP and dual X-ray absorptiometry (DXA) and report here the development and validation of this application. Inter-observer variability of the determination of % occupancy was 0.02%. Predicted and reference FM values were significantly related in females (R2 = 0.949, SEE = 2.83) and males (R2 = 0.907, SEE = 2.71). Differences between predicted and measured FM values were small (0.02 kg, p = 0.96 and 0.07 kg, p = 0.96) for females and males, respectively. No significant bias was found; limits of agreement ranged from 5.6 to −5.4 kg for females and from 5.6 to −5.7 kg for males. These promising results indicate that DP is a practical and valid method for personal body composition assessments.
Background: Obesity is chronic health problem. Screening for the obesity phenotype is limited by the availability of practical methods. Methods: We determined the reproducibility and accuracy of an automated machine-learning method using smartphone camera-enabled capture and analysis of single, two-dimensional (2D) standing lateral digital images to estimate fat mass (FM) compared to dual X-ray absorptiometry (DXA) in females and males. We also report the first model to predict abdominal FM using 2D digital images. Results: Gender-specific 2D estimates of FM were significantly correlated (p < 0.001) with DXA FM values and not different (p > 0.05). Reproducibility of FM estimates was very high (R2 = 0.99) with high concordance (R2 = 0.99) and low absolute pure error (0.114 to 0.116 kg) and percent error (1.3 and 3%). Bland–Altman plots revealed no proportional bias with limits of agreement of 4.9 to −4.3 kg and 3.9 to −4.9 kg for females and males, respectively. A novel 2D model to estimate abdominal (lumbar 2–5) FM produced high correlations (R2 = 0.99) and concordance (R2 = 0.99) compared to DXA abdominal FM values. Conclusions: A smartphone camera trained with machine learning and automated processing of 2D lateral standing digital images is an objective and valid method to estimate FM and, with proof of concept, to determine abdominal FM. It can facilitate practical identification of the obesity phenotype in adults.
Limitations of body mass index (BMI) as a measure of body fat and the need for practical methods to estimate body fat reinforce interest in smartphone two-dimensional digital imaging and bioe-lectrical impedance analysis (BIA). Compared to dual x-ray absorptiometry (DXA), we determined differences in body fat mass (FM) estimated with smartphone single lateral standing digital image (SLSDI) and bioimpedance analysis (BIA) in 188 healthy adults (69 females and 119 males). SLSDI FM estimates were similar to DXA values but BIA underestimated (p<0.0001) FM. We tested the hypothesis that fluid imbalance, expansion of the extracellular water (ECW), designated as ECW to intracellular water ratio (ECW/ICW), affects the BIA-dependent differences. With BMI>25 kg/m2, 54 male rugby players, compared to 40 male non-rugby players, had greater (p<0.001) BMI and fat-free mass but less (p<0.001) FM and ECW/ICW. SLSDI and DXA FM estimates were not different in both groups; BIA underestimated (p<0.001) FM in the non-rugby men. This finding is consistent with expansion of ECW in individuals with excess body fat due to increased adipose tissue mass and its water content. Unlike SLSDI, BIA predictions of FM are affected by altered fluid distribution associated with increased adipose tissue. These findings establish the validity, practicality, and convenience of smartphone SLSDI to estimate FM for healthcare providers in clinical and field settings.
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