Assessing Body Composition via a Smartphone Computer Vision Application: Reliable but Biased Compared to BODPOD and InBody

Accurate assessment of body composition is essential for monitoring health status, fitness progress, and disease risk. Traditional methods such as air displacement plethysmography (BODPOD) and bioelectrical impedance analysis (BIA) are widely used to estimate body fat percentage (BF%), fat mass (FM), and fat-free mass (FFM), but can be costly or inaccessible. Smartphone applications utilizing computer vision (CV) offer a promising alternative. This study evaluated the agreement and test-retest reliability of a smartphone-based CV application (CV _app ) compared to BODPOD and the InBody BIA device. Forty-nine adults (ages 18–70; 29 females, 27 racial and ethnic minority participants) completed two consecutive measurements using BODPOD, InBody, and CV _app in a single session. Differences in BF%, FM, and FFM estimates were analyzed using repeated-measures ANOVA. Agreement metrics included mean absolute error (MAE), root mean square error (RMSE), concordance correlation coefficients (CCC), and Bland-Altman analysis. Subgroup analyses examined differences by sex and minority status. The CV _app yielded significantly higher BF% (mean=+2.2%, p =0.004) and FM (+1.5 kg, p =0.015) compared to BODPOD, and lower FFM than InBody (mean=−1.84 kg, p =0.043). The CV _app agreement with BODPOD (MAE=4.0, RMSE=5.0, CCC=0.86) was lower than with the InBody (MAE=3.3, RMSE=4.3, CCC=0.89), with wider limits of agreement. All methods showed excellent test-retest reliability (ICC>0.99). A significant Sex×Method interaction was observed for BF% ( p <0.001), FM ( p <0.001), and FFM ( p =0.013), with females showing greater overestimation of BF% (mean=+3.9%) and FM (mean=+2.7kg, p <0.001), and underestimation of FFM (mean=−2.5kg, p <0.001) by the CV _app . Among minority participants, BF% and FM estimates from the CV _app were significantly higher than BODPOD (mean=+2.7%, p =0.009; mean=+1.8kg, p =0.031), and FFM was lower (mean=−1.8kg, p =0.035), with significant Method×Minority Group interactions for FM and FFM ( p <0.03). While the CV _app was reliable, its estimates differed from other common body composition methods and revealed population-specific biases, highlighting the need for more accurate and equitable algorithms.