Assessing body composition via a smartphone computer vision application: High repeatability but method-dependent...

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 relative agreement and same session repeatability under standardized conditions of a smartphone-based CV application (CVapp) 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 CVapp 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 CVapp yielded 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 CVapp agreement with BODPOD (MAE = 4.0, RMSE = 5.0, CCC = 0.86) was weaker than with the InBody (MAE = 3.3, RMSE = 4.3, CCC = 0.89), with wider limits of agreement. All methods showed excellent within session repeatability under standardized conditions (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=+2.7 kg, p < 0.001), and underestimation of FFM (mean=-2.5 kg, p < 0.001) by the CVapp. Among Minority participants, BF% and FM estimates from the CVapp were significantly higher than BODPOD (mean=+2.7%, p = 0.009; mean=+1.8 kg, p = 0.031), and FFM was lower (mean=-1.8 kg, p = 0.035), with significant Method×Minority Group interactions for FM and FFM (p < 0.03). These findings support cautious use of CVapp-derived values for within-person monitoring under similar conditions but suggest they should not be treated as interchangeable with established comparator methods or used as standalone diagnostic estimates of body composition. Future efforts should address the need for more accurate, transparent, and equitable algorithms that are validated across diverse populations and testing contexts.