Agreement of anthropometric and body composition measures predicted from 2D smartphone images and body impedance scales with criterion methods

Background/objectives: Body composition and anthropometry assessment from two-dimensional smartphone images is possible through advancement of computational hardware and artificial intelligence (AI) techniques. This study established agreement of a novel smartphone assessment, compared with traditional bioelectrical impedance analysis (BIA), and criterion measures. Subjects/methods: Body composition of 929 adults was measured using DXA (GE lunar iDXA), a foot-to-foot BIA machine (TANITA BC-313), and predictions from two-dimensional smartphone images. Anthropometry measures were also collected. Body composition and anthropometry estimates were compared via concordance coefficient correlation (CCC), equivalence testing, Bland–Altman analysis, and root mean square error (RMSE). Results: 2D smartphone image predictions for percent body fat (%BF) (males: CCC = 0.90 and RMSE = 2.9, and females: CCC = 0.90 and RMSE = 2.8) reported greater agreement with DXA measures than the BIA measures (males: CCC = 0.66 and RMSE = 5.6, and females: CCC = 0.79 and RMSE = 4.6). All anthropometry 2D smartphone image predictions had a strong agreement with criterion measurements (CCC ≥ 0.84 and RMSE ≤ 3.3). Body composition and anthropometry measures predicted by the 2D smartphone images were clinically equivalent at ±2.5 and ±5.0% thresholds. BIA %BF was not equivalent at either threshold; with only female BIA fat-free mass equivalent at the ±5% threshold. Conclusion: Body composition predictions from 2D smartphone application images provide a promising alternative to BIA scales for in-home body composition assessment. Future research should assess the validity of this method for longitudinally tracking body composition and indicating an individual's potential risk of chronic diseases.