Consumer Technologies for Personalized Health: Feasibility of Five‐Compartment Body Composition Self‐Assessment Using Mobile Devices

ABSTRACT Objective Monitoring body composition is essential for assessing nutritional status and detecting early muscle loss, yet most methods require clinical settings. This study evaluated a self‐assessment model that uses consumer technologies for personalized and data‐driven body composition monitoring. Methods We developed a consumer‐accessible five‐compartment model that integrates body volume from smartphone‐based three‐dimensional optical imaging with total body water from smartwatch‐based bioelectrical impedance analysis. Body volume and total body water were calibrated against air displacement plethysmography and a clinical‐grade bioimpedance system, then combined within a five‐compartment framework to estimate fat‐free mass and fat mass. Estimates from the accessible model were compared with corresponding five‐compartment estimates derived from reference laboratory methods using linear regression and root mean square error. Results In 30 adults, smartphone body volume ( r 2 = 0.97, RMSE = 2.65 L) and smartwatch total body water ( r 2 = 0.98, RMSE = 1.54 L) showed strong agreement with laboratory measures but required offset corrections to remove biases. After calibration, fat‐free mass and fat mass estimates closely matched the laboratory‐derived five‐compartment model ( r 2 > 0.96, RMSE = 2.10 kg). Conclusions Properly calibrated smartphones and smartwatches can yield multicompartment body composition estimates that closely match laboratory standards and support precise, low‐cost monitoring in remote and resource‐limited settings.