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

Abstract

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. 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. 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). 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.