Related Experiment Video
Updated: Jun 25, 2026
Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging
Published on: June 7, 2024
Mobile applications for body composition estimation: functionality, current findings, and future directions
Taiara S Poltronieri1, Bruna R da Silva2, Jonathan Bennett3
1Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada; Postgraduate Program in Medical Sciences, Endocrinology, Faculty of Medicine, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
Objectives:
To provide an overview of the functionality, research findings, and future directions on mobile apps for estimating body composition.
Methods:
A nonsystematic literature search (April 2023 - February 2026) was conducted across scientific databases, online search engines, and digital marketplaces to identify apps with scientific backing. Data on apps methodologies were extracted from associated papers and websites. Developers were contacted to address technical gaps, and only apps with responses were included.
Results:
Out of the 18 studies, 11 apps for body composition prediction were identified, with complete technical data available for 5 apps. Studies reflect available approaches used to capture two-dimensional whole-body images, employing digital anthropometry techniques, such as three-dimensional electronic tape measurements and artificial intelligence, to extract body shape and anthropometric data. The apps demonstrated good accuracy in predicting fat mass percentage; however, most showed reduced accuracy in individuals with higher adiposity. Studies also evaluated fat mass, fat-free mass, and appendicular fat-free mass, and the apps generally presented satisfactory predictive performance. However, important limitations remain, including reduced accuracy at the individual-level, insufficient validation across diverse populations, and limited evidence on longitudinal tracking capabilities.
Conclusion:
Mobile apps offer a promising, cost-efficient, and accessible approach for body composition estimation. Nevertheless, further validation and improvements in accuracy are needed to support its clinical application.
