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Related Experiment Video

Updated: Jun 25, 2026

Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging
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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.

Nutrition (Burbank, Los Angeles County, Calif.)
|June 23, 2026
PubMed
Summary

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This summary is machine-generated.

Mobile apps show promise for estimating body composition, offering accessible and cost-effective methods. However, further validation is needed to improve accuracy for clinical use.

Area of Science:

  • Biomedical Informatics
  • Health Technology Assessment
  • Digital Health

Background:

  • Mobile applications are increasingly utilized for health monitoring and data collection.
  • Estimating body composition is crucial for assessing health status and managing chronic diseases.
  • Traditional methods for body composition analysis can be costly and inaccessible.

Purpose of the Study:

  • To review the functionality, research findings, and future prospects of mobile applications designed for body composition estimation.
  • To identify and evaluate existing mobile apps with scientific validation for body composition analysis.

Main Methods:

  • A non-systematic literature search was performed across scientific databases and digital marketplaces.
  • Apps with scientific backing were identified, and their methodologies were extracted from publications and websites.
Keywords:
Artificial intelligenceBody compositionImaging, Three-dimensional, Mobile, AppMobile applications

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  • Developers were contacted to resolve technical data gaps, with only responsive apps included.

    • Main Results:

    • Eleven mobile apps for body composition prediction were identified from 18 studies, with complete technical data for five.
    • Apps utilize 2D whole-body imaging and digital anthropometry (e.g., 3D electronic tape, AI) for data extraction.
    • Apps showed good accuracy for fat mass percentage but reduced accuracy in individuals with higher adiposity; generally satisfactory for fat mass and fat-free mass prediction.
    • Limitations include reduced individual-level accuracy, insufficient diverse population validation, and limited longitudinal tracking evidence.

    Conclusions:

    • Mobile apps present a promising, cost-effective, and accessible tool for body composition estimation.
    • Further validation and accuracy improvements are essential for widespread clinical adoption.
    • Future research should focus on enhancing accuracy, validating across diverse populations, and exploring longitudinal tracking capabilities.