Visual body composition assessment methods: A 4-compartment model comparison of smartphone-based artificial intelligence for body composition estimation in healthy adults.

Journal: Clinical nutrition (Edinburgh, Scotland)
PMID: 36215866

Abstract

BACKGROUND & AIMS: Visual body composition (VBC) estimates produced from smartphone-based artificial intelligence represent a user-friendly and convenient way to automate body composition remotely and without the inherent geographical and monetary restrictions of other body composition methods. However, there are limited studies that have assessed the reliability and agreement of this method and thus, the aim of this study was to evaluate VBC estimates compared to a 4-compartment (4C) criterion model.

Authors

  • Austin J Graybeal
    University of Southern Mississippi, School of Kinesiology & Nutrition. Electronic address: austin.graybeal@usm.edu.
  • Caleb F Brandner
    University of Southern Mississippi, School of Kinesiology & Nutrition.
  • Grant M Tinsley
    Energy Balance & Body Composition Laboratory; Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA. grant.tinsley@ttu.edu.

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