BRIEF RESEARCH REPORT article
Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1577049
A Predictive Model for Body Water and Fluid Balance Using 3D Smartphone Anthropometry
Provisionally accepted
Austin J Graybeal1*
Abby T Compton1Sydney H Swafford1Caleb F Brandner2Molly F Johnson1Maria G Kaylor1Hunter Haynes1
Jon Stavres1- 1University of Southern Mississippi, Hattiesburg, United States
- 2The University of Iowa, Iowa City, Iowa, United States
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Background: Body fluid volumes, including total body water (TBW), extracellular fluid (ECF), and intracellular fluid (ICF), are crucial indicators of body composition, and the distribution of these fluids is essential for assessing hydration status and fluid accumulation. Although fluid volumes are commonly measured with bioelectrical impedance devices, several challenges hinder the application of this technique. However, 3D smartphone scanning applications that automate body volumes and other anthropometrics estimates may provide a viable alternative to body fluid assessments. Methods: A total of 338 participants underwent fluid volume assessments using bioelectrical impedance spectroscopy (BIS) and collected body volumes and anthropometric data using a 3D smartphone scanning application. Then, LASSO regression was used to develop new TBW and ECF prediction model in a subset of participants (n=272), which was subsequently tested in the remaining participants (n=66). Smartphone-derived ICF was calculated as the difference between smartphone-predicted TBW and ECF. Fluid overload and imbalance were determined using ECF/TBW and ECF/ICF, respectively, and subsequently predicted from the retained variables using receiver operating characteristic curve analyses and logistic regression.Results: Estimates from each of the newly-developed prediction models were not significantly different from the estimates produced using BIS (all p≥0.70) and revealed acceptable agreement (TBW: R2=0.91, RMSE=3.24 L; ECF: R2=0.94, RMSE=1.10 L; ICF: R2=0.87, RMSE=2.29 L) when evaluated in the testing sample (n=66), although proportional bias was observed (p<0.001). Smartphone-predicted fluid overload (AUC: 0.81 [95%CI: 0.70, 0.92]; sensitivity+specificity: 1.53[95%CI: 1.39, 1.67]) and imbalance (AUC: 0.76 [95%CI: 0.64, 0.88]; sensitivity+specificity: 1.40 [95%CI: 1.24, 1.56]) demonstrated acceptable diagnostic performance.
Keywords: Anthropometry, Body Composition, 3D, Body fluid, Water retention, Fluid balance, overhydration, Edema
Received: 17 Feb 2025; Accepted: 05 Jun 2025.
Copyright: © 2025 Graybeal, Compton, Swafford, Brandner, Johnson, Kaylor, Haynes and Stavres. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Austin J Graybeal, University of Southern Mississippi, Hattiesburg, United States
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