Objective: To investigate the accuracy of foot-to-foot impedance methodology for the prediction of total body water and whether leg length rather than stature should be used in the prediction of total body water. Design: Cross-sectional study using volunteers from the community. Setting: University laboratory. Subjects: 57 subjects (29 male; 28 female) aged 19±56 y. Interventions: Total body water was measured using a deuterium oxide dilution technique. Total body water was also predicted using foot-to-foot impedance apparatus (Tanita Inc, Tokyo, Japan, Model TBF 305). Results: Mean values for predicted and measured total body water differed by 0.7 l. However this bias was not constant across all individuals with a progressive underestimation of total body water by foot-to-foot impedance technology as the water content of the body increases. Also the use of leg length did not improve the accuracy of the prediction equation. Conclusions: At the population level predictions of total body water obtained from foot-to-foot impedance technology compare well with measured total body water. However the signi®cant correlation between the difference between predicted and measured total body water and the absolute value for total body water is a concern especially if the technology is used for body composition assessment during a weight loss program. Sponsorship: RJH was in receipt of a Queensland University of Technology Vacation Scholarship.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.