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Assessing body composition via a smartphone computer vision application: High repeatability but method-dependent agreement compared with BODPOD and Inbody.

Published in:PLoS Digital Health Vol. 5; no. 9; pp. 1 - 24
Main Authors: Sisohor, Jahliya, Ambegaonkar, Jatin P., Lindsey, Bryndan, Wei, Qi, Shaul, Yosef, Martin, Joel
Format: pictorial research tables/charts Journal Article
Published: Public Library of Science 9/2/2026
Online Access:View this record in EBSCOhost
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      dt: 9/2/2026
      vid: 5
      iid: 9
      pid: 16707
      pub: Public Library of Science
      place: San Francisco, California
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        196691284
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        196691284
        10.1371/journal.pdig.0000983
        196691284
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        atl: Assessing body composition via a smartphone computer vision application: High repeatability but method-dependent agreement compared with BODPOD and Inbody.
      aug:
        au:
          Sisohor, Jahliya
          Ambegaonkar, Jatin P.
          Lindsey, Bryndan
          Wei, Qi
          Shaul, Yosef
          Martin, Joel
        affil: Sports Medicine Assessment Research & Testing (SMART) Laboratory, George Mason University, Virginia, United States of America
      sug:
        subj:
          Body Composition Evaluation
          Mobile Applications
          Smartphone
          Sex Factors
          Race Factors
          Ethnic Groups
          Plethysmography
          Image Processing, Computer Assisted
          Electric Impedance
          Human
          Male
          Female
          Adult
          Middle Age
          Aged
          Comparative Studies
          Anthropometry
          Repeated Measures
          Data Analysis Software
          Descriptive Statistics
          One-Way Analysis of Variance
          Post Hoc Analysis
          Intraclass Correlation Coefficient
          Chi Square Test
          Confidence Intervals
          Test-Retest Reliability
          Fat Free Mass
          Pearson's Correlation Coefficient
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Aged: 65+ years
          Male
          Female
      pubtype: Academic Journal
      doctype:
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
      ab:
    language: English
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