ArticleJournal of human nutrition and dietetics : the official journal of the British Dietetic Association2023
Validity and reliability of a mobile digital imaging analysis trained by a four-compartment model.
Article in Journal of human nutrition and dietetics : the official journal of the British Dietetic Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Smartphone and tablet-based 3D scanner for anthropometric assessments in adults: a systematic review of reliability, validity, and accuracy.Biomedical engineering online · 2025Pooled it
- Validity of non-traditional measures of obesity compared to total body fat across the life course: A systematic review and meta-analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Pooled it
- Development of a metabolic syndrome prediction model using smartphone-derived digital anthropometry.The British journal of nutrition · 2026Article
- The impact of different body surface area prediction equations on ventricular dilatation prevalence in youth soccer players.Frontiers in cardiovascular medicine · 2025Article
- A predictive model for body water and fluid balance using 3D smartphone anthropometry.Frontiers in physiology · 2025Article
- Development and Validation of a Method of Body Volume and Fat Mass Estimation Using Three-Dimensional Image Processing with a Mexican Sample.Nutrients · 2024Article
- Exploring gait velocity as a predictor of cardiometabolic disease risk in young adults.Frontiers in sports and active living · 2024Article
- Smartphone three-dimensional imaging for body composition assessment using non-rigid avatar reconstruction.Frontiers in medicine · 2024Article
- Equations for smartphone prediction of adiposity and appendicular lean mass in youth soccer players.Scientific reports · 2023Article
- Evaluation of automated anthropometrics produced by smartphone-based machine learning: a comparison with traditional anthropometric assessments.The British journal of nutrition · 2023Article
- Predicting Bone Mineral Content from Smartphone Digital Anthropometrics: Evaluation of an Existing Application and the Development of New Prediction Models.Journal of clinical densitometry : the official journal of the International Society for Clinical DensitometryArticle
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Authors and funding
3 authors.
Funding
Abstract
backgroundDigital imaging analysis (DIA) estimates collected from mobile applications comprise a novel technique that can collect body composition estimates remotely without the inherent restrictions of other research-grade devices. However, the accuracy of the artificial intelligence used in DIA is reliant on the accuracy of the developmental methods. Few DIA applications are trained by multicompartment models, but this developmental strategy may be most accurate. Thus, the aim of the present study was to assess the precision and agreement of a DIA application with developmental software trained by a four-compartment (4C) model using an actual 4C model as the criterion method.
methodsFor this cross-sectional study, body composition estimations were collected from 102 participants (63 females, 39 males) using the methods necessary for a rapid 4C model and a DIA application using two different smartphones.
resultsIntraclass correlation coefficients (0.96-0.99; all p < 0.001) and root mean square coefficients of variation (0.5%-3.0%) showed good reliability for body fat percentage, fat mass and fat-free mass. There were no significant mean differences between the 4C model or the DIA estimates for the total sample, by sex, and for non-Hispanic White (n = 61) and Black/African-American (n = 32) participants (all p > 0.050). DIA estimates demonstrated equivalence with the 4C model for all variables but revealed proportional biases that underestimated body fat percentage (both β = -0.25; p < 0.001) and fat mass (both β = -0.07; p < 0.010) at higher degrees of each variable.
conclusionsDIA applications trained by a 4C model are reliable and produce body composition estimates equivalent to an actual 4C model.
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