ArticleJournal of diabetes science and technology2023
Body Composition Prediction-BOMP: A New Tool for Assessing Fat and Lean Body Mass.
Article in Journal of diabetes science and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Relative fat mass as an estimator of body fat percentage in Chilean adults.European journal of clinical nutrition · 2024Article
- Body Composition Prediction-BOMP: A New Tool for Assessing Fat and Lean Body Mass.Journal of diabetes science and technology · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPragmatic and easy-to-use alternatives to estimating body composition, such as lean body mass and fat mass, could be valuable tools for assessing the risk of diabetes or other metabolic diseases. Previous work has shown how demographic and anthropometric data could be used in a neural network to estimate body composition with high precision. However, there is still a need for a publicly available and user-friendly format before these results can have clinical impact.
methodsWe used data from 18 430 NHANES participants and stepwise linear regression with inclusion of linear, interactions, and quadratic terms to model lean body and fat mass. HTML and Javascript was used to develop a webapp as a frontend of the model.
resultsThe models had a correlation cofficent R = 0.99-0.98 (P < .001) withstandard error of estimate [SEE] = 2.07-2.05.
conclusionsThe results indicate that it is possible to develop a "white-box" model with high precision.The proof of concept webapp is available as open source under the MIT license.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.