ArticleNPJ digital medicine2023
Development and validation of an accurate smartphone application for measuring waist-to-hip circumference ratio.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline 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
- A Neurosurgeon's Guide to Mobile Health Application Development With a Case Study for Cervical Myelopathy.Neurosurgery · 2026Article
- Prediction of total and regional body composition from 3D body shape.NPJ digital medicine · 2024Article
- Smartphone-based abdominal inclination angles for assessing abdominal obesity: Convergent validity and reliability study.Digital healthArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Waist-to-hip circumference ratio (WHR) is now recognized as among the strongest shape biometrics linked with health outcomes, although use of this phenotypic marker remains limited due to the inaccuracies in and inconvenient nature of flexible tape measurements when made in clinical and home settings. Here we report that accurate and reliable WHR estimation in adults is possible with a smartphone application based on novel computer vision algorithms. The developed application runs a convolutional neural network model referred to as MeasureNet that predicts a person's body circumferences and WHR using front, side, and back color images. MeasureNet bridges the gap between measurements conducted by trained professionals in clinical environments, which can be inconvenient, and self-measurements performed by users at home, which can be unreliable. MeasureNet's accuracy and reliability is evaluated using 1200 participants, measured by a trained staff member. The developed smartphone application, which is a part of Amazon Halo, is a major advance in digital anthropometry, filling a long-existing gap in convenient, accurate WHR measurement capabilities.
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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.