Evidence map›Paper›PMID 36632007›Full record

ArticleThe British journal of nutrition2023

Evaluation of automated anthropometrics produced by smartphone-based machine learning: a comparison with traditional anthropometric assessments.

Austin J Graybeal, Caleb F Brandner, Grant M Tinsley

Open access · hybridAbstract read
In one paragraph

Article in The British journal of nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
5.6field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

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  17. 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 Densitometry
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Austin J GraybealSchool of Kinesiology & Nutrition, College of Education and Human Sciences, University of Southern Mississippi, Hattiesburg, MS39406, USA.ORCID 0000-0003-4520-9230
Caleb F BrandnerSchool of Kinesiology & Nutrition, College of Education and Human Sciences, University of Southern Mississippi, Hattiesburg, MS39406, USA.
Grant M TinsleyDepartment of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX79409, USA.ORCID 0000-0002-0230-6586
University of Southern Mississippi · USTexas Tech University · US

Funding

Training and Mentoring Core P20GM103476 · NIGMS · UNIVERSITY OF SOUTHERN MISSISSIPPI · PI MICHAEL R GARRETT · 2012 to 2026
$60.2M
NIGMS NIH HHS P20 GM103476
6 · The paper itself

Abstract

Automated visual anthropometrics produced by mobile applications are accessible and cost effective with the potential to assess clinically relevant anthropometrics without a trained technician present. Thus, the aim of this study was to evaluate the precision and agreement of smartphone-based automated anthropometrics against reference tape measurements. Waist and hip circumference (WC; HC), waist:hip ratio (WHR) and waist:height ratio (W:HT) were collected from 115 participants (69 F) using a tape measure and two smartphone applications (MeThreeSixty

Indexed as

SmartphoneAnthropometryBody Mass IndexHumansWaist CircumferenceWaist-Hip RatioBody composition assessmentDigital anthropometryMobile healthObesity

Identifiers

PMID36632007
PMCPMC10442791
OpenAlexW4315754466

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.