Evidence map›Paper›PMID 41267501›Full record

ArticleThe British journal of nutrition2026

Development of a metabolic syndrome prediction model using smartphone-derived digital anthropometry.

Caleb F Brandner, Grant M Tinsley, Abby T Compton, Sydney H Swafford, Molly F Johnson, Maria G Kaylor, Hunter Haynes, Jon Stavres, Austin J Graybeal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The British journal of nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Caleb F BrandnerDepartment of Health and Human Physiology, University of Iowa, Iowa City, IA, USA.
Grant M TinsleyDepartment of Kinesiology and Sports Management, Texas Tech University, Lubbock, TX, USA.ORCID https://orcid.org/0000-0002-0230-6586
Abby T ComptonSchool of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA.
Sydney H SwaffordSchool of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA.
Molly F JohnsonSchool of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA.
Maria G KaylorSchool of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA.
Hunter HaynesSchool of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA.
Jon StavresDepartment of Kinesiology, Nutrition, and Health, College of Education, Health, and Society, Miami University, Oxford, OH45056, USA.ORCID https://orcid.org/0000-0003-0564-7252
Austin J GraybealDepartment of Kinesiology, https://ror.org/0405mnx93Harris College of Nursing and Health Sciences, Texas Christian University, Fort Worth, TX76129, USA.ORCID https://orcid.org/0000-0003-4520-9230

Funding

Tracking and Evaluation CoreU54GM115428 · NIGMS · UNIVERSITY OF MISSISSIPPI MED CTR · PI GOMEZ-SANCHEZ, CELSO ENRIQUE · 2016 to 2025
$38.2M
NIGMS NIH HHS U54 GM115428
6 · The paper itself

Abstract

Integrating metabolic syndrome (MetS) screening procedures into routine care remains challenging. Traditional anthropometric and body composition assessments, while useful, have drawbacks that limit their application. However, automated anthropometrics produced from smartphone scanning applications may offer a solution. This study aimed to determine whether smartphone-derived anthropometrics could effectively predict both MetS and its severity. A total of 281 participants underwent a MetS screening assessment to determine fasting blood pressure, lipids, glucose and waist circumference and completed a smartphone scanning assessment (MeThreeSixty

Indexed as

AnthropometryMetabolic SyndromeSmartphoneAdultAgedFemaleHumansMaleMiddle AgedWaist Circumference3D scanningCardiometabolic healthDigital imagingMobile applicationSmartphone

Identifiers

PMID41267501
PMCPMC12825942

What OpenQuestion holds

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LicenceCC BY
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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.