Evidence mapPaperPMID 34305707Full record

ReviewFrontiers in psychology2021

Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review.

Paola Piqueras, Alfredo Ballester, Juan V Durá-Gil, Sergio Martinez-Hervas, Josep Redón, José T Real

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
106citing papers in PubMed, 1 pooled it
16.9field-weighted citation impact, top 1% 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

106 citing papers in PubMed, 1 synthesis or guideline pooled it, 203 citations in OpenAlex.

  1. Pooled it
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  11. Overweight and obesity in Jordanian adults before and after COVID-19: a multi-index comparison.Archives of public health = Archives belges de sante publique · 2026
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46 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 1 country.

Paola PiquerasInstituto de Biomecánica de Valencia, Universitat Politècnica de Valencia, Valencia, Spain.
Alfredo BallesterInstituto de Biomecánica de Valencia, Universitat Politècnica de Valencia, Valencia, Spain.
Juan V Durá-GilInstituto de Biomecánica de Valencia, Universitat Politècnica de Valencia, Valencia, Spain.
Sergio Martinez-HervasService of Endocrinology and Nutrition, Hospital Clínico Universitario de Valencia, Valencia, Spain.
Josep RedónDepartment of Internal Medicine, Hospital Clínico de Valencia, University of Valencia, Valencia, Spain.
José T RealService of Endocrinology and Nutrition, Hospital Clínico Universitario de Valencia, Valencia, Spain.
Hospital Clínico Universitario de Valencia · ESUniversitat Politècnica de València · ESBiomechanics Institute of Valencia · ESCentro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Obesity is characterized by the accumulation of an excessive amount of fat mass (FM) in the adipose tissue, subcutaneous, or inside certain organs. The risk does not lie so much in the amount of fat accumulated as in its distribution. Abdominal obesity (central or visceral) is an important risk factor for cardiovascular diseases, diabetes, and cancer, having an important role in the so-called metabolic syndrome. Therefore, it is necessary to prevent, detect, and appropriately treat obesity. The diagnosis is based on anthropometric indices that have been associated with adiposity and its distribution. Indices themselves, or a combination of some of them, conform to a big picture with different values to establish risk. Anthropometric indices can be used for risk identification, intervention, or impact evaluation on nutritional status or health; therefore, they will be called anthropometric health indicators (AHIs). We have found 17 AHIs that can be obtained or estimated from 3D human shapes, being a noninvasive alternative compared to X-ray-based systems, and more accessible than high-cost equipment. A literature review has been conducted to analyze the following information for each indicator: definition; main calculation or obtaining methods used; health aspects associated with the indicator (among others, obesity, metabolic syndrome, or diabetes); criteria to classify the population by means of percentiles or cutoff points, and based on variables such as sex, age, ethnicity, or geographic area, and limitations.

Indexed as

3D human shapesanthropometric health indicatorsfat distributionhealthobesityrisk identification

Identifiers

PMID34305707
PMCPMC8299753
OpenAlexW3177991731

What Socratic 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 Socratic graph.