Cited by 47Body Composition Measurement TechniquesObesity, Physical Activity, DietElectrical and Bioimpedance Tomography

Bioelectrical impedance vector analysis in obese and overweight children.

Open access
de-Mateo-Silleras, Beatriz, de-la-Cruz-Marcos, Sandra, Alonso-Izquierdo, Laura, Camina-Martín, Mª Alicia, Marugán-de-Miguelsanz, José Manuel, Redondo-del-Río, Mª Paz·2019·PLoS ONE

Abstract

INTRODUCTION: BMI is the most commonly used indicator to evaluate overweight and obesity, but it cannot distinguish changes in body composition. Over recent years, it has been demonstrated that bioelectrical impedance analysis (BIA) is a more accurate method for analyzing body composition. Bioelectrical impedance vector analysis (BIVA) has revealed its effectiveness as an indicator of nutritional status and hydration. OBJECTIVE: To assess the usefulness of bioimpedance analysis on the study of body composition in a group of children with overweight and obesity. MATERIALS AND METHODS: Cross-sectional observational study. The anthropometric parameters of 167 (79 were older than 12 years) overweight and obese children were recorded. Their body composition was analyzed using BIA and BIVA, and was classified based on different criteria. Concordance was analyzed (intraclass correlation coefficient, Bland-Altman analysis and weighted Kappa coefficient). The BIVA of the subgroups was compared using the Mahalanobis distance and Hotelling's T2. Statistical significance was considered for p<0.05. RESULTS: The BMI revealed that the majority of the assessed subjects were obese, although 12% had a normal percentage of fat mass (%FM). The classification by Z-BMI and Z-%FM significantly discriminate between subjects with different levels of adiposity. In children over the age of 12, the classification of fat mass index also discriminates significantly between obesity and non-obesity. As anticipated, in the tolerance ellipses, most of the individual vectors were situated in the left lower quadrant. CONCLUSIONS: BIVA reflects differences in the bioelectric patterns of children who are classified as being overweight or obese (BMI) and who have different levels of %FM and FMI. BIVA permits a fast and easy monitoring of the evolution of the nutritional state and changes associated with body composition, and it identifies those children whose body compartments may be precisely estimated using traditional BIA methods.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0211148

Cites

53 of 54 references verified1 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • BIA is the most used tool to assess body composition worldwide.

    “These characteristics make BIA the most used tool to assess body composition worldwide (22).”

    From Development and Cross-Validation of a Predictive Equation for Fat-Free Mass in Brazilian Adolescents by Bioelectrical Impedance (2022) · cites this paper as [22]

    The Discussion describes bioimpedance analysis as “one of the most used in clinical practice,” and the Introduction lists BIA among methods commonly used in clinical settings. Neither passage establishes that it is the single most used body-composition tool worldwide. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. [36]Hirschler V, Calcagno ML, Aranda C, Maccallini G, Jadzinsky M (2006) Síndrome metabólico en la infancia y su asociación con insulinorresistencia. Arch Argent Pediatr 104(6): 486– 491.
  2. [38]Rodríguez PN, Bermúdez EF, Rodríguez GS, Spina MA, Zeni SN, Friedman SM, et al (2008) Composición corporal en niños preescolares: comparacion entre métodos antrpométricos simples, bioimpedancia y absorciometría de doble haz de rayos X. Arch Argent Pediatr 106(2): 102– 109. 18661034

Cited by

Sorted by citations

Authors
de-Mateo-Silleras, B., de-la-Cruz-Marcos, S., Alonso-Izquierdo, L., Camina-Martín, M. A., Marugán-de-Miguelsanz, J. M., & Redondo-del-Río, M. P.
Affiliations
Universidad de Valladolid (ES)
Venue
PLoS ONE
Published
2019-01-24
Volume
14
Issue
1
Pages
e0211148
ISSN
1932-6203 (ISSN-L)
OpenAlex
W2913837964
Cited by
45
References
53
Citations by year
2026: 3 · 2025: 3 · 2024: 8 · 2023: 7 · 2022: 12 · 2021: 5 · 2020: 5 · 2019: 2
FWCI
4.12
Type
journal article
Language
en
Open access
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical and Bioimpedance Tomography
Keywords
bioelectrical impedance vector analysis, childhood obesity, overweight children, body composition, body fat percentage, fat mass index, body mass index, nutritional status, hydration status, tolerance ellipses
MeSH terms
Adolescent, Body Composition, Child, Cross-Sectional Studies, Female, Humans, Male, Nutritional Status, Electric Impedance, Pediatric Obesity

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review