Evidence map›Paper›PMID 27898706›Full record

ArticlePloS one2016

Prediction of Body Mass Index Using Concurrently Self-Reported or Previously Measured Height and Weight.

Zhaohui Cui, June Stevens, Kimberly P Truesdale, Donglin Zeng, Simone French, Penny Gordon-Larsen

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Why primary obesity is a disease?Journal of translational medicine · 2019
    Review
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

6 authors.

Zhaohui CuiDepartment of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
June StevensDepartment of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Kimberly P TruesdaleDepartment of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Donglin ZengDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Simone FrenchDivision of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America.
Penny Gordon-LarsenDepartment of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.

Funding

Wave IV Data CollectionP01HD031921 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HARRIS, KATHLEEN MULLAN · 1994 to 2020
$86.1M
Research Services CoreP2CHD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN B GUZZO · 2015 to 2026
$9.6M
Childhood Obesity Prevention and Treatment Research Coordinating UnitU01HL103561 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI STEVENS, JUNE · 2010 to 2017
$7.0M
Gene-Environment Interactions and Weight GainR01HD057194 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GORDON-LARSEN, PENNY, NORTH, KARI E. · 2008 to 2019
$5.9M
NHLBI NIH HHS U01 HL103561NICHD NIH HHS P01 HD031921NICHD NIH HHS P2C HD050924NICHD NIH HHS R01 HD057194
6 · The paper itself

Abstract

objectiveTo compare alternative models for the imputation of BMIM (measured weight in kilograms/measured height in meters squared) in a longitudinal study.

methodsWe used data from 11,008 adults examined at wave III (2001-2002) and wave IV (2007-2008) in the National Longitudinal Study of Adolescent to Adult Health. Participants were asked their height and weight before being measured. Equations to predict wave IV BMIM were developed in an 80% random subsample and evaluated in the remaining participants. The validity of models that included BMI constructed from previously measured height and weight (BMIPM) was compared to the validity of models that used BMI calculated from concurrently self-reported height and weight (BMISR). The usefulness of including demographics and perceived weight category in those models was also examined.

resultsThe model that used BMISR, compared to BMIPM, as the only variable produced a larger R2 (0.913 vs. 0.693), a smaller root mean square error (2.07 vs. 3.90 kg/m2) and a lower bias between normal-weight participants and those with obesity (0.98 vs. 4.24 kg/m2). The performance of the model containing BMISR alone was not substantially improved by the addition of demographics, perceived weight category or BMIPM.

conclusionsOur work is the first to show that concurrent self-reports of height and weight may be more useful than previously measured height and weight for imputation of missing BMIM when the time interval between measures is relatively long. Other time frames and alternatives to in-person collection of self-reported data need to be examined.

Indexed as

Body Mass IndexModels, TheoreticalAdolescentAdultBody HeightBody WeightFemaleHumansLongitudinal StudiesMaleSelf ReportSurveys and QuestionnairesYoung Adult

Identifiers

PMID27898706
PMCPMC5127553

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.