Evidence map›Paper›PMID 29479462›Full record

ArticleObesity science & practice2018

Visual body size norms and the under-detection of overweight and obesity.

M Oldham, E Robinson

Abstract read
In one paragraph

Article in Obesity science & practice, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. The Real Ideal: Misestimation of Body Mass Index.Frontiers in global women's health · 2022
    Article
  10. Review
  11. Observational
  12. Review
  13. Article
  14. Article
  15. Overweight but unseen: a review of the underestimation of weight status and a visual normalization theory.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2017
    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

2 authors.

M OldhamPsychological Sciences University of Liverpool Liverpool UK.
E RobinsonPsychological Sciences University of Liverpool Liverpool UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The weight status of men with overweight and obesity tends to be visually underestimated, but visual recognition of female overweight and obesity has not been formally examined. The aims of the present studies were to test whether people can accurately recognize both male and female overweight and obesity and to examine a visual norm-based explanation for why weight status is underestimated. Methods: The present studies examine whether both male and female overweight and obesity are visually underestimated (Study 1), whether body size norms predict when underestimation of weight status occurs (Study 2) and whether visual exposure to heavier body weights adjusts visual body size norms and results in underestimation of weight status (Study 3). Results: The weight status of men and women with overweight and obesity was consistently visually underestimated (Study 1). Body size norms predicted underestimation of weight status (Study 2) and in part explained why visual exposure to heavier body weights caused underestimation of overweight (Study 3). Conclusions: The under-detection of overweight and obesity may have been in part caused by exposure to larger body sizes resulting in an upwards shift in the range of body sizes that are perceived as being visually 'normal'.

Indexed as

Body size normsobesityvisual perceptionweight misperceptions

Identifiers

PMID29479462
PMCPMC5818735

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.