Evidence map›Paper›PMID 30303471›Full record

ArticlePublic health nutrition2018

Industry use of 'better-for-you' features on labels of sugar-containing beverages.

Aimee L Brownbill, Caroline L Miller, Annette J Braunack-Mayer

Abstract read
In one paragraph

Article in Public health nutrition, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Trial
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  8. Article
  9. The Effect of Packaging Color and Health Claims on Product Attitude and Buying Intention.International journal of environmental research and public health · 2020
    Article
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

3 authors.

Aimee L Brownbill1The University of Adelaide,School of Public Health,Adelaide Health and Medical Sciences Building,Adelaide,SA5005,Australia.ORCID 0000-0002-3368-3907
Caroline L Miller1The University of Adelaide,School of Public Health,Adelaide Health and Medical Sciences Building,Adelaide,SA5005,Australia.
Annette J Braunack-Mayer1The University of Adelaide,School of Public Health,Adelaide Health and Medical Sciences Building,Adelaide,SA5005,Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo examine the ways in which sugar-containing beverages are being portrayed as 'better-for-you' (BFY) via features on product labels.

designCross-sectional audit of beverage labels.

settingAdelaide, Australia. Data on beverage labels were collected from seventeen grocery stores during September to November 2016. SUBJECTS: The content of 945 sugar-containing beverages labels were analysed for explicit and implicit features positioning them as healthy or BFY.

resultsThe mean sugar content of beverages was high at 8·3 g/100 ml and most sugar-containing beverages (87·7 %) displayed features that position them as BFY. This was most commonly achieved by indicating the beverages are natural (76·8 %), or contain reduced or natural energy/sugar content (48·4 %), or through suggesting that they contribute to meeting bodily needs for nutrition (28·9 %) or health (15·1 %). Features positioning beverages as BFY were more common among certain categories of beverages, namely coconut waters, iced teas, sports drinks and juices.

conclusionsA large proportion of sugar-containing beverages use features on labels that position them as healthy or BFY despite containing high amounts of sugar.

Indexed as

Food LabelingAustraliaBeveragesCross-Sectional StudiesDietary SugarsDiet, HealthyHumansNutritive ValueDietary SugarsAdvertisingFood labelsHealth haloMarketingSugar-sweetened beverages

Identifiers

PMID30303471
PMCPMC10260941

What OpenQuestion holds

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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.