ArticleNutrients2019
Healthy Food Labels Tailored to a High-Risk, Minority Population More Effectively Promote Healthy Choices than Generic Labels.
Article in Nutrients, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The impact of the consumer and neighbourhood food environment on dietary intake and obesity-related outcomes: A systematic review of causal impact studies.Social science & medicine (1982) · 2022Pooled it
- Effects of food policy actions on Indigenous Peoples' nutrition-related outcomes: a systematic review.BMJ global health · 2020Pooled it
- Community Engaged Research Strategies Used in Food Retail Interventions: A Scoping Review.Journal of the Academy of Nutrition and Dietetics · 2025Article
- Personalizing product sets to individual health priorities increases the healthfulness of hypothetical food choices in US adults.Scientific reports · 2025Article
- The impact of health and environmental messaging with and without product filtering in complex retail markets: the case of pulses.Frontiers in nutrition · 2024Article
- Nutrition in Times of Crisis: A Qualitative Study in Siargao Island, Philippines, during the COVID-19 Pandemic.Acta medica Philippina · 2023Article
- A Scoping Review of the Use of Indigenous Food Sovereignty Principles for Intervention and Future Directions.Current developments in nutrition · 2021Article
- The Multifaceted Dimensions of Food Choice and Nutrition.Nutrients · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The decades-long increase in obesity in the US has led to a number of policies aimed at improving diets, which are thought to play a significant role in obesity. Many of these policies seek to influence individuals' behaviors. Front-of-package labels providing salient, easily interpretable information to consumers have exhibited promise in helping people identify and choose healthier foods. However, behavioral economics may offer an opportunity to enhance label effectiveness. Tailoring labels to high-risk communities, including minority and rural populations, which have higher rates of diet-related diseases than the overall population, may increase the label's effectiveness. We conducted a choice experiment with supermarket shoppers on a rural American Indian reservation to test labels tailored to the local population relative to a generic label, which had previously been identified as highly effective in the general population. Results show that while the generic label continues to be quite effective in encouraging healthier choices, the label that is tailored to the local community is more effective, resulting in a marked increase in the premium shoppers were willing to pay for a healthy item. Tailoring healthy food labeling systems using insights from behavioral economics may increase their effectiveness.
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Registered trials
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.