Evidence map›Paper›PMID 39064780›Full record

ArticleNutrients2024

Health Belief Model Predicts Likelihood of Eating Nutrient-Rich Foods among U.S. Adults.

Abigail A Glick, Donna M Winham, Michelle M Heer, Mack C Shelley, Andrea M Hutchins

Abstract read
In one paragraph

Article in Nutrients, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

5 authors.

Abigail A GlickDepartment of Food Science & Human Nutrition, Iowa State University, Ames, IA 50011, USA.
Donna M WinhamDepartment of Food Science & Human Nutrition, Iowa State University, Ames, IA 50011, USA.ORCID 0000-0003-3924-2560
Michelle M HeerDepartment of Food Science & Human Nutrition, Iowa State University, Ames, IA 50011, USA.ORCID 0000-0003-2736-9273
Mack C ShelleyDepartments of Political Science and Statistics, Iowa State University, Ames, IA 50011, USA.ORCID 0000-0002-0414-5843
Andrea M HutchinsDepartment of Human Physiology & Nutrition, University of Colorado Colorado Springs, Colorado Springs, CO 80918, USA.

Funding

United States Department of Agriculture 3060-21650-001-000
6 · The paper itself

Abstract

Despite decades of messaging, most Americans still consume excess fats and sugars, but inadequate fiber, potassium, and calcium. Nutrient-rich foods (NRFs) have a high density of favorable nutrients related to calories. Choosing NRFs could lower risk of nutrition-related chronic diseases and aid in their control. We hypothesized that having greater knowledge of NRFs, the presence of a nutrition-related chronic disease or risk factor, and positive Health Belief Model (HBM) views would be predictive of the likelihood of eating NRFs. Through a national online survey panel, 976 adults aged 18-80 completed demographic, health, NRF knowledge, attitudes, and HBM construct questions. Participants were 77% White, 52% women, and 55% had a nutrition-related disease or risk factor. Multivariable HBM scales were generated by theory, principal components, and reliability analysis. NRF knowledge was significantly higher for women, Whites, households without children, and persons without a nutrition-related disease (all

Indexed as

Health Belief ModelHealth Knowledge, Attitudes, PracticeAdolescentAdultAgedAged, 80 and overDiet, HealthyFeeding BehaviorFemaleHumansMaleMiddle AgedNutrientsNutritive ValueUnited StatesYoung AdultNutrientschronic diseaseconsumer researchhealth belief modelnutrient densitynutrient rich foodsnutrientsnutrition knowledgestructural equation modelunder-consumed nutrients

Identifiers

PMID39064780
PMCPMC11279385

What OpenQuestion holds

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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.