Evidence map›Paper›PMID 35084446›Full record

ArticleAdvances in nutrition (Bethesda, Md.)2022

Perspective: Novel Approaches to Evaluate Dietary Quality: Combining Methods to Enhance Measurement for Dietary Surveillance and Interventions.

Maya K Vadiveloo, Filippa Juul, Mercedes Sotos-Prieto, Niyati Parekh

Abstract read
In one paragraph

Article in Advances in nutrition (Bethesda, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

4 authors.

Maya K VadivelooDepartment of Nutrition and Food Sciences, University of Rhode Island, Kingston, RI, USA.ORCID 0000-0002-0137-670X
Filippa JuulDepartment of Public Health Policy and Management, School of Global Public Health, New York University, New York, NY, USA.
Mercedes Sotos-PrietoDepartment of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, and IdiPaz (Instituto de Investigación Sanitaria Hospital Universitario La Paz), Madrid, Spain.
Niyati ParekhPublic Health Nutrition Program, School of Global Public Health, New York University, New York, NY, USA.ORCID 0000-0002-1334-0528

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Refining existing dietary assessment methods to reduce measurement error and facilitate the routine evaluation of dietary quality is essential to inform health policy. Notable advancements in technology in the past decade have enhanced the precision and transformation of dietary assessment methods with applications toward both population health and precision nutrition. Within population health, innovative applications of big data including use of automatically collected food purchasing data, quantitative measurement of food environments, and novel, yet simplified dietary quality metrics provide important complementary data to traditional self-report methods. Precision nutrition is similarly advancing with greater use of validated biomarkers for assessing dietary patterns and understanding individual variability in metabolism. Concurrently enhancing our understanding of diet-disease relations at the population health and precision nutrition levels provides tremendous potential to generate evidence needed to advance public health nutrition policy. This commentary highlights the importance of these advances toward progressing the field of dietary assessment and discusses the application of food purchasing data, data analytics, alternative dietary quality metrics, and -omics technology in population and clinical medicine.

Indexed as

NutrigenomicsPrecision MedicineDietFoodHumansNutritional Statusbiomarkersmultidimensional diet assessmentNOVA classificationnutrigenomicspersonalized dietary guidancepopulation healthprecision nutritiontechnological advancement in dietary assessment

Identifiers

PMID35084446
PMCPMC9340961

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.