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Related Experiment Video

Updated: Feb 22, 2026

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
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Published on: September 18, 2018

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Perspective: Essential Study Quality Descriptors for Data from Nutritional Epidemiologic Research.

Chen Yang1, Mariona Pinart2, Patrick Kolsteren1

  • 1Departments of Food Safety and Food Quality, Ghent University, Ghent, Belgium.

Advances in Nutrition (Bethesda, Md.)
|September 17, 2017
PubMed
Summary
This summary is machine-generated.

This study defines essential data quality characteristics for nutritional epidemiology research. Establishing these descriptors improves the reuse and interpretation of observational study data for scientific discovery.

Keywords:
data interoperabilitydata qualitydietary assessmentnutritional epidemiologyobservational study

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Area of Science:

  • Nutritional Epidemiology
  • Data Science
  • Biostatistics

Background:

  • Pooled analysis of secondary data enhances research power in nutritional epidemiology.
  • Accurate data reuse and interpretation depend on understanding study characteristics that determine data quality.
  • Existing instruments for assessing study and dietary measurement quality are varied and require synthesis.

Purpose of the Study:

  • To define essential quality characteristics for data from observational studies in nutrition.
  • To develop standardized data descriptors and controlled vocabulary for nutrition data.
  • To facilitate the identification, storage, and querying of nutrition data.

Main Methods:

  • Conducted a comprehensive literature review of existing quality assessment instruments for cohort, case-control, and cross-sectional studies, and dietary measurements.
  • Organized two face-to-face workshops with experts to identify key study characteristics impacting data quality.
  • Achieved consensus on data descriptors and controlled vocabulary through a structured process.

Main Results:

  • Selected 26 relevant instruments from 4884 retrieved papers, identifying 164 study design characteristics and 93 measurement characteristics.
  • Developed 10 data descriptors for the "study design" domain and 22 for the "measurement" domain.
  • Organized data descriptors into an ordinal scale to aid data management and querying.

Conclusions:

  • The defined data descriptors provide a framework for assessing and managing the quality of nutritional epidemiology data.
  • Standardized descriptors enhance the reliability and reproducibility of pooled analyses in nutritional research.
  • Integration with an Ontology for Nutrition Studies will further improve data repository interoperability.