A review of harmonization methods for studying dietary patterns

Venkata Sukumar Gurugubelli, Hua Fang, James M. Shikany, Salvador V. Balkus, Joshua Rumbut, Hieu Ngo, Honggang Wang, Jeroan J. Allison, Lyn M. Steffen

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations


Data harmonization is the process by which each of the variables from different research studies are standardized to similar units resulting in comparable datasets. These data may be integrated for more powerful and accurate examination and prediction of outcomes for use in the intelligent and smart electronic health software programs and systems. Prospective harmonization is performed when researchers create guidelines for gathering and managing the data before data collection begins. In contrast, retrospective harmonization is performed by pooling previously collected data from various studies using expert domain knowledge to identify and translate variables. In nutritional epidemiology, dietary data harmonization is often necessary to construct the nutrient and food databases necessary to answer complex research questions and develop effective public health policy. In this paper, we review methods for effective data harmonization, including developing a harmonization plan, which common standards already exist for harmonization, and defining variables needed to harmonize datasets. Currently, several large-scale studies maintain harmonized nutrient databases, especially in Europe, and steps have been proposed to inform the retrospective harmonization process. As an example, data harmonization methods are applied to several U.S longitudinal diet datasets. Based on our review, considerations for future dietary data harmonization include user agreements for sharing private data among participating studies, defining variables and data dictionaries that accurately map variables among studies, and the use of secure data storage servers to maintain privacy. These considerations establish necessary components of harmonized data for smart health applications which can promote healthier eating and provide greater insights into the effect of dietary patterns on health.

Original languageEnglish (US)
Article number100263
JournalSmart Health
StatePublished - Mar 2022

Bibliographical note

Funding Information:
This research was partly supported by NIH 1R56DK114514-01A1 and NIH R01DK129432 to Dr. Fang.

Publisher Copyright:
© 2021


  • Data harmonization
  • Diet quality
  • Dietary data
  • Intelligent
  • Longitudinal
  • Observation study
  • Pattern
  • Randomized controlled trial
  • Smart health

PubMed: MeSH publication types

  • Journal Article


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