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'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
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A review of harmonization methods for studying dietary patterns.

Venkata Sukumar Gurugubelli1, Hua Fang1,2, James M Shikany3

  • 1University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, Massachusetts, USA.

Smart Health (Amsterdam, Netherlands)
|March 7, 2022
PubMed
Summary

Data harmonization standardizes variables from different studies for integrated analysis. This process is crucial for nutritional epidemiology and developing smart health applications, enabling more accurate health predictions.

Keywords:
Data harmonizationdiet qualitydietary dataintelligentlongitudinalobservation studypatternrandomized controlled trialsmart health

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Area of Science:

  • Nutritional Epidemiology
  • Health Informatics
  • Data Science

Background:

  • Data harmonization standardizes variables from disparate research studies into comparable datasets.
  • Harmonized data enables more powerful examination and prediction of health outcomes.
  • Prospective harmonization involves pre-collection guidelines, while retrospective harmonization uses existing data and expert knowledge.

Purpose of the Study:

  • To review methods for effective data harmonization in nutritional epidemiology.
  • To outline considerations for future dietary data harmonization.
  • To highlight the importance of harmonization for smart health applications.

Main Methods:

  • Review of existing data harmonization methods and standards.
  • Application of data harmonization techniques to U.S. longitudinal diet datasets.
  • Identification of key components for successful harmonization, including planning, standards, and variable definition.

Main Results:

  • Several large-scale studies, particularly in Europe, maintain harmonized nutrient databases.
  • Steps have been proposed to guide retrospective harmonization processes.
  • Methods were applied to U.S. longitudinal diet datasets, demonstrating practical application.

Conclusions:

  • Future dietary data harmonization requires user agreements for data sharing and clear variable definitions.
  • Secure data storage is essential for maintaining privacy in harmonized datasets.
  • Effective harmonization is foundational for smart health applications promoting healthier eating and understanding dietary impacts on health.