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A Methodological Framework for Aggregating Branded Food Composition Data in mHealth Nutrition Databases: A Case

Antonis Vlassopoulos1,2, Stefania Xanthopoulou1, Sofia Eleftheriou2

  • 1Department of Food Science & Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.

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Summary
This summary is machine-generated.

Aggregating branded food data (BFCDs) offers a cost-effective method to update food composition databases (FCDs) for mHealth apps. This study presents a framework for BFCD data aggregation, yielding new generic food names and nutritional data.

Keywords:
branded food datadata aggregationdigital applicationsgeneric food datamHealthnutritional composition

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Area of Science:

  • Nutrition Science
  • Data Science
  • Computational Biology

Background:

  • Up-to-date food composition databases (FCDs) are crucial for mHealth applications.
  • Expanding FCDs with branded food data (BFCDs) is a potentially cost-efficient strategy.
  • A standardized framework for aggregating BFCDs is currently lacking.

Purpose of the Study:

  • To develop and evaluate a framework for aggregating data from branded food composition databases (BFCDs).
  • To assess the feasibility of using BFCDs to create generic food entries for FCDs.
  • To determine the homogeneity of aggregated nutritional data derived from BFCDs.

Main Methods:

  • A three-step grouping process was applied to 3988 products from the HelTH BFCD.
  • Foods were initially grouped by name, followed by heterogeneity testing (coefficient of variation <20%).
  • Ingredient lists and product characteristics were analyzed to reduce heterogeneity.

Main Results:

  • 347 new generic food names were proposed, derived from at least three branded products.
  • 235 generic food names were populated with aggregated nutritional data.
  • High homogeneity was observed for aggregated energy (95.3%), protein (88.6%), carbohydrate (86%), and sodium (82.6%) values (CV <40%).
  • Lower homogeneity was found for saturated fatty acids (76.3%) and total sugars (65.3%), particularly in baked goods and dairy products.

Conclusions:

  • Branded food composition databases (BFCDs) can effectively supplement existing FCDs with homogeneous, up-to-date nutritional information.
  • The proposed framework demonstrates a viable method for generating generic food data from BFCDs.
  • Applying this framework to larger datasets could enhance generic food name generation and data homogeneity, benefiting mHealth apps and other applications.