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Temporal Dietary Patterns Using Kernel k-Means Clustering.

Nitin Khanna1, Heather A Eicher-Miller2, Carol J Boushey3

  • 1School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA.

ISM ... : ... IEEE International Symposium on Multimedia ... : Proceedings. IEEE International Symposium on Multimedia
|September 27, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces temporal dietary patterns, analyzing 24-hour energy intake in US adults. It reveals distinct daily eating rhythms linked to chronic disease risk factors.

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Area of Science:

  • Nutrition Science
  • Public Health
  • Data Science

Background:

  • Chronic diseases like heart disease, diabetes, and obesity are linked to diet and nutrient intake.
  • Existing research often overlooks the temporal aspect of dietary habits.
  • Understanding the timing of food consumption is crucial for public health.

Purpose of the Study:

  • To introduce the concept of temporal dietary patterns.
  • To develop a novel construct for analyzing 24-hour energy intake patterns.
  • To identify distinct temporal dietary patterns in the adult US population.

Main Methods:

  • Utilized the National Health and Nutrition Examination Survey (NHANES) 1999-2004 dataset.
  • Proposed a novel distance metric for comparing 24-hour diet records.
  • Applied kernel k-means clustering to identify temporal dietary patterns.

Main Results:

  • Identified distinct 24-hour temporal dietary patterns for energy intake.
  • Demonstrated the utility of the proposed distance metric and clustering approach.
  • Provided insights into the temporal dimension of dietary habits in US adults.

Conclusions:

  • Temporal dietary patterns offer a new perspective on diet-related health outcomes.
  • The developed methodology can be used to analyze daily eating rhythms.
  • Further research can explore the association between identified patterns and chronic diseases.