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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
06:21

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

Smart-card-based automatic meal record system intervention tool for analysis using data mining approach.

Satoko Zenitani1, Hiromu Nishiuchi, Takahiro Kiuchi

  • 1Department of Health Communication, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Nutrition Research (New York, N.Y.)
|June 11, 2010
PubMed
Summary

The Automatic Meal Record system can predict employee obesity risk using dietary patterns. This smart-card system offers a valuable tool for workplace health interventions and nutrition assessment.

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Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq
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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
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Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq
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Area of Science:

  • Nutrition Science
  • Data Mining
  • Public Health

Background:

  • The Automatic Meal Record (AutoMealRecord) system monitors employee eating habits in company cafeterias.
  • This system has potential as a nutrition assessment tool but requires validation.
  • Previous studies have not assessed its reliability for predicting health outcomes.

Purpose of the Study:

  • To validate the reliability of the AutoMealRecord system's data.
  • To determine if dietary patterns from the system can predict current obesity.
  • To assess the system's potential as a health care intervention tool.

Main Methods:

  • Data mining approach applied to AutoMealRecord data from 899 employees.
  • Principal Component Analysis (PCA) used to identify dietary patterns.
  • Multiple linear regression analyses to assess Body Mass Index (BMI) predictability.

Main Results:

  • Five major dietary patterns were identified: healthy, traditional Japanese, Chinese, Japanese noodles, and pasta.
  • BMI positively correlated with male gender, "Japanese noodles" preference, energy intake, protein, and measurement frequency.
  • BMI negatively correlated with age, dietary fiber, and lunchtime cafeteria use (R² = 0.22).
  • The model predicted "would-be obese" participants (BMI ≥ 23) with 68.8% accuracy.

Conclusions:

  • The AutoMealRecord system demonstrates sufficient predictability of BMI.
  • The system is a valuable tool for nutrition assessment and health interventions in corporate settings.
  • Further consideration of the AutoMealRecord system for healthcare interventions is warranted.