Smart-card-based automatic meal record system intervention tool for analysis using data mining approach.
The Smart-card-based Automatic Meal Record system for company cafeterias (AutoMealRecord system) was recently developed and used to monitor employee eating habits. The system could be a unique nutrition assessment tool for automatically monitoring the meal purchases of all employees, although it only focuses on company cafeterias and has never been validated. Before starting an interventional study, we tested the reliability of the data collected by the system using the data mining approach. The AutoMealRecord data were examined to determine if it could predict current obesity. All data used in this study (n = 899) were collected by a major electric company based in Tokyo, which has been operating the AutoMealRecord system for several years. We analyzed dietary patterns by principal component analysis using data from the system and extracted 5 major dietary patterns: healthy, traditional Japanese, Chinese, Japanese noodles, and pasta. The ability to predict current body mass index (BMI) with dietary preference was assessed with multiple linear regression analyses, and in the current study, BMI was positively correlated with male gender, preference for "Japanese noodles," mean energy intake, protein content, and frequency of body measurement at a body measurement booth in the cafeteria. There was a negative correlation with age, dietary fiber, and lunchtime cafeteria use (R(2) = 0.22). This regression model predicted "would-be obese" participants (BMI >or= 23) with 68.8% accuracy by leave-one-out cross validation. This shows that there was sufficient predictability of BMI based on data from the AutoMealRecord System. We conclude that the AutoMealRecord system is valuable for further consideration as a health care intervention tool.
Department of Health Communication, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Satoko
S
Zenitani
Hiromu
H
Nishiuchi
Takahiro
T
Kiuchi
D000328:Adult; D000367:Age Factors; D015992:Body Mass Index; D057225:Data Mining / Q000379:methods*; D004032:Diet; D015930:Diet Records; D001330:Electronic Data Processing; D002149:Energy Intake; D005247:Feeding Behavior; D005260:Female; D005518:Food Preferences; D005522:Food Services / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015596:Nutrition Assessment; D009752:Nutritional Status; D009765:Obesity; D012044:Regression Analysis; D015203:Reproducibility of Results; D012737:Sex Factors; D014041:Tokyo; D055815:Young Adult