Literature DB >> 16011560

Using smart card technology to monitor the eating habits of children in a school cafeteria: 1. Developing and validating the methodology.

N Lambert1, J Plumb, B Looise, I T Johnson, I Harvey, C Wheeler, M Robinson, P Rolfe.   

Abstract

OBJECTIVE: The aim of the study was to test the feasibility of using smart card technology to track the eating behaviours of nearly a thousand children in a school cafeteria.
METHODS: Within a large boys' school a smart card based system was developed that was capable of providing a full electronic audit of all the individual transactions that occurred within the cafeteria. This dataset was interfaced to an electronic version of the McCance and Widdowson composition of foods dataset. The accuracy of the smart card generated data and the influence of portion size and wastage were determined empirically during two 5-day trials.
RESULTS: The smart card system created succeeded in generating precise data on the food choices made by hundreds of children over an indefinite time period. The data was expanded to include a full nutrient analysis of all the foods chosen. The accuracy of this information was only constrained by the limitations facing all food composition research, e.g. variations in recipes, portion sizes, cooking practices, etc. Although technically possible to introduce wastage correction factors into the software, thereby providing information upon foods consumed, this was not seen as universally practical.
CONCLUSION: The study demonstrated the power of smart card technology for monitoring food/nutrient choice over limitless time in environments such as school cafeterias. The strengths, limitations and applications of such technology are discussed.

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Year:  2005        PMID: 16011560     DOI: 10.1111/j.1365-277X.2005.00617.x

Source DB:  PubMed          Journal:  J Hum Nutr Diet        ISSN: 0952-3871            Impact factor:   3.089


  8 in total

1.  Energy intake estimation from counts of chews and swallows.

Authors:  Juan M Fontana; Janine A Higgins; Stephanie C Schuckers; France Bellisle; Zhaoxing Pan; Edward L Melanson; Michael R Neuman; Edward Sazonov
Journal:  Appetite       Date:  2014-11-07       Impact factor: 3.868

2.  qDIET: toward an automated, self-sustaining knowledge base to facilitate linking point-of-sale grocery items to nutritional content.

Authors:  Valliammai Chidambaram; Philip J Brewster; Kristine C Jordan; John F Hurdle
Journal:  AMIA Annu Symp Proc       Date:  2013-11-16

3.  Adolescents' beverage choice at school and the impact on sugar intake.

Authors:  H Ensaff; J Russell; M E Barker
Journal:  Eur J Clin Nutr       Date:  2015-10-07       Impact factor: 4.016

4.  Food Choice Architecture: An Intervention in a Secondary School and its Impact on Students' Plant-based Food Choices.

Authors:  Hannah Ensaff; Matt Homer; Pinki Sahota; Debbie Braybrook; Susan Coan; Helen McLeod
Journal:  Nutrients       Date:  2015-06-02       Impact factor: 5.717

Review 5.  How can GPS technology help us better understand exposure to the food environment? A systematic review.

Authors:  Andreea Cetateanu; Andy Jones
Journal:  SSM Popul Health       Date:  2016-12

Review 6.  How has big data contributed to obesity research? A review of the literature.

Authors:  Kate A Timmins; Mark A Green; Duncan Radley; Michelle A Morris; Jamie Pearce
Journal:  Int J Obes (Lond)       Date:  2018-07-18       Impact factor: 5.095

7.  Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor.

Authors:  Xin Yang; Abul Doulah; Muhammad Farooq; Jason Parton; Megan A McCrory; Janine A Higgins; Edward Sazonov
Journal:  Sci Rep       Date:  2019-01-10       Impact factor: 4.379

8.  Online Pre-Order Systems for School Lunches: Insights from a Cross-Sectional Study in Primary Schools.

Authors:  Nahlah Alkhunain; Jennifer Bernadette Moore; Hannah Ensaff
Journal:  Nutrients       Date:  2022-02-23       Impact factor: 5.717

  8 in total

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