Smart Food: Crowdsourcing of experts in nutrition and non-experts in identifying calories of meals using smartphone as a potential tool contributing to obesity prevention and management

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To address an increasing global health problem of obesity, further innovative initiatives are required. One such initiative is personalized messaging using mobile applications as a potential tool contributing to obesity prevention and management. In order to achieve this, there are challenges that need to be considered first including the accurate estimation of calories of meals and individuals’ calorific intakes using a smartphones. There is also a lack of evidence indicating whether novices, peers and family members can provide accurate tailored feedback on calorie intake and nutrition. The two study objectives were i. To determine the feasibility of experts in nutrition and non-experts accurately identifying calories of meals from photographs as taken on a smartphone; and ii. To inform the development a personalized messaging system for obesity prevention and management using a mobile application. This study was an experimental design using a quantitative online survey with 24 participants, consisting of 12 experts in nutrition and/or dietetics, and 12 non-experts. The non-expert group attended a training session and both groups completed an online survey. The survey consisted of 15 meals, the participants were required to view the photographs and then answer the following question for each photograph: “From viewing the above photograph, enter the number of calories you consider is in this meal? ___________Kcal OR ___________KJ”. Crowdsourcing was used. The results revealed that the percentage difference between the estimated calories count in the meals against the actual number of calories was on average +55% (SD 79.9) for the non-expert group and +8% (SD 15.1) for the expert group (t-test, P
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages1-3
Number of pages3
Publication statusPublished - 5 Nov 2014
EventIEEE International Conference on Bioinformatics and Biomedicine - Belfast
Duration: 5 Nov 2014 → …

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine
Period5/11/14 → …

Fingerprint

Crowdsourcing
Meals
Obesity
Food
Mobile Applications
Dietetics
Research Design
Smartphone

Keywords

  • obesity
  • crowdsourcing
  • food

Cite this

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title = "Smart Food: Crowdsourcing of experts in nutrition and non-experts in identifying calories of meals using smartphone as a potential tool contributing to obesity prevention and management",
abstract = "To address an increasing global health problem of obesity, further innovative initiatives are required. One such initiative is personalized messaging using mobile applications as a potential tool contributing to obesity prevention and management. In order to achieve this, there are challenges that need to be considered first including the accurate estimation of calories of meals and individuals’ calorific intakes using a smartphones. There is also a lack of evidence indicating whether novices, peers and family members can provide accurate tailored feedback on calorie intake and nutrition. The two study objectives were i. To determine the feasibility of experts in nutrition and non-experts accurately identifying calories of meals from photographs as taken on a smartphone; and ii. To inform the development a personalized messaging system for obesity prevention and management using a mobile application. This study was an experimental design using a quantitative online survey with 24 participants, consisting of 12 experts in nutrition and/or dietetics, and 12 non-experts. The non-expert group attended a training session and both groups completed an online survey. The survey consisted of 15 meals, the participants were required to view the photographs and then answer the following question for each photograph: “From viewing the above photograph, enter the number of calories you consider is in this meal? ___________Kcal OR ___________KJ”. Crowdsourcing was used. The results revealed that the percentage difference between the estimated calories count in the meals against the actual number of calories was on average +55{\%} (SD 79.9) for the non-expert group and +8{\%} (SD 15.1) for the expert group (t-test, P",
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author = "Anne Moorhead and Raymond Bond and Huiru Zheng",
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AB - To address an increasing global health problem of obesity, further innovative initiatives are required. One such initiative is personalized messaging using mobile applications as a potential tool contributing to obesity prevention and management. In order to achieve this, there are challenges that need to be considered first including the accurate estimation of calories of meals and individuals’ calorific intakes using a smartphones. There is also a lack of evidence indicating whether novices, peers and family members can provide accurate tailored feedback on calorie intake and nutrition. The two study objectives were i. To determine the feasibility of experts in nutrition and non-experts accurately identifying calories of meals from photographs as taken on a smartphone; and ii. To inform the development a personalized messaging system for obesity prevention and management using a mobile application. This study was an experimental design using a quantitative online survey with 24 participants, consisting of 12 experts in nutrition and/or dietetics, and 12 non-experts. The non-expert group attended a training session and both groups completed an online survey. The survey consisted of 15 meals, the participants were required to view the photographs and then answer the following question for each photograph: “From viewing the above photograph, enter the number of calories you consider is in this meal? ___________Kcal OR ___________KJ”. Crowdsourcing was used. The results revealed that the percentage difference between the estimated calories count in the meals against the actual number of calories was on average +55% (SD 79.9) for the non-expert group and +8% (SD 15.1) for the expert group (t-test, P

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