Context-aware Based Personalized Recommendation on Mobile for Monitoring Excessive Sugar Consumption of Thai Adolescents

Authors

  • Rodjana Suwan Computer and Communication Engineering for Capacity Building Research Center, School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand https://orcid.org/0000-0001-5634-1961
  • Punnarumol Temdee Computer and Communication Engineering for Capacity Building Research Center, School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand https://orcid.org/0000-0001-9847-157X
  • Ramjee Prasad CTIF Global Capsule (CGC), Department of Business Development and Technology, Aarhus University, Denmark

DOI:

https://doi.org/10.13052/jmm1550-4646.18618

Keywords:

Nutrition, sugar consumption, lifestyle, context-awareness, personalized recommendations

Abstract

Given the harmful effects of excessive sugar consumption, everyone should be aware of the amount of sugar they consume in daily life. Generally, managing nutrition and preventing negative effects from sugar intake requires a nutritional specialist or specialized knowledge, which may not always be readily accessible or understandable. Therefore, there is a need for a mobile application that can track excessive sugar consumption. This study proposes a context-aware personalized recommendation mobile application for monitoring excessive sugar consumption and providing individual recommendations (based on a predefined set of 144 rules) to Thai adolescents. The application is user friendly and can be used to provide recommendations to users for sugar consumption and proper exercise each day. Personal, health, and lifestyle data are collected and analyzed to provide individualized recommendations to each user. Experiments were conducted with 140 Thai adolescents aged 15 to 25 years old. Users’ preferences regarding degree of awareness in presentation style were also investigated. Users rated their satisfaction with the proposed mobile application as very high in terms of both function and personalization. In addition, fully automated recommendations were found to be the preferred degree of awareness among the test group.

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Author Biographies

Rodjana Suwan , Computer and Communication Engineering for Capacity Building Research Center, School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand

Rodjana Suwan received the bachelor’s degree in B.S. (ANIMATION) from Chiang Mai University in 2013. She is currently working as a computer technician and studying at the School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand. Her research interests include mobile application, artificial intelligence, and machine learning.

Punnarumol Temdee, Computer and Communication Engineering for Capacity Building Research Center, School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand

Punnarumol Temdee received B.Eng. in Electronic and Telecommunication Engineering, M. Eng in Electrical Engineering, and Ph.D. in Electrical and Computer Engineering from King Mongkut’s University of Technology Thonburi. She is currently a lecturer at School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand. Her research interests are social network analysis, artificial intelligence, software agent, context-aware computing, and ubiquitous computing.

Ramjee Prasad, CTIF Global Capsule (CGC), Department of Business Development and Technology, Aarhus University, Denmark

Ramjee Prasad is currently a Professor of Future Technologies for Business Ecosystem Innovation (FT4BI) in the Department of Business Development and Technology, Aarhus University, Denmark. He is the Founder President of the CTIF Global Capsule (CGC). He is also the Founder Chairman of the Global ICT Standardisation Forum for India, established in 2009. He has published over 1000 technical papers, more than 15 patents, contributed to several books and has authored, co-authored, and edited over 30 books.

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Published

2022-07-18

How to Cite

Suwan , R. ., Temdee, P. ., & Prasad, R. . (2022). Context-aware Based Personalized Recommendation on Mobile for Monitoring Excessive Sugar Consumption of Thai Adolescents. Journal of Mobile Multimedia, 18(06), 1879–1912. https://doi.org/10.13052/jmm1550-4646.18618

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Multimedia Data and Applications on the Next Generation Communication

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