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A Novel Mobile App for Personalized Dietary Advice Leveraging Persuasive Technology, Computer Vision, and Cloud Computing: Development and Usability Study
The Australian Dietary Guidelines (ADG) translate the best available evidence in nutrition into food choice recommendations. However, adherence to the ADG is poor in Australia. Given that following a healthy diet can be a potentially cost-effective strategy for lowering the risk of chronic diseases,...
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Published in: | JMIR formative research 2023-08, Vol.7, p.e46839-e46839 |
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description | The Australian Dietary Guidelines (ADG) translate the best available evidence in nutrition into food choice recommendations. However, adherence to the ADG is poor in Australia. Given that following a healthy diet can be a potentially cost-effective strategy for lowering the risk of chronic diseases, there is an urgent need to develop novel technologies for individuals to improve their adherence to the ADG.
This study describes the development process and design of a prototype mobile app for personalized dietary advice based on the ADG for adults in Australia, with the aim of exploring the usability of the prototype. The goal of the prototype was to provide personalized, evidence-based support for self-managing food choices in real time.
The guidelines of the design science paradigm were applied to guide the design, development, and evaluation of a progressive web app using Amazon Web Services Elastic Compute Cloud services via iterations. The food layer of the Nutrition Care Process, the strategies of cognitive behavioral theory, and the ADG were translated into prototype features guided by the Persuasive Systems Design model. A gain-framed approach was adopted to promote positive behavior changes. A cross-modal image-to-recipe retrieval model under an Apache 2.0 license was deployed for dietary assessment. A survey using the Mobile Application Rating Scale and semistructured in-depth interviews were conducted to explore the usability of the prototype through convenience sampling (N=15).
The prominent features of the prototype included the use of image-based dietary assessment, food choice tracking with immediate feedback leveraging gamification principles, personal goal setting for food choices, and the provision of recipe ideas and information on the ADG. The overall prototype quality score was "acceptable," with a median of 3.46 (IQR 2.78-3.81) out of 5 points. The median score of the perceived impact of the prototype on healthy eating based on the ADG was 3.83 (IQR 2.75-4.08) out of 5 points. In-depth interviews identified the use of gamification for tracking food choices and innovation in the image-based dietary assessment as the main drivers of the positive user experience of using the prototype.
A novel evidence-based prototype mobile app was successfully developed by leveraging a cross-disciplinary collaboration. A detailed description of the development process and design of the prototype enhances its transparency and provides detailed insights in |
doi_str_mv | 10.2196/46839 |
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This study describes the development process and design of a prototype mobile app for personalized dietary advice based on the ADG for adults in Australia, with the aim of exploring the usability of the prototype. The goal of the prototype was to provide personalized, evidence-based support for self-managing food choices in real time.
The guidelines of the design science paradigm were applied to guide the design, development, and evaluation of a progressive web app using Amazon Web Services Elastic Compute Cloud services via iterations. The food layer of the Nutrition Care Process, the strategies of cognitive behavioral theory, and the ADG were translated into prototype features guided by the Persuasive Systems Design model. A gain-framed approach was adopted to promote positive behavior changes. A cross-modal image-to-recipe retrieval model under an Apache 2.0 license was deployed for dietary assessment. A survey using the Mobile Application Rating Scale and semistructured in-depth interviews were conducted to explore the usability of the prototype through convenience sampling (N=15).
The prominent features of the prototype included the use of image-based dietary assessment, food choice tracking with immediate feedback leveraging gamification principles, personal goal setting for food choices, and the provision of recipe ideas and information on the ADG. The overall prototype quality score was "acceptable," with a median of 3.46 (IQR 2.78-3.81) out of 5 points. The median score of the perceived impact of the prototype on healthy eating based on the ADG was 3.83 (IQR 2.75-4.08) out of 5 points. In-depth interviews identified the use of gamification for tracking food choices and innovation in the image-based dietary assessment as the main drivers of the positive user experience of using the prototype.
A novel evidence-based prototype mobile app was successfully developed by leveraging a cross-disciplinary collaboration. A detailed description of the development process and design of the prototype enhances its transparency and provides detailed insights into its creation. This study provides a valuable example of the development of a novel, evidence-based app for personalized dietary advice on food choices using recent advancements in computer vision. A revised version of this prototype is currently under development.</description><identifier>ISSN: 2561-326X</identifier><identifier>EISSN: 2561-326X</identifier><identifier>DOI: 10.2196/46839</identifier><identifier>PMID: 37549000</identifier><language>eng</language><publisher>Canada: JMIR Publications</publisher><subject>Cognition & reasoning ; Cognitive ability ; Computer vision ; Diet ; Dietary guidelines ; Disease ; Eating behavior ; Food ; Gamification ; Information systems ; Intervention ; Nutrition ; Original Paper ; Problem solving ; Smartphones ; Telemedicine ; Usability ; User experience ; Vegetables</subject><ispartof>JMIR formative research, 2023-08, Vol.7, p.e46839-e46839</ispartof><rights>Vivienne Guan, Chenghuai Zhou, Hengyi Wan, Rengui Zhou, Dongfa Zhang, Sihan Zhang, Wangli Yang, Bhanu Prakash Voutharoja, Lei Wang, Khin Than Win, Peng Wang. Originally published in JMIR Formative Research (https://formative.jmir.org), 07.08.2023.</rights><rights>2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Vivienne Guan, Chenghuai Zhou, Hengyi Wan, Rengui Zhou, Dongfa Zhang, Sihan Zhang, Wangli Yang, Bhanu Prakash Voutharoja, Lei Wang, Khin Than Win, Peng Wang. Originally published in JMIR Formative Research (https://formative.jmir.org), 07.08.2023. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c458t-c8f632c43fb72b972af3ea403fd74d8659db12df6dd4f433a0b6de44da104e733</citedby><cites>FETCH-LOGICAL-c458t-c8f632c43fb72b972af3ea403fd74d8659db12df6dd4f433a0b6de44da104e733</cites><orcidid>0000-0002-1352-2858 ; 0000-0002-7810-6388 ; 0009-0008-9174-860X ; 0009-0009-7570-2895 ; 0000-0002-0961-0441 ; 0000-0002-3162-1788 ; 0009-0008-2495-9239 ; 0000-0002-5397-9115 ; 0009-0003-1849-3405 ; 0009-0000-5720-9479 ; 0009-0006-7367-1528</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2918545798/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918545798?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25731,27901,27902,36989,36990,38493,43871,44566,53766,53768,74155,74869</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37549000$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Guan, Vivienne</creatorcontrib><creatorcontrib>Zhou, Chenghuai</creatorcontrib><creatorcontrib>Wan, Hengyi</creatorcontrib><creatorcontrib>Zhou, Rengui</creatorcontrib><creatorcontrib>Zhang, Dongfa</creatorcontrib><creatorcontrib>Zhang, Sihan</creatorcontrib><creatorcontrib>Yang, Wangli</creatorcontrib><creatorcontrib>Voutharoja, Bhanu Prakash</creatorcontrib><creatorcontrib>Wang, Lei</creatorcontrib><creatorcontrib>Win, Khin Than</creatorcontrib><creatorcontrib>Wang, Peng</creatorcontrib><title>A Novel Mobile App for Personalized Dietary Advice Leveraging Persuasive Technology, Computer Vision, and Cloud Computing: Development and Usability Study</title><title>JMIR formative research</title><addtitle>JMIR Form Res</addtitle><description>The Australian Dietary Guidelines (ADG) translate the best available evidence in nutrition into food choice recommendations. However, adherence to the ADG is poor in Australia. Given that following a healthy diet can be a potentially cost-effective strategy for lowering the risk of chronic diseases, there is an urgent need to develop novel technologies for individuals to improve their adherence to the ADG.
This study describes the development process and design of a prototype mobile app for personalized dietary advice based on the ADG for adults in Australia, with the aim of exploring the usability of the prototype. The goal of the prototype was to provide personalized, evidence-based support for self-managing food choices in real time.
The guidelines of the design science paradigm were applied to guide the design, development, and evaluation of a progressive web app using Amazon Web Services Elastic Compute Cloud services via iterations. The food layer of the Nutrition Care Process, the strategies of cognitive behavioral theory, and the ADG were translated into prototype features guided by the Persuasive Systems Design model. A gain-framed approach was adopted to promote positive behavior changes. A cross-modal image-to-recipe retrieval model under an Apache 2.0 license was deployed for dietary assessment. A survey using the Mobile Application Rating Scale and semistructured in-depth interviews were conducted to explore the usability of the prototype through convenience sampling (N=15).
The prominent features of the prototype included the use of image-based dietary assessment, food choice tracking with immediate feedback leveraging gamification principles, personal goal setting for food choices, and the provision of recipe ideas and information on the ADG. The overall prototype quality score was "acceptable," with a median of 3.46 (IQR 2.78-3.81) out of 5 points. The median score of the perceived impact of the prototype on healthy eating based on the ADG was 3.83 (IQR 2.75-4.08) out of 5 points. In-depth interviews identified the use of gamification for tracking food choices and innovation in the image-based dietary assessment as the main drivers of the positive user experience of using the prototype.
A novel evidence-based prototype mobile app was successfully developed by leveraging a cross-disciplinary collaboration. A detailed description of the development process and design of the prototype enhances its transparency and provides detailed insights into its creation. This study provides a valuable example of the development of a novel, evidence-based app for personalized dietary advice on food choices using recent advancements in computer vision. A revised version of this prototype is currently under development.</description><subject>Cognition & reasoning</subject><subject>Cognitive ability</subject><subject>Computer vision</subject><subject>Diet</subject><subject>Dietary guidelines</subject><subject>Disease</subject><subject>Eating behavior</subject><subject>Food</subject><subject>Gamification</subject><subject>Information systems</subject><subject>Intervention</subject><subject>Nutrition</subject><subject>Original Paper</subject><subject>Problem solving</subject><subject>Smartphones</subject><subject>Telemedicine</subject><subject>Usability</subject><subject>User experience</subject><subject>Vegetables</subject><issn>2561-326X</issn><issn>2561-326X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkl2L1DAUhoso7rLOX5CACF7saPPRpPVGhlk_FsYPcFe8C2ly2s2QNjVpC-NP8deanRmXXa-SnDx5eA85WbbA-WuCK_6G8ZJWj7JTUnC8pIT_fHxvf5ItYtzmeU4w5qKiT7MTKgpWpcpp9meFvvgZHPrsa-sArYYBNT6gbxCi75Wzv8GgCwujCju0MrPVgDYwQ1Ct7ds9NqloZ0BXoG9673y7O0dr3w3TCAH9sNH6_hyp3qC185M5XqW3b9FF8jg_dNCPe-A6qpTBjjv0fZzM7ln2pFEuwuK4nmXXH95frT8tN18_Xq5Xm6VmRTkuddlwSjSjTS1IXQmiGgqK5bQxgpmSF5WpMTENN4Y1jFKV19wAY0bhnIGg9Cy7PHiNV1s5BNulXqVXVu4LPrRShdFqB5KQPGd1jRUXhOEClwXRxIh0MEYZzJLr3cE1THUHRqfWgnIPpA9vensjWz_LlIURQXkyvDoagv81QRxlZ6MG51QPfoqSlExQxhi_Df7iP3Trp5A-LVFVysYKUZWJenmgdPAxBmju0uBc3k6P3E9P4p7fj35H_ZsV-hcNlcAH</recordid><startdate>20230807</startdate><enddate>20230807</enddate><creator>Guan, Vivienne</creator><creator>Zhou, Chenghuai</creator><creator>Wan, Hengyi</creator><creator>Zhou, Rengui</creator><creator>Zhang, Dongfa</creator><creator>Zhang, Sihan</creator><creator>Yang, Wangli</creator><creator>Voutharoja, Bhanu Prakash</creator><creator>Wang, Lei</creator><creator>Win, Khin Than</creator><creator>Wang, Peng</creator><general>JMIR Publications</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>NAPCQ</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-1352-2858</orcidid><orcidid>https://orcid.org/0000-0002-7810-6388</orcidid><orcidid>https://orcid.org/0009-0008-9174-860X</orcidid><orcidid>https://orcid.org/0009-0009-7570-2895</orcidid><orcidid>https://orcid.org/0000-0002-0961-0441</orcidid><orcidid>https://orcid.org/0000-0002-3162-1788</orcidid><orcidid>https://orcid.org/0009-0008-2495-9239</orcidid><orcidid>https://orcid.org/0000-0002-5397-9115</orcidid><orcidid>https://orcid.org/0009-0003-1849-3405</orcidid><orcidid>https://orcid.org/0009-0000-5720-9479</orcidid><orcidid>https://orcid.org/0009-0006-7367-1528</orcidid></search><sort><creationdate>20230807</creationdate><title>A Novel Mobile App for Personalized Dietary Advice Leveraging Persuasive Technology, Computer Vision, and Cloud Computing: Development and Usability Study</title><author>Guan, Vivienne ; Zhou, Chenghuai ; Wan, Hengyi ; Zhou, Rengui ; Zhang, Dongfa ; Zhang, Sihan ; Yang, Wangli ; Voutharoja, Bhanu Prakash ; Wang, Lei ; Win, Khin Than ; Wang, Peng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c458t-c8f632c43fb72b972af3ea403fd74d8659db12df6dd4f433a0b6de44da104e733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Cognition & reasoning</topic><topic>Cognitive ability</topic><topic>Computer vision</topic><topic>Diet</topic><topic>Dietary guidelines</topic><topic>Disease</topic><topic>Eating behavior</topic><topic>Food</topic><topic>Gamification</topic><topic>Information systems</topic><topic>Intervention</topic><topic>Nutrition</topic><topic>Original Paper</topic><topic>Problem solving</topic><topic>Smartphones</topic><topic>Telemedicine</topic><topic>Usability</topic><topic>User experience</topic><topic>Vegetables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guan, Vivienne</creatorcontrib><creatorcontrib>Zhou, Chenghuai</creatorcontrib><creatorcontrib>Wan, Hengyi</creatorcontrib><creatorcontrib>Zhou, Rengui</creatorcontrib><creatorcontrib>Zhang, Dongfa</creatorcontrib><creatorcontrib>Zhang, Sihan</creatorcontrib><creatorcontrib>Yang, Wangli</creatorcontrib><creatorcontrib>Voutharoja, Bhanu Prakash</creatorcontrib><creatorcontrib>Wang, Lei</creatorcontrib><creatorcontrib>Win, Khin Than</creatorcontrib><creatorcontrib>Wang, Peng</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>ProQuest - Health & Medical Complete保健、医学与药学数据库</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Nursing & Allied Health Premium</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Open Access: DOAJ - Directory of Open Access Journals</collection><jtitle>JMIR formative research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guan, Vivienne</au><au>Zhou, Chenghuai</au><au>Wan, Hengyi</au><au>Zhou, Rengui</au><au>Zhang, Dongfa</au><au>Zhang, Sihan</au><au>Yang, Wangli</au><au>Voutharoja, Bhanu Prakash</au><au>Wang, Lei</au><au>Win, Khin Than</au><au>Wang, Peng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Novel Mobile App for Personalized Dietary Advice Leveraging Persuasive Technology, Computer Vision, and Cloud Computing: Development and Usability Study</atitle><jtitle>JMIR formative research</jtitle><addtitle>JMIR Form Res</addtitle><date>2023-08-07</date><risdate>2023</risdate><volume>7</volume><spage>e46839</spage><epage>e46839</epage><pages>e46839-e46839</pages><issn>2561-326X</issn><eissn>2561-326X</eissn><abstract>The Australian Dietary Guidelines (ADG) translate the best available evidence in nutrition into food choice recommendations. However, adherence to the ADG is poor in Australia. Given that following a healthy diet can be a potentially cost-effective strategy for lowering the risk of chronic diseases, there is an urgent need to develop novel technologies for individuals to improve their adherence to the ADG.
This study describes the development process and design of a prototype mobile app for personalized dietary advice based on the ADG for adults in Australia, with the aim of exploring the usability of the prototype. The goal of the prototype was to provide personalized, evidence-based support for self-managing food choices in real time.
The guidelines of the design science paradigm were applied to guide the design, development, and evaluation of a progressive web app using Amazon Web Services Elastic Compute Cloud services via iterations. The food layer of the Nutrition Care Process, the strategies of cognitive behavioral theory, and the ADG were translated into prototype features guided by the Persuasive Systems Design model. A gain-framed approach was adopted to promote positive behavior changes. A cross-modal image-to-recipe retrieval model under an Apache 2.0 license was deployed for dietary assessment. A survey using the Mobile Application Rating Scale and semistructured in-depth interviews were conducted to explore the usability of the prototype through convenience sampling (N=15).
The prominent features of the prototype included the use of image-based dietary assessment, food choice tracking with immediate feedback leveraging gamification principles, personal goal setting for food choices, and the provision of recipe ideas and information on the ADG. The overall prototype quality score was "acceptable," with a median of 3.46 (IQR 2.78-3.81) out of 5 points. The median score of the perceived impact of the prototype on healthy eating based on the ADG was 3.83 (IQR 2.75-4.08) out of 5 points. In-depth interviews identified the use of gamification for tracking food choices and innovation in the image-based dietary assessment as the main drivers of the positive user experience of using the prototype.
A novel evidence-based prototype mobile app was successfully developed by leveraging a cross-disciplinary collaboration. A detailed description of the development process and design of the prototype enhances its transparency and provides detailed insights into its creation. This study provides a valuable example of the development of a novel, evidence-based app for personalized dietary advice on food choices using recent advancements in computer vision. A revised version of this prototype is currently under development.</abstract><cop>Canada</cop><pub>JMIR Publications</pub><pmid>37549000</pmid><doi>10.2196/46839</doi><orcidid>https://orcid.org/0000-0002-1352-2858</orcidid><orcidid>https://orcid.org/0000-0002-7810-6388</orcidid><orcidid>https://orcid.org/0009-0008-9174-860X</orcidid><orcidid>https://orcid.org/0009-0009-7570-2895</orcidid><orcidid>https://orcid.org/0000-0002-0961-0441</orcidid><orcidid>https://orcid.org/0000-0002-3162-1788</orcidid><orcidid>https://orcid.org/0009-0008-2495-9239</orcidid><orcidid>https://orcid.org/0000-0002-5397-9115</orcidid><orcidid>https://orcid.org/0009-0003-1849-3405</orcidid><orcidid>https://orcid.org/0009-0000-5720-9479</orcidid><orcidid>https://orcid.org/0009-0006-7367-1528</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Cognition & reasoning Cognitive ability Computer vision Diet Dietary guidelines Disease Eating behavior Food Gamification Information systems Intervention Nutrition Original Paper Problem solving Smartphones Telemedicine Usability User experience Vegetables |
title | A Novel Mobile App for Personalized Dietary Advice Leveraging Persuasive Technology, Computer Vision, and Cloud Computing: Development and Usability Study |
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