Science, Irving K. Barber Faculty of (Okanagan)
Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
DSpace
UBCO
Sowande, Iyanuoluwa
Master of Science - MSc
University of British Columbia
Unhealthy dietary habits remain a global concern, particularly among individuals managing health conditions. While many mobile nutrition applications exist, they often emphasize calorie counting or weight loss, overlook personalization, and lack a foundation in behavioral change theories. This research presents HealKitchen, a persuasive mobile application that aims to support healthier dietary behaviors by leveraging behavioral science, artificial intelligence, and user-centered design. This research addresses five main objectives: the development of a mobile health app that aims to influence healthier dietary choices, the implementation of behavioral theories into the mobile application, the implementation of a recommendation system that delivers personalized nutritional guidance, the development and evaluation of an object detection model capable of identifying grocery items, and the assessment of the mobile application's usability, perceived usefulness, and engagement. HealKitchen integrates a range of personalized features, including a recipe recommendation system adapted to user health requirements, a recipe curation engine, and an image recognition model that identifies grocery items and provides nutritional information. The application design also draws on five foundational behavior change theories, operationalized into persuasive features that nudge users toward healthier choices. To evaluate the application, a mixed-methods user study involving 20 participants was conducted. Quantitative survey data revealed a high level of usability (mean = 4.6), perceived usefulness (mean = 4.45), and aesthetics (mean = 4.49). Thematic analysis of qualitative feedback highlighted the need for recipe diversity, improved multimedia support, and additional features, informing iterative design enhancements. This research demonstrates the feasibility of building a persuasive and theory-driven mobile health application for personalized nutrition support.
HealKitchen - A Persuasive, ArtificialIntelligence Driven, MobileApplication to Influence HealthyDietary HabitsbyIyanuoluwa SowandeA THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe College of Graduate Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)August 2025© Iyanuoluwa Sowande, 2025The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis entitled:HealKitchen - A Persuasive, Artificial Intelligence Driven,Mobile Application to Influence Healthy Dietary Habitssubmitted by Iyanuoluwa Sowande in partial fulfilment of the require-ments of the degree of Master of ScienceDr. Ifeoma Adaji, Irving K. Barber Faculty of ScienceSupervisorDr. Gema Rodr´ıguez-Pe´rez, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Abdul-Fatawu Abdulai, The University of British Columbia (Vancouver)Supervisory Committee MemberDr. Nicolas Peleato, School of EngineeringUniversity ExamineriiAbstractUnhealthy dietary habits remain a global concern, particularly amongindividuals managing health conditions. While many mobile nutrition appli-cations exist, they often emphasize calorie counting or weight loss, overlookpersonalization, and lack a foundation in behavioral change theories. Thisresearch presents HealKitchen, a persuasive mobile application that aims tosupport healthier dietary behaviors by leveraging behavioral science, artifi-cial intelligence, and user-centered design.This research addresses five main objectives: the development of a mobilehealth app that aims to influence healthier dietary choices, the implementa-tion of behavioral theories into the mobile application, the implementationof a recommendation system that delivers personalized nutritional guidance,the development and evaluation of an object detection model capable ofidentifying grocery items, and the assessment of the mobile application’susability, perceived usefulness, and engagement.HealKitchen integrates a range of personalized features, including arecipe recommendation system adapted to user health requirements, a recipecuration engine, and an image recognition model that identifies groceryitems and provides nutritional information. The application design alsodraws on five foundational behavior change theories, operationalized intopersuasive features that nudge users toward healthier choices.To evaluate the application, a mixed-methods user study involving 20participants was conducted. Quantitative survey data revealed a high levelof usability (mean = 4.6), perceived usefulness (mean = 4.45), and aesthet-ics (mean = 4.49). Thematic analysis of qualitative feedback highlightedthe need for recipe diversity, improved multimedia support, and additionalfeatures, informing iterative design enhancements.This research demonstrates the feasibility of building a persuasive andtheory-driven mobile health application for personalized nutrition support.iiiLay SummaryThis research presents HealKitchen, a mobile application designed tosupport healthy food choices, especially for individuals with specific healthconditions like diabetes or high blood pressure. Many people want to eatbetter but struggle to identify the right foods for their needs. HealKitchenaddresses this by combining health science and artificial intelligence to rec-ommend recipes and grocery items based on a user’s health conditions.HealKitchen helps users find healthy meals, manage grocery lists, andidentify nutrients of items in a grocery store. Unlike other apps that onlytrack meals after eating, HealKitchen focuses on influencing decisions whenfood choices are made. Its user-friendly features aim to encourage long-termhealthy habits by offering relevant suggestions during this phase.By guiding users with personal recommendations and nutritional infor-mation, HealKitchen aims to empower better dietary decisions and supporthealthy dietary behavior.ivPrefaceThis thesis is the original work by the author, Iyanuoluwa Sowande.This study was approved by the University of British Columbia BehavioralResearch Ethics Board (BREB) - Certificate Number: H24-01526One chapter of this thesis has already been published in a conference:I. Sowande and I. Adaji, “Design and Development of HealK-itchen - A Persuasive Application to Influence Healthy Dietary Habits”2025 IEEE 15th Annual Computing and Communication Workshopand Conference (CCWC), Las Vegas, NV, USA, 2025, pp. 611-618,doi:10.1109/CCWC62904.2025.10903959.Generative AI tools were used in this thesis to generate recipe contentfor the application, as described in Chapter 3. They were also used to checkthe correctness of grammar and to help articulate some content in this thesisvContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vContents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . xiiDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiChapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . 11.1 Overview of dietary lifestyle and its implications in the healthof individuals . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Advancements in mobile health dietary interventions . . . . . 21.3 Research Problem: Lack of mHealth Solutions Targeting Var-ious Health Challenges . . . . . . . . . . . . . . . . . . . . . . 31.4 Objectives of the Research . . . . . . . . . . . . . . . . . . . . 51.4.1 Research Questions . . . . . . . . . . . . . . . . . . . . 51.5 Significance of Research and Contributions . . . . . . . . . . 61.6 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . 7Chapter 2: Related Work . . . . . . . . . . . . . . . . . . . . . . 82.1 Role of Mobile Applications in Healthcare . . . . . . . . . . . 82.2 Behavioral Theories in Influencing Behavioral Change . . . . 92.2.1 High-Performance Cycle (HPC) . . . . . . . . . . . . . 112.2.2 Action Regulation Theory . . . . . . . . . . . . . . . . 11viCONTENTS2.2.3 Social Cognitive Theory . . . . . . . . . . . . . . . . . 122.2.4 Theory of Planned Behavior . . . . . . . . . . . . . . . 122.2.5 Self-Determination Theory . . . . . . . . . . . . . . . 132.3 Persuasive Strategies in Health Applications . . . . . . . . . . 142.4 Grocery Shopping-Focused Nutrition Apps . . . . . . . . . . . 162.5 Artificial Intelligence in Health Applications . . . . . . . . . . 182.5.1 Image Recognition Systems in Health Applications . . 182.6 Gaps and Opportunities in Current Solutions . . . . . . . . . 20Chapter 3: Systematic Literature Review . . . . . . . . . . . . 233.1 Search Strategy & Inclusion Criteria . . . . . . . . . . . . . . 233.2 Coding Themes . . . . . . . . . . . . . . . . . . . . . . . . . . 263.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.3.1 Persuasive Strategies . . . . . . . . . . . . . . . . . . . 283.3.2 Features & Functionality . . . . . . . . . . . . . . . . 303.3.3 Adherence to Evidence-Based Guidelines . . . . . . . . 313.3.4 Target Demographic . . . . . . . . . . . . . . . . . . . 323.3.5 Technology Used . . . . . . . . . . . . . . . . . . . . . 323.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Chapter 4: Design & Development of HealKitchen . . . . . . 374.1 Integration of Comprehensive Behavioral Model . . . . . . . . 374.1.1 High Performance Cycle . . . . . . . . . . . . . . . . . 384.1.2 Action Regulation Theory . . . . . . . . . . . . . . . . 394.1.3 Social Cognitive Theory . . . . . . . . . . . . . . . . . 404.1.4 Theory of Planned Behavior . . . . . . . . . . . . . . . 414.1.5 Self-Determination Theory . . . . . . . . . . . . . . . 414.2 Overview of System Architecture . . . . . . . . . . . . . . . . 424.3 Application System Component . . . . . . . . . . . . . . . . . 444.3.1 Model-View-Controller (MVC) Architecture . . . . . . 444.3.2 Database Design . . . . . . . . . . . . . . . . . . . . . 454.3.3 Recipe Recommendation System . . . . . . . . . . . . 524.3.4 Supporting Application System Features . . . . . . . 594.4 Image Recognition System Component . . . . . . . . . . . . . 624.4.1 Data Gathering . . . . . . . . . . . . . . . . . . . . . . 644.4.2 Data Annotation and Labeling . . . . . . . . . . . . . 644.4.3 Image Preprocessing . . . . . . . . . . . . . . . . . . . 654.4.4 Object Detection Model Architecture and Training . . 664.4.5 Evaluation of Image Recognition Model . . . . . . . . 69viiCONTENTS4.4.6 Nutrient Information & Integration in Mobile Appli-cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Chapter 5: Development of Mobile Application Interface . . . 755.1 Mobile Application Interface Development . . . . . . . . . . . 755.1.1 User Onboarding and Authentication System . . . . . 755.1.2 Recipe Recommendations . . . . . . . . . . . . . . . . 765.1.3 Recipe Ratings . . . . . . . . . . . . . . . . . . . . . . 775.1.4 Grocery Shopping Lists and Favorite Recipes . . . . . 775.1.5 Recipe Curation System . . . . . . . . . . . . . . . . . 795.1.6 Gamification and Rewards System . . . . . . . . . . . 815.1.7 Grocery Item Recognition System . . . . . . . . . . . 815.1.8 Application Deployment . . . . . . . . . . . . . . . . . 815.2 Persuasive Strategies in the HealKitchen Application . . . . . 82Chapter 6: Evaluation of HealKitchen . . . . . . . . . . . . . . 866.1 Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 866.1.1 Evaluation constructs . . . . . . . . . . . . . . . . . . 866.1.2 Recruiting Participants . . . . . . . . . . . . . . . . . 886.1.3 Survey Instrument . . . . . . . . . . . . . . . . . . . . 886.2 Analysis of Response . . . . . . . . . . . . . . . . . . . . . . . 896.2.1 Quantitative Analysis (Likert-scale questions) . . . . . 896.2.2 Qualitative Analysis (Open-Ended Questions - OEQ) 906.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 926.3.1 Quantitative Analysis . . . . . . . . . . . . . . . . . . 926.3.2 Overall User Ratings . . . . . . . . . . . . . . . . . . . 966.3.3 Qualitative Analysis (OEQ) . . . . . . . . . . . . . . . 976.4 Discussion and Implications . . . . . . . . . . . . . . . . . . 1026.4.1 Strengths of HealKitchen . . . . . . . . . . . . . . . . 1046.4.2 Areas for Improvement . . . . . . . . . . . . . . . . . . 1046.4.3 Iterative Development & Feature Improvement . . . . 105Chapter 7: Conclusion . . . . . . . . . . . . . . . . . . . . . . . . 1077.1 Summary of Key Findings . . . . . . . . . . . . . . . . . . . . 1077.2 Implications for Practice and Research . . . . . . . . . . . . . 1087.2.1 Practical Applications of Research Findings . . . . . . 1087.2.2 Contributions to Academic Knowledge . . . . . . . . . 1087.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1097.4 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 1107.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111viiiCONTENTSBibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138Appendix A: Dataset Components used in Recipe Generation . . . 138A.1 List of Grocery Items used to Generate Recipes . . . . 138A.2 List of Health Conditions Targeted by the Grocery Items140A.3 List of Nutrients Provided by the Grocery Items . . . 141Appendix B: Open AI Prompts for Recipe Generation and Curation142B.1 Prompt for Recipe Generation . . . . . . . . . . . . . 142B.2 Prompt for Recipe Curation . . . . . . . . . . . . . . . 143B.3 Prompt for Recipe Image Generation . . . . . . . . . . 144Appendix C: Image Dataset Composition by Class . . . . . . . . . 145ixList of TablesTable 3.1 Systematic Literature Review . . . . . . . . . . . . . . 26Table 3.2 Description of coding metrics used in review . . . . . . 28Table 4.1 Performance Metrics for Recommendation Algorithms 58Table 4.2 Summary of Model Training Configuration . . . . . . . 70Table 4.3 Object Detection Evaluation Results . . . . . . . . . . 72Table 4.4 Comparison of HealKitchen Image Recognition Modelwith Prior Research . . . . . . . . . . . . . . . . . . . 72Table 6.1 Themes Identified from Feature Improvement Feedback 101Table 6.2 Themes Identified from Overall User Impressions . . . 103Table 7.1 Grocery Items used in HealKitchen (N = 130) . . . . . 138Table 7.2 Health Conditions used in HealKitchen (N = 85) . . . 140Table 7.3 Nutrients Provided in HealKitchen (N = 40) . . . . . . 141Table 7.4 Image Classes used in Image Recognition Model Train-ing (N = 41) . . . . . . . . . . . . . . . . . . . . . . . 145xList of FiguresFigure 3.1 Selection of research articles for literature review . . . 27Figure 3.2 Bar chart showing distribution of persuasive strategies 30Figure 3.3 Bar chart showing different demographics targeted bymHealth apps . . . . . . . . . . . . . . . . . . . . . . 33Figure 3.4 Bar chart showing different technologies used byhealth applications . . . . . . . . . . . . . . . . . . . . 33Figure 4.1 System architecture of HealKitchen application . . . . 43Figure 4.2 MVC architecture . . . . . . . . . . . . . . . . . . . . 46Figure 4.3 Sample images collected in image dataset reflectingvarying image quality . . . . . . . . . . . . . . . . . . 65Figure 4.4 Image Annotation and Labeling . . . . . . . . . . . . 66Figure 4.5 Model Training and Validation Loss Results . . . . . 69Figure 4.6 Evaluation Results of Real-Time Inference . . . . . . 73Figure 5.1 User authentication and onboarding screens . . . . . 76Figure 5.2 Personalized recipe recommendations . . . . . . . . . 78Figure 5.3 Recipe rating system . . . . . . . . . . . . . . . . . . 79Figure 5.4 Grocery List & Recipe Curation System . . . . . . . . 80Figure 5.5 Gamification interface showing points, streaks, andchallenges . . . . . . . . . . . . . . . . . . . . . . . . . 82Figure 5.6 Grocery Image Recognition System . . . . . . . . . . 83Figure 5.7 Persuasive Strategies & Behavioral Change Theoriesimplemented in HealKitchen . . . . . . . . . . . . . . 85Figure 6.1 Evaluation of Ease of Use . . . . . . . . . . . . . . . . 93Figure 6.2 Evaluation of Perceived Usefulness . . . . . . . . . . . 94Figure 6.3 Evaluation of User Engagement . . . . . . . . . . . . 95Figure 6.4 Evaluation of Aesthetic Design . . . . . . . . . . . . . 96Figure 6.5 User Ratings of HealKitchen . . . . . . . . . . . . . . 97xiAcknowledgementsI would like to express my sincere gratitude to my supervisor, ProfessorIfeoma Adaji, for her mentorship and guidance throughout the course of thisresearch. Her unwavering support, insights, and thoughtful feedback wereinstrumental in shaping the direction and quality of this research, and I feeltruly privileged to have worked under her supervision.I would also like to thank the faculty and staff of Computer Scienceat The University of British Columbia, Okanagan Campus, for providing asupportive environment and access to the resources that made this researchpossible.To my colleagues at the PASCL Lab—Victor, Linda, Parinda, Emon,Jason, and everyone else—thank you for the shared knowledge, collaborativespirit, and encouragement. Your support made this journey both memorableand enjoyable.We acknowledge the support of the Natural Sciences and EngineeringResearch Council of Canada (NSERC), [funding reference number RGPIN-2022-03689].Cette recherche a e´te´ finance´e par le Conseil de recherches en sciencesnaturelles et en ge´nie du Canada (CRSNG), [nume´ro de re´fe´rence RGPIN-2022-03689].xiiDedicationI dedicate this work first and foremost to God, whose grace, guidance,and presence have sustained me throughout this journey. Every step of thisresearch has been made possible through His strength and wisdom.To my mum, Mrs Ewajesu Olufunmilayo, my cornerstone and deepestjoy, thank you for laying the foundation of resilience, discipline, and faith.Your prayers and sacrifices continue to shape who I am, and I carry yourvalues with me in all I do.To everyone who walked this journey with me, your support, kindness,and encouragement have meant more than words can express. Thank you.xiiiChapter 1Introduction1.1 Overview of dietary lifestyle and itsimplications in the health of individualsIn recent years, there has been an increasing recognition of the rolethat diet plays in preventing and managing various health conditions [200].Studies have shown a strong correlation between humans’ dietary choicesand their overall physical and mental health, indicating that what we eatcan directly impact our health either positively or negatively [12, 107]. Forinstance, the World Health Organization (WHO) reports that poor dietaryhabits contribute to approximately 11 million deaths each year, making itone of the leading global risk factors for disease and mortality [144]. Chronicillnesses such as hypertension, diabetes, and cardiovascular diseases are alsooften closely associated with unhealthy dietary patterns.On the other hand, a well-balanced nutritional diet supports immunefunction, enhances energy levels, and reduces the risk and severity of manydiseases [47, 64]. It also aids in effectively combating existing health chal-lenges and maintaining bodily health [181].This understanding of the impact of diet on health has created an empha-sis on embracing healthier dietary choices as a solution to health challenges.Various research continues to attempt to identify optimal nutrients and di-etary practices that provide the best benefits and promote long-term healthyliving [95].Similarly, more individuals are becoming conscious of the need to adopthealthier diets to improve or maintain their health in order to prevent ormanage various illnesses [171]. Studies such as the 2020–2025 Dietary Guide-lines for Americans reflect a shift away from traditional calorie counting to-ward a more holistic approach to nutrition, emphasizing balanced meals andnutrient quality [50].However, despite increased awareness, many people tend to find it chal-lenging to translate dietary awareness into consistent and informed dietarydecisions [119]. Lobstein et al. [119] acknowledged that consumers face con-11.2. ADVANCEMENTS IN MOBILE HEALTH DIETARY INTERVENTIONSfusion when attempting to incorporate nutritional advice in their dietarychoices. This difficulty is attributed primarily to the lack of nutritionalknowledge, as well as easily understandable information on the nutritionalcontent of staple food items.According to Waltner et al. [209], modern society is characterized by afast-paced environment, coupled with a challenging work schedule, and thisleads to increasingly less knowledge about food origins and nutritional value.Furthermore, the sheer volume of accessible nutritional information on theinternet, from diverse and sometimes conflicting sources, can make it difficultfor individuals to discern credible sources and understand what constitutesa truly beneficial diet, particularly when targeting specific health challenges[188]. This, in effect, prevents individuals from effectively creating a healthydietary lifestyle.There are also personal and social factors that affect the adoption of ahealthy dietary lifestyle. One significant challenge is the uncertainty indi-viduals face when deciding what to cook, especially when they have rawgrocery items at home [193]. This often leads to food waste or reliance onless healthy, fast food alternatives due to a lack of inspiration [193].Additionally, people managing health conditions may struggle to identifyrecipes that align with their specific dietary requirements and health goals[40]. These decision-making barriers highlight the need for personalized toolsthat can recommend healthy recipes based on individual health conditionsand available ingredients.1.2 Advancements in mobile health dietaryinterventionsThe highlighted challenges emphasize the need for a clear and user-friendly solution that can support individuals in developing and maintaininghealthy dietary habits over time. Effective nutritional interventions need tobe easily accessible and offer practical and personalized support that helpsindividuals adopt healthy eating in their daily routines.One promising approach is the use of mobile health (mHealth) technolo-gies, which have emerged as a viable solution to addressing health challengesby supporting improved dietary choices [128]. Mobile health applicationsprovide a convenient platform for users to access a wide range of health-related tools from their smartphones or other mobile devices. These appsare increasingly being used to support dietary behavior change by offeringfeatures such as dietary monitoring and nutritional information that allow21.3. RESEARCH PROBLEM: LACK OF MHEALTH SOLUTIONS TARGETING VARIOUS HEALTH CHALLENGESindividuals to make informed food choices and manage diet-based healthissues [194].Furthermore, the portability and availability of mobile applications allowindividuals to make informed food choices in real-world settings, whether inthe convenience of their home or while on the go [185]. These tools haveshown potential not only in increasing users’ nutritional knowledge but alsoin encouraging the formation of healthy habits [90].Recent advancements in mobile technology have also enabled the devel-opment of sophisticated mHealth application features. Some applicationsutilize artificial intelligence (AI) to analyze user data and deliver more pre-cise and relevant guidance. For instance, the COFIT mobile applicationintegrates AI-powered dietary analysis and personalized coaching to sup-port users in adopting healthier eating habits. The app allows users to logmeals and receive automated feedback based on a structured portion-controldiet model [48].These advancements have the potential to innovate how people approachnutrition and health, offering scalable, data-driven solutions to support last-ing dietary change. As such, mHealth interventions represent a practicaland scalable solution to bridging the gap between nutritional awareness andconsistent, healthy dietary behavior.1.3 Research Problem: Lack of mHealthSolutions Targeting Various HealthChallengesDespite the increasing availability of mobile health applications, manyexisting solutions are limited in scope. A large number of these applicationsare designed primarily for general wellness or weight management, offer-ing features such as calorie tracking, fitness monitoring, or basic dietaryguidance [10, 174]. For example, the popular app, MyFitnessPal [66], fo-cuses primarily on calorie tracking and weight management without offeringdisease-specific recommendations or tailored nutritional plans [45, 66].While these tools may be beneficial for the general population, they of-ten fail to address the complex and individualized needs of users managingvarious health conditions. These solutions often assume a one-size-fits-allapproach and overlook the fact that users may be combating different con-ditions, such as diabetes, hypertension, or nutrient deficiencies, that requiretailored nutritional guidance [44]. As a result, users may be left to manu-ally interpret and adapt general dietary recommendations to their unique31.3. RESEARCH PROBLEM: LACK OF MHEALTH SOLUTIONS TARGETING VARIOUS HEALTH CHALLENGESsituations, which can be confusing and error-prone.Moreover, many solutions typically lack adaptability to accommodatechanges in users’ conditions, preferences, or nutritional goals over time [172].This lack of personalization limits the effectiveness and long-term relevanceof such solutions, making them less supportive for users who desire specificdietary assistance.There is little work done to provide a solution that addresses individualhealth challenges and nutritional requirements. This gap presents a limita-tion in current mHealth tools, as individuals with diverse health conditionsoften require targeted dietary support to manage their symptoms and im-prove outcomes [172]. There remains a need for mobile interventions thatprovide both general nutritional support and disease-specific dietary sup-port.To address this gap, this research introduces HealKitchen, a persua-sive AI-driven mobile health application designed to support individuals inmaking long-term, healthier dietary decisions tailored to their specific healthconditions. HealKitchen leverages behavioral theories, nutritional science,and artificial intelligence to personalize recipe recommendations tailored tothe individual’s unique health conditions and nutrient needs. It integratesdisease-specific guidelines, nutritional requirements, and grocery recommen-dations, creating a unified solution for healthy dietary living.A distinguishing feature of HealKitchen is its foundation in establishedbehavioral theories. Research in similar domains, of healthy diet and habitformation, has shown that interventions grounded in behavioral theory aremore effective at driving long-term behavior change than those that arenot [29, 105]. By operationalizing principles from core behavioral models asdesign features, HealKitchen incorporates persuasive strategies into the userjourney. This approach ensures that the application not only informs butalso motivates and supports users in forming sustainable, healthy dietaryhabits.HealKitchen also leverages artificial intelligence (AI) technologies, in-cluding a deep learning-based food detection model, to personalize contentand deliver nutritional information to users. These capabilities make theapp responsive to user’ dietary needs while aligning recommendations withtheir health conditions and available resources.41.4. OBJECTIVES OF THE RESEARCH1.4 Objectives of the ResearchThe primary objective of this research is to design, develop, and eval-uate HealKitchen, a persuasive, AI-driven, mobile health application thatleverages behavioral change theories, personalized recommendations, andartificial intelligence to promote healthier dietary habits. This study alsoaims to assess the usability and effectiveness of the application in influencingusers’ dietary choices and supporting sustained behavior change.Specifically, the research seeks to:− Design and implement a recommendation system that delivers rele-vant recipe suggestions tailored to users’ nutritional needs and healthconditions.− Develop a food image recognition model capable of accurately iden-tifying grocery food items and identifying their nutritional values topromote nutritional knowledge.− Design and develop a mobile application, HealKitchen, based on be-havioral change theories that aims to influence long-term healthy di-etary habits, and effectively integrate the food recognition model andrecommendation algorithm into the mobile application.− Evaluate the usability, engagement, and user satisfaction of HealK-itchen through user testing and feedback analysis.− Evaluate the accuracy of both the image recognition and recommen-dation components integrated in the application.− Identify opportunities for further development by analyzing qualitativeand quantitative user feedback to guide iterative improvements.1.4.1 Research QuestionsThis research and its objectives are guided by the following researchquestions:− RQ1: How can a mobile health application be designed with the aimof influencing users’ dietary behavior toward healthier food choices?− RQ2: In what ways can behavioral theories be operationalized inthe design of a mobile health application to support dietary behav-ior change?51.5. SIGNIFICANCE OF RESEARCH AND CONTRIBUTIONS− RQ3: How accurately can an image recognition system identify groceryfood in real-world grocery shopping contexts?− RQ4: To what extent can a personalized recommendation system pre-dict relevant healthy recipes based on users’ health conditions, nutri-tional requirements, and behavioral data?− RQ5. What are the usability and user experience outcomes of a mobilehealth application that incorporates behavioral change techniques tosupport dietary behavior change?1.5 Significance of Research and ContributionsThe rise in non-communicable diseases linked to poor dietary habitshas intensified the global emphasis on health interventions [3]. However,many individuals still lack access to practical, user-friendly tools that guidethem in making health-conscious dietary decisions tailored to their uniqueconditions and preferences [3]. HealKitchen addresses this gap by offeringa digital solution that aims to promote healthy eating through personalizedrecommendations and nutritional information.This study is significant because it combines the principles of behavioraltheory with modern mobile technology to create an application that inte-grates user-specific requirements and nutritional goals. These behavioraltheories play an important role in the development of effective digital healthinterventions as they foster sustainable behavior change through persuasivedesign [29, 105].By developing an application that is both functional and user-friendly,the research contributes to the growing field of mobile health technology anddietary interventions. Conceptually, it introduces an innovative applicationthat aims to promote long-term, healthier dietary habits in a convenient andaccessible manner. This application can also contribute to the global fightagainst diet-related health issues such as obesity, diabetes, and cardiovascu-lar disease, reducing healthcare costs and improving quality of life.From a technological standpoint, this research advances mobile healthapplication development by incorporating behavioral theories and artificialintelligence in a user interface. These features position the application asa comprehensive dietary change tool rather than a simple calorie-trackingapp, distinguishing it from existing apps in the market.This research also has implications for future innovations in the field. Bydemonstrating the feasibility of integrating behavioral theories and person-61.6. THESIS OVERVIEWalized features in a mobile health application, it can create opportunities formore advanced systems that could leverage artificial intelligence for morepersonalized health solutions.Furthermore, a labeled dataset was developed to support the imagerecognition model within HealKitchen. This dataset represents a meaningfulcontribution to the body of knowledge by providing grocery object-detectiontask-specific visual data. This provides a valuable resource for researchersand developers working on similar research. By making the dataset avail-able, this work supports ongoing advancement in the field and encouragesfurther exploration of object detection systems.Ultimately, this research contributes to the body of knowledge in digitalhealth solutions and offers a functional, scalable tool that can be adaptedacross different demographics to promote better dietary lifestyles.1.6 Thesis OverviewThis chapter introduces the current landscape of dietary health inter-ventions and provides an overview of this thesis, including its motivation,research questions, and significance. Chapter 2 reviews related literature,focusing on mobile health applications, persuasive systems, artificial intel-ligence in mobile applications, and the use of behavioral theories in healthinterventions. Chapter 3 presents a systematic review of existing health tech-nologies to identify features and patterns that attempt to influence behaviorchange. Chapter 4 describes the development of the HealKitchen applica-tion, including the recommendation system, image recognition model, andthe integration of behavioral change theories. Chapter 5 builds on this bydetailing the development of the mobile interface and how the designed fea-tures were implemented. Chapter 6 presents a user study that evaluates theapplication’s perceived usefulness, ease of use, engagement, and visual aes-thetics, and presents its findings and suggested application improvements.Finally, Chapter 7 summarizes key insights, highlights limitations, and offersrecommendations for future development.7Chapter 2Related WorkTo contextualize and inform the design of HealKitchen, this chapterreviews related works on mHealth applications that attempt to influencedietary behavior. This review contributes to an understanding of the currentstate of mHealth interventions for dietary improvement and highlights thecontribution HealKitchen seeks to make. Thus, by analyzing the strengthsand limitations of existing mobile applications, this chapter aims to identifythe best practices, common challenges, and research gaps that inform therationale and design principles behind HealKitchen.2.1 Role of Mobile Applications in HealthcareTechnology has revolutionized the way we interact with the world. Rapidinnovations in various aspects of technology, such as mobile applications andartificial intelligence, have made it possible for individuals to directly accesssophisticated digital solutions and interact with them in their daily lives [28].Mobile health (mHealth) applications represent a central outcome of theseadvancements, leveraging the capabilities of smartphones and other mobiledevices to provide users with direct access to health-related services and in-terventions [172]. These applications implement a wide array of functionali-ties, designed to address common health needs such as disease management,calorie monitoring, mental health support, and preventive care.Notably, one of the more common and innovative categories withinmHealth is dietary applications. These applications are designed to influ-ence users’ eating behaviors using features and functionalities to promoteadherence to healthy dietary habits [208]. For example, the popular foodtracking app, MyFitnessPal [66], helps users monitor food intake and phys-ical activity while offering calorie and macronutrient tracking.Another app, Yuka [186], allows users to scan product barcodes whenshopping to evaluate the nutritional quality of food items and receive health-ier alternatives. Noom [189], which integrates coaching and psychologicalprinciples, focuses on building sustainable eating behaviors through goalsetting, feedback, and daily lessons.82.2. BEHAVIORAL THEORIES IN INFLUENCING BEHAVIORAL CHANGEThese tools have demonstrated positive outcomes, including increasednutritional awareness, weight loss, and improved dietary adherence. Manyof them embed persuasive features such as self-monitoring and remindersthat contribute to sustainable behavior change when implemented effectively[208].Given the widespread adoption and effectiveness of mobile health appli-cations in promoting healthy behaviors, it is logical to develop HealKitchenas a mobile application to support dietary health. A mobile format en-sures portability, ease of access, and high engagement opportunities, allow-ing users to receive personalized dietary guidance and interventions easilyin real-world contexts. As such, HealKitchen aligns with the growing trendof evidence-based mHealth interventions and leverages mobile technology toprovide a dietary behavior change intervention.2.2 Behavioral Theories in InfluencingBehavioral ChangeThe theory of behavioral change focuses on understanding how humanbehavior can be influenced to achieve positive outcomes [29]. It involvesmodeling human behavior as theoretical frameworks that describe and pre-dict behavioral responses as a basis for behavioral intervention [6, 32].In the domain of health and dietary behavior, these theories are used toguide the design and implementation of interventions that support long-termbehavioral change, making them relevant to the development of applicationslike HealKitchen [132, 146].Salazar [173] reviewed behavioral models that are used in health ser-vices and provided a logical approach to behavior change. They comparedthe relevant approaches of four behavioral change models: the Health BeliefModel, the Theory of Self-Efficacy, the Theory of Reasoned Action, and theMulti-attribute Utility Model, and established the contribution they madeto influencing positive health change despite their varying approaches. El-der et al. [64] similarly looked at how behavioral theories can effectivelyinfluence behavioral change in health patients. They affirmed that theoreti-cal models such as the Social Cognitive Theory (SCT) can be effective toolsthat primary care providers use to help their patients actualize behavioralchange.These theories are usually integrated into applications as persuasivestrategies and provide an informed background for targeting positive be-havior change through application features. Webb et al. [211] performed92.2. BEHAVIORAL THEORIES IN INFLUENCING BEHAVIORAL CHANGEa review of internet-based persuasive interventions and found that inter-ventions that incorporated behavioral change theories tend to have largerpositive effects than those that did not. Brindal et al. [34] introduced Mo-tiMate, a persuasive mobile application designed for weight managementand emotional well-being. MotiMate combined behavior change theory withpersuasive design principles such as self-monitoring and goal setting. Theirstudy showed that users experienced improved mood and increased con-fidence in managing their health goals, indicating that theory-driven appfeatures can result in meaningful behavior change.Torkamaan et al. [197] similarly developed a mobile-based mental healthapplication by integrating behavior change theories. Their application in-cluded features such as goal-setting, reminders, and feedback mechanisms.The study associated these features with improved user adherence and en-gagement. It concluded that embedding behavioral theories into app devel-opment improves the usability and effectiveness of mental health interven-tions.These findings demonstrate that digital health applications that incorpo-rate behavioral theories are more likely to influence positive and sustainedhealth behavior change [132, 146]. As such, the use of behavioral theoryis not only theoretically justified but also practically effective in designingpersuasive mobile health solutions like HealKitchen.While these behavioral theories are applied to serve as the theoreticalfoundation for application features, incorporating them into persuasive in-terface design can be challenging, as each theory offers a distinct perspectiveon how behavior change should be achieved [29]. For example, the High-Performance Cycle (HPC) focuses on influencing change by improving moti-vation and efficiency [112], whereas the Theory of Planned Behavior (TPB)emphasizes behavioral intentions as the pathway to change [8]. While eachtheory provides valuable insights and contributes to influencing behavior,translating them into concrete application design elements remains a com-plex task [29].To address this challenge, Blanke et al. [29] proposed a comprehensiveframework that systematically integrates concepts from multiple behavioraltheories into operationalizable design elements for persuasive mobile appli-cations. This model was integrated into the design of an application tosupport sustainable, healthy shopping.In the design of HealKitchen, this framework was adopted to translatebehavioral change strategies into actionable features within the application,effectively targeting the promotion of healthier dietary habits. Five behav-ioral theories were selected based on similar work done in this context by102.2. BEHAVIORAL THEORIES IN INFLUENCING BEHAVIORAL CHANGEBlanke et al [29].2.2.1 High-Performance Cycle (HPC)The High-Performance Cycle (HPC), introduced by Latham et al [121],is focused on enhancing the motivation and efficiency of users by aligningperformance goals, feedback, and perceived rewards [12]. Although it wasoriginally developed for workplace contexts, the HPC has been effectivelyadapted to health settings [12].The HPC focuses on the concept of overarching demand, which refers tothe goals that drive the intervention. It is particularly useful when long-termengagement and behavioral persistence are required, such as in the case ofdietary behavioral change.This theory has demonstrated success in improving the performance andmotivation of users [30]. The HPC’s principles have been implemented inweight management apps like Noom [189], for setting achievable goals thatreinforce user motivation. This helps users have a clear association betweentheir actions and dietary outcomes.The HPC was adopted in HealKitchen due to its focus on aligning usereffort with meaningful and rewarding health outcomes.2.2.2 Action Regulation TheoryThe Action Regulation Theory (ART) was initially proposed by Hackeret al [84], and describes how complex actions are planned and executed byusers. The ART emphasizes the division of broad tasks into goal-orientedsubtasks that can be easily carried out.Similar to the HPC, the ART focuses on goal-setting. It asserts thatsuccessful behavioral change is achieved by translating abstract goals intoclear, actionable steps, allowing users to regulate their progress toward largerobjectives [29]. This is specifically implemented through the concept ofredefinition of tasks, which involves translating the goals from the HPC intomeaningful tasks and features accessible to the user [29].In mHealth applications, ART has been implemented to simplify com-plex health behaviors into manageable tasks. For instance, the ENGAGEDapp [156] employed ART to allow users to plan and record meals, observeprogress, and adjust their behavior based on feedback. Similarly, HealK-itchen uses ART to translate the abstract goal of healthy eating into spe-cific sub-tasks like selecting recommended recipes and creating grocery lists,making the behavior more achievable.112.2. BEHAVIORAL THEORIES IN INFLUENCING BEHAVIORAL CHANGE2.2.3 Social Cognitive TheoryThe Social Cognitive Theory (SCT), introduced by Albert Bandura [18],focuses on understanding how individuals develop and maintain new behav-ioral patterns. Where previous theories viewed behavioral change as a reac-tion to external factors, the SCT primarily highlights reciprocal determinism[21], which is the idea that behavioral change occurs when a behavior, per-sonal factors, and environmental influence all interact and affect each other.The SCT emphasizes the concept of self-efficacy, which speaks to thebelief in one’s ability to successfully perform a behavior. This concept isconsidered one of the most influential factors in determining whether be-havior change will occur and be maintained over time [18].In simple terms, this theory refers to the idea that people learn by watch-ing others and that their behavior is shaped by a mix of what they see othersdo (social influence), what they believe they can do (self-efficacy), and howthe environment around them supports them.The SCT has seen extensive use in health applications. For example, theMotiMate app [34] applied SCT to encourage users to manage emotionaleating through self-monitoring, feedback, and motivational messaging.This theory is particularly important within the context of HealKitchen,as the application is targeted at influencing long-term dietary behavior. Byintegrating the concepts of the SCT into its design, HealKitchen aims tostrengthen users’ motivation in sustaining behavior.2.2.4 Theory of Planned BehaviorThe Theory of Planned Behavior (TPB) was introduced by Ajzen etal. [7] and is widely used in influencing human behavior across differentdomains. The TPB claims that behavioral change is directly influencedby users’ behavioral intentions, which are shaped by three main factors:attitudes towards the targeted behavior, subjective social norms, and users’perceived behavioral control.Attitude toward targeted behavior refers to an individual’s positive ornegative evaluation of performing the behavior. Subjective social normsreflect perceived social pressure to perform or not perform the defined be-havior, while behavioral control indicates the individual’s perception of theirability to perform the behavior, similar to the concept of self-efficacy underthe SCT.Applications like MyMealMate [83] and EatRight [162] apply TPB byproviding users with information that positively shapes their attitudes and122.2. BEHAVIORAL THEORIES IN INFLUENCING BEHAVIORAL CHANGEperceived control. Similarly, HealKitchen implements the TPB to shape pos-itive attitudes toward healthy eating via nutritional information to reinforcethe benefits of healthy eating and enhance perceived control by simplifyinggrocery planning and meal preparation.2.2.5 Self-Determination TheoryThe final behavioral theory implemented in this research is the Self-Determination Theory (SDT). This behavioral theory was developed byRyan et al [57] and explores how individuals sustain behavioral change basedon the fulfillment of internal motivation.The SDT focuses on intrinsic motivation, which defines the internal driveto perform an activity because it is inherently meaningful or satisfying, asthe most suitable influence for behavioral change [57].The SDT influences behavioral change primarily through the concepts ofautonomy, competence, and relatedness. Autonomy refers to the need of auser to feel in control of their behavior. Competence involves the ability tomaster a task or experience a sense of effectiveness when performing a task,and Relatedness reflects a feeling of connection and support to the behaviorand others.The SDT has been employed in nutrition apps to support user engage-ment. For example, Foodbot Factory [71] found success in using game-basedlearning to foster intrinsic motivation in children. HealKitchen integratesthe SDT to support user autonomy and competence. The application pro-vides users with the flexibility to choose recipes and customize their grocerylist based on their dietary preferences.The selection of these behavioral theories for HealKitchen is informedby their success in existing mHealth apps and their application in Blanke’sdesign framework [29]. Their model supports translating broad behavioralgoals into achievable tasks and integrating persuasive strategies with tech-nical features. This is aligned with HealKitchen’s objectives to influencesustainable dietary behavior through mobile application features.Research demonstrates that digital health applications that incorporatebehavioral theories are more likely to influence positive and sustained healthbehavior change [132, 172]. Therefore, the theoretical foundation used inHealKitchen not only follows successful precedents but also represents apractical design strategy for effective behavior change intervention.132.3. PERSUASIVE STRATEGIES IN HEALTH APPLICATIONS2.3 Persuasive Strategies in Health ApplicationsPersuasive strategies, derived from behavioral change theories, are es-sential design elements in mobile health (mHealth) applications aimed atinfluencing user behavior. These strategies guide users toward adopting andmaintaining healthier habits by translating abstract goals into actionablesteps [29]. In the context of nutrition-related apps, persuasive strategiesenhance user engagement, facilitate motivation, and support long-term be-havior change [172].Persuasive strategies are built on Fogg’s Behavior Model (FBM) [70],which proposes that behavior change occurs when three elements converge:motivation, ability, and a prompt. In this model, “motivation” refers to theuser’s desire to perform the behavior. “Ability”, on the other hand, reflectsthe ease or difficulty of performing the behavior. Finally, “prompt” is thetrigger that signals when the behavior should occur. For a behavior such ashealthy eating to occur, the user must be motivated to eat healthily, findit easy to select, purchase, and prepare healthier alternatives, and receiveappropriate prompts at various moments of decision-making.According to Salas et al., [172], the most commonly used persuasivestrategies in nutrition-focused mHealth applications include self-monitoring,feedback, and goal setting. These strategies consistently demonstrated ef-fectiveness in digital interventions for dietary change.Self-monitoring encourages users to record their food intake, track caloricvalues, and observe dietary patterns over time. Apps like MyFitnessPal [66],Noom [189], and Lose It! [213] implement self-monitoring through daily meallogs, integration with wearable devices, and visual food diaries.The ENGAGED app [156] allowed users to track meals from a large fooddatabase, and a study found that frequent use of the app was positivelyassociated with weight loss outcomes. Similarly, Eaton et al. [62] reportedthat users who engaged in daily food tracking demonstrated better outcomesand higher app retention rates over 12 weeks.However, while this method is powerful, it can become burdensome with-out adequate support or automation. Applications that fail to reduce frictionin the tracking process may see declines in user retention over time [37].Feedback is another essential persuasive strategy used to inform usersabout their performance relative to set goals or guidelines. It reinforces thedesired behavior by highlighting progress, identifying areas for improvement,and offering encouragement [15]. Feedback can be immediate (e.g., mealquality scores after logging a meal) or cumulative (e.g., weekly nutritionsummaries).142.3. PERSUASIVE STRATEGIES IN HEALTH APPLICATIONSApps like VegEze [92] used feedback based on vegetable intake data andbehavioral prompts. Their randomized trial showed significantly improvedvegetable consumption among users over 21 days. Similarly, Keenoa uses AIto assess nutritional quality from food images, providing personalized feed-back that enhances users’ dietary understanding [31]. Personalized feedbackis reported to be more effective than generic messages, especially when it iscontextual and timely [182].Goal setting helps users establish and pursue clear, measurable objec-tives, such as reducing caloric intake or increasing fiber consumption. Appslike MyMealMate [83] and Noom [189] prompt users to define weekly orlong-term goals, reinforced by in-app reminders and visual progress indica-tors.A study on MyMealMate found that users who set specific, time-boundgoals demonstrated greater weight loss than the control group [83]. Effec-tive goal setting incorporates personal relevance, attainability, and feedbackloops [120].Gamification, although not as common, is another strategy that remainseffective in influencing behavior change by using points, badges, levels, andchallenges to increase motivation [53]. Apps such as Foodbot Factory andNutrition Garden use gamified tasks and virtual rewards to promote healthybehaviors in both children and adults [71, 127].Gamification has proven especially effective when aligned with users’ in-trinsic motivations and values [127]. However, overuse or shallow implemen-tation can lead to disengagement if users perceive the rewards as meaningless[178].Social support is another powerful persuasive strategy. It provides userswith a sense of community and accountability for the desired behavior. Appslike Aim2Be [56, 201] incorporate virtual coaching and peer challenges, whileNoObesity [129] fosters caregiver collaborations through shared goal-settingand messaging features.Social features increase motivation and are particularly impactful forvulnerable populations, such as children and patients with chronic conditions[72].While persuasive strategies have clear benefits, some challenges hindertheir optimal use in nutrition-focused mHealth apps, including:− Over-persuasion and Intrusiveness: A primary concern is the potentialfor persuasive elements to become intrusive, leading to user annoyanceor resistance. Constant notifications, frequent pop-up reminders, oroverly competitive gamification can backfire by overwhelming users,152.4. GROCERY SHOPPING-FOCUSED NUTRITION APPSespecially those already dealing with information overload from otherapps [39].− Personalization Complexity: Creating genuinely personalized interven-tions requires access to a broad range of user data, including dietarypreferences, behavioral patterns, and shopping habits. Managing thiscomplexity without compromising user privacy or increasing cognitiveload remains a significant technical and ethical challenge [17].− Sustained Engagement Over Time: While persuasive techniques canboost short-term engagement, maintaining user interest over extendedperiods remains difficult. Without the implementation based on be-havioral theories, even initially effective persuasive elements may losetheir impact, leading to dropouts [145].Despite these strengths of persuasive strategies, nutrition applicationstend to employ only a narrow range of persuasive strategies, often focusingheavily on self-monitoring while neglecting other habit formation strategies[13].HealKitchen aims to fill this gap by integrating multiple persuasivestrategies, including goal setting, feedback, gamification, and personaliza-tion, to support dietary behavior change, while also being grounded in be-havioral theory.By integrating these features, HealKitchen aligns with the Fogg BehaviorModel, increasing users’ motivation to eat healthy, improving their abilityto choose nutritious options, and providing subtle prompts in planning andpreparing healthy meals.HealKitchen leverages FBM principles by reducing friction (via food im-age recognition and grocery planning), enhancing motivation (via tailoredrecipe recommendations), and delivering subtle prompts (via feedback andnutritional information). This alignment with persuasive theory enhancesthe app’s potential to create meaningful and sustained dietary change.2.4 Grocery Shopping-Focused Nutrition AppsGrocery shopping is an important moment in shaping individual andhousehold dietary behavior [209]. The decisions made in-store or onlinelargely determine what foods are available at home and, consequently, whatis consumed [115, 206]. According to [206], the types of foods purchasedat supermarkets are strong predictors of an individual’s nutritional quality.162.4. GROCERY SHOPPING-FOCUSED NUTRITION APPSIntervening at moments preceding purchase can therefore be more impactfulthan interventions that rely solely on tracking consumption.However, many nutrition-focused mHealth applications concentrate onfood logging, calorie counting, or post-consumption feedback [202]. Thisomission represents a missed opportunity, as behavioral interventions atthe point of purchase can effectively guide users toward healthier choices,ultimately leading to better nutrition outcomes [209].Some early efforts have attempted to address this gap by focusing ongrocery shopping contexts. For example, MyNutriCart [123] was developedto help users build personalized grocery lists based on their health goals,budget, and household size. Upon entering basic information such as age,gender, and health status, users receive customized food group targets anda recommended shopping list. The key features of MyNutriCart included:grocery recommendations tailored to financial constraints, grocery list gen-eration, and nutrient-based adjustment of grocery lists.Although MyNutriCart offered a structured and educational approach togrocery planning by generating healthy grocery lists for users, it had limitedintegration of behavioral theories and persuasive strategies. The app lackedinteractive features and supporting persuasive strategies. Additionally, itsinterface was relatively static, and there was little integration of feedback ormotivational elements to sustain long-term use [123, 152].In a pilot randomized controlled trial to evaluate the efficacy of the appin improving healthy food purchase and reducing weight, the app led to someimprovements in food purchasing and intake, such as increased householdpurchasing of vegetables and whole grains [123, 152]. However, it had nosignificant impact on weight control, and user engagement was limited, withonly 26% of participants reporting purchasing at least 70% of the recom-mended groceries consistently. The study authors concluded that a lack ofsupporting persuasive strategies, like reminders and app notifications, likelylimited sustained behavior change and suggested more intensive supportingfeatures to improve efficacy [123, 152].Another notable tool, FoodSwitch from The George Institute for GlobalHealth, offered a more interactive approach to supporting grocery decision-making [125]. It utilizes barcode scanning to deliver instant nutritionalratings for packaged foods and suggests healthier alternatives using a traffic-light labeling system. This visual approach helped users quickly assess thehealthiness of a product based on its fat, sugar, and salt content.FoodSwitch’s strengths include a real-time product assessment via bar-code scan, simple visual cues to encourage healthy choices, and an extensiveproduct database regularly updated for regional markets. However, like172.5. ARTIFICIAL INTELLIGENCE IN HEALTH APPLICATIONSMyNutriCart, FoodSwitch lacked deeper persuasive features and behaviorchange techniques (BCTs).FoodSwitch provided barcode-based nutritional information but didnot offer features such as self-monitoring, goal-setting, or feedback mech-anisms—features commonly associated with sustained behavior change[125, 132]. As such, its impact may reduce over time.Research on behavior change consistently shows that persuasive strate-gies play an important role in improving user engagement and supportinglong-term behavior change [132, 146, 172]. The absence of such features ingrocery shopping applications likely limits their long-term impact, as theinterventions are primarily limited to initial grocery decision support ratherthan sustained engagement or habit transformation.HealKitchen aims to build on these efforts by combining in-store supportthrough image recognition with goal-setting, feedback, and personalization,all grounded in behavioral change theories. By integrating these features,HealKitchen seeks to guide users from intention to action, transforming foodplanning and grocery shopping into a meaningful opportunity for long-termdietary behavior change.2.5 Artificial Intelligence in Health ApplicationsThe integration of Artificial Intelligence (AI) into mobile health(mHealth) applications has also emerged as an innovative advancement inpersonalized healthcare solutions [28]. AI technologies, particularly thoseutilizing machine learning (ML), natural language processing (NLP), anddeep learning, are increasingly implemented to enhance diagnosis, predic-tion, and health behavior interventions [65, 195].Health applications powered by AI can process user-generated data tooffer personalized insights and guidance [196]. For example, AI models canidentify patterns in dietary behaviors, physical activity, or disease symptomsand use this information to suggest individual interventions. These appli-cations often leverage smartphones, wearable devices, and cloud computingto collect and process real-time health data [196].2.5.1 Image Recognition Systems in Health ApplicationsDietary applications, implemented based on behavioral change theories,often employ various innovative techniques and features to influence change.One of such innovations in modern applications is the utilization of artificial182.5. ARTIFICIAL INTELLIGENCE IN HEALTH APPLICATIONSintelligence models to implement image recognition systems that providenutritional information and insights on dietary behavior.Early dietary assessment and nutritional recommendation applicationswere built upon self-recording techniques that were considered ineffectivedue to their overreliance on human accuracy [187].The emergence of artificial intelligence, however, provided a viable so-lution to this criticism. Studies have explored ways of utilizing machinelearning models and neural networks in managing dietary intake. For ex-ample, Meyers et al. [137] developed Im2Calories, an application that usesdeep convolutional neural networks to estimate calorie content from foodimages, demonstrating the potential of image recognition in automating di-etary management.Dietary management systems that integrate image recognition tech-niques utilize computer vision to analyze cooked food images and identifyfood items in real-time [138]. These approaches employ machine learn-ing models to perform image classification tasks that support different fea-tures in their application. These models are typically used to support self-monitoring and tracking features that appeared as one of the most commonpersuasive strategies in use [132].Early systems like DietCam [108] implemented cooked food recognitionby detecting color, shape, and texture descriptors using Support VectorLearning (SVM) classifiers. DietCam was developed for use in restaurantsettings and requires that users take pictures of images with a credit cardin the background for scaling purposes. Yoshiyuki et al [102] similarly in-troduced a mobile recognition system called FoodCam for tracking users’eating habits by automatically identifying cooked food items from photosand estimating their nutritional content.The rise of convoluted neural network (CNN) models that automaticallylearn optimal feature representations from image datasets allowed for moreadvanced food recognition systems [209]. This was exemplified in the devel-opment of Im2Calories [137], where a GoogleNet CNN model was trainedon an extensive food image dataset and achieved high accuracy in foodrecognition and calorie estimation. Chang Liu et al. [117] also developed aprototype application that implemented deep-learning food image recogni-tion algorithms to improve the accuracy of dietary assessment by analyzingthe images of food captured on mobile devices. Georg Waltner [209] sim-ilarly introduced a dietary management system that implements a shallowCNN-based food image classifier optimized for running on mobile devices toidentify food images and provide nutritional information to users.HealKitchen builds on this foundation by applying AI in its development.192.6. GAPS AND OPPORTUNITIES IN CURRENT SOLUTIONSIt implements a CNN-based image recognition model designed to identifyraw grocery food items from user-captured images in-store or at home. Thisenables the app to provide real-time nutritional information about groceryitems, helping users make healthier dietary choices before purchase or con-sumption. Unlike existing systems that support post-consumption tracking,HealKitchen uses image recognition to enable pre-consumption decision-making, bridging a gap in dietary behavior support.In addition, HealKitchen also utilizes AI-driven large language models toimplement features such as recipe curation that suggest personalized recipesbased on users’ health conditions and grocery inputs, combining AI withbehavioral change features.2.6 Gaps and Opportunities in Current SolutionsWhile the mobile health (mHealth) ecosystem has seen substantialgrowth in the development of nutrition-related applications, many of thesetools face limitations in delivering comprehensive, sustainable, and relevantdietary behavior change.Despite advances in mobile technology, artificial intelligence, and behav-ior change theory, many applications rely on minimal feature integration,offer limited personalization, and fail to support long-term healthy dietaryhabits.− Limited Long-Term Engagement and Retention: One of the most per-sistent challenges facing nutrition applications is poor user retention.While many apps are successful in attracting initial downloads andonboarding users, long-term engagement tends to decline rapidly, of-ten within the first month [11]. This decline is generally attributedto repetitive content, limited personalization, lack of meaningful feed-back, and poor integration of behavior change models that promotesustained engagement [152]. For instance, the MyNutriCart studyfound that lack of app notifications and follow-up sessions reducedcompliance and motivation to continue using the app [123, 152].HealKitchen addresses these issues by implementing personalized goal-setting, feedback, and theory-driven engagement features that canevolve with user progress. Even after users reach address initial nutri-tional requirements, the system can be adjusted to support new healthconditions.202.6. GAPS AND OPPORTUNITIES IN CURRENT SOLUTIONS− Limited Personalization and Narrow Focus on Specific Health Con-ditions: Existing nutrition applications focus on a narrow range ofconditions, primarily targeting weight loss or diabetes management[104]. This one-size-fits-all approach leaves users with multiple healthchallenges, such as hypertension or micronutrient deficiencies, under-served [103].This absence of personalized, health-specific recommendations limitsthe effectiveness of these applications in driving long-term dietarychange for users with complex health challenges. There remains agap in nutrition application design for systems that tailor recommen-dations based on individual medical needs and preferences.HealKitchen aims to fill this gap by tailoring recipe recommendationsand curation based on individual health profiles, including less com-monly targeted conditions.− Lack of Integration at the point of Dietary Decision: Few nutritionalapplications offer support during the process of making dietary de-cisions [4]. Yet this moment is highly influential in shaping dietaryoutcomes [123, 209]. If unhealthy foods are not bought, they are lesslikely to be consumed.HealKitchen addresses this by integrating recipe recommendations,grocery image recognition, and shopping list management to influencebehavior at the point of grocery decision-making.− Limited integration of Behavioral Science in Application Design: Manynutrition applications are developed without a grounding in behavioralscience, resulting in limited features that fail to promote lasting be-havior change [79]. As a result, they may include beneficial features,but they lack the theoretical background that drives meaningful andsustained behavior change. [132].Evidence suggests that mHealth apps guided by behavioral modelsare more effective than those without theoretical grounding [132, 146].HealKitchen avoids this by operationalizing its features based on be-havioral change theories, including the HPC, SCT, and TPB.This chapter has reviewed a broad range of literature and related worksin the domain of mobile health (mHealth) nutrition interventions. It showedthat mobile applications can effectively promote healthier dietary behaviorswhen they are grounded in behavioral theories and leverage persuasive tech-nologies to drive continuous engagement [29, 132, 146]. Applications like212.6. GAPS AND OPPORTUNITIES IN CURRENT SOLUTIONSVegEze [92] and MotiMate [34] illustrate the positive impact of these ele-ments across various user populations and nutritional goals.Despite these successes, the review identifies some limitations in currentmobile nutrition solutions. These include limited long-term user engagementdue to a lack of adaptive content and feedback mechanisms [11], insufficientpersonalization for users with diverse health challenges [104], inadequatesupport at the point of dietary decision-making [4], and the inconsistentapplication of behavioral theories in app design [79].HealKitchen contributes to addressing these limitations by offering atheory-driven, AI-enabled mobile application designed to support healthiergrocery shopping and meal planning habits. It integrates behavioral modelsto inform features like personalized recipe recommendations, recipe curation,grocery list management, feedback, and nutritional information.By synthesizing insights from successful applications and addressingshortcomings in user engagement and personalization, HealKitchen offersa scalable, user-centered solution that aims to transform routine groceryshopping into a dynamic and persuasive health intervention. This innova-tive approach positions HealKitchen to contribute meaningfully to the futureof mobile dietary solutions.22Chapter 3Systematic LiteratureReviewPrior to the conceptualization or design of the HealKitchen application,a systematic literature review was conducted to examine existing researchon mHealth interventions targeting dietary behavior change. This reviewevaluated these dietary interventions by considering key metrics related totheir effectiveness, usability, and overall impact on dietary behavior change.It examined peer-reviewed conference papers, research journals, and litera-ture reviews that discussed or assessed the development and implementationof existing mobile health applications focused on nutrition and dietary be-havior change.The goal of this review was to identify patterns and features in exist-ing mobile dietary interventions that influenced behavioral change. It alsohelped to identify limitations in previous literature, creating avenues forimprovement within this research.3.1 Search Strategy & Inclusion CriteriaTo carry out this literature review, a comprehensive search was con-ducted to retrieve relevant research articles on digital interventions designedto support healthy nutrition. The search was conducted in collaborationwith another researcher, using context-specific search queries across multi-ple research databases, ensuring coverage of relevant research.The databases selected for this review included PubMed, IEEE Xplore,Google Scholar, ACM Digital Library, and the Journal of Medical InternetResearch (JMIR). These databases were chosen for their relevance to theintersecting domains of health, technology, and behavioral science.PubMed was selected for its comprehensive coverage of biomedical liter-ature, which includes peer-reviewed articles relating to digital interventionsin health and nutrition. JMIR, a leading journal in digital health, provideda wealth of empirical studies and systematic reviews related to mHealth233.1. SEARCH STRATEGY & INCLUSION CRITERIAapplications and their real-world impact on health behaviors. IEEE Xplorewas selected to include technical and engineering-focused research, especiallystudies related to the design, development, and evaluation of mobile healthapplications. Google Scholar served as a supplementary database, offeringa broader search to capture any relevant studies not indexed in the otherdatabases.This combination of databases ensured a balanced and diverse represen-tation of research articles for addressing the study’s objectives, providing asolid foundation for analyzing the themes and best practices in the develop-ment of mobile nutrition applications.The search strategy included peer-reviewed conference proceedings andjournals. This was done to ensure the review captured recent innovations,as conference proceedings in fields like AI and mHealth often contain newcontributions that precede journal publication. The search strategy alsoemphasized the inclusion of studies with clear methodology, relevance touser-centered design, and evident impact on behavior change.Initial searches were conducted on the selected databases, split betweenthe two researchers. The searches were performed using the advanced searchtools available on the respective databases. This tool allowed for a Booleancombination of search queries and filtering of results to match the desiredcontext. The search queries were computed from a set of predefined key-words and applied to search within the titles and abstracts of the research ar-ticles. The search terms used included: “nutrition app”, “mobile health ap-plication”, “dietary intervention”, “healthy eating app”, “persuasive healthapplication”, and “dietary behavior change”.The scope of the search was limited to articles published in the Englishlanguage between 2014 and 2024. This ten-year window was selected tocapture current trends and innovations while also including a wide rangeof high-quality, peer-reviewed research within the rapidly evolving digitalhealth landscape.Example queries included:− (“mobile health application” OR “mHealth application” OR “dietaryapplication”) AND (“Publication year: from 2014 to 2024”) AND(“Search within: Title & Abstract”)− (“mobile health application” OR “persuasive health application” OR“dietary behavior change”) AND (“Publication year: from 2014 to2024”) AND (“Search within: Title & Abstract”)After the initial search across databases, the resulting articles were com-243.1. SEARCH STRATEGY & INCLUSION CRITERIAbined and reviewed to remove duplicates. This resulted in a total of 1,235research articles that matched the search terms and filters. Following this,both reviewers independently performed multiple stages of screening to re-fine the results and ensure alignment with the objectives of this review.After each stage, a review was performed to validate the results. Disagree-ments on inclusion were resolved through discussion until a consensus wasreached. This peer-review process was performed to minimize potential biasand strengthen the reliability of the selection procedure.First, the titles of the identified articles were screened for relevance tothis research. This resulted in the exclusion of 1,014 articles. Next, theabstracts and introductory sections of the remaining articles were reviewed,resulting in the exclusion of another 129 articles. This resulted in a finalselection of 92 articles, which formed the basis of the literature review.Articles were retained if they met the following inclusion criteria:1. The article described or evaluated an application or digital interventionthat implemented a dietary solution specifically aimed at modifyingor supporting dietary behavior change.2. The intervention was delivered through a digital platform (e.g., smart-phone, tablet, or wearable device), consistent with the scope ofmHealth.3. The study reported on outcomes relating to usability, effectiveness,and engagement.4. The study was published in a peer-reviewed journal or conference pro-ceeding.5. The study was published between 2014 and 2024, ensuring relevanceto current technological and behavioral health contexts.6. The article was written in English to allow accurate interpretation andintegration into the review.The selected articles were analyzed thematically to extract insights oneffective design principles, behavioral theories, and technological featuresrelevant to the development of the HealKitchen mobile application.Table 3.1 summarizes the search and inclusion process utilized in thisreview. Figure 3.1 provides a visual summary of the number of articlesselected at each stage of screening.253.2. CODING THEMESTable 3.1: Systematic Literature ReviewElement DescriptionDatabases Searched PubMed, IEEE Xplore, ACM Digital Library,Google Scholar, JMIRSearch Period Publications from 2014–2024Search Keywords “nutrition app”, “mobile health application”,“dietary intervention”, “healthy eating app”,“persuasive health application”, and “dietarybehavior change”Search Queries (“mobile health application” OR “mHealthapplication” OR “dietary application”) AND(“Publication year:from 2014 to 2024”) AND(“Search within: Title & Abstract”)(“mobile health application” OR “persua-sive health application” OR “dietary behaviorchange”) AND (“Publication year: from 2014to 2024”) AND (“Search within: Title &Abstract”)Publication Types Peer-reviewed journals and conference proceed-ingsLanguage English onlyReview process Dual peer-reviewer3.2 Coding ThemesThe selected research papers were analyzed using a thematic analysisapproach guided by five key evaluation metrics. This process involved iden-tifying recurring patterns and concepts across studies that align with thereview’s objectives, particularly focusing on the effectiveness of mobile appsin promoting a healthy dietary lifestyle.The primary metrics that guided this analysis included: features andfunctionality/design approach, persuasive strategies, adherence to evidence-based guidelines, target demographics, and the technology used.Each article was analyzed according to these metrics, mapping relevantdata segments onto the corresponding categories to allow for a structured263.2. CODING THEMESFigure 3.1: Selection of research articles for literature reviewcomparison across studies.Under the features and functionality theme, the review examined theimplemented functionalities within the applications and their user interfacedesign. The persuasive strategies theme focused on identifying the specificbehavioral change strategies implemented in the apps to influence dietarybehavioral change, such as goal setting, self-monitoring, and social support.Adherence to evidence-based guidelines reviewed applications that im-plemented their features based on standardized and validated research, andhow this impacted their outcomes. Target demography focused on under-standing trends in which user demographics were targeted in the develop-ment of these applications. Finally, technology used examined the techno-logical platforms and tools commonly used in developing these applications.By organizing the literature according to these metrics, the review wasable to identify common features, best practices, and limitations affectingthe effectiveness of mobile health applications.Rather than employing an open coding process where themes emergeinductively from the data, this review applied a deductive thematic analysisapproach, in which predefined metrics guided the extraction and organiza-tion of findings. This method is commonly used in persuasive technologyresearch to identify themes across literature [96].273.3. RESULTSTable 3.2: Description of coding metrics used in reviewTarget Metric DescriptionPersuasive strategies Persuasive techniques used to promote healthyeating and influence dietary behavior, such asgoal setting.Features & Functional-ityPrimary functionalities implemented as the ap-plication’s solution, e.g., meal tracking, calo-rie counting, food logging, recipe suggestions,progress tracking, etc.Adherence to Evidence-Based GuidelinesAlignment with dietary recommendations andevidence-based guidelines.Target Demographic Characteristics of the app’s intended user base,including age, gender, and health status.Technology Used The primary technology or tool used to imple-ment the solution.The findings from this review directly informed the design and develop-ment of the HealKitchen application presented in this work, ensuring thatit builds upon established best practices while addressing observed gaps inexisting research. Table 3.2 summarizes the identified metrics used in thisanalysis.3.3 ResultsThis section presents the results of the literature review that formedthe foundation of the HealKitchen application’s conceptual and technicaldevelopment. The results are presented based on the evaluation metricsused in reviewing the studies.3.3.1 Persuasive StrategiesA significant portion of the reviewed literature highlighted the use ofpersuasive strategies. As seen in Figure 3.2, the research articles reflecta diverse set of strategies employed in mHealth applications to promotehealthy nutrition.283.3. RESULTSThe most prevalent strategy was self-monitoring, occurring in about30.06% of the reviewed articles, such as [46]. This strategy highlights theimportance of empowering users to measure their dietary and health-relatedbehaviors, and is foundational in helping users become aware of their habitsand make informed decisions, often through features like activity logs andprogress tracking [5, 68].Feedback was another significant persuasive strategy occurring in about19% of the research studies, such as [36, 85]. Feedback involves providingusers with information about their activities or performance relative to theirgoals or standards [15]. This could be real-time feedback on daily nutrientintake or weekly summaries of activity levels, motivating continued adher-ence to healthy behaviors [205].Similar to feedback, goal-setting was another widely used strategy occur-ring in about 13% of the studies, such as [158]. Goal-setting was particularlycommon as it allowed users to establish measurable objectives that, whencombined with feedback and monitoring, can create a consistent cycle of be-havioral modification where users set goals, track their progress, and receivefeedback to adjust their actions accordingly [63].Other notable persuasive strategies included information provision (5%),which involved delivering educational content on nutrition and health [74,75], and social support (5%) [202, 205], which influences positive behavior forusers through interactions and feedback from peers or similar communities.Techniques like personalization (3.47%) and reminders (2.89%) [68, 136,165] were noted to offer more tailored and timely interventions and werefound to be effective in helping users stay on track with their specific dietarygoals [9].Some less common but effective strategies included gamification (2.31%)[5, 41] and education (2.31%) [49]. These strategies demonstrated a trendtoward engaging users through interactive elements and instructional con-tent.The combination of these persuasive strategies within mobile apps ex-emplified a comprehensive approach to behavior change, leveraging variouspsychological and motivational strategies to support users in adopting andmaintaining healthier dietary practices.The emphasis on self-monitoring, feedback, personalization, and goalsetting reflects a user-centric approach, where individuals are encouragedto take active roles in managing their health, supported by technology thatadapts to their personal needs and progress.293.3. RESULTSFigure 3.2: Bar chart showing distribution of persuasive strategies3.3.2 Features & FunctionalityThe review revealed a wide range of application features implementedacross existing dietary mHealth applications, which were essential for userengagement and adoption of the target behavior.One of the most prevalent features across these applications was calorie-counting functionality. Calorie counting was specifically noted in a signif-icant number of apps, such as [23, 131], revealing a trend that existingliterature leans more towards dietary monitoring than other nutritional so-lutions. These tracking functionalities, often enhanced through digital plat-forms, have been found to significantly impact user behavior by increasingaccountability and awareness of nutritional intake [22].Another popular feature among these applications was the provision ofnutritional information and educational content that helps users learn aboutthe nutritional value of various food items [27, 129].Some applications, such as [35, 168], also incorporated gamification el-ements as features, which were designed to increase user engagement bymaking the process of adopting healthier eating habits more enjoyable. Forinstance, some applications introduced challenges and quizzes with feed-back to influence drinking habits and consumption of fruits [168]. Othersused point systems, rewards, and challenges to motivate users to reach theirhealth and nutrition goals [35, 42]. These gamification features helped sus-303.3. RESULTStain user interest over time, making them more likely to engage with theapp consistently.The integration of social support features like virtual coaching withinapplications also provided an additional layer of motivation for users [86].Some apps, such as [26, 111], featured social networking boards or interac-tions with coaches or peers through in-app messaging, promoting a sense ofcommunity and accountability.These social features encourage users to share their progress, challenges,and successes with others, thereby enhancing adherence to healthy eatingpractices [97, 201].3.3.3 Adherence to Evidence-Based GuidelinesEvidence-based guidelines refer to validated approaches, derived fromscientific research and clinical expertise, and integrated in intervention tools[80, 88].Adherence to these guidelines is an important component in the devel-opment and functionality of effective mobile health (mHealth) applications,particularly those designed to improve nutrition, diet, and overall healthoutcomes[122].Integrating these guidelines into mobile apps not only enhances theircredibility but also improves the effectiveness of interventions by ensuringthat users receive safe, reliable, and scientifically validated recommendations[122].Several studies in the review reported incorporating evidence-basedguidelines within the context of their target behavior to inform the develop-ment of application features. Hendrie et al. in [91] reported incorporatingthe Australian Dietary Guidelines into a healthy eating app designed to helpadults consume more vegetables. Similarly, Lopez et al [123] developed anapp to provide a list of healthy foods to meet individuals within a budget,following the recommendations of the Dietary Guidelines for Americans.These studies noted that users of apps that adhered to evidence-basedguidelines were more likely to achieve and maintain healthier behaviors overtime [91, 133]. They also note that by adhering to these well-establishedprinciples, the applications fostered trust among users and healthcare pro-fessionals, leading to increased user engagement and consistent behaviorchange [151].Studies were also found to apply evidence-based techniques from popularauthorities such as the World Health Organization dietary recommendations313.3. RESULTS[129] or the UK Department of Health and Social Care guidelines on child-hood obesity [77].Based on the research findings, mobile apps that follow evidence-basedprinciples receive positive feedback and increase user trust [87].3.3.4 Target DemographicAs shown in Figure 3.3, the demographic distribution of the review arti-cles revealed a focus on specific population groups. The largest target groupwas obese individuals, occurring in approximately 22.83% of the articlessuch as [126, 204]. This emphasis suggests a significant concern within mo-bile health interventions about addressing obesity, which is a major globalhealth issue.Following this, general adults were the second most targeted demo-graphic, accounting for 16.30% of the apps such as [67, 101]. This is un-surprising, given that adults are generally more likely to use health-relatedapps and may be seeking tools to manage their dietary habits [179].Children and adolescents are also notable target groups, making up10.87% and 8.70% respectively [72, 131, 164]. These figures highlight thegrowing awareness of the importance of instilling healthy eating habits froma young age to prevent future health issues. Diabetes participants followed,representing 9.78% of the reviewed articles [63, 148].Other niche groups like pregnant women (3.26%) [1], elderly (2.17%)[118], and those with specific conditions such as heart disease (1.09%) [27]were also targeted, indicating that some mobile apps are designed with spe-cialized needs in mind. However, it was noted that there were no applicationsthat provided a singular solution that applied to the general population buttargeted specific health conditions within it.This demographic breakdown indicated that while there is a broad spec-trum of users being catered to, a significant portion of mobile apps areconcentrated on managing specific conditions, such as weight loss. This fo-cus suggests that app developers are responding to the most prevalent publichealth challenges.3.3.5 Technology UsedThe final metric explored in the review was the type of technology im-plemented in these applications to achieve behavioral change. The findingsrevealed that most health interventions were implemented as mobile ap-plications (86.96%) such as [204, 214]. This overwhelming result could be323.3. RESULTSFigure 3.3: Bar chart showing different demographics targeted by mHealthappsFigure 3.4: Bar chart showing different technologies used by health applica-tions333.4. DISCUSSIONattributed to the popularity and accessibility of mobile devices, which makeit easy for users to incorporate the dietary solution into their daily lives.This prevalence emphasizes the central role that mobile technology playsin the current landscape of health interventions, particularly in areas likenutrition and behavior change.Apart from mobile apps, some applications also utilize IoT devices(2.17%), such as in [81], which refer to external devices such as wearabletech that can provide data-driven insights and offer comprehensive healthmanagement solutions. A few other technologies emerged from the review,including web platforms, virtual reality, and digital tools that did not fitinto the categories of mobile and IoT devices. This diversity highlights theinnovation in using different digital platforms to reach varied populationsand achieve specific health outcomes.3.4 DiscussionThe results obtained from the review of 92 research studies providedinsights into the diverse approaches and effectiveness of mobile health(mHealth) interventions adopted in promoting healthy nutrition across di-verse population groups. These studies highlight the use of these tools acrossdiverse demographic groups, including individuals with obesity, children,adolescents, pregnant women, and the elderly.A notable focus was on addressing specific health conditions such asobesity and diabetes, with 22.83% of the applications specifically targetingthis demographic, reflecting public health urgency around these issues. This,however, revealed a gap observed across most of the analyzed applications,which is their narrow demographic focus.Many of these applications are designed for a single specific user group,without offering features that adapt to the diverse needs of a broader pop-ulation within a single platform [103].This limitation means users outside the target group may not receivepersonalized guidance that aligns with their unique health conditions ordietary goals. As a result, users often need to seek multiple apps to addressdifferent aspects of their health or dietary needs, which can be inconvenientand lead to inconsistent engagement [157].HealKitchen addresses this gap by offering a unified, personalized plat-form tailored to a wide range of users with varying health conditions andnutritional requirements. Through recommendation systems, nutritional in-formation, and adaptable features, HealKitchen serves as a tool that can343.4. DISCUSSIONmeet diverse user needs within a single application.The results also emphasized the role of persuasive strategies in drivinglong-term dietary improvements. The review showed that the presence ofthese behavior change strategies was associated with higher user retentionand behavioral change [132]. Apps that lacked such features were oftenless effective in producing sustainable changes in dietary behavior [29, 146].Therefore, integrating persuasive strategies and behavioral theories into dig-ital nutrition tools is not only a best practice but also an important conditionfor success.HealKitchen integrates persuasive strategies such as personalization,feedback, and goal-setting, implemented based on behavioral change the-ories, to encourage user engagement and promote sustainable dietary be-havior change.The review further revealed that mobile platforms dominate the land-scape of health-related technology, with mobile apps accounting for 86.96%of all platforms used. This dominance reflects the accessibility and flexibilityof mobile technology in enabling users to engage with health interventionsanytime and anywhere. Mobile apps’ capacity to integrate features such asreal-time data tracking, personalized feedback, and interactive engagementtools made them particularly effective in promoting healthy eating behaviors[163]. Reflecting these trends, HealKitchen was developed as a mobile-firstapplication to maximize accessibility and engagement.The functionality and features of an application were also shown to sig-nificantly influence user behavior when implemented based on identified per-suasive strategies. Applications that incorporated diverse features to sup-port user engagement were found to be more impactful in influencing userbehavior [178, 179].Features such as gamification elements, educational information, andAI-driven tools created a comprehensive user experience that encouragedadherence to healthier dietary practices.These insights directly informed the design of HealKitchen, which in-cluded the integration of features such as badges, weekly challenges, andstreaks to support long-term dietary behavioral change. These features wereimplemented based on theoretical research using existing behavioral changetheories.Advanced technologies like image recognition and artificial intelligencewere also adopted into the application to improve usability and help usersmake healthy, informed choices, reflecting the mobile-first, user-centeredtrends observed in the review.In summary, the insights gained from this systematic review directly353.4. DISCUSSIONshaped the comprehensive and user-focused design of HealKitchen. By ad-dressing the limitations of existing nutrition apps and building on existingstrategies, HealKitchen aims to offer an engaging and adaptable solutionthat supports healthier dietary behaviors across a diverse user base.36Chapter 4Design & Development ofHealKitchenThe Healkitchen application was developed as a full-stack applicationcomprising a cross-platform mobile application on the front end and a mod-ular server on the back end integrated with an object detection neural net-work model.The architecture of the system was designed with a research-first ap-proach that involved implementing features based on select behavioralchange theories. These features were built on a comprehensive behavioralmodel that influences change through theory-based persuasive strategies[29].4.1 Integration of Comprehensive BehavioralModelThe development of HealKitchen implements the comprehensive behav-ioral model developed by Blanke et al. [29] to translate behavioral changetheories into actionable features within the application, targeting the pro-motion of healthier dietary habits. The integration of behavioral theoriesseeks to answer research question 2, which investigates how behavioral theo-ries can be integrated into a mobile application to support dietary behaviorchange.This approach was suitable because, at the time of development, noother existing frameworks provided a clear method for embedding behavioraltheories that can impact behavioral change into mobile application design.Additionally, Blanke et al.’s work aligned with the goals of HealKitchen, as itwas developed for mobile applications supporting sustainable dietary habits.To implement this framework, five behavioral theories were used thatalign with the target behavioral change. Each theory was analyzed, exam-ining its formal definition, key concepts, and the intended areas of applica-tion as exemplified by the framework. This analysis allowed the design to374.1. INTEGRATION OF COMPREHENSIVE BEHAVIORAL MODELleverage mutual dependencies among the theories, harnessing the strengthsof one theory to compensate for the limitations of another.Blanke et al. emphasized that each theory within their model occupiesa specialized area of influence, suggesting that a comprehensive behavioralchange strategy must incorporate insights from multiple theoretical per-spectives [29]. The behavioral theories selected to guide the design of theHealKitchen application, as explained in Chapter 2, are listed below:1. High-Performance Cycle (HPC)2. Action Regulation Theory (ART)3. Social Cognitive Theory (SCT)4. Theory of Planned Behavior (TPB)5. Self-Determination Theory (SDT)The mutual dependencies between these theories were established follow-ing the procedure described in the framework through a structured processthat involved:1. Evaluating the core concepts and definitions of each theory, and iden-tifying overlaps or limitations in their approaches.2. Integrating the theories by merging complementary or overlappingconcepts and establishing mutual reliance between them.3. Accounting for similar concepts presented under different names orterminologies by summing these concepts up and using the name fromthe most dominant theory in the target area.4. Translating these concepts into formal design features that were inte-grated into the application.4.1.1 High Performance CycleThe High-Performance Cycle (HPC) was implemented in HealKitchen todefine the overarching “demand”, which represents the intended goals of theapplication. The goal of HealKitchen was defined as promoting long-termhealthy dietary behavior tailored to users’ health conditions and targetinggrocery shopping.384.1. INTEGRATION OF COMPREHENSIVE BEHAVIORAL MODELThis definition frames a healthy dietary lifestyle not as an abstract idea,but as a concrete, achievable outcome. Following the definition of the goalof the system, it was translated into operationalizable features within theapplication by implementing defined functionalities within the structure ofthe application’s backend and database.The server architecture was structured with the principles of HPC inmind by offering recipe recommendations and nutritional information thatpertain to users’ defined health conditions. Core data, such as recipes andnutritional information, was structured to align with the users’ requirementsand the defined goal.The primary limitation of the HPC is that it lacks a clear descriptionfor translating goals to actual behavior [121]. This limitation was addressedby the integration of the Action Regulation Theory (ART).4.1.2 Action Regulation TheoryThe Action Regulation Theory (ART) was implemented in the devel-opment of HealKitchen through the concept of redefinition of tasks, whichinvolves translating the goals from the HPC into meaningful functions andfeatures accessible to the user.This concept was employed to guide the transformation of the overarch-ing goal into a series of achievable user actions. The defined goal was brokendown into the following sub-tasks for the users:− Provide health information regarding health challenges and nutritionalrequirements that guide recipe recommendations− Select and prepare recommended recipes that target users’ health chal-lenges− Create and modify grocery lists linked to recommended recipes− Identify nutritional information of unfamiliar grocery items− Plan and prepare healthy dietary recipes based on available ingredients− Monitor progress and achievements in building healthy dietary habitsThese sub-tasks were supported by designing the application interfacein a user-friendly manner, presenting information in a way that matchesusers’ cognitive needs and drives behavioral change. Users can easily browse394.1. INTEGRATION OF COMPREHENSIVE BEHAVIORAL MODELhealth-appropriate recipes, view nutritional information, and add items di-rectly to their grocery list with just a few taps. This approach reflectsART’s principle of supporting users through structured task managementby breaking down complex behaviors into simple, achievable tasks.Furthermore, the ART focuses on the concept of goal-setting. This wasembedded in the design of the application by providing achievable goals thatallow users to include the application in their daily activities. Applicationfeatures such as grocery list management, weekly challenges, and progresstracking features were introduced to maintain user interaction in the applica-tion and help them remain motivated to continue toward long-term healthyeating behaviors.4.1.3 Social Cognitive TheoryThe Social Cognitive Theory (SCT) was integrated alongside the ART inthe development of HealKitchen through the concepts of self-efficacy, whichfocuses on influencing users’ perceived ability to achieve the desired behaviorchange, and self-regulation, which refers to the process by which individualsmonitor and adjust their behaviors to achieve the desired goals. The SCTwas integrated by implementing the following features:− Recipe recommendation: HealKitchen provides users with easyaccess to recipe recommendations, detailed nutritional information,and health benefits tailored to their specific conditions. This reducesthe complexity of dietary decisions, helping users build confidence intheir ability to make healthy choices independently [19, 149].− Recipe curation: To support preparing recipes, HealKitchen pro-vides recipe curation, suggesting recipes based on existing ingredientsthat users already have. This approach reduces barriers to preparinghealthy meals and strengthens users’ belief in their ability to maintainhealthy eating habits [21, 76].− Feedback and Progress Monitoring: HealKitchen also supportsthe SCT’s concept of feedback through features such as recipe ratings,progress tracking, and a rewards system. These features reinforceself-regulation and acknowledge users’ efforts, subsequently influencingusers’ belief in their ability to achieve targeted behavior [37, 132].− Social Proof : HealKitchen also integrates the SCT through the con-cept of social proof. Although it is primarily a personalized app, it404.1. INTEGRATION OF COMPREHENSIVE BEHAVIORAL MODELincorporates elements of social proof by highlighting popular recipesthat have been highly rated by users with similar health conditions.Seeing others successfully engage with healthy meals models positivebehavior and encourages new users to do the same [20, 147].By integrating these concepts of the SCT into its design, HealKitchenpromotes healthier dietary practices and also aims to strengthen users’ long-term confidence and motivation, and engagement.4.1.4 Theory of Planned BehaviorThe HealKitchen application integrates the Theory of Planned Behavior(TPB) through the concept of users’ attitudes toward behavior.This application enhances a positive attitude towards healthy dietaryhabits by providing clear and understandable information to reinforce thebenefits of healthy eating. This encourages users to associate healthy foodchoices with desirable outcomes and cultivate a favorable attitude towardthe desired behavior [140, 160].HealKitchen also supports behavioral control by simplifying the processof healthy grocery shopping and meal preparation. The application breaksdown behavioral change into manageable tasks such as selecting a recipe,providing nutritional information on ingredients, automatically generatinga grocery list, and providing cooking instructions.These features limit users’ perceived barriers to the desired habit andincrease their perception of control over the desired behavioral change [7, 21].4.1.5 Self-Determination TheoryThe final behavioral theory integrated into the design of HealKitchenwas the Self-Determination Theory (SDT). The SDT is integrated withinHealKitchen by supporting user autonomy and competence.The application provides users with the flexibility to choose recipes andcustomize their grocery list based on their dietary preferences. Rather thanrigidly dictating what users should eat, the app offers curated options thatalign with the user’s needs and empower them to make informed choices.This sense of control is crucial in sustaining internal motivation toward thetarget behavior [170].The onboarding process of the application also supports user autonomyby allowing users to set up their health profiles, indicating health challengesand nutritional needs. This allows the application to adapt to individual414.2. OVERVIEW OF SYSTEM ARCHITECTUREusers’ needs and not the other way around, building trust and engagementfrom a user’s first interaction [139].HealKitchen also supports user competence through feedback mecha-nisms and reward systems, enabling users to experience visible progress andmastery over their healthy dietary decisions. For instance, completing mul-tiple weekly challenges earns a user a badge and contributes to their score,which reflects long-term healthy engagement. HealKitchen further reinforcescompetence through the application’s nutritional information features.A central focus of the SDT is understanding how externally motivatedbehaviors can be internalized over time and become intrinsically motivated.HealKitchen supports this progression by initially engaging users with ex-ternal motivators, such as rewards, progress tracking, and recipe recommen-dations, and gradually helping them internalize the value of healthy eating.Research shows that as users begin to perceive the personal relevance andbenefits of a behavior, their motivation shifts from extrinsic drivers to in-trinsic satisfaction, thereby increasing the likelihood of continuous adherence[169].The integration of these behavioral change theories into the HealKitchenapplication provided a theoretical background to implement strategies thatcan influence change in the dietary lifestyle of the users of the app. Byincorporating five distinct but complementary theories, the application aimsto support both the cognitive and practical aspects of behavior change.Each theory contributed to the design of the application by providingspecialized concepts such as goal-setting, action planning, feedback, andautonomy, which were translated into concrete features within the mobileapplication.Having identified how the application could be designed based on thesetheories, the following section details the technical architecture and devel-opment of HealKitchen.4.2 Overview of System ArchitectureThe HealKitchen application was developed as a modular, scalable, andcross-platform system, supported by a robust backend server and enhancedby artificial intelligence-driven features to promote healthy dietary behav-ior. To effectively support its functionalities, which include a personalizedrecipe recommendation, grocery list management, grocery image object de-tection, and behavioral feedback, a robust and scalable system architecturewas required.424.2. OVERVIEW OF SYSTEM ARCHITECTUREFigure 4.1: System architecture of HealKitchen applicationThe architecture of the system reflects the prioritization of personaliza-tion and user engagement in the development of the application, aligningtechnical decisions with behavioral design principles to support healthy di-etary habits.This section outlines the technical foundation of the system, detailingthe design principles and development of the backend and front end of theapplication, as well as the integration of machine learning components forimage recognition.The architecture of the system depicted in Figure 4.1 integrates the pre-viously described behavioral theories, implementing these theories as designfeatures within the mobile app. By aligning technical design with motiva-tional theories, the system aims to support the effective translation of userintentions into actionable dietary behaviors.The implementation of the HealKitchen backend server follows the def-434.3. APPLICATION SYSTEM COMPONENTinition of the goal of the application. This goal was defined as promotinglong-term healthy dietary behavior tailored to users’ health conditions andtargeting grocery shopping.After defining the application’s goal, the requirements to achieve thiswere reviewed to identify the components needed within the application.This resulted in the division of the application logic into two core compo-nents that provide application features.The first component is the application system, consisting of a personal-ized healthy recipe recommendation system, a recipe curation system pow-ered by artificial intelligence, and a grocery management system for organiz-ing grocery lists. The second core component is an image recognition systemthat provides users access to nutritional information on grocery items whileat the grocery store, promoting efficacy for performing the desired behavior.4.3 Application System ComponentThe application system of the HealKitchen server was designed as aheadless application interface that manages data and supplies the front endwith structured information to provide functionality. It was built usingthe Flask framework, which is a lightweight and extensible Python-basedframework suitable for RESTFUL API development [153].Flask was chosen for use because of its flexibility and its suitability formicroservice-oriented architectures. The Flask framework is also known tosupport simplicity and efficient control over request routing and applica-tion middleware, making it well-suited for a modular system that integratesmultiple subsystems. This allowed for the seamless integration of a Python-based machine-learning model.4.3.1 Model-View-Controller (MVC) ArchitectureTo ensure maintainability, scalability, and enforce separation of concerns,the backend follows the Model-View-Controller (MVC) architectural pat-tern, modified to suit its headless architecture as a Backend-for-Frontend(BFF). MVC is a widely used architectural paradigm that promotes mod-ularity and maintainability within software applications [192]. The MVCpattern separates application logic into three interconnected components:The Model component of the backend system comprises the application’sdata structures and interaction with the database. It defines the entities thatmake up the content of the application and manages data-related operationsbetween them. The HealKitchen models are defined to integrate with the444.3. APPLICATION SYSTEM COMPONENTdatabase using PyMongo, the official MongoDB driver for Python. Thislayer of the architecture defines the schemas for users, recipes, grocery lists,recipe ratings, etc. The schemas were defined based on the defined goalof the system and were stored in BSON (Binary JSON) format within thedatabase, allowing for flexible storage of nested data entities and intercon-nection between data schemas.The View component of MVC traditionally represents the user interfaceof an application, but is replaced by the structured presentation of data ex-posed over the application programming interface (API) within the contextof the HealKitchen backend system. This design allows decoupled clients,such as a mobile application (in the case of HealKitchen), to render theuser interface based on structured information provided by the Backend-for-Frontend (BFF). This data is dynamically generated based on clientrequests and sent to the front end as a JavaScript Object Notation (JSON)response.The controller component serves as the intermediary between the Modeland View components. In HealKitchen, the controller handles incomingclient requests, directs application logic, processes data to modify appropri-ate models, and constructs responses to be returned via the API.Controllers are responsible for enforcing validation, authentication, andpersonalization logic, such as ensuring that recipe recommendations featuresare tailored to the user’s health profile.By encapsulating this logic in dedicated controller modules, the backendensures clean separation of concerns and scalability. This layer plays animportant role in translating the system’s goal-driven logic into actionableuser-facing features.The MVC structure implemented in the development of HealKitchen alsoaligns with the theoretical foundation described by the concept of goals fromthe HPC theory. According to the HPC theory [121], motivation is sustainedwhen users perceive their tasks as meaningful and linked to clear goals. InHealKitchen, the backend controller ensures that all features delivered tothe user reflect the application’s goals.4.3.2 Database DesignHealkitchen uses MongoDB as its primary database to persist structuredapplication data. The database was hosted on MongoAtlas, with cloud de-ployment configured in the Toronto Central region. This hosting choice wasmade to meet the requirements for persistent data residency and complianceoutlined in the research ethics protocol, ensuring that all user information454.3. APPLICATION SYSTEM COMPONENTFigure 4.2: MVC architectureis stored securely within Canadian jurisdiction.MongoDB was chosen because of its flexibility, performance, and scal-ability. MongoDB is a non-relational database, and its document-orientedstructure allows for proper nested data representation, flexible schema ma-nipulation without affecting existing data, and efficient storage and dataaccess [135].The structure of the application data was defined within the server us-ing PyMongo. PyMongo is the official MongoDB driver for Python andoffers direct interaction with MongoDB collections using intuitive Pythonsyntax, allowing the definition of schema models with expressive queries,and efficient document updates [59].PyMongo was used specifically to define the entities used in the HealK-itchen application. All entities were defined as PyMongo documents andautomatically translated to MongoDB collections using the MongoEngineObject Document Mapper, which is a high-level Python abstraction layerover PyMongo used to simplify document modeling and interaction. To464.3. APPLICATION SYSTEM COMPONENTensure low-latency access during queries, query indexes were defined on fre-quently required fields. These indices reduced the response times of queriesfor large operations based on complex data aggregation, allowing for seam-less user engagement and fast response times.The core application system of HealKitchen was further broken downinto data entities that support the application features. Each entity wasdefined individually to manage an aspect of the HealKitchen applicationlogic as defined within the HPC and interact seamlessly with other dataentities within the application. The primary data entities defined for theapplication features include:User Model and Authentication SystemAn important component of the HealKitchen application is the user man-agement and authentication system, which enables secure account creation,session handling, and user data management. The user model was designedto encapsulate all essential user information required for personalization anddietary recommendations. The model included core identification fields suchas email, password, first name, and last name to support user registrationand personalized app experiences. The central feature of the user modelwas the inclusion of health-related data specifically defined by two fields:healthChallenges and desiredNutrients. These fields were used to collectdata about the user’s health challenges and desired nutrients, which formthe basis for the tailored dietary recommendations.To appropriately manage user privacy, these fields were encrypted usingAES-256 encryption in CBC (Cipher Block Chaining) mode, a symmetric-key encryption algorithm widely used for securing sensitive information.AES (Advanced Encryption Standard) uses a 256-bit encryption key forhigh security, while the CBC mode adds an initialization vector (IV) toensure that identical plaintext inputs produce different ciphertext outputs[61]. The encryption of sensitive data ensured the HealKitchen applicationmaintained user privacy and adhered to ethical protocol.The user model also contains reference fields that support various appli-cation features, including an embedded shopping list for managing groceries,a favorites list for saved recipes, and additional fields to track user engage-ment. These include references to badges earned, challenges completed, andmetrics such as point totals and streaks, which reflect the user’s consistencyand long-term commitment to healthy behavior through the app.The defined user model enabled structured session management withinthe application. User sessions were handled using the authorization con-474.3. APPLICATION SYSTEM COMPONENTtroller following the MVC architectural pattern. The authorization con-troller supported all key authentication and authorization processes, includ-ing registration, sign-in, sign-out, and account deletion.Session management was implemented using encrypted JSON Web To-kens (JWTs) [99]. The authorization controller provides access and refreshtokens generated based on authenticated user IDs and enforces secure accessto user information throughout an application session. The system adheresto security best practices by assigning a fast-expiring access token and alonger-lived refresh token to maintain user sessions [82]. This practice re-duces the risk of security attacks, such as cross-site request forgery attacks,and ensures that user sessions remain secure throughout the application[150].Recipe Schema Modeling and Generation of Healthy RecipesThe HealKitchen application integrates personalized dietary recommen-dations tailored to individual users based on their specific health challengesand nutritional needs. This solution was implemented in two primary func-tions: recommending healthy recipes that directly address these challenges,and guiding users toward healthier choices during grocery shopping basedon their nutritional needs.The proposed solution required access to a curated and structureddatabase of healthy recipes, where each recipe is linked to specific healthconditions and nutritional deficiencies that it remedies. This created a majorchallenge in the development of the application, as at the time of develop-ment, there was no publicly accessible, curated database that directly mapsrecipes or food items to specific health conditions or nutritional deficiencies.To address this limitation, a hybrid approach was implemented to curatea custom database of healthy recipes with related health challenges andnutritional needs that they remedy. This approach combined authoritativepublic nutritional data from the United States Department of Agriculture(USDA) with AI-assisted mapping and manual health research. This methodenabled the creation of a custom database that links core grocery itemsto essential nutrients and health benefits, which would later serve as thefoundation for recipe recommendations.− Extracting and Structuring Grocery-Nutrient Data: The firststep involved assembling a structured database that maps commonhealthy grocery items to their dominant nutritional content. This wasimplemented using data from FoodData Central (FDC) [203], a com-484.3. APPLICATION SYSTEM COMPONENTprehensive nutritional reference database maintained by the USDA.FoodData Central provided detailed nutritional information on a widerange of agricultural products and was designed to serve as a com-prehensive food reference tool for dietary guidelines and scientific re-search.The selected dataset for this approach was the Foundation Foodsdataset, which was the most suitable source for raw, unprocessed gro-cery items. This dataset provides standardized nutritional profiles ofcommon agricultural products that serve as a baseline for all otherprepared and branded foods.The version of the Foundation Foods dataset used in this researchwas published on April 20, 2023, and contained approximately 1,450records. However, most of the records were duplicate entries withminor variations of the same grocery items and required processingto filter the dataset into a meaningful subset for this study. Thesedifferences were based on factors such as region of cultivation, scien-tific classification, and labeling variations. For example, “Almonds -NFY1213KT” and “Almonds- NFY1213LU” appeared as two distinctrecords, but they are simply variations of the same product (almonds)and offer identical nutritional value. To refine the dataset, the re-dundant entries were merged into a single primary food item (e.g., allvariations of almond were combined under “Almonds”).Next, other occurrences of food items that did not align with thisresearch were manually removed. These represented processed orbranded food items that did not fit the category of raw or grocery fooditems, including branded products that were not suitable for forminghealthy recipes. The resulting dataset was reduced from 1,450 recordsto a final curated list of 130 unique grocery items. Each item in thiscurated list served as a base ingredient that was used in recipe recom-mendations in HealKitchen.− Nutrient Profiling: The Food Data Central database also included areference nutritional table that provided the nutrients for each groceryitem based on its nutritional composition. Due to the large volumeof data and the scope of the research, the nutritional information ofeach grocery item was reduced to include the top five highest occurringnutrients in each item. The nutrient ranking was determined based ontwo primary factors:– Daily Value Contribution: The percentage of the average daily494.3. APPLICATION SYSTEM COMPONENTrecommended intake that the nutrient contributes when con-sumed in standard amounts.– Absolute Quantity: The actual amount of the nutrient present inthe food item.For example, if a food item contains 250mg of nutrient A (contributing30% of the recommended daily intake) and 10mg of nutrient B (con-tributing 1%), nutrient A would be prioritized over B, allowing for afocus on the most impactful nutrient for each grocery item.To maintain consistency across entries, nutrient names were also nor-malized across grocery items. Some nutrients have multiple aliases,such as “Thiamin” and “Vitamin B1”. These names were generalizedto their most common or recognizable names for ease of processingand consistency during health benefit mapping. This normalizationprocess was especially important for future use in similarity scoringand filtering during recipe recommendation.− Mapping Health Benefits to Grocery Items: After collectingthe dataset of grocery items with their nutrient profile, the next stepwas to associate each grocery item with specific health challenges orconditions it could help mitigate or support. This step was not coveredby the USDA dataset and required additional research. To gather thisinformation, a manual review was conducted referencing two highlyreputable health and nutrition resources: Healthline [89] and WebMDDiet [210].These platforms were selected because they provided credible andevidence-based information on the health benefits of food items. Themapping was implemented in three steps:1. Each food item was searched to identify its top three consistenthealth benefits across both platforms.2. Benefits that were repeated or strongly supported in both sources(e.g., if both sources noted that almonds support heart health,that benefit was retained) were extracted. This helped reducebias and filtered out one-off claims.3. The results were collated into a structured mapping that linkedeach grocery item to three primary health conditions or benefits.This process resulted in a preliminary mapping of grocery items totheir associated health conditions. However, due to the manual nature504.3. APPLICATION SYSTEM COMPONENTof the search, health condition names varied in terminology. For exam-ple, one entry might use the term “cardiovascular disease”, while an-other uses “supports heart disease”, both referencing the same healthcondition. This could lead to inconsistencies when using the data. Toalleviate this, a normalization process was performed, grouping similarterms under a standardized name. This step was crucial for creatinga consistent and reliable database schema.The result was a list of 87 unique health conditions across the 130grocery items. This final mapping of grocery items tagged with theirtop nutrients and the health conditions they help mitigate or supportformed the core content of the recipes used in the development ofHealKitchen.Details of the grocery items, health conditions, and nutrients generatedfrom these processes can be found in Appendix 7.5 (see Tables 7.1 -7.3)− Recipe Generation Using Open AI: After completing the curatedmappings between grocery items, nutrients, and health conditions, thefinal step involved generating healthy recipes that would be used inthe HealKitchen application. This step required not only combiningingredients in meaningful ways but also ensuring that the resultingrecipes aligned with the health goals associated with the curated data.To accomplish this, OpenAI’s text generation API was utilized, lever-aging its natural language generation capabilities to produce unique,health-conscious recipes. A custom prompt was created to generatethese unique and nutritionally beneficial recipes that aligned with theassociated health conditions, using the curated grocery items as thebase ingredients of the recipe. The prompt specified the fields to beincluded in the resulting data, such as the recipe name, ingredients,and cooking instructions. The prompt was also designed to enforceminimal use of processed ingredients, ensuring the recipes focus onwhole, natural foods, and to produce clear, structured outputs thatcould be easily parsed and stored in the database.This prompt generated a total of 138 unique recipes based on theidentified grocery items, each constructed using combinations of the130 grocery items and designed to support one or more of the 87documented health conditions. The generated recipes were formattedto match a defined recipe schema. These recipes were then insertedinto the database using a standard seeding process.514.3. APPLICATION SYSTEM COMPONENTThe seeding process ensured that the recipes, along with all inter-related data, including grocery items, nutrients, and diseases, werestored in a normalized, relational format within the database. Theresulting schema supports scalability and efficient database access andserves as the formal dataset of recipes that power the recommendationsystem implemented in the HealKitchen application.The prompts used to generate recipes can be found in Appendix 7.54.3.3 Recipe Recommendation SystemAn integral part of the architecture of HealKitchen, as shown in Fig-ure 4.1, is the recipe recommendation system. Personalized recommendationsystems can play an essential role in persuasive digital solutions, particularlyin applications where users’ needs are shaped by different factors. In thecontext of HealKitchen, the recommendation system was designed to providetailored healthy meal suggestions that align with users’ nutritional targets.The development of the recommendation system was guided by researchquestion 4, which aims to determine how personalized recommendation sys-tems can predict relevant healthy recipes, taking into account users’ healthconditions and nutritional needs.This recommendation system represents a core technical innovationwithin the HealKitchen application. It plays a central role in deliveringaccurate and personalized healthy meal suggestions tailored to individualusers based on their unique nutritional requirements, health challenges, andbehavioral preferences.HealKitchen implements a hybrid recommendation approach [38] thatcombines two major recommendation algorithms, content-based filtering[155] and collaborative filtering [176], to provide users with contextual, rel-evant, and health-optimized recipe suggestions.This approach enhances the variety of recommended recipes and avoidsissues such as the cold start problem [177]. The recommendation systemalso ensures the recipes are relevant to the user’s needs and takes essentialfeedback from other application users.Data Modeling and Vector EncodingTo implement this recommendation system, similarity computationswere aggregated to output a scored list of recipes. These outputs weremerged using weighted scoring to deliver the final recommendation lists.524.3. APPLICATION SYSTEM COMPONENTThe similarity scores were implemented using vector representations gen-erated from user and recipe profiles. User profiles were created from storeduser information and were composed of the following elements:− Declared health conditions (e.g., hypertension, diabetes)− Desired nutrient goals (e.g., fiber, sodium)− Favorited recipes− Past recipe ratingsSimilarly, recipe vectors were constructed for each recipe using its prop-erties, such as:− Nutrients provided− Health conditions supported− List of ingredients− Base grocery items (the primary ingredient in the recipe)All category fields within the data entities were transformed into fixed-length vectors using one-hot encoding for diseases and nutrients [167]. In-gredients and base grocery items were encoded using hash-based vector rep-resentations [212]. These standardized encodings allowed for calculatingvector operations like cosine similarity within the recommendation system.Content-Based Filtering Recommendation AlgorithmContent-based filtering is a recommendation algorithm that suggestsitems to users based on the features of those items [124]. It is a widelyadopted recommendation technique that relies on matching characteristicsof items (in this case, recipes) with a curated profile of a user [124]. Inthe development of HealKitchen, content-based filtering was implementedby constructing the user profile from explicit user input gathered during theapplication registration process.This algorithm matches each user to recipes based on vector similarity.The user vector combines their health challenges, nutrient needs, and op-tionally, the average vectors of their favorite recipes. Each recipe vectorwas similarly constructed using its nutritional properties, supported health534.3. APPLICATION SYSTEM COMPONENTconditions, ingredients, and base item. The cosine similarity was then cal-culated between the user vector and all recipe vectors to generate a rankedlist of recipes that most aligned with the user’s needs.The algorithm can be described as follows:1. Encode user profile as vector U2. Encode each recipe r as vector Rr3. Compute similarity (U,Rr) = U ·Rr|U |·|Rr|4. Rank all recipes by similarityThe content-based filtering algorithm ensured that the recommendedrecipes are not only relevant to the user’s health challenges and nutritionalrequirements but also similar to the types of meals they’ve shown a prefer-ence for in the past. Additionally, content-based filtering supports explain-ability, allowing the system to justify how each recommendation it makesmatches a user’s goals.Collaborative Filtering Recommendation AlgorithmWhile content-based filtering personalizes recommendations based on theexplicit features of the recommended items and user profile, it can be lim-ited in its ability to introduce variety and account for some other implicituser preferences. To overcome this and further enhance personalization, theHealKitchen system also incorporates a collaborative filtering recommen-dation algorithm that bases recommendations on the preferences of similarusers. The recommendation system specifically implements this by:1. Identify a set of similar users u1, u2, . . . , uk. Similar users are definedin the application using cosine similarity between user vectors.2. Aggregate all recipes highly rated or frequently favorited by theseusers.3. Compute a weighted score for each recipe based on:− Rating strength− Similarity between the target user and a similar user4. Rank aggregated recipes by this weighted score.544.3. APPLICATION SYSTEM COMPONENTCollaborative filtering techniques are utilized in recommendation sys-tems where behavioral similarities can indicate shared preferences [16, 166].In HealKitchen, the collaborative filtering algorithm captures patterns thatare not explicitly expressed by users’ declared preferences. By aggregatingrecipes highly rated by similar users, the system can recommend new recipesthat may not directly match the user’s nutrient or disease profile but are stilllikely to be of interest. This method of recommendation introduces noveltyinto the recommendation process, improving user satisfaction and discovery.Hybrid Recommendation StrategyThe recommendation algorithms were combined in a hybrid approach toimprove the quality of the recipe recommendations made to users. Hybridrecommendation systems have been shown to outperform single-strategymodels by combining the strengths of both content-based and collaborativefiltering approaches and compensating for shortcomings of each algorithm[38]. They have also been found to yield better user satisfaction in foodrecommendation systems [199].In the application, two lists of recipe recommendations were generatedfor the user. The first list contained purely content-based recipe recommen-dations, while the second list contained recipes from a hybrid recommen-dation algorithm. The first recommendation list was focused on generatingcontent-relevant recipe suggestions that directly match the user’s healthconditions, nutritional requirements, and behavioral patterns.The second recommendation list was implemented to improve the varietyin users’ recommendations by taking into account feedback from other usersof the application. The hybrid algorithm was utilized for the second list toensure content relevance for collaborative filtering results. This was donebecause the relevance of the recommendations relies heavily on the attributesof the recipes, which are not inherently subjective. Without this, a userseeking fiber as a nutrient might receive a recommendation for a diabetesrecipe based on collaborative results from a similar user seeking fiber butalso combating diabetes, though the recipe is not relevant to the first user.This approach balances user-specific relevance with peer-informed vari-ety to improve user satisfaction and engagement over time.System IntegrationThe recommendation computation was performed asynchronously usingPython’s ThreadPoolExecutor. This design allowed both algorithms to run554.3. APPLICATION SYSTEM COMPONENTin parallel, reducing system latency. The system returns two lists of 10randomly sampled top-ranked recipes from the computed recommendations.This randomization is done from a pool of all ranked recommended recipesfor a user to enhance the variety of recommendations whenever they reopenthe application.Evaluation of Recommendation SystemThe performance and accuracy of the recommendation system were as-sessed by calculating key evaluation metrics across the two recommendationlists. Due to the absence of real-time user feedback during development, tra-ditional recommendation evaluation metrics like precision or recall, whichare commonly used in collaborative systems with behavioral feedback [180],could not be directly applied. Instead, three intrinsic evaluation metrics thatare appropriate for evaluating recommendation algorithms were selected:− Content relevance: This metric quantifies how many of the recom-mended recipes match the users’ declared health profile and nutritionalrequirements. It evaluates whether the system returns recipes that areobjectively aligned with the user’s needs. Since the recipes are taggedwith nutrient and disease metadata, relevance can be determined with-out requiring subjective user feedback [124].Content relevance was evaluated by calculating the precision of therecommendation algorithms for the user’s health condition nutrientneeds. The user profile consists of their target nutrients and healthconditions, and each recommended recipe contains information aboutthe nutrients it provides and the health conditions it supports. Theevaluation algorithm compared each recommended recipe’s attributesagainst the user’s health profile and counted a recommendation asrelevant if there was at least one overlap. Precision was then com-puted as the ratio of relevant recommendations to the total number ofrecommendations.The content relevance was calculated as:Precision = |Rrelevant||Rrecommended|where Rrelevant represents the subset of recipes aligned with the user’sdietary needs, and Rrecommended represents all recommended recipes.This approach ensured that the evaluation was not generic but tied564.3. APPLICATION SYSTEM COMPONENTto the system’s intended functionality, which is to recommend recipesthat support the user’s health conditions and desired nutrients.− Diversity: Diversity measures the degree of distinction between therecommended recipes. A diverse recommendation list ensures usersexperience variety and reduces redundancy in the system. This isespecially valuable in dietary recommendation contexts where varietycontributes to sustained healthy behavior [109].Diversity was computed in this system using cosine similarity acrossrecipe vectors, which encoded nutrients, disease, and other recipe char-acteristics. The pairwise similarities between all recipe vectors in therecommendation list were calculated and then averaged. The diversityscore was finally expressed as one less the average similarity, so highervalues correspond to greater dissimilarity as described in [216].The diversity score was calculated as:Diversity = 1− 2n(n− 1)n−1∑i=1n∑j=i+1sim(vi, vj)where n is the number of recommended recipes, vi and vj are recipefeature vectors, and sim(vi, vj) is the cosine similarity between twovectors [216].− Coverage: Coverage represents the proportion of relevant recom-mendable items that are recommended across users. High coverageindicates that the recommendation system is capable of utilizing awide range of available datasets without repeating a small subset ofpopular recipes. In health applications, good coverage ensures thatusers with less common nutritional needs or diseases are still effec-tively served by the system [93].The coverage score of the recommendation systems was evaluated bymeasuring the ratio between the number of unique recipe IDs in-cluded in the recommendations and the total number of recipes inthe database across multiple recommendations. Coverage was definedby the following formula:Coverage = |Rrecommended ∩Rall||Rall|where Rrecommended is the set of unique recipes recommended, and Rallis the set of all recipes in the dataset [93].574.3. APPLICATION SYSTEM COMPONENTThese metrics were measured as a preliminary assessment of the algo-rithm’s performance using seed data of user profiles, as real user feedbackdata was unavailable while building the application, and provide a goodview of the recommendation system’s effectiveness under constraints wherereal user feedback is unavailable.Evaluation ResultsThe results of the evaluations are summarized in Table 4.1. The content-based recommendation algorithm demonstrated high coverage (70.42%), in-dicating that it successfully captures a significant portion of all relevantrecipes in its top recommendations. This suggests that the algorithm effec-tively aligns with user-defined health challenges and nutritional goals. Thecontent relevance score of 1.0 confirms that all recommended recipes aredirectly relevant to the user’s health challenges and dietary requirements.In contrast, the hybrid recommendation, which combines content-basedand collaborative filtering, exhibited lower coverage (14.08%). This reducedcoverage is primarily due to limited behavioral data (e.g., ratings and fa-vorites) available from other users, which the collaborative component relieson. Since collaborative filtering depends on patterns from user interactions,its performance is currently constrained by the limited data. However, asuser engagement increases over time, this coverage is expected to improvesubstantially.Despite its lower coverage, the hybrid model achieved a higher diversityscore (37.4%) compared to the content-based model (25.3%). This indi-cates that integrating collaborative filtering introduces greater variety intothe recommendations, potentially exposing users to a broader set of rele-vant recipes. This can enhance the user experience by avoiding repetitivesuggestions and increasing exploration within safe boundaries.Table 4.1: Performance Metrics for Recommendation AlgorithmsRecommendationAlgorithmContentRelevanceCoverage DiversityContent-Based 1.0 0.7042 0.253Hybrid 1.0 0.1408 0.374584.3. APPLICATION SYSTEM COMPONENT4.3.4 Supporting Application System FeaturesRecipe Interactions: Ratings, Favorites, Shopping ListsThe HealKitchen backend supports user interactions with recommendedrecipes as a vital component in accomplishing the desired behavioral change.These interactions extend the functionality of recipe recommendations, al-lowing users to actively engage with them through rating or favoritingrecipes and generating personalized shopping lists. These interactions areessential to building user engagement and reinforcing positive dietary be-havior.They also play an important role as additional data for improving theaccuracy and diversity of the recommendation system. These interactionsare derived as operationalizable features that support the behavioral con-cepts of self-efficacy, self-regulation, goal-setting, and feedback, which arecore elements of the Action Regulation Theory (ART) and Social CognitiveTheory (SCT).The recipe rating system in HealKitchen allows users to provide feedbackon recipes they have tried through a rating interface. Recipes are rated ona range of 1-5, and these ratings are stored in a Ratings schema in thedatabase and hold a reference to the user and recipe.These ratings serve two key purposes: Firstly, they contribute to thecollaborative filtering algorithm, as they are used to measure the popularityof recipes and the similarity of users. This follows previous research thathas shown that user-generated ratings enhance the accuracy of collaborativerecommendation systems [2]. Secondly, they are reflected to the user aspart of their engagement history, reinforcing the target behavior of enjoyinghealthy recipes and supporting the behavioral concept of feedback.Similar to recipe ratings, HealKitchen supports user interaction and feed-back through a favorite recipes feature. Users can save recipes to a favoriteslist for future reference. These recipes are stored in the favRecipes field onthe user model, which holds a list of references to recipe documents. Fa-voriting a recipe allows a user to save meals they have enjoyed and plan totry again, giving them a quick way to access such recipes.They also contribute to the content-based recommendation algorithm byproviding additional information on the preferences and tastes of a user thatinfluences the user profile and content similarity vector. This feature reflectsa user’s intentional act of performing the desired behavior and supports goal-setting and dietary planning, which are important for long-term sustainedengagement [70].594.3. APPLICATION SYSTEM COMPONENTHealkitchen also supports concepts from the ART through an automatedgrocery shopping list generator. This feature simplifies the transition fromrecipe recommendation to grocery planning and supports self-efficacy inbuilding long-term healthy dietary behavior. The shopping list system inHealKitchen exists on the user schema and consists of two lists. The firstmain list integrates ingredients pulled directly from recipes into the shop-ping lists. This allows users to add recipes they wish to prepare, and all theingredients used to prepare those recipes are added to their shopping list,simplifying the process. The second list takes additional ingredients that auser is interested in purchasing from the grocery store. Ingredients in thislist are manually added by the user, providing a single place for managing allgroceries. The shopping list feature supports goal-setting and self-efficacyby making it easy to translate recipes of interest into real-world purchasingbehavior.The ratings, favorites, and shopping list functionalities together form aninteractive feedback loop within the HealKitchen application. These fea-tures are important for maintaining behavioral engagement and supportinghealthy habit formation. They help to reinforce user decisions and healthydietary actions [51, 161].Recipe Rewards System and Gamification MechanicsThe final component of the HealKitchen application system involved theintegration of gamification elements. Gamification refers to the use of gameelements in non-game contexts and has become a powerful tool in enhancinguser engagement and promoting sustained behavioral change [25].The integration of gamification features in HealKitchen was informedby the results of the systematic literature review conducted in Chapter 3,where gamification elements were identified as a key strategy to influenc-ing behavioral change in existing nutrition and health applications. Thesestrategies not only improve user engagement but also support long-termhabit formation and behavior maintenance [172, 175].The gamification elements are embedded within the system to supportthe concepts of self-regulation and intrinsic motivation defined by the Self-Determination Theory (SDT) and Social Cognitive Theory (SCT) behavioralmodels.The core gamification elements integrated in HealKitchen include:− A streak system that tracks users’ daily interactions and consistentuse of the application.604.3. APPLICATION SYSTEM COMPONENT− Weekly challenges that provide short-term goals to promote continuousengagement.− A point-based rewards system that grants points for specific user ac-tions.− A badge system that rewards users for hitting key milestones.These features provide a sense of progress and achievement within theapplication, which is a driver for users’ consistency in building the desiredbehavior [98, 175]. They are integrated in a structured and scalable manneron the application server to support continuous user engagement in theapplication.The streak system monitors users’ daily engagement with the applicationby observing their daily activity. Each successful streak day adds 2 pointsto the user’s point total on the user schema and contributes to their badgeprogression.The streak fosters intrinsic motivation of the SDT by making users moreaware of their consistency, as research has shown that visible progress andbehavioral tracking can improve compliance with health behavior in mobileinterventions [51, 110].The weekly challenge system further supports structured engagement.Challenges are stored in the backend using a dedicated challenge model, andthe system automatically activates a new challenge weekly using a scheduledtask.These challenges are aligned with the principles of goal-setting definedby the ART and TPB by providing users with clearly defined, time-bound,and actionable goals. Users are presented with challenges such as “Pre-pare recommended meals every day for 3 days”, where users are expectedto prepare a new meal from the recommended recipes for three consecu-tive days. Upon completion, challenges add to both the user’s point totaland badge progression, reinforcing both short-term rewards and long-termachievements.The points system awards points to users for different actions in theapplication, such as consecutive streak days or completing challenges. Thepoints system is a form of incentivizing the user, providing immediate feed-back to their actions, and encouraging repeated actions, thus reinforcing thetarget behavior [98, 172].The badge system tracks cumulative achievements such as streak lengthand total points earned. When users meet certain thresholds, new badgesare unlocked and displayed on their profiles. These badges serve as symbolic614.4. IMAGE RECOGNITION SYSTEM COMPONENTrewards, satisfying the concept of self-efficacy in line with SDT principles[14, 170]. They also act as social proof when viewed on the user profile,showing visible signs of progress and achievement, an approach supportedby the literature review’s findings on the impact of social proof and feedbackin health behavior change apps.The decision to include these reward systems is grounded both in lit-erature and the review in Chapter 3, which highlighted that feedback isessential to maintaining user motivation in nutrition-focused interventions[98, 172].4.4 Image Recognition System ComponentThe second core backend component of the HealKitchen application is adynamic food image recognition system designed to assist users in identifyinggrocery items and accessing their nutritional and health information. Thisstage of research is guided by research question 3, which aims to determinehow accurately an image recognition system can identify grocery food itemsin grocery shopping contexts.Image recognition systems have emerged as a significant advancement inmobile health (mHealth) technologies, particularly in dietary management.Traditionally, dietary assessment relied heavily on self-reporting methodssuch as 24-hour dietary recalls or food diaries; however, these methods areoften prone to inaccuracies due to reliance on user memory, estimations, andconsistent compliance, leading to issues of data validity and underreportingof actual intake [187].To overcome these challenges, recent research has turned to artificial in-telligence (AI), particularly computer vision and deep learning, to automatefood identification and nutrition tracking through image analysis. These sys-tems utilize convolutional neural networks (CNNs), a class of deep neuralnetworks modeled after the human visual cortex that processes visual infor-mation hierarchically [138], to automatically extract, interpret, and classifyvisual features from images of food items [73].Convolutional neural networks (CNNs) have generally been applied inseveral domains, but have shown effectiveness in food classification tasksbecause of their ability to detect hierarchical patterns in visual data, andtheir processing time [138]. Modern image-based dietary systems such asIm2Calories [137] and Smart Diet Diary [138] have demonstrated that neu-ral networks can effectively identify food items, estimate portion sizes, andcalculate calorie content, offering users an interactive and low-effort method624.4. IMAGE RECOGNITION SYSTEM COMPONENTof tracking food intake.This functionality is particularly effective in mobile health applications,as they can bridge the gap between passive observation and active healthmanagement by allowing users to upload images of food items for immediateclassification. The Smart Diet Diary [138], for instance, uses a Faster R-CNNmodel with a region proposal network to perform object detection on fooditems and estimate calorie content. It achieved object detection accuraciesup to 92% for common items such as apples and oranges, making it a viablealternative to traditional methods.The HealKitchen application builds upon this foundation by integratingan object detection model tailored to real-time grocery shopping. While ear-lier food recognition models focused on meals or plated food and countingcalorie content, HealKitchen distinguishes itself by applying image recogni-tion to grocery items. This aligns with the application’s objective of enablinginformed decision-making during grocery runs, which is critical in shapinglong-term healthy dietary habits.A common barrier for users to make healthy decisions during groceryshopping is the lack of familiarity with certain grocery items [94]. Users mayencounter fruits, vegetables, or grains they do not recognize or understandin terms of their nutritional value. The image recognition system addressesthis by allowing users to gain insight into the nutritional composition of theitem and its implications on their health. The system then generates reciperecommendations on the fly based on the identified grocery item.As previously established, the application is grounded in the behavioraltheories of the High Performance Cycle (HPC), Action Regulation Theory(ART), and Social Cognitive Theory(SCT). The image recognition featuredirectly contributes to these theories by supporting the concepts of self-efficacy and reduced complexity.Rather than purchasing items without knowing their micro-nutrient val-ues, users are empowered with real-time nutritional knowledge, allowingthem to feel confident and competent in their dietary choices. As a re-sult, the image recognition system not only increases the grocery shoppingexperience but also reinforces users’ ability to perform the desired health be-havior independently, bridging the gap between user intention and desiredbehavioral action. Furthermore, by simplifying the process of identifyinghealthy food options while shopping and integrating them into the applica-tion backend, the system supports the goals of the application defined bythe HPC.The integration of the image recognition system in this work is guidedby the work done by Nadeem et al. in the development of Smart Diet634.4. IMAGE RECOGNITION SYSTEM COMPONENTDiary [138]. Their approach involved utilizing a labeled image dataset totrain a neural network for image object detection. This model is trainedto identify visual patterns and extract hierarchical features across varyinglevels of abstraction. This foundational strategy informs HealKitchen’s foodimage object detection system, which is tailored to address grocery items.4.4.1 Data GatheringThe performance of any image recognition system is highly dependenton the quality, quantity, and diversity of its training data [198]. The HealK-itchen image recognition system was developed to classify and provide nutri-tional insight on grocery items. To implement this, a custom dataset tailoredto the application’s requirements was compiled. The goal of the dataset wasto support a deep learning model capable of identifying grocery items andfunctioning under real-world mobile conditions, such as varying lighting,angles, and image quality. This required a dataset that was balanced andproperly annotated.Publicly available grocery image datasets were sampled from Kaggle[100] to find a suitable dataset that matched the needs of the application. Agrocery food image dataset consisting of unlabeled grocery images of vary-ing image quality was collected. Additionally, manual capture of images orgrocery items was carried out at a local grocery store using a smartphonecamera under varying lighting and background conditions. This was to en-sure the model could generalize across different user conditions when in use.The total collated dataset consisted of 28,015 images, accounting for41 categories of grocery items. This size was determined to be sufficientfor training a lightweight convolutional neural network while maintainingcomputational efficiency for mobile deployment [138]. Figure 4.3 shows asample of the images included in the dataset. Details of the grocery imageclasses used in training the model are shown in Appendix 7.54.4.2 Data Annotation and LabelingAfter collecting the image data, each image was annotated to define itscorresponding class label and region of interest (ROI). The ROI is used inobject detection models to indicate the region within an image where theobject of interest is located. The ROI is needed in the convolutional neuralnetwork to identify the features that represent the grocery item within theimage and define the class associated with those features [138].Image labeling was manually performed using the LabelImg annotation644.4. IMAGE RECOGNITION SYSTEM COMPONENTFigure 4.3: Sample images collected in image dataset reflecting varying im-age qualitytool. Each image was annotated by drawing the bounding box around theROI and accurately labeling the region with the corresponding grocery itemclass, as shown in Figure 4.4. The annotations were performed in PascalVOC format and exported to an XML file that contained annotation in-formation for each image. The annotations were validated through manualinspection to remove issues such as incorrect labels and overlapping imagebounding boxes.4.4.3 Image PreprocessingAfter correctly annotating the dataset, several preprocessing steps werecarried out to prepare the images for training. The image processing wasperformed in a Python environment before the initialization of the model654.4. IMAGE RECOGNITION SYSTEM COMPONENTFigure 4.4: Image Annotation and Labelingtraining process.Firstly, the processed image dataset was split into a training and testingdataset in the ratio of 80:20. The class distribution was preserved acrossthis process, ensuring that each food category was equally represented inall datasets. Next, a grocery dataset class was created in Python from theimage dataset for both the training and testing datasets. This class readseach grocery image into a Python object using the OpenCV package andnormalizes the image data.Each image is validated to verify that it loads correctly and has a corre-sponding annotation XML file with the defined bounding box. The boundingboxes for each image were then extracted and loaded into a dictionary alongwith other image annotation data. Next, the images were resized to a con-sistent input shape of 512 x 512 pixels and scaled to a [0, 1] vector range toimprove the training convergence.Following the image normalization, data augmentation was performed toimprove the robustness of the dataset for training. This was done by flipping,rotating, and adjusting the brightness or contrast of random images. Thisaugmentation step helps the model generalize to match real-world scenarioswhere food images might be taken from varying angles [183]. Finally, thebounding box data and class labels were converted to tensors using thePyTorch package. These tensors were used as the input later in the modeltraining.4.4.4 Object Detection Model Architecture and TrainingFollowing the dataset annotation and preprocessing, the next phase ofdevelopment involved the design and training of the object detection neural664.4. IMAGE RECOGNITION SYSTEM COMPONENTnetwork model. The goal of this phase was to deploy a lightweight butrobust object detection model capable of efficiently running inference onthe custom image dataset. To achieve this, a convolutional neural networkmodel was developed and fine-tuned using the processed grocery dataset.Convolutional neural networks (CNNs) are a proprietary model for im-age object detection as they require less preprocessing than other imageclassification algorithms [138]. As described by Nadeem et al., they are im-proved to overcome heavy computation due to varying ROI by implement-ing region proposals using a Region-based Convolutional Neural Network(RCNN) [138].To support a lightweight model, an improved version of the RCNN,Faster R-CNN, was utilized for object detection. The Faster-RCNN modelutilizes a single region proposal network to predict both the object bound-ing box and the class probability of an image [138]. This single networkeliminates the need for an external region proposal algorithm, reducing thecomputational time required to train the model. The faster-RCNN modelconsists of three neural networks:− Feature Network: Extracts rich, unique feature maps from the inputimage using a CNN backbone. These maps capture patterns necessaryfor object detection.− Region Proposal Network (RPN): Generates region proposals bypredicting object scores and bounding box coordinates, identifying po-tential regions that may contain objects.− Detection Network: Takes proposed regions from the RPN, per-forms RoI Pooling, and outputs final object classes and refined bound-ing box coordinates.To train the model, a base RCNN model was selected from the Ten-sorFlow Object Detection Model zoo [190] and fine-tuned with modifiedhyperparameters. This was done following similar research in this areaof retraining existing base models with custom data rather than writinga full model from scratch [138]. Considering the model requirements forthe HealKitchen application, where computational efficiency was prioritized,the faster rcnn resnet 50 v2 was selected from the model zoo as the startingpoint for the model training. This model was chosen because it supportsbounding box predictions as well as multi-class classification, and has beenpre-trained with the COCO dataset, allowing for faster convergence of train-ing results.674.4. IMAGE RECOGNITION SYSTEM COMPONENTThe implemented model architecture included the following components:− Input layer: The input layer which receives preprocessed groceryimages resized to 512×512 pixels with 3 RGB channels.− Backbone network: A pretrained Faster R-CNN with a ResNet-50backbone was used as a feature extractor to generate convolutionalfeature maps from input images.− Region Proposal and RoI Pooling: The Region Proposal Net-work (RPN) generates candidate object regions, which are then passedthrough a Region of Interest (RoI) pooling layer to extract fixed-sizefeatures.− Output heads: Two parallel heads were used:– A softmax classification head for predicting probabilities (in thismodel, 42 classes: 41 grocery items + 1 background).– A regression head for predicting bounding box coordinates foreach proposed region.The model was trained in a Python environment using a custom trainingscript and the PyTorch package. The first step in training the model wasto load the data into a PyTorch DataLoader class with a custom collatefunction for the training and test datasets. This was done using an imagebatch size of eight.Next, the pretrained Faster RCNN model was loaded into memory todefine the input features and define a new head for the detector with therequired number of classes. The model was configured on a CPU device dueto the unavailability of a GPU-enabled environment.Following this, the hyperparameters and optimizers used to train themodel were defined. A Stochastic Gradient Descent (SGD) optimizer func-tion was used to update the model parameters during back propagation usingthe gradient of the loss function [138]. The SGD optimizer was configuredwith a learning rate of 0.002, a momentum of 0.9 to accelerate convergence,and a weight decay factor of 0.0005 to enforce regularization and reduceoverfitting.To improve training convergence, a Step Learning Rate Scheduler with agamma value of 0.1 was applied, which reduces the learning rate by a factorof 0.1 every three epochs [138]. This learning rate decay strategy enabledfiner adjustments to model parameters in later training stages, promoting684.4. IMAGE RECOGNITION SYSTEM COMPONENTmore precise convergence. The model was run for 20 epochs with validationafter each epoch.The model training was monitored by tracking the training and valida-tion loss after each epoch. Each epoch in the training loop involved trainingthe data with batch iteration over the training dataloader object and vali-dating the data using the validation dataloader to compute the training lossand model accuracy.After each epoch, the best-performing model checkpoint was saved bycomparing the validation loss between the previously saved checkpoint andthe current model checkpoint. The model training ran for about 12 daysdue to the limitations of the computational device used in training. Oncethe model training was completed, the best model iteration was exported,defined by the accuracy and loss values, and this model was used to makeinferences within the application.The final training and validation losses of the model converged to 0.228and 0.294 over the last few epochs, reflecting satisfactory convergence behav-ior of the model under the given training conditions [78]. Table 4.2 detailsthe hyperparameters used in training the neural network.Figure 4.5: Model Training and Validation Loss Results4.4.5 Evaluation of Image Recognition ModelAfter successful training, the performance of the custom model was eval-uated using both standard object detection metrics and real-world inferencetests. The evaluation was performed to validate the model’s performanceand compatibility for real-world use.694.4. IMAGE RECOGNITION SYSTEM COMPONENTTable 4.2: Summary of Model Training ConfigurationParameter DescriptionProgramming Environment Python using PyTorch machine learningframeworkBase Model Faster R-CNN with ResNet-50 backboneBatch Size 8 images per batchOptimizer Stochastic Gradient Descent (SGD)Learning Rate 0.002Momentum 0.9Weight Decay 0.0005 (L2 regularization)Learning Rate Scheduler StepLR with gamma = 0.1 every 3 epochsNumber of Epochs 20 epochsQuantitative Evaluation of Model AccuracyTo assess the performance of the object detection model, the top-1 ac-curacy and mean average precision (mAP) of the model were computed, asthese are the most frequently used evaluation metrics for classification andobject detection models [138].The top-1 accuracy measures the accurate prediction capability of themodel for single-image detection instances across an iteration of test images[209]. This metric evaluates whether the model correctly predicted the classlabel of a single detection in each image.The mAP measures the mean of the average precision of a model acrossdifferent Intersection over Union (IoU) values [138]. These provide an overallperformance measure of the model for each object detected in the image,considering its ability to retrieve relevant instances across multiple classes.To calculate the mAP, the average recall (AR) and average precision(AP) of the model at various IoU thresholds were calculated. The IoU valuessimply represent the ratio of the intersection between the model predictionand the actual bounding box of the object. Once the AP score for eachIoU was computed, the mAP score was calculated as the mean of the APscores across all classes. In other words, the mAP score provides an overallperformance measure of the model, considering its ability to retrieve relevantinstances across multiple classes [138].The model evaluations were carried out using a custom evaluator func-704.4. IMAGE RECOGNITION SYSTEM COMPONENTtion that measures model performance on an image dataset. The evaluationresults revealed that the model achieved a top-1 accuracy of 85.8%, indi-cating high accuracy in predicting the captured grocery item. This highaccuracy demonstrates the model’s robustness in identifying grocery itemsaccurately and supports its practical application for real-world mobile-basedfood recognition tasks.We extended this accuracy measurement to include an IoU thresholdof 0.5 [116], measuring how well the bounding box predicted by the modeloverlapped the target bounding box. The model achieved an accuracy of74.0% in this instance, revealing a strong overlap between the predictionand the target images.The validation results of the mAP also reveal strong overall performance,particularly for large objects, with a mean Average Precision (mAP) of 0.64across all Intersection over Union (IoU) thresholds. The model recordedan mAP of 0.76 at 0.50 IoU (mAP@0.50), indicating very good detectionaccuracy at average overlap thresholds. The mAP at 0.75 IoU (mAP@0.50)was 0.69, suggesting the model maintains reasonable precision in predictingbounding boxes even under stricter IoU requirements.The precision and recall values, represented by mean Average Recall(mAR), further support the model’s robustness. The model recordedmAR@100 detections at 0.70, demonstrating that the model has strong re-call capabilities when considering up to 100 predictions per image.Notably, the model performs best on large objects, achieving amAP large of 0.65 and mAR large of 0.72. The detection performance forsmall objects was not computed (indicated by -1), likely due to the absenceor insufficiency of such target bounding boxes in the validation dataset.These findings suggest the model is well-suited for detecting food itemsbut may require further optimization or dataset augmentation to improveperformance on smaller items within an image.The performance of the HealKitchen grocery image recognition modelwas compared against two related systems from recent literature: the SmartDiet Diary [138] and Personalized Dietary Self-Management Using MobileVision-Based Assistance [209] that influenced the work done in HealKitchen.These works presented comparable mobile food recognition systems, usingdeep learning-based models to assist users in grocery or dietary decision-making. Table 4.4 shows the results of this comparison.The results show that the HealKitchen model achieves higher classifica-tion accuracy (85.8%) compared to the work done in [209] (7˜2.9%) andcomparable accuracy and mAP scores to the work done in [138](8˜0.1%,mAP@0.50=0.81).714.4. IMAGE RECOGNITION SYSTEM COMPONENTTable 4.3: Object Detection Evaluation ResultsMetric IoU Threshold ScoreMean Average Precision(mAP)All IoUs (0.50:0.95) 0.64mAP@0.50 0.50 0.76mAP@0.75 0.75 0.69Mean Average Recall(mAR)Up to 100 detections 0.70mAP medium Large objects 0.65mAR large Large objects 0.72Real-Time Inference and EvaluationTo further validate the effectiveness of the model in practical scenarios,a manual evaluation was conducted by me. This was achieved by runninginference on a set of random grocery images sourced from the internet toqualitatively verify the accuracy of the model. Five sample grocery itemimages were used for this evaluation. These images were chosen to representcommon grocery food categories. These images were passed through theclassifier function to simulate real-world inference operations on the modeloutput.The model classified the items found in the images, drawing boundingboxes around them and indicating the confidence level. The model’s pre-Table 4.4: Comparison of HealKitchen Image Recognition Model with PriorResearchMetric HealKitchenModelSmart Diet Di-ary [138]PersonalizedDietary System[209]Technique Faster RCNN Faster RCNN CNNAccuracy 85.8% 80.1% 72.9%mAP@0.50 0.76 0.79 N/AmAR@100 0.71 0.81 N/A724.4. IMAGE RECOGNITION SYSTEM COMPONENTFigure 4.6: Evaluation Results of Real-Time Inferencedictions were evaluated by visually inspecting the prediction and boundingbox to confirm whether the detected item matched the actual grocery item.The confidence scores were also reviewed to assess the model’s certainty inits predictions. Although this evaluation was limited in scale, it was imple-mented to supplement the quantitative evaluations performed and providea real-world context of the model’s performance.This real-world test demonstrated higher accuracy than the evaluationmetrics computed across a validation dataset. As illustrated in Figure 4.6,the model achieved an average object detection accuracy of 82% for apples,99% for bananas, 90% for beetroot, and 84% for chili peppers.These results suggest that while the model may not have reached veryhigh mAP across all classes during formal evaluation, it is still effective indetecting and classifying certain dominant or visually distinct items duringinformal tests simulating real-world settings.Discussion and LimitationsThe evaluation results, including a classification accuracy of 85.8% and amean Average Precision (mAP@50) of 0.76, indicate that the model performs734.4. IMAGE RECOGNITION SYSTEM COMPONENTwell in detecting and classifying grocery food items, especially under realisticinference conditions. These results are comparable to those reported inrelated works, demonstrating the model’s robustness for object detectionand showing that the HealKitchen model can adequately identify groceryitems in stores and can be extended to provide nutritional information ofthose items to users.The contrast between mAP-based detection scores and real-time classifi-cation accuracy, however, highlights the importance of context in evaluatingobject detection systems. While quantitative metrics provide essential per-formance baselines, they may not fully capture the model’s utility in liveenvironments where inference conditions and user expectations differ.Despite these strengths, some limitations remain. The model underper-forms on smaller objects and at higher overlap (IoU) values, likely due to theinsufficient training images for such cases and inconsistent image labeling.Additionally, class imbalance and limited data diversity may contribute toreduced generalization across underrepresented food categories.Future work will look to address these limitations through expanded andmore robust datasets, targeted data augmentation strategies, hyperparam-eter tuning, and improved class balancing. Additional experiments usingcross-dataset evaluation can also help validate the model’s generalizabilityand guide hyperparameter tuning for further optimization.4.4.6 Nutrient Information & Integration in MobileApplicationThe final phase of the image recognition system involved generating nu-tritional information for the grocery items identified by the image recogni-tion model.Following the correct identification of a food item, the nutritional infor-mation is retrieved from the Nutritionix nutrients API [141], which providesextensive nutrient data for a large range of grocery items. The returneddata is processed to select only relevant information and presented to theuser.Recipe recommendations are also generated for the identified food itemfrom the database as healthy suggestions to the user. These recommenda-tions can then be added to the grocery shopping list, creating a circular flowof processes within the application that supports the dietary change.74Chapter 5Development of MobileApplication Interface5.1 Mobile Application Interface DevelopmentThe HealKitchen mobile application was developed as a cross-platformmobile interface that presents features to users to support the target behav-ior change. Following the development of the backend features, includingthe recipe recommendation system, image recognition model, and applica-tion features, a robust mobile interface was developed to implement thefeatures and present them to users. This stage of the research addressesresearch question 1, which seeks to design a mobile application aimed atinfluencing dietary behavioral change.The mobile application was developed using React Native, a popularReact framework for building cross-platform mobile applications [130]. Thischoice enabled a consistent user experience across both Android and iOSplatforms while leveraging a single codebase.The application interface was developed to support the concept of redef-inition of tasks as defined by the ART. The redefinition of tasks was usedto transform the goals defined through the HPC and implemented on thebackend into sub-tasks and functional features integrated into the mobileinterface.The mobile interface of the HealKitchen application provides intuitiveaccess to support the targeted behavioral change. The application designemphasized simplicity and personalization to ensure that the persuasive fea-tures can be easily engaged by the user. The application features imple-mented in the mobile interface include:5.1.1 User Onboarding and Authentication SystemThe initial interface encountered by a new user is the authentication in-terface that consists of the signup and login screens. These screens, depictedin Figure 5.1, allow a user to securely gain access to the application and755.1. MOBILE APPLICATION INTERFACE DEVELOPMENTFigure 5.1: User authentication and onboarding screensprovide authorization control to the application features. The applicationsupports users’ feelings of trust in the system by ensuring that unauthorizedusers do not gain access to the application interface or any user data.The authentication system also supports the redefinition of tasks throughthe concept of personalization. Upon initial signup, users are presentedwith a personalization screen where they are required to input their healthchallenges and nutritional requirements. This information is stored securelyin the database and is used to determine the recipes to be recommended tothe user.The authentication system also implements a user account area withdirect links to frequently accessed screens in the application to simplify theuser experience.5.1.2 Recipe RecommendationsThe mobile application also integrates the recipe recommendations im-plemented by the server to provide users with healthy dietary options. Therecommendations system forms the primary functionality of the applicationand supports the ART, SCT, and TPB behavioral theories through the con-cepts of personalization, feedback, and social support.The recipe recommendations are provided by the server and displayed765.1. MOBILE APPLICATION INTERFACE DEVELOPMENTon the home screen of the application. The presentation of the recommen-dations is designed in a user-friendly format to prioritize ease of interactionand support the behavioral concepts defined in the ART.The recipes are displayed in a simple, optimized list that shows the im-portant information about the recipe in a clickable card. Upon clicking therecipe, the user is directed to a dedicated screen that provides more detailedinformation about the recipe, including the ingredients, cooking instruc-tions, and nutritional information. This display supports the redefinition oftasks and provides clarity to a user about a recipe they may be interestedin.The recommendation screens in the application also display peer-inspiredrecipe recommendations that are composed based on the preferences of sim-ilar users in the system. These recipes are displayed as a horizontal sublist,asserting that they are suggested based on similar user preferences. Thisfeature integrates the concept of social proof from the SCT, providing userswith a variety of recipes that are equally relevant to their needs. Figure 5.2shows the implementation of personalized recipe recommendations withinthe application.5.1.3 Recipe RatingsUpon browsing the recipe recommendations and selecting recipes of in-terest, users are presented with the ability to give feedback on the recipesthat they have prepared through the recipe ratings interface. The recipe rat-ings system implements the persuasive strategies of social proof and feedbackdefined under the SCT and TPB behavioral theories.Users are provided with an interface that allows them to rate a recipefrom a range of 1-5, with 1 being the lowest and 5 being the highest. Thisinterface is made intuitive for the user by using a modal with a star ratingsystem as illustrated in Figure 5.3.The feedback provided by the user through ratings is collected by thesystem and used to provide informed recipe recommendations to other users.Highly rated recipes of a user are recommended to other users with similarhealth conditions. The ratings also provide clear feedback on the user’sengagement in the application, reinforcing the desired behavior.5.1.4 Grocery Shopping Lists and Favorite RecipesSimilar to the recipe ratings, the application provides a modular interfaceto support the integration of grocery shopping lists and favorite recipes.775.1. MOBILE APPLICATION INTERFACE DEVELOPMENTFigure 5.2: Personalized recipe recommendationsThese features directly implement the application goal of targeting long-term healthy behavior through grocery shopping. Users are provided theability to add recipes to their shopping list.The shopping list also implements the persuasive strategy of reduction bysimplifying the process of adding recipe ingredients into a compact shoppinglist with a single click. Ingredients from the recipes are asynchronouslyadded to the shopping list, providing a simplified experience for the user.The shopping list is extended to allow users to manually add additionalitems to the shopping list and also remove items that are unavailable or havebeen purchased. This is done using a simple dynamic form on the mobileinterface.Users also have the ability to save a recipe for later by favoriting therecipe. These recipes are then presented on the user dashboard for easyaccess at a later time. The favorite recipes are also used to provide more785.1. MOBILE APPLICATION INTERFACE DEVELOPMENTFigure 5.3: Recipe rating systemrelevant recommendations to users. The shopping list and favorite recipesfunctionalities are implemented to support the concept of self-efficacy andself-regulation, which are vital in building the target behavior.5.1.5 Recipe Curation SystemAn innovative feature implemented in the HealKitchen mobile applica-tion is the recipe curation system. This system was targeted at promotingthe concepts of self-efficacy, goal-setting, and user intentions as describedunder the SCT, SDT, and TPB behavioral theories. These theories define auser’s motive and perceived ability to achieve the desired behavior.The recipe curation system was designed to support these features bycurating recipes based on the available ingredients in a user’s home. Theuser types in food items that they have at home, and this system generateshealthy recipe recommendations that can easily be prepared without thenecessity of grocery shopping. The recipe curation system also takes intoaccount a user’s previously registered health challenges and desired nutrientsto curate recipe recommendations.The recipe curation system was implemented using artificial intelligencemodels by employing OpenAI’s text and image generation API to generatehealthy recipe ideas based on the provided items from the user. The system795.1. MOBILE APPLICATION INTERFACE DEVELOPMENTFigure 5.4: Grocery List & Recipe Curation Systemintegrates the GPT-4 and DALLE-3 models to generate the curated recipes.To develop the recipe curation system, a text prompt was composed thatintegrates the nutrient requirements of the user, their health conditions,and the food items available to the user. The prompt also accounts forthe structure of the response, ensuring that it matches the existing recipemodel in the database. The GPT-4 model is then used via the OpenAI APIto generate the desired recipes.The recipes generated in the response from the GPT-4 model containonly text elements, so the DALLE-3 image generation model was used togenerate accompanying images for each recipe. These recipe recommenda-tions -text and images- are then presented to the user and stored in thedatabase of the application for future use.Figure 5.4 illustrates the implementation of the grocery list and recipecuration systems in the mobile application.805.1. MOBILE APPLICATION INTERFACE DEVELOPMENT5.1.6 Gamification and Rewards SystemThe mobile application also integrates gamification elements and a re-wards system implemented on the server to promote long-term user engage-ment. This strategy was informed by findings from the review presented inChapter 3, which identified gamification as an important persuasive strategyused in effective nutrition-focused health applications [172, 175].The gamification features support behavioral concepts of the SDT, SCT,and TPB, which emphasize the importance of feedback, progress, and goal-setting in behavioral change [29, 132].Users are provided with a gamified interface that tracks progress andrewards healthy behavior. The weekly challenges are presented prominentlyusing a call-to-action component at the top of the homepage. Users can com-plete these challenges and receive point rewards. Users are further rewardedfor consistent interaction with the application through the daily streak.All gamification features are seamlessly integrated into the mobile inter-face using dynamic components to display progress information. Figure 5.5,depicts the weekly challenges and badges earned by a user.5.1.7 Grocery Item Recognition SystemThe final application feature implemented in the HealKitchen mobileapplication was the integration of the image recognition system to provideusers with direct identification and nutritional information for unfamiliargrocery items.The recognition system described in Chapter 4 is presented via an inter-face that allows users to either take a picture of a grocery item or uploadone from their mobile device gallery. The uploaded image is then sent tothe image recognition model for processing, and the identified grocery item,along with its nutritional information, is presented in a user-friendly mannerto the user.This implementation enables users to quickly retrieve nutritional infor-mation and healthy recipe ideas for grocery items, along with insights intohow these foods impact their health while grocery shopping in a store. Fig-ure 5.6 shows the integration in the grocery application.5.1.8 Application DeploymentAfter development, the application was packaged and deployed to theGoogle Play Store and the Apple App Store to enable public access for815.2. PERSUASIVE STRATEGIES IN THE HEALKITCHEN APPLICATIONFigure 5.5: Gamification interface showing points, streaks, and challengesusers. Due to publishing requirements from these stores, the application iscurrently deployed under external testing tracks.5.2 Persuasive Strategies in the HealKitchenApplicationEffective behavioral change applications often incorporate persuasivestrategies to positively influence user behavior [132, 146]. These strategiesinfluence user actions without restricting freedom of choice [69].HealKitchen integrates persuasive strategies grounded in the behavioralchange theories as described in Chapter 4 using a model that transforms be-havioral change theories into operationalizable design features. The strate-825.2. PERSUASIVE STRATEGIES IN THE HEALKITCHEN APPLICATIONFigure 5.6: Grocery Image Recognition Systemgies are also in line with the findings of the systematic literature reviewconducted in Chapter 3 to help users form long-term healthy dietary habits.By embedding these strategies into core application features, the appli-cation aims to increase motivation, improve dietary decision-making, andsustain user engagement over time [143].− Personalization: Personalization is an effective persuasive strategythat increases user relevance and engagement within an application.Studies have shown that personalized content significantly increasesusers’ adherence to dietary interventions [106].HealKitchen integrates personalization by collecting user health pro-files and nutritional requirements during onboarding and using thisinformation to dynamically adjust recipe recommendations and in-appexperiences.− Social Proof: Social influence is similarly an effective persuasive strat-egy in influencing behavioral change, particularly in digital health in-terventions [43].HealKitchen integrates social proof through a collaborative filteringrecommendation algorithm that suggests recipes to users based onthe preferences of similar users within the system. It also supportssocial proof through a recipe rating system that allows users to view835.2. PERSUASIVE STRATEGIES IN THE HEALKITCHEN APPLICATIONother users’ feedback on recipes. This reinforces user decisions bynormalizing positive health behaviors and fostering community-drivenmotivation.− Goal Setting: Goal-setting theory asserts that specific, measurablegoals enhance task performance and behavioral change [120]. HealK-itchen supports goal setting by allowing users to create custom gro-cery shopping lists from recommended recipes. This structured self-regulation helps users stay focused on achievable dietary goals.− Feedback: Feedback is also an essential persuasive strategy in behav-ioral change models that supports reflection and self-monitoring [20].HealKitchen provides users with comprehensive feedback through therecipe rating system and points system.− Rewards: According to reinforcement theory, rewards following desiredbehavior increase the likelihood of recurrence of that behavior [184].In HealKitchen, users earn points for performing target actions such ascooking recommended meals or completing a weekly challenge. Thesepoints progressively unlock badges, which provide immediate positivereinforcement and promote sustained engagement with that applica-tion and desired behavior.− Gamification: Similar to rewards, HealKitchen implements gamifica-tion elements to support user engagement and retention. Gamificationapplies game mechanics such as badges, levels, and challenges to non-game contexts to increase user motivation [58]. In HealKitchen, usersengage in weekly challenges and earn badges as they progress throughchallenges. These features introduce incentives into the health journey,making healthy eating more engaging and habit-forming [53].Figure 5.7 shows how some of the persuasive strategies and behavioraltheories were implemented in the HealKitchen application.845.2. PERSUASIVE STRATEGIES IN THE HEALKITCHEN APPLICATIONFigure 5.7: Persuasive Strategies & Behavioral Change Theories imple-mented in HealKitchen85Chapter 6Evaluation of HealKitchenTo assess the design, usability, engagement, and impact of the HealK-itchen application, a formative user evaluation study was conducted. Theprimary goal of this study was to examine how users interact with the ap-plication, assess its core features in a real-world context, and identify areasrequiring improvement. By analyzing user feedback, this evaluation aimed toensure that HealKitchen is intuitive, engaging, and aligned with the behav-ioral and nutritional needs of its intended audience. This chapter addressesresearch question 5, which aims to determine users’ perceived usability ofthe app.This study, referred to as Study 1, employed a mixed-methods approachto evaluate key dimensions of user experience, including ease of use, per-ceived usefulness, aesthetic appeal, and user engagement. It served as aformative assessment of the application before full deployment and a long-term evaluation.6.1 Study DesignThe study employed a remote usability testing process, allowing partici-pants to interact with the mobile application in their natural environments.This design choice reflects best practices in human-computer interaction re-search, which emphasize ecological validity in evaluating health technologytools [24, 114].By combining both quantitative metrics and qualitative feedback, thestudy enabled a comprehensive understanding of user satisfaction and func-tional performance.6.1.1 Evaluation constructsThe study was centered around four constructs from the TechnologyAcceptance Model (TAM) and related behavioral frameworks [55]:− Perceived usefulness: This construct captures the extent to which usersbelieve the application helps them achieve meaningful outcomes, in866.1. STUDY DESIGNthis case, building healthier dietary habits. As defined by Davis [55],perceived usefulness strongly influences user satisfaction and intentionto continue using health applications.In HealKitchen, this metric assesses whether the application is per-ceived to support users in making better dietary decisions alignedwith their health goals and whether they find the features, such aspersonalized recipe recommendations, curated recipes, and nutritionalinformation, to be useful. High scores in this area suggest that userssee the app as functional and useful in their daily lives.− Ease of Use: Defined in TAM as the degree to which users find thesystem effortless and intuitive [55], this metric evaluates whether theapplication interface is intuitive, functions are clear, and the overallexperience is seamless during an interaction.For HealKitchen, the goal was to ensure that features such as shoppinglist generation and image recognition could be accessed without priortechnical expertise. A positive perception of ease of use contributesdirectly to the likelihood of repeated usage and sustained use.− User Engagement: Engagement refers to the level of attention, motiva-tion, and interaction a user sustains while using an application [142].In digital health interventions, engagement is critical to supportinglong-term behavioral change.For this study, engagement evaluates whether users feel motivated tocontinue using the application, explore its features, and stay com-mitted to their dietary journey. Components such as personalization,interactive features, and gamification elements contribute to maintain-ing user engagement.− Aesthetic Quality: Visual and sensory design greatly influence firstimpressions, emotional responses, and continued use of mobile appli-cations [113]. In HealKitchen, aesthetics evaluates the visual appealof the application interface, including the layout, color scheme, andicons.This construct is important to ensure that the app not only looksmodern and clean but also aligns with the contextual preferences ofits users. A well-designed interface can enhance usability, supportengagement, and foster a sense of trust in the application.876.1. STUDY DESIGNThis structured approach ensured the study captured both functional perfor-mance and subjective experience, providing a rounded evaluation of HealK-itchen’s strengths and limitations.6.1.2 Recruiting ParticipantsParticipants were recruited for this study following approval by the Be-havioral Research Ethics Board (BREB) of UBC Okanagan H24-01526. Acombination of online and offline recruitment strategies was employed toidentify prospective participants. Recruitment posters were distributed onpublic platforms, while snowball sampling was used to extend reach throughthe academic and community networks. Participants who expressed interestcontacted the study team directly and were screened for eligibility to matchthe inclusion criteria.Upon selection, participants were provided with an instruction manualcontaining details about the study, installation guidelines, and an informedconsent form. Once consent was obtained, participants were invited to theclosed testers group on the Google Play Store and the Apple App Store andgranted access to the HealKitchen application. Participants were requiredto install and interact with the application over the course of several ses-sions using the features, including recipe recommendations, shopping listfunctionality, recipe curation, image recognition, and gamification features.A total of 20 participants were enrolled in this study in accordance withthe ethics approval. This sample size aligns with accepted practices for ex-ploratory studies in Human-Computer Interaction (HCI), where sample sizesbetween 5 and 30 are often considered sufficient for generating meaningfulinsights [114].After interacting with the application and utilizing its functionalities andfeatures, participants completed a structured evaluation survey hosted onQualtrics. Of the 20 participants enrolled in the study, 19 completed theevaluation survey before it was closed, representing a 95% response rate.These responses formed the basis of both the quantitative and qualitativeanalyses. The feedback gathered through this study was directly used toinform iterative improvements to the HealKitchen application before thefull deployment of the application.6.1.3 Survey InstrumentThe survey instrument was developed and administered using Qualtrics,a secure online survey platform compliant with FIPAA standards for aca-886.2. ANALYSIS OF RESPONSEdemic research. The questionnaire was designed to assess the usability con-structs described above and user feedback using a combination of:− Likert scale questions (5-point scale ranging from from “Strongly Dis-agree (1)” to “Strongly Agree (5)”)− Open-ended qualitative questionsEase of use was addressed through questions focused on navigation, clar-ity, and simplicity of the application interface and presented information.Perceived usefulness was assessed via questions exploring whether the ap-plication supported users’ dietary goals and provided relevant, actionableinsights. Engagement was measured through questions related to consistentinvolvement, interest, and feature interaction. Aesthetic quality was evalu-ated through users’ perceptions of visual appeal, design layout, and overalldesign satisfaction.The survey questionnaire also included open-ended questions that wereused to capture participants’ feedback and suggestions for future improve-ments. This question distribution ensured a comprehensive and informedevaluation of the application’s user experience.Demographic data, such as age, gender, and education level, were notcollected as part of the user study as they did not align with the primaryobjective of this evaluation. At this stage of the research, the focus was onunderstanding user perceptions of the HealKitchen application, particularlyconstructs such as perceived usefulness and perceived ease of use. Theseconstructs reflect how users engage with the application and form the basisfor predicting adoption and continued use, independent of demography.This combination of structured evaluation and open-ended feedback en-abled the study to identify usability strengths, potential barriers, and designimplications for the future development of HealKitchen.6.2 Analysis of Response6.2.1 Quantitative Analysis (Likert-scale questions)Descriptive statistics were used as the primary method of analysis tosummarize participants’ initial reactions to the application. These includedthe mean, standard deviation, and frequency distribution for each surveyitem. The mean indicated the average tendency of user responses, while thestandard deviation offered insights into the variability across participants.A high mean with a low standard deviation was interpreted as strong and896.2. ANALYSIS OF RESPONSEconsistent agreement among users, indicating clear user satisfaction or ac-ceptance.Additionally, response frequency distributions were calculated to assesshow users’ ratings were distributed across the Likert scale. These distribu-tions offered further insights into the proportion of users who agreed, wereneutral, or disagreed with each statement. This visual representation ofthese distributions was presented using bar charts, which helped highlightoverall sentiment trends for each construct.This quantitative analysis provided a foundational understanding of howthe HealKitchen application was perceived across usability constructs. How-ever, these metrics alone may not capture the full range of user experiences.To support these findings, a qualitative component was integrated into thestudy, as described below.6.2.2 Qualitative Analysis (Open-Ended Questions - OEQ)While the quantitative results offered structured insights into user sat-isfaction, this study also employed open-ended questions to explore moresubtle perspectives and gain deeper contextual understanding.This mixed-methods approach follows best practices in user-centered de-sign evaluation, which suggest that combining qualitative and quantitativemethods yields a more comprehensive evaluation of digital interventions[52, 114]. Specifically, qualitative data helps to triangulate the findings,validate survey results, and uncover areas not addressed by predefined sur-vey questions [154].To that end, three open-ended questions were asked at the end of theuser testing phase:1. What aspects of this application, in your opinion, need to be improved?2. What features would you like to see in this application?3. What are your overall thoughts and impressions about the nutritionalapplication?These questions were designed to extract feedback from participants onpotential usability and design gaps in the application, as well as featureexpectations that could improve the functionality of the application. Thequestions were aligned with the constructs measured quantitatively but al-lowed for open expression, enabling users to express specific experiences,preferences, and concerns.906.2. ANALYSIS OF RESPONSEThe qualitative analysis aimed to drill down into specific user experiencesand support the Likert-scale results by identifying the underlying causes ofuser satisfaction or dissatisfaction.Thematic analysisThe open-ended responses were analyzed using thematic analysis, awidely recognized method for identifying, analyzing, and reporting patterns(themes) within qualitative data [33]. This method was chosen due to itsflexibility and ability to derive meaningful insights from user feedback, par-ticularly in digital health and human-computer interaction (HCI) contexts.The analysis employed in this study followed the six-phase principlesproposed by Braun et al [33], which involve:1. Familiarization with the response data: All responses were reviewedthoroughly to gain a general understanding and to identify preliminaryideas.2. Generating initial codes: Segments of text were assigned open codesthat described their meaning or key content. This was done manuallyusing a spreadsheet and confirmed for consistency.3. Searching for themes: Related codes were grouped into more gener-alized categories. These broader patterns were refined into potentialthemes that represented recurring ideas across responses.4. Reviewing themes: Each theme was assessed for internal consistencyand distinctiveness to ensure there were no overlapping or redundantthemes.5. Defining and naming themes: The final themes were labeled and de-scribed, with examples taken directly from user responses to supportinterpretation.6. Producing the report: A report was developed to describe each themeand its relevance to the application’s design, features, or perceivedimpact.The coding process was performed independently by me, guided byBraun et al’s principles. To strengthen the validity of the analysis andminimize individual researcher bias, the coding and identified themes werereviewed and validated by a second researcher (the supervisor). This ad-ditional step ensured that the identified themes were both accurate and916.3. RESULTSrepresentative of participant perspectives. Discrepancies in interpretationwere resolved through discussion until a consensus was reached.Following the initial review of the responses, the qualitative data weredivided into two categories for thematic analysis.1. A combined analysis of responses to the improvement and feature re-quest questions. This was done due to the similarity of codes withinthe responses to both questions and was used to derive a unified setof design and feature improvements for future development.2. An analysis of the overall impressions to evaluate general sentimentand identify themes of satisfaction or concern related to user experi-ence.The analysis of the responses was conducted using a combination ofQualtrics’ built-in analytics tools, Microsoft Excel, and Python for statisticaland descriptive evaluation.6.3 ResultsThe usability and user engagement of the HealKitchen application wereassessed through the analysis of participant survey responses to derive ac-tionable insights to inform iterative improvements to the application. Thefindings from these analyses are detailed in the subsequent sections.6.3.1 Quantitative AnalysisEase of useEase of use was measured using six Likert-scale items designed to evalu-ate how easily participants could navigate and interact with the application.The construct showed a strong central trend, with a mean score of 4.6 anda standard deviation of 0.26, indicating that participants generally agreedthat the application was easy to use.Descriptive analysis revealed that a majority of respondents (68.4%) se-lected strongly agree and 31.6% selected agree when asked whether the ap-plication was easy to use, confirming a high level of usability.In relation to the application’s interface, 94% of participants agreed(31.6%) or strongly agreed (68.4%) that they could understand the appli-cation interface and use it independently. Furthermore, 99% reported that926.3. RESULTSFigure 6.1: Evaluation of Ease of Usethey could navigate most of the application with ease. These findings sug-gest that the interface was intuitive and user-friendly, requiring minimalprior instruction.Only 5.3% of participants reported encountering difficulty with somefeatures, highlighting the general accessibility of the application’s function-ality. Additionally, 79% of participants (63.2% strongly agree, 15.8% agree)expressed confidence in their ability to incorporate the app into their dailyroutine.Overall, the consistently positive responses across all items suggest thatHealKitchen delivers a highly intuitive and user-friendly experience. A sum-mary of the descriptive statistics is presented in Figure 6.1.Perceived UsefulnessPerceived usefulness was evaluated through a series of questions address-ing the application’s ability to support healthy dietary practices. The anal-ysis, shown in Figure 6.2, revealed positive responses across participants,with a mean score of 4.45 and a standard deviation of 0.20.A total of 26.3% of respondents strongly agreed, and 52.6% agreed thatthe application could assist them in meeting their nutritional requirements.936.3. RESULTSFigure 6.2: Evaluation of Perceived UsefulnessNotably, a greater percentage (73% strongly agree, 26% agree) affirmed thatthe application could support them in improving their dietary decisions. Inaddition to this, 63.2% of participants strongly agreed and 31.6% agreed thatthe application could assist them to consistently practice healthy eating.Regarding its usefulness in grocery shopping contexts, 68.4% stronglyagreed and 26.3% agreed that the application’s features could make healthygrocery shopping more convenient. Furthermore, 63.2% of participantsstrongly agreed that the application’s features could enhance their nutri-tional knowledge.When asked about its overall relevance to daily routines, 42.1% of par-ticipants strongly agreed and 52.6% agreed that the application would beuseful in supporting their daily nutritional habits.These findings support the application’s perceived ability to assist usersin adopting long-term dietary behavior change and validate its potentialrelevance to daily life.User engagement & SatisfactionUser engagement was assessed through questions that measured partic-ipants’ interest in and motivation to continue using the application’s fea-tures. Descriptive analysis of the responses, as illustrated in Figure 6.3,946.3. RESULTSFigure 6.3: Evaluation of User Engagementindicated that participants generally found the application engaging. Theoverall mean score was 4.29 out of 5 (SD = 0.19), suggesting strong userinterest with minimal variation in response.More than half of the participants (52.6%) strongly agreed that theyenjoyed using the application, while an additional 42.1% agreed with thisstatement. Similarly, 42.1% strongly agreed and 36.8% agreed that theyenjoyed using the application to improve their nutritional knowledge.These results suggest a high level of satisfaction and interest amongusers. In terms of sustained engagement, 31.6% of participants stronglyagreed and 63.2% agreed that they were likely to continue using the ap-plication. Additionally, 57.9% strongly agreed and 31.6% agreed that theywould recommend the application to others.Collectively, these findings demonstrate strong user engagement and ahigh likelihood of continuous use and promotion of the HealKitchen appli-cation.AestheticsThe final construct assessed participants’ perceptions of the application’svisual appeal. As shown in Figure 6.4, the majority of users found the956.3. RESULTSFigure 6.4: Evaluation of Aesthetic Designdesign aesthetically pleasing, with a mean score of 4.49 out of 5 (SD =0.31), indicating generally high satisfaction with the app’s interface.Specifically, over 52% of respondents strongly agreed that the applicationwas visually appealing, and an additional 36.8% agreed. When asked aboutthe layout of elements such as buttons and icons, 73.7% of participantsstrongly agreed that the design was clear and easy to navigate.Only 5.3% expressed negative feedback regarding design, and 10.5% re-mained neutral, suggesting that the majority of users were visually satisfiedwith the app. These results suggest that the HealKitchen application’s vi-sual design was well-received and contributed positively to the overall userexperience.6.3.2 Overall User RatingsAs a final metric, participants were asked to provide an overall rating ofthe HealKitchen application on a 5-point scale based on their impression ofits usability, engagement, and potential impact. Results revealed a highlypositive reception, indicating the application has a strong foundation of usersatisfaction and perceived usefulness.As shown in Figure 6.5, the majority of participants provided high rat-ings, with 61.1% giving a rating of 5 out of 5. An additional 33.3% ratedthe application at a 4, while 5.3% scored the application a 3 out of 5. The966.3. RESULTSFigure 6.5: User Ratings of HealKitchenapplication averaged a rating of 4.58 out of 5, affirming a high level of usersatisfaction and validating the application’s potential as an engaging andeffective intervention tool for building healthy dietary habits.6.3.3 Qualitative Analysis (OEQ)To complement the quantitative findings, open-ended questions were in-cluded to elicit more detailed user feedback. These questions explored areasof the application that needed improvement, desired new features, and over-all user impressions. These questions were evaluated using thematic analy-sis. The process revealed several key insights that helped contextualize theLikert-scale results and provide actionable feedback.OEQ1 & OEQ2 - Feature Improvement & New FeaturesParticipants’ responses to the question on areas of the app that needimprovement and features they would like to see highlighted general designimprovements and new functionalities. Analysis of the responses producedthe following themes:976.3. RESULTSTheme 1: Enhanced Recipe Content & FeaturesThe most prevalent theme regarding feature improvement was for morediverse recipe content and options. Participants consistently asked for “morerecipes”, “including more menu options”, and “more recipes from differentcultures”, highlighting the limited variety of available recipes as an areafor improvement. This suggests that the application needs to incorporate amore diverse range of healthy recipes that would address users’ needs.A noteworthy concern was raised regarding the existing recipes, withone user noting that the recipe ingredients may be unavailable or may becostly to purchase. This highlights a gap in content relevance where thelimited recipe content fails to meet users’ needs. If recipes are not accessibleto a user, the application loses its practical relevance, which can lead todisengagement.Participants expressed further improvements for the recipe content, withrequests to incorporate media to facilitate a seamless experience whenpreparing the recipes. This included requests for “instructional videos”,“video recipes”, and “audio description of food recipe”. The demand forvideo and audio content indicates that users desire interactive media to im-prove engagement.Furthermore, participants requested more detailed nutritional informa-tion, such as “Tags added to each recipe to indicate which condition ittargets”, and “estimated calorie counts”. This feedback suggests a desire forfeatures that simplify complex tasks like nutritional tracking.This collective feedback shows a growing complexity in user needs andsuggests that future development should include multimedia content andadvanced data integration to meet these expectations and provide a morecomprehensive dietary management tool.Theme 2: User Interface and Experience (UI/UX) RefinementAnother prevalent theme from participant responses was a minor im-provement in the user interface design. General user interface improvementswere mentioned, with users stating “I believe the UI could be worked on”and “Just the look of the application, it can look better and the animationscan be smoother” as areas of improvement.Beyond design aesthetics, specific user experience issues were identified.This included the “plus button covers the bottom part of the recipe”, and is-sues with the “Remembered Recipes screen when adding many items to theshopping list”. While the overall impression of the application is positive,986.3. RESULTSthis feedback indicates minor design flaws that could affect user experienceand need refinement. Addressing these minor issues is crucial for demon-strating attention to detail and user-centric design.A user also suggested the “ability to allow registered users to updatetheir health challenges”. This indicates a desire for dynamic personalizationbased on evolving health needs, as users’ health goals, conditions, or prefer-ences can change over time. Enabling dynamic personalization ensures theapplication remains a relevant and useful tool for the user’s ongoing healthjourney, promoting sustained engagement and trust.Additionally, participants requested “more detailed explanations” and“snippets of explanations guiding users”, suggesting a need for improvedonboarding or contextual help to enhance user understanding and naviga-tion.Theme 3: Notifications & Smart RemindersA less prevalent but equally emergent theme occurring within the re-sponses was a request for a notification and reminder feature. Users ex-pressed a desire for proactive engagement to help them stay on track withtheir health goals. Requests such as “notifications or reminders for groceryshopping”, “notification features for when to eat”, and “a reminder of whatcould be prepared” indicate that participants perceive the application as acompanion in the dietary journey.Integrating these features would improve the application’s relevance asan active dietary behavioral intervention tool. These nudges can signif-icantly enhance user engagement and help in integrating the applicationseamlessly into daily routines, thereby increasing the application’s effective-ness in influencing the target behavior.Theme 4: External Features & AI CapabilitiesA few responses pointed towards extending the application to accommo-date external delivery service and further integration of artificial intelligenceinto the application features. These included requests for “Integration withfood delivery service applications for recipes that I can buy off already pre-pared as an option”, and the “integration of AI”. Artificial intelligence couldadapt application features to user preferences, while food delivery integra-tion would simplify the process of healthy eating for users by eliminatingmeal preparation.996.3. RESULTSTheme 5: Gamification & Community ElementsOne participant suggested “grocery store recommendations and gamemechanics for earning points while adhering to nutritional objectives”. Thesuggestion of game mechanics and points indicates that participants arereceptive to the persuasive strategies of gamification elements. Additionalgamification features can transform the routine tasks of grocery shoppinginto engaging challenges that foster sustained engagement through positivereinforcement and a sense of achievement.Similarly, incorporating social or community elements, suggested by theresponse of a “referral feature”, could further enhance user engagement byleveraging peer support and healthy competition.Theme 6: Satisfaction with Current FeaturesFinally, A few users indicated no need for new features or improvements,which supports current design strengths. This was expressed through re-sponses such as “I think the app already has enough functionalities”, “Nonecome to mind for now”, and “N/A”. These responses suggest that the currentapplication functionality is satisfactory to users.Table 6.1 summarizes the resulting themes from the analysis on featureimprovements.OEQ3 - Overall application impressionThe response from participants in response to the question about theiroverall thoughts and impressions about the app provided insights into thegeneral acceptance rate of the application. The results from this analysisproduced the following themes summarized in Table 6.2.Theme 1: Perceived Usefulness and ImpactThe prevalent theme among user impressions was one of positive per-ceived usefulness and impact. Many participants expressed that the appli-cation was useful to them and could help facilitate an improved nutritionaldiet.Phrases such as “It is a very useful application for me because I can mealprep and also eat healthier”, “It is a very wonderful app an puts a healthierway of taking care of essential vitamin and mineral needs”, “Really good andimpactful”, and “This app has helped me with my diet, and the essentialnutrients to consume” where present to support this.1006.3. RESULTSTable 6.1: Themes Identified from Feature Improvement FeedbackTheme Description Example QuotesEnhanced RecipeContent &FeaturesParticipants desired agreater variety of recipes,nutritional tags, calorieestimates, and multimediacontent.“More recipes and caloriecalculator”, “Instructionalvideos would be cool”,“Tags added to eachrecipe to indicate whichcondition it targets”User Interfaceand Experience(UI/UX)RefinementUsers identified interfaceand interaction issues suchas overlapping contentand navigation problems,and suggested morecontextual explanations.“The + button covers thebottom part of therecipe”, “The UI could beworked on...snippets ofexplanations guidingusers”, “Design can lookbetter and animationssmoother”Notifications &Smart RemindersRequests for notificationsor prompts to enhanceengagement and promoteregular app use.“Notification features forwhen to eat”, “Remindersfor grocery shopping”,“Maybe adding areminder of what could beprepared”External Features& AI CapabilitiesParticipants suggestedexpanding the appthrough AI integrationand compatibility withexternal platforms.“Integration with fooddelivery serviceapplications for recipesthat I can buy”Gamification &CommunityElementsUsers proposed gamifiedfeatures and socialengagement tools toenhance motivation.“Game mechanics forearning points”, “Referralfeature”Satisfaction withCurrent FeaturesA few respondentsexpressed contentmentwith existing features,indicating minimal desirefor change.“I think the app alreadyhas enoughfunctionalities”, “Nonecome to mind for now”1016.4. DISCUSSION AND IMPLICATIONSOne user also responded that they were “impressed ... and would liketo adopt the app in their daily life” signifying interest for continued en-gagement. This positive feedback on the application’s ability to promotehealthy eating and nutritional awareness directly aligns with the target goalof HealKitchen.This perceived impact of the application is likely the primary factorcontributing to the high overall ratings of the application. Participants wereengaged with the application because it helped them achieve the desiredoutcome of a healthier dietary lifestyle.Theme 2: Innovation and CreativityThe application was also recognized for its innovative approach. Re-sponses described the app as “very innovative”, “a creative piece”, “an in-novative and user-friendly app”, and “helpful for people who struggle tomaintain a healthy diet”. These responses suggest that users were impressedwith the uniqueness of the application features.The recipe curation feature was highlighted as a positive aspect by auser who stated, “I really like the curating feature. It’s a useful platformfor people seeking to try something new and healthy”. This suggests thatfeatures allowing users to customize or personalize their experience can pro-mote engagement and improve the application’s perceived value.Theme 3: Ease of UseAnother theme that supported the positive initial impression of the ap-plication was its ease of use. Participants described the application as “reallygood and easy to interact with ”, “simple to use”, and “easy to navigate”.These responses indicate that the app remained uncomplicated and en-gaging for users even while providing value. It also indicates that userswere able to effectively navigate through the application and interact withit, regardless of the minor design flaws.6.4 Discussion and ImplicationsThe analysis of user feedback from Study 1 reveals a positive percep-tion of the HealKitchen application among participants, while also high-lighting actionable areas for improvement. The application was generallywell-received, with users expressing satisfaction with its usability, engage-ment features, aesthetic design, and overall usefulness. These findings align1026.4. DISCUSSION AND IMPLICATIONSTable 6.2: Themes Identified from Overall User ImpressionsTheme Description Example QuotesPerceivedUsefulness andImpactParticipants valued theapp’s support in planninghealthier diets andimproving nutritionawareness.“This app has helped mewith my diet and theessential nutrients toconsume”, “I would adoptthis in my daily life”Innovation andCreativityUsers appreciated theapp’s originality, features,and creativity in solvingreal dietary challenges.“Very innovative, simpleto use”, “I really like thecurating feature. It’s auseful platform for peopleseeking to try somethingnew and healthy.”Ease of Use The app was widelydescribed as easy tonavigate and interactwith, even for first-timeusers.“Really good and easy tointeract with”, “It waseasy to navigate”, “Simpleto use”with the foundational principles of behavior change design, suggesting thatHealKitchen shows promise as a persuasive mobile health (mHealth) inter-vention for supporting healthier dietary behaviors.The quantitative analysis provided clear evidence of high user satisfac-tion, with consistently high scores across key constructs such as ease of useand perceived usefulness. This was further reinforced by the qualitativeanalysis, where participants expressed appreciation for the application’s in-novative features and visual appeal. The application’s high usability scoreis especially important as research suggests that ease of use is a primary de-terminant of user adoption and continuous engagement of persuasive healthapplications [54, 207]Despite the high application ratings, users also provided constructivefeedback identifying limitations and areas for improvement. These includethe need for expanded recipe content, improved interface design, more cul-turally diverse food options, and additional features such as reminders anda dynamic profile. These suggestions are valuable and consistent with exist-ing research, which indicates that iterative user-centered refinement is key to1036.4. DISCUSSION AND IMPLICATIONSmaintaining sustained engagement and behavioral impact in digital healthtools [159, 215].6.4.1 Strengths of HealKitchenThe results of the evaluation indicate that HealKitchen was able to suc-cessfully implement behavioral theories and persuasive strategies as designfeatures within the application. High mean scores in perceived usefulnesssuggest that users found value in the application’s features, particularlythose that offer recipe recommendations, nutritional information, and gro-cery shopping support. Furthermore, the user engagement metrics revealthat participants were motivated to use the application continuously andexpressed interest in recommending it to others.These findings showed that theory-informed persuasive design, as appliedin HealKitchen, contributes meaningfully to user satisfaction and perceivedvalue, consistent with academic research in this field [70, 143].6.4.2 Areas for ImprovementThematic analysis of open-ended responses revealed several key areasfor improvement, which are essential for the long-term adoption and ef-fectiveness of the application. While many participants described the apppositively, their feedback also revealed gaps in the current functionality.Recipe Variety and DiversityA recurring suggestion among participants was the need for a wider vari-ety of recipes, including culturally diverse options. Some participants notedthat the ingredients in some recipes were either inaccessible or unaffordableto them. This underscores the importance of personalization in nutrition-focused interventions. Ensuring that recipe content is relevant, affordable,and contextually appropriate is critical for maintaining long-term user en-gagement and practical usability of the application.User Interface and Experience (UI/UX) IssuesAlthough overall ease of use was rated positively, minor usability issueswere reported. These include overlapping design elements, navigation issueson the saved recipes page, and an inconsistent shopping list view. Addressingthese problems is important, as even small usability issues can negativelyimpact user satisfaction and retention [60]. Participants also suggested some1046.4. DISCUSSION AND IMPLICATIONSdetailed tips and instructions to help new users understand app functionality,highlighting the need for better in-app guidance and help tools.Smart reminders and notificationsParticipants expressed interest in reminders for grocery shopping, mealplanning, and recipe suggestions, reflecting a desire for the application to bemore proactive in influencing their dietary behavior. This aligns with theliterature on digital nudging, where timely, context-aware prompts have beenshown to increase adherence to health goals [134, 191]. Incorporating smartnotifications could significantly enhance the app’s behavioral influence.Personalization and Profile FlexibilityAnother key insight was the request for profile flexibility, particularlythe ability to update one’s health profile over time. As users’ health con-ditions evolve, their dietary requirements may change, requiring the app toadapt dynamically. Supporting this functionality would improve the app’slongevity and relevance [20].6.4.3 Iterative Development & Feature ImprovementFollowing the conclusion of Study 1, the insights gathered were directlyincorporated into an iterative redesign and refinement of HealKitchen. Thisapproach follows agile development practices, which emphasize user feedbackloops. The following improvements were made based on user feedback:− Resolved UI/UX issues: Design issues and layout problems, such asoverlapping content, were corrected, improving design and accessibil-ity.− Improved recipe tagging: Tags were added to recipes to indicate whichhealth condition each recipe supports, allowing for better filtering anduser understanding.− Shopping list update: Ingredient entries were simplified to show basegrocery items without quantities, making lists more practical for shop-ping.− Profile update functionality: Users can now edit their health chal-lenges directly from their profile page, increasing personalization andflexibility.1056.4. DISCUSSION AND IMPLICATIONSThese updates address identified user improvements and demonstrateresponsiveness to user feedback, which is essential for building trust andpromoting sustained engagement.106Chapter 7ConclusionThis research explored the design, development, and evaluation ofHealKitchen, a persuasive mobile application aimed at promoting health-ier dietary and grocery shopping habits. HealKitchen integrates behavioralchange theories, personalized recommendation systems, and AI-based imagerecognition into a mobile interface to empower users, specifically those withdiverse health requirements, to make informed, healthy food decisions.The development process was anchored on iterative research-drivenframeworks, including behavioral change theories and persuasive systemsdesign. These frameworks were integrated into the application as opera-tionalizable design features to encourage sustained behavior change.7.1 Summary of Key FindingsThis research revealed that HealKitchen has potential as a nutritionintervention tool that can actively and effectively influence users’ dietarybehavior. The first user study (Study 1), consisting of a mixed-methodsevaluation, confirmed that HealKitchen achieved high usability and engage-ment scores from users’ perception. Quantitative data showed positive feed-back in constructs like ease of use (mean = 4.6), perceived usefulness (mean= 4.45), and engagement (mean = 4.29). Participants also rated the overallapplication experience positively, with an average rating of 4.58 out of 5,citing the application’s perceived usefulness in improving dietary habits.Qualitative feedback revealed several themes regarding users’ percep-tions of the application. Participants praised the application’s usefulnessand potential impact while identifying specific areas for improvement. Oneprimary suggested improvement was the inclusion of more recipe optionsthat cater to the diverse needs of users. Additional suggestions includedUI refinements, rich media content, and smart notifications. These insightsinformed subsequent application updates and laid the foundation for futurework in the application development.1077.2. IMPLICATIONS FOR PRACTICE AND RESEARCH7.2 Implications for Practice and Research7.2.1 Practical Applications of Research FindingsHealKitchen exemplifies how mobile health (mHealth) tools can be de-signed to integrate behavioral science with technology to foster healthierdietary decision-making. Unlike conventional calorie trackers, HealKitchenintervenes during the grocery shopping process, a critical moment in dietarydecision-making, offering personalized, health-conscious recommendationsto foster long-term, sustainable dietary behavior.From a design standpoint, HealKitchen provides a blueprint for embed-ding persuasive strategies and behavioral change theories into health appli-cations as operationalizable design features. Its curated recipe suggestions,goal-setting, social features, and point-based rewards align with establishedhealth promotion techniques and can be replicated in similar tools aimed atdiet, exercise, or medication intervention.Practically, the application could be scaled to support dietitians, wellnesscoaches, and public health campaigns. For example, healthcare professionalscan use the app to complement in-clinic consultations, empowering users toact on recommendations independently. Furthermore, its modular architec-ture enables expansion to support localization across various cultures anddietary contexts, making it adaptable for a wider range of users.7.2.2 Contributions to Academic KnowledgeThis research contributes to the literature in several ways:1. It demonstrates how behavior change theories can be translated intoconcrete, interactive application features that aim to support long-term engagement and decision-making.2. It demonstrates the feasibility of implementing an AI-powered imagerecognition and hybrid recommendation systems to support the pro-vision of personalized dietary information.3. The mixed-methods evaluation supports existing literature by pro-viding evidence of user perceptions and qualitative insights into userexpectations and feature preferences.4. It provides a labeled image dataset for grocery image recognition andobject detection, contributing resources for future academic research.1087.3. LIMITATIONSIn contrast to generic food apps that target singular and generic dietaryconcerns (e.g., diabetes or weight loss), HealKitchen addresses a broaderrange of nutritional needs by allowing users to select from multiple healthgoals and receive tailored support. This makes it uniquely positioned in themobile health technology space.7.3 LimitationsWhile this research highlighted the strengths of HealKitchen in support-ing dietary behavioral change, several limitations were encountered duringdevelopment that influenced both the scope and outcomes of the project.These constraints, ranging from technical limitations to methodological chal-lenges, highlight areas for refinement in future research.The major limitation encountered during the development of this projectwas the unavailability of a comprehensive, verified database that connectshealth conditions with specific recipes. Unlike calorie tracking, which relieson well-established nutrient databases, disease-specific dietary recommen-dations are uncommon across sources.To address this limitation, recipe content was generated using OpenAI’slanguage model for a curated list of common health conditions. Althoughthe generated recipes were filtered for relevance, they are not yet medicallyverified. The absence of professional nutritional validation introduces animportant limitation to the clinical reliability of the content. Future workcould involve collaboration with certified nutritionists and medical profes-sionals to vet and refine the generated recipe content.Another significant limitation arose from the deprecation of commonlyused AI model finetuning libraries such as the TensorFlow Object Detec-tion API and PyTorch Mobile’s PlayTorch. These libraries, which wereonce widely used for object detection tasks and optimized mobile deploy-ment, were discontinued and lacked support for real-time mobile integration.This challenge made it difficult to deploy the trained object detection modeldirectly on the mobile device, an approach that would have minimized in-ference time and improved responsiveness for users.To overcome this limitation, a custom training pipeline was developedusing PyTorch. The model was trained on the available dataset and deployedvia a server-based API, allowing image recognition to be handled remotely.While this workaround enabled functionality, it introduced latency issuesand dependence on internet connectivity.Furthermore, the object detection model was also limited due to the1097.4. FUTURE WORKunavailability of a robust, high-quality, labeled grocery image dataset. Mostpublicly available food image datasets are limited in scope or not adequatelylabeled for the classification of grocery items.This constraint limited the diversity of the training data, which couldimpact the model’s ability to generalize effectively in real-world scenarios.To address this, the project scope was reduced to a manageable set of groceryitems, and manual efforts were employed to augment the dataset throughweb sourcing and image collection. While this approach allowed progress,the resulting dataset was still not diverse enough to support high-performingobject detection across varied lighting conditions, packaging types, andbackgrounds.Some limitations were also encountered during the evaluation of theHealKitchen application, one of which was the reliance on self-reporteddata, which may have introduced the risk of social desirability bias. Giventhe study’s focus on healthy dietary behaviors, participants might have feltinclined to provide more favorable responses to align with perceived expec-tations.Although steps were taken to ensure participant anonymity and mitigatepotential biases, the possibility of skewed responses cannot be entirely ruledout. Additionally, Study 1 involved a limited sample size of 20 participants.While this was sufficient for formative feedback, it limits the generalizabilityof the findings. A larger and more diverse participant base is necessary formore generalized validation.Another limitation faced was due to the time constraints in securingethics approval for a second user study (Study 2), which was intended toinvestigate the actual impact of the app on influencing dietary behavioralchange. This follow-up study is planned for future implementation andwill require a longitudinal design spanning approximately 6–12 months toadequately evaluate sustained behavior change over time.7.4 Future WorkBuilding upon the findings of this research and addressing the limita-tions identified during implementation and evaluation, several avenues forfuture work have been identified to improve the HealKitchen application andstrengthen its impact as a persuasive health intervention.A key area for improvement identified in user feedback relates to the va-riety and diversity of the recipe database. Future development efforts shouldfocus on expanding the range of culturally diverse recipes to accommodate1107.5. SUMMARYusers from different backgrounds and dietary preferences.Additionally, there is a need to refine the categorization of recipes basedon specific health conditions, nutritional goals, and ingredient availability.To support this, collaboration with certified dietitians and medical profes-sionals can be initiated to validate and curate recipe content that alignswith evidence-based dietary guidelines for various health conditions. Thiswill help ensure both the verified relevance and practical usability of reciperecommendations.Additionally, to address the performance limitations of the image recog-nition feature, future work could involve curating a more comprehensive anddiverse grocery image dataset. This dataset should include grocery itemsphotographed under various lighting conditions, packaging variations, andenvironmental contexts to improve model robustness. Furthermore, effortscan be made to fine-tune and optimize the object detection model usingalternative base architectures and hyperparameter configurations.The long-term goal is to adapt the model for mobile device deployment,removing dependence on external servers and reducing latency. This couldimprove efficiency and make the application more reliable in low-internetenvironments.While Study 1 provided insights into usability and engagement, a sec-ond planned user study (Study 2) that evaluates the effectiveness of theHealKitchen application in influencing actual dietary behavior could be im-plemented. Study 2 could adopt an A/B testing methodology to assessthe behavioral impact of the application’s persuasive features. Participantscould be divided into control and intervention groups to measure differ-ences in food selection, nutritional knowledge, and dietary choices over adefined period. This study could also employ both application behavioraltracking and survey-based outcomes to assess changes in dietary behavior.The outcomes of this evaluation would provide evidence of the application’seffectiveness in achieving long-term dietary change.7.5 SummaryThis research project presented the design, development, and initial eval-uation of HealKitchen, a persuasive mobile application aimed at promotinghealthy dietary behaviors through technology-driven and research-informeddesign.Grounded in behavioral change theories, persuasive technology, and user-centered design principles, HealKitchen incorporates key features such as1117.5. SUMMARYpersonalized recipe recommendations based on health challenges and nutri-ent needs, image recognition of grocery items, and gamification elements tosupport users in making informed dietary decisions.The project addressed five research questions that investigate: whethera mobile health application can be designed to influence users’ dietary be-havior toward healthier food choices, what ways behavioral theories can beoperationalized in the design of a mobile health application to support di-etary behavior change, whether an image recognition model could be trainedto classify grocery food items accurately, whether a recommendation systemcould predict relevant healthy recipes based on users health condition andnutritional requirements, and the usability and user experience outcomes ofthe developed mobile health application.A hybrid recommendation system was developed, combining content-based filtering and collaborative filtering algorithms using health-relatedfeatures and user preferences. An object detection model was also trainedusing a custom PyTorch implementation to identify grocery items. Themodel demonstrated baseline performance suitable for functional integra-tion, with plans for optimization in future iterations.A user study (Study 1) evaluated the usability, engagement, and per-ceived usefulness of the application. The findings from 19 participantsindicated strong satisfaction with the application’s features and interface.Thematic analysis of open-ended feedback revealed areas for improvement,including recipe diversity, multimedia content, user interface updates, andsmart notifications, some of which were addressed through iterative devel-opment.In summary, this thesis demonstrates the integration of persuasive strate-gies, behavioral theories, and AI technologies in mobile health applicationsto provide personalized and engaging tools to support long-term dietarychange. 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InProceedings of the 14th international conference on World Wide Web(2005), pp. 22–32. → pages 57137AppendicesAppendix A: Dataset Components used in RecipeGenerationA.1 List of Grocery Items used to Generate RecipesTable 7.1: Grocery Items used in HealKitchen (N = 130)# Item # Item # Item1 Almonds 2 Amaranth 3 Anise Seed4 Apples 5 Apricot 6 Arugula7 Artichoke 8 Asparagus 9 Avocado10 Bananas 11 Bartlett Pears 12 Basil Fresh13 Beans 14 Beets 15 Bell Peppers16 Blueberries 17 Brazil Nuts 18 Broccoli19 Brown Rice 20 Brussels 21 Buckwheat22 Canola Oil 23 Cantaloupe 24 Caraway Seed25 Carrots 26 Cashews 27 Cassava28 Cauliflower 29 Celery 30 Chard31 Cherimoya 32 Cherries 33 Chestnut34 Chia Seeds 35 Chicken 36 Cinnamon37 Cloves 38 Coconut 39 Coconut Oil40 Collard 41 Coriander Seed 42 Corn43 Cucumber 44 Dandelion 45 Egg Whites46 Egg Yolk 47 Olive Oil 48 Figs49 Garlic 50 Ginger 51 Gourd52 Grapefruit 53 Greek Yoghurt 54 Green Beans55 Green Cabbage 56 Green Olives 57 Ground Flaxseed58 Ground Turkey 59 Guava 60 Haddock Fish138# Item # Item # Item61 Hazelnuts 62 Honey 63 Hummus64 Jute 65 Kale 66 Kidney Beans67 Kiwi 68 Lettuce 69 Lima Beans70 Macadamia Nuts 71 Mango 72 Milk73 Skimmed Milk 74 Mushroom 75 Nectarines76 Nutmeg 77 Oatmeal 78 Okra79 Onions 80 Oranges 81 Oysters82 Palm Oil 83 Papayas 84 Paprika85 Parsley 86 Parsnip 87 Peaches88 Peas 89 Pecan Nuts 90 Pickles91 Pine Nuts 92 Pineapple 93 Pinto Beans94 Pistachio Nuts 95 Plantain 96 Plum97 Pollock Fish 98 Poppy 99 Potatoes100 Pumpkin Seeds 101 Quinces 102 Radishes103 Raspberries 104 Red Cabbage 105 Rhubarb106 Salmon 107 Salsa 108 Sapodilla109 Sapote 110 Semolina 111 Sesame Seeds112 Shallots 113 Soy Milk 114 Soybeans115 Spearmint 116 Spinach 117 Squash118 Starfruit 119 Strawberries 120 Sweet Potatoes121 Tamarind 122 Thyme 123 Tomatoes124 Tuna Fish 125 Turnips 126 Walnuts127 Watermelon 128 Wheat 129 White Rice130 Yam139A.2 List of Health Conditions Targeted by the GroceryItemsTable 7.2: Health Conditions used in HealKitchen (N = 85)# Disease # Disease # Disease1 Cancer 2 Diabetes 3 High Cholesterol4 High Blood Pressure 5 Breast Cancer 6 Inflammation7 Arthritis 8 Menopause 9 Ulcer10 Thyroid 11 Heart Disease 12 Weight Loss13 Mental Health 14 Cataract 15 Osteoporosis16 Irritable Bowel Syndrome 17 High Blood Sugar 18 Cataracts19 Eye Health 20 Prostate Health 21 Macular Degeneration22 Leukamia 23 Kidney Stones 24 Constipation25 Stroke 26 Fatty Liver Disease 27 Blindness28 Skin Damage 29 Thyroid Health 30 Hashimoto Disease31 Schizophrenia 32 Cardiovascular Disease 33 Alzheimers34 Asthma 35 Indigestion 36 Obesity37 Antioxidant 38 Birth Defects 39 Rhinitis40 Lung Cancer 41 Heart Health 42 Free Radical Damage43 Rhinitis 44 Lung Cancer 45 Lung Cancer46 Heart Health 47 Free Radical Damage 48 Tooth Decay49 Allergies 50 Hiv 51 Staphylococcus52 E-coli 53 Seizures 54 Dandruff55 Glaucoma 56 Dementia 57 Dehydration58 Stress 59 Kidney Infection 60 Menstral Pains61 Blood Clot 62 Osteoarthritis 63 Iodine Definciency64 Hypothyroidism 65 Bone Health 66 Depression67 Cavities 68 Liver Disease 69 Poisoning70 Diuresis 71 Common Cold 72 Insulin Sensitivity73 Muscle Scamp 74 Anxiety 75 Infertility76 Hair Loss 77 Cellular Damage 78 Nausea79 Fungal Infection 80 Lung Disease 81 Oxidative Stress82 Celiac Disease 83 Dermatitis 84 Acne85 Cough140A.3 List of Nutrients Provided by the Grocery ItemsTable 7.3: Nutrients Provided in HealKitchen (N = 40)# Nutrient # Nutrient # Nutrient1 Calcium 2 Beta-carotene 3 Carbohydrate4 Fiber 5 Catechin 6 Choline7 Copper 8 Eugenol 9 Folic Acid10 Iodine 11 Iron 12 Lauric Acid13 Limonene 14 Magnesium 15 Manganese16 MCTs 17 Molybdenum 18 Myristic Acid19 Phosphorus 20 Polyphenols 21 Phytosterols22 Potassium 23 Protein 24 Selenium25 Sodium 26 Thiamin 27 Thymol28 Vitamin A 29 Vitamin B 30 Zinc31 Vitamin B3 32 Vitamin B5 33 Vitamin B634 Vitamin B7 35 Vitamin B9 36 Vitamin C37 Vitamin D 38 Vitamin E 39 Vitamin K40 Antioxidants141Appendix B: Open AI Prompts for RecipeGeneration and CurationB.1 Prompt for Recipe Generation1 curate_structure = """ Each recipe should be a Python dictwith the following structure:2 - name: the name of the recipe3 - slug: the name trimmed , in lowercase , and hyphen -separated4 - base: the primary ingredient used in this recipe5 - ingredients: an array of ingredient objects6 - instructions: an array of cooking instructions asstrings78 The ’base’ should be the main ingredient of the recipe.910 The ’ingredients ’ object should have:11 - name: ingredient name12 - amount: per serving quantity13 - unit: unit of measurement14 - desc: (optional) description of ingredient state1516 def prompt_config(config):17 return (18 "For each item in the list {}, generate one ortwo actual healthy recipes , "19 "The individual recipes should be a list ofPython objects with the following structure:{}. The recipe should not include unhealthyingredients"20 ).format(config ["items"], curate_structure)142B.2 Prompt for Recipe Curation1 curate_structure = """ Each recipe should be a Python dictwith the following structure:2 - name: the name of the recipe3 - slug: the name trimmed , in lowercase , and hyphen -separated4 - base: the primary ingredient used in this recipe5 - ingredients: an array of ingredient objects6 - availableIngredients: the number of requiredingredients that are available in the fridge7 - additionalIngredients: the remaining ingredients needed, in the same structure as ’ingredients ’8 - instructions: an array of cooking instructions asstrings910 The ’base’ should be the main ingredient of the recipe.1112 The ’ingredients ’ object should have:13 - name: ingredient name14 - amount: per serving quantity15 - unit: unit of measurement16 - desc: (optional) description of ingredient state1718 The response should be in a valid python JSON object witha key called recipes and all text in lowercase. Theresponse should completely be in python JSON format ,with no additional information."""1920 def generate_prompt(items):21 return (22 "Given␣the␣following␣ingredients␣from␣my␣fridge:␣{},␣"23 "generate␣3␣healthy␣recipes␣using␣some␣or␣all␣of␣these␣ingredients.␣"24 ).format(",␣".join(items), curate_structure)143B.3 Prompt for Recipe Image Generation1 def generate_recipe_image_prompt(recipe_name):2 return f"Create␣an␣image␣of␣a␣food␣whose␣name␣is␣{recipe_name }."144Appendix C: Image Dataset Composition by ClassTable 7.4: Image Classes used in Image Recognition Model Training (N =41)Class Images Class ImagesAlmond 9 Apple 1808Avocado 925 Banana 1030Beetroot 553 Bell Pepper 2662Blueberries 468 Broccoli 21Brussels 11 Cabbage 204Carrot 319 Cauliflower 780Chilli 86 Corn 626Cucumber 487 Eggplant 803Garlic 100 Ginger 376Grapes 1569 Jalepeno 84Kiwi 554 Lemon 1078Lettuce 112 Mango 1010Onion 1455 Orange 568Paprika 78 Parsley 10Pear 1986 Peas 92Pineapple 1097 Pomegranate 571Potato 1516 Raddish 81Soybeans 88 Spinach 97Strawberries 1229 Tomato 2015Turnip 108 Walnut 735Watermelon 569145
Thesis/Dissertation
eng
Computer Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Adaji, Ifeoma
HealKitchen - a persuasive, artificial intelligence driven, mobile application to influence healthy dietary habits
Text