A Systematic Review of Nutrition Recommendation Systems: With Focus on Technical Aspects

Abhari, Shahabeddin; Safdari, Reza; Azadbakht, Leila; Lankarani, Kamran Bagheri; Kalhori, Sh. Rostam Niakan; Honarvar, Behnam; Abhari, Kh; Ayyoubzadeh, Seyed Mohammad; Karbasi, Zahra; Zakerabasali, Somayyeh; Jalilpiran, Yahya

doi:10.31661/jbpe.v0i0.1248

Cited by 28 publications

(37 citation statements)

References 20 publications

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“…The (Nutrition aware Food Recommender System ) nutri-FRS possess the potential for becoming the ultimate personal health guide application that can assist people in defining their health goals and guide them plan diet, menu, grocery, and physical activity to achieve those goals. Which fueled the enthusiasm among RS and medical researchers to investigate, design, and develop for nutri-FRS [184,93,42,163,160]. Over the past few decades, researchers have produced seminal contributions towards nutri-FRS to ensure user-preference, diversity, novelty, and nutritional development in diet decisions, such as [166,164,165,62,178,58,124].…”

Section: Related Literaturementioning

confidence: 99%

“…Stefanie Mika in [122], Cristoph Trattner et al in [166], Felicia Cordeiro et al in [77] and Kerry Shih-Ping in [66] outline various challenges and research problems in the domain of nutri-FRS. Shahabeddin Abhari et al in [42], Thi Ngoc et al in [165] , Weiqing Min et al [124], Thomas Theodoridis et al in [160] and Christoph Trattner et al in [166,169] studied the performance of various fundamental as well as fooddomain-specific RS algorithms and reviewed which algorithmic strategies are the best for addressing specific nutri-FRS challenges. In many of their seminal research works, such as [172,167,169,171,168,173], Christoph Trattner et al looked into the potential of crowdsourced online recipes.…”

Section: Knowledge Discovery For Nutri-frsmentioning

confidence: 99%

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Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits

Khan,

Muhammad,

Smyth

et al. 2021

Preprint

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Food-choices and eating-habits directly contribute to our long-term health. This makes the food recommender system a potential tool to address the global crisis of obesity and malnutrition. Over the past decade, artificial-intelligence and medical researchers became more invested in researching tools that can guide and help people make healthy and thoughtful decisions around food and diet. In many typical (Recommender System) RS domains, smart nudges have been proven effective in shaping users' consumption patterns. In recent years, knowledgeable nudging and incentifying choices started getting attention in the food domain as well. To develop smart nudging for promoting healthier food choices, we combined Machine Learning and RS technology with food-healthiness guidelines from recognized health organizations, such as the World Health Organization, Food Standards Agency, and the National Health Service United Kingdom. In this paper, we discuss our research on, persuasive visualization for making users aware of the healthiness of the recommended recipes. Here, we propose three novel nudging technology, the WHO-BubbleSlider, the FSA-ColorCoading, and the DRCI-MLCP, that encourage users to choose healthier recipes. We also propose a Topic Modeling based portion-size recommendation algorithm. To evaluate our proposed smart-nudges, we conducted an online user study with 96 participants and 92250 recipes. Results showed that, during the food decision-making process, appropriate healthiness cues make users more likely to click, browse, and choose healthier recipes over less healthy ones.

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Section: Related Literaturementioning

confidence: 99%

Section: Knowledge Discovery For Nutri-frsmentioning

confidence: 99%

Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits

Khan,

Muhammad,

Smyth

et al. 2021

Preprint

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“…Mohammed and Hagras [19] present a type-2 fuzzy logic-based diet recommendation system to help achieve a healthy lifestyle to control diabetes. A complete systematic review of nutrition recommendation systems with a focus on technical aspects can be found in [20]. Previous work from the authors has shown promising results for using machine learning techniques in a food recommendation system, maintaining healthy blood glucose levels on a T1D simulator during exercise [21].…”

Section: Introductionmentioning

confidence: 99%

Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities

et al. 2020

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Background. Since physical activity has a high impact on patients with type 1 diabetes and the risk of hypoglycemia (low blood glucose levels) is significantly higher during and after physical activities, an automatic method to provide a personalized recommendation is needed to improve the blood glucose management and harness the benefits of physical activities. This paper aims to reduce the risk of hypoglycemia and hyperglycemia (high blood glucose levels), and empowers type 1 diabetes patients to make decisions regarding food choices connected with physical activities. Methods. Traditional and Bayesian feedforward neural network models are developed to provide accurate predictions of the blood glucose outcome and the risks of hyperglycemia and hypoglycemia with uncertainty information. Using the proposed models, safe actions that minimize the risk of both hypoglycemia and hyperglycemia are provided as food recommendations to the patient. Results. The predicted blood glucose responses to the optimal and safe food recommendations are significantly better and safer than by taking random food. Conclusions. Simulations conducted on the state-of-the-art UVA/Padova simulator combined with Brenton’s physical activity model show that the proposed methodology is safe and effective in managing blood glucose during and after physical activities.

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“…Hybrid Recommender (HR) Systems: HR systems are based on the combination of the above-mentioned techniques. Studies have found HR systems to be the preferred approach as these systems could allow one system to cover the disadvantages of the other system [18,29]. For example, a well-known problem with the CF system is a "cold-start" problem [30].…”

mentioning

confidence: 99%

“…A survey conducted by Abhari et al, in 2019, looked specifically at nutrition recommendation systems, and using the PRISMA framework, they were able to identify 25 articles that implement various types of NR systems [29]. The survey identified that hybrid and knowledge-based recommender systems are the popular recommendation system types, with the collaborative recommendation system being the least popular type [29]. This study also highlights that the K-Means clustering algorithm, a deep learning recommender system, is not as widely used as the rule-or ontology-based algorithms [29].…”

mentioning

confidence: 99%

A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes

Joachim

Forkan

Jayaraman

et al. 2022

Sensors

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Diabetes mellitus is a serious chronic disease that affects the blood sugar levels in individuals, with current predictions estimating that nearly 578 million people will be affected by diabetes by 2030. Patients with type II diabetes usually follow a self-management regime as directed by a clinician to help regulate their blood glucose levels. Today, various technology solutions exist to support self-management; however, these solutions tend to be independently built, with little to no research or clinical grounding, which has resulted in poor uptake. In this paper, we propose, develop, and implement a nudge-inspired artificial intelligence (AI)-driven health platform for self-management of diabetes. The proposed platform has been co-designed with patients and clinicians, using the adapted 4-cycle design science research methodology (A4C-DSRM) model. The platform includes (a) a cross-platform mobile application for patients that incorporates a macronutrient detection algorithm for meal recognition and nudge-inspired meal logger, and (b) a web-based application for the clinician to support the self-management regime of patients. Further, the platform incorporates behavioral intervention techniques stemming from nudge theory that aim to support and encourage a sustained change in patient lifestyle. Application of the platform has been demonstrated through an illustrative case study via two exemplars. Further, a technical evaluation is conducted to understand the performance of the MDA to meet the personalization requirements of patients with type II diabetes.

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A Systematic Review of Nutrition Recommendation Systems: With Focus on Technical Aspects

Cited by 28 publications

References 20 publications

Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits

Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits

Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities

A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes

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