![]() Yet, efforts towards personalised nutrition recommendations have been made by Zeevi et al. 2002) but are often based on guidelines for a whole population instead of dealing with individual requirements. The use of nutrition assistance systems is promising since previous studies have shown that persuasive technologies can help people to eat healthier (Orji and Moffatt 2016).Įxisting national and international dietary guidelines constitute important informational sources for nutrition (Painter et al. Thus, health-aware recommender systems need to also incorporate different parameters related to taste and health into their systems (Elsweiler et al. Conventional recommender systems learn the users’ preferences and try to cater to them, which might enforce recommendations for unhealthy food as well (Elsweiler and Harvey 2015 Schäfer et al. 2019a) while fostering healthy eating patterns. These systems have the potential to help users navigate the growing amount of multimedia food content (Min et al. The research in recommender systems has been recently interested in food recommender systems addressing, among others, nutritional health with different approaches (Trattner and Elsweiler 2018). Finally, we discuss general knowledge acquired on the design of personalized mobile nutrition recommendations by identifying important factors, such as the users’ acceptance of the recommender’s taste, health, and personalization. We further identify system limitations influencing this result, such as a lack of diversity, mistrust in healthiness and personalization, real-life contexts, and personal user characteristics with a qualitative analysis of semi-structured in-depth interviews. The analysis of different application features shows that reflective visual feedback has a more substantial impact on healthy behaviour than the recommender (conditional \(R^2=.354\)). Our results show that Nutrilize positively affects nutritional behaviour (conditional \(R^2=.342\)) measured by the optimal intake of each nutrient. By using quantitative and qualitative measures of 34 participants during a study of 2–3 months, we provide a deeper understanding of how our nutrition application affects the users’ physique, nutrition behaviour, system interactions and system perception. Our system offers automated personalized visual feedback and recommendations based on individual dietary behaviour, phenotype, and preferences. This study investigates the impact of a mobile, personalized recommender system named Nutrilize. However, knowledge about the effects of the long-term provision of health-aware recommendations in real-life situations is limited. ![]() Recommender systems, as an integral part of mHealth technologies, address this task by supporting users with healthy food recommendations. Healthy nutrition contributes to preventing non-communicable and diet-related diseases. ![]()
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