Design Issues in Personalized Nutrition Advice Systems

Abstract

The current health status of the general public can substantially benefit from a healthy diet. Using a personalized approach to initiate healthy dietary behavior seems to be a promising strategy, as individuals differ in terms of health status, subsequent dietary needs, and their desired behavior change support. However, providing personalized advice to a wide audience over a long period is very labor-intensive. This bottleneck can possibly be overcome by digitalizing the process of creating and providing personalized advice. An increasing number of personalized advice systems for different purposes is becoming available in the market, ranging from systems providing advice about just a single parameter to very complex systems that include many variables characterizing each individual situation. Scientific background is often lacking in these systems. In designing a personalized nutrition advice system, many design questions need to be answered, ranging from the required input parameters and accurate measurement methods (sense), type of modeling techniques to be used (reason), and modality in which the personalized advice is provided (act). We have addressed these topics in this viewpoint paper, and we have demonstrated the feasibility of setting up an infrastructure for providing personalized dietary advice based on the experience of 2 practical applications in a real-life setting.

Designing and Evaluating a Nutrition Recommender System for Improving Food Security in a Developing Country

“…Numerous nutrition recommender systems have been designed and implemented worldwide, but they solely focus on users' nutritional needs and preferences and ignore economic factors when recommending meals or diets. 12,[25][26][27][28][29][30][36][37][38][39][40][41][42][43][44][45][46][47] Due to the contribution of economic factors to food security, Rules for pregnancy status • IF the user is pregnant, THEN remove Z 1 -Z n meals from the recommendations.…”

“…There is still no consensus on the use of smart systems for recommending personalized diets; moreover, as this study aimed to promote food security, meal recommendation was regarded as the output of the recommender system, which is also the case in many other studies. 12,[25][26][27][28][29][30][36][37][38][39][40][41][42][43][44][45][46][47] After eliciting system requirements, based on the agile approach, the Web-based TanSa system was designed. This system provides two principal services "recommending healthy and affordable meals for individuals and households" and "offering a tele-nutrition consulting system".…”

“…content-based (CB), collaborative filtering (CF), knowledge-based (KBS), and hybrid recommender systems (HRS). 34,36,37 Recently, recommender systems have been employed in the domain of healthy nutrition as a potential solution to help users deal with a large volume of nutrition-related data, including data on foods, nutritional values, healthy foods, recipes, etc. 12,36 As a result, various studies have been recently conducted on the design and application of recommender systems for nutrition.…”

“…12,36 As a result, various studies have been recently conducted on the design and application of recommender systems for nutrition. 12,[25][26][27][28][29][30][36][37][38][39][40][41][42][43][44][45][46][47] In 2017, a nutrition recommender system was designed in Italy. Relying on specialists' prescriptions, user profiles, and preferences, the system helps users select more appropriate meals and offers healthy and personalized meals.…”

“…content-based (CB), collaborative filtering (CF), knowledge-based (KBS), and hybrid recommender systems (HRS). 34 , 36 , 37 …”