Myfitnesspal (1) is a diet app with a food tracker, to help people keep track of what they eat. Users can set targets on their weight, the calories consumed and sports practiced. The app interacts with the users in many ways, for example, if you entered your sport exercises you are allowed to eat more calories on that day. There are no monetary costs involved for users, but the users have to put some effort into using the product: they need to add every single item they consumed. The utility, however, is relatively high for most people. Only by adding the food, the app gives you extensive feedback on your behavior. The company promotes sharing your achievements with friends, as this would increase weight-loss. You can give friends permission to view your data, or share your achievements on the large social media platforms.
The company benefits from user data and uses nutritional information for their databases. Users can add foods, information about this food and its barcodes to the database. If someone scans a barcode, he immediately receives all information available on this product and can add this to his own record. Therefore, user participation adds to the quality of the product, which makes them active co-creators of the app. The more information the app contains, the easier the app gets for users. Also, a forum is available to the users, on which they can motivate each other, and blogs offer them more information about food and exercising.
MyFitnessPal is one of the most successful lifestyle and health apps with 40 million users and has been in many top downloading list. However, it is used as an individual food tracker only. With the data available, there are many options for improving the app. The keyword for a successful diet is personalization. In the first place because not everyone wants or needs the same, and secondly because there is a choice overload in diets, foods and possible healthy behavior. A Myfitnesspal Recommendation agent could be the answer for a successful diet. Recommendation agents (RAs) are software agents that elicit the interests or preferences of individual consumers for products, either explicitly or implicitly, and make recommendations accordingly (Xiao & Benbasat, 2007). According to Tsekouras & Li (2014), the ease of generating recommendations is important for a RA to succeed. Because users already provide enormous sets of data, a RA would be easy to create for myfitnesspal. By learning from the available data, Myfitnesspal can observe successful dieting behavior and match this to the users by recommending sports, food and diets (2). The app could provide messages such as: you are over your calorie goal for today. If you walk for 25 minutes you could still reach your goal. Or: You eat more fat than the average person of your height and age. Try to cut down 10 grams per day and lose up to 5kg. In this way, without asking more from its users, MyFitnessPal can help people lose weight even better.
(2) Murthi, B.P.S., & Sarkar, S. (2003). The role of the management sciences in research on personalizaBon. Management Science, 49(10)
Xiao, Bo, and Izak Benbasat. “E-commerce product recommendation agents: use, characteristics, and impact.” MIS Quarterly 31.1 (2007): 137-209.
Tsekouras,D.,& Li T. (2014). A Car & A Room For My Perfect Date: The Role Of Perceived Effort In Personalized RecommendaBons, Working Paper