The number of people with diabetes rose from 108 million in 1980 to 422 million in 2014 [1]. The global prevalence of diabetes among adults above 18 years has risen from 4.7% in 1980 to 8.5% in 2014 [1]. The leading risk factor for type 2 diabetes is obesity [1]. Obesity is usually caused by overeating and moving too little. According to the research in the addiction and nutrition field, food is the cause of obesity, particularly palatable and energy-dense ('addictive') food [2]. Moreover, addictive behavior for food is one of the ways to regulate affective states, which are caused due to stress [3]. Affective states such as anxiety, anger, depressive mood, and other negative emotions have a correlation to eating behavior [4].
In this project, user’s context, behavior and affective state are monitored with the help of wearable sensors. Correlations between context, behavior, affective states and eating habits are investigated. The project is based on an existing sensor data collection application and will be extended with respect to further sensor integration and data analysis functionality.
The project is done in collaboration with the Oviva AG. Oviva is a startup based in Potsdam with branches in Switzerland, France, and the UK. Oviva provides and supports digital solutions for patient-oriented therapy for changing dietary behavior with a mobile application. One aspect of Oviva's treatment has been implemented with the help of neural networks, whereby the patient sends a picture of their meal and the network the food category. Based on the classified food categories, the nutritionist assigned to the patient supervises the patient’s eating behavior and provides intervention if necessary.
