Overweight and obesity affect the entire population. On a day-to-day basis, this problem relates to what people eat, why people eat what they eat and their day-to-day food choices. Towards e-health solutions that support self-management of (health) food related practices, a better understanding of eating habits is needed. Validated food measurement instruments are challenged to generate such holistic knowledge. Primarily due to their limited scope (mostly descriptive) and their long and time consuming demands. The FoodSampler research project aims to explore food informatics strategies to engage people in generating contextual knowledge of their food behaviour. It targets an increasing vulnerable group in prevention of overweight and obesity: older adults with a low Socio-Economical Status (SES). The approach combines Mixed Method Research (MMR), Research through Design (RtD) and Living Labs research. In this way a user-centric innovative process is implemented, involving end-users and experts in cycles of exploring, prototyping and testing mixed food informatics strategies. By means of contextual research in-the-wild, co-design sessions, and in-situ interventions the project seeks for direct benefits to involve the targeted group as collaborators of the design process. In FoodSampler end-users and experts will co-generate knowledge on best practices for mixed food informatics and the values of the generated knowledge to explain food behaviour.