The main aim of LABDA is to establish novel methods for advanced 24/7 movement behaviour data analysis of sensor-based data, examine the added value of advanced behavioural data analysis and multi-modal data for predicting health risk, and facilitate the use and interpretability of the advanced methods for application in science, policy, and society.

Via training-through-research projects, 13 doctoral fellows will establish novel methods for advanced 24/7 movement behaviour data analysis and assess the added value of linking multimodal data. They will develop a joint taxonomy to enable interoperability and data harmonisation. Results will be combined in an open-source LABDA toolbox of advanced analysis methods, which will include a decision tree to guide researchers and other users to the optimal method for their (research) question. The open-source toolbox of advanced analysis methods will lead to optimised, tailored public health recommendations and improved personal wearable feedback concerning 24/7 movement behaviour.


The LABDA project answers the need to improve high-dimensional data analysis in epidemiology. The increased and growing data collection in epidemiological research has highlighted the importance of innovative methods that allow the analysis of high-dimensional data while responding to epidemiological research questions. Sensor-based movement behaviour data are a good example of such data. Often, 1GB of data is collected for each study participant, from which relevant information for health needs to be identified.

When developing advanced methods, it is important to interpret findings while considering the knowledge of movement behaviour science. This requires a close interplay between data scientists and experts in human movement science, epidemiology, and public health. To advance wearable sensor data analyses and understand how different movement profiles impact health, there is a critical need for highly skilled researchers that can integrate the knowledge of data science, method development, epidemiology and public health policy, including the theoretical foundations, challenges, and limitations of various advanced analysis methods.

Therefore, LABDA aims to build a bridge between the different fields and disciplines involved in sensor-based 24/7 movement behaviour analysis: from industry-developing wearables to method engineers, researchers, and policymakers – thereby improving communication, inspiration, and knowledge exchange.