Research programme
LABDA (Learning Network for Advanced Behavioural Data Analysis)
Understanding how data derived from wearable technology can be used to identify effective changes in behaviour that are likely to result in health improvements
- Duration
- 2023 - 2027
- Contact
- Mitra Baratchi
- Funding
- EU-Marie Skłodowska-Curie Action (MSCA) Doctoral Network
- Partners
Amsterdam University Medical Center, location VUmc (coordinator), University of Southern Denmark, Norwegian University of Science and Technology, Leiden University, INSERM (France), SENS (Denmark), Glasgow Caledonian University, University of Leicester, Loughborough University
Smartwatches and other wearable technologies allow us to continuously collect data on our daily movements. Our goal is in this project is to understand how such data can be used to identify effective changes in behaviour that are likely to result in health improvements (e.g., improving cardiorespiratory fitness). This research, at the intersection of machine learning and causality, aims to develop algorithms that identify causal relationships between behavioral indicators and health outcomes.
Our project is part of the LABDA (Learning Network for Advanced Behavioural Data Analysis) European Doctoral. LABDA is an EU-funded Marie Skłodowska-Curie Action (MSCA) Doctoral network project, that brings together leading researchers in advanced movement behaviour data analysis at the intersection of data science, method development, epidemiology, public health, and wearable technology to train a new generation of creative and innovative public health researchers via training-through-research.
The main aims of LABDA are 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 contribute to reaching these aims. Together, 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, including 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.