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Wearable sensors for the assessment of physical and
Healthy eating behaviour and healthy physical activity behaviour are associated with a lower risk of developing non-communicable diseases and obesity. Unfortunately, the combined measurement of sitting or exercise with simultaneous dietary or eating behaviour is currently still very difficult. In order to offer solutions for future science, WEALTH is developing a new methodology. It uses an innovative method to process accelerometry data with ecological momentary assessment (EMA), where movement and eating behaviour are measured simultaneously within its context. This approach provides a comprehensive understanding of the context-specific relationship between physical activity and eating behaviour.
WEALTH will also develop a web-based infrastructure for processing passive sensor data from commercially available wearables, among others, to establish a system for the estimation of physical activity and eating behaviour and better surveillance of health-related behaviours. For this purpose, physical activity data will be collected both according to a structured protocol and under real environmental conditions from up to 600 adults in four European study centres in Germany, Ireland, France, and Czech republic. In Bremen, 150 participants will be invited.
Context-specific behavioural information will be recorded using EMA prompts. Machine learning procedures based on the collected raw sensor data will be used to train and validate the estimation of classified movement and eating behaviours. Finally, the behaviours will be evaluated to demonstrate the feasibility of the combined assessment methods. WEALTH will publish the results or the methods and procedures in a toolbox.
In the end, WEALTH will deliver an easy-to-use and openly accessible system for measuring physical activity and eating behaviours that will be of high value for future public health surveillance. The project is funded under the Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL).