Because the standard interpretation of the accelerometer data does not provide enough insight about the physical activity (PA) of the study participants in free-living conditions, further analysis to combine wearable and heath data requires an experienced data scientist. In the past two years we generated labelled activity data in a validation study of 35 older adults, using accelerometers and physiological sensors. Using this dataset and state of the art machine learning algorithms, together with LIACS we created multiple activity recognition models, which can be applied to free living data collections. We are now ready to interpret free-living physical activity profiles in the LUMC studies and combine them with health parameters data, such as MRI data on brain ageing and metabolic health measured by traditional clinical parameters and metabolomics.