Statistical methods for integrating accelerometry data from multiple sources

Oregon Convention Center 

Wearable devices such as accelerometers are increasingly assessed in many observational and interventional studies. However different studies often have inconsistent choices of device brands, wear positions and processing pipelines, making it challenging to compare and combine data across studies. Since accelerometry data are often recorded continuously over multiple days and have complex the time dependency structures, the existing data harmonization methods are inapplicable. We propose a new method to integrate multiday minute-level physical activity datasets from two different studies and model the shared information by common eigenvalues and eigenfunctions while allowing for batch-specific scale and rotation. The methods are applied on different batches of NHANES accelerometry data and the results demonstrate the superior performane of our proposed method in removing batch effects while preserving biological signals compared to existing approaches.