New Methods For Analyzing Wearable Device Data

Oregon Convention Center 

Modern longitudinal data from wearable devices consist of biological signals at high-frequency time points and offer unparalleled opportunities for discovering new health insights. Distributed statistical methods have emerged as a powerful tool to overcome the computational burden of estimation and inference with these intensively measured outcomes, but methodology for distributed functional regression remains limited. Developing functional regression tools is critical to appropriately modeling and understanding these data. We propose distributed estimation and inference procedures that efficiently estimate functional parameters for intensively measured longitudinal outcomes and overcome computational difficulties by leveraging recent developments in high performance computing platforms. We demonstrate the practicality of our approaches through application of our methods to accelerometer data from the NHANES data set.