NIH R01 · 2024
PROJECT SUMMARY We propose novel statistical and machine learning methods for processing and analyzing accelerometer data for studying physical activity, sedentary behavior, and sleep and their effects on outcomes such as cardiovascular health. Methods to accurately estimate and characterize physical activity, sedentary behavior and sleep are crucially needed. Accelerometers have been widely adopted as the standard objective measure of movement in free-living humans. Recent advances have spawned instruments that collect enormous amounts of data that has far outpaced the research community’s ability to meaningfully interpret them. Current studies rely on outdated methods for identifying…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.