Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors

CONCLUSION: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health.TRIAL REGISTRATION: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.PMID:38671485 | DOI:10.1186/s12966-024-01585-8

Source: Health Physics - April 26, 2024 Category: Physics Authors: Rong W Zablocki Sheri J Hartman Chongzhi Di Jingjing Zou Jordan A Carlson Paul R Hibbing Dori E Rosenberg Mikael Anne Greenwood-Hickman Lindsay Dillon Andrea Z LaCroix Loki Natarajan Source Type: research