Calibration of an Accelerometer Activity Index Among Older Women and Its Association with Cardiometabolic Risk Factors

• Activity Number: 319 - Innovative Statistical and Machine Learning Methods for Wearable Device Data
• Type: Topic Contributed
• Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Medical Devices and Diagnostics
• Author(s): Guangxing Wang* and Sixuan Wu and Kelly Evenson and Ilsuk Kang and Michael LaMonte and John Bellettiere and I-Min Lee and Annie Green Howard and Andrea LaCroix and Chongzhi Di
• Companies: Fred Hutchinson Cancer Research Center and Inspur and UNC - Chapel Hill and Fred Hutchinson Cancer Research Center and University at Buffalo – SUNY and UCSD and Harvard T.H. Chan School of Public Health and UNC - Chapel Hill and UCSD and Fred Hutchinson Cancer Research Center

Abstract

Traditional summary metrics provided by accelerometer device manufacturers, known as counts, are proprietary and manufacturer specific, making them difficult to compare studies using different devices. Alternative summary metrics based on raw accelerometry data have been introduced in recent years. However, they were often not calibrated on ground truth measures of activity-related energy expenditure for direct translation into continuous activity intensity levels. Our purpose is to calibrate, derive, and validate thresholds among women 60 years and older based on a recently proposed transparent raw data based accelerometer activity index (AAI), and to demonstrate its application in association with cardiometabolic risk factors.