New Insights into Activity Patterns in Children, Found Using Functional Data Analyzes

• Activity Number: 324 - Applications of Functional Data Analysis to Medical Studies
• Type: Topic Contributed
• Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Jeff Goldsmith* and Xinyue Liu and Judith Jacobson and Andrew Rundle
• Companies: Columbia University and Analysis Group and Columbia University and Columbia University
• Keywords: Accelerometer data ; Splines ; Correlated errors ; Interactive graphics ; Smoothing

Abstract

Continuous monitoring of activity using accelerometers and other wearable devices provides objective, unbiased measurement of physical activity in minute-by-minute or finer resolutions. While common analyses of accelerometer data focus on single summary variables, such as the total or average activity count, there is growing interest in the determinants of diurnal profiles of activity. We illustrate the use of function-on-scalar regression models, in which 24-hour diurnal profiles are outcomes, by analyzing data collected in New York City from 420 children participating in a Head Start program. Covariates of interest include season, sex, BMI Z-score, presence of an asthma diagnosis, and mother's birthplace. In some cases, including shifted activity patterns for children of foreign-born mothers and time-specific effects of asthma on activity, associations exist for covariates that are not associated with average activity count.