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


Medicine & Science in Sports & Exercise: September 2016 - Volume 48 - Issue 9 - p 1723–1729
doi: 10.1249/MSS.0000000000000968

Introduction/Purpose Continuous monitoring of activity using accelerometers and other wearable devices provides objective, unbiased measurement of physical activity in minute-by-minute or finer resolutions. Accelerometers have already been widely deployed in studies of healthy aging, recovery of function after heart surgery, and other outcomes. Although 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.

Methods We use tools from functional data analysis (FDA), an area with an established statistical literature, to treat complete 24-h diurnal profiles as outcomes in a regression model. We illustrate the use of such models by analyzing data collected in New York City from 420 children participating in a Head Start program. Covariates of interest include season, sex, body mass index z-score, presence of an asthma diagnosis, and mother’s birthplace.

Results The FDA model finds several meaningful associations between several covariates and diurnal profiles of activity. In some cases, including shifted activity patterns for children of foreign-born mothers and time-specific effects of asthma on activity, these associations exist for covariates that are not associated with average activity count.

Conclusion FDA provides a useful statistical framework for settings in which the effect of covariates on the timing of activity is of interest. The use of similar models in other applications should be considered, and we make code public to facilitate this process.

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1Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY; 2Analysis Group, New York, NY; and 3Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY

Address for correspondence: Jeff Goldsmith, Ph.D., Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 W 168th Street, New York, NY 10032; E-mail:

Submitted for publication November 2015.

Accepted for publication April 2016.

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© 2016 American College of Sports Medicine