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

Abstract

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. 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. Methods We use tools from functional data analysis (FDA), an area with an established statistical literature, to treat complete 24-hour 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, BMI 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.

Modeling Temporal Variation in Physical Activity Using Functional Principal Components Analysis

“…The use of functional data analysis is gradually becoming more common in the analysis of activity patterns in children and adolescents [9,11,23], where a day structured around school hours makes assessment of circadian activity patterns more tangible. Among adults, a recent study by [28] applied the functional principal component analysis technique to a cohort of older men to examine associations between diurnal patterns of accelerometry measured physical activity with sleep, cognitive function, and mortality.…”

Morning surge in blood pressure using a random‐effects multiple‐component cosinor model

“…Zero scores for an individual would result in their trajectory following the mean pattern. The scores would usually be estimated through numerical integration but with sparse data, as in the case of ABPM (compared with high sampling frequency data, eg, every second, that is often associated with functional data), the approximation is sometimes deemed inadequate, and in this case the scores were estimated by the principal component analysis through conditional expectation method . Using this method the scores are estimated for each individual using their repeated measures while borrowing strength from the cohort with sample estimates of the mean function, covariance, eigenvalues, and eigenfunctions …”

Expert’s Choice: 2018’s Most Exciting Research in the Field of Pediatric Exercise Science

“…However, the multivariate pattern analysis approach used by Aadland et al (3) can. Like other statistical methods recently applied to accelerometer data analysis (eg, 69,105) it is commonly used in other disciplines, but not in physical activity research.…”