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

Jeff Goldsmith¹, Xinyue Liu, Judith S Jacobson

¹1Department 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.

Medicine and Science in Sports and Exercise

|May 17, 2016

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Functional data analysis (FDA) models diurnal activity profiles from accelerometers, revealing associations not seen in average activity. This approach highlights factors like asthma and maternal birthplace influencing children's daily movement patterns.

Area of Science:

Biostatistics
Wearable Technology
Pediatric Health

Background:

Accelerometers offer objective, high-resolution physical activity monitoring, valuable in diverse health studies.
Traditional analyses often focus on summary statistics, overlooking detailed activity patterns.
There's a growing need to understand factors influencing diurnal activity profiles.

Purpose of the Study:

To apply functional data analysis (FDA) to model 24-hour physical activity profiles.
To investigate associations between covariates and diurnal activity patterns in children.
To identify factors influencing the timing and nature of physical activity.

Main Methods:

Utilized functional data analysis (FDA) regression models.
Treated complete 24-hour diurnal activity profiles as outcomes.

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Analyzed data from 420 children in a New York City Head Start program, considering season, sex, BMI z-score, asthma, and mother's birthplace.

Main Results:

Identified meaningful associations between covariates and diurnal activity profiles.
Found significant effects for covariates not related to average activity counts.
Observed shifted activity patterns in children of foreign-born mothers and time-specific effects of asthma.

Conclusions:

FDA offers a robust statistical framework for analyzing the timing of activity.
This methodology can uncover covariate effects on activity patterns missed by traditional methods.
The study encourages wider adoption of FDA for activity profile analysis, with code made available.