Research
Summer activity patterns among teenage girls: harmonic shape invariant modeling to estimate circadian cycles
1 Biostatistics and Bioinformatics Branch, Division of Epidemiology Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, 6100 Executive Blvd, Bethesda, MD, 20892, USA
2 National Center on Sleep Disorders Research, National Heart Lung and Blood, 6701 Rockledge Drive, Bethesda, MD, 20892, USA
3 Prevention Research Branch, Division of Epidemiology Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, 6100 Executive Blvd, Bethesda, MD, 20892, USA
Journal of Circadian Rhythms 2012, 10:2 doi:10.1186/1740-3391-10-2
Published: 6 May 2012Abstract
Background
Physical activity as measured by activity counts over short time intervals across a 24 h period are often used to assess circadian variation. We are interested in characterizing circadian patterns in activity among adolescents and examining how these patterns vary by obesity status. New statistical approaches are needed to examine how factors affect different features of the circadian pattern and to make appropriate covariate adjustments when the outcomes are longitudinal count data.
Methods
We develop a statistical model for longitudinal or repeated activity count data that is used to examine differences in the overall activity level, amplitude (defined as the difference between the lowest and highest activity level over a 24 hour period), and phase shift. Using seven days of continuous activity monitoring, we characterize the circadian patterns and compare them between obese and non-obese adolescent girls.
Results
We find a statistically significant phase delay in adolescent girls who were obese compared with their non-obese counterparts. After the appropriate adjustment for measured potential confounders, we did not find differences in mean activity level between the two groups.
Conclusion
New statistical methodology was developed to identify a phase delay in obese compared with non-obese adolescents. This new approach for analyzing longitudinal circadian rhythm count data provides a useful statistical technique to add to the repertoire for those analyzing circadian rhythm data.
