Abstract
Physical activity trajectories from the Trial of Activity in Adolescent Girls (TAAG) capture the various exercise habits over female adolescence. Previous analyses of this longitudinal data from the University of Maryland field site, examined the effect of various individual-, social-, and environmental-level factors impacting the change in physical activity levels over 14 to 23 years of age. We aimed to understand the differences in physical activity levels after controlling for these factors. Using a Bayesian linear mixed model incorporating a model-based clustering procedure for random deviations that does not specify the number of groups a priori, we find that physical activity levels are starkly different for about 5% of the study sample. These young girls are exercising on average 23 more minutes per day.
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Young, D. R., Mohan, Y. D., Saksvig, B. I., Sidell, M., Wu, T. T., Cohen, D.: Longitudinal predictors of moderate to vigorous physical activity among adolescent girls and young women. Under review. (2017)
Ng, S. K., McLachlan, G. J.,Wang, K., Ben-Tovim Jones, L., Ng, S.W.: A mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics 22(14), 1745 (2006)
Zhou, C., Wakefield, J.: A Bayesian mixture model for partitioning gene expression data. Biometrics 62(2), 515–525 (2006)
Richardson, S., Green, P. J.: On Bayesian analysis of mixtures with an unknown number of components (with discussion). J. Roy. Stat. Soc. B 59(4), 731–792 (1997)
Dahl, D. B.: Model-based clustering for expression data via a Dirichlet process mixture model. Bayesian Inference for Gene Expression and Proteomics. 201–218 (2006)
Flegal, J. M., Hughes, J., Vats, D.: mcmcse: Monte Carlo standard errors for MCMC. R package version 1.2–1 (2016)
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LaLonde, A., Love, T., Young, D.R., Wu, T. (2023). Clustering Adolescent Female Physical Activity Levels with an Infinite Mixture Model on Random Effects. In: Brito, P., Dias, J.G., Lausen, B., Montanari, A., Nugent, R. (eds) Classification and Data Science in the Digital Age. IFCS 2022. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-031-09034-9_25
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DOI: https://doi.org/10.1007/978-3-031-09034-9_25
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