We will conduct random coefficients analysis (RCA) to model muscle and functional mobility across time, as well as covariates that could affect outcomes, such as age, sex, physical activity, and dietary intake. RCA models trajectories from assessments nested within subjects; test of condition are represented by the interaction between a time factor and treatment condition. The RCA avoids the many pitfalls associated with traditional repeated measures ANOVA: It adjusts for within-individual dependence or autocorrelation in the data, can model nonlinear growth, does not require fixed spacing among assessments, and accommodates missing values over time. By using any available data across time, the RCA will limit the effects of missing data, reducing bias and increasing power. Based on our preliminary data, we estimate that we will be able to collect >92% of all data points.