Regression diagnostics, residual plots, and exploratory analyses will be performed to find appropriate transformations for all variables to satisfy linearity, homogeneity of variances, and normality assumptions. Descriptive statistics consisting of frequency tables and percentages for categorical variables, and means, standard deviations, medians, and ranges for continuous variables will be tabulated and presented by intervention group and time point. Exact 95% confidence intervals (CIs) will be provided for the estimated proportions and approximate 95% CIs for estimated means. The primary outcome of the intervention effect on fast 400-meter walk gait speed will be analyzed using an ANCOVA model fit with the main effect of treatment arm and adjusted for baseline values of fast 400-meter walk gait speed. The primary test will be covariate-adjusted main treatment effect of the weight loss intervention compared to the control group, and we assume a Type I error rate of 0.05 for the two-sided alternative hypothesis. Secondary measures of body composition will be analyzed similarly, using baseline values of the particular body composition measure as a covariate in the outcome model. Other data including feasibility and compliance data collected in this study will be used to inform future studies of weight loss in older adults. All statistical analyses will be conducted assuming intent-to-treat so that participants will remain assigned to the initial randomization group regardless of treatment adherence, and participants who do not adhere or discontinue treatment will still be encouraged to provide outcomes data. Our group has past success in retaining participants by maintaining frequent contact with individuals who encounter barriers to participation and creatively accommodate and counsel, as necessary, to encourage the maximum degree of participation. To ensure the results are not overly influenced by missing data, a multiple imputation sensitivity analysis of the primary outcome will be performed using both baseline and post-randomization characteristics to inform the missing data imputation, assuming observations are missing at random.