 Change in Primary Outcome6mo β0 β1PrimaryOutcomebaseline β2Intervention b0 e Including baseline score as a covariate in an analysis of covariance ANCOVA, in our case a GLLM ANCOVA analysis, is a more powerful test than a group comparison of baseline to post-intervention change. Additionally, ANCOVA is not distorted by regression towards the mean bias, whereas a change analysis is. To further explore intervention effect, investigators will fit GLMMs that: 1 adjust for baseline covariates, considered a priori, to be relevant to change in MVPA; 2 adjust for baseline variables distributed differently between Healthy Lifestyle and Healthy Finance arms; 3 test interaction terms between treatment group and other covariates; and 4 examine completers only. While investigators will make every effort to minimize attrition at the center and participant level; some drop out is expected. Investigators will explore the potential bias from non-ignorable, non-response using two methods: 1 compare respondents and non- respondents to see if they differ systematically on values of non-missing variables; and 2 assess whether data are missing completely at random MCAR, or missing at random MAR or missing not at random MNAR - non-ignorable. If missing data are ignorable MAR andor MCAR, investigators will consider imputing missing endpoint data using multiple imputation techniques. However, it is often hard to determine if missing data are ignorable; therefore, investigators will consider selection models MNAR models to assess if data are missing not at random and estimate the effect of the interventions after removing selection bias. Investigators will also consider using pattern mixture models to assess the sensitivity of our results to various assumptions of missing data patterns. Investigators previous work in child care centers and worksites demonstrate good retention. 