Statistical analysis will compare baselines scores (week 1) to midpoint scores (week, 3), and scores at the endpoint of the study (week 5). Response to treatment will be defined as an improvement of 30% or greater on the HAM-A and HAM-D17 at the endpoint of the study. The impact of prescription medication as a confounding variable in relation to the effect of CES on anxiety and depression will also be examined. Data analysis will consist of a repeated measures design that will likely use a general linear model in SPSS and hierarchical linear and quadratic growth models to assess individual change. The reason for this approach is that hierarchical analysis of individual change is advantageous when there are multiple repeated measure data patterns because it affords the research an opportunity to combine into a single analysis (Raudenbush & Byrk, 2002). We will estimate both the individual growth and mean growth for each point in the time series. Cross-validation of quadratic will occur via the estimation of a piecewise linear growth models to assess whether growth is more variable for one period over another during the study time frame.