As a consequence of this work, we expect to be able to analyze the actigraphy data of the BCOPS study with more accuracy and with more tools to find differences not only in sleep quality, but also in the corresponding disease association analyses. Using the K-statistic makes determining the quality of data sets easier and faster, as opposed to manually viewing each data set. SEM is capable of reducing the set of possible variables by grouping them and allowing investigators to select the ones best suited for that particular study. Nonlinear analysis, waiting time distributions, and the ability to use many nonstandard variables give research investigators more tools to identify differences in participants' sleep quality and in study populations. They also give researchers the ability to conduct more in-depth statistical and mathematical analysis. Ultimately, our work should help make wrist actigraphy more accurate and less expensive for research investigators and physicians who study and treat the millions of workers around the United States who suffer from sleep disorders.