Continuous glucose monitors (CGMs) generate frequent glucose measurements, and numerous studies suggest that these devices may improve diabetes management. These devices support behavior change and self-management by giving people with diabetes real-time visibility into how behavioral and lifestyle factors, i.e., meals, physical activity, sleep, stress, and medication adherence, drive their glucose levels. While earlier studies have shown that individual's actions can influence their CGM data, it has not been clear whether CGM data can provide information about these actions. This is the first study to show on a large cohort that CGM can provide information about sleep and physical activities. We first train a neural network model to determine the sequence of daily activities from CGM signals, and then extend the model to use additional data, such as individual demographics and medical claims history. Using data from 6981 participants in a Type 2 diabetes (T2D) management program, we show that a model combining an individual's CGM, demographics, and claims data is highly predictive of sleep (AUROC 0.947), and moderately predictive of a range of physical activities (AUROCs of 0.722-0.817). These results show that CGM may have wider utility as a tool for behavior change than previously known.