Recently there has been an increase in the collection of intensive longitudinal data (ILD), or data collected at many occasions over a relatively short period of time. Instead of investigating slow-developing characteristics as in more traditional longitudinal studies, ILD allow researchers to look into daily patterns, variation, or quickly-changing subject attributes. The increased availability of remote data collection devices such as smartphones and activity monitors has led to a corresponding increase in the collection of such data. When participation in a certain activity is of interest, applied researchers are often interested in using ILD to model the propensity of participation and also the risk of the event. Such interests will naturally lead to logistic, survival, and recurrent events modeling techniques. Important issues to consider with ILD include time-dependent predictors, differing time scales between predictors and response, and changing frequency of events. These issues and others will be discussed in the context of using ILD to study likelihood and risk of events, with applications to marijuana and alcohol co-use.