Mobile health (mHealth) methods allow researchers to monitor study participants in their natural environments in order to improve health-related outcomes through behavior change. MHealth investigators are interested in understanding the feasibility of supplementing or even replacing actively collected data with passively collected data to reduce participant burden, while also increasing the temporal resolution of the data. In this work, we propose a novel Bayesian dynamic functional variable selection method to explore the relations between multimodal mHealth data collected on different time scales. Specifically, our approach leverages spiked hierarchical species sampling priors to identify critical moments when a participant experiences momentary spikes in a low-frequency outcome, characterize high-frequency outcome trajectories which are related to these critical moments, and cluster the relational trends to explore potential subpopulations. We introduce continuous-time multistate Markov model priors to inform selection based on information learned at previous assessments. We demonstrate the variable selection and clustering performance of our model in various simulation settings motivated by the data structures found in mHealth studies. We then apply our model to multimodal intensive longitudinal data collected in the Pathways between Socioeconomic Status and Behavioral Cancer Risk Factors Study to explore relations between physical activity passively collected with accelerometers and mood actively collected with ecological momentary assessment methods.