A Bring-Your-Own-Device (BYOD) model for contributing mobile health (mHealth) data enables real-world data collection as patients go about their daily activities. To date, most mHealth research studies provision a specific wearable device (i.e., Fitbit) and have a constrained study period during which data is collected. A BYOD mHealth model allows for capturing data from patients’ routine lives and has efficiencies at scale, allowing researchers to better understand patient trajectories in a real-world deployment of devices. There are growing examples of BYOD data contribution for the purposes of research including the PCORnet Inflammatory Bowel Diseases (IBD) Partners (formerly Crohn’s and Colitis Foundation of America) patient-powered research network, and NIH’s All of Us Research Program, where participants can currently contribute their Fitbit data. IBD Partners allows for a wide range of wearable devices and apps to be connected to our research platform. This facilitates mHealth data contribution for those who participate in our longitudinal Internet cohort study. These mHealth contributors can connect different devices over time and can view their data trends in the IBD Partners patient portal. Participants also contribute self-reported survey data on health outcomes, such as disease activity, as well as on patient- reported outcomes such as depression and anxiety. While BYOD has many benefits, there are also challenges due to the diversity of both devices/apps and usage patterns that come with real-world data generation. As this BYOD data contribution model is still an emerging one, there is little known about how many patients will choose to contribute their mHealth data, and how those patients may differ from those who do not. We examine which brands are represented across mHealth contributors within the cohort, the patterns for device wear-time (usage) among these participants, and blocks of missingness where devices/apps were not used. In this oral abstract, we present an overview of the characteristics of a BYOD mHealth study, Precision VISSTA, which is an NIH-funded study that seeks to develop preprocessing, machine learning, and data visualization methods for mHealth data to generate precision health recommendations for patients with IBDs as the initial use case.