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The development of mobile health (mHealth) assessment machine learning models requires data gathering studies in which smartphone sensor data is gathered continuously from users’ phones as they live their lives "In-the-wild". Periodically, participants annotate their sensor data with health, wellness and context labels, which serve as ground truth for machine learning models that can predict a user’s health from their smartphone data. However, as the scale of such studies increases, it becomes difficult to analyze such data and build machine learning models that can work across increasingly diverse, heterogeneous participants. Additionally, non-visual analytics approaches have limited interpretability. This paper innovatively takes a visual analytics approach instead. We propose Visualizing COMmunity Phenotypes (VICOMP), an interactive visual analytics framework for exploring complex population-level …