More than 50 percent of the world’s population currently lives in urban areas. Urban environments, like Amsterdam, are not only economically attractive and exciting, but also more challenging and stressful than rural environments (“urban stress”), which may affect mental health. Many mental health problems in an urban context (addiction, anxiety, depression) and related problems (e.g., loneliness) do not come in isolation, but in clusters of symptoms.

In symptom network models, symptoms are not seen as indicators of an underlying latent factor, but the causal interplay of the symptoms is the mental disorder. Importantly, in network models, it is also possible to investigate the causal interplay between psychological symptoms and the external factors related to these symptoms. During this PhD project, we aim 1) to develop a network-based method to map an individual's interactions between symptoms and external (urban) factors influencing these symptoms (e.g., social interaction, physical contexts, etc.) and 2) to develop and test personalized interventions based on this method. The symptom networks are identified through modelling of rich temporal real-time data assessed with ecological momentary assessment (EMA).

The interventions are based on the retrieved causal interactions and developed with clients and clinicians using a collaborative citizen science approach. These personalized interventions are tested in multiple urban samples: a student sample in higher education, and a vulnerable sample of adolescent ethnic minorities who are either undergoing treatment in youth care or are in juvenile detention.