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Mental Health in Urban Environments: Uncovering the Black Box of Person-Place Interactions Requires Interdisciplinary Approaches.

Martina KanningLi YiChih-Hsiang YangChristina NiermannStefan Fina
Published in: JMIR mHealth and uHealth (2023)
Living in urban environments affects individuals' mental health through different pathways. For instance, physical activity and social participation are seen as mediators. However, aiming to understand underlying mechanisms, it is necessary to consider that the individual is interacting with its environment. In this regard, this viewpoint discusses how urban health research benefits from integration of socioecological and interdisciplinary perspectives, combined with innovative ambulatory data assessments that enable researchers to integrate different data sources. It is stated that neither focusing on the objective and accurate assessment of the environment (from the perspective of spatial sciences) nor focusing on subjectively measured individual variables (from the public health as well as a psychosocial perspective) alone is suitable to further develop the field. Addressing person-place interactions requires an interdisciplinary view on the level of theory (eg, which variables should be focused on?), assessment methods (eg, combination of time-varying objective and subjective measures), as well as data analysis and interpretation. Firstly, this viewpoint gives an overview on previous findings addressing the relationship of environmental characteristics to physical activity and mental health outcomes. We emphasize the need for approaches that allow us to appropriately assess the real-time interaction between a person and a specific environment and examine within-subject associations. This requires the assessment of environmental features, the spatial-temporal behavior of the individual, and the subjective experiences of the situation together with other individual factors, such as momentary affective states. Therefore, we finally focused on triggered study designs as an innovative ambulatory data assessment approach that allows us to capture real-time data in predefined situations (eg, while walking through a specific urban area).
Keyphrases
  • mental health
  • data analysis
  • physical activity
  • public health
  • electronic health record
  • big data
  • healthcare
  • blood pressure
  • sleep quality
  • bipolar disorder
  • machine learning
  • risk assessment
  • lower limb
  • life cycle