A campus digital twin framework for visualizing and managing anxiety-related stress among college students

Anxiety-related stress among college students has become a significant public health concern, affecting student well-being, academic performance, and campus operations. However, the application of digital twins for campus mental health management remains limited. This study proposes a campus-scale digital twin framework to support monitoring, analysis, and management of anxiety-related stress among college students from a geospatial perspective.

The framework is composed of three integrated layers. The data acquisition and management layer integrates aggregated stress-related indicators derived from wearable devices, campus mental health service records, and campus geospatial data. The modeling and simulation layer represents spatiotemporal dynamics of student stress patterns and enables scenario-based analysis. The user application layer provides interactive visualization and decision support tools for campus administrators, mental health professionals, and students.

Based on the proposed framework, we developed a digital twin visualization system with four primary functions. These include continuous monitoring of anxiety-related stress trends, campus-scale stress heatmap visualization to support spatiotemporal assessment and targeted intervention, digital twin-supported management and spatial planning of campus mental health facilities, and personalized low-stress path navigation with restorative space recommendations.

As a proof-of-concept, the prototype illustrates the potential feasibility of applying campus digital twin technologies to aggregated privacy-preserving stress visualization and decision support, and highlights the value of integrating geospatial intelligence with digital twin technologies. This study contributes a scalable and transferable framework that supports preventive mental health strategies, informed decision-making, and sustainable, healthy campus environments. Further empirical validation is required to evaluate system accuracy, usability, effectiveness, and ethical acceptability.