Comparing Digital Phenotype Features and Depression Detection in Student Wellness Applications

Abstract

The global escalation of mental health disorders among university students necessitates innovative approaches to early detection and intervention. Traditional screening methods, primarily reliant on self-reported questionnaires, are often limited by recall bias, low engagement, and temporal sparsity. Digital phenotyping, defined as the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices, offers a transformative paradigm. This paper presents a comprehensive registry analysis investigating the efficacy of digital phenotype features extracted from student wellness applications for depression detection. By leveraging a large-scale, anonymized registry of mobile sensor data and application interaction logs, we evaluate the predictive validity of behavioral, spatial, and temporal markers. The analysis systematically reviews passive sensing modalities, including geolocation variance, screen state transitions, and keystroke dynamics, correlating these features with validated clinical depression instruments. Our methodology employs advanced data processing techniques to handle the high dimensionality and missingness inherent in passive sensor data. Subsequent algorithmic analysis demonstrates that multimodal feature fusion significantly enhances classification performance compared to unimodal approaches. Furthermore, the discussion addresses critical ethical considerations, such as data privacy and algorithmic bias, which are paramount in deploying continuous monitoring systems in educational environments. The findings underscore the potential of digital phenotyping to augment traditional mental health assessments, providing a scalable, unobtrusive mechanism for identifying at-risk students and facilitating timely therapeutic support.