Abstract

Digital phenotyping has emerged as a promising paradigm for continuous and objective mental health monitoring using wearable and smartphone technologies. However, existing studies remain fragmented in terms of methodological consistency, model validation, and clinical applicability. This paper presents a systematic and performance-oriented review of digital phenotyping systems, synthesizing findings from 62 studies, including 16 high-relevance articles. Unlike prior reviews, this study introduces a structured computational framework that characterizes the end-to-end pipeline of digital phenotyping systems, encompassing data acquisition, feature engineering, machine learning modeling, and clinical decision support. Comparative analysis reveals that predictive models achieve accuracies ranging from 72% to 82%, with probabilistic and supervised learning approaches outperforming traditional regression techniques. However, significant gaps persist in external validation, reproducibility, and multimodal data integration. The findings highlight the need for standardized benchmarking protocols, improved algorithm transparency, and adaptive AI-driven intervention mechanisms. By bridging methodological, computational, and ethical dimensions, this study provides a foundation for the design and evaluation of next-generation digital mental health systems. This study contributes to the advancement of computational methods for scalable, data-driven mental health monitoring systems.

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