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Review
. 2025 Aug 7;25(15):4858.
doi: 10.3390/s25154858.

Integration of Multi-Modal Biosensing Approaches for Depression: Current Status, Challenges, and Future Perspectives

Xuanzhu Zhao  1 , Zhangrong Lou  1 , Pir Tariq Shah  1 , Chengjun Wu  2 , Rong Liu  3 , Wen Xie  1 , Sheng Zhang  4
Affiliations
Review

Integration of Multi-Modal Biosensing Approaches for Depression: Current Status, Challenges, and Future Perspectives

Xuanzhu Zhao et al. Sensors (Basel). .

Abstract

Depression represents one of the most prevalent mental health disorders globally, significantly impacting quality of life and posing substantial healthcare challenges. Traditional diagnostic methods rely on subjective assessments and clinical interviews, often leading to misdiagnosis, delayed treatment, and suboptimal outcomes. Recent advances in biosensing technologies offer promising avenues for objective depression assessment through detection of relevant biomarkers and physiological parameters. This review examines multi-modal biosensing approaches for depression by analyzing electrochemical biosensors for neurotransmitter monitoring alongside wearable sensors tracking autonomic, neural, and behavioral parameters. We explore sensor fusion methodologies, temporal dynamics analysis, and context-aware frameworks that enhance monitoring accuracy through complementary data streams. The review discusses clinical validation across diagnostic, screening, and treatment applications, identifying performance metrics, implementation challenges, and ethical considerations. We outline technical barriers, user acceptance factors, and data privacy concerns while presenting a development roadmap for personalized, continuous monitoring solutions. This integrative approach holds significant potential to revolutionize depression care by enabling earlier detection, precise diagnosis, tailored treatment, and sensitive monitoring guided by objective biosignatures. Successful implementation requires interdisciplinary collaboration among engineers, clinicians, data scientists, and end-users to balance technical sophistication with practical usability across diverse healthcare contexts.

Keywords: biosensors; depression; multi-modal integration; temporal dynamics; wearable technologies.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Conceptual framework of multi-modal biosensing for depression.
Figure 2
Figure 2
Classification of biosensors for depression monitoring.
Figure 3
Figure 3
Sensor fusion strategies for depression monitoring.
Figure 4
Figure 4
Clinical implementation and validation pathway.
Figure 5
Figure 5
Technical and implementation challenges in multi-modal biosensing for depression.
See this image and copyright information in PMC

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