Sensor-Equipped Digital Technologies for Monitoring and Detecting Depressive Disorders: A Systematic Review

Abstract

Background and Aims

Depression is a common and chronic mental health problem, and the diagnosis and management of depression require continuous monitoring. In this review study, sensor-based digital tools for the diagnosis and management of depression were examined. The effectiveness, usability, and limitations of these tools were evaluated and discussed.

Methods

This systematic review was conducted in November 2025 using databases including IEEE, PubMed, Scopus, and Web of Science. The search was performed in accordance with PRISMA guidelines. Peer-reviewed studies that had used digital technologies for the diagnosis, monitoring, or intervention in depression were identified. Eligible articles were included in the study after full-text assessment.

Results

In total, 41 studies met the inclusion criteria. Sample sizes in the studies ranged from 5 to 3936 participants. The study populations covered a wide range, from adolescents to older adults. Most investigations addressed various depressive disorders; some also referred to bipolar disorders or psychological distress. Overall, digital tools were categorized into smartphones, wearables, hybrid systems, and innovative platforms. These tools often used sensors such as global positioning systems (GPS), accelerometers, and heart rate monitors. Speech and facial analyzers were also employed. Data collection was carried out through active and passive monitoring of behavior, physiology, and mood.

Conclusion

Sensor-based digital tools have the capability to monitor and record the complex symptoms of depression. These data can also be used for personalized care. However, robust and standardized validation is required for clinical implementation. Future research should focus on long-term engagement and scalability of these tools while maintaining confidentiality and sensitivity, with an emphasis on specific types of depression.