Digital Technologies for Mental Health Monitoring and Intervention

Mental health disorders affect nearly one billion individuals worldwide, significantly diminishing quality of life and placing immense burdens on families, healthcare systems, and economies. Traditional methods of assessment (clinical interviews, self-reported questionnaires, and periodic evaluations) are often limited by subjectivity and lack the temporal resolution to capture the dynamic nature of conditions such as anxiety and depression.

This Research Topic invites original research articles, reviews, and perspectives that explore how emerging digital technologies are transforming the landscape of mental health care. In particular, we welcome contributions focused on wearable and contactless systems enhanced by artificial intelligence (AI) and machine learning (ML), which offer continuous, objective, and context-aware monitoring of neurophysiological and behavioral indicators.

Topics of interest include, but are not limited to:

- Physiological sensing (e.g., heart rate variability via wearables)
- Neurotechnological tools (e.g., EEG, TMS)
- Contactless monitoring (e.g., computer vision-based vital sign detection)
- Multimodal approaches integrating diverse data streams
- AI/ML algorithms for detection, prediction, and intervention
- Real-world deployment studies and clinical validations
- Ethical, privacy, and regulatory considerations

Submissions should emphasize not only technical innovation but also clinical relevance and translational impact. We particularly encourage studies that demonstrate high-performance outcomes (e.g., >90% accuracy in stress/anxiety detection) and discuss strategies for overcoming barriers such as data privacy, standardization, and long-term user engagement.

This topic aims to highlight interdisciplinary efforts at the intersection of digital health, neuroscience, engineering, and mental health, offering a platform to advance the science and implementation of next-generation mental health technologies.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Digital Health, Mental Health Monitoring, Artificial Intelligence, Wearable Devices, Machine Learning, Neurotechnology

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.