Over the past few years, there has been increasing research and clinical interest in the role of digital phenotyping in mental health. Digital phenotyping enables the collection and analysis of data from a variety of digital tools and techniques (e.g. wearable sensors, mobile devices, social media, virtual reality) to capture mental health-related behaviors and symptoms in real-time, which offers a practical tool for remotely monitoring and assessing mental health and also has the potential to increase access to more customized and responsive mental health care.
The clinical value of digital phenotyping is expected to create closed-loop systems that use digital phenotyping data to trigger personalized intervention. However, until now, there has been little evidence to show that digital phenotyping can be implemented successfully in real clinical practice, as substantial uncertainties remain.
Recent studies have recognized the development of appropriate digital phenotyping data analysis methods and the integration of digital phenotyping and digital intervention as key research priorities to accelerate the clinical adoption of digital phenotyping in mental health. Therefore, this Research Topic aims to introduce new advances in these areas, as well as providing directions for future research.
In this Research Topic, we welcome original articles, reviews, and case reports regarding, but not limited to, the following specific topics:
• innovative digital phenotyping methods for predicting mental health outcomes
• multimodal data fusion methods in digital phenotyping
• fairness, bias, and generalizability of digital phenotyping
• framework, method, and practice in the integration of digital phenotyping and digital intervention
• standards and evaluation methods in digital phenotyping and digital intervention
• ethical issues in making use of digital phenotype data to trigger digital intervention.
Over the past few years, there has been increasing research and clinical interest in the role of digital phenotyping in mental health. Digital phenotyping enables the collection and analysis of data from a variety of digital tools and techniques (e.g. wearable sensors, mobile devices, social media, virtual reality) to capture mental health-related behaviors and symptoms in real-time, which offers a practical tool for remotely monitoring and assessing mental health and also has the potential to increase access to more customized and responsive mental health care.
The clinical value of digital phenotyping is expected to create closed-loop systems that use digital phenotyping data to trigger personalized intervention. However, until now, there has been little evidence to show that digital phenotyping can be implemented successfully in real clinical practice, as substantial uncertainties remain.
Recent studies have recognized the development of appropriate digital phenotyping data analysis methods and the integration of digital phenotyping and digital intervention as key research priorities to accelerate the clinical adoption of digital phenotyping in mental health. Therefore, this Research Topic aims to introduce new advances in these areas, as well as providing directions for future research.
In this Research Topic, we welcome original articles, reviews, and case reports regarding, but not limited to, the following specific topics:
• innovative digital phenotyping methods for predicting mental health outcomes
• multimodal data fusion methods in digital phenotyping
• fairness, bias, and generalizability of digital phenotyping
• framework, method, and practice in the integration of digital phenotyping and digital intervention
• standards and evaluation methods in digital phenotyping and digital intervention
• ethical issues in making use of digital phenotype data to trigger digital intervention.
