Adolescence is an important turning point for children to grow into adults. During this time, mental health is very important for cultivating personality, forming positive mental states (e.g. self-confident and self-reliant), and setting up ideals, beliefs and life goals. Consequently, it is very necessary to evaluate and monitor teenagers’ mental health status. As one of the traditional psychometric methods, self-reporting has a wide range of applications in measuring adolescence mental health status. However, there are several limitations and issues in the case of public health scenario. For instance, self-reporting does not enable researchers to acquire large-scale data in a short period of time. Apart from this, participants’ performance may be influenced by retrieval practice effect when longitudinal studies were conducted. Moreover, as the cognitive abilities of adolescence are still in the process of development, the results of self-reporting may be affected by recall bias to a certain extent and the degree of cooperation of subjects.
Currently, teenagers are increasingly surrounded by digital products and services. For this reason, a growing fraction of their thoughts, behaviours, and preferences leave digital traces (or digital footprints) that can be relatively easily recorded, stored, and analyzed. As a large number of psychological studies have found that there are close relationships between teenagers’ behaviors and their mental health, employing digital footprints can provide a new direction for mental health assessment research. Types of digital footprints generally include usage-logs (e.g. social media activity records), language data (such as email exchanges), behavioral data generated by mobile sensors (e.g. facial and gait behavior) and audiovisual data. The advantages of employing digital footprints are numerous: it is cheap, objective, non-intrusive and ecologically valid. Moreover, participants can be tracked remotely and longitudinally. However, it is worth noticing that there are several challenges associated with using digital-footprint-based measurements. For instance, how could researchers make sure that their respondents’ privacy and anonymity are well protected?
As digital footprints are increasingly used in the public mental health sector, it is important to investigate how digital-footprint-based measures can extend the applicability of psychometrics to public mental health contexts where traditional measures were difficult to assess. In order to address this issue, this Research Topic seeks to provide an opportunity to researchers in the areas of adolescent mental health assessment to share their research findings.
We welcome submissions of Original Articles, Reviews, and Case Reports in the following subtopics, but not limited to:
• Studies which employ digital-footprint based methods that focus on adolescent Internet users’ mental health
• New or innovative techniques for identifying the mental health status of adolescent Internet users on social media (e.g. based on text analysis, machine learning, natural language processing, etc.)
• New or innovative techniques for predicting mental health of adolescent Internet users base on their language, behavioral or audiovisual data
• Studies that discuss the reliability/validity and evaluation methods of psychological modelling technology in the context of adolescent mental health
• Studies that examine ethical-related issues and privacy protection in using digital-footprint based methods
Adolescence is an important turning point for children to grow into adults. During this time, mental health is very important for cultivating personality, forming positive mental states (e.g. self-confident and self-reliant), and setting up ideals, beliefs and life goals. Consequently, it is very necessary to evaluate and monitor teenagers’ mental health status. As one of the traditional psychometric methods, self-reporting has a wide range of applications in measuring adolescence mental health status. However, there are several limitations and issues in the case of public health scenario. For instance, self-reporting does not enable researchers to acquire large-scale data in a short period of time. Apart from this, participants’ performance may be influenced by retrieval practice effect when longitudinal studies were conducted. Moreover, as the cognitive abilities of adolescence are still in the process of development, the results of self-reporting may be affected by recall bias to a certain extent and the degree of cooperation of subjects.
Currently, teenagers are increasingly surrounded by digital products and services. For this reason, a growing fraction of their thoughts, behaviours, and preferences leave digital traces (or digital footprints) that can be relatively easily recorded, stored, and analyzed. As a large number of psychological studies have found that there are close relationships between teenagers’ behaviors and their mental health, employing digital footprints can provide a new direction for mental health assessment research. Types of digital footprints generally include usage-logs (e.g. social media activity records), language data (such as email exchanges), behavioral data generated by mobile sensors (e.g. facial and gait behavior) and audiovisual data. The advantages of employing digital footprints are numerous: it is cheap, objective, non-intrusive and ecologically valid. Moreover, participants can be tracked remotely and longitudinally. However, it is worth noticing that there are several challenges associated with using digital-footprint-based measurements. For instance, how could researchers make sure that their respondents’ privacy and anonymity are well protected?
As digital footprints are increasingly used in the public mental health sector, it is important to investigate how digital-footprint-based measures can extend the applicability of psychometrics to public mental health contexts where traditional measures were difficult to assess. In order to address this issue, this Research Topic seeks to provide an opportunity to researchers in the areas of adolescent mental health assessment to share their research findings.
We welcome submissions of Original Articles, Reviews, and Case Reports in the following subtopics, but not limited to:
• Studies which employ digital-footprint based methods that focus on adolescent Internet users’ mental health
• New or innovative techniques for identifying the mental health status of adolescent Internet users on social media (e.g. based on text analysis, machine learning, natural language processing, etc.)
• New or innovative techniques for predicting mental health of adolescent Internet users base on their language, behavioral or audiovisual data
• Studies that discuss the reliability/validity and evaluation methods of psychological modelling technology in the context of adolescent mental health
• Studies that examine ethical-related issues and privacy protection in using digital-footprint based methods
