Mental health researchers have increasingly explored the use of digital mental health interventions (DMHIs) as a scalable solution to increase access to mental health support, and numerous studies have demonstrated their effectiveness in improving symptoms.
However, user adoption and engagement with these tools varies. There are various factors that may influence whether users would continue to engage with tools beyond a study setting, such as:
* User-related factors, such as the extent to which people are able to integrate use into their daily lives;
* Technology-related factors, such as the level of guidance participants receive from using an intervention and the extent to which users feel a technology is appropriate to their culture and/or values;
* Environment-related factors, such as the endorsement the app receives from a user’s family, friends and/or doctor.
The goal of this Research Topic is to better understand what factors may influence user engagement with DMHIs. In order to understand the full potential of digital mental health tools, it is important to not only look at effectiveness or 'time spent using an app' but to investigate the multitude of factors that influence high and/or long-term engagement. Furthermore, the time spent on an intervention varies between different types of interventions and across communities, and little time spent using a DMHI does not have to be a negative feature per se (eg, people may see improvement in symptoms and no longer need to use an intervention). Understanding factors that influence engagement might help us better understand what is deemed an appropriate or desired pattern of usage. Researchers, as well as practitioners, can use this knowledge to inform evaluations and development of new digital tools to support mental health.
This Research Topic will explore a broad set of themes related to user engagement, including adoption and sustained interactions, with DMHIs. We welcome Original Research, Systematic or Scoping Reviews, Opinion pieces, and Commentary articles. Relevant sub-topics include but are not limited to:
* Empirical studies highlighting contextual factors that may affect user engagement with DMHIs;
* Lessons learnt and best practices for understanding and evaluating user engagement in digital mental health studies;
* Novel digital mental health tools that may provide new knowledge on improving user engagement;
* Theoretical frameworks or models that shed insight into factors playing a role in user engagement;
* Machine learning approaches that help understand and predict patterns of user engagement with DMHIs;
* Reviews synthesizing trends and gaps in digital mental health user research.
Mental health researchers have increasingly explored the use of digital mental health interventions (DMHIs) as a scalable solution to increase access to mental health support, and numerous studies have demonstrated their effectiveness in improving symptoms.
However, user adoption and engagement with these tools varies. There are various factors that may influence whether users would continue to engage with tools beyond a study setting, such as:
* User-related factors, such as the extent to which people are able to integrate use into their daily lives;
* Technology-related factors, such as the level of guidance participants receive from using an intervention and the extent to which users feel a technology is appropriate to their culture and/or values;
* Environment-related factors, such as the endorsement the app receives from a user’s family, friends and/or doctor.
The goal of this Research Topic is to better understand what factors may influence user engagement with DMHIs. In order to understand the full potential of digital mental health tools, it is important to not only look at effectiveness or 'time spent using an app' but to investigate the multitude of factors that influence high and/or long-term engagement. Furthermore, the time spent on an intervention varies between different types of interventions and across communities, and little time spent using a DMHI does not have to be a negative feature per se (eg, people may see improvement in symptoms and no longer need to use an intervention). Understanding factors that influence engagement might help us better understand what is deemed an appropriate or desired pattern of usage. Researchers, as well as practitioners, can use this knowledge to inform evaluations and development of new digital tools to support mental health.
This Research Topic will explore a broad set of themes related to user engagement, including adoption and sustained interactions, with DMHIs. We welcome Original Research, Systematic or Scoping Reviews, Opinion pieces, and Commentary articles. Relevant sub-topics include but are not limited to:
* Empirical studies highlighting contextual factors that may affect user engagement with DMHIs;
* Lessons learnt and best practices for understanding and evaluating user engagement in digital mental health studies;
* Novel digital mental health tools that may provide new knowledge on improving user engagement;
* Theoretical frameworks or models that shed insight into factors playing a role in user engagement;
* Machine learning approaches that help understand and predict patterns of user engagement with DMHIs;
* Reviews synthesizing trends and gaps in digital mental health user research.