According to the US Centers for Disease Control and Prevention, one in every 36 people suffers from autism spectrum disorders (ASD) in 2020, and we have seen an alarming accelerating trend in the increase of people with ASD. Common symptoms of ASD include impairments in cognition, emotional regulation, and social interactions. The impairments usually lead to problematic behavioral issues, which could cause severe disruption to one's life. While clinical interventions and treatments are available, most people do not have access to such cares due to a variety of reasons, such as shortage of trained clinicians and lack of financial resources to pay for the treatments. The advancement of digital technologies, particularly wearable and mobile technologies, promises to significantly lower the cost of mental health intervention delivery and to greatly expand the access to mental health care.
Most physical illnesses can be rehabilitated within a reasonably short period of time. In contrast, ASD has no known cure and can only be managed. As such, ASD intervention is inevitably long term. Ideally, a digital health intervention for autism should have minimum involvement from the clinician once the initial training with the intervention is completed. This would place significant requirements on the design and implementation of the digital technology as well as the intervention itself. The digital mental health intervention technology must be usable and effective for long-term use. How to achieve this goal is still an open research issue.
This research topic welcomes all forms of research (original research, systematic review, case report, etc.) that would help lead to the development of usable and effective digital health intervention technology for long-term autism care and intervention use with minimum involvement of trained clinicians. Topics of interest include, but are not limited to:
* User-centered design in digital health technology for autism care and intervention
* User engagement mechanisms such as gamification and virtual reality
* Personalization mechanisms
* Learning community and peer support technology for autism care
* Self-adaptive intervention with machine learning for autism
* Self-support with chatbot and chatGPT for autism
* Validation study on the usability and efficacy of digital health intervention technologies for autism care and intervention
* Systematic review and meta-analysis of digital health for autism care and intervention
According to the US Centers for Disease Control and Prevention, one in every 36 people suffers from autism spectrum disorders (ASD) in 2020, and we have seen an alarming accelerating trend in the increase of people with ASD. Common symptoms of ASD include impairments in cognition, emotional regulation, and social interactions. The impairments usually lead to problematic behavioral issues, which could cause severe disruption to one's life. While clinical interventions and treatments are available, most people do not have access to such cares due to a variety of reasons, such as shortage of trained clinicians and lack of financial resources to pay for the treatments. The advancement of digital technologies, particularly wearable and mobile technologies, promises to significantly lower the cost of mental health intervention delivery and to greatly expand the access to mental health care.
Most physical illnesses can be rehabilitated within a reasonably short period of time. In contrast, ASD has no known cure and can only be managed. As such, ASD intervention is inevitably long term. Ideally, a digital health intervention for autism should have minimum involvement from the clinician once the initial training with the intervention is completed. This would place significant requirements on the design and implementation of the digital technology as well as the intervention itself. The digital mental health intervention technology must be usable and effective for long-term use. How to achieve this goal is still an open research issue.
This research topic welcomes all forms of research (original research, systematic review, case report, etc.) that would help lead to the development of usable and effective digital health intervention technology for long-term autism care and intervention use with minimum involvement of trained clinicians. Topics of interest include, but are not limited to:
* User-centered design in digital health technology for autism care and intervention
* User engagement mechanisms such as gamification and virtual reality
* Personalization mechanisms
* Learning community and peer support technology for autism care
* Self-adaptive intervention with machine learning for autism
* Self-support with chatbot and chatGPT for autism
* Validation study on the usability and efficacy of digital health intervention technologies for autism care and intervention
* Systematic review and meta-analysis of digital health for autism care and intervention