Mental health issues such as depression, anxiety, and others are a growing epidemic facing modern society. The Mental Health America society estimated that nearly a fifth of the adult US population suffered a mental illness in 2019 - 2020 and that 94% of these individuals did not receive any treatment. Information Technology (IT) such as wearables, digital pills, cope notes, VR, and others have been proposed and used to help address the growing mental health crisis. However, the understanding of the design, development, adoption, use, and impact of such technologies for diagnosing and treating mental health illnesses remains nascent.
Information Systems (IS) scholars are starting to study various aspects of mental health, including occupational stress, distress, and diagnosable mental health disorders. However, significant areas of opportunity remain for developing and evaluating digital technologies that could help identify or tackle anxiety disorders (e.g., generalized anxiety disorder, panic, social anxiety), mood disorders (e.g., depression, bipolar disorder), and addiction (e.g., substance abuse, chemical dependence). This Special Section seeks to expand research related to IT for mental health and spearhead an ongoing research agenda related to this subject in the IS discipline. We are specifically seeking contributions that improve our understanding of how IT could be leveraged to identify mental health conditions and improve mental health. We encourage a wide range of content, including theory, qualitative and quantitative approaches, and design science for mental health for this Special Section.
Irrespective of the topic, the focus on how IT is being used or developed to identify mental health conditions or improve mental health should be evident (IT is a central theme of the paper). Research that examines the negative impacts of technology (e.g., antecedents to technostress) do not fit the theme. We welcome research that uses or employs various types of methods and analysis, including qualitative methods, quantitative methods, archival and observational research methods, mixed methods research, design science research, and Artificial Intelligence (AI)-enabled analytics methods, including machine learning, deep learning, text mining, and network science.