Human-Centered and Computational Understanding for the Design and Adaptation of Mental Health and Well-being Interventions

Abstract

As many as 20% of Americans suffer from diagnosable mental health disorders, but those overwhelmed with physiological and economic burdens cannot prioritize seeking support for their mental health and well-being. There are many evidence-based psychosocial interventions (EBPIs) that have been proven to be effective in treating mental health conditions. Recent initiatives to improve engagement in mental health care through technology have generated an abundance of promising digital mental health solutions. However, symptoms of stress, anxiety, and depression remain overlooked and in constant tension with life demands and disruptions, making it challenging to integrate such solutions into everyday life. My dissertation research examines the tensions between everyday life demands and mental health and well-being, where I design systems that integrate adaptations of EBPIs into everyday contexts to promote engagement. My work intersects three well-being contexts: (1) the COVID-19 pandemic, (2) co-morbid cancer and depression, and (3) workplace stress. First, I examine the situated contexts using human-centered and computational methods grounded on holistic frameworks to reveal challenges rooted in tensions among multiple needs that get in the way of engaging in mental health and well-being activities. I conduct this research in the COVID-19 pandemic and co-morbid cancer and depression contexts to demonstrate that these challenges are present at the individual, organizational, and population scales. Second, I identify modification targets to existing evidence-based psychosocial interventions that can be enhanced through the use of technology to ease the tensions among needs and to directly integrate adapted interventions into the relevant contexts. I describe the development of the collaborative behavioral activation system aimed at improving the collaboration and engagement of patients and providers in depression care. I also describe the development of a just-in-time micro-intervention system aimed at reducing stress in the workplace. Lastly, I deploy these technology-enhanced mental health and well-being systems in real-world contexts to evaluate their effectiveness in improving engagement. Through such deployment, I highlight implementation challenges to integrating patient-provider collaborative technology into a clinical care practice as well as individual, contextual, and intervention-related factors that may influence real-time engagement in digitized interventions. Across three well-being contexts, my dissertation demonstrates that contextual and continuous adaptations of EBPIs can improve engagement in mental health and well-being care. My dissertation makes theoretical contributions through the development of holistic frameworks, methodological contributions through the development of computational frameworks, and artifact contributions through the development of technology-enhanced mental health and well-being intervention systems and through the design recommendations that arise from real-world deployments.