Projects

Investigating Social Networks & Technology Resources for Collaborative Self-Management of Depression

Eleanor Burgess successfully defended her dissertation proposal! Now the hard work begins. She gave us a brief background and overview of her study:

Approximately 16.2 million U.S. adults experienced a depressive episode in 2016. Symptoms of depression include negative thoughts, feelings of sadness, lack of enjoyment of activities, agitation, sleep disruption, and lack of motivation. Addressing these challenges requires individuals to self-manage their condition to prevent or reduce the intensity of future episodes. While we know much in the field of Human-Computer Interaction (HCI) about the self-management of individuals with chronic physical illness, we know less about these activities for individuals managing depression. This lack of understanding makes it difficult to develop solutions to support their work. Furthermore, HCI studies investigating self-management in mental health often focus on individual activities. This perspective is reflected in current individual-focused self-tracking and skills practice technologies for depression self-management. Yet, my preliminary work published in CSCW 2019 points to the importance of collaboration and social interaction as a key ingredient of self-management for individuals managing depression. However, collaborative work in this context can be challenging – supporting mental health and ongoing management raises concerns about issues such as reciprocity and burden. Therefore, while social connections are clearly important to people managing depression, these same relationships can also be the source of conflicts. Understanding the benefits and challenges of collaborative self-management will be important for future design to support these activities.

My first goal is to understand how people conceptualize the work of collaborative self-management. I am examining questions of when, how, and why these interactions occur. For instance, what external dimensions and contextual factors affect these activities and interactions? Some factors influencing collaboration were identified in my preliminary study (relationship roles, mood, and location), but I hope to identify other factors.

My second goal is to investigate how technologies shape these interactions, looking at the broader set of tools that individuals use in day-to-day life. I aim to understand how individuals utilize their technology ecosystems to access support, particularly the ways that certain assemblages of technologies enable and constrain desired collaboration. I also seek to surface challenges – aspects where current technology ecosystems do not meet individual’s goals and desires.

Third, I intend to develop socio-technical design insights regarding how we might better support collaborative self-management, with a focus on communication technologies. Specifically, I believe that by better understanding the tensions embedded in social technologies (e.g., social media), we can better support individual’s ongoing collaborative self-management work, their decision-making, and their communication practices, through informed design.

We are excited to learn more about collaborative self-management of depression!

 

Previous work:

Eleanor R. Burgess, Kathryn E. Ringland, Jennifer Nicholas, Ashley Knapp, Jordan Eschler, David C. Mohr, Madhu C. Reddy. 2019. “I think people are powerful”: The sociality of individuals managing depression. In Proceedings of the 2019 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’19). ACM Press.