As mental health concerns grow among Asian American communities, there is an urgent need for culturally-relevant support. While digital mental health treatments (DMHTs) offer new opportunities for interventions, they have largely been focused on non-minority populations in the U.S. (e.g., Caucasians, females, who are middle-aged). Through co-design sessions with Asian Mental Health Collective (AMHC), this study examines the unique mental health challenges faced by Asian Americans, intergenerational connections and elements of culturally-relevant mental health support. The co-design sessions resulted in two prototypes: A multifunctional community hub, to strengthen connections and resource access, and a storytelling app, for sharing and preserving cultural narratives. These prototypes drew from community co-design sessions to include elements of storytelling, community-centered approaches, and intergenerational engagement in addressing mental health concerns among Asian Americans. Leveraging the heterogeneous and similar cultural experiences among Asian Americans, this paper presents and discusses nuances and considerations for digital mental health technology (DMHT) designs for minority communities.

Anxiety disorders are the most common mental health problem, and cognitive-behavioral therapy is one of the most widely used, evidence-based treatments. While several mobile apps for anxiety that integrate cognitive-behavioral therapy (CBT) techniques exist, ma- jor challenges remain concerning uptake and engagement. Personalization is one strategy that can be used to improve client engagement, and integrating therapist input is one mechanism for such personalization. This study aims to understand therapist practices and identify new possibilities for delivering intervention content between face-to-face CBT therapy sessions. It comprised semi-structured interviews, followed by a series of ideation activities, and thematic analysis of the data. The results showed the central role of clients in shaping the content of therapy sessions, their challenges with homework practice, and therapists’ diverse practices. Analysis of the ideation activities elaborated the potential role of therapists in the personalization of apps for anxiety. We conclude with takeaways for designers of personalized mental health mobile applications.

Software engineers exhibit higher burnout and suicide rates compared to many other information workers. Consequently, mental wellbeing is a growing concern to technology organizations. To better understand the challenges of supporting mental wellbeing in the context of the work of software engineering, we conducted 14 interviews with software engineers. We examine the different aspects of their lived experiences with mental wellbeing at work, their strategies for managing mental wellbeing, the challenges they face in using these strategies, and recommendations they have for mental wellbeing technologies. We contribute to the HCI literature by discussing how mental wellbeing should be considered within the context of work across individual, team, and organization levels, and highlight the need for integrating mental wellbeing into the technologies employees use at work.

While the HCI field increasingly examines how digital tools can support individuals in managing mental health conditions, it remains unclear how these tools can accommodate these conditions' temporal aspects. Based on weekly interviews with five individuals with depression, conducted over six weeks, this study identifies design opportunities and challenges related to extending technology-based support across fluctuating symptoms. Our findings suggest that participants perceive events and contexts in daily life to have marked impact on their symptoms. Results also illustrate that ebbs and flows in symptoms profoundly affect how individuals practice depression self-management. While digital tools often aim to reach individuals while they feel depressed, we suggest they should also engage individuals when they are less symptomatic, leveraging their energy and motivation to build habits, establish plans and goals, and generate and organize content to prepare for symptom onset.

The increasing ubiquity of health sensing technology holds promise to enable patients and health care providers to make more informed decisions based on continuously-captured data. The use of sensor-captured patient-generated data (sPGD) has been gaining greater prominence in the assessment of physical health, but we have little understanding of the role that sPGD can play in mental health. To better understand the use of sPGD in mental health, we interviewed care providers in an intensive treatment program (ITP) for veterans with post-traumatic stress disorder. In this program, patients were given Fitbits for their own voluntary use. Providers identified a number of potential benefits from patients' Fitbit use, such as patient empowerment and opportunities to reinforce therapeutic progress through collaborative data review and interpretation. However, despite the promise of sensor data as offering an "objective" view into patients' health behavior and symptoms, the relationships between sPGD and therapeutic progress are often ambiguous. Given substantial subjectivity involved in interpreting data from commercial wearables in the context of mental health treatment, providers emphasized potential risks to their patients and were uncertain how to adjust their practice to effectively guide collaborative use of the FitBit and its sPGD. We discuss the implications of these findings for designing systems to leverage sPGD in mental health care.

Talk therapy is a common, effective, and desirable form of mental health treatment. Yet, it is inaccessible to many people. Enabling peers to chat online using effective principles of talk therapy could help scale this form of mental health care. To understand how such chats could be designed, we conducted a two-week field experiment with 40 people experiencing mental illnesses comparing two types of online chats-chats guided by prompts, and unguided chats. Results show that anxiety was significantly reduced from pre-test to post-test. User feedback revealed that guided chats provided solutions to problems and new perspectives, and were perceived as "deep," while unguided chats offered personal connection on shared experiences and were experienced as "smooth." We contribute the design of an online guided chat tool and insights into the design of peer support chat systems that guide users to initiate, maintain, and reciprocate emotional support.

Objective: While depression and anxiety are common mental health issues, only a small segment of the population has access to standard one-on-one treatment. The use of smartphone apps can fill this gap. An app recommender system may help improve user engagement of these apps and eventually symptoms. Methods: IntelliCare was a suite of apps for depression and anxiety, with a Hub app that provided app recommendations aiming to increase user engagement. This study captured the records of 8057 users of 12 apps. We measured overall engagement and app-specific usage longitudinally by the number of weekly app sessions ("loyalty") and the number of days with app usage ("regularity") over 16 weeks. Hub and non-Hub users were compared using zero-inflated Poisson regression for loyalty, linear regression for regularity, and Cox regression for engagement duration. Adjusted analyses were performed in 4561 users for whom we had baseline characteristics. Impact of Hub recommendations was assessed using the same approach. Results: When compared to non-Hub users in adjusted analyses, Hub users had a lower risk of discontinuing IntelliCare (hazard ratio = 0.67, 95% CI, 0.62-0.71), higher loyalty (2- to 5-fold), and higher regularity (0.1-0.4 day/week greater). Among Hub users, Hub recommendations increased app-specific loyalty and regularity in all 12 apps. Discussion/Conclusion: Centralized app recommendations increase overall user engagement of the apps, as well as app-specific usage. Further studies relating app usage to symptoms can validate that such a recommender improves clinical benefits and does so at scale.