Frontline workers (FLWs) in gender-based violence (GBV) service provision regularly engage in intense emotional labor to provide survivors of GBV with essential, often life-saving, services. However, FLWs experience intense burnout, resulting in turnover rates as high as 50% annually and a critical loss of services for survivors. In order to design digital burnout interventions in a context where so few exist, we recruited 15 FLWs for a 3-stage qualitative study where they used two existing applications to reflect on, and reimagine, concrete design features necessary to address FLW burnout in GBV service provision. We contribute important findings regarding designing specifically for empathy-based stress (EBS) in frontline work contexts, preferences for activities, desired interactivity, among other requirements for interventions. We synthesize our design recommendations through an example scenario of a collaborative just-in-time adaptive intervention (co-JITAI) system that integrates peer-based support that can adapt to users' changing needs and contexts over time.

Early detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm. Furthermore, how patients who have recovered from schizophrenia view these AI models has been underexplored. To address this gap, we first conducted semi-structured interviews with 28 patients and reflexive thematic analysis, which revealed a disconnect between AI predictions and patient experience, and the importance of the social aspect of relapse detection. In response, we developed a prototype that used patients' Facebook data to predict relapse. Feedback from seven patients highlighted the potential for AI to foster collaboration between patients and their support systems, and to encourage self-reflection. Our work provides insights into human-AI interaction and suggests ways to empower people with schizophrenia.

Frontline workers (FLWs) provide essential services necessary for a functioning society, but the nature of their work contributes to their high rates of burnout. This burnout impacts not only their own health, but also has dire consequences for the people most in need of their services. Digital interventions for burnout may provide scalable, accessible, and cost-effective care. However, such interventions have been designed to intervene at the individual-level and have been inadequate in sustainably alleviating burnout. In addition, these interventions have not considered how we might integrate more effective organizational-level strategies or reflect FLWs' own experiences and the unique contexts of frontline work in their design. One important, yet understudied, domain of frontline work that experiences high rates of burnout is gender-based violence (GBV) service provision in nonprofit organizations. Using a community-based participatory research approach, we ran 8 co-design workshops with FLWs and supervisors from various nonprofit GBV organizations in a large metropolitan city in the United States to understand their experiences, hopes, and perceptions of technology for burnout support within their organizations. We found that participants greatly valued their support systems and supervisory relationships at work. And while there was a desire to use technology as support tools for burnout, participants were wary of how it could conflict with these support systems and important organizational values. We contribute a multilevel framework for digital burnout interventions and argue for a methodological shift to an assets-based approach, reframing the future of research and design of burnout interventions to center and reflect the needs, values, and lived experiences of FLWs.

Mental health symptoms are commonly discovered in primary care. Yet, these settings are not set up to provide psychological treatment. Digital interventions can play a crucial role in stepped care management of patients' symptoms where patients are offered a low intensity intervention, and treatment evolves to incorporate providers if needed. Though digital interventions often use smartphone and wearable sensor data, little is known about patients' desires to use these data to manage mental health symptoms. In 10 interviews with patients with symptoms of depression and anxiety, we explored their: symptom self-management, current and desired use of sensor data, and comfort sharing such data with providers. Findings support the use digital interventions to manage mental health, yet they also highlight a misalignment in patient needs and current efforts to use sensors. We outline considerations for future research, including extending design thinking to wraparound services that may be necessary to truly reduce healthcare burden.

Young adults have high rates of mental health conditions, yet they are the age group least likely to seek traditional treatment. They do, however, seek information about their mental health online, including by filling out online mental health screeners. To better understand online self-screening, and its role in help-seeking, we conducted focus groups with 50 young adults who voluntarily completed a mental health screener hosted on an advocacy website. We explored (1) catalysts for taking the screener, (2) anticipated outcomes, (3) reactions to the results, and (4) desired next steps. For many participants, the screener results validated their lived experiences of symptoms, but they were nevertheless unsure how to use the information to improve their mental health moving forward. Our findings suggest that online screeners can serve as a transition point in young people's mental health journeys. We discuss design implications for online screeners, post-screener feedback, and digital interventions broadly.

Online informal support networks may provide a critical source of support for young people who self-injure. While these platforms are often intended to mitigate digital harm, there is limited understanding of how individuals use peer support venues to seek self-injury related support and the specific contingencies of supportive exchanges. The present mixed-methods study was designed to explore the types of concerns members express on a mobile peer support application and the types of responses that they receive. Specifically, our aims were to (1) understand the prevalence of peer support types exchanged and (2) surface more nuanced themes within these categories of support. We also explore the relationship between support sought through posts and received through comments. Findings have important theoretical implications for understanding support seeking and provision through a mobile peer support app, which can help guide the design and optimization of peer-driven platforms for individuals who self-injure.

In this paper, we explore the use of virtual reality (VR) in assisting individuals who self-injure. Past work on self-injury in HCI has focused almost exclusively on mobile applications and message boards. As VR systems become more common, it is worth exploring what unique affordances of the technology can be leveraged to support self-injury reduction and cessation. Research on VR intervention and self-injury treatment informed the design of three novel virtual reality experiences. Nineteen interviews were conducted with individuals with current, or a past history of, self-injury with the goals of uncovering overall impressions of the perceived efficacy of VR with this population, as well as better understanding key mechanisms which impact their experience. Our analysis reveals four key elements common across all experiences: transportation, embodiment, immersion/distraction, and sense of control, and additional themes within each unique experience. We discuss the implications of these findings for future intervention design.