Psychotherapy delivery relies on a negotiation between patient self-reports and clinical intuition. Growing evidence for technological support of psychotherapy suggests opportunities to aid the mediation of this tension. To explore this prospect, we designed a prototype of a clinical decision support system (CDSS) for treating veterans with post-traumatic stress disorder in a Prolonged Exposure (PE) therapy intensive outpatient program. We conducted a two-phase interview study to collect perspectives from practicing PE clinicians and former PE patients who are United States veterans. Our analysis distills opportunities for a CDSS (e.g., offering homework review at a glance, aiding patient conceptualization) and larger challenges related to context and deployment (e.g., navigating Veterans Affairs). By reframing our findings through three human-centered perspectives (distributed cognition, situated learning, infrastructural inversion), we highlight the complexities of designing a CDSS for psychotherapists in this context and offer theory-aligned design considerations.

Centering humans in explainable artificial intelligence (XAI) research has primarily focused on AI model development and highstake scenarios.However, as AI becomes increasingly integrated into everyday applications in often opaque ways, the need for explainability tailored to end-users has grown more urgent.To address this gap, we explore end-users' perspectives on embedding XAI into daily AI application scenarios.Our findings reveal that XAI is not naturally accepted by end-users in their daily lives.When users seek explanations, they envision XAI design that promotes contextualized understanding, empowers adoption and adaption to AI systems, and considers multistakeholders' values.We further discuss supporting users' agency in XAI non-use and alternatives to XAI for managing ambiguity in AI interactions.Additionally, we provide design implications for XAI design at personal and societal levels.These include understanding users through a computational rationality lens, adaptive design that coevolves with users, and advancing the "society-in-the-loop" vision with everyday XAI.

Informal caregivers perform an important role in taking care of family members with chronic disease. Informal caregivers' mental health can be negatively impacted by life-changing events (e.g., patients' diagnosis, care transitioning, etc.). This leads the caregiver to suffer from interpersonal and intrapersonal conflicts, causing a sense of disorientation and escalating malaise. In this study, we investigated informal caregivers' experiences of facing conflicts and life-changing events by qualitatively analyzing the data from online health communities. We categorized conflicts using a psychodynamic framework. We further looked at the interplay of life-changing events and conflicts and how this leads to caregivers' sense-making and decisions to mediate conflicts. We also found that online health communities provide support by helping caregivers interpret and navigate conflicts and raising awareness of the temporal resolution of life-changing events. We conclude this study by discussing designing online health communities to better support such practice.

Many people do not get enough physical activity. Establishing routines to incorporate physical activity into people’s daily lives is known to be effective, but many people struggle to establish and maintain routines when facing disruptions. In this paper, we build on prior self-experimentation work to assist people in establishing or improving physical activity routines using a framework we call “reflective iteration.” This framework encourages individuals to articulate, reflect upon, and iterate on high-level “strategies” that inform their day-to-day physical activity plans. We designed and deployed a mobile application, Planneregy, that implements this framework. Sixteen U.S. college students used the Planneregy app for 42 days to reflectively iterate on their weekly physical exercise routines. Based on an analysis of usage data and interviews, we found that the reflective iteration approach has the potential to help people find and maintain effective physical activity routines, even in the face of life changes and temporary disruptions.

Today, clinicians have limited visibility into the quality of homework exercises that occur outside of the clinical context; however, understanding patient performance in these exercises is essential for guiding patient-centered care. To address this, we present the Clinician Homework Review (CHR), a unique measure and interface that displays similarity ratings calculated using sensor-captured patient-generated data (sPGD; i.e. heart rate, phone usage, ambient noise, and physical activity) for therapeutic exercises outside of the clinical setting within the post-traumatic stress disorder (PTSD) treatment context. Through concept testing sessions with 10 clinicians, we examine how sPGD can be leveraged to measure and investigate what contributes to patient performance in a therapeutic exercise. We also share in-depth information regarding clinician interpretation and planned use of data displayed by CHR in clinical sessions with patients. We frame our results in the context of situated objectivity and propose the notion of "perceived reference weight," which describes the significance attributed to contextualized data. In doing so, we support clinical decision-making in PTSD therapy.

Making and executing physical activity plans can help people improve their physical activity levels. However, little is known about how people make physical activity plans in everyday settings and how people can be assisted in creating more successful plans. In this paper, we developed and deployed a mobile app as a probe to investigate the in-the-wild physical activity planning experience for 28 days with 17 participants. Additionally, we explored the impact of presenting successful and unsuccessful planning records on participants’ planning behaviors. Based on interviews before, during, and after the deployment, we offer a description of what factors participants considered to fit their exercise plans into their existing routines, as well as factors leading to plan failures and dissatisfaction with planned physical activity. With access to historical records, participants derived insights to improve their plans, including trends in successes and failures. Based on those findings, we discuss the implications for better supporting people to make and execute physical activity plans, including suggestions for incorporating historical records into planning tools.

As we look to use Wear OS (formerly known as Android Wear) devices for fitness and health monitoring, it is important to evaluate the reliability of its ecosystem. The goal of this paper is to understand the reliability weak spots in Wear OS ecosystem. We develop a state-aware fuzzing tool, Vulcan, without any elevated privileges, to uncover these weak spots by fuzzing Wear OS apps. We evaluate the outcomes due to these weak spots by fuzzing 100 popular apps downloaded from Google Play Store. The outcomes include causing specific apps to crash, causing the running app to become unresponsive, and causing the device to reboot. We finally propose a proof-of-concept mitigation solution to address the system reboot issue.