The number of older adults who are homebound with depressive symptoms is increasing. Due to their homebound status, they have limited access to trained mental healthcare support, which leaves this support often to untrained family caregivers. To increase access, a growing interest is placed on using technology-mediated solutions, such as voice-assisted intelligent personal assistants (VIPAs), to deliver mental health services to older adults. To better understand how older adults and family caregivers intend to interact with a VIPA for mental health interventions, we conducted a participatory design study during which 6 older adults and 7 caregivers designed VIPA-human dialogues for various scenarios. Using conversation style preferences as a starting point, we present aspects of human-likeness older adults and family caregivers perceived as helpful or uncanny, specifically in the context of the delivery of mental health interventions, which helps inform potential roles VIPAs can play in mental healthcare for older adults.

People with multiple chronic conditions (MCC) face challenges planning health care collaboratively with primary care clinicians, particularly when their priorities conflict. These challenges intensify with symptoms of anxiety or depression. Elicitation of patients' values is promoted as a means to aligning patient and clinician priorities in primary care, and as a component of psychotherapy for anxiety and depression. But, these approaches remain siloed. We conducted a qualitative interview study to understand patients' preferences for Technology Enabled Services (TESs) to coordinate values elicitation across primary and mental health care settings. Many participants preferred face-to-face elicitation by a mental health clinician; some preferred elicitation via telehealth and some preferred self-directed elicitation. Participants' preferences were influenced by: 1) how they perceived the rationale and benefits of values elicitation; 2) how they perceived the training and credibility of people facilitating elicitation; and 3) how they perceived their own capacity to engage in values elicitation. Participants also shared numerous concerns about values elicitation that warrant critical examination of TESs to support it.

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.

Digital tools have potential to support collaborative management of mental health conditions, but we need to better understand how to integrate them in routine healthcare, particularly for patients with both physical and mental health needs. We therefore conducted interviews and design workshops with 1) a group of care managers who support patients with complex health needs, and 2) their patients whose health needs include mental health concerns. We investigate both groups' views of potential applications of digital tools within care management. Findings suggest that care managers felt underprepared to play an ongoing role in addressing mental health issues and had concerns about the burden and ambiguity of providing support through new digital channels. In contrast, patients envisioned benefiting from ongoing mental health support from care managers, including support in using digital tools. Patients' and care managers' needs may diverge such that meeting both through the same tools presents a significant challenge. We discuss how successful design and integration of digital tools into care management would require reconceptualizing these professionals' roles in mental health support.

Digital tools can support individuals managing mental health concerns, but delivering sufficiently engaging content is challenging. This paper seeks to clarify how individuals with mental health concerns can contribute content to improve push-based mental health messaging tools. We recruited crowdworkers with mental health symptoms to evaluate and revise expert-composed content for an automated messaging tool, and to generate new topics and messages. A second wave of crowdworkers evaluated expert and crowdsourced content. Crowdworkers generated topics for messages that had not been prioritized by experts, including self-care, positive thinking, inspiration, relaxation, and reassurance. Peer evaluators rated messages written by experts and peers similarly. Our findings also suggest the importance of personalization, particularly when content adaptation occurs over time as users interact with example messages. These findings demonstrate the potential of crowdsourcing for generating diverse and engaging content for push-based tools, and suggest the need to support users in meaningful content customization.

The last decade has seen increased reports of mental health problems among college students, with college counseling centers struggling to keep up with the demand for services. Digital mental health tools offer a potential solution to expand the reach of mental health services for college students. In this paper, we present findings from a series of design activities conducted with college students and counseling center staff aimed at identifying needs and preferences for digital mental health tools. Results emphasize the social ecosystems and social support networks in a college student's life. Our findings highlight the predominant role of known peers, and the ancillary roles of unknown peers and non-peers (e.g., faculty, family) in influencing the types of digital mental health tools students desire, and the ways in which they want to learn about mental health tools. We identify considerations for designing digital mental health tools for college students that take into account the identified social factors and roles.

Psychosocial disability involves actual or perceived impairment due to a diversity of mental, emotional, or cognitive experiences. While assistive technology for psychosocial disabilities has been understudied in communities such as ASSETS, advances in computing have opened up a number of new avenues for assisting those with psychosocial disabilities beyond the clinic. However, these tools continue to emerge primarily within the framework of "treatment," emphasizing resolution or improvement of mental health symptoms. This work considers what it means to adopt a social model lens from disability studies and incorporate the expertise of assistive technology researchers in relation to mental health. Our investigation draws on interviews conducted with 18 individuals who have complex health needs that include mental health symptoms. This work highlights the potential role for assistive technology in supporting psychosocial disability outside of a clinical or medical framework.

Digital interventions offer great promise for supporting health-related behavior change. However, there is much that we have yet to learn about how people respond to them. In this study, we present a novel mixed-methods approach to analysis of the complex and rich data that digital interventions collect. We perform secondary analysis of IntelliCare, an intervention in which participants are able to try 14 different mental health apps over the course of eight weeks. The goal of our analysis is to characterize users' app use behavior and experiences, and is rooted in theoretical conceptualizations of engagement as both usage and user experience. In the first aim, we employ cluster analysis to identify subgroups of participants that share similarities in terms of the frequency of their usage of particular apps, and then employ other engagement measures to compare the clusters. We identified four clusters with different app usage patterns: Low Usage, High Usage, Daily Feats Users, and Day to Day users. Each cluster was distinguished by its overall frequency of app use, or the main app that participants used. In the second aim, we developed a computer-assisted text analysis and visualization method - message highlighting - to facilitate comparison of the clusters. Last, we performed a qualitative analysis using participant messages to better understand the mechanisms of change and usability of salient apps from the cluster analysis. Our novel approach, integrating text and visual analytics with more traditional qualitative analysis techniques, can be used to generate insights concerning the behavior and experience of users in digital health contexts, for subsequent personalization and to identify areas for improvement of intervention technologies.