Individually, AI and Extended Reality (XR) have improved patient-oriented care across healthcare domains. Additionally, recent reviews have highlighted the significant synergistic effects of AI and XR in various fields, including healthcare. This review aims to identify existing AI-XR applications for patient-oriented health and well-being, analyze their benefits, derive trends, identify challenges, and outline future perspectives. Articles of any study design published before 9 July 2024 were systematically collected from six databases. After the initial screening of 4,612 articles, 64 matched the eligibility criteria. The applications demonstrated patient benefits, including improved clinical outcomes, engagement, and accessibility. Machine learning, deep learning, and conversational avatars were combined with XR’s interactive environments to enable personalized rehabilitation and physical therapy, mental health, and developmental care interventions. Associated challenges included the technological immaturity of current applications, lack of large-scale validations, and ethical concerns. While innovation in rehabilitation applications reached its peak, future directions include the need for comprehensive validation and investments in technical, regulatory, and clinical integration. Another perspective highlights the potential of combining XR environments with recent trends in generative AI, especially for mental health interventions. Generalized AI-XR paradigms can guide user-centered development through improved human–computer interaction in various computational research fields.

Stories about overcoming personal struggles can effectively illustrate the application of psychological theories in real life, yet they may fail to resonate with individuals' experiences. In this work, we employ large language models (LLMs) to create tailored narratives that acknowledge and address unique challenging thoughts and situations faced by individuals. Our study, involving 346 young adults across two settings, demonstrates that personalized LLM-enhanced stories were perceived to be better than human-written ones in conveying key takeaways, promoting reflection, and reducing belief in negative thoughts. These stories were not only seen as more relatable but also similarly authentic to human-written ones, highlighting the potential of LLMs in helping young adults manage their struggles. The findings of this work provide crucial design considerations for future narrative-based digital mental health interventions, such as the need to maintain relatability without veering into implausibility and refining the wording and tone of AI-enhanced content.

This paper characterizes the mental health technology "kits" of individuals managing depression: the specific technologies on their digital devices and physical items in their environments that people turn to as part of their mental health management. We interviewed 28 individuals living across the United States who use bundles of connected tools for both individual and collaborative mental health activities. We contribute to the HCI community by conceptualizing these tool assemblages that people managing depression have constructed over time. We detail categories of tools, describe kit characteristics (intentional, adaptable, available), and present participant ideas for future mental health support technologies. We then discuss what a mental health technology kit perspective means for researchers and designers and describe design principles (building within current toolkits; creating new tools from current self-management strategies; and identifying gaps in people's current kits) to support depression self-management across an evolving set of tools.

Challenges in engagement with digital mental health (DMH) tools are commonly addressed through technical enhancements and algorithmic interventions. This paper shifts the focus towards the role of users' broader social context as a significant factor in engagement. Through an eight-week text messaging program aimed at enhancing psychological wellbeing, we recruited 20 participants to help us identify situational engagement disruptors (SEDs), including personal responsibilities, professional obligations, and unexpected health issues. In follow-up design workshops with 25 participants, we explored potential solutions that address such SEDs: prioritizing self-care through structured goal-setting, alternative framings for disengagement, and utilization of external resources. Our findings challenge conventional perspectives on engagement and offer actionable design implications for future DMH tools.

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains.

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.

IMPORTANCE: Progression of HIV disease, the transmission of the disease, and premature deaths among persons living with HIV (PLWH) have been attributed foremost to poor adherence to HIV medications. mHealth tools can be used to improve antiretroviral therapy (ART) adherence in PLWH and have the potential to improve therapeutic success. OBJECTIVE: To determine the efficacy of WiseApp, a user-centered design mHealth intervention to improve ART adherence and viral suppression in PLWH. DESIGN, SETTING, AND PARTICIPANTS: A randomized (1:1) controlled efficacy trial of the WiseApp intervention arm (n = 99) versus an attention control intervention arm (n = 101) among persons living with HIV who reported poor adherence to their treatment regimen and living in New York City. INTERVENTIONS: The WiseApp intervention includes the following components: testimonials of lived experiences, push-notification reminders, medication trackers, health surveys, chat rooms, and a "To-Do" list outlining tasks for the day. Both study arms also received the CleverCap pill bottle, with only the intervention group linking the pill bottle to WiseApp. RESULTS: We found a significant improvement in ART adherence in the intervention arm compared to the attention control arm from day 1 (69.7% vs 48.3%, OR = 2.5, 95% CI 1.4-3.5, P = .002) to day 59 (51.2% vs 37.2%, OR = 1.77, 95% CI 1.0-1.6, P = .05) of the study period. From day 60 to 120, the intervention arm had higher adherence rates, but the difference was not significant. In the secondary analyses, no difference in change from baseline to 3 or 6 months between the 2 arms was observed for all secondary outcomes. CONCLUSIONS: The WiseApp intervention initially improved ART adherence but did not have a sustained effect on outcomes.

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.

Young adults have high rates of mental health conditions, but most do not want or cannot access formal treatment. We therefore recruited young adults with depression or anxiety symptoms to co-design a digital tool for self-managing their mental health concerns. Through study activities-consisting of an online discussion group and a series of design workshops-participants highlighted the importance of easy-to-use digital tools that allow them to exercise independence in their self-management. They described ways that an automated messaging tool might benefit them by: facilitating experimentation with diverse concepts and experiences; allowing variable depth of engagement based on preferences, availability, and mood; and collecting feedback to personalize the tool. While participants wanted to feel supported by an automated tool, they cautioned against incorporating an overtly human-like motivational tone. We discuss ways to apply these findings to improve the design and dissemination of digital mental health tools for young adults.

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.

Recent research has explored computational tools to manage workplace stress via personal sensing, a measurement paradigm in which behavioral data streams are collected from technologies including smartphones, wearables, and personal computers. As these tools develop, they invite inquiry into how they can be appropriately implemented towards improving workers' well-being. In this study, we explored this proposition through formative interviews followed by a design provocation centered around measuring burnout in a U.S. resident physician program. Residents and their supervising attending physicians were presented with medium-fidelity mockups of a dashboard providing behavioral data on residents' sleep, activity and time working; self-reported data on residents' levels of burnout; and a free text box where residents could further contextualize their well-being. Our findings uncover tensions around how best to measure workplace well-being, who within a workplace is accountable for worker stress, and how the introduction of such tools remakes the boundaries of appropriate information flows between worker and workplace. We conclude by charting future work confronting these tensions, to ensure personal sensing is leveraged to truly improve worker well-being.

Depression is a challenging condition that requires individuals to manage their moods and emotions over time. Within CSCW, there has been an interest in understanding how individuals seek and share support on social media and in online communities. However, less attention has been paid to how collaboration as an aspect of self-management of depression unfolds in people's daily lives. In this paper, we explore the collaborative self-management work of 28 individuals managing depression who live in the United States. Data collection included remote semi-structured interviews with an associated cognitive mapping exercise. Our findings describe who participants turn to for day-to-day collaborative support, how collaborative activities are enacted (across both mood-focused and preventative support practices), and where these often technology-mediated interactions occur across text, phone, video, and picture-based channels. We discuss collaborative self-management in the depression support context, including key characteristics: agency, reciprocity, time, and interaction. We also present a four-step model of how the process occurs over time (awareness, planning, interaction, and reflection). We conclude by discussing how technology ecosystems support individuals' collaborative self-management.

Collaborative Care Programs (CCPs) integrate mental health services into primary care settings to help patients access much needed treatment. Technologies could increase the effectiveness of CCPs, but we know little about what collaboration challenges technologies must address in this complex clinical setting. To investigate these challenges and technology opportunities, we conducted interviews and contextual inquiries with 30 patients and providers in an obstetric CCP. Using the Parallel Journeys Framework as a lens, we uncover new collaboration challenges (e.g., weighing risks and benefits of treatment, conflicting opinions and ambiguous responsibilities) at the intersection of patients' obstetric and psychosocial care journeys. We discuss new CSCW implications and technology opportunities, such as the importance of addressing support gaps in cyclical experiences, and the need to resolve provider conflicts to refocus on patient needs. These contributions inform how technologies can support patient engagement and collaboration with providers to access and receive treatment, as well as improve health outcomes.

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.

In pursuit of mental wellness, many find that behavioral change is necessary. This process can often be difficult but is facilitated by strong social support. This paper explores the role of social support across behavioral change journeys among young adults, a group at high risk for mental health challenges, but with the lowest rates of mental health treatment utilization. Given that digital mental health tools are effective for treating mental health conditions, they hold particular promise for bridging the treatment gap among young adults, many of whom, are not interested in - or cannot access - traditional mental healthcare. We recruited a sample of young adults with depression who were seeking information about their symptoms online to participate in an Asynchronous Remote Community (ARC) elicitation workshop. Participants detailed the changing nature of social interactions across their behavior change journeys. They noted that both directed and undirected support are necessary early in behavioral change and certain needs such as informational support are particularly pronounced, while healthy coping partnerships and accountability are more important later in the change process. We discuss the conceptual and design implications of our findings for the next generation of digital mental health tools.

A growing body of work has emphasized the need for customizability and flexibility in mobile health technologies to increase support user autonomy. However, customization may be burdensome for people with motivational and cognitive challenges, such as those with mental illnesses, and the optimal level and type of customizability are unclear. Based on 32 interviews with people who experience symptoms of depression and anxiety, we examine how individuals use and customize mental health apps to manage their symptoms. Our findings suggest that participants’ engagement with the apps is affected by their level of energy and motivation, depending on the severity of symptoms. Customization is deemed desirable when the required user effort does not exceed users’ mental and motivational capacity and when ample resources are available. We discuss how customizable systems can increase autonomy without overburdening users in the context of mental health.

Much human-computer interaction work related to depression focuses on the population level (e.g., studying social media hashtags related to depression) or evaluates prototypes for digital interventions to manage depression. However, little is known about how people living with depression perceive and manage technology use, such as time spent on social media per day. For this study, we interviewed 30 individuals living with depression to explore their technology and social media use. We find that these individuals demonstrated emergent practices related to self-regulation, such as learning to monitor and adjust technology use to improve their emotional, cognitive, and behavioral health. Our findings add a human-centered viewpoint to the relationship between living with depression and technology and social media use. We present design implications of these findings for better empowering individuals with depression to encourage their natural inclinations to self-regulate technology and social media use.

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.

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.

Millions of Americans struggle with depression, a condition characterized by feelings of sadness and motivation loss. To understand how individuals managing depression conceptualize their self-management activities, we conducted visual elicitations and semi-structured interviews with 30 participants managing depression in a large city in the U.S. Midwest. Many depression support tools are focused on the individual user and do not often incorporate social features. However, our analysis showed the key importance of sociality for self-management of depression. We describe how individuals connect with specific others to achieve expected support and how these interactions are mediated through locations and communication channels. We discuss factors influencing participants' sociality including relationship roles and expectations, mood state and communication channels, location and privacy, and culture and society. We broaden our understanding of sociality in CSCW through discussing diffuse sociality (being proximate to others but not interacting directly) as an important activity to support depression self-management.

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.

In this paper, we present a novel approach for the intrinsic mapping of anatomical surfaces and its application in brain mapping research. Using the Laplace-Beltrami eigen-system, we represent each surface with an isometry invariant embedding in a high dimensional space. The key idea in our system is that we realize surface deformation in the embedding space via the iterative optimization of a conformal metric without explicitly perturbing the surface or its embedding. By minimizing a distance measure in the embedding space with metric optimization, our method generates a conformal map directly between surfaces with highly uniform metric distortion and the ability of aligning salient geometric features. Besides pairwise surface maps, we also extend the metric optimization approach for group-wise atlas construction and multi-atlas cortical label fusion. In experimental results, we demonstrate the robustness and generality of our method by applying it to map both cortical and hippocampal surfaces in population studies. For cortical labeling, our method achieves excellent performance in a cross-validation experiment with 40 manually labeled surfaces, and successfully models localized brain development in a pediatric study of 80 subjects. For hippocampal mapping, our method produces much more significant results than two popular tools on a multiple sclerosis study of 109 subjects.