Digital peer-to-peer mental health tools have shown promise in supporting the well-being of those receiving help and giving it (i.e. helper therapy), but promoting engagement remains a challenge. We examine whether the framing of helper therapy exercises motivates active user participation and how user characteristics shape differential effects of the framings in a publicly deployed interactive text messaging-based mental health program. Among 3,817 users randomized to different helper therapy framings, we find causal evidence that framings which emphasize helpng oneself increase written engagement rates as much as 4.6% over other framings, with even larger effects seen among minoritized identities. These self-focused framings also elicited messages with more positive, trust, and anticipation-related words and fewer fear, anger, disgust, and sadness words. Our findings highlight the importance of centering the user in the framing of digital intervention content, and personalizing digital mental health tools to align with a diversity of user identities.

Abstract Although competition is a common feature of digital games, nuances of when and why aspects of competition influence players' motivation and well-being have been surprisingly underexplored, especially through the lens of self-determination theory (SDT). In this critical review, we: (1) describe how a mini-theory of SDT, cognitive evaluation theory (CET), can help predict when and why aspects of competition will alternatively satisfy or frustrate basic psychological needs in digital games with downstream effects on players' motivation and well-being; (2) apply the Motivation, Engagement and Thriving in User Experience (METUX) model to outline ways competition in digital games can influence motivation and well-being at multiple levels; and (3) prioritize future research directions. Finally, we argue that digital games, given their diversity, adaptability and massive reach, represent an especially powerful context for studying competition, motivation and well-being. Research Highlights This critical review integrates self-determination theory (SDT)-guided models and research related to HCI, sports psychology and well-being supportive design to advance understanding of competition in digital games. Introduces a new taxonomy of competition relevant to SDT and digital games, including macro-level categories, general elements and specific features. Presents a competition and digital gaming specific cognitive evaluation theory (CET) model linking different aspects of competition in digital games to basic psychological need satisfaction and frustration, motivation, health and well-being. Applying the METUX model, we map out ways researchers and game makers can think about aspects of competition in digital games at multiple levels or spheres of influence. Prioritizes future directions for research, specifically related to experimentally manipulating digital feedback and digital representations of self and others in digital games.

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.

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.

Although engagement in self-directed wellness exercises typically declines over time, integrating social support such as coaching can sustain it. However, traditional forms of support are often inaccessible due to the high costs and complex coordination. Large Language Models (LLMs) show promise in providing human-like dialogues that could emulate social support. Yet, in-depth, in situ investigations of LLMs to support behavior change remain underexplored. We conducted two randomized experiments to assess the impact of LLM agents on user engagement with mindfulness exercises. First, a single-session study, involved 502 crowdworkers; second, a three-week study, included 54 participants. We explored two types of LLM agents: one providing information and another facilitating self-reflection. Both agents enhanced users' intentions to practice mindfulness. However, only the information-providing LLM agent, featuring a friendly persona, significantly improved engagement with the exercises. Our findings suggest that specific LLM agents may bridge the social support gap in digital health interventions.

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.

Traditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and potential limitations of LLMs in this context remain underexplored. To address this, we conducted interviews and focus group discussions with 15 university students and 6 experts, during which a technology probe for generating personalized advice for managing procrastination was presented. Our results highlight the necessity for LLMs to provide structured, deadline-oriented steps and enhanced user support mechanisms. Additionally, our results surface the need for an adaptive approach to questioning based on factors like busyness. These findings offer crucial design implications for the development of LLM-based tools for managing procrastination while cautioning the use of LLMs for therapeutic guidance.

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.

Without a nuanced understanding of users' perspectives and contexts, text messaging tools for supporting psychological wellbeing risk delivering interventions that are mismatched to users' dynamic needs. We investigated the contextual factors that influence young adults' day-to-day experiences when interacting with such tools. Through interviews and focus group discussions with 36 participants, we identified that people's daily schedules and affective states were dominant factors that shape their messaging preferences. We developed two messaging dialogues centered around these factors, which we deployed to 42 participants to test and extend our initial understanding of users' needs. Across both studies, participants provided diverse opinions of how they could be best supported by messages, particularly around when to engage users in more passive versus active ways. They also proposed ways of adjusting message length and content during periods of low mood. Our findings provide design implications and opportunities for context-aware mental health management systems.

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.

Adopting new psychological strategies to improve mental wellness can be challenging since people are often unable to anticipate how new habits are applicable to their circumstances. Narrative-based interventions have the potential to alleviate this burden by illustrating psychological principles in an applied context. In this work, we explore how stories can be delivered via the ubiquitous and scalable medium of text messaging. Through formative work consisting of interviews and focus group discussions with 15 participants, we identified desirable elements of stories about mental health, including authenticity and relatability. We then deployed story-based text messages to 42 participants to explore challenges regarding both the stories' content (e.g., specific versus generalized) and format (e.g., story length). We observed that our stories helped participants reflect on and identify flaws in their thinking patterns. Our findings highlight design implications and opportunities for mental wellness interventions that utilize stories in text messaging services.

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.

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 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.

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.

In face-to-face interaction, moving with and mimicking the body movements of communication partners has been widely demonstrated to affect interpersonal processes, including feel- ings of affiliation and closeness. In this paper, we examine effects of movement and mimicry in robot-mediated communication. Participants were instructed to get to know their partner, a confederate, who interacted with them via a telepresence robot. The robot either (a) mimicked the participant's body orientation (mimicry condition), (b) mimicked pre-recorded movements of another participant (random movement condition), or (c) did not move during the interaction (static condition). Results showed that mimicry and random movement had similar effects on participants' perceptions of similarity and closeness to their partners and that these effects depend on the participant's gender and level of self-monitoring. The findings suggest that the social movements of a telepresence robot affect interpersonal processes and that these effects are shaped by individual differences.

As social media is becoming an increasingly important source of public information, companies, organizations and individuals are actively using social media platforms to promote their products, services, ideas and ideologies. Unlike promotional campaigns on TV or other traditional mass media platforms, campaigns on social media often appear in stealth modes. Campaign promoters often try to influence people's behaviors/opinions/decisions in a latent manner such that the readers are not aware that the messages they see are strategic campaign posts aimed at persuading them to buy target products/services. Readers take such campaign posts as just organic posts from the general public. It is thus important to discover such campaigns, their promoter accounts and how the campaigns are organized and executed as it can uncover the dynamics of Internet marketing. This discovery is clearly useful for competitors and also the general public. However, so far little work has been done to solve this problem. In this paper, we study this important problem in the context of the Twitter platform. Given a set of tweets streamed from Twitter based on a set of keywords representing a particular topic, the proposed technique aims to identify user accounts that are involved in promotion. We formulate the problem as a relational classification problem and solve it using typed Markov Random Fields (T-MRF), which is proposed as a generalization of the classic Markov Random Fields. Our experiments are carried out using three real-life datasets from the health science domain related to smoking. Such campaigns are interesting to health scientists, government health agencies and related businesses for obvious reasons. Our results show that the proposed method is highly effective.