Storytelling is a powerful tool for supporting perinatal mental health, allowing mothers process experiences, reduce isolation, and foster connection. Although generative AI is increasingly used in therapeutic and peer-support interventions, it remains unclear how AI-generated stories are perceived compared to narratives grounded in lived experience. We conducted a mixed-methods study to investigate the perceptions of AI-generated and human-authored stories. Using MTurk, we collected 400 responses from female participants in their perinatal stages. Participants rated stories on believability, engagement, relevance, and emotional catharsis. Results showed that AI-generated stories were often described as polished and coherent but emotionally distant, while human-authored stories, adapted from online communities, were viewed as raw, culturally specific, and emotionally resonant. Emotional catharsis was tied less to positivity than to the progression of emotions. These findings underscore the continued centrality of lived-experience narratives in perinatal support and suggest that any use of AI storytelling in this domain should be cautious, transparently bounded, and clearly secondary. We discuss implications for designing human-centered storytelling technologies that preserve authenticity while carefully delimiting the role of AI in perinatal mental health contexts.

Perinatal mental illness is a prevalent global concern impacting pregnant women and new mothers. Women frequently utilize technology for perinatal mental health (PMH) assistance, including smartphone applications, web-based platforms, social media, and online support groups. This study aims to understand mothers' perceived acceptability and effectiveness of mental health technologies and the challenges they face while adopting those technologies to navigate their PMH journey. We conducted in-depth semi-structured interviews with mothers (n=15) who were either pregnant or in their postpartum. Additionally, we gathered data (1600 posts and corresponding 10,000 comments) from online perinatal support communities to explore the discussions concerning mothers' utilization of technology during the perinatal period. Our findings elucidate the diverse experiences of women around professional help, social support, anonymity, and misinformation when adopting and using perinatal technologies and how these experiences shape how they envision future PMH technologies and their aspects. Based on these findings, we recommend developing future evidence-based technologies that continuously support mothers throughout pregnancy and postpartum while advocating for existing technologies to prioritize transparency, empowering mothers to make informed decisions and enhance their digital literacy regarding PMH resources.

HCI aspires to achieve equitable outcomes in our sociotechnical interventions. Prior work suggests that improved evaluation and reporting of intervention disparities can help achieve more equitable outcomes. We analyzed socioeconomic disparities in hybrid and online-only local support communities by quantitatively operationalizing the access-adoption-adherence-effectiveness (AAAE) framework. The hybrid intervention demonstrated statistically significant socioeconomic status (SES)-based disparities in adoption of community's asynchronous online platform, while the online-only intervention demonstrated statistically significant disparities in access and adherence to synchronous online classes. Our findings suggest that disparities in earlier elements of the framework ''pipeline'' may mask later disparities due to survivorship bias, highlighting the importance of comprehensively examining the four elements of the framework. Using the example of a parenting education community, we reflect on using the AAAE framework as a rigorous tool to evaluate a sociotechnical intervention's disparities, avoid survivorship bias, identify the most pertinent support needed, and aid efforts toward sustained and equitable outcomes.

US military veterans (USMVs) are a vulnerable population with an elevated risk of mental health issues and suicide. Peer support, especially through mobile technology, has proven effective in addressing mental health related challenges, but ensuring long-term engagement remains a concern. This study explores the opportunity of designing persuasive technology, particularly persuasive reminders, to enhance engagement in peer support interventions for veterans. We followed community-based participatory research with ten veterans to identify specific peer support processes that can benefit from persuasive reminders and to uncover the underlying community values and needs to guide design. The findings emphasize the importance of designing reminders that focus on personalized strategies, effective delivery of success stories, understanding motivation levels, careful language selection, actionable reminders, and mutual accountability. The study advocates context-specific design and highlights the need for a broader user-centered persuasion design perspective to cater to veterans’ unique needs.

Postpartum depression (PPD) is one of the most prevalent mental health disorder following childbirth. Mothers utilize social media and online forums throughout postpartum to seek mental health support. In this paper, we aim to gain a comprehensive understanding of the topics of naturally occurring online discussions and the online social support dynamics associated with PPD. We qualitatively analyze posts and comments from three postpartum depression support communities corresponding to well-known online social platforms (Reddit, What to expect, and Babycenter). We discovered that the three communities share common themes of discussion that include the causes, stressors, symptoms, and coping mechanisms for PPD. Seeking both informational and emotional support through venting and storytelling was prevalent, and emotional support seeking was more common in Reddit than the other two communities. We provide recommendations for future postpartum depression research and design considerations based on pervasive help-seeking approaches.

Mobile mental health applications are seen as a promising way to fulfill the growing need for mental health care. Although there are more than ten thousand mental health apps available on app marketplaces, such as Google Play and Apple App Store, many of them are not evidence-based, or have been minimally evaluated or regulated. The real-life experience and concerns of the app users are largely unknown. To address this knowledge gap, we analyzed 2159 user reviews from 117 Android apps and 2764 user reviews from 76 iOS apps. Our findings include the critiques around inconsistent moderation standards and lack of transparency. App-embedded social features and chatbots were criticized for providing little support during crises. We provide research and design implications for future mental health app developers, discuss the necessity of developing a comprehensive and centralized app development guideline, and the opportunities of incorporating existing AI technology in mental health chatbots.

Post-Traumatic Stress Disorder (PTSD) is one of the most common mental health disorders prevalent in the US. Most alarming, PTSD occurs at double the rate for combat veterans compared to the general population. Severity of PTSD is associated with risk taking behaviors such as substance abuse, non-suicidal self-injury, sexual risk behaviors, among other negative behaviors. Psychological disorders are often preceded by crisis events, thus monitoring for crisis events can help prevent risky behavior in veterans. Ecological momentary assessment techniques are effective in capturing possible crisis events for veterans. Mobile apps are commonly used to gather such behavioral changes in participants. Crisis events collected from m-health can be analyzed for the identification of long- term PTSD risk. Early identification of risk can help in planning intervention to mitigate the risk. Many scholars have used traditional statistical and machine learning methods for the prediction of mental health issues in individuals. But these models lack transparency in how decisions are made. Providing justifications for the predictions can increase the reliability of the model. Our research focused on developing an explainable prediction model using class association rules to identify veterans at risk of persistent PTSD. The generated association rules serve as precursors to the long-term crisis in veterans. Results of the analysis showed that having no family support, little or no interest in hobbies, stress and lack of sleep are some of the influencing factors of persistent PTSD in veterans.

Targeting the right group of workers for crowdsourcing often achieves better quality results. One unique example of targeted crowdsourcing is seeking community-situated workers whose familiarity with the background and the norms of a particular group can help produce better outcome or accuracy. These community-situated crowd workers can be recruited in different ways from generic online crowdsourcing platforms or from online recovery communities. We evaluate three different approaches to recruit generic and community-situated crowd in terms of the time and the cost of recruitment, and the accuracy of task completion. We consider the context of Alcoholics Anonymous (AA), the largest peer support group for recovering alcoholics, and the task of identifying and validating AA meeting information. We discuss the benefits and trade-offs of recruiting paid vs. unpaid community-situated workers and provide implications for future research in the recovery context and relevant domains of HCI, and for the design of crowdsourcing ICT systems.

Alcoholics Anonymous (AA) is the largest grassroots peer support group for any health condition. While AA meeting attendance is particularly important for people who are newly sober, newcomers often have trouble finding meetings because of a lack of global up-to-date meeting list due to preference for regional autonomy in AA's organizational structure. Detection of regional webpages containing meetings and extraction of day, time, and address of meetings from those pages are essential steps in making the information available and up-to-date in a global meeting list. However, varied structure of the webpages and the meetings pose challenges in achieving the goal with traditional information retrieval methods. In this paper we propose HAIR: a semi-automated human-aided information retrieval technique and explore its potential to solve this problem. We describe future directions in developing this critical tool and discuss major implications of our work in pointing to the importance of context-specific rather than context-agnostic semi-automated in-formation retrieval techniques by conceptualizing the proposed methods and results in a broader context.

People in recovery from substance use disorders seek peer support through online health communities like InTheRooms.com (ITR). This community provides the unique opportunity to study video-mediated health peer support online, as it hosts over 100 weekly video meetings for fellowships like Alcoholics Anonymous (AA). We describe two synergistic investigations of participants' use, perceptions, and tensions around video meetings on ITR: an online questionnaire, and in-depth inter-views with active site members. We discuss four themes that may be important to other peer-support health communities: opportunities for video-mediated support, synergy with face-to-face contact, challenge of transparency of norms, and the importance of constructive moderation.

How do individuals in twelve-step fellowships like Alcoholics Anonymous (AA) and Narcotics Anonymous (NA) interpret and enact "anonymity?" In this paper, we answer this question through a mixed-methods investigation. Through secondary analysis of interview data from 26 participants and an online questionnaire (N=285) we found three major interpretations of anonymity among AA and NA members: "unidentifiability," "social contract," and "program over individual." While unidentifiability has been the focus of computing investigations, the other interpretations provide a significant and novel lens on anonymity. To understand how and when the unidentifiability interpretation was most likely to be enacted, we conducted a quantitative analysis of traces of activity in a large online recovery community. We observed that members were less likely to enact "unidentifiability" if they were more connected to the particular community and had more time in recovery. We provide implications for future research on context-specific anonymity and implications for design in online recovery spaces and similar sensitive contexts.

Human posture identification for motion controlling applications is becoming more of a challenge. We present a posture classification system using skeletal-tracking feature of Microsoft Kinect sensor. Posture recovery is carried out by detecting the human body joints, its position, and orientation at the same time. Angular representation of the skeleton data makes the system very robust and avoids problems related to human body occlusions and motion ambiguities. The implemented system is tested on a class of relatively common postures comprising hundreds of human pose instances by different people, where our classifier shows an average accuracy of 94.9%, 96.7% and 96.9% for linear, exponential and priority based matching systems respectively.