Personal informatics (PI) systems are widely used in various domains such as mental health to provide insights from self-tracking data for behavior change. Users are highly interested in examining relationships from the self-tracking data, but identifying causality is still considered challenging. In this study, we design DeepStress, a PI system that helps users analyze contextual factors causally related to stress. DeepStress leverages a quasi-experimental approach to address potential biases related to confounding factors. To explore the user experience of DeepStress, we conducted a user study and a follow-up diary study using participants' own self-tracking data collected for 6 weeks. Our results show that DeepStress helps users consider multiple contexts when investigating causalities and use the results to manage their stress in everyday life. We discuss design implications for causality support in PI systems.
doi.org/10.1145/3613904.3642766
During the grieving process, physical objects often serve as catalysts for remembering and honouring the relationship with departed loved ones. Leveraging a participatory design approach, we created Be.side, a fully customisable multi-modal artefact that incorporates scent, sound, and heartbeat stimulation and acts as a touch-point between the deceased and the bereaved. We conducted a four-week study with three participants to understand how the artefact, continuously attuned to each participant, helped to continue bonds with the deceased. Our results show that Be.side’s bespoke elements helped participants to evoke memories of the deceased. Participants created personalised rituals for remembrance. They sustained bonds by not only interacting with Be.side but also participating in the research. Finally, highlighting that remembrance can both provide comfort and deepen sadness, we discuss future design considerations.
doi.org/10.1145/3613904.3642386
Recent increases in self-harm and suicide rates among youth have coincided with prevalent social media use; therefore, making these sensitive topics of critical importance to the HCI research community. We analyzed 1,224 direct message conversations (DMs) from 151 young Instagram users (ages 13-21), who engaged in private conversations using self-harm and suicide-related language. We found that youth discussed their personal experiences, including imminent thoughts of suicide and/or self-harm, as well as their past attempts and recovery. They gossiped about others, including complaining about triggering content and coercive threats of self-harm and suicide but also tried to intervene when a friend was in danger. Most of the conversations involved suicide or self-harm language that did not indicate the intent to harm but instead used hyperbolical language or humor. Our results shed light on youth perceptions, norms, and experiences of self-harm and suicide to inform future efforts towards risk detection and prevention.
doi.org/10.1145/3613904.3642489
Children with high-functioning autism (HFA) often face challenges in emotional recognition and expression, leading to emotional distress and social difficulties. Conversational agents developed for HFA children in previous studies show limitations in children’s learning effectiveness due to the conversational agents’ inability to dynamically generate personalized and contextual content. Recent advanced generative Artificial Intelligence techniques, with the capability to generate substantial diverse and high-quality texts and visual content, offer an opportunity for personalized assistance in emotional learning for HFA children. Based on the findings of our formative study, we integrated large language models and text-to-image models to develop a tool named EmoEden supporting children with HFA. Over a 22-day study involving six HFA children, it is observed that EmoEden effectively engaged children and improved their emotional recognition and expression abilities. Additionally, we identified the advantages and potential risks of applying generative AI to assist HFA children in emotional learning.
doi.org/10.1145/3613904.3642899
A growing market for mental health applications and increasing evidence for the efficacy of these applications have made apps a popular mode of mental healthcare delivery. However, given the gravity of mental illnesses, the potential harms of using these applications must be continually investigated. In this study, we conducted a thematic analysis using user-comments left on depression self-management applications. We analyzed 6,253 reviews from thirty-six, systematically selected apps from the Google Play and Apple App stores. We identified four themes regarding the potential, unintentional harms caused by these applications. This study uniquely contributes to the literature by examining the reported harms to users caused by depression self-management apps and contextualizing them in an ethical framework. We provide recommendations to developers for creating ethical depression self-management apps and resources for practitioners and consumers to aid in screening apps.
doi.org/10.1145/3613904.3642178