Nudging participants with text-based reflective nudges enhances deliberation quality on online deliberation platforms. The effectiveness of multimodal reflective nudges, however, remains largely unexplored. Given the multi-sensory nature of human perception, incorporating diverse modalities into self-reflection mechanisms has the potential to better support various reflective styles. This paper explores how presenting reflective nudges of different types (direct: persona and indirect: storytelling) in different modalities (text, image, video and audio) affects deliberation quality. We conducted two user studies with 20 and 200 participants respectively. The first study identifies the preferred modality for each type of reflective nudges, revealing that text is most preferred for persona and video is most preferred for storytelling. The second study assesses the impact of these modalities on deliberation quality. Our findings reveal distinct effects associated with each modality, providing valuable insights for developing more inclusive and effective online deliberation platforms.
https://dl.acm.org/doi/10.1145/3706598.3714189
Managing complex chronic illness is challenging due to its unpredictability. This paper explores the potential of voice for automated flare-up forecasts. We conducted a six-week speculative design study with individuals with endometriosis, tasking participants to submit daily voice recordings and symptom logs. Through focus groups, we elicited their experiences with voice capture and perceptions of its usefulness in forecasting flare-ups. Participants were enthusiastic and intrigued at the potential of flare-up forecasts through the analysis of their voice. They highlighted imagined benefits from the experience of recording in supporting emotional aspects of illness and validating both day-to-day and overall illness experiences. Participants reported that their recordings revolved around their endometriosis, suggesting that the recordings’ content could further inform forecasting. We discuss potential opportunities and challenges in leveraging the voice as a data modality in human-centered AI tools that support individuals with complex chronic conditions.
https://dl.acm.org/doi/10.1145/3706598.3714040
This paper critically examines the machine learning (ML) modeling of humans in three case studies of well-being technologies. Through a critical technical approach, it examines how these apps were experienced in daily life (technology in use) to surface breakdowns and to identify the assumptions about the “human” body entrenched in the ML models (technology design). To address these issues, this paper applies agential realism to decenter foundational assumptions, such as body regularity and health/illness binaries, and speculates more inclusive design and ML modeling paths that acknowledge irregularity, human-system entanglements, and uncertain transitions. This work is among the first to explore the implications of decentering theories in computational modeling of human bodies and well-being, offering insights for more inclusive technologies and speculations toward posthuman-centered ML modeling.
https://dl.acm.org/doi/10.1145/3706598.3713940
With psychiatry lagging behind other medical fields in terms of innovation in instruments and methods, AI provides it an opportunity to catch up. Advocates of digital phenotyping promise to provide an objective tool that detects symptoms by analysing data from personal devices. We argue that digital phenotyping requires a more reflexive and critical approach to its design and an alignment of the clinicians' interests in generating relevant evidence with the needs of service users who seek tools to manage their condition. We propose a felt informatics approach, situating digital phenotyping design within the problem space of pragmatist aesthetics. Within this perspective, felt life becomes a central object and a site for digital phenotyping design. This paper reveals the ways diagnostic data mediates mental ill health experience, emphasises the cultivation of aesthetic sensibility as a fundamental element of digital phenotyping and includes design considerations for practitioners and researchers.
The mobile health market is rapidly developing, but few apps follow evidence-based guidelines. Literature recommends personalized systems grounded in behavioral science, involving healthcare professionals in design to maximize effectiveness. To address this, we propose a metamodel to guide designers. This article discusses its application to low back pain self-management, focusing on four patient profiles: Unmotivated, Cautious, Depressed, and Confident. We evaluated the app over one month with 60 users. Of these, 32 users received a version of the application tailored to their profile, and 28 users received a version of the application without tailoring (no recommendations or motivational messages). We assessed user experience, engagement and psychological characteristics involved in the behavior change process. Results showed satisfactory user experience, impact of tailoring on user behavior and features to reduce fears and false beliefs and increase self-efficacy. Further efforts are needed to increase user engagement and observe an impact on long-term behavior.
https://dl.acm.org/doi/10.1145/3706598.3713224
Remote patient monitoring can significantly enhance post-operative home recovery for cancer patients, yet its effectiveness is often hindered by low patient engagement. Reassurance has been identified as a key factor in improving engagement. Our study explored how cancer patients seek reassurance through a Patient Public Involvement workshop with former patients. This involved developing personas for participants to navigate reassurance scenarios and share their post-operative experiences. Based on this, we co-created a reassurance journey map to illustrate when reassurance is needed, the behaviours patients use to seek it, and how it can be effectively provided. Our findings highlight three key design principles: the limitations of digital technology in offering reassurance, the personalised nature of reassurance, and the need for holistic integration. These are intended to inform the design of reassurance-focused RPM systems that better support cancer patients during home recovery. Practical design recommendations are also provided for developers and clinicians.
https://dl.acm.org/doi/10.1145/3706598.3714134
Image-based sexual abuse (IBSA) refers to the nonconsensual creating, taking, or sharing of intimate images, including threats to share intimate images. Despite the significant harms of IBSA, there is limited data on its prevalence and how it affects different identity or demographic groups. This study examines prevalence of, impacts from, and responses to IBSA via a survey with over 16,000 adults in 10 countries. More than 1 in 5 (22.6%) respondents reported an experience of IBSA. Victimization rates were higher among LGBTQ+ and younger respondents. Although victimized at similar rates, women reported greater harms and negative impacts from IBSA than men. Nearly a third (30.9%) of victim-survivors did not report or disclose their experience to anyone. We provide large-scale, granular, baseline data on prevalence in a diverse set to aid in the development of effective interventions that address the experiences and intersectional identities of victim-survivors'.
https://dl.acm.org/doi/10.1145/3706598.3713545