Loss of decisional capacity, coupled with the increasing absence of reliable human proxies, raises urgent questions about how individuals’ values can be represented in Advance Care Planning (ACP). To probe this fraught design space of high-risk, high-subjectivity decision support, we built an experience prototype (ACPAgent) and asked 15 participants in 4 workshops to train it to be their personal ACP proxy. We analysed their coping strategies and feature requests and mapped the results onto axes of agent autonomy and human control. Our findings show a surprising 86.7% agreement with ACPAgent, arguing for a potential new role of AI in ACP where agents act as personal advocates for individuals, building mutual intelligibility over time. We propose that the key areas of future risk that must be addressed are the moderation of users’ expectations and designing accountability and oversight over agent deployment and cutoffs.

Despite technology's pervasive integration into aged care to address aging populations and labor shortages, its deployment often overlooks the critical role of care workers who shape the experience of care through technologies. This paper presents a four-month ethnographic study at a long-term residential aged care facility in Singapore, involving 37 staff members from frontline workers to senior management. We examine how they adopt, implement, and use three distinct digital technologies at different stages of maturity in the delivery of care. Adopting an infrastructuring lens, our findings, presented through six affective vignettes, reveal the invisible labor involved in ''patchworking'' technologies implemented in a top-down, fragmented manner as well as the significant impacts on workers' liability and professional agency. The discussion unpacks the invisible burden of organizational unpreparedness in technology integration, the resulting negotiation of care work's meaning and power differentials with care workers for decent work.

We explored the potential of Large Language Models (LLMs) to facilitate laypersons' selection of treatment goals within a complex medical decision-making context.Using ChatGPT-4o, we developed an LLM-enhanced tool to guide users through goal elicitation, clarification, and revision.Our findings demonstrate that LLM features can effectively support these key aspects of decision-making.However, the absence of human interaction, the lack of patientand context-specific treatment information, and the risk of information overload due to unconstrained access to LLM-generated content present significant risks.To balance the benefits and risks, we propose that LLM-enhanced facilitation tools for asynchronous, independent use should be clinician-initiated, constrain broad information search, and focus on creating a safe space for the exploration of laypersons' preferences and goals regarding the difficult challenges in balancing treatment and tradeoffs for quality of life.

In serious illness contexts, caregivers tasked with making decisions for incapacitated patients face unique challenges. Current tools for values elicitation, primarily patient-oriented, may not fully meet caregiver needs. This study, involving 12 caregivers, adopted a research-through-design approach to evaluate caregiver-facing values elicitation tools. The findings reveal caregivers’ need for more support in understanding their own values, discerning patient values, and reconciling these perspectives. While caregivers seek tools for consensus building within the family, they prefer using them individually rather than in a shared manner. The study also highlights a general unawareness of the importance of values in decision-making. This lack of understanding points to the need for tools that educate as well as aid in decision-making. Finally, tool designers must balance top-down directives with bottom-up values in their designs. This research informs the development of more effective caregiver-facing values elicitation tools.

Digital contact tracing can limit the spread of infectious diseases. Nevertheless, barriers remain to attain sufficient adoption. In this study, we investigate how willingness to participate in contact tracing is affected by two critical factors: the modes of data collection and the type of data collected. We conducted a scenario-based survey study among 220 respondents in the United States (U.S.) to understand their perceptions about contact tracing associated with automated and manual contact tracing methods. The findings indicate a promising use of smartphones and a combination of public health officials and medical health records as information sources. Through a quantitative analysis, we describe how different modalities and individual demographic factors may affect user compliance when participants are asked to provide four key information pieces for contact tracing.

Counsellors use Motivational Interviewing (MI) to boost intrinsic motivation for healthier lifestyles by supporting 1) the identification of personal values and 2) the linking of these values to personal reasons for change. Research is needed to increase the reach of such interventions through the use of online tools. Thus, we explored the use of defamiliarization and poetic gameplay in a serious game, to make habitual health choices difficult and ‘unfamiliar’ in order to promote players’ contemplation about their personal reasons for health. Our evaluation indicates that the players demonstrated evidence of processes of change such as self-reevaluation, environmental reevaluation, and improving decisional clarity. We observed that these methods could promote dialogic reflection with different points of view, but was less effective at promoting transformative reflection that would lead to change. We conclude with a discussion of promising future directions for research.

Previous work has looked closely at the challenges of using patient-generated data to enable remote assessment and monitoring by healthcare professionals. In this paper, we examine family caregivers who act as proxies for patients who may not have the capacity of capturing the necessary data. We worked with occupational therapists to develop an application for remote assessment of the safety of patients' homes by occupational therapists with the assistance of family caregivers. We evaluated the application with family caregivers and found two features unique to communication between family caregivers and healthcare professionals: Caregivers want to be able to direct healthcare professionals' attention to support problem-solving at home, and they include their perspective on how to best meet the patient's health needs. We discuss the importance of these findings for home systems in the domain of long-term chronic care.

On-the-go text-editing is difficult, yet frequently done in everyday lives. Using smartphones for editing text forces users into a heads-down posture which can be undesirable and unsafe. We present EYEditor, a heads-up smartglass-based solution that displays the text on a see-through peripheral display and allows text-editing with voice and manual input. The choices of output modality (visual and/or audio) and content presentation were made after a controlled experiment, which showed that sentence-by-sentence visual-only presentation is best for optimizing users' editing and path-navigation capabilities. A second experiment formally evaluated EYEditor against the standard smartphone-based solution for tasks with varied editing complexities and navigation difficulties. The results showed that EYEditor outperformed smartphones as either the path OR the task became more difficult. Yet, the advantage of EYEditor became less salient when both the editing and navigation was difficult. We discuss trade-offs and insights gained for future heads-up text-editing solutions.

Improving volunteer performance leads to better caregiving in dementia care settings. However, caregiving knowledge systems have been focused on eliciting and sharing expert, primary caregiver knowledge, rather than volunteer-provided knowledge. Through the use of an experience prototype, we explored the content of volunteer caregiver knowledge and identified ways in which such non-expert knowledge can be useful to dementia care. By using lay language, sharing information specific to the client and collaboratively finding strategies for interaction, volunteers were able to boost the effectiveness of future volunteers. Therapists who reviewed the content affirmed the reliability of volunteer caregiver knowledge and placed value on its recency, variety and its ability to help bridge language and professional barriers. We discuss how future systems designed for eliciting and sharing volunteer caregiver knowledge can be used to promote better dementia care.

We present EDITalk, a novel voice-based, eyes-free word processing interface. We used a Wizard-of-Oz elicitation study to investigate the viability of eyes-free word processing in the mobile context and to elicit user requirements for such scenarios. Results showed that meta-level operations like highlight and comment, and core operations like insert, delete and replace are desired by users. However, users were challenged by the lack of visual feedback and the cognitive load of remembering text while editing it. We then studied a commercial-grade dictation application and discovered serious limitations that preclude comfortable speak-to-edit interactions. We address these limitations through EDITalk's closed-loop interaction design, enabling eyes-free operation of both meta-level and core word processing operations in the mobile context. Finally, we discuss implications for the design of future mobile, voice-based, eyes-free word processing interface.

Engagement in dementia is typically measured using behavior observational scales (BOS) that are tedious and involve intensive manual labor to annotate, and are therefore not easily scalable. We propose AVEID, a low cost and easy-to-use video-based engagement measurement tool to determine the engagement level of a person with dementia (PwD) during digital interaction. We show that the objective behavioral measures computed via AVEID correlate well with subjective expert impressions for the popular MPES and OME BOS, confirming its viability and effectiveness. Moreover, AVEID measures can be obtained for a variety of engagement designs, thereby facilitating large-scale studies with PwD populations.

Volunteers are an important resource at long-term care homes because they can supply services, such as engagement activities, that over-burdened care staff struggle to provide. However, volunteers without sufficient training are often challenged in responding to dementia-linked behaviors, which can lead to frustrating difficulties during interaction. Additionally, short-staffed care homes have difficulties in training and maintaining volunteers. To better support volunteers in providing engagement activities for people with dementia without a high training burden, we created VITA, a tablet-based system that supplies carefully designed profiling and guidance using our dementia-appropriate engagement activity kit. Our evaluation indicated that the instructional guide supplied by VITA significantly improves volunteers' ability to facilitate engagement activities with people with dementia, approaching the level of engagement achievable by professional therapists.

Controlled experiment, an approach that has been adopted from research methods in psychology, is now widely used in HCI. Design an effective controlled experiment is not necessarily easy, even for experienced researchers. Existing controlled experiment designing tools focused more on the process of designing, but they are often not intuitive and guided enough for less experienced users to use. In this demo paper, we introduce NexP (Next Experiment Tool), a web-based open-source tool for designing controlled experiments. NexP introduces a 5-step approach to guide users through the experimental design process, and helped them to better understand the experimental design process, making it both a useful and educational tool.

We present CoFaçade, a novel approach to helping elders reach their goals with IT products by working collaboratively with helpers. In this approach, the elder uses an interface with a small number of triggers, where each trigger is a single button (or card) that can execute a procedure. The helper uses a customization interface to link triggers to procedures that accomplish frequently-recurring high-level goals with IT products. Customization can be done either locally or remotely. We conducted an experiment to compare the CoFaçade approach with a baseline approach where helpers taught elders to perform IT tasks. Our results showed that CoFaçade can reduce helpers' time and effort, reduce elders' frustration, and improve elders' success rate in completing IT tasks.