HCI increasingly engages with the concept of time, with many studies focusing on how temporal relations are produced and reconfigured through computing technologies. However, less attention is paid to the impact of temporality on researchers themselves and specifically their capacity for ethical reflection and action. This paper explores the interplay between temporal features and ethical reflection in HCI research projects. Through a reflective multi-ethnography of nine research projects, we examine how temporal features can influence ethical capacity across the lifecycle of research. The study employs infrastructural and chronopolitical analysis (after Star and Sharma) and offers a range of considerations for researchers to help activate greater temporal awareness for everyday wisdom, to acknowledge temporal power, mobilise responsibility, counter isolation in interdisciplinarity, and promote collective action and dynamic methodologies for temporal research. The study responds to calls for more ethnographic approaches to temporality that address specific HCI needs and focus on practice.

Globally, about 40% of women and people assigned female at birth live under laws that restrict or prohibit access to safe abortion care. Even where abortion is legal, socio-cultural stigma and health inequities hinder timely, equitable access. Technologies have been developed to support abortion seekers and providers in overcoming barriers to information, safe abortion care, and support. However, research on abortion care technologies is fragmented, spanning medical and computing publications, and lacking a consolidated understanding. To address this gap, we conducted a scoping review of 92 studies, examining technological applications, contexts of use, research methods, ethical considerations, and pathways to impact. This analysis informs the HCI research agenda for abortion care, highlighting future directions, and fostering reflection on design, ethics, and meaningful impact. We call on HCI researchers to move beyond telemedicine and U.S.-centric perspectives, re-politicize abortion care technologies, and consider temporality in delivering timely abortion care amid broader sociopolitical constraints.

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.

Digital mental health interventions (DMHIs) have potential to provide effective and accessible care to entire populations, but low client uptake and engagement are significant problems. Few prior studies explore the lived experiences of non-engagers, because reaching this population is inherently difficult. We present an observational inquiry into the barriers to sign-up and early use of a DMHI, along with reasons for initial interest in the DMHI. We collected 205 online questionnaire responses and 20 interviews from self-referring participants across four healthcare ecosystems in the UK and US. Questionnaire results revealed that uncertainty about DMHI usefulness and usability were the main barriers to uptake, whereas forgetting about it, not finding time for it and not finding it useful were the main barriers to early engagement. Participants reported multiple reasons for considering the DMHI, reflecting the contextual, subjective nature of mental health. Our thematic analysis generated themes around (1) the need for human connection, (2) the impact of self-stigma on help-seeking, (3) the lack of knowledge around DMHIs and psychological therapy, (4) the desire for personally relevant care, and (5) the fluctuating, perennial nature of mental health. We discuss implications for DMHI design, implementation and future research, as well as transdisciplinary opportunities.

Self-monitoring of mood and lifestyle habits is the cornerstone of many therapies, but it is still hindered by persistent issues including inaccurate records, gaps in the monitoring, patient burden, and perceived stigma. Smartwatches have the potential to deliver enhanced self-reports, but their acceptance in clinical mental health settings is unexplored and rendered difficult by a complex theoretical landscape and need for a longitudinal perspective. We present the Mood Monitor smartwatch application for mood and lifestyle habits self-monitoring. We investigated patient acceptance of the app within a routine 8-week digital therapy. We recruited 35 patients of the UK’s National Health Service and evaluated their acceptance through three online questionnaires and a post-study interview. We assessed the clinical feasibility of the Mood Monitor by comparing clinical, usage, and acceptance metrics obtained from the 35 patients with a smartwatch with those from an additional 34 patients without a smartwatch (digital treatment as usual). Findings showed that the smartwatch app was highly accepted by patients, revealed which factors facilitated and impeded this acceptance, and supported clinical feasibility. We provide guidelines for the design of self-monitoring on a smartwatch and reflect on the conduct of human-computer interaction research evaluating user acceptance of mental health technologies.

CHI papers researching healthcare human-computer interaction (HCI) are increasingly reporting the use of “thematic analysis” (TA). TA refers to a range of flexible and evolving approaches for qualitative data analysis. Its increased use demonstrates a change in research practices, and with that the emergence of new local standards. We need to understand and reflect upon these emerging local practices, including departures from what is advocated as quality TA practice more generally. Toward this, we conducted a scoping review of a decade of CHI publications (2012 – 2021) that researched healthcare and termed their analysis approach “thematic analysis”; 78 papers reporting a total of 100 TAs were included. We contribute a description of 1) the contexts in which TA is being used, 2) the TA approaches being conducted, and 3) how TA is being reported. Drawing on this, we discuss opportunities to improve research practice when using TA in healthcare HCI.

User acceptance is key for the successful uptake and use of health technologies, but also impacted by numerous factors not always easily accessible nor operationalised by designers in practice. This work seeks to facilitate the application of acceptance theory in design practice through the Technology Acceptance (TAC) toolkit: a novel theory-based design tool and method comprising 16 cards, 3 personas, 3 scenarios, a virtual think-space, and a website, which we evaluated through workshops conducted with 21 designers of health technologies. Findings showed that the toolkit revised and extended designers’ knowledge of technology acceptance, fostered their appreciation, empathy and ethical values while designing for acceptance, and contributed towards shaping their future design practice. We discuss implications for considering user acceptance a dynamic, multi-stage process in design practice, and better supporting designers in imagining distant acceptance challenges. Finally, we examine the generative value of the TAC toolkit and its possible future evolution.

In the last decade, the number of articles on HCI and health has increased dramatically. We extracted 139 papers on depression, anxiety and bipolar health issues from 10 years of SIGCHI conference proceedings. 72 of these were published in the last two years. A systematic analysis of this growing body of literature revealed that most innovation happens in automated diagnosis, and self-tracking, although there are innovative ideas in tangible interfaces. We noted an overemphasis on data production without consideration of how it leads to fruitful interventions. Moreover, we see a need to promote ethical practices for involvement of people living with affective disorders. Finally, although only 16 studies evaluate technologies in a clinical context, several forms of support and intervention illustrate how rich insights are gained from evaluations with real patients. Our findings highlight potential for growth in the design space of affective health technologies.

Sign'Maths is an online dictionary that gives deaf students access to mathematical definitions in Sign Language. In this study, we investigate a new approach that uses Sign Language as a means to navigate and access online content.