IntroductionAlthough remote patient management (RPM) shows promise for improving chronic heart failure (HF) management, its effectiveness depends on technology acceptance, usability, and patient engagement. However, patients are often involved only after systems are implemented, limiting opportunities to align RPM with their needs. This study explored patients' expectations, wishes, and concerns regarding future RPM using participatory design methods to inform more patient-centred RPM.MethodsPatients with HF using RPM were recruited from a Dutch hospital for individual, semi-structured interviews. We employed a co-constructing stories approach, inviting participants to imagine future scenarios of remote care. Guided by narrative prompts about potential developments in RPM, participants were encouraged to discuss how such technologies might shape their experiences. During the interviews, one researcher concurrently created sketches of the participants' answers, creating an evolving trace of the dialogue. The visual data were subsequently analysed using Annotated Visual Analysis to identify themes.ResultsSix patients were interviewed, producing 7 h of audio and 18 A3 pages of sketch notes. Four key themes emerged: Data Feedback, reflecting patients' desire to receive clear and constructive feedback that supports self-management beyond alert-based monitoring, to be able to take more responsibility for their health through RPM; Invasiveness, including camera-based monitoring or collection of non-medical data, was perceived as a barrier to adoption, although concerns were reduced when monitoring was clearly explained and considered proportional to disease severity; Adaptability and Integration highlighted the need for RPM to extend support beyond monitoring and to remain useful in daily life; Healthcare Provider contact emphasised that human interaction is irreplaceable, with empathy from healthcare professionals fostering trust in the system and patients' confidence in managing their health.ConclusionInviting patients to reflect on remote care provided valuable insights into their needs and expectations for future RPM. Participatory design methodologies facilitated rich discussions about patients' experiences, needs, and expectations regarding future RPM. Patients emphasised RPM should be actionable, minimally invasive, adaptable to daily life, and complemented by human contact. This highlights the importance of socio-technical systems that empower self-management while maintaining professional support. These findings can guide more human-centred RPM designs for HF care.

As data-driven technologies like remote patient monitoring (RPM) reshape healthcare, designers are increasingly in demand to implement new technologies seamlessly into existing workflows. Yet working in healthcare remains uniquely constrained: data is sensitive, access is restricted, and clinicians have limited time to engage. While prior work has identified these challenges, few studies have examined how designers actually navigate them in practice. This paper presents findings from interviews with 11 designers working across academic and healthcare settings. The study surfaces a repertoire of 27 tactics designers use to work around limited access to data and stakeholders, and structures them into the TORCH toolkit. The tactics include using alternative data sources, learning by reverse engineering systems, aligning with ongoing clinical initiatives, and prioritising micro-engagements with clinicians. To further illustrate how these tactics can be operationalised, we reflect on two design cases where designers employ novel techniques such as synthetic dataset construction, open source proxy datasets, and micro-workshops with clinicians. By shifting the conversation from domain constraints to design capabilities, this paper contributes a practical toolkit for designers engaging with the clinical field and a call to explore alternative ways of working in healthcare settings.

Conceptual data modeling is a central activity in data work, yet how such models are created remains understudied. While data attributes play a key role, modeling is also shaped by tasks, tools, developers’ prior experiences, and often unfolds collaboratively between diverse stakeholders. In this study, we invited 22 participants with varying expertise in pairs to collaboratively sketch conceptual data models. We captured screen recordings, their evolving sketches, and conversations. Through a mixed-methods approach combining thematic analysis of dialogue with an examination of model artifacts, we identify how communication and collaboration patterns influenced the process. Our findings reveal a range of collaborative strategies and representations, as well as distinct ways dialogue shaped the emergence and expression of shared conceptual models. These insights deepen understanding of Human-Data Interaction in collaborative data work and point to design opportunities for tools that better support communication, negotiation, and sensemaking of data.

Sound offers unique affordances for interaction designers, enabling immediate, embodied, and expressive experiences. Yet sound is often treated as peripheral, limited to simple notifications rather than being explored as a central design material. Designing meaningful auditory feedback typically requires low-level programming, audio-specific expertise, and fragmented toolchains that hinder earlystage experimentation. We present TESI (Tangible and Embodied Sound Interaction), a modular toolkit that reduces these barriers by combining sensor-integrated hardware, open-source middleware, and a visual programming environment for real-time sensor-to-sound mapping. TESI enables interaction designers to prototype adaptive soundscapes across networked tangible interfaces with minimal setup. We describe the toolkit's architecture, illustrate its use through examples, and discuss future directions for embodied sound design in distributed systems. CCS Concepts center dot Human-centered computing -> User interface toolkits; center dot Applied computing -> Sound and music computing.

Listening to heart and lung sounds — auscultation — is one of the first and most fundamental steps in a clinical examination. Despite being fast and non-invasive, it demands years of experience to interpret subtle audio cues. Recent deep learning methods have made progress in automating cardiopulmonary sound analysis, yet most are restricted to simple classification and offer little clinical interpretability or decision support. We present StethoLM, the first audio–language model specialized for cardiopulmonary auscultation, capable of performing instruction-driven clinical tasks across the full spectrum of auscultation analysis. StethoLM integrates audio encoding with a medical language model backbone and is trained on StethoBench, a comprehensive benchmark comprising 77,027 instruction–response pairs synthesized from 16,125 labeled cardiopulmonary recordings spanning seven clinical task categories: binary classification, detection, reporting, reasoning, differential diagnosis, comparison, and location-based analysis. Through multi-stage training that combines supervised fine-tuning and direct preference optimization, StethoLM achieves substantial gains in performance and robustness on out-of-distribution data. Our work establishes a foundation for instruction-following AI systems in clinical auscultation.

People's identities change during life transitions, e.g., studying abroad. They bring everyday objects that embody memories and reflect their identities during such moves. To assist in these transitions, we ask how people's human identities could be influenced by their objects through an artificial agent. This paper presents an exploratory research-through-design study around how people undergoing life transitions experience conversing with their everyday objects through a chatbot. Drawing on a two-week field deployment and interviews with 12 participants, we contribute (1) a conceptualization of 'trans-embodiment' describing the asynchronous imagination of object and human identities on the chatbot, (2) empirical evidence of the resulting emotional and reflective experiences, and (3) three types of object identities for designing conversational agents that role-play objects. Our contributions sum up to triangulating human-agent-object identity as trans-embodiment in supporting life transitions.

Federated Learning (FL) offers a powerful paradigm for training models on decentralized data, but its promise is often undermined by the immense complexity of designing and deploying robust systems. The need to select, combine, and tune strategies for multifaceted challenges like data heterogeneity and system constraints has become a critical bottleneck, resulting in brittle, bespoke solutions. To address this, we introduce Helmsman, a novel LLM-based multi-agent framework that automates the end-to-end synthesis of federated learning systems from high-level user specifications. It emulates a principled research and development workflow through three collaborative phases: (1) interactive human-in-the-loop planning to formulate a sound research plan, (2) modular code generation by supervised generative agent teams, and (3) a closed-loop of autonomous evaluation and refinement in a sandboxed simulation environment. To facilitate rigorous evaluation, we also introduce AgentFL-Bench, a new benchmark comprising 16 diverse tasks designed to assess the system-level generation capabilities of LLM-driven agentic systems in FL. Extensive experiments demonstrate that our approach generates solutions competitive with, and often superior to, established hand-crafted baselines. Our work represents a significant step towards the automated engineering of complex decentralized AI systems.

Commonly attributed smart home challenges for both primary and secondary users have been researched extensively, yet there is a gap in understanding mental models and their influence on how both types of users internalize and domesticate smart home lighting systems including bulbs, lamps, smart plugs, switches, and presence sensors. Our study targets the perceptual distinction between both users from a long-term domestication perspective. We conducted an in-situ ethnographic study with 12 households in the Netherlands and a subsequent online survey with 93 participants. Our research offers three contributions. By analyzing guided diaries, interviews, and surveys, we characterize the intricate mental models (spatial, contextual, functional, and metaphorical mapping) within and across households. Second, we addressed the distinctiveness and importance of the post-domestication phase in smart home lighting systems. Lastly, we present design recommendations to improve future smart home system designs aimed at long-term, evolving use.

AIMS:Methods of non-invasive remote patient monitoring (RPM) for heart failure (HF) remain diverse. Understanding factors that influence the effectiveness of RPM on HF-related and all-cause hospitalizations, mortality, and emergency department visits is crucial for developing successful RPM interventions. This meta-analysis aims to synthesize and compare existing literature on RPM components that impact HF-related and all-cause hospitalizations, mortality and emergency department visits in HF patients. METHODS AND RESULTS:A systematic search of electronic databases (PubMed, EMBASE, CENTRAL) identified randomized controlled trials from January 2012 to June 2023, comparing non-invasive RPM interventions for HF with usual care. A random-effects meta-analysis assessed outcomes, and additional analyses identified effective RPM components. A total of 41 studies with 16 312 patients (mean follow-up: 9.88 ± 6.37 months) were included. RPM was associated with lower mortality risk (pooled odds ratio [OR] 0.81 95% confidence interval [CI] 0.69-0.95; I2 = 0.39) and reduced first HF hospitalization risk (pooled OR 0.78, 95% CI: 0.70-0.87; I2 = 0.21) compared to usual care. RPM interventions with a self-management module (p < 0.001) and education module (p = 0.028) significantly lowered HF-related hospitalizations. Video calls during RPM interventions further reduced HF-related (p = 0.047) and all-cause hospitalizations (p < 0.001). CONCLUSION:This meta-analysis confirms the efficacy of RPM in reducing HF-related hospitalizations and mortality. Effective components include self-management, education modules, and video communication. However, heterogeneity among interventions challenges the overall evaluation. Modernizing RPM with advanced technologies like non-invasive sensors, artificial intelligence, and cardiac telerehabilitation could enhance its potential.

Designers have ample opportunities to impact the healthcare domain. However, hospitals are often closed ecosystems that pose challenges in engaging clinical stakeholders, developing domain knowledge, and accessing relevant systems and data. In this paper, we introduce a making-oriented approach to help designers understand the intricacies of their target healthcare context. Using Remote Patient Monitoring (RPM) as a case study, we explore how manually crafting synthetic datasets based on real-world observations enables designers to learn about complex data-driven healthcare systems. Our process involves observing and modeling the real-world RPM context, crafting synthetic datasets, and iteratively prototyping a simplified RPM system that balances contextual richness and intentional abstraction. Through this iterative process of sensemaking through making, designers can still develop context familiarity when direct access to the actual healthcare system is limited. Our approach emphasizes the value of hands-on interaction with data structures to support designers in understanding opaque healthcare systems.

This studio invites participants to explore tangible and embodied interaction beyond standalone interfaces into interconnected ecologies of smart objects, focusing on sound as a primary design element. Through a series of hands-on experimentations, participants will prototype with a modular toolkit, exploring how sound can move beyond simple notification functions to create rich and adaptive soundscapes. Drawing from the practices of digital lutherie and sound design, in particular mapping and interaction design strategies, participants will learn how to map sensor data to sound using interactive machine learning techniques based on artificial neural networks. This studio explores sustainable practices by adopting a modular approach to create adaptable systems, while promoting community engagement, open source contributions, and wider accessibility.

Background Many promising artificial intelligence (AI) and computer-aided detection and diagnosis systems have been developed, but few have been successfully integrated into clinical practice. This is partially owing to a lack of user-centered design of AI-based computer-aided detection or diagnosis (AI-CAD) systems. Objective We aimed to assess the impact of different onboarding tutorials and levels of AI model explainability on radiologists’ trust in AI and the use of AI recommendations in lung nodule assessment on computed tomography (CT) scans. Methods In total, 20 radiologists from 7 Dutch medical centers performed lung nodule assessment on CT scans under different conditions in a simulated use study as part of a 2×2 repeated-measures quasi-experimental design. Two types of AI onboarding tutorials (reflective vs informative) and 2 levels of AI output (black box vs explainable) were designed. The radiologists first received an onboarding tutorial that was either informative or reflective. Subsequently, each radiologist assessed 7 CT scans, first without AI recommendations. AI recommendations were shown to the radiologist, and they could adjust their initial assessment. Half of the participants received the recommendations via black box AI output and half received explainable AI output. Mental model and psychological trust were measured before onboarding, after onboarding, and after assessing the 7 CT scans. We recorded whether radiologists changed their assessment on found nodules, malignancy prediction, and follow-up advice for each CT assessment. In addition, we analyzed whether radiologists’ trust in their assessments had changed based on the AI recommendations. Results Both variations of onboarding tutorials resulted in a significantly improved mental model of the AI-CAD system (informative P=.01 and reflective P=.01). After using AI-CAD, psychological trust significantly decreased for the group with explainable AI output (P=.02). On the basis of the AI recommendations, radiologists changed the number of reported nodules in 27 of 140 assessments, malignancy prediction in 32 of 140 assessments, and follow-up advice in 12 of 140 assessments. The changes were mostly an increased number of reported nodules, a higher estimated probability of malignancy, and earlier follow-up. The radiologists’ confidence in their found nodules changed in 82 of 140 assessments, in their estimated probability of malignancy in 50 of 140 assessments, and in their follow-up advice in 28 of 140 assessments. These changes were predominantly increases in confidence. The number of changed assessments and radiologists’ confidence did not significantly differ between the groups that received different onboarding tutorials and AI outputs. Conclusions Onboarding tutorials help radiologists gain a better understanding of AI-CAD and facilitate the formation of a correct mental model. If AI explanations do not consistently substantiate the probability of malignancy across patient cases, radiologists’ trust in the AI-CAD system can be impaired. Radiologists’ confidence in their assessments was improved by using the AI recommendations.

Aims Urbanization is related to non-communicable diseases such as congestive heart failure (CHF). Understanding the influence of diverse living environments on physiological variables such as heart rate variability (HRV) in patients with chronic cardiac disease may contribute to more effective lifestyle advice and telerehabilitation strategies. This study explores how machine learning (ML) models can predict HRV metrics, which measure autonomic nervous system responses to environmental attributes in uncontrolled real-world settings. The goal is to validate whether this approach can ascertain and quantify the connection between environmental attributes and cardiac autonomic response in patients with CHF. Methods and results A total of 20 participants (10 healthy individuals and 10 patients with CHF) wore smartwatches for 3 weeks, recording activities, locations, and heart rate (HR). Environmental attributes were extracted from Google Street View images. Machine learning models were trained and tested on the data to predict HRV metrics. The models were evaluated using Spearman's correlation, root mean square error, prediction intervals, and Bland-Altman analysis. Machine learning models predicted HRV metrics related to vagal activity well (R > 0.8 for HR; 0.8 > R > 0.5 for the root mean square of successive interbeat interval differences and the Poincar & eacute; plot standard deviation perpendicular to the line of identity; 0.5 > R > 0.4 for the high frequency power and the ratio of the absolute low- and high frequency power induced by environmental attributes. However, they struggled with metrics related to overall autonomic activity, due to the complex balance between sympathetic and parasympathetic modulation. Conclusion This study highlights the potential of ML-based models to discern vagal dynamics influenced by living environments in healthy individuals and patients diagnosed with CHF. Ultimately, this strategy could offer rehabilitation and tailored lifestyle advice, leading to improved prognosis and enhanced overall patient well-being in CHF. Graphical Abstract

Artificial Intelligence (AI) decision-making tools for radiology demonstrated potential capacity to improve radiology work in several tasks such as tumor detection. However, relatively low acceptance in clinical practice demonstrates the challenge of incorporating end-users’ lived experience and their opinions to improve the interaction between clinicians and AI solutions. Therefore, we conducted semi-structured interviews with radiologists and technicians who had lived experience with current or the prior generations of radiology AI tools (e.g., Computer Aided Decision tools). Three key themes were elicited. Firstly, the role of AI, addresses how radiology professionals interact with radiology AI; the second theme, adoption in practice, discusses the requirements for easy usage and smooth transition; the third theme, building appropriate trust, explores influencing factors of clinicians’ trust towards radiology AI. Our findings call attention to the adoption of actionable recommendations on the interaction design and the importance of individual tailored functionalities in radiology AI systems.

Today many children encounter Internet of Things (IoT) devices and systems of connected products in their daily contexts and activities such as learning, play, and helping in the household. When designing computational systems that children can understand and use, making sense of a child’s perspective of system behaviors is a challenge for HCI practitioners and researchers, particularly when targeting an informal, undirected learning experience. To build design knowledge on how children approach and make sense of novel interactive system concepts, we have designed and implemented a system of ten connected components, which was deployed at a local Maker Faire. This case study reports on design requirements, data design process, observations from and reflections on the deployment with respect to the young visitors’ interactions with the implemented system behaviors. We conclude with a discussion of insights and future possibilities.