Background:
Heart failure (HF) is a chronic, progressive condition where the heart cannot pump enough blood to meet the body’s needs. In addition to the daily challenges that HF poses, acute exacerbations can lead to costly hospitalizations and increased mortality. High health care costs and the burden of HF have led to the emerging application of new technologies to support people living with HF to stay well while living in the community. However, many digital solutions have not involved consumers and health care professionals in their design, leading to poor adoption. The SmartHeart project aimed to codevelop a smart health ecosystem to support the early detection of HF deterioration and encourage self-care, potentially preventing hospitalizations.

Objective:
This study aims to provide an overview of the SmartHeart project by describing our approach to designing the SmartHeart system, outlining its features, and describing the planned pilot study to determine the feasibility of the system.

Methods:
We used the Integrate, Design, Assess, and Share (IDEAS) framework to guide the development of the SmartHeart system, involving users (people with HF and their caregivers) and stakeholders (health care providers involved in the management of HF) in its design. SmartHeart is a complete remote heart health monitoring and automated feedback delivery system. It includes 2 user interfaces for patients: an Amazon Alexa conversational agent and a smartphone app. The system collects physiological, symptom, and behavioral data through wireless sensors and self-reports from users. These data are processed and analyzed to provide personalized health insights, self-care support, and alerts in case of health deterioration. The system also includes a web-based user interface for health care professionals, allowing them to access data, send messages to users, and receive notifications about potential health deterioration. A single-arm, multicenter pilot trial (N=20) is planned to determine the feasibility and acceptability of SmartHeart before evaluation through a randomized controlled trial. The primary outcome will be a description of the study's feasibility (recruitment, attrition, engagement, and changes in self-care).

Results:
The SmartHeart study started in January 2021 on procurement of funding. Recruitment for the pilot trial started in August 2024 and will be completed by March 2025. We have currently enrolled 12 participants. Follow-up of all participants will be completed by the end of May 2025.

Conclusions:
We have co-designed and developed a complete remote heart health monitoring and automated feedback delivery system for the early detection of HF deterioration and prevention of HF-related hospitalizations. The next step is a pilot study, which will provide valuable information on feasibility and preliminary effects to inform a larger evaluation trial. SmartHeart has the potential to augment existing health services and help people with HF stay well while living in the community.

Mental Workload (MWL) is a construct widely used in HCI to assess the cognitive demand users must exert to perform a task. Research in human factors, however, has suggested several issues regarding its definitions, scales, and applications. This paper, first, introduces debates surrounding the MWL concept and its most popular measure, the NASA-TLX. We present a systematic review of CHI papers involving MWL and highlight severe issues in its application. Finally, through a validation experiment, we assess the convergent validity and sensitivity of two MWL instruments—NASA-TLX and MRQ. Our findings reveal disagreements in the definitions of MWL and severe drawbacks in NASA-TLX and its applications. Our validation study also presents evidence for a lack of convergent validity and sensitivity of MWL subjective scales in HCI tasks. Our findings recommend caution when employing NASA-TLX in user studies and highlight the need for an MWL definition that is agreed upon within the HCI community.

We explore the metaphorical "daily memory pill"concept - a brief pictorial lifelog recap aimed at reviving and preserving memories. Leveraging psychological strategies, we explore the potential of such summaries to boost autobiographical memory. We developed an automated lifelogging memory prosthesis and a research protocol (Automated Memory Validation "AMV") for conducting privacy-aware, in-situ evaluations. We conducted a real-world lifelogging experiment for a month (n=11). We also designed a browser "Pixel Memories"for browsing one-week worth of lifelogs. The results suggest that daily timelapse summaries, while not yielding significant memory augmentation effects, also do not lead to memory degradation. Participants' confidence in recalled content remains unaltered, but the study highlights the challenge of users' overestimation of memory accuracy. Our core contributions, the AMV protocol and "Pixel Memories"browser, advance our understanding of memory augmentations and offer a privacy-preserving method for evaluating future ubicomp systems.

The growing sophistication of Large Language Models allows conversational agents (CAs) to engage users in increasingly personalized and targeted conversations. While users may vary in their receptiveness to CA persuasion, stylistic elements and agent personalities can be adjusted on the fly. Combined with image generation models that create context-specific realistic visuals, CAs have the potential to influence user behavior and decision making. We investigate the effects of linguistic and visual elements used by CAs on user perception and decision making in a charitable donation context with an online experiment (n=344). We find that while CA attitude influenced trust, it did not affect donation behavior. Visual primes played no role in shaping trust, though their absence resulted in higher donations and situational empathy. Perceptions of competence and situational empathy were potential predictors of donation amounts. We discuss the complex interplay of user and CA characteristics and the fine line between benign behavior signaling and manipulation.

Text summaries, images, and mind maps are well-known methods for priming readers to better engage with content. Previously, these “primes” needed to be hand-crafted, limiting their use. The advent of generative technologies makes the automatic creation of custom primes for any passage a realistic possibility. Here, we evaluate the efficacy of primes generated using AI on reading comprehension, reading speed, and re-engagement during mobile reading, which is notorious for its frequent interruptions. We used a mobile platform to present a reading task with an interruption to 44 readers (21 with English as a first language). We found that AI primes increased reading speed by an average of 7% for all readers in the initial reading task with no loss of comprehension and that visual primes had a significant interruption recovery effect for people whose first language was not English. Across all readers, text primes had both the initial reading speed increase and were overall most preferred.

Designers often engage with video to gain rich, temporal insights about the context of users, collaboratively analyzing it to gather ideas, challenge assumptions, and foster empathy. To capture the full visual context of users and their situations, designers are adopting 360° video, providing richer, more multi-layered insights. Unfortunately, the spherical nature of 360° video means designers cannot create tangible video artifacts such as storyboards for collaborative analysis. To overcome this limitation, we created Tangi, a web-based tool that converts 360° images into tangible 360° video artifacts, that enable designers to embody and share their insights. Our evaluation with nine experienced designers demonstrates that the artifacts Tangi creates enable tangible interactions found in collaborative workshops and introduce two new capabilities: spatial orientation within 360° environments and linking specific details to the broader 360° context. Since Tangi is an open-source tool, designers can immediately leverage 360° video in collaborative workshops.

Individuals tend to apply preferences and beliefs as heuristics to effectively sift through the sheer amount of information available online. Such tendencies, however, often result in cognitive biases, which can skew judgment and open doors for manipulation. In this work, we investigate how individual and contextual factors lead to instances of confirmation bias when seeking, evaluating, and recalling polarising information. We conducted a lab study, in which we exposed participants to opinions on controversial issues through a Twitter-like news feed. We found that low-effortful thinking, strong political beliefs, and content conveying a strong issue amplify the occurrences of confirmation bias, leading to skewed information processing and recall. We discuss how the adverse effects of confirmation bias can be mitigated by taking bias-susceptibility into account. Specifically, social media platforms could aim to reduce strong expressions and integrate media literacy-building mechanisms, as low-effortful thinking styles and strong political beliefs render individuals especially susceptible to cognitive biases.

Background
Interventions targeting social media use show mixed results in improving well-being outcomes, particularly for persons with problematic forms of smartphone use. This study assesses the effectiveness of an intervention app in enhancing well-being outcomes and the moderating role of persons' perceptions about problematic smartphone use (PSU).

Methods
In a randomized controlled trial, N = 70 participants, allocated to the intervention (n = 35) or control condition (n = 35), completed weekly online surveys at baseline, post-intervention, and follow-up. Participants from the intervention condition received personalized full-screen nudges to reduce their social media app use. This secondary analysis focuses on the repeatedly assessed outcomes well-being, positive affect, negative affect, and perceived stress. Linear mixed models were computed.

Results
No significant time x group effects were found, but intervention condition participants reported higher well-being and lower negative affect and stress levels at follow-up. Only one significant moderation was found, indicating that participants reporting higher PSU levels benefited more from the intervention in reducing stress.

Conclusions
The intervention was partly effective and particularly beneficial in reducing stress among smartphone users with higher PSU, highlighting the need to tailor interventions. Present findings need to be replicated by future research using a larger sample size.

In the recent years, studies on health consequences of smartphone usage time have increased, yet findings on the effectiveness of usage interventions remain unclear. This preregistered study investigates the effectiveness of a planning intervention to reduce total smartphone usage time. Additionally, it examines the interventions’ underlying mechanisms of self-efficacy, intention, action, and coping planning. A primary analysis of a randomized controlled trial, with data collected at three measurement points was conducted. Three cohorts of university students were recruited during the period prior to the end-of-term exams. A total of N = 787 participants were allocated to either an intervention condition (n = 389) or a control condition (n = 398). At baseline measurement (T1) the intervention condition formed up to three actions and three coping plans. Self-reported self-efficacy, intention, action, and coping planning as well as objectively measured smartphone usage were assessed up to a three-weeks follow-up. The effectiveness of the intervention and the mediating mechanisms were evaluated using linear mixed models. The analysis revealed no significant effect on total smartphone usage time. With respect to the interventions underlying mechanisms, results show a significant indirect effect of self-efficacy at T2, on a reduction in total smartphone usage time at T3 but no evidence for intention, action, or coping planning.

Computing systems are increasingly designed to adapt to users' cognitive states and mental models. Yet, cognitive biases affect how humans form such models and, therefore, they can impact their interactions with computers. To better understand this interplay, we conducted a scoping review to chart how Human-Computer Interaction (HCI) researchers study cognitive biases. Our findings show that computing systems not only have the potential to induce and amplify cognitive biases but also can be designed to steer users' behaviour and decision-making by capitalising on biases. We describe how HCI researchers develop algorithms and sensing methods to detect and quantify the effects of cognitive biases and discuss how we can use their understanding to inform system design. In this paper, we outline a research agenda for more theory-grounded research and highlight ethical issues when researching and designing computing systems with cognitive biases in mind as they affect real-world behaviour.

Researchers have proposed various approaches to increase motivation for learning. However, in many cases, they are designed to boost motivation but fall short of creating learning habits. Gamification and competition between peers are widely used to urge people to, for example, do more steps in a day. E-learning platforms often use leaderboards to visualize peer progress, but their effectiveness is limited to when users open the platform. In this work, we use notifications to inform peers of each other's progress in real-time to increase motivation for learning. In an in-the-wild study with 19 participants, we investigate the impact of peer-awareness on learning by comparing real-time notifications (sent immediately after peers' progress) with interval-based notifications (sent at random intervals). Contrary to our initial expectations, our findings reveal that interval-based notifications are more effective in promoting learning activities. We introduce the Peer-Aware Learning System (PALS), a mobile application designed to simulate peer effects and provide insights from our user study on notification timing strategies for enhancing learning motivation.

Recent work on augmented reality (AR) has explored the use of adaptive agents to overcome attentional issues that negatively impact task performance. However, despite positive technical evaluations, adaptive agents have shown no significant improvements to user task performance in AR. Furthermore, previous works have primarily evaluated such agents using abstract tasks. In this paper, we develop an agent that observes user behaviour and performs appropriate actions to mitigate attentional issues in a realistic sense-making task in AR. We employ mixed methods to evaluate our agent in a between-subject experiment (N=60) to understand the agent's effect on user task performance and behaviour. While we find no significant improvements in task performance, our analysis revealed that users’ preferences and trust in the agent affected their receptiveness of the agent's recommendations. We discuss the pitfalls of autonomous agents and highlight the need to shift from designing better Human–AI interactions to better Human–AI collaborations.

Object selection and manipulation are the foundation of VR interactions. With the rapid development of VR technology and the field of virtual object selection and manipulation, the literature demands a structured understanding of the core research challenges and a critical reflection of the current practices. To provide such understanding and reflections, we systematically reviewed 106 papers. We identified classic and emerging topics, categorized existing solutions, and evaluated how success was measured in these publications. Based on our analysis, we discuss future research directions and propose a framework for developing and determining appropriate solutions for different application scenarios.

Attention management systems seek to minimize disruption by intelligently timing interruptions and helping users navigate multiple tasks and activities. While there is a solid theoretical basis and rich history in HCI research for attention management, little progress has been made regarding their practical implementation and deployment. Building sophisticated attention management systems requires a great variety of sensors, task- and user models, and multiple devices while considering the complexity of user context and human behavior. Novel AI technologies, such as generative systems, reinforcement learning, and large language models, open new possibilities to create intelligent, practical, and user-centered attention management systems. This proposed workshop aims to bring together researchers and practitioners from diverse backgrounds to discuss and formulate a research agenda to advance attention management systems using novel AI tools to manage and mitigate interruptions from computing systems effectively.

We present a fully-printable method to embed interactive information inside 3D printed objects. The information is invisible to the human eye and can be read using thermal imaging after temperature transfer through interaction with the objects. Prior methods either modify the surface appearance, require customized devices or not commonly used materials, or embed components that are not fully 3D printable. Such limitations restrict the design space for 3D prints, or cannot be readily applied to the already deployed 3D printing setups. In this paper, we present an information embedding technique using low-cost off-the-shelf dual extruder FDM (Fused Deposition Modeling) 3D printers, common materials (e.g., generic PLA), and a mobile thermal device (e.g., a thermal smartphone), by leveraging the thermal properties of common 3D print materials. In addition, we show our method can also be generalized to conventional near-infrared imaging scenarios. We evaluate our technique against multiple design and fabrication parameters and propose a design guideline for different use cases. Finally, we demonstrate various everyday applications enabled by our method, such as interactive thermal displays, user-activated augmented reality, automating thermal triggered events, and hidden tokens for social activities.

AI systems are increasingly incorporated into human decision-making. Yet, human decision-makers are often affected by their cognitive biases. In critical settings, such as medical diagnosis, criminal judgment, or information consumption, these cognitive biases hinder optimal decision outcomes, thereby resulting in dangerous decisions and negative societal impact. The use of AI systems can amplify and exacerbate cognitive biases in their users. In this workshop, we seek to foster discussions on ongoing research around cognitive biases in human-AI collaboration and identify future research directions to understand, quantify, and mitigate the effects of cognitive biases. We will explore cognitive biases appearing in various contexts of human-AI collaboration: what can cause them?; how can we measure, model, mitigate, and manage cognitive biases?; and how can we utilise cognitive biases for the greater good? We will reflect on workshop discussions to form a research community around cognitive biases and bias-aware systems.