Background: Problematic social media use has been linked to reduced well-being and impulse control difficulties. While digital self-control apps show potential for reducing general app usage, they often lack customization, leading to limited effectiveness and increased user resistance. Their impact on problematic social media use remains uncertain. Objective: This study evaluates the effectiveness of the Wellspent app, a customizable mobile intervention app designed to promote self-regulated social media use by targeting user-defined problematic app use and offering tailored behavioral nudges. Methods: In a 3-week randomized controlled trial, 70 iPhone users (mean age 26.2, SD 5.6 years; 47/70, 67% female), regularly using at least 1 social media app, were randomly assigned to an intervention (n=35) or control group (n=35). The intervention group received personalized full-screen reminders with the option to quit or continue social media app use whenever an app session exceeded a self-defined time limit. Participants completed weekly online surveys measuring problematic social media use, problematic smartphone use, self-efficacy, and daily screen time on their most problematic app. Linear mixed models tested intervention effects. Results: While no significant reduction in problematic social media use or increase in self-efficacy was observed, the intervention group showed a significant reduction in daily screen time on their most problematic app by approximately 29 minutes (estimate=-29.35, SE 6.84, 95% CI -42.79 to -15.99; P<.001), and a significant decrease in perceived problematic smartphone use (estimate=-0.46, SE 0.18, 95% CI -0.80 to -0.11; P=.01). Conclusions: The Wellspent app demonstrated short-term efficacy in reducing problematic smartphone use. By allowing users to tailor interventions to their personal goals, the app shows promise as a self-directed tool to support healthier digital habits. Further research should explore long-term effects and feature-specific impacts.

Background and objectives: Heart failure requires complex and daily self-care that many patients struggle with for a range of reasons including limited health literacy, cognitive impairment, comorbidities, and emotional distress. This study describes the user-centred design and development of a mobile app (SmartHeart) to support comprehensive self-monitoring and improve self-care engagement for people with heart failure. Methods: Building on previous co-design research and expert panel feedback, we developed an initial Figma prototype following user-centred design principles. Two online sessions were conducted with adults living with heart failure (n=7), including a focus group session and a follow-up individual feedback session. The same participants took part in both sessions to provide feedback on the functionality, aesthetics, navigation, and content. Data were analysed deductively based on heuristic principles of user interface design, with findings informing the iterative development of the SmartHeart mobile app. The functional app was tested in-home by two participants over two weeks to evaluate real-world usability and gather contextual feedback to inform further refinement. Results: The SmartHeart prototype was developed through expert workshops and user feedback. Participants emphasised simplicity, leading to a streamlined design with clear navigation, adaptable graphics, and larger fonts. The app’s health tracking features were iteratively improved. User-driven modifications included personalised threshold alerts, simplified symptom reporting, and integrated medication reminders. Participants reported high satisfaction with the prototype interface and health monitoring capabilities; however, formative testing identified reliability issues that are being addressed prior to pilot evaluation. Findings primarily inform design refinements before evaluating clinical effectiveness. Conclusion: The SmartHeart app was refined through user-centred design process involving direct feedback from individuals with heart failure, resulting in a self-care tool with user-friendly features, to be further evaluated in future research. These user-driven enhancements support self-care engagement and highlight the app’s potential for real-world use and broader clinical integration.

The visual span, defined as the number of letters that can be accurately recognized in a single eye fixation, is a fundamental sensory constraint on reading speed. While well-studied on desktops, visual span in virtual reality (VR) remains largely unexplored, despite the increasing use of text-heavy VR applications. This gap is critical, as VR's unique constraints (e.g., limited angular resolution, optical distortions, and vergence-accommodation conflict) may fundamentally restrict text intake. We present the first empirical study to directly measure visual span in VR using the trigram paradigm and compare it to a matched desktop baseline. Although the profile shape of the visual span was similar across conditions, its size was significantly reduced in VR, averaging 4.28 letters versus 10.72 on desktop (a ≈60% reduction). These findings reveal a fundamental limitation and lay the groundwork for designing more readable and efficient text experiences in immersive environments.

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.

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.

Active and healthy ageing depends on maintaining physical and cognitive activity, but it is still challenging to motivate older adults to participate in regular training. This paper describes the iterative design and evaluation of a digital platform for increasing older adults' motivation to perform physical and cognitive exercises. The digital solution was designed and evaluated in four iterations with a total of 13 older adults. The first stage focused on identifying effective communication methods, including different formats of instructional delivery and feedback, as well as tone. The second stage explored the combination of physical activity with cognitively stimulating activities, such as brain games, sport, and hobbies, to find the most motivating combinations. The final stage developed the prototype further by integrating motivational elements into one coherent design, emphasizing clarity, guidance, and user agency. The final evaluation reviewed the overall design, including the importance of adaptive systems that dynamically adjust the difficulty level to align with users' physical and cognitive abilities to increase motivation. This study contributes to the growing field of participatory design within digital health interventions, aligning with best practices that emphasize the need for dynamic user involvement in all stages of development.

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.

Novel consumer neurotechnologies allow users to track their cognitive states and processes, such as attention and mental workload (MWL). However, data on these inherently complex, abstract, and invisible cognitive processes can be challenging to interpret, and little is known about how users make sense of their data. In this work, we explore how people understand and reflect on MWL through six semi-structured interviews and a follow-up experience sampling study. We examine how people conceptualize MWL, distinguish it from related concepts such as stress, what they consider high and low workload in their daily lives, and how they connect workload to emotional states. We discuss these user perceptions and identify barriers to MWL self-tracking, such as lack of trust in the data and ambiguity of the MWL concept, and propose five design guidelines to make cognitive tracking tools more intelligible and meaningful for users.