As Artificial Intelligence (AI) conversational agents become widespread, people are increasingly using them for health information seeking. The use of off-the-shelf conversational agents for health information seeking could place high metacognitive demands (the need for extensive monitoring and control of one’s own thought process) on individuals, which could compromise their experience of seeking health information. However, currently, the specific demands that arise while using conversational agents for health information seeking, and the strategies people use to cope with those demands, remain unknown. To address these gaps, we conducted a think-aloud study with 15 participants as they sought health information using our off-the-shelf AI conversational agent. We identified the metacognitive demands such systems impose, the strategies people adopt in response, and propose considerations for designing beyond off-the-shelf interfaces to reduce these demands and support better user experiences and affordances in health information seeking.

Abstract Older adults living with multiple chronic conditions (MCC) can considerably benefit from collecting and reflecting on their health data. Many older adults collect their health data using various approaches, such as digital tools or handwritten notebooks. However, in these approaches, the act of collecting data does not itself yield insights; sensemaking and reflection happen only if individuals later review their accumulated records. The daily process of data collection thus offers limited opportunity for individuals to actively engage with their data or find the process personally meaningful and enjoyable. Personal data input visualizations using physical tokens offer a promising solution that can help individuals recognize evolving patterns while collecting data and discover meaningful insights in a more serendipitous and engaging manner. Yet, there is a limited understanding of whether and how older adults living with MCC might adopt physical input visualizations to collect data and reflect on their health, and how the tangible, expressive, and personalizable nature of this process supports their sensemaking and reflection. In this paper, we present the results of our interview and diary studies in which older adults living with MCC inputted health data using physical tokens over two weeks. Our findings highlight the diverse and unique needs of older adults for tracking personal health data, illustrating how they adapt strategies and personalize physical input visualizations to align with their individual needs. We demonstrate how older adults integrated input visualizations into daily routines and leveraged tangible markers to reflect on patterns and behaviors, while enjoying the process of tracking and focusing on personal expression and meaningful reflection. Finally, we provide design considerations for supporting older adults with MCC when inputting health data through physical tokens. All data and supplemental materials are available at https://osf.io/7ak9x/overview?view_only=63ce1356b4b94a1abea6d9247c8a2f26 .

Journaling plays a crucial role in managing chronic conditions by allowing patients to document symptoms and medication intake, providing essential data for long-term care. While valuable, traditional journaling methods often rely on static, self-directed entries, lacking interactive feedback and real-time guidance. This gap can result in incomplete or imprecise information, limiting its usefulness for effective treatment. To address this gap, we introduce PATRIKA, an AI-enabled prototype designed specifically for people with Parkinson's disease (PwPD). The system incorporates cooperative conversation principles, clinical interview simulations, and personalization to create a more effective and user-friendly journaling experience. Through two user studies with PwPD and iterative refinement of PATRIKA, we demonstrate conversational journaling's significant potential in patient engagement and collecting clinically valuable information. Our results showed that generating probing questions PATRIKA turned journaling into a bi-directional interaction. Additionally, we offer insights for designing journaling systems for healthcare and future directions for promoting sustained journaling.

There is a vast amount of unstructured text data generated every day analyzing and making sense of these text-based datasets is a complex, cumbersome task. The existing visualization tools that analyze text data leveraging Natural Language Processing (NLP) techniques, are often tailored for structured text-based data. They also fail to support reading, a crucial analysis task to validate the output of NLP techniques. We designed and developed TextVista, an NLP-enriched visualization tool that supports analysts during their analysis of unstructured text with temporal references. Our tool combines techniques including clustering, sentiment analysis, and threat detection with three views that visualize high-level patterns in the data to encourage reading. We report on TextVista’s iterative design process, which included a focus group to distill design requirements, a think-aloud interview study with data analysts to understand their impressions of the tool, and a diary study to assess its long-term usage. Through this process, we identified how TextVista supported the analysis of unstructured text with temporal references using NLP techniques and fostered methods to promote reading in situ. TextVista also encouraged serendipity when analyzing data via its question-focused overviews and flexible avenues to explore data.

Stroke is one of the leading causes of disability worldwide. The efficacy of stroke recovery is determined by various factors, including patient adherence to their rehabilitation program. Effective communication between healthcare providers and patients is crucial for promoting patients’ adherence to rehabilitation programs. Aiming to support patient-healthcare provider communication during inpatient stroke rehabilitation, we (1) conducted semi-structured interviews with healthcare providers with expertise in inpatient stroke recovery to extract design requirements for visualizing stroke recovery progress. Using these design requirements, we (2) designed a data visualization tool representing stroke recovery. We (3) sought feedback on the visualization designs from healthcare providers and patients and integrated their feedback into the designs. Informed by the results of our studies, we provided several considerations for designing future visualization tools for patients and providers to communicate during inpatient stroke rehabilitation.

Large displays can provide the necessary space and resolution for comprehensive explorations of data visualizations. However, designing and developing visualizations for such displays pose distinct challenges. Identifying these challenges is essential for data visualization designers and developers creating data visualizations on large displays. In this study, we aim to identify the challenges designers and developers encounter when creating data visualizations for large displays. We conducted semi-structured interviews with 13 experts experienced in creating data visualizations for large displays and, through affinity diagramming, categorized the challenges. We identified several challenges in designing, developing, and evaluating data visualizations on large displays, as well as building infrastructure for large displays. Design challenges included scaling visual encodings, limited design tools, and adopting design guidelines for large displays. In the development phase, developers faced difficulties working away from large displays and dealing with insufficient tools and resources. During the evaluation phase, researchers encountered issues with individuals' unfamiliarity with large display technology, interaction interruptions by technical limitations such as cursor visibility issues, and limitations in feedback gathering. Infrastructure challenges involved environmental constraints, technical issues, and difficulties in relocating large display setups. We share the lessons learned from our study and provide future directions along with research project examples to address these challenges.

This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper-educators and researchers in data visualization-identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes People, Goals & Assessment, Environment, Motivation, Methods, Materials, and Change. Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting opportunities and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.

We were approached by a group of healthcare providers who are involved in the care of chronic patients looking for potential technologies to facilitate the process of reviewing patient-generated data during clinical visits. Aiming at understanding the healthcare providers' attitudes towards reviewing patient-generated data, we (1) conducted a focus group with a mixed group of healthcare providers. Next, to gain the patients' perspectives, we (2) interviewed eight chronic patients, collected a sample of their data and designed a series of visualizations representing patient data we collected. Last, we (3) sought feedback on the visualization designs from healthcare providers who requested this exploration. We found four factors shaping patient-generated data: data & context, patient's motivation, patient's time commitment, and patient's support circle. Informed by the results of our studies, we discussed the importance of designing patient-generated visualizations for individuals by considering both patient and healthcare provider rather than designing with the purpose of generalization and provided guidelines for designing future patient-generated data visualizations.

Stacked graphs are a visualization technique popular in casual scenarios for representing multiple time-series. Variations of stacked graphs have been focused on reducing the distortion of individual streams because foundational perceptual studies suggest that variably curved slopes may make it difficult to accurately read and compare values. We contribute to this discussion by formally comparing the relative readability of basic stacked area charts, ThemeRivers, streamgraphs and our own interactive technique for straightening baselines of individual streams in a ThemeRiver. We used both real-world and randomly generated datasets and covered tasks at the elementary, intermediate and overall information levels. Results indicate that the decreased distortion of the newer techniques does appear to improve their readability, with streamgraphs performing best for value comparison tasks. We also found that when a variety of tasks is expected to be performed, using the interactive version of the themeriver leads to more correctness at the cost of being slower for value comparison tasks.

What will a truly large iPad be like? Will it have a touchscreen at the front, or will some other changes be forced by the sheer sizeof the device? We mocked up a working device using a 17-inch Macbook laptop screen. The device size was too large for us to comfortably hold with one hand while using the other hand for touch input, so we placed the touch pad at the back. Hence, wecall our device a BackPad. In the first experiment, we compared user performance with our 17-inch BackPad and a normal iPad in game and typing tasks. The results on the game completion time and score were similar, and users liked our large screen,while time but not spelling errors were different in the BackPad versus the iPad. For the second experiment, we compared the front touchscreen versus the back trackpad user performance on same sized devices. Similar results to the first experiment were found on game completing time and score.

Several studies have been done measuring preferred hand grip strength, but none of them has measured preferred hand strength on a PDA or similar device when it is held and used. We measured dominant hand strength in two conditions similar to real PDA use, resting fore-arms on a table and holding the PDA without table support. We found that adult participants squeeze the device with their preferred hand significantly more than with their nonpreferred hand while holding. In addition, we examined users' hand strength while they were tapping on the back of the device with their right and left index fingers. Our results were different than expected from previous studies, as we found that there was no significant difference in dominant and non dominant hand strength during back tapping. Also participants' non preferred hand strength was not significantly different with their preferred hand when they tap on the back of the device. The results show that in such functional use during tapping, the dominant and non-dominant hands are used similarly which will contribute to future designs for PDAs and their interfaces. Our results may also contribute to design for more comfortable devices for users with hand disabilities.