Public health studies generate extensive datasets providing important insights into human health. The Norwegian Mother, Father, and Child Cohort Study (MoBa) is a longitudinal cohort study capturing information on pregnancy and early childhood. This information helps uncover the genetic underpinnings of traits or diseases drawing interest from researchers in public health. Non-experts are also attracted to the study, both to understand their contributions as data donors and relevant health determinants. However, the complexity of MoBa data hinders its exploration, analysis, and dissemination. We present a design study exploring the needs and uses of the MoBa dataset in a mixed-user context and introducing the MoBa GWAS Explorer, a web-based visual tool for exploration and analysis of MoBa data by a mixed audience. This tool supports experts in exploring and analyzing MoBa data interactively. Though designed primarily for researchers, we explored the potential for onboarding strategies to make this tool more approachable for non-experts. We conducted a qualitative study with both user groups to evaluate their experience with the tool and its usability. Our evaluation indicates that the application, along with the integrated onboarding, has potential to serve both expert and non-expert groups. Supplementary materials for this study are available at https://osf.io/k5bvj/.
In the precision medicine paradigm, oncological treatment leverages complex ensemble datasets of similar patients to estimate the outcomes for a current patient. A key challenge is developing and deploying easy-to-understand AI predictive models for the outcomes of a specific patient, based on patient data from multiple institutions. We describe the lessons learned from the development and deployment of an interactive dashboard to support the analysis of individual head and neck cancer patient outcomes based on cohort data. As required by the project, the dashboard design aims to handle a large client base. The dashboard combines an AI solution with a multi-view interface featuring domain-specific plots to facilitate the visual analysis of patient outcomes and to quickly stratify new patients into risk groups. A year after the successful public deployment of the dashboard, we evaluate it with clinician domain experts. We report the feedback and we reflect on the lessons learned through this experience.
Large Language Models (LLMs) are increasingly used for generating and adapting visualizations for different user groups. While recent efforts have focused on adapting visualizations to users’ cognitive and perceptual abilities, how LLMs cater to the distinct interests and subjective priorities of various stakeholder groups remains largely unexplored. Specifically, LLMs utilise rhetorical elements to prioritise data stories, which can shape user interpretation. We present a systematic approach to assessing how LLMs adapt their visualization rhetoric to match the priorities of different user personas in a healthcare context. Based on qualitative interviews with population health stakeholders, we demonstrate LLMs’ capabilities for (i) understanding user tasks and priorities from interview data, (ii) adapting visualizations to these priorities, and (iii) justifying design choices for the adaptations. Population health data presents an excellent space for experimentation, given: (a) the diversity of stakeholders (e.g., commissioners, population health experts, data analysts, and the public); and (b) the varied purposes and key messages for which visualizations are designed. We reflect on patterns in LLM reasoning about persona-specific design choices—in light of an established analytical framework for rhetorical visualization—and propose open questions to promote safer, more responsible practices in LLM-assisted visualization.
The complexity of intervention studies to assess physical activity (PA) is increasing, resulting in vast amounts of data being recorded in laboratory settings. Recent studies extend datasets with measurements outside the lab using wearable devices, allowing for a bridge to be built between the lab and real-life applications. Such heterogeneous, multigranular datasets impose various challenges for data analysis and visualization, and require tailored approaches to support domain experts. Contrarily, it enables data-driven hypothesis generation, which is particularly valuable in interdisciplinary contexts where theory-driven approaches fall short due to a lack of well-established theoretical foundations. While lab conditions are extensively handled in existing visual interfaces, measurements in everyday life situations are often neglected, yet have an essential impact on the capturing of PA. To facilitate exploration of this data, we propose a visual analytics application consisting of multiple linked views comprising a BiPlot, Variable Distribution plots, and a dense-pixel visualization allowing experts to generate novel interdisciplinary hypotheses based on laboratory and everyday life measurements. We evaluate our application by conducting an expert study with an end user of the application, showcasing the application’s benefits to support experts in solving tasks regarding exploration, pattern identification, association, and comparison.
Injury prevention in sports requires understanding how bio-mechanical risks emerge from movement patterns captured in real-world scenarios. However, identifying and interpreting injury prone events from raw video remains difficult and time-consuming. We present VAIR, a visual analytics system that supports injury risk analysis using 3D human motion reconstructed from sports video. VAIR combines pose estimation, bio-mechanical simulation, and synchronized visualizations to help users explore how joint-level risk indicators evolve over time. Domain experts can inspect movement segments through temporally aligned joint angles, angular velocity, and internal forces to detect patterns associated with known injury mechanisms. Through case studies involving Achilles tendon and Anterior cruciate ligament (ACL) injuries in basketball, we show that VAIR enables more efficient identification and interpretation of risky movements. Expert feedback confirms that VAIR improves diagnostic reasoning and supports both retrospective analysis and proactive intervention planning.
