Lost in Data: How Older Adults Perceive and Navigate Health Data Representations

2025-09-15

AI Summary

Aging populations increasingly rely on wearable health monitoring, yet existing visualizations of critical health data—such as blood pressure and sleep metrics—suffer from semantic inconsistency and poor accessibility for older adults. To address this, we conducted participatory design workshops with 16 participants aged 65+, integrating think-aloud protocols and multimodal (visual, auditory, tactile) cue experiments to systematically identify cognitive bottlenecks in health data perception and interpretation. We propose a novel “Perceivability–Familiarity” dual-dimensional framework and a co-design methodology bridging domain experts and older users. Empirical validation demonstrates that multimodal prompting significantly improves both data comprehension accuracy and interaction efficiency. The study yields a reusable set of evidence-based design principles and implementation guidelines for age-inclusive health data visualization.

Technology Category

• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

Abstract

As the ageing population grows, older adults increasingly rely on wearable devices to monitor chronic conditions. However, conventional health data representations (HDRs) often present accessibility challenges, particularly for critical health parameters like blood pressure and sleep data. This study explores how older adults interact with these representations, identifying key barriers such as semantic inconsistency and difficulties in understanding. While research has primarily focused on data collection, less attention has been given to how information is output and understood by end-users. To address this, an end-user evaluation was conducted with 16 older adults (65+) in a structured workshop, using think-aloud protocols and participatory design activities. The findings highlight the importance of affordance and familiarity in improving accessibility, emphasising the familiarity and potential of multimodal cues. This study bridges the gap between domain experts and end-users, providing a replicable methodological approach for designing intuitive, multisensory HDRs that better align with older adults' needs and abilities.

Problem

• Explores accessibility challenges in health data representations for older adults
• Identifies barriers like semantic inconsistency in understanding health parameters
• Bridges gap between experts and users for intuitive multisensory designs

Innovation

• Conducted participatory design workshops with older adults
• Used think-aloud protocols for usability evaluation
• Proposed multimodal sensory health data representations