Designing Data Visualizations for ADHD Users : Effects on Satisfaction and Cognitive Load

Sammanfattning

Attention Deficit/Hyperactivity Disorder (ADHD) can significantly influence how individuals process information and interact with digital interfaces. While cognitive load has been studied in user experience research, the specific needs of ADHD users remain underexplored. This study investigates how data visualization strategies within recommender systems affect cognitive load and user satisfaction among individuals with ADHD compared to neurotypicals. Two high-fidelity movie recommender system prototypes were developed in Figma: one utilizing progressive disclosure with additional contextual information (e.g., tags, genre, ratings, and duration), and another presenting minimal information. Usability testing was conducted with both ADHD and neurotypical participants. Cognitive load was measured using the NASA-TLX, while user satisfaction was assessed using the USE Questionnaire. Semi-structured interviews were conducted to complement the quantitative findings with qualitative insights. The results indicate that participants with ADHD experienced significantly lower cognitive load and higher satisfaction when using the prototype that provided additional information through progressive disclosure. In contrast, the minimal-data interface increased perceived workload and reduced satisfaction among ADHD users. Neurotypical users also preferred the prototype providing more information, although the effects were less pronounced. Qualitative data revealed that ADHD users relied heavily on information such as ratings, genres, tags, and running time when making decisions and consistently expressed a desire for greater transparency and access to information. The findings suggest that accessible data visualization can support decision-making and reduce cognitive load for users with ADHD. Future research should examine alternative data visualization and interface design strategies, investigate visual attention through eye-tracking methods, and explore how these findings transfer to other digital environments, such as e-commerce platforms, where users must navigate large amounts of information and make complex decisions.