Neuroinformatics -Bridging Cognition, AI, and Digital Health

Apps for patients and users are increasingly developed to support self-management of chronic conditions such as Multiple Sclerosis (MS). The series of studies describes the design and evaluation of a mobile application that helps individuals monitor symptoms, track activities, manage stress, and prepare for medical appointments. Insights from patients, healthcare professionals, and social media discussions guided the design process, ensuring that the solution addressed real user needs. The application was refined through iterative prototyping and usability testing, leading to improvements in functionality and data visualization. Overall, it illustrates how digital health tools can enhance patient engagement, motivation, and self-awareness in everyday disease management.

Design principles

Research participants

Bild: Martine Oppegaard Jakobsen, University of Bergen

There are emerging AI-based methods for the early detection of cognitive decline, drawing on imaging, clinical, and language data. These approaches demonstrate high diagnostic potential and continue to improve with advances in deep learning. Together, they point toward more accurate and accessible tools for supporting early intervention in cognitive health.

There is growing interest in digital systems and games for cognitive testing and training in older adults, yet validated self-assessment tools remain limited. Clinical solutions are more reliable but often inaccessible, while many apps lack scientific validation despite wide availability. Future development should combine clinical rigor with user-centered design to create engaging, evidence-based tools for cognitive health in aging.

Iterations of Mini-Cog test

We have developed two digital artifacts for testing cognitive decline based on the medically validated Mini-Cog test. The first version is a simple, clinician-oriented tool, while the second integrates gamification elements such as narratives, rewards, and emojis to increase engagement.

This figure illustrates the clock-drawing component of the Mini-Cog test. The left image shows the initial step of setting the clock face, while the right image depicts the task of assigning the correct time by positioning the clock hands.

User experience results

User feedback and usability testing indicate that the gamified version enhances enjoyment and interaction while preserving the test’s diagnostic integrity.

The integration of assistive robots in Japan nursing care through interviews and observations in three nursing homes. Robots such as Paro, Pepper, and Qoobo were evaluated for their roles and user experiences. The findings highlight user satisfaction and the therapeutic and entertainment value of these robots. Their potential to support elderly care is clear. However, better strategies are needed to fully utilize and integrate them into daily practice.