Data-driven User Profiling and Personalization in Tiimo: Towards Characterizing Time Management Behaviors of Neurodivergent Users of a Scheduling Application

Deficits with time management and other cognitive functions can stem from multiple causes and be found across different diagnostic conditions. At the same time, cognitive function can differ within diagnostic classes, which calls for adaptable and personalized assistance. A great deal of literature on cognitive assistive technology (CAT) focus on diagnostic populations rather than cognitive impairments across different conditions. This study reports the initial steps towards a data-driven approach to map out the characteristics and behavior of users of a time management app, Tiimo, originally targeting children with ADHD. Based on results from a questionnaire and analysis of user activity data, findings indicate a tendency of attracting a more heterogeneous user population compared to the originally intended target group, thus supporting the need for a more complex and data-driven ‘design for all’ approach to CAT rather than delimitations based on diagnostic groups. Preliminary findings from the analysis of activity data across user groups and diagnoses show that users generally schedule fewer than five daily activities and most often in the morning, suggesting a potential emphasis on support particularly during morning routines. However, the analysis also highlights the need for more data points to enable assessment of progress, motivation, and effectiveness of the technology. Next steps include a more detailed analysis of user activity that takes different types of behavior and other relevant factors into account by applying NLP to further develop data-driven approaches to user profiling and personalization in time management apps for neurodivergent users.