## 5 Discussion  
After conducting the pilot study and analyzing the results, this work has demonstrated promising contributions as well as clear limitations. The contributions extracted from the results can be summarized as follows. * There exists a strong correlation between age and several monitored metrics. * A significant inverse correlation is found in the mobile's time of use, the frequency of locking and unlocking the device, and the number of actions performed in each open app. Consequently, younger individuals exhibit more intensive use of the device. Additionally, younger people tend to use entertainment apps more frequently. * One of the most valuable findings is the strong inverse correlation in the use of the device while in motion. Generally, but particularly in applications such as WhatsApp and Chrome, the older the user, the fewer apps they use while moving. This finding is closely related to the dual-tasking test commonly used to diagnose cognitive decline. * Concerning the use of WhatsApp, there is a significant difference in the type of action performed depending on whether the user is in motion or stationary, as well as depending on their age. As individuals age, they use complex actions such as writing  
Figure 5: Decision tree of user profiles and how they are classified in each age group. First line in intermediate nodes is the feature and the value by which it is split; second line is the value of Gini impurity of a node; third line is the number of samples in the node; fourth line is the class distribution (the first position includes the participants from 20 to 34 years, the second includes participants from 35 to 59 years, and the third includes participants from 60 to 75 years); and the last line is the class with more samples. messages, viewing multimedia files, or sending images less frequently while walking. However, these actions do not correlate with age when the user is stationary; they perform these actions similarly and with a comparable frequency regardless of age. * Further analysis of the metrics related to messaging reveals that young adults write more than older users in both stationary and in-motion scenarios. There is a similar ratio between the number of deleted items and the number of typed items when the user is stationary. However, this correlation changes to the opposite direction when in motion, as older adults tend to correct more errors and write at a slower pace. These findings provide valuable insights into the relationship between age and mobile device usage patterns, specifically in relation to cognitive decline and messaging behavior on WhatsApp. The work sheds light on the different ways in which age impacts mobile interactions and helps us to understand the potential implications of these findings. However, it is essential to acknowledge the limitations of the study to guide future research and further explore this intriguing area of investigation. Comparisons between different users are always based on intrinsic differences in how people use their mobile devices, which makes it difficult to obtain statistically robust results. An important bias is that differences in use may be the result of generational differences, rather than normal cognitive decline. A typical example is the strong correlation between age and the deletion of characters when writing messages. Is this related to the fact that we make more mistakes as we get older or that the current generation of older adults is more concerned about writing correctly? Another limiting aspect is the simple distinction between standing and moving activities. Based on existing studies showing the relationship between cognitive impairment and alterations in gait parameters [16; 17], we are currently working on studying the interaction with mobile devices and specific gait parameters obtained through body tracking [26].