## 1 Introduction  
The use of mobile devices as a tool in our daily lives is universally widespread. This general use allows them to serve as a valuable source of information for the analysis of people's performance in their daily activities [1]. In their common usage, mobile devices and their applications can collect data about location, movement, battery usage, etc. Apart from sensor data, it is particularly interesting that interactions between users and their mobile devices also provide valuable information related to human behavior. Indeed, it has been shown that this analysis can support the diagnosis of diseases [2] but should play a complementary role in the doctor-patient relationship. In regard to dementia, [PERSON] [3] demonstrated the potential of mobile applications to facilitate diagnostic support, minimizing bias, with greater patient independence. Indirectly, such approaches can reduce healthcare costs and improve the overall quality of life of older people [4]. This makes the analysis of smartphone interactions highly valuable, both in the general field of human-computer Interaction (HCI) and in healthcare, whether for diagnostic or treatment purposes.  
This article is part of the project \"Mobile computing-based Multitasking for Mild cognitive impairment Monitoring and early Screening (M\({}^{4}\)S)\", which aims to contribute to the early diagnosis of cognitive decline by monitoring dual daily tasks in terms of interactions with mobile devices. One of the most common pre-dementia stages is known as mild cognitive impairment [5] (MCI). It is defined as \"cognitive decline greater than expected for an individual's age and education level, but that does not interfere notably with activities of daily life\" [6]. One of the main motivations of this project is the increasing prevalence of MCI, with epidemiological studies estimating it to be between 3% to 19% in adults older than 65 years, and with a risk of it progressing to dementia (11-33% cases) after 2 years. The prevalence increases over time, being up to 50% for those who finally progress to dementia within 5 years [7]. These statistics show that dementia is one of the most common disorders among older adults, and the number of people affected is projected to increase by up to three times by 2050 [8].  
The aim of this study is to determine which tasks performed with a mobile device show significant differences between population cohorts of different ages and whether this difference is more relevant when the tasks are performed while walking. This study is conducted with people who have no diagnosed dementia; therefore, the aim is to establish a ground truth regarding how the normal cognitive decline of aging affects the use of mobile devices, both while stationary and on the move.  
This work is based on a previous study [9] where we defined the HuSBIT-10 taxonomy for the main types of tasks performed with mobile devices (distinguishing between automatic, psychomotor, production, consumption, and exploration tasks). Based on this taxonomy, the cognitive load was studied by measuring it with electroencephalography (EEG), to extract those tasks with a more significant cognitive load. This study helped to determine the tasks to be monitored and analyzed in the current work.  
After explaining the context, motivation, and objective of this work during the introduction section, the rest of the paper continues with a review of related works in the literature in Section 2. Next, the description of the experimental protocol, materials, and methods (the BIPapp monitoring application and the generated dataset) is provided in Section 3. The statistical method and results are explained in Section 4. The user profiling model to classify people regarding the use of mobile devices and age is also developed in Section 4. Section 5 discusses the results' scope, their meaning, and the possible limitations of the experiment. Finally, Section 6 concludes the paper, discussing the goals accomplished and the contributions of this work.