## 4 Results
### Statistical Analysis
#### 4.1.2 Results of the Statistical Approach  
Based on the first hypothesis about verifying how people use their mobile devices differently depending on their age, a statistical analysis was performed using the data obtained. The data of users who completed the experiment were organized into stages of adulthood according to [PERSON]'s theory [29], in order to analyze changing trends in the use of mobile devices. Table 2 describes each age range. To distinguish each user's events, we used a variable named \"identifier\". Participants also provided their subjective ability to use technology. A criterion for inclusion was that the participants typically used mobile devices in their daily tasks, including communication, shopping, managing finances, and searching for information on the internet. The fixed variables were age and the type of physical activity (standing or moving). The rest of the variables were selected based on the information collected and those that differentiated the use of the device from one user to another. Sex/gender and the technological ability of the participants were variables that were collected, but, due to the limited population, they were not considered in the study as no meaningful statistical \begin{table}
\begin{tabular}{c c} \hline
**Range of Rxy Values** & **Interpretation** \\ \hline \(0.00\leq\mid\mathrm{rxy}\mid<0.10\) & Null correlation \\ \(0.10\leq\mathrm{rxy}\mid<0.30\) & Weak correlation \\ \(0.30\leq\mid\mathrm{rxy}\mid<0.50\) & Moderate correlation \\ \(0.50\leq\mid\mathrm{rxy}\mid<1.00\) & Strong correlation \\ \hline \end{tabular}
\end{table} 
Table 1: Boundaries of [PERSON]’s correlation coefficient [28]. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Total** & \ \begin{tabular}{c} **High** \\ **Ability** \\ \end{tabular}  & \begin{tabular}{c} **Medium** \\ **Ability** \\ \end{tabular}  & **Low Ability** & **Men** & **Women** \\ \hline Young adults [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 59; 58; 59; 59;  
conclusions could be drawn. Inferential analysis of the data was then performed using Pearson's correlation coefficient. Firstly, Table 3 summarizes this correlation regarding mobile use and age. To provide greater detail on the use of mobile devices, the applications utilized by users were classified by the type of content offered to users. Five types were distinguished: financial, commercial, shopping, entertainment, and utility. As Table 4 depicts, the most frequently used applications were commercial, utility, and entertainment. The significance of the analysis lies in differentiating the use of the mobile device while the user is standing or in motion. Therefore, starting from the previous classification, an exploration was performed of the applications utilized by users on the move and the degree of correlation that existed with respect to age (Table 5). Focusing on WhatsApp and Facebook, the two applications most often used, Table 6 shows that the older the age of users, the fewer actions they performed on the application. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Stationary**} & \multicolumn{2}{c}{**On the Move**} \\ \hline WHATSAPP & & & \\ \hline WhatsApp events vs. age & \(-0.578\) & \(-0.545\) \\ Open conversation vs. age & \(-0.169\) & \(-0.476\) \\ Viewing and listening to multimedia files vs. age & \(-0.194\) & \(-0.502\) \\ Writing vs. age & \(-0.566\) & \(-0.551\) \\ Sending files and camera vs. age & \(0.370\) & \(-0.640\) \\ \hline FACEBOOK & & & \\ \hline Facebook events vs. age & \(0.194\) & \(0.068\) \\ Facebook likes vs. age & \(0.156\) & \(<0.01\) \\ Facebook commenting vs. age & \(0.526\) & \(<0.01\) \\ Facebook sharing vs. age & \(0.070\) & \(<0.01\) \\ Notification vs. age & \(0.078\) & \(0.086\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Correlation coefficients of actions performed in WhatsApp and Facebook vs. age.