## 5 Evaluation  
The findings of assessing the quantitative data acquired from the participants in the execution of the tasks identified in Table 2 with the app features are presented in this section. Results are based on the execution time for each task and the application of the UEQ. Regarding the execution times for each task, Table 3 presents the average time (in seconds) that users took to complete the tasks. As expected, results show that tasks that change the original food plan for logging purposes manifest higher interaction costs. Each extra food added to the plan (lettuce or ice cream in the tasks, also presented in Figure 9) required 8.6 s of the user's time. The most challenging UI tasks required manually searching for new food, since the keypress increases interaction costs. For example, exchanging food for an equivalent (3.6 s, also presented in Figure 10) reduces by more than half the time taken to execute the task (when compared to the 8.6 s adding extra food). The rationale is that to exchange food for an equivalent, a small list of equivalents is presented, while for adding an extra food, the user must search for food. Preference learning techniques based on customized food preferences can be a solution to help reduce the time required  
Figure 11: Consumption of water. Figure 12: Statistics of consumption of macronutrients. for logging extra food. The food search converts into a list of preferences, which will bring the execution times to the level of those of exchanging for an equivalent. For the user to visualize a meal presented in the food plan required 3.5 s; this is related to the time taken to access the meal and the dish composition interaction (see Figure 4). Logging one meal by confirming the original food plan only required 1.2 s (accessing the meal tab and clicking on the consume button--last image of Figure 4). Given the similarity of the tasks that were designed considering the mental process of the user, several tasks have similar implementations. Water consumption outside the food plan (last image of Figure 11) required 2.3 s to complete the task. Based on the UEQ scale benchmark, the product's overall rating is excellent, as Table 4 and Figure 13 show. The highest mean score is efficiency, with a mean of 2.59 (\(\sigma~{}=~{}0.26\)), followed by novelty and attractiveness, with a mean of 2.48 (\(\sigma=0.20\) and \(\sigma=0.40\), respectively). This suggests that the product is considered easy to use, unique and innovative, and users like interacting with it. The scales of the UEQ can be grouped into pragmatic quality (perspicuity, efficiency, dependability) and hedonic quality (stimulation, originality). Pragmatic quality describes task-related quality aspects, and hedonic quality refers to the non-task-related quality aspects. Since attractiveness is defined as the overall impression of the software application, it is considered an individual dimension. In Table 5, the mean of the three pragmatic and hedonic quality aspects is calculated. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Confidence Intervals (\(p\) = 0.05) per Scale**} \\ \hline
**Scale** & **Mean** & **Std. Dev.** & **Confidence** & **Confidence Interval** & **Rating** \\ \hline Attractiveness & 2.48 & 0.20 & 0.10 & 2.38 & 2.58 & Excellent \\ \hline Perspicuity & 2.13 & 0.42 & 0.20 & 1.92 & 2.33 & Excellent \\ \hline Efficiency & 2.59 & 0.26 & 0.13 & 2.47 & 2.72 & Excellent \\ \hline Dependability & 2.19 & 0.25 & 0.12 & 2.07 & 2.31 & Excellent \\ \hline Stimulation & 2.27 & 0.45 & 0.22 & 2.04 & 2.49 & Excellent \\ \hline Novelty & 2.48 & 0.40 & 0.20 & 2.29 & 2.68 & Excellent \\ \hline \hline \end{tabular}
\end{table} 
Table 4: UEQ results for the overall rating.