The sample (n=75) comprised young students between 19 and 32 years of age (22.65 ± 3.12 years). Most were women (92%, 69/75) and exclusively dedicated to studying (79%, 59/75); only 8% (n=6) were working as nursing technicians.

Regarding the semester of the students (n=75) at the time of data collection, the following distribution was observed: 1% (n=1) in the second semester; 27% (n=20) in the third semester; 17% (n=13) in the fourth semester; 9% (n=7) in the fifth semester; 9% (n=7) in the sixth semester; 12% (n=9) in seventh semester, and 24% (n=18) in the eighth semester.

As for the use of the Internet in their personal and academic environments (multiple-choice questions), 131 answers were obtained, distributed as follows: 46% (60/131) accessed the Internet on their laptops; 34% (45/131) on mobile phones; 13% (17/131) on desktops, and 7% (9/131) on tablets.

The results draw attention to the issue of connectivity from small-sized mobile devices, thereby confirming the global trend of popularization of these technologies among young people [24].

Regarding the sociodemographic characteristics of specialist nurses (n=5), 100% (n=5) were female, 80% (n=4) had a master’s degree, and 20% (n=1) had a doctorate. As for the area of experience, the participants were distributed in the following areas: education and/or providing care for pain (40%, 2/5), nursing informatics (40%, 2/5), and medical surgical (20%, 1/5). All used mobile technologies and the Internet for personal and academic reasons every day.

The application of NASA TLX enabled identification of the rates, weights, and magnitudes assigned to 6 assessment dimensions, which were used to calculate the overload. Tables 1 and 2 show the results obtained from the assessment performed by students and specialists, as well as the mean overload for each group, respectively.

Students and specialists obtained the highest means in these respective dimensions: “Performance” (58.47 ± 24.19, 73 ± 28.85) and “Mental Demand” (57.10 ± 22.27, 51 ± 29.45). It should be noted that the dimension “Physical Demand” (29.27 ± 27.91, 10 ± 8.66) was identified as the one that least contributed to mental workload during the educational intervention. In general, the distribution of rates assigned by the participants for each dimension had the same behavior (Figure 3).

The repeated measures one-way ANOVA was used to identify statistically significant differences in the mean values of the 6 dimensions of mental workload for the group of students (Table 3).

To identify the significant variables according to the ANOVA test, the Bonferroni test was applied, with no statistically significant difference in the mean values of the following pairs: “Mental Demand” and “Performance,” “Physical Demand” and “Temporal Demand,” “Physical demand” and “Frustration,” “Temporal Demand” and “Effort,” and also “Temporal Demand” and “Frustration.” For the other possible comparisons, significant differences between means were identified (Table 4).

The ANOVA test could not be performed for comparison of means of assessment of the instrument dimensions due to the small number of specialist nurses. With regard to the mental workload index (overload), the specialists’ mean was higher (50.20 ± 7.28) when compared with the students’ mean (47.87 ± 16.85). Among the students, 2 outliers were identified (S47 and S67), which had high overload means (90.33 and 100), moving away from the means of most students (Figure 4).

The increasing inclusion of VLOs and mobile devices for teaching and learning in nursing has also been raising concerns about the assessment of mental workload that these resources can cause in their users. The assessment of these factors becomes essential to develop new directions for technological productions using these resources.

The analysis of the perceived mental workload from a given task is complex and personal, because it includes both specific characteristics of the task and the effort required for its development, which also has a direct relationship with the individual’s personal factors such as motivation, skills [3].

From the application of the NASA TLX instrument after an educational intervention using a VLO, we present the discussion of our results, starting with the sample characterization, followed by an analysis of individual assessment rates of the 6 dimensions of the instrument and the overload of students and specialists.

During the analysis of the students’ sociodemographic characteristics, it was found that although UFSC provides free Wi-Fi networks for students, students report that they most frequently access the Internet at their own homes 63% (70/111). This finding can be explained by the fact that the students are more focused on actual classes at the university, using other spaces to complement their online learning.

The fact that 7% (8/111) of the students access the Internet regularly during commuting also drew attention, confirming the global trend of mobility of individuals made possible by the use of mobile devices. Currently, technologies are very popular, especially for providing easy access to information, regardless of time or space [25].

Regarding rates, the 6 dimensions were similarly assessed by both students and specialists, with the dimensions “Performance” and “Mental Demand” standing out, as these had the highest assessment means.

The dimension “Performance” aimed at measuring how satisfied participants felt with their own level of performance for the assessment of pain using the m-OVADor. It had the highest rate of assessment, both among students (58.47 ± 24.19) and specialists (73 ± 28.85).

The assessment indicates a positive contribution of technology to learning acute pain assessment using mobile devices, suggesting it as a viable process to be used in the teaching-learning process in nursing, comparable to results of previous studies [7-9].

By contrast, “Mental Demand” had the second highest rate, both among students (57.20 ± 22.27) and specialists (51 ± 29.45), indicating that the educational intervention through m-OVADor demanded a greater mental workload, especially regarding the amount of mental activity for execution (think, decide, remember, look, and search).

This may be associated with the fact that the simulation of pain assessment in clinical scenarios requires constant involvement and judgment by the individuals, and may also be due to accessing content from mobile devices.

By contrast, “Physical Demand,” defined as the amount of physical activity that the online educational intervention demands from the individual, was indicated by students (29.27 ± 27.91) and specialists (10 ± 8.66) as the dimension that contributed least to the mental workload, just as reported in a previous nursing study [18].

For some authors [26], this result is justified by the low physical demand required for the handling of small mobile devices, which are very popular nowadays. The result is justified by both the familiarity of these individuals with access to the Internet, and the use of their own mobile devices in their academic and personal daily life, which would facilitate interaction with them. In addition, there is the project presentation, which sought to simplify the presentation layout of m-OVADor, thereby facilitating navigation through clinical scenarios.

The presence of 2 outliers (S47 and S67), which were distanced from the mean of most participants, with high levels of overload from the online educational intervention, can be explained by the individual perception of these individuals at the time of data collection, occurring at the end of the school year, when all students had examinations and term papers.

It must be considered that the assessment of mental workload is considered a complex and personal function, which also involves the specific characteristics of the task, the effort for its performance, and the direct relationship between motivation, emotional state, skills, among others [7].

Still, workload can be influenced by the nature of the task, especially when considering that the educational intervention with m-OVADor included access to 3 different clinical scenarios, in which there were a diversity of resources (animation, short texts with definitions and/or descriptions, assessment exercises, and also the clinical judgment to determine nursing diagnoses and interventions), required for the assessment of each presented context.

One should also consider that mental workload generated from VLOs can relate to factors such as the complexity of the individual, resources used for the development of the activity, and factors unrelated to content, such as the multimedia resources used [27-29].

In this sense, one should also reflect on increasing access to mobile devices with Internet access, and the potential of these technologies when applied in the teaching-learning process in nursing.

Arguably these technologies have become popular, especially for the flexibility that allows users to quickly access information at any place or time, to expand their potential to transform individuals’ attitudes and behaviors [30,31].

Specifically in the context of this study, an assessment of mental workload has indicated that the use of these technologies is feasible in higher education in nursing, and can influence the way students build their knowledge, establishing an innovative process for teaching/learning.

The NASA TLX instrument demonstrated applicability for the assessment of mental workload from the use of m-OVADor. The results enabled analysis of the sources related to students’ and specialists’ mental workload from the use of a VLO accessed from small mobile devices. The analysis revealed that the dimension “Mental demand” obtained a greater weight both from the students’ and specialists’ perceptions, which is expected from technologies for online learning, due to the level of attention that they require. Although the study achieved the proposed aim, in no way did it intend to exhaust the possible approaches to the theme. The development of other studies to explore the theme from other perspectives is recommended, to deepen our understanding of the effects of mental workload on learning outcomes and even when planning new strategies for mobile learning in nursing education as well as for validation of the instrument in Brazil.