Bruxism

In this research, 32.4% of students had bruxism, a result similar to those found in other studies on bruxism in adolescents, ranging from 5.3% to 47.5%, during both night and day (^{Arman, Petruninaitė, Grigalauskienė, & Slabšinskienė, 2016}; ^{Thomaz, Cangussu, & Assis, 2013}; ^{Carra et al., 2011}; Emodi ^{Perlman et al., 2016}; ^{Itani et al., 2013}; ^{Murrieta et al., 2014}; ^{Prado et al., 2018}; ^{Sousa et al., 2018}; ^{Türkoğlu, Akça, Türkoğlu, & Akça, 2013}; ^{Van Selms, Visscher, Naeije, & Lobbezoo, 2012}). It is difficult to compare prevalence studies on the subject due to the variation of age groups, different diagnostic methods, and non-specification of the type of bruxism. Another point that must be considered is the population profile under study. In addition, many studies do not correctly distinguish age groups, and childhood and adolescence are frequently found in the same group. This is not adequate, as occlusal adjustment occurs during childhood, which can change the prevalence described in studies when not considered.

We found that the prevalence of bruxism varies greatly, with the lowest value (5.3%) found by ^{Itani et al. (2013}). The author had already conducted four studies with identical methodology in Japanese adolescents and the bruxism percentage was low in all outcomes. Moreover, in addition to bruxism, the author also evaluated arousal disorders and obtained an expressive value. The highest value (47.5%) was reported by ^{Murrieta et al. (2014}), who investigated the prevalence of bruxism, dental class, and their association in Mexican adolescents. Perhaps this high percentage is due to the clinical examination of the oral cavity, although the author justifies the result by considering the genetic factor.

When comparing studies with similar methodology and population to our study, we found similar prevalence. As in this work, ^{Serra-Negra et al. (2018}), who developed their research in Brazil, diagnosed bruxism only based on self-administered questionnaire. The author obtained a 36.8% prevalence of bruxism in their sample and attributed the value to poor sleep quality among university students. Another Brazilian study conducted by ^{Prado et al. (2018}) points to a 23.9% prevalence of bruxism, but the author only analyzed adolescents aged 12 years, which hinders further comparisons. ^{Sousa et al. (2018}) also developed their research in northeastern Brazil and followed the same methodology as the other referenced works, obtaining a 22.2% prevalence of bruxism in their sample. This represents a reduction of more than 10% in affected individuals compared to this study, despite the fact that both studies were conducted in Brazil and in the same region.

Regarding the age group surveyed (14-19 years old), we found no significant difference (p-value 0.068) between age groups of 14-16 and 17-19 years. However, despite the fact that more than one third attend the first year of high school, we found that being a second-year high school student generated greater risk of bruxism (p-value 0.001). When analyzing the p-value corresponding to each year of high school, we found a significant value only for the second year (0.023). This can be explained by the population profile, in which many students were in grades below their age and needed to develop work activities to provide extra income. Thus, it could be inferred that these individuals carry more responsibilities than other adolescents and are possibly under greater emotional burden, which corroborate the hypothesis of this research.

The studies conducted on bruxism show a long period of diagnosis of the parafunctional habit, ranging from 12 to 20 years (Emodi ^{Perlman et al., 2016}; ^{Serra-Negra et al., 2018}; ^{Solano, Molina, & Hernández, 2019}; ^{Sousa et al., 2018}; ^{Türkoğlu et al., 2013}; ^{Van Selms et al., 2012}). Corroborating this research, ^{Serra-Negra et al (2018}) found the age of 17 years, but it should be highlighted that the author only studied adolescents aged 16-18 years. This is due to the fact that the collection was conducted at a university where individuals have more responsibilities and tasks, possibly contributing to the development of arousal disorders. ^{Solano et al. (2019}) studied a broader age group compared to ^{Serra-Negra et al. (2018}) and found a higher prevalence of bruxism for the age group of 15-20 years, justifying the fact using the same aspects mentioned by the latter author. ^{Van Selms et al. (2012}) and Emodi ^{Perlman et al. (2016}) found 14.5% and 15.1%, respectively. Both studies were carried out in schools and without the inclusion of adolescents up to 19 years of age. Thus, the findings corroborate those of the other aforementioned studies, considering that the most advanced age group of the sample was the one that presented the highest bruxism percentage. In agreement with the World Health Organization (WHO), ^{Sousa et al. (2018}) and Türkoğlu et al. (2013) showed average of 12 years; however, both included only individuals aged 12 years in their studies.

Regarding sex, more than half respondents were female and presented higher risk of bruxism (p-value 0.009). ^{Van Selms et al. (2012}), ^{Itani et al. (2013}), and ^{Solano et al. (2019}) observed that bruxism was higher among females. ^{Itani et al. (2013}) justify this fact by concluding that female individuals tend to have higher frequency of arousal disorders, one of which is bruxism. Although ^{Solano et al. (2019}) did not point to an explanation for the fact, it was found that their studied sample showed predominance of female respondents. It is also noteworthy that ^{Van Selms et al. (2012}) only detected the prevalence of females in sleep bruxism but there was no difference in the daytime bruxism. The author did not provide an explanation for this fact.

^{Sousa et al. (2018}) found prevalence of males and justified the finding by assuming that males are more agitated and motivated to contain their emotions. In this way, the practice of involuntary movements would be an emotional escape. In contrast, other studies have concluded that there is no difference between sexes (^{Arman et al., 2016}; Emodi ^{Perlman et al., 2016}; ^{Türkoğlu et al., 2013}). In fact, the work by ^{Türkoğlu et al. (2013}) was carried out with two groups (bruxism and no bruxism) and both had similar characteristics, including sex and age. The author points out in the text that no difference was observed between sex and bruxism, regardless of age group.

Regarding schooling, more than half of mothers and fathers had less than 11 years of schooling, that is, they did not complete the educational cycle. As for maternal education, the bruxism percentage among children was similar (p-value 0.562). Among the most recent studies on the subject, only ^{Sousa et al. (2018}) also analyzed this variable and found that 38.7% of mothers had seven years of schooling and 46.5% of them had schooling that ranged from 7 to 11 years. In agreement, both Brazilian studies showed that the general schooling was less than 11 years.

Among participants, most were not benefited by the Bolsa Família program, although the monthly household income was up to one minimum wage. Data seem to be contradictory but a possible explanation for the fact is the fear of jeopardizing their eligibility and, consequently, their family in some way when filling out the questionnaire, as individuals may not always meet all the inclusion criteria for the program. We found similar household income among participants (one and two minimum wages) and even with salary stratification, there was not a marked salary discrepancy. We found no significant association between bruxism and the Bolsa Família benefit (p-value 0.971) and monthly household income in minimum wages (p-value 0.364).

Screen time

Screen time of up to two hours per day has been advocated as ideal by the American Academy of Pediatrics since 2001 (^{AAP, 2001}); however, a more recent study by ^{Hu et al. (2018}) suggested three hours per day. Despite the addition of one hour, these is still well below the average use of the population, with six to seven and a half hours per day (^{Rideout et al., 2010}; ^{Wallace, 2015}). In this work, the value described by AAP (2001) was considered, which is applied in other studies on the subject (^{Feng, Zhang, Du, Ye, & He, 2014}; ^{Maras et al., 2015}; ^{Saunders & Vallance, 2016}; ^{Wu, Tao, Zhang, Zhang, & Tao, 2015}). In the researched sample, 59.7% excessively used of screens, with values being similar to those found in other studies, such as those by ^{Feng et al. (2014}), reporting 72.2%; ^{Saunders et al. (2016}), 50%; and ^{Wu et al. (2015}), 48.2%.

We found that more than half of participants aged 14-16 years used screens excessively. In this age group, most cases of bruxism were also found, a result that corroborates the hypothesis of this research. Other authors conducted their work in university environments and participants had average age of 18-18.96 years (^{Feng et al., 2014}; ^{Wu et al., 2015}; ^{Zeeni, Doumit, Kharma, & Sanchez-Ruiz, 2018}). Research carried out in schools indicated average age of 12.34-14.1 years (^{Maras et al., 2015}; ^{Merritt, LaQuea, Cromwell, & Ferguson, 2016}). Notably, studies cover one of the phases of adolescence and, regardless of location, individuals have an average age in agreement with that established in this research. However, we found no statistically significant difference between screen time and age group (p-value 0.132).

Regarding bruxism, we noticed that most second-year high-school students had excess screen time, followed by third-year and first-year students. The findings referring to the second year corroborate ^{Tenório et al. (2010}), who found prevalence of almost half, compared to 1^st, 2^nd, and 3^rd high-school students, both during the week and weekends. However, in this study, screen use by third-year students was slightly higher. ^{Tenório et al. (2010}) justifies that these students are focused on their studies and dedicate themselves to preparatory courses, so they have less time to using screens. Although university admission is still the goal for most students, we highlight that teaching methods have varied over the past 10 years. Therefore, the increase in the use of screens for educational purposes is plausible. This fact would also justify why screen time is greater among third-year students than among first-year students. However, we found no statistically significant association between screen time and high-school year (p-value 0.479).

We found no statistically significant difference between screen time and sex (p-value 0.320). These findings agree with other studies that evaluated the same variables and did not observe sex differences (^{Feng et al., 2014}; ^{Zeeni et al., 2018}). On the other hand, some studies have concluded that males use screens more, especially during weekends (^{Tenório et al., 2010}) and spend greater time using computers/video games (^{Rey-López, 2011}). In contrast, Wu et al. (2016) found that females spend more time using screens due to social media use.

We noticed that more than half of participants had some type of religion, and the prevalence of excess screen time was also more than half, regardless of having a religion or not (p-value 0.196). Although both values are close, we found a slight increase among those who did not have a religion. A possible justification for this is the fact that some religious doctrines limit access to electronic devices. This topic has not been discussed in other studies, thus limiting the debate on the subject.

We also noted that more than half of mothers whose children exceeded the recommended screen time studied for up to 11 years, with the percentage of mothers who studied for more than 11 years being slightly higher (p-value 0.180). As for paternal education, we observed that schooling greater than 11 years generated greater risk of excessive screen time (p-value 0.037). Data suggest that longer parental schooling led to longer screen time by adolescents. These findings are different from those described in literature, as ^{Atkin et al. (2014}) found longer screen time among those whose parents had lower schooling. ^{Sousa and Silva (2017}) pointed that adolescents with guardians who studied less than eight years were more likely to watch television in excess. ^{Tenório et al. (2010}) reported that 45.1% of students with screen time longer than recommendations had mothers with more than eight years of schooling. According to ^{Rey-Lopes et al. (2011}), adolescents whose parents completed higher education watch less television, whereas those whose parents only completed high school have more screen time, as they watch more television and use more computers and video games. ^{Babey, Hastert, and Wolstein (2013}) correlated screen time devoted to television with parents’ lower educational levels. The discrepancy in results when compared to literature may be due to the profile of participants, in which higher educational level leads to improvement in the financial condition and, thus, an increase in access to electronic devices.

In this study, most individuals who used screens in excess did not receive the Bolsa Família benefit, and monthly household income above two minimum wages generated a greater risk of excessive screen time (p-value 0.001). Thus, it seems that better financial condition is directly proportional to the use of screens. However, the findings differ from the literature, as ^{Sousa and Silva (2017}) reported that those with lower economic levels were more likely of experiencing excessive total screen time. ^{Smith-Menezes, Duarte, and Silva RJS (2012}) also associated the excessive use of computers, television, and video games with low-income adolescents, as well as ^{Babey et al. (2013}), who associated time spent watching television to lower household income. This discordance of results can be explained by the different screen time criteria used. ^{Babey et al. (2013}) and ^{Smith-Menezes et al. (2012}) reported screen use but did not include smartphones, for example, in addition to the use of different methods and sociodemographic characteristics.

Association between bruxism and screen time

We found that excessive screen time generated a greater risk of bruxism (p-value 0.008). Despite no other field research being found in the literature on the topic, a recent integrative review carried out based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria concluded that anxiety, stress, and depression are associated with bruxism and screen time in adolescence (^{Anjos et al., 2020}). In another study, published by the latter original author, the same mental factors were associated with bruxism in adolescents. However, cause and effect cannot be determined since most studies included in both reviews adopted a cross-sectional design (^{Anjos et al., 2020}).