Artigos

Introduction

Sedentary behavior (SB) is characterized by energy expenditure below ?1.5 METs as a result of activities performed during waking time in sitting, lying or reclining position1. This behavior has been considered an emerging risk factor among youth due to the unfavorable relationship with several health indicators2. Another aspect that has been explored in the context of SB refers to screen time (ST), especially sedentary ST1, in view of the negative association with obesity3, mental health4 and metabolic risk2.
Another fact implicit in ST extends to cognitive demand, that is, ST can occur in two categories, mentally active (reading, working, studying) and passive (watching television [TV], listening to music), in which only mentally passive ST was associated with mental health problems5,6. Furthermore, mentally passive ST was inversely associated with self-concept7 and longer uninterrupted SB periods in adolescents8.
Considering these factors, it is recommended that youth should not exceed two hours per day of recreational ST, with recommendation to limit the time of this behavior 9. However, it is observed that approximately 52% of children and adolescents exceed the guideline of ?2h/day10, and in Brazil, this percentage is even higher, around 70%11. In addition, in recent years, there has been a significant increase in total ST12, and between 2002 and 2010, adolescents aged 11-15 years increased total ST by 1.4 h/day and 2.1 h/day, respectively10.
The current descriptive epidemiology of time spent on screen devices has indicated that the available data are mostly on total ST and time spent watching TV. However, there seems to be a tendency of decreasing the time spent on these traditional devices (e.g. TV, video game [VG])10. In this way, the evolution of multifunctional devices seems to contribute to changes in trends and device use, and this fact has been the main factor that justify high ST levels, considering that more current screens (smartphone, tablet, etc.) are increasingly more present in the daily lives of youth. Therefore, it is necessary to analyze these change patterns, as well as the tendency of maintaining these behaviors throughout adolescence. However, few longitudinal studies have explored the extent and nature of these changes10,13.
If, on the one hand, there is evidence of moderate tracking of TV and VG time in childhood and adolescence14,15, current screen standards have been changing due to the possibility of coexistence between devices and media13. In this way, the stability and interrelation between different types of screen require investigations that discriminate specific uses. From this perspective, there is lack of studies describing screen use during adolescence13,14. More recently, Rosenberg et al.16 pointed out that in different age cohorts among Australian adolescents, there were changes in the time spent in different ST, highlighting the heterogeneous nature of this behavior in adolescence.
Therefore, there are few studies that have identified tracking patterns (maintenance or changes over time) of screen devices in current times, since this information can contribute to the development of targeted interventions during a period of development and consolidation of behaviors. In addition, it is believed that exposure of youth to ST may be different in high and low-middle income countries and vary by domain of sedentary behavior, furthermore, in countries with lower-middle income, as is the case in Brazil, groups with high socioeconomic status indicate a greater probability of high screen time when compared to groups with low socioeconomic status17. Thus, the objective of the study was to verify the tracking screen time on different electronic devices in school-aged adolescents during a three-year follow-up.






















Methods

Population and sample

This longitudinal, observational, school-based study was carried out in two stages. Baseline took place between October 2015 and May 2017 and had the participation of a representative sample of sixth-grade students with median age of 11.7 (0.6) years of both sexes, enrolled in public schools in the city of Londrina- PR. Details of the baseline sample selection process have been previously described7. This study considered 394 students eligible to participate in the follow-up in 2019, who presented complete accelerometry data (57%) at baseline, and the follow-up had sample composed of 151 students (80 girls), with median age of 14.9 years18. It is worth mentioning that, due to the Covid-19 pandemic, data collection was interrupted, which made it impossible to fully recruit eligible subjects. Participants, parents and/or legal guardian signed the free and informed consent form. Both moments were approved by the Ethics and Research Committee of the State University of Londrina, under opinion No. 1.281.324 of 10/09/2015 (baseline) and No. 3.389.373 of 06/13/2019 (follow-up), according to norms of Resolution 466/2012, of the National Health Council on research involving human beings.


Anthropometry

Body mass was measured on Seca model 813 portable digital scale. Height was determined on Harpenden portable stadiometer. All procedures were performed according to standardized techniques19. Based on collected information, body mass index was calculated.

Screen time (ST)

To measure ST, a questionnaire derived from the Health Behavior in School-aged Children protocol was used20, with the following questions: “On a normal day, how many hours do you: (a) Watch television [TV] (open programming)” (ICC:0.90); (b) “Spend time watching television, smartphone or tablet [TST] (Movies/Series/Videos) Ex.: Netflix. Youtube” (ICC:0.33); (c) “Videogames/Mobile Games [VG]” (ICC:0.54); (d) “Uses the smartphone to chat (WhatsApp/ Facebook/ Instagram)” (ICC: 0.84); (e) “Uses the computer [PC] for leisure (playing games, surfing the internet)” (ICC:0.72). Questions contained six possible answers: I) none; II) <1hr; III) between 1 and 2 hours; IV) between 2 and 3 hours; V) between 3 and 4 hours; VI) >4 hours. For analysis purposes, ST was categorized (?2h/day, 2 to 3.9 h/day and ?4 h/day) and also dichotomized according to recommendation (?2h/day and >2h/day) for each device9.

Socioeconomic characteristics

To identify the socioeconomic level of participants, a questionnaire proposed by the “Associação Brasileira de Empresas e Pesquisas” was used21, which assigns scores for consumption items. For analysis purposes, the total score was adopted at both times.

Statistical analysis

For dropout analysis of the study (remain and/or give up), the Mann-Whitney U test was applied. For sample characterization, median and interquartile range values were used, and for comparison between baseline and follow-up, the Wilcoxon test for continuous data stratified by sex was used. The intraclass correlation coefficient (ICC) was used to verify the reproducibility of the ST questionnaire. Tracking was analyzed using contingency tables, and the McNemar test was used to test differences between proportions of subjects during follow-up. To verify the strength of agreement between the proportions of subjects who remained or changed classification, the Kappa index was used according to the following interpretation: k ? 0.20 = low; k 0.21-0.40 = weak; k 0.41-0.60 = moderate; k 0.61-0.80 = good and k ? 0.81 = very good22. The significance level adopted was 5%.










Results

Subjects who remained and dropped out the study were similar in chronological age (p=0.127), socioeconomic level (p=0.190), TV (p=0.399), TST (p=0.627), smartphone (p=0.433) and PC use time (p=0.748). The sample characterization variables showed significant increases during follow-up (p<0.001), with more evident increase for body mass (38% for boys and 17% for girls) (Table 1).

** INSERT TABLE 1 HERE**

Table 2 presents the proportion of subjects according to ST group in both phases of the study. It is noteworthy that, among analyzed devices, TST time showed increase in the proportion of time above 4h/day of approximately 382% between baseline and follow-up for boys (3.3 to 15.9%; p<0.001). Likewise, the same was observed for girls, in even greater proportions (2.0 to 19.2%; p<0.001), with percentage variation of 860%, as well as for smartphones (4.9 to 17.2%; p<0.001), reflecting a 251% increase in the proportion of girls classified with time above 4 h/day from baseline to follow-up.

** INSERT TABLE 2 HERE**

The percentage of subjects who remained or changed to the ST classification (?2h and >2h) between times are presented in Table 3, according to gender. According to the kappa index, the tracking classification was low for both sexes (k<0.20; p>0.05) and weak for TV time only for girls (k=0.22; p<0.05). Among boys, 54.3% and 57.9% of subjects remained in the same TV and VG time classification, respectively. On the other hand, 68.6%, 34.4% and 30% of subjects migrated to >2h/day from ST to TST, smartphone and PC respectively (p<0.05).
For girls, 62% for TV time, 59.5% for VG and 84.8% for PC remained in the same classification. With regard to TST and smartphone, 67.1% and 42.1% respectively, changed the classification to ST over 2h/day (p<0.05).

** INSERT TABLE 3 HERE**

ST tracking among subjects who spent ?2h/day varied between 26.8% and 68.5% for TST and PC, respectively, while tracking for ST over 2h/day ranged from 4.0% to 16.1% for PC and TV. In contrast, the proportion of subjects who migrated to high ST was around 38.6% and 67.8% for smartphone and TST (p<0.05). Although the McNemar test did not show statistical significance, devices with the highest percentages of changes below 2h/day were TV time (26.2%), followed by VG time (17.6%) (Figure 1).

** INSERT FIGURE 1 HERE**




























Discussion

This study demonstrated that after three years of follow-up during adolescence, tracking was low for the ST of different electronic devices in both sexes. This finding indicates that adolescent are markedly changing their use of the different screen devices analyzed during the follow-up period. In addition, despite the fact that most subjects maintained the same ST classification in more traditional devices (TV, VG and PC), TST and smartphone had the highest proportions of subjects that migrated to high ST.
The implications of these findings point to future directions in order to assess the possible impacts of these changes in time spent on screens, as a result of the use of the most current devices, on health risk markers. Analyzing the changes in TST (~68%) observed in both sexes, along with smartphone use varying from 34% among boys to 48% among girls, these factors may contribute to an increased risk of overweight and obesity within the studied sample.Although the causal relationship is unclear, smartphone, tablet and video game use is associated with greater number of risk factors for obesity, such as intake of high-sugar beverages, physical inactivity and inadequate sleep when compared to time of watching TV23. In addition, watching TV on portable devices is associated with higher adiposity index when compared to broadcasting open programming, especially for girls24.
Especially for more traditional devices (TV and VG), low tracking indicates that there have been changes in individual trajectories patterns, that is, youth are reallocating the time spent on these devices, mainly because the time spent on smartphone and TST increased during the follow-up. Other studies have shown divergent results in relation to these more traditional devices, with tracking values ranging from low25 to moderate15,26. However, the aforementioned studies date back more than a decade, during which time there has been a rapid adoption and popularity of these screens13.
Among analyzed screens, TV and VG were those with the highest percentage of reduction during the follow-up and this fact may be due to technological evolution, which in turn favors the change in the time spent on more current devices. The decrease in time spent on more traditional screens is in line with the findings of a recent review that showed a relatively small reduction in the time spent viewing these screens10. Furthermore, in the last decade, it has been observed that unifunctional devices (TV, VG) have been replaced by portable screens since the access of smartphones and tablets among youth has increased13 thus, these data reiterate the time reallocation to more current screens10.
In this sense, the present study observed that more current media, such as smartphones (instant messaging) and TST (for watching movies, series and videos), were responsible for the high percentage of changes in the proportion of young people who migrated to a group of high ST at follow-up. Rideout and Robb27 point out that at the age of 14 years, the proportion of adolescents using smartphones increased by 22% from 2016 to 2019, and online video content increased by 35% in this period; in addition, 39% of the total time spent is dedicated to video watching activities. In Brazil, this reality is similar, with smartphone use being the main device used to access the internet, so that from 2012 to 2021, it increased by 11%, and watching videos, series, movies online was the main activity responsible for this increase.
Another relevant finding pertains to the socioeconomic level of the present sample. Although no differences were observed in the changes in scores between the baseline and follow-up, the assessed youth were classified as having a medium-low socioeconomic level. This finding aligns with the ICT Kids Online Brazil28 survey, which demonstrated that, to the higher proportion of exclusive use of smartphones in lower socioeconomic classes when compared to higher classes.
However, there is scarcity of longitudinal studies that evaluate the most recent ST data10, which makes comparison with other studies difficult. In the present research, the time spent using the smartphone showed significant increase, especially for girls, corroborating a cohort of Australian adolescents, but the media content that increased the most among girls was the social network16.
In fact, the presence of screens today has become a worrying factor among government agencies9,29. Recent data have indicated that digital media has potential benefits and risks in mental, cognitive, psychosocial and physical aspects29. In adolescents, there is still no consensus of the effects of different screen types on cognitive health, as well as whether there is adequate time in order to favor the development of this aspect30. In this perspective, studies like this one can help to understand the change pattern of each type of ST and whether these changes have different effects on health outcomes.
The literature presents some theories that explain the negative effects of ST on adolescents, one of them is the hypothesis of time displacement, that is, activities in front of the screen suppress activities considered healthy, such as socializing, reading books and practicing physical activities31. Another finding points out that the type of screen media can contribute to the development of literacy skills, prosocial behaviors and, to a limited extent, may even reduce the risk of depression when compared to individuals without any ST29.
Digital media are integrated into the daily lives of children and adolescents and the guidelines on the use of ST involve some principles, such as healthy management and balanced monitoring. In addition, screen type (TV, smartphone, computer, video game), period of the week (weekdays and weekends), contents, contexts and individual characteristics are important factors when evaluating the effects of ST29. It is assumed that the advancement of digital media brings the challenge of evaluating them due to the speed with which they develop; therefore, it is essential that discussions are aimed at aspects highlighted above16.
Finally, the present study fills some gaps, regarding the longitudinal analyses of the various screen devices, including current devices, as well as media activity that contributes to the engagement of youth. These results imply directions for future studies that enable observing whether changes and/or maintenance of the time spent using different devices can positively or negatively impact the various health outcomes in children and adolescents.
The absence of information on the use of social networking media, as well as the overlapping of some screen types among categories used and sample loss are acknowledged as study limitations. Strong points of this study include the longitudinal design, the analyzed population, considering that adolescence is an important phase in the development and consolidation of behaviors, the evaluation of the most current devices, and the most current media contents.

Conclusion

ST in different electronic devices showed low tracking, indicating that the device use patterns changed over the three years of follow-up. This finding indicates that subjects are replacing the time spent on traditional devices (TV, VG and PC) to others such as the smartphone. Overall, TST and smartphone use was the behavior that increased most over the three-year follow-up period.

Implications and contributions

Longitudinal analyses of different screen devices, particularly more recent ones, indicate that adolescents are altering their viewing patterns based on the type of media. This highlights that the multifunctionality of devices is a significant factor contributing to these changes and consequently to increased engagement time. Therefore, for research to advance in this field, questionnaires or other measures that capture such information should assess these multitasking behaviors to produce estimates that more accurately reflect current realities.




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