Artigos

INTRODUCTION
Screen time, recently defined as part of a construct of sedentary behavior1, is characterized by the time spent by the individual interacting with a computer, cell phone, tablet, and television screen. Concerns about how this behavior impacts the health of the population have prompted investigations by researchers around the world, identifying detrimental effects on mental health, body weight gain, musculoskeletal pain, as well as sleep and vision problems2-6. Because of these concerns, international guidelines such as the Canadian 24-Hour Movement Guidelines7 aim for a limit of 3 hours per day of screen interaction during leisure time for adults and older people, with evidence indicating a significant decrease in this behavior after the recommendations were implemented8.
When looking at the Brazilian scenario, there is a population reality that has an essential relationship with screen time. Recent research9-10 reveals that a significant proportion of the population, ranging from children to the older, has access to interactive screens and increased daily screen time between 2016 and 2021. In a perspective that includes only the use of cell phones, 9 out of 10 households in Brazil contained the device by the year 2021, and the device insertion rate was predicted to increase consecutively between the current year 2023 and 202811.
Given the overview of harmful effects caused by screen time and the population contingent on access to these technologies in Brazil, it is crucial to identify the factors that influence the population's adoption of this health risk behavior. To this end, this research aimed to analyze and present information from a Brazilian national survey conducted between 2016 and 2022, using them as possible predictors for excessive screen time in adults and identifying which factors are associated with this behavior. Additionally, we sought to verify the immediate impact of the COVID-19 pandemic on the evolution of excessive screen time in the Brazilian adult population.

METHODS
A cross-sectional study was conducted using the Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Inquiry (VIGITEL), with a database corresponding to 2016 to 2022. The system, implemented in Brazil in 2006, uses data extracted annually using a telephone survey with the adult Brazilian population over 18 years living in the 26 Brazilian state capitals and the Federal District. VIGITEL was approved by the National Ethics Committee on Research for Human Beings of the Ministry of Health (CAAE: 65610017.1.0000.0008), aiming at the continuous monitoring of risk and protection factors that determine chronic diseases while generating a baseline for future monitoring of these factors12.
The investigation of excessive screen time was addressed in the block of questions related to daily physical activity using two questions; the first: "In your free time, do you use your computer, tablet, or cell phone to participate in social networks such as Facebook, to watch movies, or to entertain yourself with games?". Those who answered "yes" went on to the following question: "On average, how many hours of your free time (excluding work) do you use a computer, tablet, or cell phone per day?". Among the options, "less than 1 hour", "between 1 and 2 hours", "between 2 and 3 hours", "between 3 and 4 hours", "between 4 and 5 hours", "between 5 and 6 hours", and "more than 6 hours". Thus, excessive screen time was considered for the option "more than 6 hours" of use per day, considering it to be a cutoff point above that recommended in international guidelines for screen time7, justifying the investigation of excessive behavior.
Regarding the sociodemographic profile, the composition variables were gender (female and male), age (18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64 years and older) organized for analysis purposes in 18 to 39 years, 40 to 59 and >60 years, skin color (white, black or brown, yellow and indigenous), marital status (single, married, in a stable union, widowed, separated or divorced, and did not want to inform), marital status (single, married, stable union, widowed, separated or divorced, did not want to inform), an education level (0 to 8 years, 9 to 11 years, and ?12 years), living alone (no/yes), and region of the country where they live (North, Northeast, Midwest, Southeast, and South).
For the behavioral profile, we used information about cigarette smoking, alcohol abuse, and physical inactivity. The variable related to cigarette smoking was dichotomized into "no" and "yes." For alcohol abuse, men were asked: "In the past 30 days, have you consumed five or more doses of alcoholic beverages on a single occasion? The question was modified for women, indicating a decrease to four or more doses. We considered physically inactive the adult who did not practice any physical activity in their free time in the last three months, did not make intense physical effort at work, did not walk or ride a bike to work or school for at least 20 minutes to and from work, and was not responsible for heavy cleaning at home.
The analyses were weighted by the rake method13, with the categorical organization of the independent variables. Poisson Regression was operationalized considering excessive screen time as the dependent variable, with robust adjustment for crude and adjusted analyses of the variables of interest. The growth in the prevalence of excessive screen time was verified using Prais-Winsten Regression from 2016 to 2022. Prevalence ratios (PR) with a 95% confidence interval (CI 95%) were used to identify significant associations. All analyses were conducted using Stata software (version 15.1).

RESULTS
The data collected on the sociodemographic and behavioral profiles of the 254,600 survey participants can be seen in Table 1. In the descriptive analysis, it was observed that 54% of the sample was composed of women. The mean age of the participants was 41.0 years (standard deviation 16.5), and the black or brown skin color comprised 53.1% of the sample. Regarding marital status, education, living alone, and region where they live, 43.2% were single, 38.6% studied from 9 to 11 years, 3.1% live alone, and most reside in the Southeast region (44.7%). The behavioral profile of the participants indicated that 9.6% smoke cigarettes, 19.4% abuse alcohol, and 13.8% are physically inactive.
The prevalence of excessive screen time in the sample was 7.03% (95% CI: 6.76-7.32). Men had a higher prevalence of the outcome, while the bigger range found between groups occurred in the age of the participants, with 1% for the older and 12% for young adults. Pre-pandemic prevalence was 6.5%, while post-pandemic prevalence was 10.3%. The prevalence of the outcome in stratified analysis by groups is presented in Table 2, and the results of the adjusted analysis in the Poisson Regression model, through which the factors associated with excessive screen time were revealed.
In crude analysis, female gender, age 18 to 39 years and 40 to 59 years, black or brown skin color, single, in a stable union, widowed, and separated or divorced, education of 9 to 11 years and ?12 years, not living alone, the North, Northeast, and Southeast regions, smoking, alcohol abuse, physical inactivity, and the period from 2021 to 2022 were associated with excessive screen time in the sample. However, in the adjusted analysis, the remaining factors significantly associated with excessive screen time were age 18 to 39 years and 40 to 59 years, marital status single, in a stable union, separated or divorced, education ranges 9 to 11 years, the North, Northeast and Southeast regions, smoking, alcohol abuse, physical inactivity, and the period 2021 to 2022.
Time trend analysis related to excessive screen time from 2016 to 2022 is presented in Figure 1. An annual coefficient of variation equal to 0.87 (95% CI: 0.19-1.56; p-value= 0.02) was identified by the Prais-Winsten Regression, indicating that excessive screen time has increased by approximately 0.9 percentage points per year. In comparison between the years analyzed separately, it was observed that the most significant increases in the proportion of the outcome occurred from the year 2020 onward.

DISCUSSION
The analysis about excessive screen time when interacting with computers, tablets, or cell phones showed that being between 18 and 39 years old, single, in a stable union, separated or divorced, having schooling between 9 and 11 years old, living in the North, Northeast and Southeast regions, smoking cigarettes, abusive alcohol consumption, and being physically inactive are factors associated with greater use of screens by adults in Brazil.
In line with the findings of this study, we found that younger adults seem to have a diversified portfolio for interaction with screens compared to older adults in the Brazilian population14, contributing to greater adherence to the behavior associated with interactive screens. In addition, other studies conducted in Brazil15-17 indicate a high prevalence of excessive screen time among adolescents, with older adolescents, who are close to or have reached adulthood, being more prevalent than younger adolescents. Although this population profile is not included in the survey analyzed in this research, it is important to note that the quick change between adolescence and adulthood is marked by transitions related to autonomy and decision-making, where lifestyle habits are established and consolidated18, which may be perpetuated for the following stages of life.
The study conducted by Cavazzoto et al.19 revealed that single individuals over 30 years are more likely to watch television for more than 3 hours a day compared to married individuals of both sexes. In this sense, besides the interaction time with televisions can be used as a proxy for the excessive screen time investigated in this research, it is believed that the lack of a partner during leisure time may predispose single, separated, or divorced individuals to greater use of technologies to establish communications, enhanced by the use of social network applications.
The association found between excessive screen time, and intermediate level of education corroborates the study by Silva et al.20, in which it is evidenced that individuals with up to high school education in Brazil are more likely to spend more time on screen when compared to individuals with lower levels of education. Although the income information was not collected, in a study that analyzed the prevalence of addiction to digital technologies in 64 countries21, it was found that low-middle-income regions had the highest rates of addiction to smartphones and social media, and this is a possible explanation in a perspective that uses education as a proxy for income, indicating that Brazilians who spend more time interacting with screens come from middle-income families, considering the association found with the middle-income bracket.
Regarding the regions where the participants live, the Southeast and the North are those with the highest mobile broadband connection per home9, with the Southeast being, in particular, the region with the highest access when the service is not available in places they usually go to22, suggesting a lifestyle tied to connectivity, justifying in part the associations to excessive screen time behavior by the possibility of accessing Internet content.
The behavioral approaches about cigarette smoking, alcohol abuse, and physical activity, brought up by the survey in this research, are related to excessive screen time, possibly because they are people who tend not to take proper care of their health, even though they are behaviors that can be modified. A recent meta-analysis23 corroborates the evidence that health risk behaviors such as cigarette smoking and alcohol consumption positively correlate with internet content addiction. Although the inconsistency found in the literature24 causes uncertainty about the characterization of screen time as an addictive behavior, scientific evidence25 reveals long-term morphological changes in brain regions closely involved in reward processing emotional regulation and executive functions of young adults who have a continuous pattern of binge drinking (drinking four or five doses of alcoholic beverages on a single occasion), reinforcing a possible relationship that encompasses excessive screen time and alcohol abuse in the same etiopathogenesis that predisposes the individual to adhere to compulsive addictive behaviors.
On the other hand, for individuals who spend 6 hours or more of the day interacting with screens, organizing the remaining day to perform tasks that require regularity may become a complex condition to achieve and even more challenging when dealing with adults with daily work schedules. In addition, other factors may have mediated the relationship between excessive screen use and physical inactivity, such as the advent of the COVID-19 pandemic, declared as a public health emergency of international concern by the World Health Organization (WHO) in March 202026.
The social restrictions imposed by the pandemic to contain virus transmission generated a new scenario of social interaction around the world, being communicated about the need for greater attention to excessive screen time as a behavior that could replace healthy habits such as regular physical activity27, something that could be observed in the adult population28,29, including in Brazil, where young adults had a higher incidence of physical inactivity during the first wave of the pandemic20. In contrast, adherence to physical exercise, mainly outdoors, contributed to individuals having a better mental and general health perception during the pandemic30.
As of 2016, an increasing prevalence of excessive screen time was observed in the Brazilian population, with the most significant increases occurring especially after 2020. In Brazil, based on the temporal analysis conducted in this research, it can be projected that this excessive behavior will be part of the daily lives of about 20% of adults in 10 years if measures aimed at mitigation are not implemented. This trend converges with the increase in the average time of screen interaction in other countries31, emphasizing access to streaming services in the recreational domain among young adults32.
To better understand this perspective that seems to reveal itself on a global scale, it is worth noting that the technological expansion that occurred concomitantly with the advent of the COVID-19 pandemic brought innovative mechanisms to social life, such as digital interventions mediated by artificial intelligence, which can manifest itself through algorithms in predictive analysis of behavioral patterns that create a digital ecosystem of unprecedented opportunities33. There was, therefore, a favorable scenario for interaction with screens during the pandemic due to the reduced possibilities for entertainment and social interaction, enhanced by the offer of exciting content for the user who stayed connected.

LIMITATIONS
Due to the type of survey sampling, there is no capacity to generalize the data to the entire Brazilian population, extrapolating from the capital cities and the Federal District to include residents of smaller municipalities in the state's interior. In addition, not knowing the occupational status of the participants is a significant limitation since the time spent on the workday can impact the distribution of other activities throughout the day.
Although the choice of the cutoff point of ? 6 hours per day to characterize excessive screen time has not been addressed in other scientific studies conducted with the Brazilian population, it should be noted that another analysis using the recommended cutoff point of < 3 hours per day in screen time was operationalized, presenting the same associations found in this research, except for the region, and sensitive changes in the magnitude of the effects in the other variables with statistical significance. It corroborated the option choice differentiating a typical behavior from an excessive one.
On the other hand, as strengths, this research informs the factors associated with excessive screen time in a study with a national sample of the capital cities of Brazil and the Federal District, identifying the main risk groups for the behavior, besides providing a temporal analysis of the outcome in recent years, even generating comparisons between the period before and after the advent of the COVID-19 pandemic.

CONCLUSION
This study aimed to assess the risk factors associated with excessive screen time in a population sample between the years 2016 and 2022 in Brazil and found that younger adults tend to spend more time interacting with screens when compared to older adults, which may be justified for a portion of individuals by the maintenance of that behavior that occurs since adolescence and is perpetuated into adulthood. Other modifiable predictive factors were found, such as smoking, alcohol abuse, and physical inactivity, highlighting the importance of a comprehensive look at people who seem to have less care for their overall health. The advent of the COVID-19 pandemic has increased interaction with computer screens, cell phones, and tablets, especially in the recreational domain, and generated a global perspective of the increasing prevalence of excessive screen time, which may affect about 1 in 5 Brazilian adults in 10 years.
The current investigation on excessive screen time promotes a better understanding of the sociodemographic and behavioral characteristics of Brazilian adults living in the capital cities of Brazil and the Federal District. However, it is suggested that new studies be carried out focusing on the peripheral populations nationwide, with the insertion of more specific questions related to the pattern of screen use, as well as studies with a qualitative approach that promote a greater description of the reasons that lead to excessive use and the contents of interest to users. Thus, in addition to the ability to infer the current risk and protective factors for screen time in different population profiles, additional information can expand the construct about this behavior that is harmful to health.

REFERENCES

1. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, Chastin SFM, et al. Sedentary Behavior Research Network (SBRN) - Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):75. Published 2017 Jun 10. doi:10.1186/s12966-017-0525-8.
2. Oswald TK, Rumbold AR, Kedzior SGE, Moore VM. Psychological impacts of "screen time" and "green time" for children and adolescents: A systematic scoping review. PLoS One. 2020;15(9):e0237725. Published 2020 Sep 4.
3. Santos RMS, Mendes CG, Sen Bressani GY, et al. The associations between screen time and mental health in adolescents: a systematic review. BMC Psychol. 2023;11(1):127. Published 2023 Apr 20.
4. Wang X, Li Y, Fan H. The associations between screen time-based sedentary behavior and depression: a systematic review and meta-analysis. BMC Public Health. 2019;19(1):1524. Published 2019 Nov 14.
5. Costigan SA, Barnett L, Plotnikoff RC, Lubans DR. The health indicators associated with screen-based sedentary behavior among adolescent girls: a systematic review. J Adolesc Health. 2013;52(4):382-392.
6. Foreman J, Salim AT, Praveen A, et al. Association between digital smart device use and myopia: a systematic review and meta-analysis. Lancet Digit Health. 2021;3(12):e806-e818.
7. Ross R, Chaput JP, Giangregorio LM, et al. Canadian 24-Hour Movement Guidelines for Adults aged 18-64 years and Adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab. 2020;45(10 (Suppl. 2)):S57-S102.
8. Rollo S, Lang JJ, Roberts KC, et al. Health associations with meeting the Canadian 24-hour movement guidelines for adults: Results from the Canadian Health Measures Survey. Health Rep. 2022;33(1):16-26.
9. Instituto Brasileiro de Geografia e Estatística. Pesquisa nacional por amostra de domicílios contínua: acesso à internet e à televisão e posse de telefone móvel celular para uso pessoal 2021 [Internet]; 2022. Comunicações e Transparência Pública; [cited 2023 Aug 1]; Available from: https://biblioteca.ibge.gov.br/visualizacao/livros/liv101963_informativo.pdf
10. Cardoso PC, Caldeira TCM, Sousa TM de, Claro RM. Changes in Screen Time in Brazil: A Time-Series Analysis 2016-2021. American Journal of Health Promotion. 2023;37(5):681-684.
11. Statista [Internet]. 2023. Share of population in Brazil that use a smartphone from 2015 to 2026; [cited 2023 Jul 3]; Available from: https://www.statista.com/forecasts/625406/smartphone-user-penetration-inbrazil
12. Brasil. Vigitel Brasil 2021 [Internet]; 2021. Vigilância de Fatores de Risco e Proteção Para Doenças Crônicas por Inquérito Telefônico: Estimativas Sobre Frequência e Distribuição Sociodemográfica de Fatores de Risco e Proteção para Doenças Crônicas nas Capitais dos 26 Estados Brasileiros e no Distrito Federal em 2021; [cited 2023 Jul 3]; Available from: https://www.gov.br/saude/ptbr/centrais-de-conteudo/publicacoes/svsa/vigitel/vigitel-brasil-2021-estimativassobre-frequencia-e-distribuicao-sociodemografica-de-fatores-de-risco-eprotecao-para-doencas-cronicas
13. Bernal RTI, Iser BPM, Malta DC, Claro RM. Sistema de Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico (Vigitel): mudança na metodologia de ponderação. Epidemiol. Serv. Saúde. 2017; 26(4):701-712.
14. Silva DR, Collings P, Araujo RHO, Barboza LL, Szwarcwald CL, Werneck AO. Correlates of screen-based behaviors among adults from the 2019 Brazilian National Health Survey. BMC Public Health. 2021;21(1):2289.
15. Schaan CW, Cureau FV, Sbaraini M, Sparrenberger K, Kohl Iii HW, Schaan BD. Prevalence of excessive screen time and TV viewing among Brazilian adolescents: a systematic review and meta-analysis. J Pediatr (Rio J). 2019;95(2):155-165.
16. Braga IAF, Maroneze LR, Oliveira LR, Santos AW dos, Andrade AM de. Screen time and its association with adolescents’ lifestyles in a city in the Brazilian Northeast. 2022;11(15):e560111537671.
17. Guedes DP, Desiderá RA, Gonçalves HR. Prevalence of excessive screen time and correlates factors in Brazilian schoolchildren. Rev. Bras. Ativ. Fís. Saúde. 2018;23:1-10.
18. van Sluijs EMF, Ekelund U, Crochemore-Silva I, et al. Physical activity behaviours in adolescence: current evidence and opportunities for intervention. Lancet. 2021;398(10298):429-442.
19. Cavazzotto TG, de Lima Stavinski NG, Queiroga MR, et al. Age and SexRelated Associations between Marital Status, Physical Activity and TV Time. Int J Environ Res Public Health. 2022;19(1):502. Published 2022 Jan 3.
20. Silva DR, Werneck AO, Malta DC, et al. Incidence of physical inactivity and excessive screen time during the first wave of the COVID-19 pandemic in Brazil: what are the most affected population groups?. Ann Epidemiol. 2021;62:30-35.
21. Meng SQ, Cheng JL, Li YY, et al. Global prevalence of digital addiction in general population: A systematic review and meta-analysis. Clin Psychol Rev. 2022;92:102128.
22. Instituto Brasileiro de Geografia e Estatística. Pesquisa nacional por amostra de domicílios contínua: acesso à internet e posse de telefone móvel celular para uso pessoal 2021 [Internet]; 2022. Comunicações e Transparência Pública; [cited 2023 Aug 1]; Available from: https://biblioteca.ibge.gov.br/visualizacao/livros/liv101963_informativo.pdf
23. Wang J, Hao QH, Tu Y, et al. Assessing the Association Between Internet Addiction Disorder and Health Risk Behaviors Among Adolescents and Young Adults: A Systematic Review and Meta-Analysis. Front Public Health. 2022;10:809232. Published 2022 Apr 1.
24. King DL, Haagsma MC, Delfabbro PH, Gradisar M, Griffiths MD. Toward a consensus definition of pathological video-gaming: a systematic review of psychometric assessment tools. Clin Psychol Rev. 2013;33(3):331-342.
25. Pérez-García JM, Cadaveira F, Canales-Rodríguez EJ, et al. Effects of Persistent Binge Drinking on Brain Structure in Emerging Adults: A Longitudinal Study. Front Psychiatry. 2022;13:935043. Published 2022 Jun 23. doi:10.3389/fpsyt.2022.935043.
26. Cucinotta D, Vanelli M. WHO Declares COVID-19 a Pandemic. Acta Biomed. 2020;91(1):157-160. Published 2020 Mar 19.
27. World Health Organization. Excessive screen use and gaming considerations during COVID19 [Internet]; 2020. Regional Office for the Eastern Mediterranean; [cited 2023 Aug 1]; Available from: https://apps.who.int/iris/handle/10665/333467
28. Chew HSJ, Lopez V. Global Impact of COVID-19 on Weight and WeightRelated Behaviors in the Adult Population: A Scoping Review. Int J Environ Res Public Health. 2021;18(4):1876.
29. Meyer J, McDowell C, Lansing J, et al. Changes in Physical Activity and Sedentary Behavior in Response to COVID-19 and Their Associations with Mental Health in 3052 US Adults [published correction appears in Int J Environ Res Public Health. 2020 Sep 23;17(19):]. Int J Environ Res Public Health. 2020;17(18):6469.
30. Colley RC, Bushnik T, Langlois K. Exercise and screen time during the COVID-19 pandemic. Health Rep. 2020;31(6):3-11.
31. Trott M, Driscoll R, Irlado E, Pardhan S. Changes and correlates of screen time in adults and children during the COVID-19 pandemic: A systematic review and meta-analysis. EClinicalMedicine. 2022;48:101452.
32. Wagner BE, Folk AL, Hahn SL, Barr-Anderson DJ, Larson N, NeumarkSztainer D. Recreational Screen Time Behaviors during the COVID-19 Pandemic in the U.S.: A Mixed-Methods Study among a Diverse PopulationBased Sample of Emerging Adults. International Journal of Environmental Research and Public Health. 2021; 18(9):4613.
33. Ting DSW, Carin L, Dzau V, Wong TY. Digital technology and COVID-19. Nat Med. 2020;26(4):459-461.