High levels of computer use for leisure and social network access among Brazilian university students

Altos níveis de uso de computador para lazer e acesso a redes sociais entre estudantes universitários brasileiros

Abstract

Abstract  Excessive computer use is a common behavior among university students. This study’s objective, conducted with Brazilian university students, was to estimate the prevalence and association between computer use for academic studies/research and high computer use for leisure and social network access, as well as to examine the associations of sociodemographic characteristics, university affiliation, and physical activity with high computer use for leisure and social network access, considering the stratification for computer use for academic studies/research. A cross-sectional study was conducted with 1,011 university students. The outcome was high computer use for leisure, accessing social networks. The variables analyzed included computer use for studies/research, along with sociodemographic variables, university affiliation, and physical activity. The prevalence of the outcome was 17.0%. There was no association between computer use for studies/research and computer use for leisure. However, among university students with lower computer use for studies/research, increasing age was associated with lower prevalence (PR: 0.92; 95% CI: 0.88-0.96), whereas a greater number of computers at home and longer university enrollment were associated with higher prevalences of high computer use for leisure. In conclusion, there was a high prevalence of computer use for leisure, this behavior among university students being associated with age, time spent at university, and the number of computers/laptops available for access, particularly in the context of lower computer use for academic activities.

Key words:
Cross-sectional studies; Screen time; Students

Resumo

Resumo  O uso excessivo do computador é um comportamento comum entre os estudantes universitários. O objetivo deste estudo, conduzido com estudantes universitários brasileiros, foi estimar a prevalência e a associação entre o uso de computador para estudos/pesquisas acadêmicas e o elevado uso do computador para lazer e acesso às redes sociais, bem como examinar as associações de características sociodemográficas, de vínculo com a universidade e atividade física com o elevado uso do computador para o lazer e acesso às redes sociais, considerando a estratificação pelo uso do computador para estudos/pesquisas acadêmicas. Realizou-se um estudo transversal com 1.011 estudantes universitários. O desfecho foi o elevado uso do computador para o lazer, com acesso às redes sociais. As variáveis analisadas incluíram o uso do computador para estudos/pesquisas, juntamente com variáveis sociodemográficas, de vínculo com a universidade e atividade física. A prevalência do desfecho foi de 17,0%. Não houve associação entre o uso do computador para estudos/pesquisas e o uso no lazer. Porém, entre os estudantes universitários com menor uso do computador para os estudos/pesquisas, o aumento da idade associou-se com menor prevalência (RP: 0,92; IC95%: 0,88-0,96), enquanto maior número de computadores em domicílio e maior tempo de vínculo com a universidade foram associados a maiores prevalências do elevado uso do computador para o lazer. Conclui-se que houve elevada prevalência do uso do computador no lazer, estando esse comportamento entre os estudantes universitários associado à idade, ao tempo de permanência na universidade e à quantidade de computadores/laptops disponíveis para acesso, particularmente no contexto de menor uso do computador para atividades acadêmicas.

Palavras-chave:
Estudos transversais; Tempo de tela; Estudantes

INTRODUCTION

Insufficient levels of physical activity are associated with the development of chronic non-communicable diseases, such as cardiovascular diseases, type II diabetes, and certain types of cancer¹. In addition, high exposure to sedentary behavior may increase health risks regardless of physical activity levels². High exposure to sedentary behavior does not represent an absence of physical activity, as these behaviors coexist simultaneously²,³.

Sedentary behavior can be characterized as conduct that involves an energy expenditure of ≤1.5 Metabolic Equivalents (METs) in a sitting, lying, or reclining position³. On the other hand, screen time refers to the time spent on screen-based behaviors, for example, on computers, that can be performed while being sedentary³. Screen time has increased worldwide, particularly among university students, partly due to the growing need to use computers and laptops⁴.

With the advancement of the digital era, the use of technologies for learning has expanded, promoting changes in lifestyle habits, especially among university students⁵,⁶. In this context, university students may be more vulnerable to excessive sedentary behavior, with possible negative impacts on both physical and mental health⁶.

In Brazil, studies have investigated sedentary behavior among university students⁶. However, sociocultural and regional particularities still require further investigation, considering the country’s continental dimensions and its socioeconomic diversity⁷. In the Northeast region, the state of Bahia stands out for its large number of students enrolled in higher education⁸. In this context, understanding technology use among university students in this state is relevant for supporting more effective health promotion strategies⁴.

Leisure time and frequent social network access among university students can largely occur through smartphones⁹. However, computer use in the context of studies and research, especially for the execution of academic tasks, as well as the recreational use of computers and engagement with digital platforms, such as social networks, which represent common behaviors in contemporary society⁵, can occur simultaneously.

This pattern favors prolonged sedentary behavior, reducing the time dedicated to social interaction⁴. Thus, identifying profiles more exposed to this behavior may support institutional policies and initiatives aimed at promoting more active and healthier lifestyles⁶. On the other hand, the relevance of using technologies, such as computers, for academic studies and research is widely recognized¹⁰.

Based on this context, this study’s objectives, conducted with Brazilian university students, were: (1) to estimate the prevalence and association between computer use for academic studies/research and high computer use for leisure, accessing social networks; (2) to examine the associations of sociodemographic characteristics, university affiliation, and physical activity with high computer use for leisure and social network access, considering the stratification for computer use for academic studies/research.

METHOD

This epidemiological cross-sectional study is part of the research project entitled “Lifestyle and Quality of Life of Students at Federal Universities in the State of Bahia: Analysis of Repeated Surveys”, which monitors risk factors for chronic non-communicable diseases among students at Federal Universities in the state of Bahia, with surveys conducted in 2019 and 2023. The analyses presented in this article refer to data collected in 2023. The study was approved by five Research Ethics Committees (approval numbers: 2,767,041; 2,795,177; 2,915,077; 3,033,773; and 6,138,116).

The study population consisted of undergraduate students enrolled in on-campus courses at Federal Universities (FUs) located in the state of Bahia, Brazil, including institutions headquartered in the state and campuses of universities based in other states. Six institutions participated: Federal University of Recôncavo da Bahia (UFRB), Federal University of Vale do São Francisco (UNIVASF), Federal University of Bahia (UFBA), Federal University of Southern Bahia (UFSB), Federal University of Western Bahia (UFOB), and the University for International Integration of Afro-Brazilian Lusophony (UNILAB). The state of Bahia, located in the Northeast region of Brazil, has a population of 14,141,626 inhabitants⁷. Data from the Higher Education Census indicate an increase in undergraduate enrollment in recent decades, associated with the expansion of the federal higher education network⁸.

Students who consented to participate were included regardless of sex and age. After data tabulation, students with special enrollment status (individuals already holding a higher education degree enrolled in undergraduate programs), students enrolled in technical programs, and those under 18 years of age were excluded. These criteria were described in the Free and Informed Consent Term (FICT) and verified through specific questions in the data collection instrument.

The sample size was defined using the equation proposed by Luiz and Magnanini¹¹. A prevalence of 50%, a 95% Confidence Level, and a sampling error of three percentage points were adopted for the target population of 49,140 university students, corresponding to the total number of students enrolled across all participating campuses. Subsequently, 40% was added to account for potential losses and 15% for association analyses. The final calculated sample size was 1,682 students, and participation occurred through convenience sampling.

Data collection took place between February 1 and December 16, 2023, through an electronic questionnaire sent by email to students at the participating institutions, with support from course coordinators and administrative sectors. Furthermore, students were approached in classrooms before or after classes on different days of the week and invited to participate using laptops or smartphones. The data collection team consisted of undergraduate students not involved in the study and graduate students enrolled in master’s programs.

Information was collected using a questionnaire with 68 objective questions, with an average completion time of 30 minutes. The dependent variable was the time reported in hours and/or minutes for screen-based activities, such as computer use for leisure and social network access¹²,¹³. The statements measured were: a) On a weekday, from Monday to Friday, how much time on average do you spend performing the following activities; b) On a Saturday or Sunday, how much time on average do you spend performing the following activities.

High computer use was the outcome, defined as spending 138 minutes or more per day on this behavior during weekdays and 300 minutes or more per day during weekends¹⁴. Other responses were grouped into the "other" category. The cutoff points were derived from a previous study conducted with Brazilian university students, which used Receiver Operating Characteristic curve analysis¹⁴. Specifically, the cutoff points for computer use for leisure corresponded with the thresholds of the best balance between sensitivity (weekdays: 45.5%; weekend: 25.4%) and specificity (weekdays: 63.5%; weekend: 84.7%) for discriminating negative self-rated health in that population.

The main independent variable and stratification was computer use time for academic studies and research¹²,¹³. Participants reported the amount of time (hours and/or minutes) spent using a computer for studies and research on a weekday (Monday to Friday) and on a weekend day (Saturday or Sunday). A weighted average was calculated as follows: [(weekday × 5) + (weekend × 2)] / 7. Computer usage time for studies and research was defined in tertiles (lower: up to 145.71 minutes/day; intermediate: 145.72 to 257.14 minutes/day; and higher: 257.15 or more minutes/day).

The other variables were considered independent and control variables, including sociodemographic and academic characteristics: sex (male, female), age, in years (quantitative variable), marital status (with partner, without partner), self-reported skin color (white, black, or brown; students reporting yellow or Indigenous skin color were excluded due to low frequency, 0.5% and 0.3%, respectively), study period (daytime, nighttime), length of university enrollment (1, 2, 3, or ≥4 years), and number of desktop or laptop computers available in the household (none, 1, 2, 3, or 4 or more). Moderate-to-vigorous physical activity (independent and control variable) was assessed using the short version of the International Physical Activity Questionnaire (IPAQ), classifying students as active (≥150 min/week) or insufficiently active (≤149 min/week), with vigorous activity time weighted by two.

Data were tabulated in Microsoft Excel and analyzed using SPSS software, version 25.0. The listwise procedure (exclusion of participants) was performed to identify those with missing data for one or more variables investigated in this study. Due to the non-probabilistic sampling procedure, sample weights were applied¹⁵ based on the census of federal higher education institutions¹⁶. Descriptive analyses of absolute and relative frequencies were performed. In bivariate analyses, chi-square tests for heterogeneity and for linear trend were used.

For the first objective, the association between computer use time for studies and research and the dependent variable was evaluated in crude and adjusted analyses. In the adjusted model, all control variables were included and progressively removed according to the highest p-value, retaining those with p ≤0.20; for the second objective, associations between the exploratory variable (sociodemographic characteristics, university affiliation, and physical activity) and the dependent variable were analyzed in each category of stratification variable, and adjusted for all control variables that were removed according to the highest p-value, retaining those with p ≤0.20. In both analyses, Poisson regression with robust variance was used to estimate Prevalence Ratios (PR) and their respective 95% Confidence Intervals (95% CI), adopting a significance level of 5%.

RESULTS

A total of 1273 university students submitted the questionnaire; however, 20 declined participation, two were excluded for reporting enrollment in postgraduate programs, and one was excluded for being under 18 years of age. The final sample comprised 1250 university students. Using the listwise procedure considering all variables, the sample for this study consisted of 1011 university students. When comparing the final sample with the sample from this study, there were significant losses for the variables sex (p: 0.047) and skin color (p: 0.021).

Table 1 shows a distribution of characteristics of samples across tertiles of computer use for academic studies and research. Overall, most participants were female (57.1%), without a partner (79.2%), self-identified as brown (41.5%), had been enrolled at the university for four years or more (44.4%), attended daytime courses (70.1%), had one computer available at home (50.6%), and were physically active (77.5%). The mean age was 26.1 years (SD = 8.3). Across tertiles of computer use for academic studies and research, a higher proportion of females was observed in the intermediate tertile (63.8%), whereas daytime students were more frequent in the highest tertile (81.4%). The distribution of marital status, skin color, physical activity, and age was similar across tertiles.

Table 1
Description of the sample of university students from federal universities in the state of Bahia, Brazil, 2023.

Variables			Computer time for academic studies and research
	All		Lower		Intermediate		Higher
	n	%	n	%	n	%	n	%
	-		343	34.2	355	33.8	313	32.0
Categorical variables
Sex
Male	369	42.9	140	49.4	114	36.2	115	43.1
Female	642	57.1	203	50.6	241	63.8	198	56.9
Marital status
Without partner	778	79.2	260	77.7	277	79.7	241	80.3
With partner	233	20.8	83	22.3	78	20.3	72	19.7
Self-reported skin color
White	288	26.4	101	26.1	100	26.9	87	26.1
Black	279	32.1	93	31.5	99	28.9	87	36.0
Brown	444	41.5	149	42.4	156	44.2	139	37.9
Length of university enrollment
1 year	201	20.9	80	20.1	69	17.4	52	25.5
2 years	216	21.7	81	28.7	76	21.3	59	14.5
3 years	164	13.0	52	12.7	59	13.6	53	12.8
4 years or more	430	44.4	130	38.5	151	47.6	149	47.2
Study period
Daytime	711	70.1	228	62.5	248	67.2	235	81.4
Nighttime	300	29.9	115	37.5	107	32.8	78	18.6
Number of desktop or laptop computers available in the household
None	78	10.1	25	16.4	21	6.2	32	7.5
1	536	50.6	184	48.5	190	54.9	162	48.4
2	246	25.0	86	22.4	90	24.1	70	28.7
3	73	7.1	23	6.6	24	6.8	26	7.9
4 or more	78	7.2	25	6.1	30	8.0	23	7.5
Physical activity (min/wk)
Active	767	77.5	254	76.9	271	77.2	242	78.6
Insufficiently active	244	22.5	89	23.1	84	22.8	71	21.4
Numerical variable	n	M (SD)	n	M (SD)	n	M (SD)	n	M (SD)
Age, in years	1011	26.1 (8.3)	343	26.1 (8.9)	355	25.9 (8.5)	313	26.4 (7.3)

• Note. n: Absolute frequency; %: Weighted relative frequency; Lower: up to 145.71 minutes/day; Intermediate: 145.72 to 257.14 minutes/day; Higher: 257.15 or more minutes/day; min: minutes; wk: week; M: Weighted mean; SD: Standard deviation.

The prevalence of prolonged computer use for leisure among university students was 17.0%. No association was observed between computer use by university students for studies and research and high computer use for leisure and social network access, using lower computer use for studies and research as the reference category (intermediate, crude analysis, RP: 1.28, 95% CI: 0.94-1.74, adjusted analysis, RP: 1.20, 95% CI: 0.88-1.64; higher, crude analysis, RP: 0.98, 95% CI: 0.70-1.38, adjusted analysis, RP: 0.99, 95% CI: 0.70-1.40), adjusted for age, length of university enrollment, and number of desktop or laptop computers available in the household.

Among students with lower computer use for academic studies and research (Table 2), high leisure-time computer use was associated with age (PR: 0.92; 95% CI: 0.88-0.96) and with length of university enrollment (p for trend: 0.005), with students enrolled for four years or more showing a higher prevalence than those enrolled for one year (PR: 2.27; 95% CI: 1.19-4.31). A linear association was also observed for the number of computers available in the household (p for trend < 0.001).

Table 2
Adjusted analysis between each exploratory characteristic and high computer use for leisure time, considering lower computer use for academic studies and research among students at federal universities in the state of Bahia, Brazil, 2023.

Variables	PRadjusted	95% CI	p
Sex			0.698*
Male	1.00
Female	0.91	0.57-1.46
Age, in years	0.92	0.88-0.96	<0.001**
Marital status			0.909*
Without partner	1.00
With partner	0.96	0.46-1.99
Self-reported skin color			0.849*
White	1.00
Black	0.82	0.40-1.65
Brown	0.91	0.53-1.54
Length of university enrollment			0.005**
1 year	1.00
2 years	1.11	0.54-2.29
3 years	1.92	0.90-4.12
4 years or more	2.27	1.19-4.31
Study period			0.191*
Daytime	1.00
Nighttime	0.67	0.37-1.22
Number of desktop or laptop computers available in the household			<0.001**
None	1.00
1	7.64	1.07-54.35
2	8.38	1.15-61.18
3	7.33	0.90-59.92
4 or more	17.26	2.38-125.29
Physical activity (min/wk)			0.900*
Active	1.00
Insufficiently active	1.03	0.62-1.72

• Note. PR: Prevalence Ratios; 95% CI: 95% Confidence Interval; p*: Wald test for heterogeneity; p**: Wald test for linear trend; min: minutes; wk: week; Adjusted for age, study period, length of university enrollment, and number of desktop or laptop computers available in the household.

Only age remained associated with higher leisure-time computer use among students with intermediate computer use for academic studies and research (Table 3). The prevalence was 14% lower for each additional year of age (PR: 0.86; 95% CI: 0.79-0.93; p < 0.001), while all other exploratory characteristics showed no significant associations (p > 0.05).

Table 3
Adjusted analysis between each exploratory characteristic and high computer use for leisure time, considering intermediate computer use for academic studies and research among students at federal universities in the state of Bahia, Brazil, 2023.

Variables	PRadjusted	95% CI	p
Sex			1.000*
Male	1.00
Female	1.00	0.66-1.51
Age, in years	0.86	0.79-0.93	<0.001**
Marital status			0.970*
Without partner	1.00
With partner	0.99	0.53-1.85
Self-reported skin color			0.544*
White	1.00
Black	1.00	0.56-1.78
Brown	1.24	0.77-1.99
Length of university enrollment			0.179**
1 year	1.00
2 years	1.70	1.00-2.90
3 years	0.99	0.45-2.19
4 years or more	1.72	0.99-3.00
Study period			0.407*
Daytime	1.00
Nighttime	1.20	0.78-1.84
Number of desktop or laptop computers available in the household			0.299**
None	1.00
1	0.71	0.35-1.45
2	0.57	0.25-1.28
3	0.82	0.34-1.98
4 or more	0.52	0.21-1.30
Physical activity (min/wk)			0.195*
Active	1.00
Insufficiently active	1.31	0.87-1.98

• Note. PR: Prevalence Ratios; 95% CI: 95% Confidence Interval; p*: Wald test for heterogeneity; p**: Wald test for linear trend; min: minutes; wk: week; Adjusted for age years old, length of university enrollment, and physical activity.

Among students with higher computer use for academic studies and research (Table 4), none of the investigated characteristics were significantly associated with high leisure time computer use. Students' age showed a borderline inverse association (PR: 0.94; 95% CI: 0.88-1.01; p: 0.076), while the remaining variables were not associated with the outcome (p > 0.05).

Table 4
Adjusted analysis between each exploratory characteristic and high computer use for leisure time, considering higher computer use for academic studies and research among students at federal universities in the state of Bahia, Brazil, 2023.

Variables	PRadjusted	95% CI	p
Sex			0.965*
Male	1.00
Female	1.01	0.58-1.75
Age, in years	0.94	0.88-1.01	0.076**
Marital status			0.122*
Without partner	1.00
With partner	0.46	0.17-1.23
Self-reported skin color			0.997*
White	1.00
Black	1.02	0.55-1.91
Brown	1.02	0.55-1.89
Length of university enrollment			0.554**
1 year	1.00
2 years	1.54	0.76-3.12
3 years	0.80	0.28-2.27
4 years or more	1.38	0.71-2.51
Study period			0.067*
Daytime	1.00
Nighttime	1.71	0.96-3.02
Number of desktop or laptop computers available in the household			0.670**
None	1.00
1	1.11	0.39-3.20
2	0.55	0.16-1.91
3	1.87	0.56-6.23
4 or more	1.33	0.36-4.83
Physical activity (min/wk)			0.770*
Active	1.00
Insufficiently active	1.09	0.61-1.96

• Note. PR: Prevalence Ratios; 95% CI: 95% Confidence Interval; p*: Wald test for heterogeneity; p**: Wald test for linear trend; min: minutes; wk: week; Adjusted for age, marital status, and study period.

DISCUSSION

This study showed that approximately 17 out of every 100 undergraduate students enrolled in federal institutions in Bahia had prolonged computer use for leisure and social network access. Computer use for academic studies/research was not associated with longer leisure time computer use. Among students who used computers less for studying and research, age was associated with a lower prevalence of the outcome, while greater availability of computers at home and a university enrollment length of four years or more were associated with a higher prevalence of this behavior.

The prevalence of prolonged computer use for leisure in this study was higher. This result is similar to that observed in a systematic review on screen time (computer and television), which reported prevalence of 16.5% (≥4 hours/day)⁶. However, a study with university students from the state of Bahia (Brazil) found a prevalence of 56.1% for computer use for leisure and social network access plus computer use for studies and research, when considering ≥2 hours/day¹⁷. These findings suggest that, even in the university context and despite the presence of other technologies such as smartphones, the computer remains a frequent means of leisure and studies.

Many studies do not distinguish screen time by device, often including television, computers, video games, tablets, and smartphones simultaneously⁶, which makes comparisons with the present results difficult. Moreover, variation in cutoff points used to define high exposure to screen time also limits comparisons⁶. Nevertheless, the cutoff point adopted in this study was based on the prediction of self-rated health¹⁴, a classical indicator used to identify groups at risk of morbidity and premature mortality¹⁸.

In this study, the computer use for studies and research performed by university students was not associated with a higher prevalence of computer use for leisure and social network access. This finding suggests cumulative and simultaneous exposure to screen-based behaviors¹⁷. The digitalization of academic activities may reduce the distinction between study-related and leisure computer use, increasing overall screen time. Thus, computer use for academic purposes may additionally contribute to recreational screen exposure¹⁹.

Among university students, increasing age was associated with a lower prevalence of computer use for leisure and social network access, in conditions of lower and intermediate computer use for studies/research. This pattern of excess computer use for leisure is frequently observed among younger people²⁰ and may reflect generational differences in digital habits and online engagement. Although academic demands tend to increase throughout university years, older students generally report lower screen time⁶, possibly because they prioritize leisure activities involving less digital exposure.

In this study, sex was not associated with prolonged computer use for leisure. Although higher screen time values are often observed among women⁶, possibly also related to information-seeking behaviors²¹, leisure screen use tends to be more prevalent among men²¹. Given the still inconclusive information regarding sex differences in computer use during university years²², further studies could explore potential motivators of this behavior.

Additionally, the number of desktops and laptops in the household was associated with prolonged computer use for leisure among university students, only among students with lower computer use for studies/research. The number of devices may reflect socioeconomic conditions²³, possibly related to employment status or higher income⁶, facilitating greater access to this equipment for recreational use. Thus, easy access to computers/laptops and less involvement in academic activities can exacerbate leisure time spent on social networks.

No differences in prolonged computer use for leisure and social network access were observed according to study period. A systematic review of sedentary behavior among university students highlighted the heterogeneity of associated factors and inconsistencies across studies⁶. Therefore, the lack of association observed in the present study suggests that screen-related behaviors may be influenced by factors beyond academic schedules, affecting students similarly across different study periods.

On the other hand, among university students with longer university enrollment, a higher prevalence of computer use for leisure and social network access was observed among those who dedicated less time to using computers for studies/research. These findings may be related to the context of the course curricula, with a higher occurrence of activities related to mandatory internships in the final years, which may minimize the use of computers for studies and research, but favor other activities related to leisure, such as seeking means related to relaxation and communication with peers through social networks²⁴.

Screen time is an important risk factor for health²⁵. In the university context, computer use is frequent due to academic demands⁶, and additional leisure use may increase risks due to the cumulative effect of sedentary behaviors and prolonged sitting time²⁵. In this sense, university routines could incorporate opportunities for active leisure as a strategy to mitigate these impacts²⁶.

In this study, leisure time computer use was similar between active and insufficiently active students in different categories of computer use for studies/research. This finding was not unexpected, since physical activity and sedentary behavior are different behaviors and should not be interpreted as direct opposites²⁵. A university student may meet the recommended levels of physical activity and still spend many hours sitting or using screens throughout the day²⁷. Moreover, screen time reflects only one component of sedentary behavior and does not necessarily represent total sedentary exposure²⁵. In the university setting, academic demands frequently require prolonged computer use, which may contribute to additional screen exposure regardless of physical activity level.

Some limitations should be considered. Online data collection may have excluded students without internet access or electronic devices²⁸. To minimize this bias, the study was disseminated at different moments of the semester and through in-person invitations. Sampling weights were also applied to reduce potential selection bias and improve sample representativeness for students from federal institutions in Bahia²⁹. In addition, the data were self-reported, which may generate response bias, although the measures used demonstrated satisfactory validity and reproducibility¹²,¹³,³⁰. Finally, although other devices such as smartphones are widely used to access social networks⁹, this study focused on computer use, a central tool in academic activities.

CONCLUSIONS

In conclusion, the prevalence of prolonged computer use for leisure and access to social networks was high among university students. There was no direct association between university students' behavior regarding computer use for studies/research and high levels of computer use during leisure time. On the other hand, among university students with lower and intermediate time dedicated to computer use for studies and research, advancing age was associated with a reduction in the prevalence of the outcome. This pattern was observed in relation to the length of university enrollment and the number of devices available at home (desktop and laptop computers), with stronger associations observed among university students with less time dedicated to studies and research using computers.

• How to cite this article
  Carvalho PLS, Santana DP, Matos CS, Santos AJ, Sousa TF. High levels of computer use for leisure and social network access among Brazilian university students. Rev Bras Cineantropom Desempenho Hum 2026, 28:e111985. DOI: https://doi.org/10.1590/1980-0037.2026v28e111985
• Funding
  This study was supported by an undergraduate research scholarship from the Scientific Initiation Program (ICB) at the State University of Santa Cruz, through Call No. 47/2023, awarded to the first author.
• Ethical approval
  Ethical approval was obtained from the local Human Research Ethics Committees – Federal University of Recôncavo da Bahia (protocol no. 88803818.3.1001.0056), Federal University of Bahia (protocol no. 88803818.3.3001.5531), Federal University of Western Bahia (protocol no. 88803818.3.3004.8060), University of International Integration of Afro-Brazilian Lusophony (protocol no. 88803818.3.3002.5576), and Federal University of Southern Bahia (protocol no. 88803818.3.3007.8467) and were written in accordance with the standards set by the Declaration of Helsinki.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

REFERENCES

• ¹ González K, Fuentes J, Márquez JL. Physical inactivity, sedentary behavior and chronic diseases. Korean J Fam Med. 2017;38(3):111-5. https://doi.org/10.4082/kjfm.2017.38.3.111 PMid:28572885.
  » https://doi.org/10.4082/kjfm.2017.38.3.111
• ² Patterson R, McNamara E, Tainio M, De Sá TH, Smith AD, Sharp SJ, et al. Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis. Eur J Epidemiol. 2018;33(9):811-29. https://doi.org/10.1007/s10654-018-0380-1 PMid:29589226.
  » https://doi.org/10.1007/s10654-018-0380-1
• ³ Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al. Sedentary Behavior Research Network (SBRN) – Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):75. https://doi.org/10.1186/s12966-017-0525-8 PMid:28599680.
  » https://doi.org/10.1186/s12966-017-0525-8
• ⁴ Deliens T, Deforche B, De Bourdeaudhuij I, Clarys P. Determinants of physical activity and sedentary behaviour in university students: a qualitative study using focus group discussions. BMC Public Health. 2015;15(1):201. https://doi.org/10.1186/s12889-015-1553-4 PMid:25881120.
  » https://doi.org/10.1186/s12889-015-1553-4
• ⁵ Dong H, Cirillo C, Diana M. Activity involvement and time spent on computers for leisure: an econometric analysis on the American Time Use Survey dataset. Transportation. 2018;45(2):429-49. https://doi.org/10.1007/s11116-017-9789-8
  » https://doi.org/10.1007/s11116-017-9789-8
• ⁶ Franco DC, Ferraz NL, Sousa TF. Sedentary behavior among university students: a systematic review. Rev Bras Cineantropom Desempenho Hum. 2019;21:e56485. http://doi.org/10.5007/1980-0037.2019v21e56485.
• ⁷ IBGE: Brazilian Institute of Geography and Statistics. Brazilian Institute of Geography and Statistics: 2022 demographic census [Internet]. Rio de Janeiro: IBGE; 2023 [cited 2026 Apr 8]. Available from: https://censo2022.ibge.gov.br/
  » https://censo2022.ibge.gov.br/
• ⁸ INEP: National Institute of Educational Studies and Research Anísio Teixeira. Higher education census [Internet]. Brasília: INEP; 2024 [cited 2026 Apr 8]. Available from: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/censo-da-educacao-superior
  » https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/censo-da-educacao-superior
• ⁹ Candussi CJ, Kabir R, Sivasubramanian M. Problematic smartphone usage, prevalence and patterns among university students: A systematic review. J Affect Disord Rep. 2023;14:100643. https://doi.org/10.1016/j.jadr.2023.100643
  » https://doi.org/10.1016/j.jadr.2023.100643
• ¹⁰ Almaiah M, Alfaisal R, Salloum S, Al-Otaibi S, Al Sawafi O, Al-Maroof R, et al. Determinants influencing the continuous intention to use digital technologies in higher education. Electronics (Basel). 2022;11(18):2827. https://doi.org/10.3390/electronics11182827
  » https://doi.org/10.3390/electronics11182827
• ¹¹ Luiz RR, Magnanini MMF. A lógica da determinação do tamanho da amostra em investigações epidemiológicas. Cad Saude Colet. 2000;8(2):9-28.
• ¹² de Sousa TF, Fonseca SA, José HPM, Nahas MV. Validade e reprodutibilidade do questionário Indicadores de Saúde e Qualidade de Vida de Acadêmicos (Isaq-A). Arq Ciênc Esporte. 2013;1(1):21-30.
• ¹³ Sousa TFD, Farias GS, Franco DC. Medida do comportamento sedentário do questionário isaq-a para aplicação em universitários: análises de reprodutibilidade e validade concorrente. Rev Bras Ciênc Mov. 2020;28(1):155. https://doi.org/10.31501/rbcm.v28i1.10478
  » https://doi.org/10.31501/rbcm.v28i1.10478
• ¹⁴ Sousa TF, Silva ES, Ferreira MS, Gordia AP, Quadros TMB. Tempo de tela como discriminador da autoavaliação do nível de saúde negativa dos universitários. Arq Ciênc Esporte. 2020;8(1):1-7. https://doi.org/10.29327/263389.8.1-7
  » https://doi.org/10.29327/263389.8.1-7
• ¹⁵ Valliant R. Comparing alternatives for estimation from nonprobability samples. J Surv Stat Methodol. 2020;8(2):231-63. https://doi.org/10.1093/jssam/smz003
  » https://doi.org/10.1093/jssam/smz003
• ¹⁶ FONAPRACE: National Forum of Pro-Rectors of Student Affairs. V National Survey of the Socioeconomic and Cultural Profile of Undergraduate Students at Federal Institutions of Higher Education: 2018 [Internet]. Brasília: FONAPRACE; 2019 [cited 2026 Apr 8]. Available from: https://www.andifes.org.br/wp-content/uploads/2019/05/V-Pesquisa-Nacional-de-Perfil-Socioeconomico-e-Cultural-dos-as-Graduandos-as-das-IFES-2018.pdf
  » https://www.andifes.org.br/wp-content/uploads/2019/05/V-Pesquisa-Nacional-de-Perfil-Socioeconomico-e-Cultural-dos-as-Graduandos-as-das-IFES-2018.pdf
• ¹⁷ Lourenço C, Sousa T, Fonseca S, Virtuoso JV Jr, Barbosa A. Comportamento sedentário em estudantes Universitários. Ativ Fis Saude. 2016;21(1):67-77. https://doi.org/10.12820/rbafs.v.21n1p67-77
  » https://doi.org/10.12820/rbafs.v.21n1p67-77
• ¹⁸ Mutz J, Lewis CM. Cross-classification between self-rated health and health status: longitudinal analyses of all-cause mortality and leading causes of death in the UK. Sci Rep. 2022;12(1):459. https://doi.org/10.1038/s41598-021-04016-x PMid:35013388.
  » https://doi.org/10.1038/s41598-021-04016-x
• ¹⁹ Kaewpradit K, Ngamchaliew P, Buathong N. Digital screen time usage, prevalence of excessive digital screen time, and its association with mental health, sleep quality, and academic performance among Southern University students. Front Psychiatry. 2025;16:1535631. https://doi.org/10.3389/fpsyt.2025.1535631 PMid:40195967.
  » https://doi.org/10.3389/fpsyt.2025.1535631
• ²⁰ Politte‐Corn M, Nick EA, Kujawa A. Age‐related differences in social media use, online social support, and depressive symptoms in adolescents and emerging adults. Child Adolesc Ment Health. 2023;28(4):497-503. https://doi.org/10.1111/camh.12640 PMid:36751140.
  » https://doi.org/10.1111/camh.12640
• ²¹ Jones S, Johnson-Yale C, Millermaier S, Pérez FSUS. College students’ internet use: race, gender and digital divides. J Comput Mediat Commun. 2009;14(2):244-64. https://doi.org/10.1111/j.1083-6101.2009.01439.x
  » https://doi.org/10.1111/j.1083-6101.2009.01439.x
• ²² Milesi C, Perez-Felkner L, Brown K, Schneider B. Engagement, persistence, and gender in computer science: results of a smartphone ESM study. Front Psychol. 2017;8:602. https://doi.org/10.3389/fpsyg.2017.00602 PMid:28487664.
  » https://doi.org/10.3389/fpsyg.2017.00602
• ²³ Pérez JJ, Bernal E, Giraldo JS. Methodology to evaluate the residential electrical stock appliances according to socioeconomic status. IJEEP. 2019;9(3):114-20. https://doi.org/10.32479/ijeep.7179
  » https://doi.org/10.32479/ijeep.7179
• ²⁴ Astleitner H, Schlick S. The social media use of college students: Exploring identity development, learning support, and parallel use. Active Learn High Educ. 2025;26(1):231-54. https://doi.org/10.1177/14697874241233605
  » https://doi.org/10.1177/14697874241233605
• ²⁵ Ekelund U, Steene-Johannessen J, Brown WJ, Fagerland MW, Owen N, Powell KE, et al. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. Lancet. 2016;388(10051):1302-10. https://doi.org/10.1016/S0140-6736(16)30370-1 PMid:27475271.
  » https://doi.org/10.1016/S0140-6736(16)30370-1
• ²⁶ Özkara AB, Mulazimoglu O, Somoglu MB, Kirkbir F, Tokul E, Aydemir B, et al. Can leisure education effect on screen time and perceived leisure benefits for college students? Front Psychol. 2024;15:1477857. https://doi.org/10.3389/fpsyg.2024.1477857 PMid:39640044.
  » https://doi.org/10.3389/fpsyg.2024.1477857
• ²⁷ Chastin S, McGregor D, Palarea-Albaladejo J, Diaz KM, Hagströmer M, Hallal PC, et al. Joint association between accelerometry-measured daily combination of time spent in physical activity, sedentary behaviour and sleep and all-cause mortality: a pooled analysis of six prospective cohorts using compositional analysis. Br J Sports Med. 2021;55(22):1277-85. https://doi.org/10.1136/bjsports-2020-102345 PMid:34006506.
  » https://doi.org/10.1136/bjsports-2020-102345
• ²⁸ Bethlehem J. Selection bias in web surveys. Int Stat Rev. 2010;78(2):161-88. https://doi.org/10.1111/j.1751-5823.2010.00112.x
  » https://doi.org/10.1111/j.1751-5823.2010.00112.x
• ²⁹ Haddad C, Sacre H, Zeenny RM, Hajj A, Akel M, Iskandar K, et al. Should samples be weighted to decrease selection bias in online surveys during the COVID-19 pandemic? Data from seven datasets. BMC Med Res Methodol. 2022;22(1):63. https://doi.org/10.1186/s12874-022-01547-3 PMid:35249541.
  » https://doi.org/10.1186/s12874-022-01547-3
• ³⁰ Perez O, Garza T, Hindera O, Beltran A, Musaad SM, Dibbs T, et al. Validated assessment tools for screen media use: A systematic review. PLoS One. 2023;18(4):e0283714. https://doi.org/10.1371/journal.pone.0283714 PMid:37053175.
  » https://doi.org/10.1371/journal.pone.0283714

Edited by

• Scientific Editor:
  Diego Augusto Santos Silva

Publication Dates

• Publication in this collection
  24 Aug 2026
• Date of issue
  2026

History

• Received
  08 Apr 2026
• Accepted
  16 July 2026