ORIGINAL ARTICLE

Int. J. Public Health, 23 February 2022

Volume 67 - 2022 | https://doi.org/10.3389/ijph.2022.1604217

Prevalence and Associated Factors of Excessive Recreational Screen Time Among Colombian Children and Adolescents

    SA

    Silvia A. González 1,2*

    OL

    Olga L. Sarmiento 3

    AF

    Alberto Florez-Pregonero 4

    PT

    Peter T. Katzmarzyk 5

    JC

    Jean-Philippe Chaput 2,6

    MS

    Mark S. Tremblay 2,6

  • 1. School of Epidemiology and Public Health, Faculty of Medicine, Ottawa, ON, Canada

  • 2. Healthy Active Living and Obesity Research Group, Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada

  • 3. School of Medicine, Universidad de los Andes, Bogotá, Colombia

  • 4. School of Education, Pontificia Universidad Javeriana, Bogotá, Colombia

  • 5. Pennington Biomedical Research Center, Baton Rouge, LA, United States

  • 6. School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada

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Abstract

Objectives: Excessive recreational screen time (RST) is associated with detrimental effects for physical, psychological and cognitive development. This article aims to describe the prevalence of excessive RST among Colombian preschoolers, children and adolescents and explore its factors associated.

Methods: We analyzed data from the National Survey of Nutrition 2015. The sample included 4,503 preschoolers, 5,333 school-aged children and 6,623 adolescents. Poisson regression models with robust variance were conducted to estimate prevalence ratios and determine associated factors of excessive RST.

Results: Fifty percent of preschoolers, 61% of school-aged children and 73% of adolescents in Colombia had excessive RST. Positive associations were observed with the availability of TV in the child’s bedroom, the availability of video games at home, and eating while using screens. A negative association with rural area was observed for all age groups.

Conclusion: The majority of Colombian children and adolescents have excessive RST. Younger preschoolers, older school-aged children, wealthiest children and those from urban areas should be targeted by interventions to decrease RST. These interventions should promote limiting the availability of electronic devices in children’s bedrooms and not eating in front of screens.

Introduction

Sedentary behavior, defined by the Sedentary Behavior Research Network as “any waking behavior characterized by an energy expenditure ≤1.5 metabolic equivalents (METs), while in a sitting, reclining or lying posture” [1] has been identified as an important risk factor for poor health. Excessive time spent in sedentary behaviors, mainly recreational screen time (RST), has been associated with detrimental effects on health and development in children including unfavorable body composition, lower fitness, higher cardiometabolic risk scores, greater energy intake and poorer diet quality, lower measures of self-esteem and prosocial behaviors, lower quality of life and lower academic achievement [25]. Concerningly, there is a large proportion of children and adolescents around the world who spend excessive time in sedentary pursuits, and may have a higher risk of those conditions [6, 7].

The high prevalence of excessive sedentary time globally is likely a result of greater access to labor-saving technologies, motorized vehicles and screen-based entertainment which have led to a shift from active lifestyles to greater involvement in activities that require lower energy expenditure [4, 8]. In response to this situation, countries like Canada and Australia, and recently the World Health Organization, have developed public health guidelines to promote a reduction in sedentary behaviors [5, 9, 10]. These guidelines focus their message on the reduction of RST such as watching TV, playing videogames or using computers or tablets for non-school related purposes, as these are some of the main sedentary behaviors with important implications for health [4]. Using the cut-off point of Australian and Canadian sedentary behavior guidelines for children and adolescents, global data indicate that the prevalence of meeting sedentary behavior guidelines ranges from 7% in Estonia and China to 85% in Bangladesh [6, 1113].

An important element to advance in the design of interventions to reduce sedentary behaviors is to identify the main factors positively or negatively associated to these behaviors. Sedentary behaviors can be driven by factors from multiple environments or at different levels of influence, therefore, these factors can be better understood and addressed from a context-specific approach, such as a socio-ecological framework of health behaviors [14]. The socio-ecological model applied to sedentary behaviors states that there are characteristics or variables at the individual, social, organizational/community, environmental, and policy level that can influence these behaviors [14]. Using a socio-ecological approach, a systematic review identified certain consistent positively associated factors at the individual, interpersonal and environmental levels, such as age, weight, Afro-American ethnicity, food consumption in front of the TV and built environment variables such as playground density and greater access to play and sports infrastructure [15]. However, the associations with sex and socioeconomic status found in this review were inconsistent, and evidence from low- and middle-income countries was absent.

In Colombia, screen time has been included as a relevant indicator for nutrition surveillance since 2005 [16], and its assessment has evolved over time. In the first version of the National Survey of Nutrition (ENSIN for its name in Spanish), screen time was assessed among 5–12 year-old children as TV viewing and videogame use [17]. In the 2010 survey, the same indicator was included and extended to adolescents aged 13–17 years [18]. For the most recent 2015 survey the indicator was modified to include time spent using other devices such as tablets, portable videogame devices and mobile phones for recreational purposes, and extended the population of interest to include preschool children in addition to the previously included age groups. Therefore, this is the first study in Colombia and among the first in Latin America that examines engagement in excessive RST in a nationally representative sample of pre-schoolers, children and adolescents between 3 and 17 years old. To the best of our knowledge, this is the first time that 2015 ENSIN data on RST among children and adolescents is presented. This initial approach to a broader range of recreational screen-based activities in a national sample with a larger age range provides a unique opportunity to help inform public health policies that contribute to decrease sedentary behavior in Colombia. In this context, this paper aims to describe the prevalence of excessive RST among Colombian preschoolers, children and adolescents and explore the factors associated with excessive RST within a socio-ecological framework.

Methods

Study Design and Participants

We analyzed data from the sedentary behavior component of ENSIN 2015. ENSIN is a cross-sectional population health survey with national representativeness of urban and rural areas of Colombia [19]. The sample of this survey was selected with a stratified, multistage probability cluster sampling design. The sub-sample for the sedentary behavior component included 4,503 preschoolers (3–5 years), 5,333 school-aged children (6–12 years) and 6,623 adolescents (13–17 years). The survey was administered by the Colombian Institute of Family Welfare.

Data Collection

Data were collected by trained nutritionists in the household setting between December 2015 and November 2016, using computer-assisted personal interview technology with a data collection system specifically designed for ENSIN. Data collection teams were regularly accompanied by researchers for quality control purposes. For preschoolers and school-aged children, one of the parents or the main caregiver responded to the survey, while adolescents self-reported their responses. Parents provided signed informed consent and children between 12 and 17 years provided informed assent before conducting the survey. The ENSIN protocol was approved by the Profamilia Institutional Review Board on Research involving Human Subjects and the Colombian National Institutes of Health (file number 2-2015, 26 February 2015). The secondary data analyses were approved by the University of Ottawa (file number H-06-19-3564). Data collection details according to the type of variables (dependent vs. potential associated factors) are described below.

Dependent Variables

Among preschoolers, RST was assessed with the Questionnaire for the Measurement of Physical Activity and Sedentary Behaviors in preschool to fourth grade children (C-MAFYCS), which has low internal consistency for the sedentary behavior scale (Cronbach’s alpha = .31) and moderate to good reproducibility mainly for items such as computer and videogame use (Kappa = .71 and .62, respectively) [20]. Engagement in screen-based activities was assessed with three questions: “During the last 7 days, did (name of the child) watch television, movies or videos in the TV? During the last 7 days, did (name of the child) play or listen to music in the computer? And during the last 7 days, did (name of the child) play with consoles such as X-Box, Play-Station, manual videogames such as Nintendo DS, etc., play with a cellphone, smartphone, iPad or other tablets?” Each of these questions was informed with the amount of time spent in each activity with the following questions: How much time did he/she spend on weekdays? And how much time did he/she spend on weekends? (for each day) The total time in all screen-based activities per weekdays and weekend days was estimated in minutes and the average screen time per day, per weekday and per weekend day was calculated. A dichotomous variable to indicate if the preschooler achieved excessive RST was calculated (excessive vs. not excessive RST). Excessive RST was defined as engaging in more than 1 h of RST per day among preschoolers under 5 years of age or more than 2 h of RST per day among 5-year-olds, in accordance with the WHO and the Canadian sedentary behavior guidelines [9, 21].

Among school-aged children and adolescents, RST was assessed with the following questions adapted from the US Youth Risk Behavior Surveillance System (YRBSS) [22]: “On an average school day or weekday, how many hours did (name of the child)/you watch TV?; On an average school day or weekday, how many hours did (name of the child)/you play video games or use a computer for something that is not schoolwork?; On a weekend day, how many hours did (name of the child)/you watch TV? And, on a weekend day, how many hours did (…) play video games or use a computer for something that is not schoolwork?” The question inquiring about videogames and computer use included the following examples of devices and activities to consider: Xbox, PlayStation, iPod, iPad or other tablet, smartphone, cellphone, YouTube, Facebook or other social media and internet. The response options were: 1) Did not watch TV/played videogames or used the computer, 2) less than 1 h, 3) between 1 and 1:59 h, 4) between 2 and 2:59 h, 5) between 3 and 3:59 h, 6) between 4 and 4:59 h, and 7) 5 h or more. A screen time score was calculated converting these categories as follows: 1 and 2 = 0 h, 3 = 1 h, 4 = 2 h, 5 = 3 h, 6 = 4 h and 7 = 5 h. With these values we calculated a weighted mean score of daily screen time, as previously estimated in other studies [23]. A dichotomous variable to indicate excessive RST was created using a cut-off of more than 2 h of screen time, according to the sedentary behavior guidelines in the Canadian 24-h movement guidelines [9].

Potential Associated Factors

We selected variables that were potentially relevant for the analysis of RST at different levels according to the socio-ecological framework of sedentary behaviors [14].

Intrapersonal Level

Sex (female vs. male), age, ethnicity (Afro-Colombian vs. Indigenous vs. Other ethnic identity), overweight (yes vs. no), physical activity program participation (yes vs. no), physical activity (meeting vs. not meeting the WHO physical activity guidelines), and food frequency consumption variables (yes vs. no) were included at the individual level. Sociodemographic variables (i.e., sex, age, ethnicity) were measured with the Household Questionnaire designed for ENSIN [19]. Overweight (including obesity) was ascertained based on body mass index, calculated from objective measurements of height and weight, and categorized according to WHO growth standards and reference tables [24, 25]. Physical activity program participation (organized activities, sports clubs, and Ciclovías for school-aged children and adolescents, and programs at the school and community levels among adolescents) was assessed with questions designed for the Physical Activity and Sedentary Behavior Questionnaire for ENSIN [19]. Physical activity was proxy-reported (by parents or caregivers) for pre-schoolers and school-aged children and self-reported by adolescents. For pre-schoolers, physical activity was assessed with the C-MAFYCS questionnaire, and for school-aged children and adolescents it was assessed with a question adapted from the YRBSS. A dichotomous variable to indicate if the child/adolescent met the physical activity guidelines was created, using a cut-off of 180 min of physical activity per day with at least 60 min of energetic play for preschool children, and 7 days of being active at least 60 min per day for school-age children [26]. More details on the physical activity assessment are described elsewhere [27]. Food consumption variables were measured with the Food Frequency Questionnaire designed for ENSIN [19] and specific food groups were incorporated in the analysis including: charcuterie products, sodas, snacks and fried foods at least three times per week; fast food consumption at least once per week; and daily consumption of candy. We also assessed usual food consumption while using screens during the last month.

Household Level

At the household level, we included wealth index, area of residence (urban vs. rural), and TV and video game device availability in the child’s bedroom. To estimate wealth index, data on asset ownership, availability of public utilities and materials used for housing construction were collected in the socio-demographic section of the survey. Then, the index was calculated using a principal component approach and categorized into quartiles. TV and videogame availability were assessed with questions adapted from the Neighborhood Impact on Kids (NIK) survey [28].

Environmental and Community Level

At the community level we included parks availability in the neighbourhood and perceived safety in the parks, which were assessed with questions adapted from the NIK survey [28]. For the natural environment level, we included the geographic region where the children lived at the time of the survey (Orinoquia-Amazonia vs. Atlantic vs. Central vs. Eastern vs. Pacific vs. Capital District).

Statistical Analysis

Descriptive statistics (means, standard deviations, and frequencies) were estimated for demographic and contextual characteristics of the sample and chi-squared tests were conducted to determine differences between groups. Poisson regression models with robust variance were conducted to estimate prevalence ratios (PR) and determine associated factors of excessive RST. Three types of models were run for each age group: 1) bivariate models with potential associated factors, 2) multivariable model 1 included the variables available for the whole sample that showed at least marginally significant associations (p < .10) and were adjusted for age and sex and 3) multivariable model 2 included the food consumption and program participation variables that had p-values <.10 in the bivariate analyses and were conducted as a sub-analysis with a smaller sample size. The reason for running two different multivariable models was that not all the participants had the same covariates available, as some components of ENSIN had different selection probabilities. Therefore, multivariable model 1 included the whole sample, and multivariable model 2 explored additional variables with the sub-sample of participants that also had food consumption and programs attendance data available. Variables with p-values <.05 in the multivariable models were considered associated factors of excessive RST. We used STATA 14.0 (StataCorp, College Station, TX, United States) for the analyses, with the survey (SVY) module for complex samples to take into account the clustering of data and the characteristics of the study design, sample weights and missing data. Respondents with missing data on the main variables of interest were excluded from the study (4 observations). Missing data on covariates are described in the footnotes for Tables 1 and 2.

TABLE 1

Preschool children
Sociodemographic and other potentially relevant variables for screen-time Total sample Males Females
n % SE N % SE n % SE p-value
Sex
 Female 2,195 48.5 1.21 NA NA
 Male 2,308 51.5 1.21 NA NA
Age (years)
 3–4 2,998 70.1 1.03 1,515 70.4 1.47 1,483 69.8 1.35 .746
 5 1,505 29.9 1.03 793 29.6 1.47 712 30.2 1.35
Ethnicity
 Afro-colombian 441 9.1 .78 222 8.2 .81 219 10.0 1.11 .178
 Indigenous 480 4.7 .63 246 4.5 .71 234 4.9 .71
 Other 3,544 86.2 .84 1819 87.3 .94 1725 85.1 1.16
Overweight
 Yes 505 11.0 .76 300 13.4 1.22 205 8.5 .82 .001
 No 3,856 89.0 .76 1,934 86.6 1.22 1,922 91.5 .82
Physically activeb
 Yes 1,075 24.4 1.1 635 28.6 1.6 440 19.92 1.39 <.001
 No 3,428 75.6 1.1 1,673 71.4 1.6 1755 80.08 1.39
Food intakec
 Charcuterie 3 times per week or more
  Yes 1,077 34.8 1.49 557 35.4 1.83 520 34.2 2.10 .625
  No 1,950 65.2 1.49 1,012 64.6 1.83 938 65.8 2.10
 Sodas 3 times per week or more
  Yes 1,393 45.6 1.38 731 46.4 1.98 662 44.8 1.83 .560
  No 1,780 54.4 1.38 888 53.6 1.98 892 55.2 1.83
 Snacks 3 times per week or more
  Yes 1,332 44.3 1.40 689 44.4 1.90 643 44.1 1.78 .914
  No 1,908 55.8 1.40 956 55.6 1.90 952 55.9 1.78
 Fried foods 3 times per week or more
  Yes 2,008 53.4 1.24 1,017 52.2 1.77 991 54.6 1.68 .337
  No 1,751 46.6 1.24 908 47.8 1.77 843 45.5 1.68
 Fast food once per week or more
  Yes 213 9.9 .93 112 10.4 1.30 101 9.5 1.31 .652
  No 1,599 90.1 .93 804 89.6 1.30 795 90.5 1.31
 Candy once per day or more
  Yes 1,426 42.6 1.21 711 40.2 1.67 715 45.1 1.71 .044
  No 2,168 57.4 1.21 1,136 59.8 1.67 1,032 54.9 1.71
 Usually eats while using screens
  Yes 2,463 62.5 1.27 1,274 62.3 1.62 1,189 62.7 1.84 .860
  No 1,683 37.5 1.27 859 37.7 1.62 824 37.3 1.84
Wealth Quartiles
 First 2,371 41.1 1.31 1,197 40.4 1.58 1,174 41.9 1.90 .655
 Second 1,086 26.3 1.18 575 27.6 1.50 511 24.9 1.56
 Third 713 20.3 1.22 365 19.6 1.34 348 21.0 2.04
 Fourth 333 12.3 1.01 171 12.4 1.35 162 12.2 1.35
Area
 Urban 3,208 72.4 1.20 1,631 71.8 1.47 1,577 72.9 1.71 .593
 Rural 1,293 27.6 1.20 675 28.2 1.47 618 27.1 1.71
TV available at the child’s bedroom
 Yes 1734 42.3 1.15 906 44.3 1.58 828 40.1 1.55 .057
 No 2,767 57.7 1.15 1,400 55.7 1.58 1,367 59.9 1.55
Video games devices availability
 Yes 554 14.0 .84 295 13.7 1.12 259 14.2 1.32 .788
 No 3,947 86.0 .84 2011 86.3 1.12 1936 85.8 1.32
Parks availability in the neighborhood
 Yes 2,376 55.4 1.37 1,273 56.9 1.70 1,103 53.8 1.94 .187
 No 2,125 44.6 1.37 1,033 43.1 1.70 1,092 46.3 1.94
Safety perceptiond
 Is safe to play in the park 1,793 75.7 1.57 977 77.3 1.97 816 74.0 2.22 .233
 Is not safe to play in the park 583 24.3 1.57 296 22.7 1.97 287 26.0 2.22
Geographic región
 Atlantic 932 24.4 1.03 490 25.0 1.26 442 23.7 1.44
 Eastern 799 17.0 1.24 401 16.2 1.42 398 17.8 1.61
 Orinoquia-Amazonia 775 3.9 .27 395 3.9 .53 380 4.0 .47
 Capital District 318 14.0 1.30 161 14.3 1.46 157 13.7 2.14
 Central 969 24.2 .93 497 24.9 1.35 472 23.5 1.34
 Pacific 710 16.5 .70 364 15.8 .94 346 17.3 1.17

Sociodemographic and lifestyle characteristics of 4,503 preschoolers from Colombia. National Survey of Nutrition, Colombia, 2015.

a

The total sample size for preschoolers was 4,503 children. However the following variables had missing values: ethnicity = 38, BMI = 142, area, TV availability, video games availability and parks availability = 2.

b

Physically active is defined as meeting the WHO physical activity guidelines using a cut-off of 180 min of physical activity per day with at least 60 min of energetic play.

c

Food intake variables are available only for the sub-sample selected for the food intake component of the survey, therefore the sample sizes are smaller.

d

Safety perception was assessed only among those who reported having a park in their neighborhood.

n, sample size; SE, standard error; NA, not applicable.

Table 2

School aged children Adolescents
Sociodemographic and other potentially relevant variables for screen-time Total sample Males Females Total sample Males Females
n % SE n % SE N % SE p-value n % SE n % SE n % SE p-value
Sex
 Female 2,556 48.0 1.12 NA NA NA 3,329 49.6 .99 NA NA NA
 Male 2,777 52.0 1.12 NA NA 3,294 50.4 .99 NA NA
Age (years)
 6–9 3,015 58.8 1.10 1,535 58.1 1.47 1,480 59.5 1.73 .56 NA NA NA NA
 10–12 2,318 41.2 1.10 1,242 41.9 1.47 1,076 40.5 1.73 NA NA NA
 13–15 NA NA NA NA 4,007 60.3 .99 2,009 59.0 1.45 1,998 61.7 1.36 .179
 13–17 NA NA NA 2,616 39.7 .99 1,285 41.0 1.45 1,331 38.3 1.36
Ethnicity
 Afro-colombian 452 9.8 .93 233 9.6 1.09 219 10.1 1.06 .834 557 9.2 .94 316 9.7 1.19 241 8.7 .96 .238
 Indigenous 671 5.9 .78 333 5.7 1.23 338 6.1 1.06 652 4.5 .80 261 4.0 .83 391 5.0 .89
 Other 4,165 84.3 1.11 2,191 84.8 1.48 1974 83.8 1.39 5,361 86.3 .82 2,689 86.3 1.05 2,672 86.3 .99
Overweight
 Yes 1,255 25.8 1.15 668 27.1 1.60 587 24.5 1.51 .221 1,234 18.1 .75 447 14.9 .98 787 21.4 1.11 <.001
 No 3,894 74.2 1.15 2019 73.0 1.60 1875 75.5 1.51 5,120 81.9 .75 2,692 85.2 .98 2,428 78.6 1.11
Participation in organized activities and programsc
 Cicloviasd
  Yes 83 2.0 .45 51 1.9 .410 32 2.0 .84 .925 459 15.4 1.15 274 20.7 1.86 185 10.1 1.13 <.001
  No 5,250 98.0 .45 2,726 98.1 .410 2,524 98.0 .84 2,999 84.6 1.15 1,408 79.3 1.86 1,591 89.9 1.13
 Community physical activity programs (e.g. aerobics)
  Yes NA NA NA NA 797 20.2 1.19 289 15.9 1.25 508 23.9 1.77 <.001
  No NA NA NA 2,840 79.8 1.19 1,351 84.1 1.25 1,489 76.1 1.77
 Physical activity programs at school
  Yes NA NA NA NA 1889 47.9 1.29 984 50.5 1.68 905 45.4 1.87 <.001
  No NA NA NA 2006 52.1 1.29 912 49.5 1.68 1,094 54.6 1.87
 Sport Clubs
  Yes 2,237 41.7 1.18 1,477 54.3 1.75 760 28.1 1.50 <.001 1,104 27.9 1.16 759 38.0 1.72 345 16.7 1.28 <.001
  No 3,096 58.3 1.18 1,300 45.7 1.75 1796 72.0 1.50 2,671 72.1 1.16 1,228 62.0 1.72 1,443 83.3 1.28
 Organized groups (dance, martial arts, etc)
  Yes 484 8.3 .69 171 5.4 .73 313 11.4 1.13 <.001 719 19.9 1.18 249 15.9 1.52 470 23.1 1.70 .002
  No 4,849 91.7 .69 2,606 94.6 .73 2,243 88.6 1.13 2,778 80.1 1.18 1,300 84.1 1.52 1,478 76.9 1.70
Physically activee
 Yes 1722 31.1 1.40 1,042 35.8 1.91 680 26.0 1.62 <.001 848 13.3 .76 605 18.8 1.12 243 7.6 .87 <.001
 No 3,611 68.9 1.40 1735 64.2 1.91 1876 74.0 1.62 5,775 86.8 .76 2,689 81.2 1.12 3,086 92.4 .87
Food intakef
 Yes 1,256 37.2 1.33 692 40.0 1.93 564 34.1 1.92 .039 1,878 39.3 1.08 966 40.7 1.63 912 37.8 1.52 .212
 No 2,422 62.9 1.33 1,249 60.0 1.93 1,173 65.9 1.92 3,018 60.7 1.08 1,527 59.3 1.63 1,491 62.2 1.52
 Sodas 3 times per week or more
  Yes 1,965 51.5 1.64 997 51.8 2.07 968 51.1 2.15 .791 3,420 61.9 1.04 1,832 65.5 1.29 1,588 58.0 1.48 <.001
  No 2,045 48.5 1.64 1,108 48.2 2.07 937 48.9 2.15 2,159 38.1 1.04 1,022 34.5 1.29 1,137 42.0 1.47
 Snacks 3 times per week or more
  Yes 1,753 50.0 1.52 916 52.3 2.07 837 47.6 1.81 .059 2,527 49.5 1.18 1,272 49.1 1.60 1,255 49.9 1.54 .706
  No 2,194 50.0 1.52 1,133 47.7 2.07 1,061 52.4 1.81 2,763 50.5 1.18 1,365 50.9 1.60 1,398 50.1 1.54
 Fried foods three times per week or more
  Yes 2,584 56.2 1.37 1,378 56.9 1.96 1,206 55.6 1.73 .612 3,766 60.0 1.09 1904 60.9 1.38 1862 59.0 1.49 .333
  No 2029 43.8 1.37 1,032 43.2 1.96 997 44.4 1.73 2,370 40.0 1.09 1,172 39.1 1.38 1,198 41.0 1.49
 Fast food once per week or more
  Yes 329 12.4 1.21 165 11.7 1.60 164 13.2 1.87 .556 1,041 25.1 1.14 553 26.1 1.60 488 24.1 1.46 .337
  No 2,179 87.6 1.21 1,126 88.3 1.60 1,053 86.9 1.87 2,986 74.9 1.14 1,450 73.9 1.60 1,536 75.9 1.46
 Candy once per day or more
  Yes 2,136 53.9 1.50 1,064 52.7 2.01 1,072 55.1 1.83 .338 2,692 51.3 1.24 1,284 49.4 1.71 1,408 53.2 1.60 .091
  No 2,277 46.2 1.50 1,233 47.3 2.01 1,044 45.0 1.83 2,786 48.7 1.24 1,420 50.6 1.71 1,366 46.8 1.60
 Usually eats while using screens
  Yes 3,208 68.3 1.24 1,651 66.2 1.83 1,557 70.6 1.62 .074 4,711 74.7 1.02 2,353 74.2 1.22 2,358 75.2 1.29 .497
  No 1808 31.7 1.24 969 33.8 1.83 839 29.4 1.62 1829 25.3 1.02 906 25.8 1.22 923 24.8 1.29
Wealth Quartiles
 First 2,867 39.2 1.56 1,479 39.0 2.06 1,388 39.4 1.78 .575 3,342 38.1 1.31 1,658 38.9 1.49 1,684 37.3 1.71 .673
 Second 1,289 25.6 1.20 708 25.8 1.49 581 25.4 1.64 1,537 23.3 .96 770 23.6 1.21 767 23.0 1.21
 Third 763 19.7 1.03 382 18.7 1.35 381 20.7 1.44 1,097 21.2 .94 545 20.9 1.27 552 21.5 1.24
 Fourth 414 15.5 1.14 208 16.4 1.59 206 14.5 1.34 647 17.5 1.11 321 16.7 1.39 326 18.2 1.41
Area
 Urban 4,016 73.7 1.35 2,087 74.0 1.77 1929 73.4 1.57 .782 4,816 73.8 1.29 2,300 73.2 1.56 2,516 74.4 1.44 .436
 Rural 1,317 26.3 1.35 690 26.0 1.77 627 25.6 1.57 1,807 26.2 1.29 994 26.8 1.57 813 25.6 1.44
TV available at the child’s bedroom
 Yes 1807 39.6 1.32 921 39.0 1.79 886 40.3 1.76 .601 2,363 41.6 1.07 1,193 42.0 1.50 1,170 41.3 1.41 .723
 No 3,525 60.4 1.32 1855 61.0 1.79 1,670 59.7 1.76 4,259 58.4 1.07 2,100 58.0 1.50 2,159 58.7 1.41
Video games devices availability
 Yes 1,452 33.7 1.28 782 35.2 1.85 670 32.0 1.70 .208 3,835 62.2 1.01 1904 61.7 1.39 1931 62.7 1.30 .552
 No 3,881 66.3 1.28 1995 64.8 1.85 1886 68.0 1.70 2,788 37.8 1.01 1,465 38.3 1.39 1,398 37.3 1.30
Parks availability in the neighborhood
 Yes 3,121 61.4 1.54 1,650 62.6 1.89 1,471 60.1 1.90 .263 4,203 66.8 1.12 2,179 69.1 1.32 2024 64.5 1.43 .006
 No 2,212 38.6 1.54 1,127 37.4 1.89 1,085 39.9 1.90 2,420 33.2 1.12 1,115 30.9 1.32 1,305 35.5 1.43
Safety perceptiong
 Is safe to play in the park 2,371 72.1 1.58 1,271 72.7 2.12 1,100 71.4 2.14 .652 3,130 72.2 1.22 1718 76.9 1.64 1,412 67.2 1.79 <.001
 Is not safe to play in the park 750 27.9 1.58 379 27.3 2.12 371 28.6 2.14 1,073 27.8 1.22 461 23.2 1.64 612 32.8 1.79
Geographic region
 Atlantic 786 23.5 1.54 418 23.4 2.05 368 23.6 1.67 .759 1,340 24.4 1.06 778 25.6 1.43 562 23.2 1.23 .682
 Eastern 809 17.9 1.88 409 17.1 2.05 400 18.8 1.91 1,149 18.0 1.57 587 17.7 1.63 562 18.4 1.77
 Orinoquia-Amazonia 1,560 3.3 .24 802 3.2 .32 758 3.4 .25 1,248 3.1 .20 440 3.2 .32 808 3.0 .25
 Capital District 367 14.5 1.39 193 14.2 1.85 174 14.7 1.60 382 12.3 1.12 202 11.9 1.64 180 12.7 1.27
 Central 1,193 24.4 1.22 630 25.4 1.68 563 23.2 1.31 1,636 24.4 .90 807 23.7 1.26 829 25.1 1.19
 Pacific 618 16.5 .93 325 16.7 1.19 293 16.2 1.22 868 17.8 .83 480 17.8 1.04 388 17.7 1.13

Sociodemographic and lifestyle characteristics of 11,956 children and adolescents from Colombia. National Survey of Nutrition, Colombia, 2015.

a

The total sample size for school-aged children was 5,333 children. However the following variables had missing values: ethnicity = 45, BMI = 184 and TV availability = 1.

b

The total sample size for adolescents was 6,623. However the following variables had missing values: ethnicity = 53, BMI = 269, and TV availability = 1.

c

Participation in organized activities and programs was assessed only among school-age children and adolescents who reported knowing those programs.

d

Ciclovias are defined as a program that closes the streets to motorized vehicles, usually Sundays and holidays, for recreational and exercise purposes.

e

Physically active is defined as meeting the WHO physical activity guidelines.

f

Food intake variables are available only for the sub-sample selected for the food intake component of the survey, therefore the sample sizes are smaller.

g

Safety perception was assessed only among those who reported having a park in their neighborhood.

n, sample size; SE, standard error; NA, not applicable.

Results

Sociodemographic characteristics of the sample and other descriptive statistics for this analysis are presented in Tables 1, 2. Approximately 48% of the sample for each age group were females. About 9% and 5% of the children and adolescents were Afro-Colombian or indigenous, respectively. Overweight prevalence was 11.0% for preschoolers, 25.8% among school-aged children, and 18.1% among adolescents. Male preschoolers had a higher prevalence of overweight compared to females, while female adolescents had a higher prevalence of overweight compared to male adolescents. Low proportions of active children were observed across all age groups and were lower among females. More than 60% of preschoolers and school-aged children, and over 70% of adolescents reported eating in front of screens. Over 70% of children and adolescents lived in urban areas, about 40% had a TV available in their bedroom, and availability of video game devices ranged from 14% among preschoolers to more than 60% among adolescents (Tables 1, 2).

Prevalence of Excessive Recreational Screen-Time

Table 3 presents the overall prevalence of excessive RST and the prevalence according to potentially relevant variables. We found that 50% of preschoolers, 61% of school-aged children and 73% of adolescents in Colombia had excessive RST (Table 3). This prevalence did not differ by sex. Among preschoolers, younger children (3–4 years) had a greater prevalence of excessive RST, while in school-aged children this prevalence was higher for older children (10–12 years). Indigenous preschoolers, children and adolescents had a lower prevalence of excessive RST. Among school-aged children, those with overweight or who were physically inactive, had greater prevalences of excessive RST.

TABLE 3

Sociodemographic and other potentially relevant variables for screen-time Preschool childrena School aged childrenb Adolescentsb
P (%) CI SE p-value P (%) CI SE p-value P (%) CI SE p-value
Total 50.1 (47.5–52.7) 1.294 61.0 (58.4–63.6) 1.303 72.5 (70.5–74.4) .964
Sex
 Female 50.5 (46.8–54.2) 1.856 .74 60.2 (56.7–63.6) 1.724 .52 73.0 (70.2–75.5) 1.332 .636
 Male 49.7 (46.4–53.1) 1.661 61.7 (58.1–65.2) 1.778 72.1 (69.2–74.7) 1.383
Age
 3–4 56.4 (53.4–59.3) 1.494 <.001 NA NA NA NA
 5 35.5 (31.6–39.5) 1.978 NA NA
 6–9 NA NA 57.2 (54.0–60.4) 1.597 <.001 NA NA
 10–12 NA 66.4 (62.8–69.7) 1.723 NA
 13–15 NA NA NA NA 72.5 (70.1–74.8) 1.176 .972
 13–17 NA NA 72.5 (69.5–75.4) 1.458
Ethnicity
 Afro-colombian 42.3 (35.4–49.5) 3.551 .001 56.0 (49.6–62.1) 3.140 <.001 73.5 (68.3–78.1) 2.449 <.001
 Indigenous 37.0 (29.2–45.6) 4.147 26.6 (18.3–36.9) 4.677 54.1 (47.0–61.1) 3.543
 No ethnic identity reported 51.6 (48.7–54.5) 1.443 63.9 (61.1–66.7) 1.396 73.3 (71.1–75.4) 1.079
Overweight
 Yes 47.0 (39.6–54.6) 3.771 .382 69.5 (65.0–73.6) 2.163 <.001 75.8 (71.7–79.4) 1.935 .153
 No 50.6 (47.7–53.5) 1.443 58.1 (55.1–61.1) 1.505 72.4 (70.0–74.6) 1.143
Physically activec
 Yes 51.1 (45.8–56.4) 2.653 .646 54.1 (49.7–58.4) 2.188 <.001 69.1 (63.8–74.0) 2.553 .129
 No 49.8 (47.0–52.6) 1.387 64.1 (61.1–67.0) 1.456 73.0 (71.0–75.0) .992
Wealth Quartiles
 First (poorest) 36.7 (33.6–39.9) 1.564 <.001 45.5 (41.8–49.2) 1.859 <.001 61.5 (58.4–64.6) 1.542 <.001
 Second 57.2 (52.5–61.8) 2.333 65.8 (61.2–70.2) 2.245 74.0 (69.9–77.7) 1.949
 Third 62.0 (55.5–68.2) 3.206 74.2 (69.5–78.3) 2.219 84.4 (81.1–87.1) 1.498
 Fourth (wealthiest) 60.3 (51.7–68.4) 4.208 74.5 (68.3–81.6) 3.334 80.0 (73.9–85.0) 2.771
Area
 Urban 56.0 (52.9–59.1) 1.553 <.001 67.0 (64.1–69.8) 1.423 <.001 77.2 (75.0–79.3) 1.077 <.001
 Rural 34.8 (30.6–39.1) 2.132 44.1 (40.0–48.2) 2.068 59.2 (55.2–63.1) 1.990
TV available at the child’s bedroom
 Yes 58.5 (54.8–62.0) 1.802 <.001 71.5 (67.9–74.8) 1.709 <.001 80.8 (78.0–83.2) 1.307 <.001
 No 44.1 (40.9–47.2) 1.578 54.1 (50.7–57.5) 1.697 66.6 (63.9–69.2) 1.334
Video games devices availability
 Yes 67.1 (61.0–72.7) 2.925 <.001 79.0 (75.2–82.3) 1.779 <.001 81.6 (79.1–83.8) 1.160 <.001
 No 47.4 (44.8–50.0) 1.304 51.9 (48.9–54.8) 1.493 57.6 (54.5–60.6) 1.548
Parks availability in the neighborhood
 Yes 57.0 (53.4–60.6) 1.805 <.001 67.1 (63.8–70.3) 1.609 <.001 75.3 (72.8–77.5) 1.180 <.001
 No 41.6 (38.2–45.2) 1.750 51.2 (47.6–54.9) 1.837 67.0 (64.1–69.7) 1.401
Safety perceptiond
 Is safe to play in the park 57.0 (52.7–61.1) 2.115 .968 66.8 (63.1–70.3) 1.788 .706 75.3 (72.6–77.9) 1.311 .899
 Is not safe to play in the park 57.1 (50.4–63.6) 3.336 68.0 (62.0–73.5) 2.893 75.0 (70.3–79.2) 2.231
Geographic region
 Atlantic 39.8 (36.2–43.7) 1.881 <.001 48.0 (42.3–53.8) 2.903 <.001 65.8 (62.2–69.2) 1.736 .001
 Eastern 50.1 (44.0–56.2) 3.088 67.2 (62.1–71.8) 2.432 71.3 (65.4–76.5) 2.789
 Orinoquia-Amazonia 47.6 (35.8–59.6) 6.056 52.5 (47.3–57.7) 2.609 66.8 (60.7–72.5) 2.961
 Capital District 70.6 (61.8–78.0) 4.088 72.4 (63.9–79.5) 3.926 80.4 (71.1–87.2) 4.011
 Central 54.0 (49.0–59.0) 2.508 64.5 (60.0–68.8) 2.210 78.1 (75.2–80.8) 1.396
 Pacific 42.9 (37.9–48.1) 2.571 59.3 (53.2–65.2) 3.031 70.8 (66.1–75.1) 2.261

Prevalence of excessive recreational screen-time among Colombian preschoolers, school-aged children and adolescents. National Survey of Nutrition, Colombia, 2015.

a

Excessive recreational screen-time in pre-schoolers was defined as engaging in more than 1 hour of screen time per day among children under 5 years of age or more than 2 hours of screen time per day among 5-year-olds.

b

Excessive recreational screen-time in school-aged children and adolescents was defined as engaging in more than 2 h of screen time per day.

c

Physically active is defined as meeting the WHO physical activity guidelines.

d

Safety perception was assessed only among those who reported having a park in their neighborhood.

P, prevalence; CI, 95% confidence intervals; SE, standard error; NA, not applicable.

The prevalence of excessive RST differed by socioeconomic status, with those in the third and fourth wealth quartiles having a higher prevalence across all age groups. Also, children and adolescents from urban areas, with TV in the bedroom, video-game devices available and those with park availability in the neighborhood had higher prevalence of excessive RST. According to geographic region, children and adolescents living in the capital district had a higher prevalence of excessive RST (Table 3).

Associated Factors of Excessive Screen Time Among Preschoolers

Figure 1 and Supplementary Table 1 presents the associated factors of excessive RST among preschoolers. According to multivariable model 1, younger preschoolers (3–4 year-olds), those with TV availability in their bedroom, video-game devices available, and those with parks available in their neighbourhood were more likely to have excessive RST compared to their counterparts. Also, preschoolers in the lowest wealth quartile, those living in rural areas, and those living in the Atlantic or Pacific region were less likely to have excessive RST (Figure 1A). According to multivariable model 2, soda consumption three or more times per week and eating while using screens were positively associated with having excessive RST (Figure 1B).

FIGURE 1

FIGURE 1

Prevalence ratios and 95% confidence intervals of associated factors of spending excessive recreational screen-time among Colombian preschoolers. Excessive screen-time was defined as spending more than 1 h of screen-time per day among children under 5 years, and engaging in more than 2 h of screen-time per day among 5-year-olds. (A) Mutivariable model 1 (B) Mutivariable model 2 including food consumption variables. Reference categories were male sex, 5 years of age, no ethnic identity reported, fourth wealth quartile, urban area, no TV availability, no video games availability, no parks availability, Capital District region, no consumption of the food items included, and no eating while using screens. National Survey of Nutrition, Colombia, 2015.

Associated Factors of Excessive Screen Time Among School-Aged Children

According to multivariable model 1, among school-aged children, those who had a TV in their bedroom or video-game device availability were more likely to have excessive RST. Younger age, Indigenous ethnicity, those in the lowest socioeconomic level, and those living in rural areas were less likely to have excessive RST (Figure 2A; Supplementary Table 2). According to multivariable model 2, there was a positive association with the usual consumption of foods in front of screens (Figure 2B; Supplementary Table 2).

FIGURE 2

FIGURE 2

Prevalence ratios and 95% confidence intervals of associated factors of spending excessive recreational screen-time among Colombian school-aged children. Excessive screen-time was defined as spending more than 2 h of screen-time per day. (A) Mutivariable model 1 (B) Mutivariable model 2 including food consumption and program participation variables. Reference categories were male sex, 10–12 years of age, no ethnic identity reported, no overweight, not meeting physical activity guidelines, fourth wealth quartile, urban area, no TV availability, no video games availability, no parks availability, Capital District region, not attending Ciclovía, not participating in sport clubs, not participating in organized groups, no consumption of the food items included and not eating while using screens. National Survey of Nutrition, Colombia, 2015.

Associated Factors of Excessive Screen Time Among Adolescents

According to multivariable model 1, Afro-Colombian adolescents, those in the third wealth index quartile and those who had TV available in their rooms or video-games available were more likely to have excessive RST than their counterparts. On the contrary, those living in rural areas were less likely to have excessive RST than their counterparts (Figure 3A; Supplementary Table 3). Program participation variables were not associated in the bivariate models and therefore were not included in model 2. According to multivariable model 2, adolescents who usually eat in front of screens were more likely to have excessive RST (Figure 3B; Supplementary Table 3).

FIGURE 3

FIGURE 3

Prevalence ratios and 95% confidence intervals of associated factors of spending excessive recreational screen-time among Colombian adolescents. Excessive screen-time was defined as spending more than 2 h of screen-time per day. (A) Mutivariable model 1 (B) Mutivariable model 2 including food consumption variables. Reference categories were male sex, 16–17 years of age, no ethnic identity reported, fourth wealth quartile, urban area, no TV availability, no video games availability, no parks availability, Capital District region, no consumption of the food items included and not eating while using screens. National Survey of Nutrition, Colombia, 2015.

Discussion

Our results indicate that a large proportion of Colombian children and adolescents have excessive RST and the prevalence increases with age. Approximately five out of every 10 preschoolers, six out of every 10 school-aged children, and seven out of every 10 adolescents had excessive RST. Associated factors differed across age groups, but consistent positive associations were observed with the availability of TV in the child’s bedroom, the availability of video games, and eating while using screens. Also, a negative association with rural area was observed for all age groups. Factors at the intrapersonal, household and environmental levels are discussed below.

At the intrapersonal level RST associated factors included age, ethnicity, consumption of energy dense foods and food intake while using screens. Younger preschoolers (3–4 years old) and older school-aged children (10–12 years old) were more likely to have excessive RST. Our findings for school-aged children are consistent with other studies that have reported a positive association between age and screen time [29, 30]. However, among preschoolers, our results, which contribute to the limited studies on this population, showed an opposite association, which can be related to the fact that the sedentary behavior guidelines used as cut-offs in this analysis are stricter for younger children [31]. It is important that public health messages consider this difference in the sedentary behavior guidelines according to age. The observed associations with ethnicity suggest that children belonging to Indigenous communities use screens less and this situation should be preserved. On the contrary, Afro-Colombian adolescents showed an increased likelihood of having excessive RST, which indicates that initiatives or programs to decrease sedentary behaviors should have a special focus on this population and further research is needed to better understand the drivers of this association. Our findings for food consumption are aligned with other studies, showing that children and adolescents with excessive RST have a greater consumption of energy dense foods and drinks [32] and tend to eat their meals while using screens [32, 33]. These associations are concerning considering that these obesogenic behaviors are present at a high proportion from early ages. Specific public health messaging for parents and children should be designed not only to reduce RST but also to avoid practices like eating while watching TV or other screens. The dietary guidelines of Brazil, for example, encourage sitting with the family to eat meals in an appropriate environment paying full attention to the act of eating and without engaging in another activity [34].

At the household level, living in a rural area and electronic devices availability emerged as consistent associated factors across all age groups. The negative association with rural area was a common correlate [17, 35] that highlights the importance of focusing interventions on populations living in the continuously growing urban areas. The association of RST and sitting time with devices availability has been previously reported, mainly for school-aged children and adolescents [3537]. In our study we observed that this is a relevant correlate from early age, which is a reason for concern considering that TV availability in the bedroom contributes to an obesogenic environment [38]. Future actions should take into account that having a TV in the bedroom and video-game availability are modifiable factors, and removing them from children’s bedrooms can contribute to limiting screen time, as demonstrated by Atkin et al. [39] in a longitudinal study. Our results also indicate that the wealthiest children and adolescents are more likely to have excessive RST than their poorer counterparts. This association was also reported in a systematic review and meta-analysis that found a positive relationship between socioeconomic level and excessive RST in adolescents form low-middle income countries, and opposite to the association observed in high income countries [40]. These different patterns in the association between RST and socioeconomic level resemble the patterns proposed by the obesity transition stages, where earlier stages are characterized by higher prevalence, in this case of obesity, among the wealthiest people and later stages show higher prevalence among those in the lowest socioeconomic status [41]. These results can suggest a lifestyle transition that require urgent action to reduce the high prevalence of RST among the wealthiest children and to prevent the increase of these behaviors among the most vulnerable ones.

At the environmental level, we did not find a statistically significant association of RST with the availability of physical activity programs. Previous evidence on Ciclovía participation, showed that children who attended this program had lower total sedentary time on sundays, however RST was not specifically assessed in this study [42]. The lack of associations observed in our results can be understood by the fact that the programs included in the survey are focused on physical activity promotion, and may indicate the need to have specific interventions or strategies aimed at reducing RST together with the already existing physical activity programs to have an impact on the children and adolescent’s movement behaviors in Colombia. In this regard, it is important to highlight that there is a lack of policies and interventions to reduce screen time among Colombian children and adolescents, as previously reported by a group of experts in the Colombian Report Card of Physical Activity [43]. We also observed a counter-intuitive positive association of excessive RST with parks availability. This finding could be related to safety conditions of the existing parks, as suggested by the non-statistically significant positive association between excessive RST and lack of safety perception observed in the bivariate analysis for preschoolers and school-aged children. In terms of geographic region, we also observed that preschoolers from the Atlantic and Pacific regions, and adolescents from the Atlantic region were less likely to have excessive RST compared to their counterparts from Bogotá (the capital district). This could be probably influenced by differences in internet accessibility and cultural norms on the use of leisure time that should be further studied.

Our estimates seem higher than the prevalence of excessive RST reported in previous versions of the ENSIN survey [17]. However, it is important to mention that our results may not be comparable, since different questionnaires have been used and the most recent version of ENSIN includes a wider range of screen-devices. In the global context, our results are similar to the engagement in RST reported by very high-income countries like Canada, Denmark, England and Qatar [6]. Having a prevalence comparable to those observed in rich countries may be indicative of the lifestyle transition that low- and middle-income countries are experiencing and reinforces the urgent need to proactively implement actions to decrease risk behaviors such as excessive RST [44]. In the current context of the COVID-19 pandemic that has drastically changed the routine and movement behaviors of children and adolescents it would be expected that the observed estimates have largely increased, as observed in other countries [45]. This adds to the urgency for the design and implementation of strategies to reduce RST. These actions should align with the call made by the WHO-UNICEF- Lancet Commission to place children as the center of the Sustainable Development Goals. Specifically, greater regulation of advertising and commercial governance to protect children from the exposure to marketing of unhealthy products and harmful contents of videogames should be priorities for decision-makers [46].

Our results should be interpreted considering strengths and limitations of the study. The main strength is that our estimates inform the situation of RST for a nationally representative sample of 3- to 17-year-old individuals from Colombia, the broadest age range evaluated at the national level, to the best of our knowledge. Also, the survey data used for this analysis provides a wide range of contextual covariates of relevance to understand the RST situation in Colombia. Limitations include the cross-sectional design of the survey, which does not allow to make causal inferences from our findings, and the use of self- and proxy-report of screen time engagement. Despite self-report being the most feasible method of assessment of RST for population surveys, it is important to recognize that this measure of screen time can be influenced by social desirability bias and recall bias and may lead to a certain degree of misclassification. Also, the questionnaire used to ascertain engagement in RST did not allow for a continuous variable of the total time engaged in RST. In addition, the questionnaire used for preschoolers showed a low internal consistency for the sedentary behaviors section [20], which suggests low agreement between the items to assess the sedentary behaviors construct. However, for the analyses conducted in this paper, not all of the items were taken into account given that the variable of interest was RST and not sedentary behaviors as a broad construct. Finally, RST at the school setting may be underestimated, since the questionnaire used for preschoolers only inquired about activities conducted out of the school setting. For school-aged children and adolescents, the context of the screen time-related activities was not assessed, but the questions used inquired exclusively about non schoolwork-related activities.

In conclusion, the majority of Colombian children and adolescents have excessive RST and several factors were identified that can be considered in the design of strategies to decrease these behaviors. The non-modifiable factors identified in this study can guide interventions aimed at decreasing RST among children and youth, for target populations such as younger preschoolers, older school-aged children, children and adolescents from urban areas and the wealthiest children and adolescents. Significant modifiable factors, such as availability of electronic devices, provide guidance for promising strategies that can be considered as part of a comprehensive initiative to reduce RST, such a public health campaigns to reduce the availability of TVs and electronic devices in children’s bedrooms and not eating in front of the TV.

Statements

Ethics Statement

The studies involving human participants were reviewed and the ENSIN protocol was approved by the Profamilia Institutional Review Board on Research involving Human Subjects and the Colombian National Institutes of Health (file number 2-2015, 26 February 2015). The secondary data analyses were approved by the University of Ottawa (file number H-06-19-3564). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

Conceptualization, SG, OS, and MT; Data curation, SG; Formal analysis, SG; Data interpretation, SG, OS, AF-P, PK, J-PC, and MST; Writing—original draft, SG; Writing—critical review and editing, OS, AF-P, PK, J-PC, and MT. All authors have read and agreed to the submitted version of the manuscript.

Funding

SG was funded by the Government of Ontario and the University of Ottawa through the Ontario Trillium Scholarship for doctoral studies.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.ssph-journal.org/articles/10.3389/ijph.2022.1604217/full#supplementary-material

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Summary

Keywords

children, surveillance, adolescents, screen exposure, sedentary behaviors

Citation

González SA, Sarmiento OL, Florez-Pregonero A, Katzmarzyk PT, Chaput J-P and Tremblay MS (2022) Prevalence and Associated Factors of Excessive Recreational Screen Time Among Colombian Children and Adolescents. Int J Public Health 67:1604217. doi: 10.3389/ijph.2022.1604217

Received

24 April 2021

Accepted

21 January 2022

Published

23 February 2022

Volume

67 - 2022

Edited by

Lena Isabel Barrera Vergara, University of Valle, Colombia

Reviewed by

Marta Lima-Serrano, Sevilla University, Spain

Gerardo Zavala, University of York, United Kingdom

Updates

Copyright

*Correspondence: Silvia A. González,

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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