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Screen time increases overweight and obesity risk among adolescents: a systematic review and dose-response meta-analysis

Abstract

Background

Adolescence is a critical period in human life, associated with reduced physical activity and increased sedentary behaviors. In this systematic review and dose-response meta-analysis, we evaluated the association between screen time and risk of overweight/obesity among adolescents.

Methods

A systematic search in electronic databases, including PubMed, Embase, and Scopus was performed up to September 2021. All published studies evaluating the association between screen time and risk of overweight/obesity among adolescents were retrieved. Finally, a total of 44 eligible studies were included in the meta-analysis.

Results

The results of the two-class meta-analysis showed that adolescents at the highest category of screen time were 1.27 times more likely to develop overweight/obesity (OR = 1.273; 95% CI = 1.166–1.390; P < 0.001; I-squared (variation in ES attributable to heterogeneity) = 82.1%). The results of subgrouping showed that continent and setting were the possible sources of heterogeneity. Moreover, no evidence of non-linear association between increased screen time and risk of overweight/obesity among adolescents was observed (P-nonlinearity = 0.311).

Conclusion

For the first time, the current systematic review and meta-analysis revealed a positive association between screen time and overweight/obesity among adolescents without any dose-response evidence.

Trial registration

The protocol of the current work has been registered in the PROSPERO system (Registration number: CRD42021233899).

Peer Review reports

Background

Adolescence is a critical period regarding physical activity-related behaviors since regular physical activity decreases and sedentary behavior increases in this period [1, 2]. Screen-related physical activities like television watching are very common among adolescents particularly in modern societies; it is reported that adolescents spend about 3h per day on screen activities [3]. Screen time constitutes an important part of adolescents’ life, and they are major TV users [4, 5]. In a study, 57% of adolescents reported watching TV every day (average time in a day: 109 minutes) [6]. According to some recent evidence, increased screen-related sedentary behaviors led not only to obesity growth [7], but also to mental problems among adolescents [8,9,10,11,12].

Sedentary behavior guidelines recommend less than 2h per day of recreational screen time for the youth [13]. However, it has been estimated that more than 50% of adolescents exceed this time [14]. In a report from the Health Behavior in School-Age Children (HBSC), which was performed among adolescents aged 11, 13, and 15 years in 41 European and North American countries, 56–65% of the adolescents spent 2h or more per day watching television [15, 16].

Sedentary behaviors are characterized by activities with low energy expenditure (< 1.5 metabolic equivalents) in a sitting position like television watching or other screen behaviors [17]. Such behaviors are an important risk factor for cardio-metabolic disease in adulthood [18,19,20,21]. In adolescents, obesity is associated with dyslipidemia, glucose intolerance, and hypertension [22]. In several population-based studies [23,24,25], high screen time was positively associated with high blood pressure (BP), high low-density lipoprotein (LDL) cholesterol, and triglyceride (TG) (P < 0.05).

Numerous studies have reported the association between screen time and adiposity among adolescents; however, the results are inconsistent. Some studies reported increased odds of obesity by increasing screen time [22, 26, 27]. For example, Cheng [26] included 2201 Chinese adolescents and reported increased odds of obesity for those with more than 2h of screen time per day (1.53; 95%CI = 0.95–2.09; P < 0.001). In contrary, in another population-based study in the school setting, Lopez-Gonzalez evaluated 1,319,293 adolescents aged 12–14 years old and reported no significant association between obesity and screen time [28]. Several other studies also reported no association between obesity and screen time [28,29,30,31]. Meanwhile, in some other studies, only watching television or playing video games for more than 3h per day increased the risk of obesity among adolescents [32,33,34]. More surprisingly, in a study by De-Lima et al. [35], a non-significant reduced risk of excess weight was observed by increased screen time of more than 4h per day (P = 0.87; 95% CI = 0.59–1.30).

As mentioned, there is an inconsistency between the results of different studies regarding the association between screen time and overweight/obesity among adolescents. In today’s digital age, screen time is almost unavoidable and it has drastically increased among children and adolescents, especially during the coronavirus disease 2019 (COVID-19) pandemic. Excessive screen time may have adverse health consequences because it replaces healthy behaviors and habits like physical activity and sleep routine [36, 37]. However, currently, there is no systematic review and meta-analysis evaluating the association between screen time and obesity among adolescents. More importantly, no study in this filed has focused on such dimensions as the type of screen (TV, PC, DVD, video games, etc.), duration of screen use, and several other factors affecting the association between overweight/obesity and screen time.

Therefore, in this systematic review and meta-analysis, we systematically searched and analyzed all the available literature evaluating the association between overweight/obesity and screen time among adolescents. We also classified the results according to numerous factors, including geographical distribution, screen type, setting, obesity status, as well as the quality and sample size of studies to identify the possible determinants of these associations.

Methods

The results were reported according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist (Sup. Table 1) [38]. The protocol’s registration code in PROSPERO is CRD42021233899.

Search strategy, selection of studies, inclusion and exclusion criteria

A total of 6291 articles were retrieved through searching electronic databases, including PubMed, Embase, and Scopus up to September 2021 (Fig. 1). The search strategy for PubMed is provided in Sup. Table 2, and it has been adopted for each electronic database. A total of 44 manuscripts were eligible to be included in the final meta-synthesis.

Fig. 1
figure 1

Study Flowchart

The inclusion criteria were as follows: 1) studies with observational designs (case control, cross-sectional or cohort studies with the baseline or cross-sectional measurement of study parameters), 2) studies evaluating the relationship (OR, RR, or HR) between screen time and risk of overweight/obesity, and (3) studies conducted only among adolescents (age ≥ 10–20 years). The studeis that did not provide an OR, RR, or HR or those with adjustment for confounders were excluded from the analysis.

The PICO model (patients, intervention, comparison, outcome), which is one of the most widely used models for formulating clinical questions, was used for selecting the studies (Table 1).

Table 1 The PICO criteria used for the systematic review

Data extraction and quality assessment

Data extraction was done by two authors in a standard EXCELL datasheet. The data sheet included the following information: name of first author and journal, publication year, country, setting, age range, number of participants, study design, adjusted covariate, gender, definition of overweight/obesity and screen time, overweight/obesity status, weight, height, screen time measurement tools, and main results. Any disagreements between reviewers were resolved by discussion. The methodological quality of studies were assessed using the Agency for Healthcare Research and Quality (AHRQ) checklist [54] (Table 2).

Table 2 Agency for Healthcare Research and Quality (AHRQ) checklist to assess quality of the cross-sectional studies

Definitions

The Oxford English Dictionary defines screen time as “the time spent using a device such as a computer, or games” [55]. In the current meta-analysis, according to the World Health Organization (WHO), screen time was defined as “the time spent passively watching screen-based entertainment (TV, computer, and mobile devices); this does not include active screen-based games where physical activity or movement is required” [56]. Therefore, TV watching, smart phone use, internet and computer use, and video games that are played in sedentary position are considered as screen time. As previously described by the WHO, adolescence is defined as the age range of 10–19 years old [57]. Overweight and obesity were defined as follows: (a) as Z score for the body mass index (BMI) for age with the cut-off points of > 1 to ≤2 standard deviations for overweight and values > 2 standard deviations for obesity [58]; (b) as the international age and sex specific cut-offs of BMI [≥85th percentile and less than 95th percentile for overweight and ≥ 95th for obesity [46]; and (c) as BMI cut-off of overweight 25 ≤ BMI ≤ 30 kg/m2 and obesity BMI ≥ 30 kg/m2 [59].

Statistical analysis

STATA version 13 (STATA Corp, College Station, TX, USA) was used for data analysis and P-values less than 0.05 were considered as statistically significant. The studies reporting the odds ratio (OR) of overweight/obesity in people with highest versus lowest screen time were included in the two-class dose-response meta-analysis. In the two-class meta-analysis, the pooled OR with 95% confidence intervals (CI) were estimated using a weighted random-effect model (the DerSimonian-Laird approach). If the number of participants in the categories were not provided, equal number of participants in each category was assumed. Cochran’s Q and I-squared tests were used to identify between-study heterogeneity as follows: I2 <25%, no heterogeneity; I2 = 25–50%, moderate heterogeneity; and I2 > 50%, high heterogeneity [60].

The possible sources of heterogeneity were identified using subgrouping approach. For subgrouping, the possible confounders were chosen (e.g., continent, screen type, age group, setting, overweight/obesity status, sample size, and quality of study). Begg’s funnel plot was used to evaluate the publication bias followed by the Egger’s regression asymmetry test and Begg’s adjusted rank correlation for formal statistical assessment of funnel plot asymmetry. Because of an evidence of publication bias, trim-and-fill method was used for estimating potentially missing studies due to publication bias in the funnel plot and adjusting the overall effect estimate. For dose-response meta-analysis, only the studies that reported at least three categories for screen time and the odds of overweight/obesity were included in the dose-response meta-analysis. Accordingly, 13 different studies published in five articles were included [61,62,63,64,65,66,67,68,69,70,71,72,73]. The median point in each screen time category was identified; when medians were not reported, approximate medians were estimated using the midpoint of the lower and upper limits. When the lowest or highest screen time categories were open-ended, the screen time was calculated by assuming the similar interval for those categories and estimating the mid-point. The reference category was the lowest one, assuming OR and CIs of 1 for it. The potential non-linear associations were assessed using random-effects dose-response meta-analysis by defining the restricted cubic splines with three knots at fixed percentiles (10, 50, and 90%) of distribution, and were used to calculate study-specific ORs.

Results

Study characteristics

General characteristics of included studies are represented in Table 3. In the meta-analysis of the odds of overweight/obesity among high screen-user adolescents, a total of 44 studies were included. Also, some of the studies reported the results separately for both genders [28], or reported the separate results for each of the screen types [31, 33, 43, 46, 74], or according to overweight/obesity status [29, 41]. The study by Velásquez-Rodríguez [48] reported separate results for healthy adolescents and adolescents with insulin resistance. Generally, the studies had a cross-sectional design, or cross-sectional data from cohort studies were used for data analysis [32]. The age range of the participants in the included studies was 10–19 years old. The studies had been performed in the United States [29, 41, 43, 74], Brazil [22, 27, 35, 47], Egypt [44, 52], China [26, 39], Iran [45], Indonesia [34], Japan [32], Nigeria [51], Pakistan [42], Nepal [75], Bangladesh [31], Qatar [30], Australia [33], Mexico [28], India [46], Finland [48], Netherland [40], Turkey [49], England [50], and South Korea [53]. The screen time was assessed by validated questionnaires and overweight and obesity definitions were according to (a) as Z score for the BMI for age with the cut-off points of > 1 to ≤2 standard deviations for overweight and values > 2 standard deviations for obesity [58]; (b) as the international age and sex specific cut- offs of BMI [≥ 85th percentile and less than 95th percentile for overweight and ≥ 95th for obesity [46] and (c) as BMI cut-off of overweight 25 ≤ BMI ≤ 30 kg/m2 and obesity BMI ≥ 30 kg/m2 [59]. All the studies included in the meta-analysis reported an adjusted OR that was adjusted according to the confounders, including age, gender, race, nationality, dietary behaviors, parents’ education, occupation, and socio-economic status.

Table 3 The characteristics of studies that evaluated the association between overweight and obesity risk by increased screen time among adolescents

The results of the meta-analysis

In the current meta-analysis, after searching the electronic databases, a total of 6291 articles were retrieved (Fig. 1). After removing 2600 duplicated studies and 1535 records according to title/abstarct irrelevancy, 2156 artciles remained for final full-text screening. Then, 2112 manuscripts were removed due to not meeting the inclusion criteria. Finally, 44 manuscripts with a total number of 112,489 participants were included in the final meta-analysis. The included stdueis had a cross-sectional design and recruitted both genders.

The results of the two-class meta-analysis is presented in Fig. 2. As can be seen, adolescents in the highest category of screen time were 1.27 times more likely to develop overweight/obesity compared to those in the lowest category (OR = 1.273; 95%CI = 1.166–1.390; P < 0.001; I-squared = 82.1%).

Fig. 2
figure 2

Odds ratio (OR) with 95% confidence interval (CI) of overweight/ obesity in highest versus lowest screen user adolescents. I2 represents the degree of heterogeneity

The results of subgrouping is shown in Table 4. Subgrouping according to continent reduced heterogeneity to some degree. For example, in the studies carried out in the United States, the heterogeneity reduced to 39.9%. Similarly, setting also was a possible source of heterogenity since subgropuing by setting reduced the heterogenity of community-based studies to 21.2%. However, other parameters were not potent sources of heterogeneity.

Table 4 Subgroup analysis for the odds of overweight/ obesity in highest versus lowest screen-user adolescents

The results of dose-response relationship between screen time and overweight/obesity is presented in Fig. 3. There was no evidence of non-linear association between increased screen time and risk of overweight/obesity (P-nonlinearity = 0.311).

Fig. 3
figure 3

Dose–response association between screen time and odds of overweight/ obesity. Linear relation (solid line) and 95% CI (dashed lines) of pooled OR of obesity by 1 min/day increment of screen time (p- nonlinearity = 0.310) among adolescents

Funnel plots indicating publication bias are presented in Fig. 4. The results of Begg’s and Egger’s tests showed some evidence of publication bias (Egger’s P-value = 0.001; Begg’s P-value = 0.001). Therefore, trim-and-fill analysis was performed (Fig. 5) and the obtained results were reported (95%OR = 1.472; 95% CI = 1.083–2.068; P < 0.001).

Fig. 4
figure 4

Begg’s funnel plot (with pseudo 95% CIs) of the odds of overweight/ obesity in highest versus lowest screen time categories among adolescents

Fig. 5
figure 5

Filled funnel plot with pseudo 95% confidence limits for studies evaluating the association between screen time and overweight/ obesity among adolescents [OR = 1.472; CI = 1.083, 2.068; P < 0.001]

Discussion

In the current meta-analysis, for the first time, we summarized the results of studies that evaluated the association between screen time and overweight/obesity risk among adolescents. In addition, in a large sample size (n = 112,489), we witnessed that high screen time was associated with 1.27-time higher chance of overweight/obesity among adolescents. No evidence of non-linear association was observed in the dose-response analysis.

Previous population-based studies have revealed the obesity-promoting effects of high screen time. Lopez-Gonzalez [28] evaluated more than 7511 registered schools and reported that high screen time was considered as an obesogenic factor. Several other studies also revealed that screen time more than two or 3h per day increased the risk of obesity [26, 33]. Internet addicted adolescents had also elevated risk of obesity in one study [27]. However, several other studies reported no significant association between obesity and screen time [30, 35, 41]. The possible strong reason for this inconsistency might be attributed to the type of screen (e.g., TV, video games, PCs, etc.) used in different studies.

In this study, we also performed subgroup analysis. According to the results, the studies that defined video games as their screen failed to reveal a positive association between screen time and obesity [31, 39, 41]. In our meta-analysis, video games alone or in combination with other screen types failed to show a positive obesity-promoting effect. In the study by Sun et al. [32], the positive association between video game playing and risk of obesity was only observed among girls and not boys. Zulfiqar et al. [33], also reported the positive association between obesity and TV watching, but not for video games. Several studies even showed the negative association between active video games and obesity. In the study by Strahan et al. [76], active video games reduced the chance of obesity among adolescents. This is possibly because some video games can increase physical activity and physical health. In a meta-analysis by Primack et al., video games were associated with 69% improve in psychological therapy outcomes and 50% improve in physical activity outcomes [77]. In another study by Williams, active video games were introduced as effective tools to improve physical activity among adolescents and were considered as a more acceptable and sustainable approach than many conventional methods [78].

In our meta-analysis, the most important obesogenic screen was TV (OR = 1.813; 95%CI: 1.420–2.315, P < 0.001). In the study by Franceschin et al. [22], adolescents watching TV for more than 2h per day had almost doubled chance of being obese compared to those watching TV for less than 2h per day (OR = 1.73; 95% CI = 1.24–2.42); but the association was not significant for playing video games or using the PC. Therefore, it seems that TV watching is a stronger motivator of obesity among adolescents. Also, the age group is a determinant of screen type use and the consequent obesity. In our study, most of the included studies had been performed in adolescents less than 15 years old and the association between screen time and overweight/obesity in this age group was stronger (P < 0.001) because at lower ages, children and adolescents have less structured time than older adolescents and most of this unstructured time is filled by watching TV [51, 79]. Another important finding in our subgrouping was the role of setting. In school-based studies, the association between screen time and overweight/obesity was more pronounced than other study types (OR = 1.405; 95% CI: 1.228–1.608; P < 0.001) because adolescents usually have more tendency to eat calorie foods in restaurants. Most of the adolescents buy lunch at school canteen and restaurants and are more likely to develop overweight and obesity [80].

High screen time, as a sedentary behavior, reduces lipoprotein lipase activity (LPL) and leads to reduced plasma triglycerides’ absorption by skeletal muscles, reduced HDL level and postprandial increase in serum lipids, that consequently results in fat deposition in vessels or adipose tissue [81,82,83]. Moreover, increased screen time increases food intake. Previous studies revealed that television watching increases motivated response to food intake and snacking behavior among children and adolescents [4, 84,85,86,87]; this is also true for video games [88,89,90] and personal computer use [91, 92]. More importantly, several TV food advertisements promote the consumption of junk food and fast foods and increase the risk of obesity [93,94,95,96,97,98]. Therefore, the association between obesity and screen use is a multi-dimensional factor. Also, the results of included studies in our meta-analysis were reported for both genders; therefore, it was not possible to give gender-specific results.

This study had some limitations. First, this study had a cross-sectional design, which precludes causal inference. Second, the data collection method for screen time measurement was self-reported questionnaires that might be biased. Third, we were not able to perform subgroup analysis for some important confounders, such as eating food while watching screen, type of video games (active or non-active), and gender because the articles had not mentioned such information.

However, this is the first meta-analysis reporting the association between screen time and overweight/obesity among adolescents. We raised concerns among parents, health care professionals, educators, and researchers about the effects of screen time on the health of adolescents. Our study has some important clinical and health implications for policy makers to develop strategies to encourage adolescents to be more physically active and to apply some restrictions for school-based meal servings. They can also improve access of adolescents to opportunities for physical activity, as is the case with state laws related to the quantity and quality of physical education. Also, parents should pay more attention to the adolescents’ screen-based behaviors and apply some at-home restrictions. Setting restrictions on screen use at certain times is a great way to protect adolescents from potentially harmful online activities and encourages them to use their time appropriately.

Conclusion

The current meta-analysis is the first study providing quantitative results for the association between different screen types and overweight/obesity among adolescents. Further studies are warranted to focus on the effects of gender, different screen types and video games to better explain the discrepancies in the obtained results.

Availability of data and materials

The data that support the findings of this study are available from Tabriz University of Medical Sciences but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of corresponding author.

References

  1. Suchert V, Hanewinkel R, Isensee B. Screen time, weight status and the self-concept of physical attractiveness in adolescents. J Adolesc. 2016;48:11–7.

    Article  PubMed  Google Scholar 

  2. Pate RR, Mitchell J, Byun W, Dowda M. Sedentary behaviour in youth. Br J Sports Med. 2011;45(11):906e913.

    Article  Google Scholar 

  3. Hardy LL, Dobbins T, Booth ML, Denney-Wilson E, Okely AD. Sedentary behaviours among Australian adolescents. Aust New Zealand. J Public Health. 2006;30(6):534e540.

    Google Scholar 

  4. Domoff SE, Sutherland E, Yokum S, Gearhardt AN. The association of adolescents’ television viewing with body mass index percentile, food addiction, and addictive phone use. Appetite. 2021:157;104990.

  5. Fairman RT, Weaver SR, Akani BC, Dixon K, Popova L. “You have to vape to make it through”: E-cigarette outcome expectancies among youth and parents. Am J Health Behav. 2021;45(5):933–46.

    Article  PubMed  Google Scholar 

  6. Rideout V, Robb M. The common sense census: media use by tweens and teens: Common Sense; 2021. p. 1–104.

    Google Scholar 

  7. Throuvala MA, Griffiths MD, Rennoldson M, Kuss DJ. The role of recreational online activities in school-based screen time sedentary behaviour interventions for adolescents: a systematic and critical literature review. Int J Ment Heal Addict. 2021;19:1065–115.

  8. Twenge JM, Martin GN, Campbell WK. Decreases in psychological well-being among American adolescents after 2012 and links to screen time during the rise of smartphone technology. Emotion. 2018;18(6):765–80.

    Article  PubMed  Google Scholar 

  9. Zhou J, Li Z, Meng H, Chang Y-C, Peng N-H, Wei B. Chinese parental awareness of Children's COVID-19 protective measures. Am J Health Behav. 2021;45(4):657–64.

    Article  PubMed  Google Scholar 

  10. Schroeder K, Kubik MY, Sirard JR, Lee J, Fulkerson JA. Sleep is inversely associated with sedentary time among youth with obesity. Am J Health Behav. 2020;44(6):756–64.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Kamolthip R, Fung XC, Lin C-Y, Latner JD, O'Brien KS. Relationships among physical activity, health-related quality of life, and weight stigma in children in Hong Kong. Am J Health Behav. 2021;45(5):828–42.

    Article  PubMed  Google Scholar 

  12. Lutz M, Vargas C, Stojanova J, Arancibia M. Diet and depressive disorders. Arch Clin Psychiatry (São Paulo). 2021;48:117–22.

    Google Scholar 

  13. Tremblay MS, LeBlanc AG, Janssen I, Kho ME, Hicks A, Murumets K, et al. Canadian sedentary behaviour guidelines for children and youth. Appl Physiol Nutr Metab. 2011;36(1):59–64.

    Article  PubMed  Google Scholar 

  14. Sisson SB, Church TS, Martin CK, Tudor-Locke C, Smith SR, Bouchard C, et al. Profiles of sedentary behavior in children and adolescents: the US National Health and nutrition examination survey, 20012006. Int J Pediatr Obes. 2009;4(4):353–9.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Currie C, Zanotti C, Morgan A. Social determinants of health and well-being among young people health behaviour in school-aged children (HBSC): international report from the 2009/2010 survey. In: Health policy for children and adolescents: WHO Regional Office for Europe; 2012. p. 1–252.

    Google Scholar 

  16. Tang D, Bu T, Feng Q, Liu Y, Dong X. Differences in overweight and obesity between the north and south of China. Am J Health Behav. 2020;44(6):780–93.

    Article  PubMed  Google Scholar 

  17. Coombs NA, Stamatakis E. Associations between objectively assessed and questionnaire-based sedentary behaviour with BMI-defined obesity among general population children and adolescents living in England. BMJ Open. 2015;5(6):e007172.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Guillaume M, Lapidus L, Björntorp P, Lambert A. Physical activity, obesity, and cardiovascular risk factors in children. The Belgian Luxembourg child study II. Obes Res. 1997;5(6):549–56.

    Article  CAS  PubMed  Google Scholar 

  19. Burke V, Beilin LJ, Simmer K, Oddy WH, Blake KV, Doherty D, et al. Predictors of body mass index and associations with cardiovascular risk factors in Australian children: a prospective cohort study. Int J Obes. 2005;29(1):15–23.

    Article  CAS  Google Scholar 

  20. Martinez-Gomez D, Rey-López JP, Chillón P, Gómez-Martínez S, Vicente-Rodríguez G, Martín-Matillas M, et al. Excessive TV viewing and cardiovascular disease risk factors in adolescents. The AVENA cross-sectional study. BMC Public Health. 2010;10:274.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Mota J, Ribeiro JC, Carvalho J, Santos MP, Martins J. Television viewing and changes in body mass index and cardiorespiratory fitness over a two-year period in schoolchildren. Pediatr Exerc Sci. 2010;22(2):245–53.

    Article  PubMed  Google Scholar 

  22. Franceschin MJ, da Veiga GV. Association of cardiorespiratory fitness, physical activity level, and sedentary behaviour with overweight in adolescents. Revista Brasileira de Cineantropometria e Desempenho Humano. 2020;22:1–12.

    Article  Google Scholar 

  23. Safiri S, Kelishadi R, Qorbani M, Abbasi-Ghah-Ramanloo A, Motlagh ME, Ardalan G, et al. Screen time and its relation to cardiometabolic risk among children and adolescents: the CASPIAN-III study. Iran J Public Health. 2015;44(1):35–44.

    Google Scholar 

  24. Ulaganathan V, Kandiah M, Shariff ZM. A case–control study on the association of abdominal obesity and hypercholesterolemia with the risk of colorectal cancer. J Carcinogenesis. 2018;17.

  25. Putrawan HA, Antariksa B, Yunus F, Basalamah MA, Nurwidya F. Prevalence of pulmonary hypertension in indonesian patients with stable chronic obstructive pulmonary disease. J Nat Sci Biol Med. 2019;10(1):49.

    Google Scholar 

  26. Cheng L, Li Q, Hebestreit A, Song Y, Wang D, Cheng Y, et al. The associations of specific school-and individual-level characteristics with obesity among primary school children in Beijing, China. Public Health Nutrition. 2020;23(10):1838–45.

    Article  PubMed  Google Scholar 

  27. Dalamaria T, De Jesus Pinto W, Dos Santos Farias E, De Souza OF. Internet addiction among adolescents in a western brazilian amazonian city. Revista Paulista de. Pediatria. 2021;39.

  28. Lopez-Gonzalez D, Partida-Gaytán A, Wells JC, Reyes-Delpech P, Avila-Rosano F, Ortiz-Obregon M, et al. Obesogenic lifestyle and its influence on adiposity in children and adolescents, evidence from mexico. Nutrients. 2020;12(3).

  29. Haidar A, Ranjit N, Archer N, Hoelscher DM. Parental and peer social support is associated with healthier physical activity behaviors in adolescents: a cross-sectional analysis of Texas school physical activity and nutrition (TX SPAN) data. BMC Public Health. 2019;19(1).

  30. Kerkadi A, Sadig AH, Bawadi H, Thani AAMA, Chetachi WA, Akram H, et al. The relationship between lifestyle factors and obesity indices among adolescents in Qatar. Int J Environ Res Public Health. 2019;16(22).

  31. Saha M, Adhikary DK, Parvin I, Sharma YR, Akhter F, Majumder M. Obesity and its risk factors of among school children in Sylhet, Bangladesh. J Nepal Health Res Counc. 2018;16(2):205–8.

    PubMed  Google Scholar 

  32. Sun Y, Sekine M, Kagamimori S. Lifestyle and overweight among Japanese adolescents: the Toyama birth cohort study. J Epidemiol. 2009;19(6):303–10.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zulfiqar T, Strazdins L, Dinh H, Banwell C, D'Este C. Drivers of overweight/obesity in 4-11 year old children of Australians and immigrants; evidence from growing up in Australia. J Immigr Minor Health. 2019;21(4):737–50.

    Article  PubMed  Google Scholar 

  34. Collins AE, Pakiz B, Rock CL. Factors associated with obesity in Indonesian adolescents. Int J Pediatr Obes. 2008;3(1):58–64.

    Article  PubMed  Google Scholar 

  35. De Lima TR, Moraes MS, Andrade JHC, De Farias JM, Silva DAS. Associated factors with the isolated and simultaneous presence of overweight and abdominal obesity in adolescents. Revista Paulista de Pediatria. 2020;38.

  36. Alyoubi RA, Kobeisy SA, Souror HN, Alkhaldi FA, Aldajam MA, Allebdi KS, et al. Active screen time habits and headache features among adolescents and young adults in Saudi Arabia. Int J Pharm Res Allied Sci. 2020;9(4):81–6.

    Google Scholar 

  37. Amigo I, Peña E, Errasti JM, Busto R. Sedentary versus active leisure activities and their relationship with sleeping habits and body mass index in children of 9 and 10 years of age. J Health Psychol. 2016;21(7):1472–80.

    Article  PubMed  Google Scholar 

  38. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–9.

    Article  PubMed  Google Scholar 

  39. Zhang Y, Zhang X, Li J, Zhong H, Pan CW. Associations of outdoor activity and screen time with adiposity: findings from rural Chinese adolescents with relatively low adiposity risks. BMC Public Health. 2020;20(1).

  40. De Jong E, Visscher TLS, Hirasing RA, Heymans MW, Seidell JC, Renders CM. Association between TV viewing, computer use and overweight, determinants and competing activities of screen time in 4- to 13-year-old children. Int J Obes. 2013;37(1):47–53.

    Article  Google Scholar 

  41. Rincón-Pabón D, Urazán-Hernández Y, González-Santamaría J. Association between the time spent watching television and the sociodemographic characteristics with the presence of overweight and obesity in Colombian adolescents (secondary analysis of the ENSIN 2010). Plos One. 2019;14(5):e0216455.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Mansoori N, Nisar N, Shahid N, Mubeen SM, Ahsan S. Prevalence of obesity and its risk factors among school children in Karachi, Pakistan. Trop Doct. 2018;48(4):266–9.

    Article  PubMed  Google Scholar 

  43. Godakanda I, Abeysena C, Lokubalasooriya A. Sedentary behavior during leisure time, physical activity and dietary habits as risk factors of overweight among school children aged 14-15 years: case control study. BMC Res Notes. 2018;11(1):186.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Talat MA, El Shahat E. Prevalence of overweight and obesity among preparatory school adolescents in urban Sharkia governorate, Egypt. Egypt Pediatr Assoc Gaz. 2016;64(1):20–5.

    Google Scholar 

  45. Moradi G, Mostafavi F, Azadi N, Esmaeilnasab N, Nouri B. Evaluation of screen time activities and their relationship with physical activity, overweight and socioeconomic status in children 10-12 years of age in Sanandaj, Iran: a cross-sectional study in 2015. Med J Islam Repub Iran. 2016;30(1).

  46. Watharkar A, Nigam S, Martolia DS, Varma P, Barman SK, Sharma RP. Assessment of risk factors for overweight and obesity among school going children in Kanpur, Uttar Pradesh. Indian J Community Health. 2015;27(2):216–22.

    Google Scholar 

  47. De Lucena JMS, Cheng LA, Cavalcante TLM, Da Silva VA, De Farias JC. Prevalence of excessive screen time and associated factors in adolescents. Revista Paulista de Pediatria. 2015;33(4):407–14.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Velásquez-Rodríguez CM, Velásquez-Villa M, Gómez-Ocampo L, Bermúdez-Cardona J. Abdominal obesity and low physical activity are associated with insulin resistance in overweight adolescents: a cross-sectional study. BMC Pediatr. 2014;14:258.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Ercan S, Dallar YB, Önen S, Engiz O. Prevalence of obesity and associated risk factors among adolescents in Ankara, Turkey. J Clin Res Pediatr Endocrinol. 2012;4(4):204–7.

    PubMed  PubMed Central  Google Scholar 

  50. Drake KM, Beach ML, Longacre MR, MacKenzie T, Titus LJ, Rundle AG, et al. Influence of sports, physical education, and active commuting to school on adolescent weight status. Pediatrics. 2012;130(2):e296–304.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Adesina AF, Peterside O, Anochie I, Akani NA. Weight status of adolescents in secondary schools in port Harcourt using body mass index (BMI). Ital J Pediatr. 2012;38:31.

    Article  PubMed  PubMed Central  Google Scholar 

  52. El-Gilany AH, El-Masry R. Overweight and obesity among adolescent school students in Mansoura, Egypt. Child Obes. 2011;7(3):215–22.

    Article  Google Scholar 

  53. Byun W, Dowda M, Pate RR. Associations between screen-based sedentary behavior and cardiovascular disease risk factors in Korean youth. J Korean Med Sci. 2012;27(4):388–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Cho CE, Taesuwan S, Malysheva OV, Bender E, Tulchinsky NF, Yan J. Trimethylamine-N-oxide (TMAO) response to animal source foods varies among healthy young men and is influenced by their gut microbiota composition: a randomized controlled trial. Mol Nutr Food Res. 2017;61:1600324.

    Article  CAS  Google Scholar 

  55. Oxford University Press. Oxford English Dictionary. Oxford: Oxford University Press; 2020.

    Google Scholar 

  56. World Health Organization. Guidelines on physical activity, sedentary behaviour and sleep for children under 5 years of age. 2020; Available from: https://apps.who.int/iris/handle/10665/311664.

    Google Scholar 

  57. Organization, W.H. Adolescent health in the South-East Asia region. 2020; Available from: https://www.who.int/southeastasia/health-topics/adolescent-health.

    Google Scholar 

  58. DO., M. The new WHO child growth standards. Paediatr Croat Suppl. 2008;52(SUPP.1):13–7.

    Google Scholar 

  59. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320:1240–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Anandam A, Akinnusi M, Kufel T, Porhomayon J, El-Solh AA. Effects of dietary weight loss on obstructive sleep apnea: a meta-analysis. Sleep Breath. 2013;17:227–34.

    Article  PubMed  Google Scholar 

  61. Li L, Shen T, Wen LM, Wu M, He P, Wang Y, et al. Lifestyle factors associated with childhood obesity: a cross-sectional study in Shanghai, China. BMC Res Notes. 2015;8(1).

  62. Bhadoria AS, Kapil U, Kaur S. Association of duration of time spent on television, computer and video games with obesity amongst children in national capital territory of Delhi. Int J Prev Med. 2015.

  63. Bingham DD, Varela-Silva MI, Ferrão MM, Augusta G, Mourão MI, Nogueira H, et al. Socio-demographic and behavioral risk factors associated with the high prevalence of overweight and obesity in portuguese children. Am J Hum Biol. 2013;25(6):733–42.

    Article  PubMed  Google Scholar 

  64. Koleilat M, Harrison GG, Whaley S, McGregor S, Jenks E, Afifi A. Preschool enrollment is associated with lower odds of childhood obesity among WIC participants in LA County. Matern Child Health J. 2012;16(3):706–12.

    Article  PubMed  Google Scholar 

  65. Taylor AW, Winefield H, Kettler L, Roberts R, Gill TK. A population study of 5 to 15 year olds: full time maternal employment not associated with high BMI. The importance of screen-based activity, reading for pleasure and sleep duration in children's BMI. Matern Child Health J. 2012;16(3):587–99.

    Article  PubMed  Google Scholar 

  66. Balaban G, Motta ME, Silva GA. Early weaning and other potential risk factors for overweight among preschool children. Clinics (Sao Paulo). 2010;65(2):181–7.

    Article  Google Scholar 

  67. Fulton JE, Wang X, Yore MM, Carlson SA, Galuska DA, Caspersen CJ. Television viewing, computer use, and BMI among U.S. children and adolescents. J Phys Act Health. 2009;6(Suppl 1):S28–35.

    Article  PubMed  Google Scholar 

  68. Khader Y, Irshaidat O, Khasawneh M, Amarin Z, Alomari M, Batieha A. Overweight and obesity among school children in Jordan: prevalence and associated factors. Matern Child Health J. 2009;13(3):424–31.

    Article  PubMed  Google Scholar 

  69. Steele RM, Van Sluijs EMF, Cassidy A, Griffin SJ, Ekelund U. Targeting sedentary time or moderate- and vigorous-intensity activity: independent relations with adiposity in a population-based sample of 10-y-old British children. Am J Clin Nutr. 2009;90(5):1185–92.

    Article  CAS  PubMed  Google Scholar 

  70. Da Costa Ribeiro I, Taddei JAAC, Colugnatti F. Obesity among children attending elementary public schools in Sao Paulo, Brazil: a case-control study. Public Health Nutr. 2003;6(7):659–63.

    Article  Google Scholar 

  71. Lagiou A, Parava M. Correlates of childhood obesity in Athens, Greece. Public Health Nutr. 2008;11(9):940–5.

    Article  PubMed  Google Scholar 

  72. Stettler N, Signer TM, Suter PM. Electronic games and environmental factors associated with childhood obesity in Switzerland. Obes Res. 2004;12(6):896–903.

    Article  PubMed  Google Scholar 

  73. Utter J, Scragg R, Schaaf D. Associations between television viewing and consumption of commonly advertised foods among New Zealand children and young adolescents. Public Health Nutr. 2006;9(5):606–12.

    Article  PubMed  Google Scholar 

  74. Hu EY, Ramachandran S, Bhattacharya K, Nunna S. Obesity among high school students in the United States: risk factors and their population attributable fraction. Prev Chronic Dis. 2018;15:E137.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Piryani S, Baral KP, Pradhan B, Poudyal AK, Piryani RM. Overweight and its associated risk factors among urban school adolescents in Nepal: a cross-sectional study. BMJ Open. 2016;6(5):e010335.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Strahan BE, Elder JH. Video game playing effects on obesity in an adolescent with autism spectrum disorder: a case study. Autism Res Treat. 2015;2015.

  77. Primack BA, Carroll MV, McNamara M, Klem ML, King B, Rich M, et al. Role of video games in improving health-related outcomes: a systematic review. Am J Prev Med. 2012;42(6):630–8.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Williams WM, Ayres CG. Can active video games improve physical activity in adolescents? A review of RCT. Int J Environ Res Public Health. 2020;17(2):669.

    Article  PubMed Central  Google Scholar 

  79. Rivera IR, Silva MA, Silva RD, Oliveira BA, Carvalho AC. Physical inactivity, TV-watching hours and body composition in children and adolescents. Arq Bras Cardiol. 2010;95(2):159–65.

    Article  PubMed  Google Scholar 

  80. Goyal JP, Kumar N, Parmar I, Shah VB, Patel B. Determinants of overweight and obesity in affluent adolescent in Surat city, South Gujarat region, India. Indian J Commun Med. 2011;36(4):296–300.

    Article  Google Scholar 

  81. Pitanga FJG, Alves CFA, Pamponet ML, Medina MG, Aquino R. Screen time as discriminator for overweight, obesity and abdominal obesity in adolescents. Revista Brasileira de Cineantropometria e Desempenho Humano. 2016;18(5):539–47.

    Article  Google Scholar 

  82. Edwardson CL, Gorely T, Davies MJ, Gray LJ, Khunti K, Wilmot EG. Association of sedentary behaviour with metabolic syndrome: a meta-analysis. Plos One. 2012;7(4):e34916.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Storz MA. The role of vegan diets in lipotoxicity-induced beta-cell dysfunction in type-2-diabetes. J Popul Ther Clin Pharmacol. 2020;27(SP2):e22–38.

    Article  PubMed  Google Scholar 

  84. Temple JL, Giacomelli AM, Kent KM, Roemmich JN, Epstein LH. Television watching increases motivated responding for food and energy intake in children. Am J Clin Nutr. 2007;85(2):355–61.

    Article  CAS  PubMed  Google Scholar 

  85. Borghese M, Tremblay M, Leduc G, Boyer C, Belanger P, LeBlanc A, et al. Television viewing and food intake pattern of normal weight, overweight, and obese 9-11 year-old Canadian children. Obes Rev. 2014;15:232.

    Google Scholar 

  86. Taveras EM, Sandora TJ, Shih MC, Ross-Degnan D, Goldmann DA, Gillman MW. The association of television and video viewing with fast food intake by preschool-age children. Obesity (Silver Spring). 2006;14(11):2034–41.

    Article  Google Scholar 

  87. Mariona P, Roy A. Survey on lifestyle and food habits of patients with PCOS and obesity. J Complement Med Res. 2021;11(5):93.

    Google Scholar 

  88. Chaput JP, Visby T, Nyby S, Klingenberg L, Gregersen NT, Tremblay A, et al. Video game playing increases food intake in adolescents: a randomized crossover study. Am J Clin Nutr. 2011;93(6):1196–203.

    Article  CAS  PubMed  Google Scholar 

  89. Cessna T, Raudenbush B, Reed A, Hunker R. Effects of video game play on snacking behavior. Appetite. 2007;49(1):282.

    Article  Google Scholar 

  90. Chaput JP, Tremblay A, Pereira B, Boirie Y, Duclos M, Thivel D. Food intake response to exercise and active video gaming in adolescents: effect of weight status. Br J Nutr. 2015;115(3):547–53.

    Article  PubMed  CAS  Google Scholar 

  91. Shi L, Mao Y. Excessive recreational computer use and food consumption behaviour among adolescents. Ital J Pediatr. 2010;36(1):1–4.

    Article  Google Scholar 

  92. Fulton JE, Wang X, Yore MM, Carlson SA, Galuska DA, Caspersen CJ. Television viewing, computer use, and BMI among US children and adolescents. J Phys Act Health. 2009;6(s1):S28–35.

    Article  PubMed  Google Scholar 

  93. Gilbert-Diamond D, Emond JA, Lansigan RK, Rapuano KM, Kelley WM, Heatherton TF, et al. Television food advertisement exposure and FTO rs9939609 genotype in relation to excess consumption in children. Int J Obes. 2017;41(1):23–9.

    Article  CAS  Google Scholar 

  94. Ustjanauskas AE, Harris JL, Schwartz MB. Food and beverage advertising on children's web sites. Pediatr Obes. 2014;9(5):362–72.

    Article  CAS  PubMed  Google Scholar 

  95. Lee B, Kim H, Lee SK, Yoon J, Chung SJ. Effects of exposure to television advertising for energy-dense/nutrient-poor food on children's food intake and obesity in South Korea. Appetite. 2014;81:305–11.

    Article  PubMed  Google Scholar 

  96. Dibildox J. Analysis of TV, advertising and other behavioral determinants of overweight and obesity in childhood. Salud Publica Mex. 2014;56(Suppl 2):s162–6.

    PubMed  Google Scholar 

  97. Kar S, Khandelwal B. Fast foods and physical inactivity are risk factors for obesity and hypertension among adolescent school children in east district of Sikkim, India. J Nat Sci Biol Med. 2015;6(2):356.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Leman MA, Claramita M, Rahayu GR. Predicting factors on modeling health behavior: a systematic review. Am J Health Behav. 2021;45(2):268–78.

    Article  PubMed  Google Scholar 

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Acknowledgements

The research protocol was approved and supported by Student Research Committee, Tabriz University of Medical Sciences (Grant Number: 68712).

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All authors have read and approved the manuscript; MAF and PH, supervised the project, performed the search, extraction and wrote the first draft of the manuscript, MAF also analyzed the data. GS was involved in search, extraction and revision of the paper. GS and ES, were involved in data extraction and SA was involved in data analysis and manuscript revision.

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Correspondence to Mahdieh Abbasalizad Farhangi.

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Haghjoo, P., Siri, G., Soleimani, E. et al. Screen time increases overweight and obesity risk among adolescents: a systematic review and dose-response meta-analysis. BMC Prim. Care 23, 161 (2022). https://doi.org/10.1186/s12875-022-01761-4

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Keywords

  • Screen time
  • Overweight
  • Obesity
  • Dose-response
  • Adiposity
  • Meta-analysis