Background
Methods
Search strategy, selection of studies, inclusion and exclusion criteria
PICO criteria | Description |
---|---|
Participants | Adolescents population |
Exposure (Interventions) | Highest category of screen time |
Comparisons | Lowest category of screen time |
Outcome | Overweight/ obesity |
Study design | Observational studies with the design of cross-sectional, case control or cohort |
Data extraction and quality assessment
ARHQ Methodology Checklist items for Cross-Sectional study | Hu J [99] | Cheng L [100] | |||||||||
1) Define the source of information (survey, record review) | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ||
2) List inclusion and exclusion criteria for exposed and unexposed subjects (cases and controls) or refer to previous publications | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ||
3) Indicate time period used for identifying patients | ⊕ | ⊕ | ⊕ | _ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ||
4) Indicate whether or not subjects were consecutive if not population-based | ⊕ | – | – | ⊕ | – | – | – | – | – | ||
5) Indicate if evaluators of subjective components of study were masked to other aspects of the status of the participants | – | – | – | U | – | – | – | – | – | ||
6) Describe any assessments undertaken for quality assurance purposes (e.g., test/retest of primary outcome measurements) | – | – | U | U | U | U | U | U | ⊕ | ||
7) Explain any patient exclusions from analysis | ⊕ | ⊕ | ⊕ | _ | ⊕ | – | ⊕ | – | ⊕ | ||
8) Describe how confounding was assessed and/or controlled. | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | – | ⊕ | ⊕ | ||
9) If applicable, explain how missing data were handled in the analysis | ⊕ | – | ⊕ | ⊕ | ⊕ | – | ⊕ | ⊕ | ⊕ | ||
10) Summarize patient response rates and completeness of data collection | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | – | ⊕ | ⊕ | ⊕ | ||
11) Clarify what follow-up, if any, was expected and the percentage of patients for which incomplete data or follow-up was obtained | – | – | – | ⊕ | – | – | – | – | – | ||
Total score | 8 | 6 | 7 | 7 | 7 | 4 | 6 | 6 | 8 | ||
ARHQ Methodology Checklist items for Cross-Sectional study | Godakanda I [43] | ||||||||||
1) Define the source of information (survey, record review) | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ||
2) List inclusion and exclusion criteria for exposed and unexposed subjects (cases and controls) or refer to previous publications | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ||
3) Indicate time period used for identifying patients | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ||
4) Indicate whether or not subjects were consecutive if not population-based | ⊕ | – | – | – | – | – | ⊕ | – | – | ||
5) Indicate if evaluators of subjective components of study were masked to other aspects of the status of the participants | U | U | U | – | – | U | U | U | – | ||
6) Describe any assessments undertaken for quality assurance purposes (e.g., test/retest of primary outcome measurements) | U | U | U | – | ⊕ | U | U | U | – | ||
7) Explain any patient exclusions from analysis | _ | _ | _ | – | ⊕ | ⊕ | _ | _ | – | ||
8) Describe how confounding was assessed and/or controlled. | ⊕ | ⊕ | – | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ||
9) If applicable, explain how missing data were handled in the analysis | ⊕ | ⊕ | ⊕ | – | – | ⊕ | – | – | – | ||
10) Summarize patient response rates and completeness of data collection | ⊕ | – | – | – | ⊕ | – | – | – | – | ||
11) Clarify what follow-up, if any, was expected and the percentage of patients for which incomplete data or follow-up was obtained | ⊕ | – | – | – | – | ⊕ | – | – | – | ||
Total score | 8 | 5 | 4 | 4 | 7 | 7 | 5 | 4 | 4 | ||
ARHQ Methodology Checklist items for Cross-Sectional study | |||||||||||
1) Define the source of information (survey, record review) | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ |
2) List inclusion and exclusion criteria for exposed and unexposed subjects (cases and controls) or refer to previous publications | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ |
3) Indicate time period used for identifying patients | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ |
4) Indicate whether or not subjects were consecutive if not population-based | – | ⊕ | – | ⊕ | – | – | ⊕ | – | – | ⊕ | ⊕ |
5) Indicate if evaluators of subjective components of study were masked to other aspects of the status of the participants | – | – | – | – | – | – | – | – | – | – | – |
6) Describe any assessments undertaken for quality assurance purposes (e.g., test/retest of primary outcome measurements) | – | U | – | U | – | – | – | U | – | ⊕ | ⊕ |
7) Explain any patient exclusions from analysis | – | ⊕ | ⊕ | ⊕ | – | – | ⊕ | ⊕ | ⊕ | ⊕ | ⊕ |
8) Describe how confounding was assessed and/or controlled. | U | ⊕ | – | ⊕ | U | U | ⊕ | ⊕ | U | ⊕ | ⊕ |
9) If applicable, explain how missing data were handled in the analysis | – | – | ⊕ | – | – | – | ⊕ | – | – | ⊕ | ⊕ |
10) Summarize patient response rates and completeness of data collection | – | ⊕ | – | ⊕ | – | – | ⊕ | ⊕ | – | ⊕ | ⊕ |
11) Clarify what follow-up, if any, was expected and the percentage of patients for which incomplete data or follow-up was obtained | – | – | – | – | – | – | ⊕ | – | – | – | – |
Total score | 3 | 7 | 5 | 7 | 3 | 3 | 8 | 6 | 4 | 8 | 8 |
Definitions
Statistical analysis
Results
Study characteristics
Journal/ Year/ First author | Country | Setting/ num | Design | Age (y)/ gender | Overweight/ obesity status and definition | ST definition | Adjusted variables | Main findings |
---|---|---|---|---|---|---|---|---|
Revista Paulista de Pediatria/ 2021/ Dalamaria T [27] | Brazil | School/ 1387 | Cross-sectional | 14–18/ both | Obesity/ ≥85th percentile of age | Internet addiction | None | Increased odds of obesity in internet addicted adolescents [OR = 1.1; CI = 0.9–3.18]. Not adjusted |
BMC Public Health/ 2020/ Zhang Y [39] | China | School/ 2264 | Cross-sectional | 12–15/ both | Obesity/ ≥85th percentile of age | TV, VG, PC | Age, sex, being the single child, ethnic minority, fruit and vegetable intake, sleep time, parents’ Education, fathers’ occupation. | Non-significant association between screen time and odds of obesity. |
Nutrients/ 2020/ Lopez-Gonzalez D [28] | Mexico | School/309 girl; 340 boys | Cross-sectional | 12–17/ both | Overweight/ obesity defined as ≤95th and ≥ 85th and ≥ 95th percentile of age respectively | TV, electronic games | Stratified by age and sex | Non-significant association between obesity and screen time. |
Rev Bras Cineantropometri Desempenho Hum/ 2020/ Franceschin MJ [22] | Brazil | School/ 1015 | Cross-sectional | 15.3/ both | Overweight/ obesity defined as 1 ≤ BMI Z-score < 2 | TV, Video game or PC | Sex, age, type of school attended and dietary energy intake | A significant increased odds of overweight/ obesity in those with more than 2 hours per day TV watching (1.73 (1.24–2.42). The OR for PC and video games was 1.01 (0.71–1.45). |
Revista Paulista de Pediatria/ 2020/ De Lima TR [35] | Brazil | School/ 583 | Cross-sectional | 11–17/ both | Overweight defined as BMI Z-score ≥ 1 | TV, Video game or PC | Gender, maternal schooling, alcohol consumption, smoking, screen time-sedentary behavior | Non-significant reduced risk of excess weight by increased screen time of more than 4 hours/day (0.87 CI = 0.59–1.30) |
Public Health Nutrition/ 2020/ Cheng L [26] | China | School/ 2201 | Cross-sectional | 10/ both | Obesity/ ≥95th percentile of age | TV/video games/ PC/iPad/ phone | Sex, age and school location (rural or urban) with school as a random effect | Increased odds of obesity for those with more than 2 hours/ d screen time (1.53; CI = 0.95–2.09) |
J Immigrant Minor health/ 2019/ Zulfiqar T [33] | Australia | Community/ 2115 girls and 2000 boys | Cross-sectional | 10–11/ both | Overweight/ obesity +BMI ≥ 25 kg/m2 | TV, electronic games | Sleep issues, breastfeeding, birth weight, siblings, foreign language spoken at home, maternal work status, family SEP, maternal partnership status | TV watching of more than 3 hours/ day in weekends was associated with odds of obesity in boys (1.4 (1.0,1.9) and girls (1.5 (1.1,1.9) P < 0.05 |
In J Environ Res Pub Health/ 2019/ Kerkadi A [30] | Qatar | Community/ 1161 | Cross-sectional | 14–18/ both | Overweight 25 ≤ BMI ≤ 30 kg/m2 and obesity BMI ≥ 30 kg/m2 | TV, Video game or PC | Age, nationality | No significant association between screen time of more than 2 hours/ day and risk of overweight/ obesity (OR = 1; CI = 0.7–1.4) |
Plos One/ 2019/ Pabon et al. [41] | USA | Community/ 2358 + 546 | Cross-sectional | 13–17/ both | Overweight/ obesity defined as 1 ≤ BMI Z-score < 2 | TV, Video game | Age, sex, socioeconomic level, geographic area, ethnic group and exposure to television and / or video games. | No significant association between increased screen time and risk of overweight or obesity. |
BMC Public Health/ 2019/ Haidar A [29] | USA | School/ 6716 | Cross-sectional | 14.88/ both | Overweight/ obesity defined as ≤95th and ≥ 85th and ≥ 95th percentile of age respectively | TV, DVD, movies | Grade, gender, ethnicity, weight, SES, parents’ education level | No significant association between increased screen time and risk of overweight or obesity. |
J Nepal Health Res Counc/ 2018/ Saha M [31] | Bangladesh | School/ 288 | Cross-sectional | 10–14/ both | Obesity defined as ≥95th percentile of age | TV, Video game, PC | None | No significant association between increased screen time and risk of overweight or obesity. |
Tropical Doctor/ 2018/ Mansouri N [42] | Pakistan | School/ 887 | Cross-sectional | 11–15/ both | Overweight defined as ≤95th and ≥ 85th percentile of age | TV | Age, sex, type of school, sleeping soft drink consumption | Watching TV more than 2 hours/ day was associated with increased risk of overweight (6.42 (4.32–9.54) P < 0.0001) |
Prev Chronic Dis/ 2018/ Hu EY [74] | USA | School/ 15,624 | Cross-sectional | 14–18/ both | Obesity defined as ≥95th percentile of age | TV, Video or computer game, PC use | Age, sex, and race/ethnicity | Increased risk of obesity for those with more than 3 hours/ day TV watching (1.38 (1.09–1.76) and more than 3 hours video game or PC use (1.19 (0.98–1.43) |
BMC Res Notes/ 2018/ Godakanda I [43] | USA | School/ 880 | Cross-sectional | 14–15/ both | Overweight defined as BMI Z-score ≥ 1 | TV, Video/ DVD | Age, sex, ethnicity, schooling years | Television watching time ≥ 2 h/day (2.6 (1.7–3.8) and Video/DVD watching ≥2 h/day (3.1 (1.8–5.3) were associated with increased risk of overweight. |
Egypt Ped Assoc Gazette/ 2016/ Talat MA [44] | Egypt | School/ 900 | Cross-sectional | 12–15/both | Overweight/ obesity defined as ≤95th and ≥ 85th and ≥ 95th percentile of age respectively | TV | Age, gender, SES | More than 2 hours TV watching was associated with increased risk of obesity (1.36 CI = 0.45–6.8; P = 0.048) |
BMJ Open/ 2016/ Piryani S [75] | Nepal | School/ 360 | Cross-sectional | 16–19/ both | Overweight defined as BMI Z-score ≥ 1 | TV | Age, sex, ethnicity, type of school, mother’s educational and occupation, family type, number of siblings, SES, watching TV and fruit consumption | Watching TV more than 2 hours/ day was associated with increased risk of obesity (OR = 8.86 (3.90 to 20.11) < 0.001 |
Med J Islamic Rep Iran/ 2016/ Moradi G [45] | Iran | School/ 2506 | Cross-sectional | 10–12/ both | Overweight/ obesity defined as ≤95th and ≥ 85th and ≥ 95th percentile of age respectively | TV, VG | Age, sex, SES | Screen time was associated with increased risk of overweight and obesity (1.41 (1.17–1.69) |
Indian J Comm Health/ 2015/ Watharkar A [46] | India | School/ 806 | Cross-sectional | 12–15/ both | Overweight/ obesity defined as ≤95th and ≥ 85th and ≥ 95th percentile of age respectively | TV, PC, cell phone | None | Increased risk of overweight obesity for those with more than 2 hours TV watching (OR = 3.72; CI = 2.38–5.83) or more than 2 hours computer or mobile phone use (OR = 1.68; CI = 1.09–2.57) |
Revista Paulista de Pediatria/ 2015/ De Lucena JMS [47] | Brazil | School/ 2874 | Cross-sectional | 14–19/ both | Overweight 25 ≤ BMI ≤ 30 kg/m2 and obesity BMI ≥ 30 kg/m2 | TV, PC, VG | None | Excessive screen time was associated with increased risk of overweight/ obesity (1.25 (0.93–1.67) |
BMC Pediatr/ 2014/ Velásquez-Rodríguez CM [48] | Finland | Community/ 120 | Cross-sectional | 10–18/ both | Overweight defined as ≤95th and ≥ 85th percentile of age | TV | None | Increased risk of overweight in excessive TV watchers among adolescents with insulin resistance (OR = 2.39; CI = 0.94–6.05) but not among healthy adolescents. |
Int J Obes/ 2013/ De Jong E [40] | Netherland | School/ 2004 + 2068 | Cross-sectional | 10–13/ both | Overweight 25 ≤ BMI ≤ 30 kg/m2 and obesity BMI ≥ 30 kg/m2 | TV, PC | Family characteristics and lifestyle nutrition behaviours | No significant association between TV watching more than 1.5 hours or PC use of more than 30 minutes and overweight/ obesity. |
JCRPE/ 2012/ Ercan S [49] | Turkey | School/ 8848 | Cross- sectional | 11–18/ both | Overweight 25 ≤ BMI ≤ 30 kg/m2 and obesity BMI ≥ 30 kg/m2 | TV, PC | None | Increased risk of overweight and obesity for those with more than 2 hours TV watching or PC use. |
Pediatrics/ 2012/ Drake KM [50] | England | School/ 1718 | Cross-sectional | 12–18/ both | Overweight/ obesity defined as ≤95th and ≥ 85th and ≥ 95th percentile of age respectively | TV, DVD, video game | Adolescent demographics (gender, grade in school, race [white/nonwhite]);screen time; academic performance; employment status; diet quality (fast food, fruit and vegetable consumption over the past week) | Screen time of 7.1–14 and > 14 hours/week was associated with increased obesity risk of OR = 1.28 CI = 1.06, 1.55; P < 0.05 and OR = 1.37 CI = 1.09, 1.71; P < 0.01 respectively. |
J Korean Med Sci/ 2012/ Byun W [53] | Korea | Community/ 1033 | Cross-sectional | 12–18/ both | Overweight/ obesity defined as ≥95th percentile of age | TV, PC, video game | Age, sex, annual household income, and moderate-to-vigorous physical activity | Increased risk of overweight and obesity was observed by increased screen time |
Ital J Pediatr / 2012/ Adesina AF [51] | Nigeria | School/ 690 | Cross-sectional | 10–19/ both | Overweight/ obesity defined as ≤95th and ≥ 85th and ≥ 95th percentile of age respectively | TV | None | Increased risk of overweight and obesity was observed by increased screen time |
Childhood Obesity/ 2011/ El-Gilany AH [52] | Egypt | School/ 953 | Cross-sectional | 14–19/ both | Overweight defined as ≤95th and ≥ 85th percentile of age | TV, PC | Age, sex, socioeconomic level, geographic area, ethnicity | Increased risk of overweight/ obesity for those with more than 2 hours TV watching (2.6 (1.7–3.9) or more than 2 hours computer use (1.8 (1.3–2.5) |
J Epidemiol/ 2009/ Sun Y [32] | Japan | School/ 2842 | Cross-sectional data of an original cohort | 12–13/ both | Overweight 25 ≤ BMI ≤ 30 kg/m2 | TV, VG | Age, parental overweight, and other lifestyle variables | Watching TV more than 3 hours/ d was associated with increased risk of overweight in boys (OR = 1.79; CI = 1.21–2.67 and girls OR = 2.37; CI = 1.55–3.62; P < 0.001 |
Int J Pediatr Obes/ 2008/ Collins AE [34] | Indonesia | School/ 1758 | Cross-sectional | 12–15/ both | Obesity defined as BMI ≥ 25 kg/m2 | PC, PS | None | Increased risk of obesity in those with more than 3 hours/ d PC use (OR = 1.85; CI = 1.04–3.29) or play station use (OR = 1.94; CI = 1.23–3.05) |
The results of the meta-analysis
Group | No. of studies* | OR (95% CI) | P within group | P between group * | P heterogeneity | I2, % |
---|---|---|---|---|---|---|
Total | 44 | 1.273 1.166 1.390 | < 0.001 | < 0.001 | 82.1 | |
Continent | < 0.001 | |||||
America | 11 | 1.115 1.002 1.241 | 0.046 | 0.083 | 39.9 | |
Europe | 10 | 1.080 0.966 1.208 | 0.276 | 0.002 | 66.2 | |
Asia | 11 | 2.014 1.450 2.798 | < 0.001 | < 0.001 | 90.9 | |
Oceania | 8 | 1.099 0.927 1.304 | 0.278 | 0.056 | 49.1 | |
Africa | 4 | 1.646 1.018 2.660 | 0.042 | < 0.001 | 86.9 | |
Screen type | < 0.001 | |||||
TV | 16 | 1.813 1.420 2.315 | < 0.001 | < 0.001 | 86.7 | |
PC | 3 | 1.467 0.950 2.265 | 0.509 | 0.159 | 45.7 | |
VG | 5 | 1.114 0.808 1.536 | 0.084 | 0.014 | 67.9 | |
TV + VG | 5 | 1.094 0.959 1.248 | 0.184 | 0.107 | 47.5 | |
VG + PC | 2 | 1.106 1.030 1.187 | 0.005 | 0.612 | 0 | |
TV + VG + PC | 13 | 1.068 0.974 1.172 | 0.163 | 0.002 | 60.7 | |
Age group | < 0.001 | |||||
< 15 | 23 | 1.375 1.131 1.672 | 0.001 | < 0.001 | 81.9 | |
≥ 15 | 6 | 1.470 1.076 2.008 | 0.016 | < 0.001 | 82.8 | |
Both | 15 | 1.126 1.032 1.228 | 0.008 | < 0.001 | 76.4 | |
Setting | < 0.001 | |||||
School | 31 | 1.405 1.228 1.608 | < 0.001 | < 0.001 | 86.6 | |
Community | 13 | 1.109 1.040 1.182 | 0.002 | 0.229 | 21.2 | |
Obesity status | < 0.001 | |||||
Obesity | 11 | 1.109 0.964 1.275 | 0.150 | 0.001 | 67.0 | |
Overweight | 9 | 1.567 1.282 1.916 | < 0.001 | < 0.001 | 84.1 | |
Overweight/ obesity | 24 | 1.271 1.105 1.463 | 0.001 | < 0.001 | 87 | |
Sample size | < 0.001 | |||||
1000 > | 11 | 2.024 1.303 3.144 | 0.002 | < 0.001 | 90.9 | |
1000–5000 | 27 | 1.121 1.049 1.198 | 0.001 | < 0.001 | 59.9 | |
≥ 5000 | 6 | 1.323 1.017 1.722 | 0.037 | 0.001 | 75.7 | |
Study quality * | < 0.001 | |||||
Low | 0 | – | – | – | – | |
Moderate | 31 | 1.259 1.085 1.461 | < 0.001 | < 0.001 | 80.3 | |
High | 13 | 1.282 1.146 1.435 | 0.002 | < 0.001 | 84.4 | |
Adjusted covariates | < 0.001 | |||||
Age, sex, nationality, SES | 7 | 1.239 1.116 1.377 | < 0.001 | 0.586 | 0 | |
Age, sex, nationality, SES, other demographic variables | 14 | 1.454 1.251 1.690 | < 0.001 | < 0.001 | 87.3 | |
Age, sex, nationality, SES, other demographic variables, dietary habits | 23 | 1.091 0.982 1.212 | 0.107 | < 0.001 | 64.8 |