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Effect of intervention programs in schools to reduce screen time: a meta-analysis Please cite this article as: Friedrich RR, Polet JP, Schuch I, Wagner MB. Effect of intervention programs in schools to reduce screen time: a meta-analysis. 2014;90:232-41.

Abstracts

OBJECTIVE:

to evaluate the effects of intervention program strategies on the time spent on activities such as watching television, playing videogames, and using the computer among schoolchildren.

SOURCES:

a search for randomized controlled trials available in the literature was performed in the following electronic databases: PubMed, Lilacs, Embase, Scopus, Web of Science, and Cochrane Library using the following Keywords randomized controlled trial, intervention studies, sedentary lifestyle, screen time, and school. A summary measure based on the standardized mean difference was used with a 95% confidence interval.

DATA SYNTHESIS:

a total of 1,552 studies were identified, of which 16 were included in the meta-analysis. The interventions in the randomized controlled trials (n = 8,785) showed a significant effect in reducing screen time, with a standardized mean difference (random effect) of: -0.25 (-0.37, -0.13), p < 0.01.

CONCLUSION:

interventions have demonstrated the positive effects of the decrease of screen time among schoolchildren.

Child; Adolescent; School health; Sedentary lifestyle


OBJETIVO:

avaliar os efeitos das estratégias dos programas de intervenção sobre o tempo dedicado a atividades como assistir à televisão, jogar videogame e usar computador em escolares.

FONTE DOS DADOS:

foi realizada busca de estudos controlados randomizados, disponíveis nas bases de dados eletrônicas PubMed, Lilacs, Embase, Scopus, Web of Science e Cochrane Library, com os descritores: randomized controlled trial, intervention studies, sedentary lifestyle, screen time e school. Medida de sumário baseada na diferença das médias padronizadas foi usada com intervalo de confiança de 95%.

SÍNTESE DOS DADOS:

foram identificados 1.552 estudos, dos quais 16 foram incluídos na metaanálise. As intervenções nos estudos controlados randomizados (n = 8.785) apresentaram efeito significativo na redução do tempo em frente à tela, com diferença das médias padronizadas (efeito randômico): -0,25 (-0,37; -0,13), p < 0,01.

CONCLUSÃO:

as intervenções mostraram efeitos positivos na redução do tempo em frente à tela em escolares.

Criança; Adolescente; Saúde escolar; Estilo de vida sedentário


Introduction

Although the World Health Organization recommends that children and adolescents should not spend more than two hours a day in front of the television, computers, or video games, a population-based study performed in Brazil, the National Survey of Schoolchild's Health (Pesquisa Nacional de Saúde do Escolar - PeNSE) demonstrated that 78% of eight-graders watched television for two or more hours daily. This indicator ranged from 71% to 82.3% in the Brazilian capitals.11. World Health Organization (WHO). Global recommendations on physical activity for health. Geneva: WHO; 2010. p. 58. , 22. Brasil. Ministério do Planejamento Orçamento e Gestão. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional de Saúde do Escolar. Rio de Janeiro: IBGE; 2013.

The longer periods of time during which children and adolescents engage in activities such as watching television, playing video games, and using the computer are associated with several health problems, including arterial hypertension,33. Pardee PE, Norman GJ, Lustig RH, Preud'homme D, Schwimmer JB. Television viewing and hypertension in obese children. Am J Prev Med. 2007;33:439-43. metabolic syndrome,44. Mark AE, Janssen I. Relationship between screen time and metabolic syndrome in adolescents. J Public Health (Oxf). 2008;30:153-60. and overweight, as reported in several international55. Dietz WH Jr, Gortmaker SL. Do we fatten our children at the television set? Obesity and television viewing in children and adolescents. Pediatrics. 1985;75:807-12. , 66. Dennison BA, Erb TA, Jenkins PL. Television viewing and television in bedroom associated with overweight risk among low-income preschool children. Pediatrics. 2002;109:1028-35. , 77. Lumeng JC, Rahnama S, Appugliese D, Kaciroti N, Bradley RH. Television exposure and overweight risk in preschoolers. Arch Pediatr Adolesc Med. 2006;160:417-22. , 88. Marshall SJ, Biddle SJ, Gorely T, Cameron N, Murdey I. Relationships between media use, body fatness and physical activity in children and youth: a meta-analysis. Int J Obes Relat Metab Disord. 2004;28:1238-46. , 99. Jouret B, Ahluwalia N, Cristini C, Dupuy M, Nègre-Pages L, Grandjean H, et al. Factors associated with overweight in preschool-age children in southwestern France. Am J Clin Nutr. 2007;85:1643-9. and Brazilian studies.1010. Fonseca Vde M, Sichieri R, da Veiga GV. Factors associated with obesity among adolescents. Rev Saude Publica. 1998;32: 541-9. , 1111. Mondini L, Levy RB, Saldiva SR, Venâncio SI, de Azevedo Aguiar J, Stefanini ML. Overweight, obesity and associated factors in first grade schoolchildren in a city of the metropolitan region of São Paulo, Brazil. Cad Saude Publica. 2007;23:1825-34. , 1212. Campagnolo PD, Vitolo MR, Gama CM. Factors associated with excessive television watching among adolescents. Rev Bras Med Esporte. 2008;14:197-200. , 1313. Silva KS, Nahas MV, Hoefelmann LP, Lopes AS, Oliveira ES. Associations between physical activity, body mass index, and sedentary behaviors in adolescents. Rev Bras Epidemiol. 2008;11:159-68. , 1414. 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;95:159-65. , 1515. Coelho LG, Cândido AP, Machado-Coelho GL, Freitas SN. Association between nutritional status, food habits and physical activity level in schoolchildren. J Pediatr (Rio J). 2012;88: 406-12. They are also associated with negative behavioral changes, such as changes in sleep,1616. Cain N, Gradisar M. Electronic media use and sleep in school-aged children and adolescents: a review. Sleep Med. 2010;11: 735-42. , 1717. Hart CN, Cairns A, Jelalian E. Sleep and obesity in children and adolescents. Pediatr Clin North Am. 2011;58:715-33. , 1818. Thompson DA, Christakis DA. The association between television viewing and irregular sleep schedules among children less than 3 years of age. Pediatrics. 2005;116:851-6. in interpersonal relationships and attention,1919. Jolin EM, Weller RA. Television viewing and its impact on childhood behaviors. Curr Psychiatry Rep. 2011;13:122-8. and increased aggression.20 20. Bushman BJ, Huesmann LR. Short-term and long-term effects of violent media on aggression in children and adults. Arch Pediatr Adolesc Med. 2006;160:348-52. , 2121. Huesmann LR, Taylor LD. The role of media violence in violent behavior. Annu Rev Public Health. 2006;27:393-415.

Excessive time in front of the screen is also associated with food, especially with low intake of fruits and vegetables,2222. Lowry R, Wechsler H, Galuska DA, Fulton JE, Kann L. Television viewing and its associations with overweight, sedentary lifestyle, and insufficient consumption of fruits and vegetables among US high school students: differences by race, ethnicity, and gender. J Sch Health. 2002;72:413-21. and with excessive intake of high-calorie foods and those with high content of fats, sugars, and sodium. Additionally, it influences the choice of foods, as the children are exposed to unhealthy food advertisements.2323. Aktas Arnas Y. The effects of television food advertisement on children's food purchasing requests. Pediatr Int. 2006;48:138-45. , 2424. Halford JC, Gillespie J, Brown V, Pontin EE, Dovey TM. Effect of television advertisements for foods on food consumption in children. Appetite. 2004;42:221-5. Some studies have also indicated an association with eating disorders.25 25. Moriarty CM, Harrison K. Television exposure and disordered eating among children: a longitudinal panel study. J Commun. 2008;58:361-81. , 2626. Harrison K, Hefner V. Media exposure, current and future body ideals, and disordered eating among preadolescent girls: a longitudinal panel study. J Youth Adolesc. 2006;35:153-63. , 2727. Dohnt H, Tiggemann M. The contribution of peer and media influences to the development of body satisfaction and self-esteem in young girls: a prospective study. Dev Psychol. 2006;42:929-36.

Therefore, several strategies have focused on changing the sedentary lifestyle with a decrease in daily screen time through intervention programs, especially in the prevention of obesity.2828. Robinson TN. Reducing children's television viewing to prevent obesity: a randomized controlled trial. JAMA. 1999;282:1561-7. , 29 29. Gortmaker SL, Peterson K, Wiecha J, Sobol AM, Dixit S, Fox MK, et al. Reducing obesity via a school-based interdisciplinary intervention among youth: Planet Health. Arch Pediatr Adolesc Med. 1999;153:409-18. , 3030. Robinson TN, Killen JD, Kraemer HC, Wilson DM, Matheson DM, Haskell WL, et al. Dance and reducing television viewing to prevent weight gain in African-American girls: the Stanford GEMS pilot study. Ethn Dis. 2003;13:S65-77.

Children and adolescents constitute the primary target of these strategies, which represent the possibility of health promotion and protection against obesity and future chronic diseases.3131. Barlow SE; Expert Committee. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007;120:S164-92. , 3232. Enes CC, Slater B. Obesity in adolescence and its main determinants. Rev Bras Epidemiol. 2010;13:163-71. Therefore, the school is an important scenario to promote educational practices and to motivate individuals to adopt healthy lifestyle habits and maintain them throughout adulthood.3333. Telama R, Yang X, Viikari J, Välimäki I, Wanne O, Raitakari O. Physical activity from childhood to adulthood: a 21-year tracking study. Am J Prev Med. 2005;28:267-73.

This study presents the main results of a meta-analysis aimed to evaluate the effects of interventions, conducted in the school environment, on the time dedicated to activities such as watching television, playing video games, and using a computer.

Methods

This was a meta-analysis based on search performed in Lilacs, PubMed, Web of Science, Scopus, Embase, and Cochrane Library electronic databases, between 1998 and August of 2012, using the following Keywords

Randomized Controlled Trial, Intervention Study, Sedentary Lifestyle, Media, Screen Time, Television, Computer, Video Games, Children, Adolescents, Overweight, Obesity, Food and Nutrition Education, Physical Education, Physical Activity, Schools.

A search was also performed using the references of relevant studies and systematic reviews that addressed the topic of interest. The following inclusion criteria were used for study selection: randomized controlled trials; publications since 1998 (including that year); schoolchildren aged 4 to 19 years; pre- and post-measurement of time spent watching television, playing video games, or using the computer; and interventions and programs that focused on changes in sedentary behavior aiming to reduce screen time, with a minimum duration of three months, conducted in the school environment. Since the present review included studies with pre- and post- measurement of screen time, the following were also used as eligibility criteria: interventions that focused on obesity prevention and changes in lifestyle through nutrition education and physical activity. In these studies, reduction of screen time was a secondary outcome.

The internal quality of the studies was assessed using the allocation concealment criteria proposed by the Cochrane Collaboration3434. Higgin JP, Green S, editores. Cochrane handbook for systematic reviews of interventions. West Sussex: The Cochrane Collaboration and John Wiley & Sons Ltd.; 2008. and complemented by the Jadad et al.3535. Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ, et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials. 1996;17:1-12. scale. When assessing the allocation concealment criteria, the studies were classified into four categories: Category A or Adequate, meaning that the process of allocation was adequately reported; Category B or Undetermined, meaning that the allocation process was not described, but was mentioned in the text of the randomized trial; Category C or Inadequate, stating that the process of allocation was inadequately reported; Category D or Not Used, stating that the study was not randomized. Studies classified as A and B, through allocation concealment analysis, were included. Those classified as C and D were excluded from the review, as they were not considered as properly performed.3434. Higgin JP, Green S, editores. Cochrane handbook for systematic reviews of interventions. West Sussex: The Cochrane Collaboration and John Wiley & Sons Ltd.; 2008.

The criteria described by Jadad et al. to evaluate internal quality used in this study were randomization, double-blind masking, losses, and exclusions. A maximum of five points could be obtained. A study was considered poor quality if its score was less than or equal to three points.3535. Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ, et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials. 1996;17:1-12.

After searching for studies in the electronic databases, study selection started with the analysis of titles and abstracts by two reviewers according to the inclusion criteria. When the abstract lacked information, the study was read in full. Subsequently, only studies classified as A and B, according to the allocation concealment criteria, were included in the review.

Information was independently extracted by two reviewers to collect data from the selected studies. The results were cross-checked to verify concordance, and discordant results were resolved by consensus. The assessment by the reviewers was not masked regarding the authors and the study results.

For the statistical analysis, randomized controlled trials were entered into the meta-analysis, and the time spent in low-intensity activities such as watching television, playing video games, and using the computer was assessed in hours/day.

A summary measure based on the standardized mean difference (SMD) was used for the outcome studied. In order to obtain that summary measure and their respective 95% confidence intervals (95% CI) a model of fixed or random effects was followed, depending on the heterogeneity between studies. The test of consistency (I22. Brasil. Ministério do Planejamento Orçamento e Gestão. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional de Saúde do Escolar. Rio de Janeiro: IBGE; 2013. ) was used to assess heterogeneity between studies, and a random effects model was used for I22. Brasil. Ministério do Planejamento Orçamento e Gestão. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional de Saúde do Escolar. Rio de Janeiro: IBGE; 2013. > 50%.36 36. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539-58. , 3737. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557-60. The I22. Brasil. Ministério do Planejamento Orçamento e Gestão. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional de Saúde do Escolar. Rio de Janeiro: IBGE; 2013. test describes total variability due to heterogeneity; values equal to zero do not represent heterogeneity between studies; values below 25% represent low variability; intermediate values between 25 and 50%, moderate; and values greater than 50%, represent high variability.3636. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539-58. The effect of interventions was also analyzed using the magnitude scale for statistical effect proposed by Cohen in 1988,3838. Cohen J. Statistical power analysis for the behavioral sciences. 2a ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988. through SMD analysis. Statistical analysis was performed using the Review Manager (RevMan) software. Version 5.2. (Copenhagen, DN). The results were presented using forest plot graphs.

Results

Figure 1 summarizes the flow chart of the study selection process. Initially, 1,552 studies were identified; of these, 1,373 were found by searching electronic databases and 179 through the references of relevant studies and systematic reviews that addressed the topic of interest.

Figure 1
Flow chart of the study selection process.

Subsequently, the studies identified were imported into Endnote(r) reference manager, release X6; then, 402 duplicate studies were removed. A total of 1,150 studies were identified, of which 931 were excluded after a thorough analysis of title and summary demonstrated that they did not fit the inclusion criteria. Due to lack of information in the summary, 219 studies were analyzed in full; of these, 190 were excluded because they did not fit the inclusion criteria. After analyzing the eligibility, 29 studies were selected for the quality check according to the allocation concealment criteria. Studies classified as C and D were excluded, totaling four. Thus, 24 studies were selected for data collection, as they were classified as A and B. Of these, nine were excluded, as they did not have sufficient data for inclusion in the meta-analysis. Thus, 16 studies were included in this systematic review.2828. Robinson TN. Reducing children's television viewing to prevent obesity: a randomized controlled trial. JAMA. 1999;282:1561-7. , 2929. Gortmaker SL, Peterson K, Wiecha J, Sobol AM, Dixit S, Fox MK, et al. Reducing obesity via a school-based interdisciplinary intervention among youth: Planet Health. Arch Pediatr Adolesc Med. 1999;153:409-18. , 3030. Robinson TN, Killen JD, Kraemer HC, Wilson DM, Matheson DM, Haskell WL, et al. Dance and reducing television viewing to prevent weight gain in African-American girls: the Stanford GEMS pilot study. Ethn Dis. 2003;13:S65-77. , 3939. Sahota P, Rudolf MC, Dixey R, Hill AJ, Barth JH, Cade J. Randomised controlled trial of primary school based intervention to reduce risk factors for obesity. BMJ. 2001;323: 1029-32. , 4040. Story M, Sherwood NE, Himes JH, Davis M, Jacobs DR Jr, Cartwright Y, et al. An after-school obesity prevention program for African-American girls: the Minnesota GEMS pilot study. Ethn Dis. 2003;13:S54-64. , 4141. Fitzgibbon ML, Stolley MR, Schiffer L, Van Horn L, KauferChristoffel K, Dyer A. Hip-Hop to Health Jr. for Latino preschool children. Obesity (Silver Spring). 2006;14:1616-25. , 4242. Foster GD, Sherman S, Borradaile KE, Grundy KM, Vander Veur SS, Nachmani J, et al. A policy-based school intervention to prevent overweight and obesity. Pediatrics. 2008;121:e794-802. , 4343. Jones D, Hoelscher DM, Kelder SH, Hergenroeder A, Sharma SV. Increasing physical activity and decreasing sedentary activity in adolescent girls-the Incorporating More Physical Activity and Calcium in Teens (IMPACT) study. Int J Behav Nutr Phys Act. 2008;5:42. , 4444. Weintraub DL, Tirumalai EC, Haydel KF, Fujimoto M, Fulton JE, Robinson TN. Team sports for overweight children: the Stanford Sports to Prevent Obesity Randomized Trial (SPORT). Arch Pediatr Adolesc Med. 2008;162:232-7. , 4545. Gentile DA, Welk G, Eisenmann JC, Reimer RA, Walsh DA, Russell DW, et al. Evaluation of a multiple ecological level child obesity prevention program: Switch what you Do, View, and Chew. BMC Med. 2009;7:49. , 4646. Lubans DR, Morgan PJ, Callister R, Collins CE. Effects of integrating pedometers, parental materials, and e-mail support within an extracurricular school sport intervention. J Adolesc Health. 2009;44:176-83. , 4747. Singh AS, Chin A Paw MJ, Brug J, van Mechelen W. Dutch obesity intervention in teenagers: effectiveness of a school-based program on body composition and behavior. Arch Pediatr Adolesc Med. 2009;163:309-17. , 4848. Sacher PM, Kolotourou M, Chadwick PM, Cole TJ, Lawson MS, Lucas A, et al. Randomized controlled trial of the MEND program: a family-based community intervention for childhood obesity. Obesity (Silver Spring). 2010;18:S62-8. , 4949. Bjelland M, Bergh IH, Grydeland M, Klepp KI, Andersen LF, Anderssen SA, et al. Changes in adolescents' intake of sugar-sweetened beverages and sedentary behaviour: results at 8 month mid-way assessment of the HEIA study-a comprehensive, multi-component school-based randomized trial. Int J Behav Nutr Phys Act. 2011;8:63. , 5050. Puder JJ, Marques-Vidal P, Schindler C, Zahner L, Niederer I, Bürgi F, et al. Effect of multidimensional lifestyle intervention on fitness and adiposity inpredominantly migrant preschool children (Ballabeina): cluster randomised controlled trial. BMJ. 2011;343:d6195. , 5151. Ezendam NP, Brug J, Oenema A. Evaluation of the Web-based computer-tailored FATaintPHAT intervention to promote energy balance among adolescents: results from a school cluster randomized trial. Arch Pediatr Adolesc Med. 2012;166:248-55.

Regarding the characteristics of the selected studies, most intervention programs were performed in the United States, with duration > six months, and included the participation of the families (Table 1).

Table 1
Characteristics of the randomized controlled trials included in the systematic review.

Considering the internal quality of the included studies, through its analysis by allocation concealment,3434. Higgin JP, Green S, editores. Cochrane handbook for systematic reviews of interventions. West Sussex: The Cochrane Collaboration and John Wiley & Sons Ltd.; 2008. the allocation process was considered adequate in 11 studies (category A), and in five of them, the process was not described, but mentioned in the text of the randomized trial (category B). Regarding the assessment according to the Jadad et al. scale, all 35 were considered as poor quality. The characteristics of the included studies are described in Table 1.

None of the studies applied the intervention programs aiming to reduce the screen time alone, but combined with other components, including nutrition education and physical activity. Moreover, in some of them, the interventions were conducted with extracurricular activities after school hours.3030. Robinson TN, Killen JD, Kraemer HC, Wilson DM, Matheson DM, Haskell WL, et al. Dance and reducing television viewing to prevent weight gain in African-American girls: the Stanford GEMS pilot study. Ethn Dis. 2003;13:S65-77. , 4040. Story M, Sherwood NE, Himes JH, Davis M, Jacobs DR Jr, Cartwright Y, et al. An after-school obesity prevention program for African-American girls: the Minnesota GEMS pilot study. Ethn Dis. 2003;13:S54-64. , 4444. Weintraub DL, Tirumalai EC, Haydel KF, Fujimoto M, Fulton JE, Robinson TN. Team sports for overweight children: the Stanford Sports to Prevent Obesity Randomized Trial (SPORT). Arch Pediatr Adolesc Med. 2008;162:232-7. Furthermore, screen time in hours per day was the measurement method used in most studies. The characteristics of the intervention program strategies are detailed in Table 2.

Table 2
Characteristics of the intervention programs.

To assess screen time, 16 studies were entered into the meta-analysis, and results with 8,785 participants showed a statistically significant effect of interventions on the decrease of screen time, with SMD (random effect): - 0.25 hours/day (95% CI = - 0.37, - 0.13), p < 0.01 between the intervention group and the control group, with a magnitude of effect considered to be small. There was heterogeneity between the studies with high variability (I22. Brasil. Ministério do Planejamento Orçamento e Gestão. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional de Saúde do Escolar. Rio de Janeiro: IBGE; 2013. = 85%) (Figure 2).

Figure 2
Forest plot for the studies comparing the intervention with the control group for interventions aimed at reducing the time in front of the screen (hours/day) in schoolchildren.

Discussion

This systematic review with meta-analysis allows a preliminary insight into the impact of interventions implemented in schools, focusing on sedentary behavior by reducing screen time, considered important in the prevention of obesity in children and adolescents.

When analyzing the international literature, relevant results were also observed in the decrease of sedentary behavior in children with SMD: - 0.29 (95% CI = - 0.35, - 0.22) in the meta-analysis presented by Kamath et al., and in adolescents in the study by Biddle et al. with SMD: - 0.192 (95% CI: - 0.30, - 0.08).5252. Kamath CC, Vickers KS, Ehrlich A, McGovern L, Johnson J, Singhal V, et al. Clinical review: behavioral interventions to prevent childhood obesity: a systematic review and metaanalyses of randomized trials. J Clin Endocrinol Metab. 2008;93:4606-15. , 5353. Biddle SJ, O'Connell S, Braithwaite RE. Sedentary behaviour interventions in young people: a meta-analysis. Br J Sports Med. 2011;45:937-42.

In schoolchildren, the result of the meta-analysis by Maniccia et al. was also positive regarding interventions to decrease time spent in front of the TV with SMD: - 0.15 (95% CI: - 0.23, - 0.06),5454. Maniccia DM, Davison KK, Marshall SJ, Manganello JA, Dennison BA. A meta-analysis of interventions that target children's screen time for reduction. Pediatrics. 2011;128:e193-210. a similar result to that observed in the present study. According to a systematic review by Schmidt et al., strategies to decrease screen time showed positive results; in most studies, the interventions were conducted in the school environment.5555. Schmidt ME, Haines J, O'Brien A, McDonald J, Price S, Sherry B, et al. Systematic review of effective strategies for reducing screen time among young children. Obesity (Silver Spring). 2012;20:1338-54. A controversial meta-analysis by Wahi et al. observed no changes in screen time between the intervention group and the control group, with SMD (mean difference): - 0.90 (95% CI: - 3.47, 1.66).5656. Wahi G, Parkin PC, Beyene J, Uleryk EM, Birken CS. Effectiveness of interventions aimed at reducing screen time in children: a systematic review and meta-analysis of randomized controlled trials. Arch Pediatr Adolesc Med. 2011;165:979-86.

The meta-analysis of randomized controlled trials also demonstrated that interventions aimed at decreasing sedentary time presented a statistically significant effect in reducing body mass index with SMD: - 0.89 (95% CI: - 1.67, - 0.11) in the intervention group compared to the control group. In this same review, the qualitative analysis of randomized controlled trials and longitudinal and cohort studies concluded that watching television for two or more hours a day is associated with increased body composition, low self-esteem, and lower school performance in children and adolescents of school age (5 to 17 years).5757. Tremblay MS, LeBlanc AG, Kho ME, Saunders TJ, Larouche R, Colley RC, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act. 2011;8:98.

In many studies included in the present review, interventions that focused on sedentary behavior aimed to reduce the time dedicated to activities such as watching television, playing video games, and using the computer. Moreover, the measurement of physical inactivity was assessed through screen time.

Of the studies included in this review, no intervention programs aimed solely to reduce screen time; they were combined with other components, including nutrition education and physical activity. This suggests that strategies aimed at changing sedentary behavior and reducing screen time should focus on both physical activity and nutrition education, aspects that should be considered in public policy planning in the healthcare are. Although some studies have observed no association between screen time and physical activity,5858. Fernandes RA, Júnior IF, Cardoso JR, Vaz Ronque ER, Loch MR, de Oliveira AR. Association between regular participation in sports and leisure time behaviors in Brazilian adolescents: a cross-sectional study. BMC Public Health. 2008;8:329. , 5959. Dumith SC, Hallal PC, Menezes AM, Araújo CL. Sedentary behavior in adolescents: the 11-year follow-up of the 1993 Pelotas (Brazil) birth cohort study. Cad Saúde Pública. 2010; 26:1928-36. a reduction in screen time and promotion of physical activity are crucial aspects of intervention programs.

This practice can be conducted at school and during leisure time, as their health benefits, amply documented in the literature, are associated with skeletal health (bone mineral content and density),60 60. Linden C, Ahlborg HG, Besjakov J, Gardsell P, Karlsson MK. A school curriculum-based exercise program increases bone mineral accrual and bone size in prepubertal girls: two-year data from the pediatric osteoporosis prevention (POP) study. J Bone Miner Res. 2006;21:829-35. , 6161. MacKelvie KJ, Khan KM, Petit MA, Janssen PA, McKay HA. A school-based exercise intervention elicits substantial bone health benefits: a 2-year randomized controlled trial in girls. Pediatrics. 2003;112:e447. , 6262. MacKelvie KJ, Petit MA, Khan KM, Beck TJ, McKay HA. Bone mass and structure are enhanced following a 2-year randomized controlled trial of exercise in prepubertal boys. Bone. 2004; 34:755-64. increase in flexibility and aerobic capacity,6363. Carrel AL, Clark RR, Peterson SE, Nemeth BA, Sullivan J, Allen DB. Improvement of fitness, body composition, and insulin sensitivity in overweight children in a school-based exercise program: a randomized, controlled study. Arch Pediatr Adolesc Med. 2005;159:963-8. , 6464. Kahn EB, Ramsey LT, Brownson RC, Heath GW, Howze EH, Powell KE, et al. The effectiveness of interventions to increase physical activity. A systematic review. Am J Prev Med. 2002; 22:73-107. and an inverse association with cardiovascular risk factors.6363. Carrel AL, Clark RR, Peterson SE, Nemeth BA, Sullivan J, Allen DB. Improvement of fitness, body composition, and insulin sensitivity in overweight children in a school-based exercise program: a randomized, controlled study. Arch Pediatr Adolesc Med. 2005;159:963-8. , 6565. Hansen HS, Froberg K, Hyldebrandt N, Nielsen JR. A controlled study of eight months of physical training and reduction of blood pressure in children: the Odense schoolchild study. BMJ. 1991;303:682-5. , 66 66. Krause MP, Hallage T, Gama MP, Goss FL, Robertson R, da Silva SG. Association of adiposity, cardiorespiratory fitness and exercise practice with the prevalence of type 2 diabetes in Brazilian elderly women. Int J Med Sci. 2007;4: 288-92. , 6767. Mcmurray RG, Harrell JS, Bangdiwala SI, Bradley CB, Deng S, Levine A. A school-based intervention can reduce body fat and blood pressure in young adolescents. J Adolesc Health. 2002; 31:125-32. , 6868. Perichart-Perera O, Balas-Nakash M, Ortiz-Rodríguez V, Morán-Zenteno JA, Guerrero-Ortiz JL, Vadillo-Ortega F. A program to improve some cardiovascular risk factors in Mexican school age children. Salud Publica Mex. 2008;50:218-26. Furthermore, regular physical activity, when started in childhood and/or adolescence, protects against physical inactivity in adulthood,69 69. Aarnio M, Winter T, Peltonen J, Kujala UM, Kaprio J. Stability of leisure-time physical activity during adolescence--a longitudinal study among 16-, 17- and 18-year-old Finnish youth. Scand J Med Sci Sports. 2002;12:179-85. , 7070. Azevedo MR, Araújo CL, Cozzensa da Silva M, Hallal PC. Tracking of physical activity from adolescence to adulthood: a population-based study. Rev Saúde Pública. 2007;41:69-75. , 7171. Maia JA, Lefevre J, Claessens A, Renson R, Vanreusel B, Beunen G. Tracking of physical fitness during adolescence: a panel study in boys. Med Sci Sports Exerc. 2001;33:765-71. even though many studies showed no association between screen time and level of physical activity.

Regarding the interventions described in the studies, the family is emphasized as an important component, especially the involvement of parents in promoting healthy habits; this fact should be considered and encouraged by intervention programs, as children are influenced by the parents' habits. Therefore, the recommendations provided at school should be followed at home, through parents' positive examples to their children. Current scientific evidence suggests that intervention programs have better results when the strategies include the family component.7272. Katz DL, O'Connell M, Njike VY, Yeh MC, Nawaz H. Strategies for the prevention and control of obesity in the school setting: systematic review and meta-analysis. Int J Obes (Lond). 2008;32:1780-9. , 7373. Epstein LH. Family-based behavioural intervention for obese children. Int J Obes Relat Metab Disord. 1996;20:S14-21.

The limitations of this meta-analysis include a small number of trials, with some exclusions due to lack of suitable data for effect size calculation. Moreover, most of the included trials were performed with a small sample, and all were considered as poor quality according to the Jadad et al. scale, as they did not describe the allocation concealment in detail, the randomization procedure, blinding, losses, and exclusions. Furthermore, no Brazilian study was included in this review, as they did not meet the inclusion criteria.

This systematic review may be subject to publication bias, as trials that reported beneficial effects of certain interventions are more often published, at the expense of those that did not describe positive effects.

Another limitation of the included trials is related to the intervention programs, as most of them did not have the reduction of screen time as specific objective, but aimed to promote and encourage physical activity and healthy eating habits. For this reason, intervention studies with pre- and post-measurements of screen time in which this variable was considered a secondary outcome were included in the review, after comprehensive discussions among the project team members.

It should be emphasized that, although the time spent using television, computers, and video games is representative of frequently sedentary activities, the assessment should also consider analyses of time spent in the car, sitting and resting, situations involving traffic, work, and leisure activities.7474. Tremblay MS, Colley RC, Saunders TJ, Healy GN, Owen N. Physiological and health implications of a sedentary lifestyle. Appl Physiol Nutr Metab. 2010;35:725-40.

Moreover, the self-reported sedentary behavior evaluated by questionnaires was considered the methodological choice of most trials to assess sedentary behavior among schoolchildren. However, this method does not allow for accurate measures as those obtained with motion sensors, such as accelerometers. For many authors, sedentary behavior is generally defined as time spent < 1.5 METs.7575. Pate RR, O'Neill JR, Lobelo F. The evolving definition of "sedentary". Exerc Sport Sci Rev. 2008;36:173-8. , 7676. Owen N, Leslie E, Salmon J, Fotheringham MJ. Environmental determinants of physical activity and sedentary behavior. Exerc Sport Sci Rev. 2000;28:153-8. Therefore, the combination of these two methods could be used to measure sedentary behavior.

The present review suggests the need for well-designed, randomized controlled trials with good methodological criteria to assess the effect of interventions, especially in Brazilian populations, as well as interventions whose main strategy is to reduce screen time.

The present results should be interpreted with caution, and may also help to plan future research. The evidence in this systematic review with meta-analysis suggests that changes in sedentary behavior, by reducing the time spent in activities such as watching television, playing video games, and using computers, are possible through intervention programs in schools, although the effects are small.

Acknowledgements

Roberta Roggia Friedrich received a doctoral grant from CNPQ. The authors would also thank the Post-graduation Program in Child and Adolescent Health of Faculdade de Medicina of the Universidade Federal do Rio Grande do Sul (UFRGS).

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  • Funding National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq).
  • Please cite this article as: Friedrich RR, Polet JP, Schuch I, Wagner MB. Effect of intervention programs in schools to reduce screen time: a meta-analysis. 2014;90:232-41.

Publication Dates

  • Publication in this collection
    May-Jun 2014

History

  • Received
    27 Aug 2013
  • Accepted
    18 Sept 2013
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