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The International Journal of Behavioral Nutrition and Physical Activity logoLink to The International Journal of Behavioral Nutrition and Physical Activity
. 2011 Sep 21;8:98. doi: 10.1186/1479-5868-8-98

Systematic review of sedentary behaviour and health indicators in school-aged children and youth

Mark S Tremblay 1,✉, Allana G LeBlanc 1, Michelle E Kho 2, Travis J Saunders 1, Richard Larouche 1, Rachel C Colley 1, Gary Goldfield 1, Sarah Connor Gorber 3
PMCID: PMC3186735  PMID: 21936895

Abstract

Accumulating evidence suggests that, independent of physical activity levels, sedentary behaviours are associated with increased risk of cardio-metabolic disease, all-cause mortality, and a variety of physiological and psychological problems. Therefore, the purpose of this systematic review is to determine the relationship between sedentary behaviour and health indicators in school-aged children and youth aged 5-17 years. Online databases (MEDLINE, EMBASE and PsycINFO), personal libraries and government documents were searched for relevant studies examining time spent engaging in sedentary behaviours and six specific health indicators (body composition, fitness, metabolic syndrome and cardiovascular disease, self-esteem, pro-social behaviour and academic achievement). 232 studies including 983,840 participants met inclusion criteria and were included in the review. Television (TV) watching was the most common measure of sedentary behaviour and body composition was the most common outcome measure. Qualitative analysis of all studies revealed a dose-response relation between increased sedentary behaviour and unfavourable health outcomes. Watching TV for more than 2 hours per day was associated with unfavourable body composition, decreased fitness, lowered scores for self-esteem and pro-social behaviour and decreased academic achievement. Meta-analysis was completed for randomized controlled studies that aimed to reduce sedentary time and reported change in body mass index (BMI) as their primary outcome. In this regard, a meta-analysis revealed an overall significant effect of -0.81 (95% CI of -1.44 to -0.17, p = 0.01) indicating an overall decrease in mean BMI associated with the interventions. There is a large body of evidence from all study designs which suggests that decreasing any type of sedentary time is associated with lower health risk in youth aged 5-17 years. In particular, the evidence suggests that daily TV viewing in excess of 2 hours is associated with reduced physical and psychosocial health, and that lowering sedentary time leads to reductions in BMI.

Keywords: Inactivity; sitting; TV; body composition; fitness; metabolic syndrome, cardiovascular disease; self-esteem; pro-social behaviour, academic achievement

Introduction

Engaging in regular physical activity is widely accepted as an effective preventative measure for a variety of health risk factors across all age, gender, ethnic and socioeconomic subgroups [1-6]. However, across all age groups, levels of physical activity remain low [7-12] and obesity rates continue to rise [10,11,13,14]; collectively threatening the persistent increase in life expectancy enjoyed over the past century and efforts to counteract the inactivity and obesity crisis [15].

This inactivity crisis is especially important in the pediatric population as recent data from the Canadian Health Measures Survey [8] suggest that only 7% of children and youth aged 6-19 years participate in at least 60 minutes of moderate- to vigorous-intensity physical activity per day, thus meeting the current physical activity guidelines from Canada [16], the U.S. [6], the U.K [17], Australia [18] and the World Health Organization (WHO) [5]. However, even for those children and youth who meet current guidelines, there remains 23 hours per day for school, sleep, work, and discretionary time. Several sources report that children and youth spend the majority of their discretionary time engaging in sedentary pursuits (e.g. watching television (TV) or playing video games) [8,19-28]. Canadian children and youth are spending an average of 8.6 hours per day, or 62% of their waking hours being sedentary [8]. Similar trends are being reported in the U.S. where children and youth spend an average of 6-8 hours per day being sedentary [22-28]. Accumulating evidence shows that, independent of physical activity levels, sedentary behaviours are associated with increased risk of cardio-metabolic disease, all-cause mortality, and a variety of physiological and psychological problems [29-31]. Therefore, to maximize health benefits, approaches to resolve the inactivity crisis should attempt to both increase deliberate physical activity and decrease sedentary behaviours, especially in the pediatric population. However, to date, public health efforts have focused primarily on physical activity and have paid little attention to the mounting evidence to support sedentary behaviour as a distinct behaviour related to poor health.

A recent scoping review identified review articles, meta-analyses, and grey literature that examined the relationship between sedentary behaviour and health [32]. The large majority of this information reported on the relationship between screen time and body composition and did not include other indicators of health [23-25]. Furthermore, none of these reviews followed the rigorous process of a systematic review and are therefore not able to be used to inform the development of clinical practice guidelines. As a result, to our knowledge, there are no systematic, evidence-based sedentary behaviour guidelines for any age group, anywhere in the world. Guidelines that do exist are largely based on expert opinion or narrative literature reviews [33,34].

Therefore, the purpose of this systematic review was to gather, catalog, assess and evaluate the available evidence examining sedentary behaviours in relation to selected health outcomes in children and youth 5-17 years of age and present a summary of the best available evidence. Specifically, the review presents available evidence for minimal and optimal thresholds for daily sedentary time in children and youth, and when possible, how thresholds differ across health outcome or demographic status (i.e. age, gender). The information gathered in this review can serve to guide future research and inform the development of evidence-based clinical practice guideline recommendations for safe and healthy amounts of daily sedentary behaviour in the pediatric population.

Methods

Study Inclusion Criteria

The review sought to identify all studies that examined the relationship between sedentary behaviour and a specific health outcome in children and youth (aged 5-17 years). All study designs were eligible (e.g. cross sectional, retrospective, prospective, case control, randomized controlled trial (RCT), longitudinal). Longitudinal studies were included if the data presented in the article was consistent with the age limits that were set (i.e. if the study looked at participants at age 10 and then again at age 30, only baseline measurements from age 10 were used).

Studies were included only if there was a specific measure of sedentary behaviour. Eligible exposures of sedentary behaviours included those obtained via direct (e.g., measurements of sitting, or low activity measured by accelerometer) and self-reported (e.g., questionnaires asking about TV watching, video gaming, non-school computer use, and screen time - composite measures of TV, video games, computers) methods. Sedentary behaviour was often measured as a composite measure of all time engaging in sedentary behaviours including screen time outside of school hours. Six health indicators were chosen based on the literature, expert input, and a desire to have relevant measures from a range of holistic health indicators (i.e. not only physical health, but also emotional, mental and intellectual health). The six eligible indicators in this review were:

1. Body composition (overweight/obesity measured by body mass index (BMI), waist circumference, skin folds, bio-impedance analysis (BIA), dual-energy x-ray absorptiometry (DXA or DEXA));

2. Fitness (physical fitness, physical conditioning, musculoskeletal fitness, cardiovascular fitness);

3. Metabolic syndrome (MS) and cardiovascular disease (CVD) risk factors (unfavourable lipid levels, blood pressure, markers for insulin resistance or type 2 diabetes);

4. Self-esteem (self-concept, self-esteem, self efficacy);

5. Behavioural conduct/pro-social behaviour (child behaviour disorders, child development disorder, pro-social behaviour, behavioural conduct, aggression);

6. Academic achievement (school performance, grade-point average).

No Language or date limits were imposed in the search. The following definitions were used to help guide the systematic review [31]:

- Sedentary: A distinct class of behaviours (e.g. sitting, watching TV, playing video games) characterized by little physical movement and low energy expenditure (≤ 1.5 METs).

- Sedentarism: Engagement in sedentary behaviours characterized by minimal movement, low energy expenditure, and rest.

- Physically active: Meeting established physical activity guidelines (e.g. see Tremblay et al. 2011 for Canadian Physical Activity Guidelines [16]).

- Physical inactivity: The absence of physical activity, usually reflected as the proportion of time not engaged in physical activity of a pre-determined intensity and therefore not meeting established physical activity guidelines.

Study Exclusion Criteria

As the volume of literature on sedentary behaviour was anticipated to be very high, to control the feasibility of this project, the following sample size limits were set a priori: population based studies (observational, cross sectional, cohort, and retrospective studies) were required to have a minimum sample size of 300 participants; RCTs, and intervention studies were required to have at least 30 participants. Studies of 'active gaming' (e.g., Nintendo Wii™, Microsoft Kinect™, Sony's Playstation Move™, video arcades, etc.) were excluded. Finally, studies that defined sedentary behaviour as 'failing to meet physical activity guidelines' were excluded from the review.

Search strategy

The following electronic bibliographic databases were searched using a comprehensive search strategy to identify relevant studies: Ovid MEDLINE(R) (1950 to February Week 2 2010), Ovid EMBASE (1980 to 2010 Week 07), and Ovid psycINFO (1806 to February Week 3 2010). The search strategy was created by a single researcher (JM) and run by a second researcher (AL). The search strategies can be found in Additional file 1. The search was limited to studies looking at 'school-aged' children and youth (mean age of 5-17 years). Articles were extracted as text files from the OVID interface and imported in to Reference Manager Software (Thompson Reuters, San Francisco, CA). Duplicate articles were first removed using Reference Manager Software, and any remaining duplicates were removed manually. All articles were given a unique reference identification number in the database.

Titles and abstracts of potentially relevant articles were screened by two reviewers (AL and one of GG, MT, RC, RL or TS) and full text copies were obtained for all articles meeting initial screening by at least one reviewer. Two independent reviewers examined all full text articles (AL and one of GG, MT, RC, RL or TS) and any discrepancies were resolved by discussion and consensus between the two reviewers. If the reviewers were unable to reach consensus, a third reviewer was asked to look at the article in question. Consensus was obtained for all included articles.

Twelve key content experts were contacted and asked to identify the most influential papers from their personal libraries examining sedentary behaviour and health in the pediatric age group. Government documents from the U.S [6], the U.K. [17], and Australia [18] were used for reference and to help guide the review process.

Data extraction

Standardized data extraction tables were created; data extraction was completed by one reviewer (AL) and checked by another (one of GG, RC, RL, or TS) for accuracy. Information was extracted regarding study characteristics (i.e. year, study design, country, number of participants, age), type of sedentary behaviour, measure of sedentary behaviour (i.e. direct, or indirect), and health outcome. Reviewers were not blinded to the authors or journals when extracting data.

Risk of bias assessment

The Downs and Black checklist was used to asses study quality [35]. This 27 point checklist assesses the quality of reporting (e.g. "Are the main findings of the study clearly described"); external validity (e.g. "Were the subjects asked to participate representative of the entire population from which they were recruited"); internal validity (e.g. "Were subjects randomized to intervention groups"); and power (e.g. "Was there sufficient power such that the difference being due to chance is less than 5%"). The maximum score a study can receive is 32, with higher scores indicating better quality. Inter-rater reliability was calculated using Cohen's kappa.

Quality of evidence was determined by the study design and by Downs and Black score. Level of evidence was used to explain the quality of available studies and the confidence of the findings [36]. RCTs were considered to have the highest level of evidence while anecdotal reports were considered to have the lowest evidence. See Table 1 for more details. When possible, studies were examined for differences among age and gender subgroups.

Table 1.

Criteria for assigning level of evidence to a recommendation

Level of evidence Criteria
Level 1 - Randomized control trials without important limitations
Level 2 - Randomized control trials with important limitations
- Observational studies (non-randomized clinical trials or cohort studies) with overwhelming evidence
Level 3 - Other observational studies (prospective cohort studies, case-control studies, case series)
Level 4 - Inadequate or no data in population of interest
- Anecdotal evidence or clinical experience
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Adapted from: Lau DC et al. 2007 [36]

Analysis

A meta-analysis was performed with the data that were sufficiently homogeneous in terms of statistical, clinical, and methodological characteristics using Review Manager Software 5.0 (The Cochrane Collaboration, Copenhagen Denmark). Pooled estimates for the meta-analysis and their 95% confidence intervals were obtained using the random effects estimator of DerSimonian-Laird [37]. Studies were weighted by the inverse of their variance. Cochrane's Q was used to test for heterogeneity among studies and the I2 (squared) index [10] was used to determine the degree of heterogeneity [38]. Funnel plots were used to assess publication bias (data not shown). Qualitative syntheses were conducted for remaining studies.

Results

Description of studies

After de-duplication, the preliminary search of electronic databases, reference lists, and grey literature identified 5,291 potentially relevant articles (Figure 1). Of these, 3,299 were identified in MEDLINE, 1,016 in EMBASE, 912 in psycINFO, and 64 through key informants, government documents, and bibliographies. After a preliminary review of titles and abstracts, 828 articles were included for detailed assessment of the full text article. Of these, 232 met the criteria for study inclusion (8 RCTs, 10 intervention studies, 37 longitudinal studies and 177 cross sectional studies). Individual study characteristics can be seen in Table 2. Reasons for excluding studies included: ineligible population (e.g. ineligible age or sample size) (n = 161), ineligible exposure (e.g. diet, physical activity) (n = 145), ineligible measure of sedentary behaviour (i.e. not meeting physical activity guidelines) (n = 19), ineligible outcome (n = 60), ineligible analysis (e.g. analysis focused on content of screen time versus duration of screen time, analysis focused on active video gaming) (n = 60), and 'other' (n = 216) (e.g. commentary article or methodological paper). Some studies were excluded for multiple reasons. Some articles (n = 9) could not be retrieved due to missing or incorrect reference information.

Figure 1.

Figure 1

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Flow of information through the different phases of the review.

Table 2.

Summary of characteristics of included studies

n analyzed
First Author Year Country Grade Age Range Mean age Total Boys Girls Units of sedentary behaviour Exposure Outcome
RANDOMIZED CONTROLLED TRIALS
Epstein LH [265] 1995 US 8-12 10.1 61 hour week TV BC
Epstein LH [50] 2008 US 4-7 6 70 37 33 hour day TV BC
Goldfield GS [264] 2006 Canada 8-12 10.4 30 13 17 min day TV BC
Gortmaker SL [57] 1995 US 11.7 1295 668 627 hour day TV BC
Hughes AR [262] 1991 Scotland 5-11 8.8 134 59 74 hour day SB BC
Robinson TN [58] 1999 US 192 hour week TV, GAMES BC
Robinson TN [221] 2003 US 8-10 9.5 61 0 61 hour week TV BC, SE
Shelton D [263] 2007 Australia 3-10 7.5 43 20 23 hour day TV BC
INTERVENTION STUDIES
Epstein LH [56] 2000 US 8-12 10.5 76 24 52 hour month SB, ST BC, FIT
Epstein LH [59] 2004 US 8-12 9.8 60 23 39 times week TV BC
Epstein LH [60] 2005 US 8-16 58 28 30 hour day SB, TV BC
Gentile DA [61] 2009 US 9.6 1323 685 674 hour day ST BC
Goldfield GS [52] 2007 Canada 8-12 10.4 30 13 17 hour day SB BC, SE
Harrison M [62] 2003 Ireland 10.2 312 177 135 min day TV, ST BC
Ochoa MC [53] 2007 Spain 6-18 11.6 370 196 174 hour week TV BC
Salmon J [51] 2008 Australia 1011 10.8 311 152 159 hour day TV BC
Simon C [54] 2002 France 11.7 954 468 486 hour day TV, COMP BC, SE
Tanasescu M [55] 2000 Puerto Rico 7-10 9.2 53 22 31 hour day TV BC
LONGITUDINAL STUDIES hour
Aires L [83] 2010 Portugal 11-19 345 147 198 hour day SCREEN BC, FIT
Berkey CS [76] 2003 US 10-15 11887 5120 6767 hour day TV, GAMES BC
Bhargava A [77] 2008 US 7635 min day TV BC
Blair NJ [68] 2007 England 5.5 591 287 304 hour day SB, TV BC
Borradaile KE [86] 2008 US 11.2 1092 501 591 hour week TV BC
Burke V [71] 2006 Australia 7.6/10.8 1569 630 648 hour week SCREEN BC
Chen JL [78] 2007 Chinese 7-8 7.52 307 147 160 hour day TV, GAMES BC
Danner FW [66] 2008 US 7334 3674 3660 hour day TV BC
Dasgupta K [215] 2006 Canada 12.7/15.1/17.0 662 319 343 hour week SB, TV MS
Day RS [85] 2009 US 8-14 556 277 279 min day TV BC
Dietz WH [181] 1985 US 12-17 2153 hour day TV BC
Elgar FJ [79] 2005 Wales 11.7 654 293 361 hour week TV BC
Elgar FJ [79] 2005 Wales 15.3 392 181 211 hour week TV BC
Ennemoser M [237] 2007 German 6-8 332 min day TV SE, AA
Fulton JE [84] 2009 US 10-18 472 245 227 min day TV BC
Gable S [70] 2007 US 8000 hour day TV BC
Hancox RJ [88] 2004 New Zealand 5-15 1013 hour day TV BC, MS
Hancox RJ [72] 2006 New Zealand 5-15 603 372 339 hour day SCREEN BC
Henderson VR [67] 2007 US 11-19 2379 0 2379 hour day TV, SCREEN BC
Hesketh K [80] 1997 Australia 5-10 7.6 1278 630 648 hour day SCREEN BC
Hesketh K [80] 1997 Australia 8-13 10.7 1278 630 648 hour day SCREEN BC
Hesketh K [64] 2009 Australia 5-10 7.7 1943 972 971 hour day TV, GAMES BC
Hesketh K [64] 2009 Australia 8-13 1569 816 753 hour day TV, GAMES BC
Jackson LA [223] 2009 US 12 500 235 265 hour day COMP, SCREEP SE
Jago R [82] 2005 US 5-6 6.5 138 65 73 min hr SB, TV BC
Janz KF [73] 2005 US 5.6/8.6 378 176 202 hour day SCREEN BC
Johnson JG [41] 2007 US hour day TV AA
Kaur H [75] 2003 US 12-17 2223 1149 1074 hour day TV BC
Lajunen HR [128] 2007 Finland 15-19 5184 hour SB BC
Lonner W [238] 1985 US 9-19 14.2 367 hour day TV AA
Maffeis C [89] 1998 Italy 8.7 298 148 150 min day SCREEN BC
Mistry K [229] 2007 US hour day TV PRO
Mitchell JA [49] 2009 UK 11-12 11.8 5434 2590 2844 hour day SB BC, FIT
Must A [87] 2007 US 10-17 156 0 156 hour day SB, SCREEN BC
O'Brien M [69] 2007 US 2-12 653 hour week TV BC
Parsons TJ [74] 2005 England/Scotland/Wales 11/16 17733 hour day TV BC
Purslow LR [63] 2008 England 8-9 345 176 169 min day SB BC
Timperio A [65] 2008 Australia 10-12 344 152 192 times week SB, SCREEN BC
Treuth MS [29] 2007 US 11.9 984 0 984 min day SB BC
Treuth MS [27] 2009 US 13.9 984 0 984 min day SB BC
Wosje,K.S [205] 2009 US 6.75-7.25 214 hour day SCREEN FIT
CROSS SECTIONAL STUDIES
Al SH [192] 2009 International 12-18 17715 8503 9212 hour day TV BC
Albarwani S [207] 2009 Oman 15-16 529 245 284 hour week TV, COMP FIT
Alves JG [191] 2009 Brazil 7-10 733 407 326 hour day TV BC
Aman J [218] 2009 Sweden 11-18 14.5 2093 1016 991 hour week TV, COMP MS
Andersen LF [155] 2005 Norway 8-14 1432 702 730 hour day TV BC
Andersen RE [142] 1998 US 8-16 4063 1985 2071 hour day TV BC
Anderson SE [103] 2008 US 4-12 8 2964 1509 1455 hour day TV BC
Armstrong CA [213] 1998 US 9.28 588 304 284 hour day TV FIT
Asante PA [183] 2009 US 3-13 8.5 324 182 142 hour day SCREEN BC
Aucote HM [163] 2009 Australia 5-6 11.09 393 198 195 hour week TV, GAMES BC
Barlow SE [151] 2007 US 6-17 12.1 52845 hour day TV BC
Basaldua N [109] 2008 Mexico 6-12 8.9 551 278 273 hour day TV BC
Bellisle F [123] 2007 France 9-11 1000 500 500 hour day TV BC
Berkey CS [90] 2000 US Sep-14 10769 4620 6149 hour day TV BC
Beyerlein A [105] 2008 Germany 4.5-7.3 4967 2585 2382 hour day TV BC
Boone JE [164] 2007 US 15.9 9155 4879 4276 hour week SCREEN BC
Boone-Heinonen J [104] 2008 US 11-21 9251 hour SB BC
Boutelle KN [130] 2007 US 16-18 1726 890 836 hour day TV BC
Brodersen NH [235] 2005 England 11.8 4320 2578 1742 hour week SB SE, PRO
Bukara-Radujkovic G [96] 2009 Bosnia 11-12 11.5 1204 578 626 hour day TV, COMP BC
Butte NF [119] 2007 US 6-17 10.8 897 441 456 hour day SCREEN BC
Caldas S [245] 1999 US 4-19 34542 hour day TV AA
Carvalhal MM [131] 2007 Portugal 10-11 3365 1755 1610 hour day TV, COMP BC
Chaput J [154] 2006 Canada 5-10 6.6 422 211 211 hour day SCREEN BC
Chen MY [78] 2007 Taiwan 13-18 15.03 660 351 309 hour day TV, COMP BC, SE, PRO
Chowhan J [232] 2007 Canada 12-15 2666 hour day TV PRO
Christoforidis A [95] 2009 Greece 4-18 11.41 1549 735 814 hour day SCREEN BC, FIT
Collins AE [149] 2008 Indonesia 12-15 1758 815 916 hour day TV, COMP BC
Colwell J [200] 2003 Japan 12-13 305 159 146 hour day SCREEN BC, PRO
Cooper H [247] 1999 US 7-11 424 225 199 hour day TV AA
Crespo CJ [177] 2001 US 8-16 4069 1994 2075 hour day TV BC
Da CR [157] 2003 Brazil 7-10 446 107 107 hour day TV BC
Dasgupta K [215] 2007 Canada 13-17 1267 hour week SCREEN MS
Delva J [125] 2007 US 11265 5274 5991 hour week TV BC
Dietz WH [181] 1985 US 12-17 6671 hour day TV AA
Dietz WH [181] 1985 US 6-11 6965 hour day TV BC, AA
Dollman J [211] 2006 Australia 6 10-11 843 439 404 min Day TV FIT
Dumais SA [255] 2009 US 10-12 15850 hour TV AA
Dominick JR [225] 1984 US 10, 11 14-18 250 110 140 hour Day TV, GAME SE, PRO
Eisenmann JC [175] 2002 US 14-18 15143 hour day TV BC
Eisenmann JC [113] 2008 US' 16.2 12464 6080 6384 hour day TV BC
Ekelund U [134] 2006 Europe 9-16 1921 911 1010 hour day TV BC, MS
Fetler M [249] 1984 US 6 10603 hour day SCREEN AA
Forshee RA [201] 2004 US 12-16 14 2216 1075 1141 hour day TV BC
Forshee RA [188] 2009 US 5-18 1459 734 725 hour week SCREEN BC
Gaddy GD [257] 1986 US 5074 hour day TV AA
Giammattei J [140] 2003 US 11-14 12.6 385 186 199 hour day TV BC
Gibson S [156] 2004 England 7-18 1294 655 639 min day TV BC
Gomez LF [150] 2007 Colombia 5-12 11137 5539 5598 hour day TV, GAMES BC
Gordon-Larsen P [176] 2002 US 11-19 15.9 12759 6290 6496 hour week TV, GAMES BC
Gortmaker SL [143] 1996 US 10-15 11.5 746 388 358 hour day TV BC
Gortmaker SL [57] 1999 US 6-11 1745 min week TV SE, AA
Gortmaker SL [57] 1999 US 12-17 1745 min week TV SE, AA
Graf C [167] 2004 Germany 6.8 344 177 167 hour day TV, COMP BC
Grusser SM [40] 2005 Germany 6 11.83 323 175 148 hour day TV AA
Hardy LL [133] 2006 Australia 11-15 2750 1446 1304 hour day SCREEN FIT
Hernandez B [178] 1999 Mexico 9-16 461 244 217 hour day TV BC
Hirschler V [144] 2009 Argentina 7-11 8.9 330 168 162 hour day TV BC
Holder MD [222] 2009 Canada 8-12 375 252 262 hour day SCREEN SE
Hume C [190] 2009 Netherlands 13 580 277 303 hour day SCREEN BC
Islam-Zwart K [195] 2008 US 480 198 282 hour day TV BC
Jackson LA [223] 2009 US 12.18 515 259 256 hour day GAMES, COMP AA
Janssen I [166] 2004 Canada 11-16 5890 2812 3078 hour day TV, COMP BC
Janz K [174] 2002 US 4-6 5.3 462 216 246 hour day TV BC
Jaruratanasirikul S [241] 2009 Thailand 7-12 15.9 1492 562 929 hour GAMES AA
Johnson CC [41] 2007 US 12 1397 0 1397 hour day SB SE
Katzmarzyk PT [197] 1998 Canada 9-18 784 423 361 min day TV BC, FIT
Katzmarzyk PT [184] 1998 Canada 640 356 284 hour day TV BC, FIT
Kautiainen S [135] 2005 Finland 14-18 6515 2916 3599 hour day SCREEN BC
Keith TZ [256] 1986 US high school seniors 28051 hour day TV AA
Klein-Platat C [165] 2005 France 12 2714 1357 1357 hour week SB BC
Kosti RI [196] 2007 Greece 12-17 2008 1021 987 hour day TV BC
Kristjansson AL [243] 2009 Iceland 14-15 5810 2807 3004 hour day TV AA
Kuntsche E [230] 2006 International 11-15 31177 hour day TV PRO
Kuriyan R [117] 2007 India 6-16 598 324 274 hour day TV BC
Lagiou A [160] 2008 Greece 10-12 633 316 317 hour day TV, GAMES BC
Lajous M [92] 2009 Mexico 11-18 13.9 9132 3519 5613 hour day TV BC
Lajunen HR [128] 2007 Finland 17.6 4098 1981 2117 hour week COMP BC
Lasserre AM [116] 2007 Switzerland 10.1-14.9 12.3 5207 2621 2586 hour day TV BC
Laurson KR [107] 2008 US 7-12 709 318 391 hour week SCREEN BC
Lazarou C [217] 2009 Cyprus 11.7 622 306 316 hour day TV MS
Leatherdale ST [11] 2008 Canada 14-19 25416 12806 12610 hour day TV BC, PRO
Lioret S [127] 2007 France 3-14 1016 528 488 hour day SB, TV, COMP BC
Lobelo F [208] 2009 US 14-18 5210 0 5210 hour day SCREEN FIT
Lowry R [173] 2002 US 15349 7445 7828 hour day TV BC
Lutfiyya MN [118] 2007 US 5-17 7972 hour day TV BC
Maffeis C [114] 2008 Italy 8-10 9.3 1837 924 913 hour day TV BC
Mark AE [220] 2008 US 12-19 15.9 1803 1005 798 hour day TV BC, MS
McMurray RG [187] 2000 US 10-16 12.7 2389 1149 1240 hour day TV BC
Mihas C [193] 2009 Greece 12-17 14.4 2008 1021 987 hour day SCREEN BC
Mikolajczyk RT [194] 2008 Germany 11-17 13.5 4878 2433 2445 hour low/high SB BC
Moraes SA [135] 2006 Mexico 6-14 8.0/11.3 662 343 339 hour week
Morgenstern M [94] 2009 Germany/US 10-17 12.8 4810 2294 2516 hour day SCREEN BC
Morgenstern M [94] 2009 Germany/US 12-16 14 4473 2239 2234 hour day SCREEN BC
Mota J [199] 2006 Portugal 14.6 450 220 230 hour day TV, COMP BC
Muller MJ [179] 1999 Germany 5-7 1468 739 729 hour day TV BC
Nagel G [193] 2009 Germany 6-9 7.57 1079 498 hour day TV, GAMES BC
nastassea-Vlachou K [240] 1996 Greece 6-13 4690 2279 2411 hour day TV AA
Nawal LM [148] 1998 US 5-18 62976 hour day TV, COMP BC
Nelson MC [233] 2006 US 7-12 11957 5979 5978 hour day SCREEN PRO
Neumark-Sztainer D [224] 2004 US 11-18 14.9 4746 2382 2364 hour week TV SE, PRO
Nogueira JA [45] 2009 Brazil 8.3-16.8 13 326 204 122 hour day SB BC
Obarzanek E [180] 1994 US 9-10 10.1 2379 0 2379 hour week TV BC
Ohannessian CM [226] 2009 US 14-16 14.99 328 138 190 hour day SCREEN SE, PRO, AA
Ortega FB [122] 2007 Spain 13-18.5 15.4 2859 1357 1502 hour day SB BC
Overby NC [219] 2009 Norway 6-19 723 375 348 min day TV
Ozmert E [42] 2002 Turkey 689 343 346 hour day TV PRO, AA
Padez C [99] 2009 Portugal 7-9 3390 1696 1694 hour day TV BC
Page RM [234] 2001 Philippine 15.1 3307 1267 1819 hour week TV PRO
Pate RR [210] 2006 US 12-19 15.4 3287 1686 1601 hour day TV FIT
Patrick K [169] 2004 US 11-15 12.7 878 407 471 min day TV BC
Pratt C [101] 2008 US 12 1458 223 1235 hour day SB BC
Purath J [185] 1995 US 3-5 365 189 176 hour day TV BC, MS
Ramos E [126] 2007 Portugal 13 2161 1045 1116 min week SB, TV, COMP BC
Rapp K [138] 2005 Germany 6.2 2140 1015 1125 hour day TV BC
Ridley-Johnson R [252] 1983 US 5-8 290 hour day TV AA
Roberts DF [250] 1984 US 539 hour week TV AA
Robinson TN [58] 1999 US 12.4 971 0 971 hour day TV BC
Ruangdaraganon N [141] 2002 Thailand 6-12 9.4 4197 2126 2035 hour day TV BC
Russ SA [147] 2009 US 6-17 54863 28153 26710 hour day SCREEN BC, SE
Sakamoto A [236] 1994 Japan 4-6 307 165 142 times week GAMES PRO
Sakamoto A [236] 1994 Japan 4-6 537 287 250 hour week COMP, GAMES PRO
Sakamoto A [236] 1994 Japan 4-5 118 118 0 hour week COMP, GAMES PRO
Salmon J [136] 2006 Australia 5-12 1560 743 817 hour day TV BC
Sardinha LB [48] 2008 Portugal 9-10 9.8 308 161 147 hour day SB MS
Scott LF [254] 1958 US 6-7 407 hour TV AA
Sharif I [244] 2006 US 10-14 6522 3169 3353 hour day TV, GAMES PRO, AA
Sharif I [260] 2010 US 9-15 12 4508 2209 2299 hour day TV, GAMES AA
Shejwal B [246] 2006 India 16.05 654 368 286 hour day TV AA
Shields M [162] 2006 US/Can 2-17 8661 hour day SB, TV BC
Shin N [239] 2004 US 6-13 9 1203 605 598 min day TV AA
Singh GK [106] 2003 US 10-17 46707 24072 22635 hour day TV BC
Singh GK [106] 2003 US 10-17 46707 24072 22635 hour day TV BC
Skoric MM [258] 2009 Singapore 8-12 10 333 180 153 hour TV, GAMES AA
Smith BJ [161] 2007 Fiji 11-16 443 200 245 hour day TV BC
Spinks AB [124] 2007 Australia 5-12 518 282 236 min week SB, SCREEN BC
Steffen LM [98] 2009 US 8-11 526 256 270 hour day TV BC
Stettler N [168] 2004 Switzerland 8 872 410 462 hour day TV, GAMES BC
Sugiyama T [47] 2007 US 12-19 15.9 4508 2295 2213 hour day SB MS
Sun Y [91] 2009 Japan 12-13 . 5753 2842 2911 hour day TV BC
Taylor WC [158] 2002 US 6-15 11.1 509 231 278 kcal day SB BC
te Velde SJ [129] 2007 International 9-14 11.4 12538 6256 6282 hour day TV, COMP BC
Thompson AM [189] 2009 Canada 3, 7, 11 1777 795 982 min day TV BC
Toschke AM [112] 2008 Germany 5-6 4884 hour day TV BC
Toschke AM [121] 2007 Germany 5-6 5472 hour day TV BC
Trang NHHD [146] 2009 Australia 11-16 2660 1332 1328 hour day SCREEN BC
Tremblay MS [172] 2003 Canada 7-11 7261 hour day TV BC
Treuth MS [27] 2009 US 11-12 11.9 1579 0 1579 hour day SB BC
Tsai H [153] 2007 Taiwan 11-12 2218 1146 1072 hour day TV BC
Tsai H [145] 2009 Taiwan 11-12 1329 615 672 hour day SB, TV BC
Tucker LA [212] 1987 US 15.7 406 406 0 hour day TV FIT, SE, PRO
Tucker LA [206] 1986 US 15.7 379 379 0 hour day TV FIT
Tucker LA [214] 1996 US 9-10 9.8 262 162 100 hour day TV FIT
Ussher MH [231] 1007 England 13-16 2623 hour day TV PRO, AA
Utter J [171] 2003 US 14.9 4480 2240 2240 hour day SCREEN BC
Utter J [152] 2007 New Zealand 5-14 1743 959 784 hour day TV, COMP BC
Vader AM [97] 2009 US 11, 7 11594 6162 5432 hour day TV BC
van Schie EG [261] 1997 Netherlands 10-14 11.5 346 171 175 hour day SCREEN PRO, AA
van Zutphen M [159] 2007 Australia 4-12 8 1926 939 987 min day TV BC
Vandewater EA [170] 2004 US 1-12 6 2831 1444 1387 hour day SB, SCREEN BC
Vaughan C [198] 2007 Australia 11-18 14 443 189 254 hour day SCREEN BC
Vicente-Rodriguez G [110] 2008 Spain 13-18.5 1960 1012 948 hour day TV, GAMES BC
Violante R [137] 2005 Mexico 6-14 8624 258 4366 hour day TV BC
Wake M [186] 2003 Australia 5-13 9.1 2862 1445 1417 hour week SCREEN BC
Walberg HJ [251] 1984 US 2-6 13 2890 1445 1445 hour day TV AA
Walberg HJ [253] 1982 US 17 2001 1031 970 hour day TV AA
Waller CE [202] 2003 China 6-11 9 880 hour week TV BC
Wang Y [120] 2007 US 11.9 498 218 280 hour day SCREEN BC
Welch WW [248] 1986 Australia 3-4 9 9 1960 TV AA
Wells JC [108] 2008 Brazil 10-12 4452 2193 2258 hour day TV BC, MS
Whitt-Glover MC [24] 2009 US 6-19 749 351 398 min day SB BC
Wiggins J [227] 1987 US 4-12 483 252 231 min day TV SE, AA
Wolf AM [203] 1998 US 11-14 552 0 552 hour day TV BC
Wong SL [100] 2009 Canada 15.5 25060 12806 12254 hour day SB, SCREEN BC
Zabinski MF [132] 2007 US 11-15 878 425 453 hour day SB BC
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SB, sedentary behaviour; TV, television viewing; COMP, computer time; GAME, video game playing; SCREEN, composite measure of 2 or more screen activities (i.e. television viewing, computer time, or video game playing); BC, body composition; MS, measures of metabolic syndrome and/or cardiovascular disease (e.g. insulin resistance, blood pressure); SE, self-esteem; PRO, pro-social behaviour; AA, academic achievement.

Table 2 provides a summary of all studies included in the review. The majority of the studies included in this systematic review were cross sectional (n = 177). In total, data from 983,840 participants were included in this review. Studies ranged from 30 participants in intervention studies and RCTs, to 62,876 participants in cross sectional observational investigations. Articles were published over a 51 year period from 1958 to 2009, and included participants ranging from 2-19 years of age. Although the scope of the review focused on those 5-17 years of age, studies that had a range below 5 years or over 17 years were not excluded as long as the mean age was between 5-17 years. Included studies involved participants from 39 countries; there were a greater number of articles reporting on female-only data than those reporting on male-only data. Translators were contracted to read non-English articles and complete any necessary data extraction for studies that met inclusion criteria (n = 8).

Of the 232 studies, 170 studies reported data on body composition, 15 on fitness, 11 on MS and CVD, 14 on self-esteem, 18 on pro-social behaviour, and 35 on academic achievement. The majority of studies (n = 223) used indirect measures to assess sedentary behaviour (i.e. parent-, teacher-, or self-report questionnaires). There were 14 studies [24,27,28,39-49] that directly measured sedentary behaviour with accelerometers and one that directly measured television viewing through a monitoring device [50]. The direction of the association between increased sedentary behaviour and health outcomes were similar between direct and indirect measures. Meta-analysis was conducted for RCTs examining change in body mass index.

Risk of bias assessment

Risk of bias assessment was completed for all included studies (Additional file 2). The mean Downs and Black score was 20.7 (range = 16-26). The studies were then split into groups and labeled as 'high quality' (score 23-26, n = 36), 'moderate quality' (score 19-22, n = 169), and 'lower quality' (score 16-18, n = 27). Quality of study did not affect the outcome of the study; in other words, both lower quality and high quality studies showed a positive relationship between increased time spent sedentary and health risk. Inter-reviewer assessment using the Downs and Black tool was very high (kappa = 0.98).

Data Synthesis

Body composition

Of the 232 studies included in this review, 170 examined body composition, with the majority of these focusing on the relationship between overweight and obesity and time spent watching TV (Table 3). Body composition was measured in a variety of ways including body mass index (BMI), sum of skin folds, percent body fat and various composite measures (e.g. BMI + sum of skin folds). Of the 8 RCTs, 7 showed that decreases in sedentary time lead to reductions in body weight (see meta-analysis below for details). Intervention studies reported desirable changes in body weight, BMI, and weight status among children and youth who successfully decreased their sedentary time [51-60]. Three intervention studies [61-63] reported that although sedentary behaviour decreased, there was no change in weight status (measured through BMI and skinfold thickness); however, these studies had relatively short follow-up periods (~1 year) and no control group leading the authors hypothesized that a longer follow up period was needed to detect a significant change in body composition. While nine-teen longitudinal studies reported that children who watched greater amounts of TV at baseline saw steeper increases in BMI, body weight and fat mass over time [64-82], nine longitudinal studies reported no significant relationship between time spent sedentary and weight status or fat mass [61-63,83-89]. Of the 119 cross sectional studies, 94 reported that increased sedentary time was associated with one or more of increased fat mass, increased BMI, increased weight status and increased risk for being overweight [28,90-182]. Risk for obesity increased in a dose response manner with increased time spent engaging in sedentary behaviours [92,106,110,128,156,178]. Twenty-five cross sectional studies reported no significant relationship between sedentary time and weight status [24,85,137,183-204]. One study [131] reported an effect in boys but not girls and one showed an effect in girls but not boys [139]. One study showed that among boys, being underweight was associated with more screen time [111]. The level of evidence reporting on the relationship between sedentary behaviour and body composition was of moderate quality and was classified as Level 2 with a mean Downs and Black score of 20.6 (standard deviation: ± 1.9).

Table 3.

Summary table of results showing relation between sedentary behaviour and measures of body composition

Type of Study Number of Studies Number of participants Narrative recommendation and main findings
RCT 8 1886 Reductions in sedentary behaviour are directly related to improved body composition.
Intervention 10 3547 TV watching and overweight/obesity were related in a dose-response manner (i.e. those who watched more TV were more likely to be overweight/obese).
Longitudinal 33 85753 TV watching and overweight/obesity were related in a dose-response manner (i.e. those who watched more TV were more likely to be overweight/obese).
Cross sectional 119 691759 > 2 hrs of sedentary behaviour related to increased risk of being overweight or obese.
Total of all studies 170 782884 Meta-analysis was performed on randomized controlled studies that looked at change in BMI. They found an effect of -0.89 kg/m2 (95% CI of -1.67 to -0.11, p = 0.03) decrease in mean BMI in the intervention group.
> 2 hrs of sedentary behaviour per day is associated with an increased risk for overweight/obesity. This risk increases in a dose-response manner.
Each additional hour of TV viewing increased risk for obesity. > 2 hrs/day significantly increased risk for overweight/obesity.
Mean Downs and Black score = 20.9 (± 1.9), Level 2 evidence.
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Fitness

Fifteen studies assessed the relationship between time spent engaging in sedentary behaviour and fitness (Table 4). Increased time spent being sedentary was associated with decreased scores for overall physical fitness, VO2 max, cardiorespiratory fitness, and musculoskeletal fitness. An intervention reported that targeting decreased sedentary behaviour lead to increases in aerobic fitness [56]. This study (n = 13 boys and 26 girls, mean age = 10.5 years) showed that an intervention to decrease targeted sedentary behaviours (watching TV, playing computer games, talking on the telephone, or playing board games) led to increases in both physical activity and non-targeted sedentary behaviours. Longitudinal evidence was conflicting. One longitudinal study showed that > 2 hours per day of TV and computer use was associated with decreased musculoskeletal fitness [205]; while the second longitudinal study found no association between increased screen time and decreased fitness. Eight of 12 cross sectional studies showed that greater than 2 hours of screen time per day was associated with decreased VO2max, lower cardiorespiratory fitness, and lower aerobic fitness [95,206-212]. Two studies showed weak relationships between television watching and fitness [197,213]. Two studies showed no consistent association between television viewing and aerobic and musculoskeletal fitness [184,214]. The level of evidence related to fitness was classified as Level 3 with a mean Downs and Black score of 20.9 (standard deviation: ± 2.1), indicating moderate quality of reporting.

Table 4.

Summary table of results showing relation between sedentary behaviour and fitness

Type of Study Number of Studies Number of participants Narrative recommendation and main findings
RCT 0
Intervention 1 76 Reductions in sedentary behaviour lead to increased fitness.
Longitudinal 2 561 One study showed no association whereas one study showed higher musculoskeletal fitness in those watching < 2 hrs of TV per day.
Cross sectional 12 17227 > 2 hrs of screen time per day is associated with better VO2max scores, better musculoskeletal and cardiorespiratory fitness scores.
Total of all studies 15 17864 Those watching less than 2 hours of TV a day showed higher results for fitness testing and more favourable bone health.
Mean Downs and Black score = 20.6 (± 2.1), Level 3 evidence.
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Metabolic syndrome and risk for cardiovascular disease

Eleven studies assessed the relationship between time spent engaging in sedentary behaviour and risk factors for MS and CVD (Table 5). All of the studies reported that increased sedentary time was associated with increased risk for MS or CVD. However, the results of these studies should be viewed with caution as the proportion of children and youth who have measurable health risk factors for MS or CVD is quite low. Longitudinal studies found that those watching more than 2 hours of television per day had higher serum cholesterol levels [88] and were more likely to have high blood pressure [215] than their peers who watched less TV. Cross sectional studies reported that high levels of screen time and self-reported sedentary behaviour were associated with increased risk for high systolic and diastolic blood pressure [47,108,216,217], higher HbA1 c [218], fasting insulin [134,216], insulin resistance [48,219], and MS [220]. These risk factors increase in a dose response manner with increased screen time [216,220]. One cross sectional study reported a significant relationship between watching TV and increased cholesterol in adolescents, but not in younger children [185]. The level of evidence for MS and CVD risk factors was classified as Level 3 with a mean Downs and Black score of 21.7 (standard deviation: ± 2.1), indicating moderate quality of reporting.

Table 5.

Summary table of results showing relation between sedentary behaviour and markers for metabolic syndrome and cardiovascular disease

Type of Study Number of Studies Number of participants Narrative recommendation and main findings
RCT 0
Longitudinal 2 1675 > 2 hr of TV per day is associated with higher serum cholesterol levels. > 1.2 hrs of TV per day is associated with increased systolic blood pressure.
Cross sectional 9 17339 > 2 of screen time per day is associated with higher blood pressure and increased risk for metabolic syndrome.
Intervention 0
Total of all studies 11 19014 Increased screen time is associated with increased risk for markers of metabolic syndrome and cardiovascular disease. Risk increases in a dose-response manner.
Mean Downs and Black score = 21.7 (± 2.0), Level 3 evidence.
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Self esteem

Fourteen studies assessed the relationship between time spent engaging in sedentary behaviour and self-esteem (Table 6). One RCT aimed to increase physical activity and decrease TV viewing [221], leading to a trend in improvements in self-esteem (P = 0.26) and concerns with body shape (p = 0.03). Intervention studies that targeted changes in sedentary behaviour produced inverse changes in physical self-worth and self-esteem [52,54]. Cross sectional studies showed that increased screen time was associated with higher depressive symptoms, low self-esteem, and decreased perceptions of self-worth [44,115,147,212,221-223]. There was evidence for a dose-response relationship as each additional hour of screen time seemed to increase the risk for lower self-esteem [147]. Two studies [224,225] reported that increased TV viewing was associated with decreased self-esteem in boys but not girls, and increased aggression in girls but not boys. Two studies showed no significant relationship [226,227]. One study [228] showed a significant relationship between increased TV viewing and decreased self-esteem in adolescents but not in young children. The level of evidence for studies examining self-esteem was classified as Level 3 with a mean Downs and Black score of 21.0 (standard deviation: ± 2.4) indicating moderate quality of reporting.

Table 6.

Summary table of results showing relation between sedentary behaviour and self-esteem

Type of Study Number of Studies Number of participants Narrative recommendation and main findings
RCT 1 61 Girls who decreased sedentary behaviour had lower body dissatisfaction and showed a trend towards improved self-esteem.
Intervention 2 984 Decreases in sedentary behaviour lead to improved self worth and self-esteem.
Longitudinal 0
Cross sectional 11 71068 Those with higher reported sedentary behaviour had poorer scores on self worth. This association seems to increase in a dose-response manner
Total of all studies 14 72113 Each additional hour of TV viewing was associated with decreases in self-worth and self-concept.
Mean Downs and Black score = 21.0 (± 2.4), Level 3 evidence.
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Pro-social behaviour

Eighteen studies assessed the relationship between time spent engaging in sedentary behaviour and pro-social behaviour (Table 7). The one longitudinal study examining the relationship between sedentary behaviour and pro-social behaviour found that sustained TV exposure (i.e. ≥ 2 hours per day) was a significant risk factor for behavioural problems [229]. Cross sectional studies reported similar findings. Those who watched less TV were more emotionally stable, sensitive, imaginative, outgoing, self-controlled, intelligent, moralistic, college bound, and less likely to be aggressive or to engage in risky behaviour [42,115,230-235]. Two studies found a significant relationship between increased computer use and behaviour problems in boys [111,236] but not girls. One study showed that increased TV viewing was associated with aggression in girls but not boys [225]. The level of evidence for studies reporting on pro-social behaviour was classified as Level 3 with a mean Downs and Black score of 19.9 (standard deviation: ± 1.3) indicating moderate quality of reporting.

Table 7.

Summary table of results showing relation between sedentary behaviour and pro-social behaviour

Type of Study Number of Studies Number of participants Narrative recommendation and main findings
RCT 0
Longitudinal 1 2707 Watching > 2 hrs of TV per day is a risk factor for social behaviour problems
Intervention 0
Cross sectional 17 91934 Individuals watching > 3 hrs of TV per day are more likely to exhibit poor social behaviours and be more aggressive. Limited evidence to suggest this relationship is stronger in boys.
Total of all studies 18 94391 > 2 hrs of TV per day is associated with poor pro-social behaviour.
Those watching less than 3 hrs of TV per day scored more positively in aspects of pro-social behaviour
Mean Downs and Black score = 19.9 (± 1.34), Level 3 evidence.
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Academic achievement

Thirty five studies assessed the relation between time spent engaging in sedentary behaviour and academic achievement (Table 8). Academic achievement was measured in a variety of ways but included measures of I.Q., school grades, grade point average (GPA), performance on standardized tests, and self-report questionnaires (e.g. students rated their own level of academic achievement). The longitudinal studies included in this review found that children who watched higher amounts of TV had greater difficulties with attention as teenagers [41], showed lower progression for reading level [237], and performed worse on cognitive tests [238] than those watching less than one hour of television per day. The majority of cross sectional studies (75%) reported that children and youth who watched higher levels of TV tended to spend less time doing homework, studying, and reading for leisure which may lead to a decrease in academic achievement [42,181,239-255]. This association increased in a dose response manner [181,244,248]. Ten of the cross sectional studies found no significant relationship [57,226,227,238,256-261]. One study [228] found that this relationship was significant in adolescents but not younger children. The evidence for academic achievement was classified as Level 3 with a mean Downs and Black score of 19.2 (standard deviation: ± 2.1) indicating moderate quality of reporting.

Table 8.

Summary table of results showing relation between sedentary behaviour and academic achievement

Type of Study Number of Studies Number of participants Narrative recommendation and main findings
RCT 0
Longitudinal 3 3530 Watching > 1 hr of TV per day is associated with attention difficulties.
Intervention 0
Cross sectional 32 157637 > 2 hrs of screen time per day resulted in lower academic achievement.
Intervention 0
Total of all studies 35 161167 > 2 hrs of screen time per day is negatively associated with academic achievement.
Dose-response relation between time spent playing video games, watching TV and using the computer (for non-academic purposes). > 3 hrs/day associated with poor school performance and lower I.Q. scores.
Mean Downs and Black score = 19.1 (± 2.1), Level 3 evidence.
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Quantitative data synthesis

Data for each of the outcomes were assessed to determine if they were sufficiently homogeneous to make meta-analysis appropriate. The only outcome for which data were consistently collected and reported and for which the characteristics of the studies were similar enough to undertake a meta-analysis was body composition. However, this was only for the RCTs; the longitudinal, cross sectional and intervention studies that examined body composition had too many inconsistencies to allow for a quantitative synthesis of results.

Change in mean BMI before and after the intervention (at the longest point of follow-up for each study) was used as the point estimate for the meta-analysis of the RCT data. Of the 8 RCTs, only 6 had data that could be used to calculate the change in BMI after the intervention [50,58,221,262-264] (the other two reported on prevalence of overweight and obesity) [57,265]. Of the remaining six studies, one [50] examined standardized estimates of BMI only and one [262] presented only median change in BMI and not a mean change. Study authors were contacted for missing information, but no additional data was made available and thus these studies were excluded from the meta-analysis. Meta-analysis of the 4 RCTs that remained revealed an overall significant effect of -0.89 kg/m2 (95% CI of -1.67 to -0.11, p = 0.03) indicating an overall decrease in mean BMI associated with the interventions (Figure 2). The Chi square test for heterogeneity was not significant but the I2 was 46% indicating that there was low to moderate heterogeneity in the data. The funnel plot showed no indication of publication bias (data not shown).

Figure 2.

Figure 2

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Meta-analysis of randomized controlled studies examining decreases in sedentary behaviour and effect on body mass index.

Meta-analyses were not undertaken for other outcomes or study designs because there was substantial heterogeneity in the units of measures and type of reporting of sedentary behaviour, as well as the specific measures of each outcome. For example, when reporting on the relation between time spent watching TV and overweight and obesity, one study may report the relation between the frequency of TV watching and skin fold thickness, whereas another may examine the relation of daily volume of TV watching and BMI. Even for studies that examined the same outcome, for instance BMI, some would report the proportion overweight or obese, while others would report mean BMI. In addition, some studies reported on data for males or females only, while others reported only overall estimates and many were missing key information about participant characteristics or study design. As a result, we were unable to determine common point estimates and associated measures of errors for many of the studies. Due to the scope of the review, it was not feasible to contact every author for individual data to re-run the analyses. Developing reporting standards for primary studies examining the relationship between sedentary behaviour and health would help to ensure that appropriate data are available for future meta-analyses.

Discussion

Based on this systematic review of 232 studies, sedentary behaviour (assessed primarily through increased TV viewing) for more than 2 hours per day was associated with unfavourable body composition, decreased fitness, lowered scores for self-esteem and pro-social behaviour and decreased academic achievement in school-aged children and youth (5-17 years). This was true for all study designs, across all countries, using both direct and indirect measurements, and regardless of participant sample size. All studies examining risk factors for MS and CVD disease reported that increased sedentary time was associated with increased health risk; however, the included studies examined a wide range of risk factors, and thus there was insufficient evidence to draw conclusions on the relationship for metabolic risk as a whole.

High heterogeneity of the included studies limited meta-analysis to RCTs examining the relationship between television viewing and BMI. This revealed a trend to support the hypothesis that decreased time spent sedentary is associated with decreases in BMI. This result should be interpreted cautiously, given that it is only based on a small number of RCTs and that only half of the RCTs included in the review were included in the meta-analysis. Nonetheless, this meta-analysis of RCTs, which are considered to be the highest quality of research evidence, coupled with the qualitative syntheses of data from the other study designs, provides consistent evidence of the inverse relationship between sedentary behaviour and health outcomes, and that reducing sedentary behaviour can improve body composition. Furthermore, this finding was consistent with the results of observational studies and previous reviews [19-21,23,25].

Studies included in this review used primarily indirect measures (i.e. parent, teacher, and self-report questionnaires) to assess time spent engaging in sedentary behaviour. Those studies that did use direct (i.e. accelerometer) measures found that children and youth are spending a large proportion of their day (up to 9 hours) being sedentary [24,27,29,39-47,49,178]. Therefore, for some children and youth, a viable approach to improving health may be to work towards a reduction of at least some of their sedentary behaviours either through smaller, micro-interventions (e.g. interrupting prolonged sedentary time), or lager macro-interventions (e.g. population-based interventions and public health initiatives). Decreasing sedentary time is important for all children and youth, but it may be may be especially important to promote gradual decreases in the most sedentary group as a stepping stone to meeting sedentary behaviour guidelines [266].

Strengths and limitations

Strengths of this review included a comprehensive search strategy, a-priori inclusion and exclusion criteria and analyses, and inclusion of non-English language articles. We included direct and indirect measures of sedentary behaviour and focused on 6 diverse health indicators in children and youth. Although efforts were made to include grey literature (e.g. by contacting key informants and reviewing government documents), we did not include conference proceedings and other types of grey literature because it was impractical and unfeasible to sift through all unpublished work, and also because of limitations in the quality of reporting in conference abstracts [267,268]. We do not anticipate that additional, unpublished work would change the results.

Our study has limitations, including the types of outcome measurements and analyses reported in the primary studies and primary study quality. The scope of this review was large and included a great deal of health indicators and measurement tools. A more detailed meta-analysis would have allowed us to estimate the overall effect sizes for each outcome. However, due to the heterogeneity of the data, it was impossible to complete such analysis. Furthermore, some studies had missing information on participant characteristics making it impossible to determine if basic demographics act as a confounder for the relationship between sedentary behaviour and health. Many studies also grouped their variables into tertiles, or groups that also took into account physical activity level. Although it was still possible to ascertain information regarding the association between level of sedentary behaviour and health indicators, it made it very difficult to compare the information across studies. Similarly, very few studies measured time spent being sedentary directly (i.e. with direct observation or accelerometry). Previous work [269,270] has shown significant differences between direct and indirect measures of physical activity; similar work needs to be completed with respect to sedentary behaviour to gain a better understanding of possible biases in previous studies. Indirect measurements of sedentary behaviour often lead to grouping for analyses. This may lead to bias in the results of the systematic review as many studies arbitrarily grouped their participants as ''high users" if they watched more than 2 hours of television per day. This could perhaps be falsely leading us to conclude that 2 hours is the critical cut-point or threshold. Further work using direct (i.e. accelerometer) measures of sedentary behaviour and screen time as continuous variables will help to clarify if a cut-point of 2 hours is in fact biased.

The final important limitation of this review was the type of primary studies that were available for analysis. Studies with small sample sizes were excluded; however we do not believe that this had a significant impact upon the strength or direction of associations observed in this review. The majority of studies (78.4%) included in this review were cross sectional, observational studies, using indirect (i.e. parent-, teacher, or self-report) measurements of sedentary behaviour. Cross sectional data make it impossible to infer causation and results should therefore be interpreted with caution. However, it should be noted that due to ethical considerations, it may be impossible to conduct a RCT on the effects of long periods of sedentary behaviours in children and youth. Due to the large and diverse sample sizes available in population-based cross sectional research, and given that this information demonstrates similar trends as those seen in RCTs and intervention studies, we believe that the evidence presented in this review provides important insights into the relationship between sedentary behaviour and health outcomes in school-aged children and youth.

Future work

The purpose of this review was to provide an evidence base to inform clinical practice sedentary behaviour guidelines for children and youth [266]. Future work is needed to translate this information into clinical practice guidelines and disseminate this information to health care providers and the general public. While this review was limited to children and youth, similar work is needed to inform sedentary guidelines for young children aged 0-5 years, adults, and older adults.

As the accessibility and popularity of multiple forms of screen-based technology increases among the pediatric population, future work needs to continue to focus on media engagement. Specifically, with increasing popularity for hand-held, portable devices, 'sedentary multitasking' is becoming increasingly common. Children and youth are able to watch television, talk on the phone, and use the computer at the same time. This is a relatively new phenomenon and we are currently unaware what, if any, are the health effects associated with this high level of 'multi-screen' time. This is also true for the effect of advancements in technology and their associated health effects. For example, 'active video gaming' (e.g., Nintendo Wii™, Microsoft Kinect™, Sony's Playstation Move™) is advertised as an effective mode of physical activity. Although it is true that some games can require sufficient energy expenditure for health benefits [271], the socio-cognitive and physiological aspects of remaining indoors for long periods are unknown. Furthermore, children and youth can learn quite quickly how to use minimal gestures (e.g., using wrist movement only) to play the game thereby substantially reducing energy expenditure.

Finally, as described above, the vast majority of the current evidence has been based on self-report questionnaires focused on TV viewing and body composition. It is now clear that these two variables are related. Future work needs to move beyond this relationship and focus on other modes of sedentarism (e.g., prolonged sitting, passive transport) and other associated health indicators. To do this, objective measures of the time, type and context of sedentary pursuits will be needed in combination with robust and standardized measures of health indicators.

Conclusions

Physical inactivity and sedentary behaviour are pervasive and persistent public health challenges to overcome. This review demonstrates that there is a need to advocate for increases in physical activity AND decreases in sedentary behaviour. It is believed that a multi-level, multi-sectoral approach is required for this to be successful [11]. Ultimately, resolving the problem of inactivity requires a sustained change in individual daily activity and sedentary patterns. From a public health perspective, a reduction in sedentary behaviour may be easier than increasing physical activity per se because there are fewer restrictions (i.e. no need to change clothing or use special equipment), and can be easily attained with minimal burden to a person's time or financial resources.

This systematic review summarizes the current evidence examining the relationship between sedentary behaviours and a series of health indicators. It was determined that increased sedentary time was associated with negative health outcomes in both boys and girls; this was true across all study designs with the majority of studies (85.8%) reporting similar relationships. The majority of current work has focused on television viewing and body composition and suggests that children and youth should watch less than 2 hours of TV per day during their discretionary time. Furthermore, children and youth should try to minimize the time they spend engaging in other sedentary pursuits throughout the day (e.g. playing video games, using the computer for non-school work or prolonged sitting). This work can be used to inform the development of evidence-based sedentary behaviour recommendations for children and youth.

List of Abbreviations

BMI: Body Mass Index; CVD: Cardiovascular disease; DXA or DEXA: Dual-energy x-ray absorptiometry; MS: Metabolic syndrome; RCT: Randomized controlled trial; TV: Television.

Competing interests

All authors received partial financial support from the Public Health Agency of Canada; no other competing interests exist.

Authors' contributions

MT was responsible for the initiation, conceptualization and design of the systematic review; oversaw the data collection and extraction, analysis, and interpretation of data and was responsible for revising the manuscript critically for important intellectual content. AL was responsible for conducting the search, data collection and extraction, the risk of bias assessment, analysis and interpretation of data, and drafting the manuscript. MEK was responsible for the design and methodology of the review and revising the manuscript critically for important intellectual content. SCG was responsible for the design and methodology of the manuscript, conducting the meta-analysis, and revising the manuscript critically for important intellectual content. RC, GG, TS and RL were responsible for data collection and extraction, risk of bias assessment, and were responsible for revising the manuscript critically for important intellectual content. JM was responsible for the generation of systematic review search terms. MS was responsible for methodology of the review. All authors have read and approved the final manuscript. MT is the guarantor of the paper.

Supplementary Material

Additional file 1

Search strategy.

Click here for file (107KB, DOC)
Additional file 2

Search strategy.

Click here for file (564KB, DOC)

Contributor Information

Mark S Tremblay, Email: [email protected].

Allana G LeBlanc, Email: [email protected].

Michelle E Kho, Email: [email protected].

Travis J Saunders, Email: [email protected].

Richard Larouche, Email: [email protected].

Rachel C Colley, Email: [email protected].

Gary Goldfield, Email: [email protected].

Sarah Connor Gorber, Email: [email protected].

Acknowledgements

The authors are grateful to Jessie McGowan and Margaret Sampson for their contributions to this project.

Michelle Kho is funded by a Fellowship Award and Bisby Prize from the Canadian Institutes of Health Research. Travis Saunders is supported by a Doctoral Research Award and Richard Larouche is supported by a Banting and Best Doctoral Award from the Canadian Institutes of Health Research. Partial funding for the completion of this review came from the Public Health Agency of Canada. The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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