Open Access

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

  • Mark S Tremblay1Email author,
  • Allana G LeBlanc1,
  • Michelle E Kho2,
  • Travis J Saunders1,
  • Richard Larouche1,
  • Rachel C Colley1,
  • Gary Goldfield1 and
  • Sarah Connor Gorber3
International Journal of Behavioral Nutrition and Physical Activity20118:98

DOI: 10.1186/1479-5868-8-98

Received: 8 April 2011

Accepted: 21 September 2011

Published: 21 September 2011

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 [16]. However, across all age groups, levels of physical activity remain low [712] 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, 1928]. 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 [2228]. 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 [2931]. 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 [2325]. 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. 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. 2.

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

     
  3. 3.

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

     
  4. 4.

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

     
  5. 5.

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

     
  6. 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

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.
https://static-content.springer.com/image/art%3A10.1186%2F1479-5868-8-98/MediaObjects/12966_2011_Article_492_Fig1_HTML.jpg
Figure 1

Flow of information through the different phases of the review.

Table 2

Summary of characteristics of included studies

      

nanalyzed

    

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

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, 3949] 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 [5160]. Three intervention studies [6163] 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 [6482], nine longitudinal studies reported no significant relationship between time spent sedentary and weight status or fat mass [6163, 8389]. 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, 90182]. 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, 183204]. 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.

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, 206212]. 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.

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.

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, 221223]. 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.

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, 230235]. 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.

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, 239255]. 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, 256261]. 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.

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, 262264] (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).
https://static-content.springer.com/image/art%3A10.1186%2F1479-5868-8-98/MediaObjects/12966_2011_Article_492_Fig2_HTML.jpg
Figure 2

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 [1921, 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, 3947, 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.

Declarations

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.

Authors’ Affiliations

(1)
Healthy Active Living and Obesity Research, Children's Hospital of Eastern Ontario Research Institute
(2)
Department of Physical Medicine and Rehabilitation, Johns Hopkins University
(3)
Office of the Task Force on Preventive Health Care, Public Health Agency of Canada

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