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The Effectiveness of Interventions on Sustained Childhood Physical Activity: A Systematic Review and Meta-Analysis of Controlled Studies

  • Jamie Sims ,

    Affiliations The British Heart Foundation Centre on Population Approaches for Non-Communicable Disease Prevention, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom, Department of Sport Development and Management, University of Chichester, Chichester, United Kingdom

  • Peter Scarborough,

    Affiliation The British Heart Foundation Centre on Population Approaches for Non-Communicable Disease Prevention, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

  • Charlie Foster

    Affiliation The British Heart Foundation Centre on Population Approaches for Non-Communicable Disease Prevention, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

The Effectiveness of Interventions on Sustained Childhood Physical Activity: A Systematic Review and Meta-Analysis of Controlled Studies

  • Jamie Sims, 
  • Peter Scarborough, 
  • Charlie Foster



Increased physical activity (PA) has been associated with a reduction in non-communicable disease risk factors and outcomes. However, interventions to increase childhood PA typically produce small to negligible effects. Recent reviews are limited due to lack of post-intervention follow-up measurement. This review aimed to examine measured effects at least six months post-intervention.

Methods and Findings

We searched PubMed, MEDLINE, EMBASE, PsychINFO, ScienceDirect, SportDiscus and Google Scholar between 1st January 1991 and 1st November 2014 for controlled studies reporting six-month post-intervention measurement for children aged 5 to 18 years. 14 studies met inclusion criteria; 12 reported moderate-to-vigorous PA (MVPA) (n = 5790) and 10 reported total PA (TPA) (n = 4855). We calculated overall effect estimates and 95% CI’s using random effects modelling with inverse variance weighting. Mean difference was calculated for MVPA, with standardised mean difference calculated to TPA due to measurement variation. Meta-regression assessed heterogeneity by continuous level variables. Negligible mean difference in MVPA existed in favour of the intervention group, amounting to 1.47 (95% CI -1.88, 4.82) mins/day compared to controls, while no difference was recorded on TPA. Sub-group analyses revealed males (2.65 mins/day: 95% CI 2.03, 3.27) reported higher levels of MVPA than females (-0.42 mins/day: 95% CI -7.77, 6.94), community settings (2.67 mins/day: 95% CI 2.05, 3.28) were more effective than school settings (1.70 mins/day: 95% CI -4.84, 8.25), and that treatment (4.47 mins/day: 95% CI -0.81, 9.76) demonstrated greater effects than population approaches (1.03 mins/day: 95% CI -2.54, 4.60). Meta-regression revealed no significant differences by factor on pooled effects. Significant heterogeneity existed between studies and potential for small study effects was present.


Improved PA levels subsequent to intervention were not maintained six month post-intervention. A potentially useful avenue of future research is to specifically explore community treatment of high risk individuals.

Review Registration

PROSPERO CRD42014007545


The health consequences of insufficient lifespan physical activity (PA) have been widely reported [1] and strongly associated with increased all-cause mortality [2] and non-communicable diseases [3] such as cardio-vascular disease [4,5], Type II diabetes [6], depression [7], osteoporosis [8] and specific cancers [9], through elevated risk factors such as hypertension, blood glucose, poor cardio-respiratory fitness and adiposity [1012]. Increased PA is associated with promoting improved energy balance [5,13], bone density [14] and functional movement skills [15].

The inception of many of the above risks have been observed as commencing in childhood [16,17], with a lack of PA leading to impaired childhood health outcomes [18], increased risk factors and subsequent ill-health outcomes in adulthood, and a compromised attitude towards PA [16,19]. PA behaviour tracks ‘reasonably well’ across time, although stability reduces in adolescence and periods of transition [20]. In addition, evidence indicates that PA levels enter a broad decline in later childhood and adolescence [21], resulting in insufficient levels of PA during transition into adulthood [22,23].

The effectiveness of interventions to increase childhood PA has been systematically reviewed; specifically investigating preventative [24,25], treatment-based [26], school-based [27,28] and community-based studies [29] as well as comparative policy reviews [30]. The magnitude of measured effects on levels of PA following intervention has typically been small and, when taking into account consistently high levels of heterogeneity, risk of small sample bias and an over-reliance on self-report measurement, caution is essential when interpreting positive findings. In addition, reviews typically included studies reporting measurement of PA or sedentary behaviour within limited times of day (e.g. school recess, travel time or after-school period), thereby failing to account for potential substitution [31].

These shortfalls were partially addressed in a recent systematic review by Metcalf, Henley and Wilkin [32] who investigated the effectiveness of interventions on levels of childhood PA across 30 controlled studies. Meta-analyses revealed only small-to-negligible effect on levels of Moderate to Vigorous Physical Activity (MVPA) and Total Physical Activity (TPA) immediately following intervention as measured by accelerometry, highlighting the potential for self-report bias in previous reviews and the importance of drawing data from studies specifically reporting whole-day PA [32]. However, with the exception of Lai et al. [28], which focused exclusively on school-based interventions, published reviews provide little detail regarding the maintenance of effects on whole-day PA in children and therefore do not account for the potential effects of habit formation [33] and stage of change [34].


Given the shortfall in the literature, the primary objective was to conduct a systematic review to explore the effect of interventions on maintained whole-day childhood PA, including studies that measured physical activity level with either accelerometers or questionnaire. Furthermore, it was necessary to explore sustained effect sizes following a period of at least six months post-intervention.


Search Strategy

The search encompassed PubMed, MEDLINE, EMBASE, PsychINFO, ScienceDirect, SportDiscus and Google Scholar (first 1,000) for studies published between January 1991 and November 2014. Reference lists of included studies and relevant published reviews were hand searched for additional studies. Only English terms were used and only English language studies were included (see Table 1).

Study selection

Peer-reviewed studies were included if they utilised a trial design incorporating a non-PA control group, irrespective of whether randomisation was used. No restriction was applied regarding intervention duration, delivery personnel or setting. Inclusion required an intervention(s) targeting PA levels in non-clinical children or adolescents aged between 5–18 years inclusive. Studies must have utilised a measure of MVPA or TPA spanning at least two domains of physical activity obtained either by objective measurement or validated self-report measure. Finally, studies must have presented follow-up measurement data at least six months post-intervention for the same participants measured at baseline and included at least 50% follow-up measurement rate from baseline.

The lead researcher (JS) examined the titles of all studies identified from the initial database results and excluded all publications that were unambiguously irrelevant and duplications. Abstracts were then examined by the lead researcher (JS) and allocated to ‘relevant,’ ‘irrelevant’ and ‘undecided’ groups, with all undecided studies discussed with a second researcher (PS) and resolved through discussion. Full text articles were then accessed and reviewed by the lead researcher (JS), with the second researcher (PS) cross-checking all included studies and the third researcher cross-checking a 10% sample of excluded studies (CF).

Data extraction and standardisation

We extracted author(s), project title, nation, design, inclusion criteria, randomisation procedure where applicable, intervention and control descriptions, length of follow-up, losses to follow-up and/or drop out, measurement strategy, secondary outcome measures and results. Self-report or objective measurement was recorded, with the specific questionnaire or accelerometer and the length of the measurement period. Participant characteristics were extracted on relative gender percentages, baseline age, baseline BMI or zBMI scores as well as baseline TPA and MVPA levels. Extracted data were entered into an Excel spreadsheet [35] for the purposes of recording and standardisation. Measurement strategy and measurement tools, along with target outcome and quality of reporting, varied considerably between studies necessitating a number of assumptions and transformations.

TPA was measured using either an accelerometer [3641] or questionnaire [4244]. MVPA was also measured using accelerometer [3639,45,46] or questionnaire [43,44,4750]. To permit meta-analysis on mean differences [51], MVPA effects were transformed into minutes per day. Where Moderate PA and Vigorous PA were presented separately [44] they were combined [52]. Where only Moderate or Vigorous PA was reported [48] this was taken to be sufficiently conceptually similar to MVPA and entered into the meta-analyses as an equivalent main effect. Where MVPA was presented as a percentage of TPA [37,40] the means and standard deviations were multiplied out to provide minutes per day. If effects were given as amount of change [47], this change was added to baseline figures to arrive at a follow-up effect. Where TPA was presented on a log scale [36], means and standard deviations were transformed using standard procedures [52]. Where geometric means were reported [48], it was assumed that these corresponded to the arithmetic means. Where inter-quartile range was reported as the indication of dispersion [48], the quartile points were plotted on an assumed normal distribution and the corresponding standard deviations were entered into the analysis. Where data were presented for separate experimental groups, primarily by gender but also for staggered intervention cohorts, the numbers, means and standard deviations were combined for entry into the meta-analyses [52]. Where specific data was missing from a paper two attempts were made to contact the correspondence author by email.

Statistical analysis

The group sizes, means and standard deviations were entered into Stata 13 [53], with MVPA and TPA analysed as separate outcomes. The effect sizes of all outcome-relevant studies were combined to provide the overall effect for both MVPA and TPA. The planned outputs were overall effect estimates and 95% confidence intervals using random effects modelling with inverse variance weighting. Random effects was chosen a priori as a moderate to high degree of heterogeneity was anticipated between studies [54]. Initial analysis of the papers revealed TPA to have been measured and reported using varied instruments, therefore the effect calculation for TPA used standardised mean difference, while mean difference was calculated for MVPA given the relative suitability of reported measurements to be standardised into mins/day.

Subgroup analyses

A priori subgroup analyses were planned for: participant characteristics (gender, age and cohort size); intervention characteristics (prevention vs. treatment, PA included vs. PA not included, intervention duration and school vs. community setting), and outcome characteristics (objective vs. subjective measurement and post-intervention follow-up delay).


Literature search

The searches were conducted and completed in February 2014. The initial search of databases resulted in 15,696 identified studies, with 13 additional studies identified from relevant systematic reviews. Removal of duplicates and analysis of titles then allowed unambiguously ineligible studies to be excluded, leaving a sub-total of 1,493. Scrutiny of abstracts of the remaining studies revealed 138 potentially relevant studies. Full text articles were then reviewed, producing a total of 18 preliminarily studies. Four further studies were excluded at the data extraction stage, leaving 14 studies for the final systematic review. A PRISMA flow-chart [55] of the study selection process is provided in Fig 1.

Fig 1. PRISMA Flow Chart Summarising the Study Selection Process.

Study characteristics

A study-by-study description of the individual characteristics of included studies is provided in Table 2. Seven of the fourteen studies were conducted in the USA [36,38,39,43,4749], two in Australia [37,45], one in China [41] with one in Hong Kong [44], and one each in Denmark [46], Israel [42] and Portugal [50]. All but one of the studies, therefore, were conducted in high-income nations according to the World Bank economic classifications, with one conducted in the upper middle-income bracket [41]. Overall, five Cluster Randomised Controlled Trials [39,40,44,47,49,50], three Randomised Controlled Trials [36,41,48], two Randomised Prospective Studies [38,42], one Cluster Randomised Prospective Study [43], one Nested Randomised Controlled Trial [37] and one Controlled Longitudinal Trial [46].

Participant characteristics.

The number of participants ranged between the smallest study of 41 [42] and the largest of 3,714 [43]. The median sample size was 255, with a total number of 7883 participants. Overall, 51.27% of the participants were female (range: 0% to 100%, median 49.5%) with one study recruiting only females [40] and one using only males [39]. Only two studies reported gender-specific results [46,48]. Mean baseline age was 10.67 (± 1.91), with eight studies targeting participants of UK primary education age [37,38,4144,46,47] and six studies targeting UK secondary education age participants [36,39,40,4850]. Three studies were treatment orientated [37,42,47], recruiting specifically overweight or obese participants, with the remainder being promotional or preventative.

Intervention characteristics.

Studies provided extra physical education classes in curriculum time [43,46,50], PA delivery outside curriculum time [39,42], counselling [37], goal-setting sessions [50], incentive-based interventions [38] and peer-modelling [36,49] either singularly or in combination. Five studies [36,40,41,49,50] reported explicitly grounding an intervention strategy in Social Cognitive Theory [56]. Seven studies [40,43,44,46,47,49,50] involved a school setting within the intervention delivery, and seven in a community setting; three in a standard community approach [36,38,41,48] two in a primary care setting [37,42], one as part of a scout group [39]. Intervention duration lasted between six weeks [44] and three years [43,46], with a median of three months.

Control characteristics.

None of the control groups included a PA component, excepting those comparisons which were made between additional PA and ‘normal practice’ in which case the participants completed standard physical education classes within curriculum time. Differences in characteristics between baseline and intervention groups were reported in all cases, with no comparisons deemed to be at high risk of bias. Studies using a cluster-design reported methods to ensure groups were comparable at baseline.

Outcome characteristics.

Twelve studies reported MVPA [3640,43,44,4650] and ten studies [3644,46] reported TPA, seven using objective [3641,46] and seven using self-report measures [4244,4750]. The self-report measures included standardised questionnaires, 24-hour recall and participation tick-sheets. Of the fourteen studies eight reported both TPA and MVPA [3640,43,44,46]. Follow-up measurement ranged from six months [39,41,44,49] to four years [46], with a median delay of nine months post-intervention. Loss to follow up ranged from 0% [38,39] to 48% [48] with the median loss being 21%.

Study quality

Quality was assessed using the Methodology Checklist for Randomised Controlled Trials [57]. Overall there was a high number of ‘uncertain’ verdicts against the papers, potentially indicating the reporting of relevant information within the published articles was more pertinent than the actual methodological quality of the studies (Fig 2). Participants lost to follow-up ranged from 0% to 50%, with studies reporting analyses of attrition characteristics. Eight of the nine studies utilising cluster-randomised design reported appropriate statistical techniques by which to account for clustering within the aggregate outcomes. A visual inspection of funnel plots for both outcomes suggested the possibility of small-study effect (Fig 3).

Fig 2. Overall Assessed Risk of Bias within the 14 Included Studies.

Fig 3. Funnel Plots Showing the Observed Effects for the 12 studies reporting MVPA (left) and the 10 studies reporting TPA (right).

Overall effect estimates

The collated results from twelve included studies showed weak evidence for a small increase in MVPA in favour of the intervention group with a mean difference of 1.47 minutes per day (95% CI -1.88, 4.82; p = 0.39) (Fig 4). For the ten studies reporting TPA the analysis showed no difference between the pooled effects of the intervention and those for the control group, with a standardised mean difference of -0.13 (95% CI -0.74, 0.48; p = 0.67) (Fig 5). Of most successful studies Araujo-Soares et al. [50] reported a mean difference 59 mins/day (95% CI 21.44, 96.56; p = 0.002) of additional MVPA and Nemet et al. [42] reported a standardised mean difference of 0.82 (95% CI 0.18, 1.47, p = 0.01). I2 values of 98% for MVPA (p < 0.001) and TPA (p < 0.001) revealed high levels of statistical heterogeneity between studies within both outcomes and a consequential requirement for caution with interpreting the results.

Fig 4. Forest Plot of Mean Difference in Change in MVPA between Intervention (n = 3212) and Control (n = 2578).

Groups across the 12 included studies reporting MVPA data.

Fig 5. Forest Plot Showing the Standardised Mean Difference in Change in TPA between Intervention (n = 2749) and Control (n = 2106).

Groups across the 10 included studies reporting TPA data.

Subgroup analysis

There were no significant differences in outcomes across the majority of study level characteristics, summarised in Table 3. Individual meta-regressions of MVPA and TPA by continuous level covariates confirmed the lack of statistical significance. Exceptions included male participants showing a mean difference (p < 0.001) in MVPA between intervention and control groups at post-intervention follow-up measurement, approximately equivalent to 2.65 mins/day and community-based interventions showed an effect (p < 0.01). However Jago et al. [39], due to a significant effect and tight confidence intervals, accounted for the majority of the weighting within the pooled effects on these sub-groups; removal of this paper from the sub-group analysis produced non-significant results. The relative success of community-based interventions may be due in part to small study effect (Fig 3), in which systematic bias is introduced into meta-analyses due to publications bias against studies with small cohorts with non-significant effects [52]. Lastly, the treatment-based subgroup [37,47] that approached significance (p < 0.10) for MVPA, and also the Nemet et al. [42] study, were all conducted in community settings, potentially indicating that treatment and community approaches may cluster to promote sustained PA.

Table 3. Summary of Effects and p Values for MVPA and TPA Outcomes by Sub-Group across 14 Studies.


There was a statistically non-significant (p = 0.39) mean difference in favour of intervention, approximating to a mean improvement of 1.47 minutes per day of MPVA compared to controls, although this figure is well below the sensitivity threshold of the utilised measurement tools. This result falls well short of the recommended improvements of PA for children [1] and is unlikely to be clinically significant even if maintained over time. There was no statistically significant (p = 0.87) difference in standardised mean difference of TPA. In the case of Cui et al. [49], the control group was assessed at six months post-baseline, rather than post-intervention, although one-study removed sensitivity analyses revealed no meaningful change to overall or sub-group effects. A similar analysis for Hovell et al. [48] was conducted given this papers reporting of geometric, rather than arithmetic, means with no differences found on the sub-group effects.

In PA studies it is typically not possible to blind participants or instructors to allocation, opening a potential source of bias into the delivery [58]. In addition, the measurement was often conducted by researchers not blinded to allocation [59], although sub-group analysis revealed no difference between self-report and objective measures for MVPA or TPA. Levels of heterogeneity apparent between studies that used self-report was consistently high across both outcomes (MVPA I2 = 98%; TPA I2 = 97%), potentially compromising the sensitivity of this measurement strategy to reliably demarcate significant from non-significant results in small studies.

Negligible effect on the main outcomes was consistent with Metcalf et al. [32], who conducted a meta-analysis on 30 studies measured by accelerometer immediately post-intervention, with Dobbins et al. [27], who reviewed 44 studies specifically regarding school-based interventions, and with Kamath et al. [24], who reviewed 18 studies on PA levels following interventions within a wider review into prevention of childhood obesity. Also concordant with Metcalf et al. [32], findings indicated that intervention duration was not associated with increased PA levels at follow-up, with an emergent trend that favoured studies implemented in a community setting, those that used a treatment approach and those with smaller cohort sizes, potentially implicating a cluster of factors associated with greater intervention success. However, it was not possible to distinguish between specific factors or rule out small study bias. No evidence for harmful effects of intervention on PA was indicated.

The strengths of the current review lay in the specificity and uniqueness of the inclusion criteria regarding methodological approach, requiring follow-up measurement to have occurred at least six months post-intervention, presenting a meaningful analysis to the literature. Limitations included the relatively small number of included studies which left subgroups underpowered within the analyses. In addition, the use of exclusively English language publications introduced a potential for English bias. While the use of a single researcher to conduct the primary identification and extraction procedure may be seen to constitute a weakness the specificity of the inclusion criteria, particularly the clear requirement for a six-month post-intervention follow-up measurement, reduced the likelihood of selection error.

This review reinforced previous evidence that PA interventions have little measured effect on TPA or MVPA levels in children, either immediately post-intervention or at six-month follow-up. The possibility remains that the included studies, plus PA interventions in general, were ineffective due to insufficiencies in intensity, duration, delivery quality, theoretical grounding and implementation or measurement sensitivity. Although the benefits of PA in childhood are intuitive, evidence has yet to support this viewpoint and resources may be better invested in alternative approaches to achieve positive effects. In terms of recommendations for future research, we suggest the inclusion of a rigorously implemented and reported follow-up measurement stage is incorporated into the method, as further publication of pre-post studies will not meaningfully add to the existing literature.

At the time of writing no publication had specifically investigated the maintenance of PA levels at follow-up; this represented an important gap in knowledge addressed by the current review. Sub-group analysis revealed a potential area of promise with the utilisation of PA intervention to treat of high risk children and warrants further investigation. The challenge remains to ensure that high methodological quality, particularly regarding measurement tools, is adhered to in future studies in order to build a meaningful evidence base.

Supporting Information

S1 File. Extraction Data from Fourteen Included Studies.



The authors would like to thank Nia Roberts for initial advice regarding search strategy and Dr David Lubans for the provision of additional data.

Author Contributions

Conceived and designed the experiments: JS PS CF. Performed the experiments: JS. Analyzed the data: JS PS CF. Contributed reagents/materials/analysis tools: JS PS CF. Wrote the paper: JS PS CF.


  1. 1. World Health Organisation (2010) Global recommendations on physical activity for health. Geneva, Switzerland.
  2. 2. Löllgen H, Böckenhoff A, Knapp G (2009) Physical activity and all-cause mortality: an updated meta-analysis with different intensity categories. International journal of sports medicine 30: 213–224. pmid:19199202
  3. 3. Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, et al. (2012) Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. The Lancet 380: 219–229.
  4. 4. Sattelmair J, Pertman J, Ding EL, Kohl HW, Haskell W, Lee I-M, et al. (2011) Dose response between physical activity and risk of coronary heart disease a meta-analysis. Circulation 124: 789–795. pmid:21810663
  5. 5. Chief Medical Officer C (2004) At least five a week: Evidence on the impact of physical activity and its relationship to health. London.
  6. 6. Jeon CY, Lokken RP, Hu FB, Van Dam RM (2007) Physical Activity of Moderate Intensity and Risk of Type 2 Diabetes A systematic review. Diabetes Care 30: 744–752. pmid:17327354
  7. 7. Mammen G, Faulkner G (2013) Physical activity and the prevention of depression: a systematic review of prospective studies. American journal of preventive medicine 45: 649–657. pmid:24139780
  8. 8. French SA, Fulkerson JA, Story M (2000) Increasing weight-bearing physical activity and calcium intake for bone mass growth in children and adolescents: a review of intervention trials. Preventive Medicine 31: 722–731. pmid:11133340
  9. 9. Wolin K, Yan Y, Colditz G, Lee I (2009) Physical activity and colon cancer prevention: a meta-analysis. British journal of cancer 100: 611–616. pmid:19209175
  10. 10. Lee D-c, Artero EG, Sui X, Blair SN (2010) Review: Mortality trends in the general population: the importance of cardiorespiratory fitness. Journal of Psychopharmacology 24: 27–35.
  11. 11. Chaput J, Leduc G, Boyer C, Bélanger P, LeBlanc A, Borghese MM, et al. (2014) Objectively measured physical activity, sedentary time and sleep duration: independent and combined associations with adiposity in canadian children. Nutrition & Diabetes 4: e117.
  12. 12. Steinbeck KS (2001) The importance of physical activity in the prevention of overweight and obesity in childhood: a review and an opinion. Obesity reviews 2: 117–130. pmid:12119663
  13. 13. Westerterp KR (2013) Physical activity and physical activity induced energy expenditure in humans: measurement, determinants, and effects. Frontiers in physiology 4.
  14. 14. Bailey DA, Martin AD, McKay HA, Whiting S, Mirwald R (2000) Calcium accretion in girls and boys during puberty: a longitudinal analysis. Journal of Bone and Mineral Research 15: 2245–2250. pmid:11092406
  15. 15. Duncan MJ, Stanley M, Wright SL (2013) The association between functional movement and overweight and obesity in British primary school children. BMC sports science, medicine and rehabilitation 5: 11. pmid:23675746
  16. 16. Biddle SJ, Gorely T, Stensel DJ (2004) Health-enhancing physical activity and sedentary behaviour in children and adolescents. Journal of sports sciences 22: 679–701. pmid:15370482
  17. 17. Boreham C, Riddoch C (2001) The physical activity, fitness and health of children. Journal of sports sciences 19: 915–929. pmid:11820686
  18. 18. Ekelund U, Luan Ja, Sherar LB, Esliger DW, Griew P, Cooper A, et al. (2012) Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents. Jama 307: 704–712. pmid:22337681
  19. 19. Sallis JF, Prochaska JJ, Taylor WC (2000) A review of correlates of physical activity of children and adolescents. Medicine and science in sports and exercise 32: 963–975. pmid:10795788
  20. 20. Telama R (2009) Tracking of physical activity from childhood to adulthood: a review. Obesity facts 2: 187–195. pmid:20054224
  21. 21. Dumith SC, Gigante DP, Domingues MR, Kohl HW (2011) Physical activity change during adolescence: a systematic review and a pooled analysis. International Journal of Epidemiology 40: 685–698. pmid:21245072
  22. 22. Gordon-Larsen P, Nelson MC, Popkin BM (2004) Longitudinal physical activity and sedentary behavior trends: adolescence to adulthood. American journal of preventive medicine 27: 277–283. pmid:15488356
  23. 23. Corder K, Sharp SJ, Atkin AJ, Griffin SJ, Jones AP, Ekelund U, et al. (2013) Change in objectively measured physical activity during the transition to adolescence. British journal of sports medicine: bjsports-2013-093190.
  24. 24. Kamath CC, Vickers KS, Ehrlich A, McGovern L, Johnson J, Singhal V, et al. (2008) Behavioral interventions to prevent childhood obesity: a systematic review and metaanalyses of randomized trials. The Journal of Clinical Endocrinology & Metabolism 93: 4606–4615.
  25. 25. Waters E, de Silva-Sanigorski A, Hall BJ, Brown T, Campbell KJ, Gao Y, et al. (2011) Interventions for preventing obesity in children. Cochrane Database Syst Rev 12: 00.
  26. 26. Oude Luttikhuis H, Baur L, Jansen H, Shrewsbury VA, O’Malley C, Stolk RP, et al. (2009) Interventions for treating obesity in children. Cochrane Database Syst Rev 1.
  27. 27. Dobbins M, Husson H, DeCorby K, LaRocca RL (2013) School-based physical activity programs for promoting physical activity and fitness in children and adolescents aged 6 to 18. Cochrane Database Syst Rev 2.
  28. 28. Lai SK, Costigan SA, Morgan PJ, Lubans DR, Stodden DF, Salmon J, et al. (2014) Do School-Based Interventions Focusing on Physical Activity, Fitness, or Fundamental Movement Skill Competency Produce a Sustained Impact in These Outcomes in Children and Adolescents? A Systematic Review of Follow-Up Studies. Sports Medicine 44: 67–79. pmid:24122775
  29. 29. van Sluijs EMF, McMinn AM, Griffin SJ (2008) Effectiveness of interventions to promote physical activity in children and adolescents: systemic review of controlled trials. British Journal of Sports Medicine 42: 653–657. pmid:18685076
  30. 30. NICE (2010) Promoting physical activity, active play and sport for pre-school and school-age children and young people in family, pre-school, school and community settings. National Institute for Health and Clinical Excellence.
  31. 31. Ridgers ND, Timperio A, Cerin E, Salmon J (2014) Compensation of Physical Activity and Sedentary Time in Primary School Children. Medicine and science in sports and exercise.
  32. 32. Metcalf B, Henley W, Wilkin T (2012) Effectiveness of intervention on physical activity of children: Systematic review and meta-analysis of controlled trials with objectively measured outcomes (EarlyBird 54). British Medical Journal 345: 1–11.
  33. 33. Lally P, Van Jaarsveld CH, Potts HW, Wardle J (2010) How are habits formed: Modelling habit formation in the real world. European Journal of Social Psychology 40: 998–1009.
  34. 34. Marshall SJ, Biddle SJ (2001) The transtheoretical model of behavior change: a meta-analysis of applications to physical activity and exercise. Annals of behavioral medicine 23: 229–246. pmid:11761340
  35. 35. Microsoft (2013) Microsoft Excel. Redmond, Washington: The Microsoft Corporation.
  36. 36. Black MM, Hager ER, Le K, Anliker J, Arteaga SS, DiClemente C, et al. (2010) Challenge! Health promotion/obesity prevention mentorship model among urban, black adolescents. Pediatrics 126: 280–288. pmid:20660556
  37. 37. Wake M, Baur LA, Gerner B, Gibbons K, Gold L, Gunn J, et al. (2009) Outcomes and costs of primary care surveillance and intervention for overweight or obese children: The LEAP 2 randomised controlled trial. Pediatrics 339: No-Specified.
  38. 38. Roemmich JN, Lobarinas CL, Barkley JE, White TM, Paluch R, Epstein LH (2012) Use of an Open-Loop System to Increase Physical Activity. Pediatric Exercise Science 24: 384–398. pmid:22971555
  39. 39. Jago R, Baranowski T, Baranowski JC, Thompson D, Cullen KW, Watson K, et al. (2006) Fit for Life Boy Scout badge: Outcome evaluation of a troop and Internet intervention. Preventative Medicine 42: 181–187.
  40. 40. Dewar DL, Morgan PJ, Plotnikoff RC, Okely AD, Collins CE, Batterham M, et al. (2013) The nutrition and enjoyable activity for teen girls study: A cluster randomized controlled trial. American Journal of Preventative Medicine 45: 313–317.
  41. 41. Chen J-L, Weiss S, Heyman MB, Lustig RH (2010) Efficacy of a child-centred and family-based program in promoting healthy weight and healthy behaviors in Chinese American children: a randomized controlled study. Journal of Public Health 32: 219–229. pmid:19933120
  42. 42. Nemet D, Barkan S, Epstein Y, Friedland O, Kowen G, Eliakim A (2005) Short- and long-term beneficial effects of a combined dietary-behavioral-physical activity intervention for the treatment of childhood obesity. Pediatrics 115: e443–449. pmid:15805347
  43. 43. Nader PR, Stone EJ, Lytle LA, Perry CL, Osganian SK, Kelder S, et al. (1999) Three-year maintenance of improved diet and physical activity: the CATCH cohort. Child and Adolescent Trial for Cardiovascular Health. Arch Pediatr Adolesc Med 153: 695–704. pmid:10401802
  44. 44. McManus AM, Masters RS, Laukkanen RM, Yu CC, Sit CH, Ling F (2008) Using heart-rate feedback to increase physical activity in children. Preventive medicine 47: 402–408. pmid:18590757
  45. 45. Dewar DL, Morgan PJ, Plotnikoff RC, Okely AD, Batterham M, Lubans DR (2014) Exploring changes in physical activity, sedentary behaviors and hypothesized mediators in the NEAT girls group randomized controlled trial. Journal of Science and Medicine in Sport.
  46. 46. Bugge A, El-Naaman B, Dencker M, Froberg K, Holme IMK,McMurray RG, et al. (2012) Effects of a Three-Year Intervention: The Copenhagen School Child Intervention Study. Medicine & Science in Sports & Exercise 44: 1310–1317.
  47. 47. Wright K, Giger JN, Norris K, Suro Z (2013) Impact of a nurse-directed, coordinated school health program to enhance physical activity behaviors and reduce body mass index among minority children: A parallel-group, randomized control trial. Child Health Care 50: 727–737.
  48. 48. Hovell MF, Nichols JF, Irvin VL, Schmitz KE, Rock CL, Hofstetter CR, et al. (2009) Parent-child training to increase preteens' calcium, physical activity, and bone density: A controlled trial. American Journal of Health Promotion 24: 118–128. pmid:19928484
  49. 49. Cui Z, Shah S, Yan L, Pan Y, Gao A, Shi X, et al. (2012) Effect of a school-based peer education intervention on physical activity and sedentary behaviour in Chinese adolescents: a pilot study. BMJ Open 2.
  50. 50. Araujo-Soares V, McIntyre T, MacLennan G, Sniehotta FF (2009) Development and exploratory cluster-randomised opportunistic trial of a theory-based intervention to enhance physical activity among adolescents. Psychology & Health 24: 805–822.
  51. 51. Borenstein M, Hedges LV, Higgins JP, Rothstein HR (2011) Introduction to meta-analysis: John Wiley & Sons.
  52. 52. Higgins J, Green S, editors (2009) Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.2 [updated September 2009]: The Cochrane Collaboration.
  53. 53. StataCorp (2013) Stata Statistical Software: Release 13. College Station, TX: StataCorp LP.
  54. 54. Higgins J, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. Bmj 327: 557–560. pmid:12958120
  55. 55. Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine 151: 264–269. pmid:19622511
  56. 56. Bandura A (2001) Social cognitive theory: An agentic perspective. Annual review of psychology 52: 1–26. pmid:11148297
  57. 57. NICE (2012) Process and Methods Guide—Appendix C: Methodology Checklist: Randomised Controlled Trials. London.
  58. 58. Foster C, Hillsdon M, Thorogood M, Kaur A, Wedatilake T (2005) Interventions for promoting physical activity. Cochrane database of systematic Reviews 1.
  59. 59. Wood L, Egger M, Gluud LL, Schulz KF, Jüni P, Altman DG, et al. (2008) Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study. Bmj 336: 601–605. pmid:18316340
  60. 60. Shah S, van der Sluijs CP, Lagleva M, Pesle A, Lim K-S, Bittar H, et al. (2011) A partnership for health: working with schools to promote healthy lifestyle. Australian family physician 40: 1011. pmid:22146334
  61. 61. Liu A, Ma G, Zhang Q, Ma W (2003) [Reliability and validity of a 7-day physical activity questionnaire for elementary students]. Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi 24: 901–904. pmid:14575604
  62. 62. Godin G, Shephard RJ (1985) A simple method to assess exercise behavior in the community. Canadian journal of applied sport sciences Journal canadien des sciences appliquees au sport 10: 141–146. pmid:4053261
  63. 63. Monzavi R, Dreimane D, Geffner ME, Braun S, Conrad B, Klier M, et al. (2006) Improvement in risk factors for metabolic syndrome and insulin resistance in overweight youth who are treated with lifestyle intervention. Pediatrics 117: e1111–e1118. pmid:16682491