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Multilevel analysis of the Be Active Eat Well intervention: environmental and behavioural influences on reductions in child obesity risk

International Journal of Obesity volume 36, pages 901–907 (2012)Cite this article

Subjects

Abstract

Background:

The Be Active Eat Well (BAEW) community-based child obesity prevention intervention was successful in modestly reducing unhealthy weight gain in primary school children using a multi-strategy and multi-setting approach.

Objective:

To (1) examine the relationship between changes in obesity-related individual, household and school factors and changes in standardised child body mass index (zBMI), and (2) determine if the BAEW intervention moderated these effects.

Methods:

The longitudinal relationships between changes in individual, household and school variables and changes in zBMI were explored using multilevel modelling, with measurement time (baseline and follow-up) at level 1, individual (behaviours, n=1812) at level 2 and households (n=1318) and schools (n=18) as higher levels (environments). The effect of the intervention was tested while controlling for child age, gender and maternal education level.

Results:

This study confirmed that the BAEW intervention lowered child zBMI compared with the comparison group (−0.085 units, P=0.03). The variation between household environments was found to be a large contributor to the percentage of unexplained change in child zBMI (59%), compared with contributions from the individual (23%) and school levels (1%). Across both groups, screen time (P=0.03), sweet drink consumption (P=0.03) and lack of household rules for television (TV) viewing (P=0.05) were associated with increased zBMI, whereas there was a non-significant association with the frequency the TV was on during evening meals (P=0.07). The moderating effect of the intervention was only evident for the relationship between the frequency of TV on during meals and zBMI, however, this effect was modest (P=0.04).

Conclusions:

The development of childhood obesity involves multi-factorial and multi-level influences, some of which are amenable to change. Obesity prevention strategies should not only target individual behaviours but also the household environment and family practices. Although zBMI changes were modest, these findings are encouraging as small reductions can have population level impacts on childhood obesity levels.

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References

  1. Flynn MAT, McNeil DA, Maloff B, Mutasingwa D, Wu M, Ford C et al. Reducing obesity and related chronic disease risk in children and youth: A synthesis of evidence with ‘best practice’ recommendations. Obes Rev 2006; 7: 7–66.

    Article  Google Scholar 

  2. Bronfenbrenner U . Ecology of the family as a context for human development: research perspectives. Dev Psychol 1986; 22: 723–742.

    Article  Google Scholar 

  3. Nutbeam D, Harris E . Theory in a Nutshell: A Practical Guide to Health Promotion Theories. McGraw-Hill: Sydney, Australia, 2004.

    Google Scholar 

  4. World Health Organization. Ottawa Charter for Health Promotion, WHO: Geneva, 1986.

  5. Lobstein T, Baur L, Uauy R . Obesity in children and young people: a crisis in public health. Obes Rev 2004; 5: 4–85.

    Article  Google Scholar 

  6. Procter KL . The aetiology of childhood obesity: a review. Nutr Res Rev 2007; 20: 29–45.

    Article  PubMed  Google Scholar 

  7. Vartanian LR, Schwartz MB, Brownell KD . Effects of soft drink consumption on nutrition and health: a systematic review and meta analysis. Am J Public Health 2007; 97: 667–675.

    Article  PubMed  Google Scholar 

  8. Sanigorski AM, Bell AC, Swinburn BA . Association of key foods and beverages with obesity in Australian school children. Public Health Nutr 2007; 10: 152–157.

    Article  Google Scholar 

  9. Davison KK, Birch LL . Childhood overweight: a contextual model and recommendations for future research. Obes Rev 2001; 2: 159–171.

    Article  CAS  PubMed  Google Scholar 

  10. Davison KK, Birch LL . Obesigenic families: parents’ physical activity and dietary intake patterns predict girls’ risk of overweight. Int J Obes 2002; 26: 1186–1193.

    Article  Google Scholar 

  11. Campbell K, Crawford D, Ball K . Family food environment and dietary behaviors likely to promote fatness in 5–6 year-old children. Int J Obes 2006; 30: 1272–1280.

    Article  CAS  Google Scholar 

  12. Gable S, Lutz S . Household, parent, and child contributions to childhood obesity. Fam Relations 2000; 49: 293–300.

    Article  Google Scholar 

  13. Gibson LY, Byrne SM, Davis EA, Blair E, Jacoby P, Zubrick SR . The role of family and maternal factors in childhood obesity. Med J Australia 2007; 186: 591–595.

    PubMed  Google Scholar 

  14. Wang Y . Cross-national comparison of childhood obesity: the epidemic and the relationship between obesity and socioeconomic status. Int J Epidemiol 2001; 30: 1129–1136.

    Article  CAS  PubMed  Google Scholar 

  15. Golan M, Crow S . Targeting parents exclusively in the treatment of childhood obesity: long-term results. Obes Res 2004; 12: 357–361.

    Article  PubMed  Google Scholar 

  16. de Silva-Sanigorski AM, Economos C . Evidence of multi-setting approaches for obesity prevention: translation to best practice. In: Waters E, Seidell J, Swinburn BA (eds). Preventing Childhood Obesity: Evidence, Policy and Practice. Blackwell Publishing Ltd: Oxford, 2010, pp 57–63.

    Chapter  Google Scholar 

  17. Economos CD, Hyatt RR, Goldberg JP, Must A, Naumova EN, Collins JJ et al. A community intervention reduces BMI z-score in children: shape up Somerville first year results. Obesity 2007; 15: 1325–1336.

    Article  Google Scholar 

  18. Taylor RW, McAuley KA, Barbezat W, Strong A, Williams SM, Mann JI . APPLE Project: 2-y findings of a community-based obesity prevention program in pimary school age children. Am J Clin Nutr 2007; 86: 735–742.

    Article  CAS  Google Scholar 

  19. Sanigorski AM, Bell AC, Kremer PJ, Cuttler R, Swinburn BA . Reducing unhealthy weight gain in children through community capacity-building: results of a quasi-experimental intervention program, Be Active Eat Well. Int J Obes 2008; 32: 1060–1067.

    Article  CAS  Google Scholar 

  20. de Silva-Sanigorski AM, Bell AC, Kremer P, Nichols M, Crellin M, Smith M et al. Reducing obesity in early childhood: results from Romp & Chomp, an Australian community-wide intervention program. Am J Clin Nutr 91: 831–840.

  21. Millar L, Kremer P, de Silva-Sanigorski A, McCabe MP, Mavoa H, Moodie M et al. Reduction in overweight and obesity from a 3-year community-based intervention in Australia: the ‘It's Your Move!’ project. Obes Rev 2011; 12 (Suppl 2): 20–28.

    Article  PubMed  Google Scholar 

  22. Sanigorski AM, Bell AC, Kremer PJ, Swinburn BA . High childhood obesity in an Australian population. Obesity 2007; 15: 1908–1912.

    Article  PubMed  Google Scholar 

  23. Bell AC, Simmons A, Sanigorski AM, Kremer PJ, Swinburn BA . Preventing childhood obesity: the sentinel site for obesity prevention in Victoria, Australia. Health Promot Int 2008; 23: 328–336.

    Article  PubMed  Google Scholar 

  24. Davies P, Roodvelt R, Marks G . Standard Methods for the Collection and Collation of Anthropometric Data in Children. Commonwealth of Australia: Canberra, 2001.

    Google Scholar 

  25. Vidmar S, Carlin J, Hesketh K, Cole T . Standardizing anthropometric measures in children and adolescents with new functions for egen. Stata J 2004; 4: 50–55.

    Article  Google Scholar 

  26. Edmunds LD, Ziebland S . Development and validation of the Day in the Life Questionnaire (DILQ) as a measure of fruit and vegetable questionnaire for 7–9 year olds. Health Educ Res 2002; 17: 211–220.

    Article  CAS  PubMed  Google Scholar 

  27. Sallis JF, Proschaska JJ, Wendell CT . A review of correlates of physical activity of children and adolescents. Med Sci Sports Exerc 2000; 32: 963–975.

    Article  CAS  PubMed  Google Scholar 

  28. Raudenbush SW, Bryk AS . Hierarchical Linear Models: Applications and Data Analysis Methods. Sage Publications: Thousand Oaks, CA, 2002.

    Google Scholar 

  29. Rasbash J, Charlton C, Browne WJ, Healy M, Cameron B . MLwiN Version 2.02. Centre for Multilevel Modelling: University of Bristol, Bristol, UK, 2005.

    Google Scholar 

  30. Hox JJ . Multilevel Analysis: Techniques and Applications. Lawrence Erlbaum Associates, Inc.: Mahwah, New Jersey, 2002.

    Book  Google Scholar 

  31. Rasbash J, Steele F, Browne W, Prosser B . A User's Guide to MLwiN 2.02. Centre for Multilevel Modelling, University of Bristol: London, 2004.

    Google Scholar 

  32. Forshee RA, Anderson PA, Storey ML . Sugar-sweetened beverages adn body mass index in children and adolescents: A meta-analysis. Am J Clin Nutr 2008; 87: 1662–1671.

    Article  CAS  PubMed  Google Scholar 

  33. Ludwig DS, Peterson KE, Gortmaker SL . Relation between consumption of sugar-sweetened drinks and childhood obesity: a prospective, observational analysis. Lancet 2001; 357: 505–508.

    Article  CAS  Google Scholar 

  34. Hancox RJ, Milne BJ, Poulton R . Association between child and adolescent television viewing and adult health: a longitudinal birth cohort study. Lancet 2004; 364: 257–262.

    Article  Google Scholar 

  35. Epstein LH, Roemmich JN, Robinson JL, Paluch RA, Winiewicz DD, Fuerch JH et al. A randomized trial of the effects of reducing television viewing and computer use on body mass index in young children. Arch Pediatr Adolesc Med 2008; 162: 239–245.

    Article  PubMed  Google Scholar 

  36. Dietz WH, Gortmaker SL . Do we fatten our children at the television set? Obesity and television viewing in children and adolescents. Pediatrics 1985; 75: 807–812.

    Google Scholar 

  37. Marshall SJ, Biddle SJH, Gorely T, Camerson N, Murdey I . Relationships between media use, body fatness and physical activity in children and youth: A meta analysis. Int J Obes 2004; 28: 1238–1246.

    Article  CAS  Google Scholar 

  38. Caroli M, Argentieri L, Cardone M, Masi A . Role of television in childhood obesity prevention. Int J Obes 2004; 28: S104–S108.

    Article  Google Scholar 

  39. Coon KA, Goldberg J, Rogers BL, Tucker KL . Relationships between use of television during meals and children's food consumption patterns. Pediatrics 2001; 107: 1–9.

    Article  Google Scholar 

  40. van Zutphen M, Bell AC, Kremer PJ, Swinburn BA . Association between the family environment and television viewing in Australian children. J Paediatr Child Health 2007; 43: 458–463.

    Article  Google Scholar 

  41. Salmon J, Timperio A, Telford A, Carver A, Crawford D . Association of family environment with children's television viewing and with low level of physical activity. Obes Res 2005; 13: 1939–1951.

    Article  Google Scholar 

  42. Barradas DT, Fulton JE, Blanck HM, Huhman M . Parental influences on youth television viewing. J Pediatr 2007; 151: 369–373.

    Article  PubMed  Google Scholar 

  43. Rose G, Day S . The population mean predicts the number of deviant individuals. BMJ 1990; 301: 1031–1034.

    Article  CAS  PubMed  Google Scholar 

  44. Butte N, Ellis K . Comment on “Obesity and the environment: where do we go from here?” Science 2003; 301: 598.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This research was supported by the Commonwealth Department of Health and Ageing, the Victorian Department of Human Services and the Victorian Health Promotion Foundation (VicHealth). Andrea de Silva-Sanigorski was supported by a VicHealth Public Health Research Fellowship and the Jack Brockhoff Child Health and Wellbeing Program. We would like to acknowledge the Deakin University evaluation staff, Colac Otway Shire, Colac Area Health, and the staff, students and parents from the participating communities.

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Authors and Affiliations

  1. The Jack Brockhoff Child Health and Wellbeing Program, The McCaughey Centre: VicHealth Centre for the Promotion of Mental Health and Community Wellbeing, Melbourne School of Population Health, University of Melbourne, Melbourne, Victoria, Australia

    B A Johnson, P J Kremer & A M de Silva-Sanigorski

  2. WHO Collaborating Centre for Obesity Prevention, Deakin University, Burwood, New South Wales, Australia

    B A Swinburn

Authors
  1. B A Johnson
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  2. P J Kremer
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  3. B A Swinburn
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  4. A M de Silva-Sanigorski
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Correspondence to B A Johnson.

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Johnson, B., Kremer, P., Swinburn, B. et al. Multilevel analysis of the Be Active Eat Well intervention: environmental and behavioural influences on reductions in child obesity risk. Int J Obes 36, 901–907 (2012). https://doi.org/10.1038/ijo.2012.23

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