Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Pediatric Endocrinology and Metabolism

Editor-in-Chief: Kiess, Wieland

Ed. by Bereket, Abdullah / Cohen, Pinhas / Darendeliler, Feyza / Dattani, Mehul / Gustafsson, Jan / Luo, Feihong / Mericq, Veronica / Roth, Christian / Toppari, Jorma

Editorial Board Member: Battelino, Tadej / Buyukgebiz, Atilla / Cassorla, Fernando / Chrousos, George P. / Cutfield, Wayne / Fideleff, Hugo L. / Hershkovitz, Eli / Hiort, Olaf / LaFranchi, Stephen H. / Lanes M. D., Roberto / Mohn, Angelika / Root, Allen W. / Rosenfeld, Ron G. / Werther, George / Zadik, Zvi

12 Issues per year


IMPACT FACTOR 2016: 1.233

CiteScore 2016: 1.09

SCImago Journal Rank (SJR) 2016: 0.527
Source Normalized Impact per Paper (SNIP) 2016: 0.602

Online
ISSN
2191-0251
See all formats and pricing
More options …
Volume 28, Issue 5-6 (May 2015)

Issues

Obesogenic environments: environmental approaches to obesity prevention

Tobias Lipek
  • Hospital for Children and Adolescents, Center of Paediatric Research, University Hospital of Leipzig, Liebigstr. 20, D-04103 Leipzig, Germany
  • LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Philipp-Rosenthal-Str. 27, D-04103 Leipzig, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Ulrike Igel / Ruth Gausche
  • Hospital for Children and Adolescents, Center of Paediatric Research, University Hospital of Leipzig, Liebigstr. 20, D-04103 Leipzig, Germany
  • CrescNet, Philipp-Rosenthal-Str. 27b, D-04103 Leipzig, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Wieland Kiess
  • Hospital for Children and Adolescents, Center of Paediatric Research, University Hospital of Leipzig, Liebigstr. 20, D-04103 Leipzig, Germany
  • LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Philipp-Rosenthal-Str. 27, D-04103 Leipzig, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Gesine Grande
Published Online: 2015-04-30 | DOI: https://doi.org/10.1515/jpem-2015-0127

Abstract

Childhood obesity is a major concern for public health. There are multiple factors (e.g., genetic, social, and environmental) that contribute to unhealthy weight gain. Drawing from findings on “obesogenic environments” and core principles of preventive strategies to reduce health inequalities, this paper gives an overview of recent childhood prevention programs that target aspects of the physical environment (“environmental changes”). Out of the ten reviews we screened (including more than 300 studies), we identified very few that addressed aspects of the environment. We focus here on 14 programs that follow different approaches to environmental changes (e.g., access to/quality of playgrounds, changes in school cafeterias). Altering the environment offers opportunities for healthier behaviors and seems to be an effective strategy to prevent childhood obesity. However, the evaluation of those (mostly) multidimensional interventions does not allow drawing firm conclusions about the single effect of environmental changes. We conclude that obesity prevention programs should combine person-based and environmental approaches.

Keywords: childhood obesity; environment design; prevention; review

Childhood obesity – causes and consequences

Obesity in children is a major concern, not only for the affected individuals but also for public health. Although prevalence rates in many industrialized countries around the world have started stabilizing since 2004 (1), they still remain at a high level. Due to the direct and indirect implications of childhood obesity, effective treatment and prevention interventions are of utmost importance (2). During the last 3 decades, a number of empirical and theoretical works have focused on the development of obesity in children. This paper gives an overview of strategies to prevent childhood obesity. First, we briefly summarize recent findings on different factors associated with childhood obesity. Second, we present core principles of preventive strategies aimed at reducing inequalities in health in general and obesity in particular. Third, we depict selected intervention (prevention) studies that explicitly include “environmental changes” and the role of obesogenic environments in childhood obesity. Finally, we draw conclusions from existing studies and suggest future programs.

Genetics

During the last 2 decades, there has been a shift from a monogenetic approach toward seeing obesity as a multigenetic disease. From a genetic point of view, obesity is currently seen as a “complex, multifactorial condition with high hereditability” (3). Family studies with twin and adopted children indicate a significant influence of genes on an individual’s predisposition to developing obesity, accounting for up to 70% of heritability estimates for BMI (4, 5) [reviewed in (6)], whereas monogenic reasons for obesity are rare. In search for the actual responsible gene loci, comprehensive search strategies have evolved during the last decade. Starting from a candidate gene approach, where individuals were tested for being carriers of known genetic variants of metabolism, genome-wide linkage studies concentrated on genetic transmission of linked genes on one chromosome within one family, but the identified common variants could only explain a small proportion of the risk for obesity (7).

The most recent change in the search doctrine was the implementation of genome-wide association studies (GWAS), screening for genetic comminalities in hundreds of thousands of subjects (rather than small samples of candidate families) and analyzing such large data without a priori knowledge or a hypothesis. New risk alleles could be identified, but the effect of each of the alterations was rather disappointing, with an effect of approx. 0.17 kg/m2 (7, 8). Some of the alleles found seem to play a different role depending on the age of the respective children, indicating that, e.g., intrauterine environment could temper adipogenic effects of some of the genetic variants such as the obesity-related allele FTO (9, 10). Summing up, gene-by-environment interactions are not yet sufficiently understood (7). With this in mind, current studies focus on epigenetic modifications like the methylation status of gene promotors that might be associated with an individual’s obesity in later life (3, 7, 11).

Socioeconomic influences

There is a vast body of research demonstrating associations between parental socio-economic status and obesity in children in industrialized countries (12). In addition to parental occupation and income, educational attainment seems to be the strongest predictor for childhood obesity (12). Social determinants of childhood obesity in a German population are discussed in detail in the works of Lange et al. (13). They reported lower parental educational level, a lower degree of professional education, low income, nationality (German vs. non-German), limited living space per person, and single parenthood as the most relevant predictors for being overweight or obese (13).

Obesogenic environments

Obesogenic environments are “the sum of influences that the surroundings, opportunities, or conditions of life have on promoting obesity in individuals or populations” (14, p. 564). They comprise a resident’s aggregate socioeconomic status (SES) as well as aspects of their social and physical environments. Evidence suggests that children living in low SES areas are more often overweight or obese (12, 15, 16). Aspects of the social environment (social cohesion, collective socialization and trust, in particular) seem to be relevant for unhealthy weight gain in children and adolescents (15).

Regarding the physical environment, Feng et al. (17) summarize three domains that may influence obesity: 1) facilities for physical activity (i.e., parks, playgrounds, sports clubs that promote active play and sports); 2) land use and transportation (i.e., mixed land use, walkability, access to public transport or walking/cycling paths that facilitate active commuting to school/work); and 3) foodscape (availability of healthy or unhealthy food). In this sense it was reported that fewer resources for recreation (e.g., parks, playgrounds), sports or active commuting (street connectivity, land-use-mix) and a high density of fast food outlets are related to overweight and obesity in children and adolescents (15, 18, 19). Recent studies also suggest that air pollution may cause higher mortality among individuals with type 2 diabetes (20, 21), increase the risk for insulin resistance in children (22), and increase the risk for asthma in children, especially in the overweight (23). However, it is possible to show that children living closer to parks and recreational spaces are less likely to experience weight gain (24). Moreover, the availability of food outlets in the neighborhood was not necessarily associated with unhealthy diets but was associated with higher consumption of fruit per day (25).

Summing up, the development of overweight and obesity during childhood is a result of complex mechanisms at different levels of influence (individual, social, environmental) (Figure 1). There are various theoretical or conceptual frameworks describing this complexity. Socioecological models, for example, point out the influences of intrapersonal, interpersonal, organizational, community, and policy levels as well as their interactions, which are relevant for an individual’s health (26). The “full obesity system map” illustrates its complexity by mapping the clusters of individual and social psychology, individual activity, activity environment, food consumption, food production, individual physiology, and physiology that influences an individual’s energy balance (27).

To choose the most relevant and preventable factors contributing to childhood obesity for future intervention studies, it would be appealing to know which factors contribute most to childhood obesity. This approach is accounted for by the concept of “attributable risk to obesity”. On the basis of four German studies comprising >34,000 individuals, Plachta-Danielzik et al. (28) identified parental obesity, parental smoking, and parental education levels to be the most relevant attributable risk factors for childhood overweight – each of them representing targets for prevention that are hard to change. Consequently, realizing that individual determinants are more important than environmental for explaining the development of childhood obesity (29) does not necessarily mean that environmental approaches should be deemed invalid.

Treatment and prevention

Given the complexity of causes, the “answer” to the so-called “obesity epidemic” also needs to address various determinants at different levels of influence. However, there are some difficulties concerning the prevention and treatment of childhood overweight or obesity. Firstly, parents often do not acknowledge their children’s weight status (30) or have little intention of of participating in intervention or prevention programs, particularly if their child is “only” overweight (31). However, parents of obese children that are already under treatment are aware of their child’s health problem (32). Moreover, it is still not clear whether or not the medical treatment of obesity in children and adolescents is effective. Treating children that are already obese is difficult and cost-intensive, and adverse effects mainly due to pharmacological therapies are not infrequent (33). Parental overweight and a higher pre-intervention BMI also reduces the effectiveness of childhood obesity interventions (34). In addition, children from disadvantaged families who are at a higher risk of becoming overweight or obese may benefit even less from interventions, particularly if traditional person-based education programs are applied (35). It is therefore important to prevent childhood obesity as early as possible using population-based, multidimensional approaches that may influence even those groups that are hard to reach individually (36).

Multiple determinants of childhood obesity [adapted from Sallis et al. (37)].
Figure 1:

Multiple determinants of childhood obesity [adapted from Sallis et al. (37)].

Preventive strategies

In general, interventions to reduce health inequalities may operate at different levels, aiming at 1) strengthening individuals (using person-based educational approaches), 2) strengthening communities (by building social cohesion and mutual support), 3) improving living and working conditions (by reducing exposure to health damaging environments and improving conditions), and 4) promoting healthy macro policies (38). Taking into account that obesity is “the result of people responding normally to the obesogenic environments they find themselves in” (39, p. 804), preventing or treating obesity requires multidimensional strategies focusing on individual, familial, institutional, and environmental levels of influence (40, 41).

Intervention approaches – from individuals to environments

There is evidence that community-based approaches are the most promising or effective strategies in preventing obesity. Especially for children who have little control over the social and environmental conditions of their lives, comprehensive community-based interventions are appropriate (42). According to Foltz et al., “communities are commonly referred to as networks or groups of individuals who share common beliefs, values, or culture [...] but can also be individuals who reside and work in common geographic locales and share a variety of common institutions [...] and resources [...]” (43, p. 402). Thus, community-based interventions comprise the access to the target group (setting approach) as well as contextual changes in different types of environments such as physical, economic, political, and sociocultural environments (14).

In this work, we put emphasis on intervention studies to prevent childhood obesity that employ changes of the environment with the goal of creating opportunities for healthier choices regarding mostly physical activity and food intake for children and adolescents.

Materials and methods

To obtain an overview of current literature for this work, we screened PubMed/MEDLINE for relevant review articles. Searching for “childhood obesity intervention”, we selected ten review articles focusing on obesity-related prevention studies (not treatment) in children (35, 37, 42, 44–50). Eight of the selected reviews comprised interventions targeting childhood obesity prevention (encompasing 314 studies meeting the respective inclusion criteria of reviewers in total, not corrected for double-listing), two reviews focused on interventions to improve physical activity (37, 50). Moreover, single studies not being currently included in available reviews were retrieved and reference lists of selected articles from the above-mentioned reviews were screened for additional articles. Studies were included if they explicitly contained environmental changes. We use the term “environmental change” [according to the definition of the Centers for Disease Control and Prevention (CDC) (51)] for interventions that focus on the physical environment and create opportunities for healthier choices (e.g., park renovations – physical activity; installation of bike lanes – active transport; healthier options in school cafeteria – healthier diet). We did not include interventions that were solely aimed at improving aspects of the social environment (e.g., social cohesion, trust between neighbors). Moreover, we identified studies focusing on increasing physical activity by environmental modification only, of which we will discuss three. Most of the recent studies employed multidimensional interventions, making it impossible to disentangle the single effect of environmental modifications from the whole effect of more complex interventions on the BMI of participating children.

Result of studies on environmental changes

Effects of built-environment modifications only

Boarnet et al. (52) evaluated California’s “Safe Routes to School” legislation (SR2S), which allocated more than $66 million to improvements on routes to school, like installation of bicycle lanes during Autumn 2003. Results of retrospective questionnaires on commuting behavior completed by parents showed an increased probability to walk or cycle to school after completion of construction works (52).

The effects of providing a safe space to play in afternoons and on weekends were investigated in another project. Directed at students of a school with children from prekindergarten through sixth grade, their school yard was opened for extra hours and monitored by attendants to prevent bullying, vandalism and entering of unrelated adults or older children. Compared to a school in a neighboring district with similar sociodemographic characteristics, it could be observed that the number of children being outdoors and physically active was 84% higher. Moreover, a decline in sedentary behavior among students during the course of the project was also observed (53).

Several groups assessed the effect of public park renovations. These interventions also represent incentives to be more physically active. Tester and Baker were able to show increased frequency of visits to and overall physical activity in a park that underwent physical playfield renovations compared to a park where similar action was desisted (54) (Table 1, section I).

Table 1

Childhood obesity prevention programs addressing obesogenic environments.

Multidimensional interventions

Searching for studies that examined the effect of environmental changes to create opportunities for health promoting behaviors, we identified predominantly complex multidimensional interventions. Mostly, environmental changes were part of setting-based interventions (i.e., they were conducted in schools, kindergartens, or community centers) that also comprised educational approaches. In none of the studies the effect of single components was analyzed, only the total effectiveness of the entire intervention (Table 1, section II).

In a program targeted at children (activity lessons in school, sessions on healthy nutrition), parents (promotion of physical activity, healthy food, limitation of screen-time), and teachers (support for lessons), the built environment was altered to promote physical activity during breaks or school time with climbing walls, balls, cords, or stilts. Comparison to standard was yielded by cluster randomization. Results showed an increase of approximately 11% for aerobic fitness and a decrease of 5%–10% in body fat for the intervention group, with no effect on participants’ BMIs compared to the BMIs of controls. In reaction to the results, some Cantonal health promotion programs in Switzerland implemented modules of this intervention (55).

A school-based trial in the Netherlands included students aged 12–14 years who underwent a multidimensional health promotion intervention. It consisted of educational components (classes in biology and physical education, a computer-based information program) as well as environmental changes such as specific advice to the school canteen proposing smaller portion sizes, “healthier” products, or restricted access to vending machines. Additionally, posters labeling foodstuff into red, yellow, or green categories were installed. Twelve-month follow-up measurements showed reduced skinfold thicknesses, lower consumption of sugar-containing beverages, and less screen time (only in boys) in the intervention schools (56, 57).

The effect of additional weekly physical activity classes and classes on healthy nutrition for parents and students was assessed in another school-based obesity-prevention intervention in Chile. From the “environmental change” perspective, school kiosks were advocated to offer healthier choices to students and were advised how they could remain profitable. Whereas it was possible to decrease BMI z-scores in boys and to improve physical fitness in both genders, the kiosk-intervention did not seem to contribute to this effect as registered proportion of healthy foods sold by kiosks did not change during the intervention period (58).

The school-based Healthier Options for Public Schoolchildren (HOPS) intervention combined measures targeting healthy nutrition by school-meal modifications and the implementation of school gardens as well as physical activity through 10–15 min “desk-side physical activity” during regular lessons (involving math and spelling tasks). Moreover, students and parents were taught about good nutrition and physical activity during lessons and via monthly newsletters. After 2 years of intervention, significantly more students in the intervention group stayed within the normal weight (<85th percentile) compared to the control group (52.1% vs. 40.7%). Moreover, students in the intervention group improved their academic performances in math (59).

The effects of multiple interventions on physical activity and BMI were evaluated in another school-based trial. In addition to creating new opportunities for physical activity in and after school time, the promotion of active commuting to school, and the education of teachers and parents, additional measures were undertaken in the “out-of-school environment”. Reduced entry fees to sport areas, public transport to physical activity sites, and improvements of bicycle lanes around schools were implemented. Four years after randomization, students in the intervention group showed a lower increase in BMI than their age- and gender-adjusted controls, the rate of students practicing physical activity was higher, and screen time was lower in the intervention group (60, 61).

The project “Shape up Somerville” (SUS) evaluated a community-based environmental change intervention. The program included the improvement of opportunities for physical activity around the school (traffic calming, information on safe routes to school, walking school bus), within the school (new equipment) as well as community-wide instruments such as approving restaurants to SUS guidelines. Results taken 2 years after 1 year of intervention showed that BMI z-scores of children in the intervention community decreased by 0.06 compared to controls. Prevalence of overweight/obesity decreased in males and females, and remission increased in males and females in the intervention group compared to controls (62, 63).

Although the Australian intervention program “Romp und Chomp” focused mainly on capacity building in the participating communities by increasing the awareness and qualifications for health promotion in local organizations, it also resulted in noteworthy measures of “environmental changes”. In addition to the provision of water in childcare centers, childcare policies regarding healthy eating and physical activity were changed. Moreover, childcare professionals were trained in physical activity and nutrition related skills. After 3 years of intervention, the proportion of children with healthy weights was significantly higher in the intervention group (64).

Quasi-experimental trial: moving to opportunity

For individuals, the easiest way to change one’s living environment is to move to a different area. The unique social experiment “moving to opportunity” (65, 66) conducted in the cities of Baltimore, Boston, Chicago, Los Angeles, and New York investigated the health effects of moving from a high-poverty area to a more affluent one. The study group evaluated the development of body weight and the level of glycated haemoglobin (HbA1c) of 1788 randomly selected women and their children who were given vouchers to move. Thus, they were able to instantaneously improve their potentially depriving and health-damaging environment. After 10–15 years, randomization results show a lower prevalence for overweight and obesity (BMI >35, BMI >40) and a lower proportion of elevated levels of glycated haemoglobin (HbA1c) in the intervention group (65). The authors were not able to explain the mechanisms underlying this effect. Nonetheless, this intervention shows the public health potential of modifying environmental factors for health improvement (Table 1, section III).

Interventions in progress

A French obesity prevention trial (EPODE – Ensemble Prévenons l’Obésité des Enfants) focused on school- and family-based interventions but was additionally supported by the local councils (67). EPODE has been transferred to more than 500 communities worldwide from its origins on the basis of clear network recommendations (68). The EPODE program clearly contains aspects of environmental changes including “rearrangement of school play-grounds, the installation of multisport courts in neighborhoods, the development of baby gym facilities and activities, and improvements to the ‘walkability’ of the town” (68, p. 306). Unfortunately, although the program is being expanded internationally (69, 70), until now no detailed evaluation has been published.

In 2012, the New York City Obesity Task Force (NYOTF) was launched (71). Together with all relevant departments and agents, strategies aimed at reducing obesity, addressing disparities between communities, reducing preventable health conditions, and lowering health care spending were elaborated. The project explicitly recognizes obesity as an environmental problem (between others) that can be addressed by environmental improvements such as availability of healthier food and water in public places (e.g., water jets and salad bars in schools and universities), making public spaces safer and more attractive for physical activity and active transport, and promoting building design to encourage physical activity (71). It also involves media campaigns as well as educational approaches. Moreover, the city published “Active Design Guidelines”, a collection of strategies to increase opportunities for physical activity in the built environment that is directed to designers, developers, and policy makers. In December 2013, some indicators already showed improvements, with final results expected in 2016 (72) (Table 1, section IV).

Discussion

Today, most interventions are multidimensional following the recommendations of the Cochrane Review on obesity prevention in children (47). Regarding the large number of screened community-based obesity prevention studies in total, we could only identify a small number that explicitly focused on “environmental changes” (i.e., modifications of the “obesogenic environment”). The majority of the included studies were school-based, while some addressed the whole community. Drawing back to the above-mentioned intervention strategies, programs focused mostly on person-based educational approaches, whereas the improvement of living conditions (“environmental change”) to offer healthier choices played a minor role. Only a few trials (Romp & Chomp, NY OTF, EPODE) were directed to capacity building (strengthening communities) and macro-policy changes. All of our included studies showed effects on physical activity or weight-related outcomes in children and adolescents in the desired direction. However, we have to take publication biases into account. In addition, this paper does not claim to be an exhaustive systematic review but an overview on interventions targeting aspects of the “obesogenic environment”. Hence, we can neither draw conclusions about the effectiveness of “environmental” interventions per se nor on their effectiveness compared to “traditional” person-based approaches. Moreover, multidimensional intervention studies are difficult to evaluate, and there are large differences regarding the complexity of evaluation designs (e.g., only outcome evaluation vs. complex evaluation including process, impact, and outcome). Furthermore, studies were not able to distinguish which part of the intervention was the most effective and which part showed no effect. This is particularly characteristic of the evaluation of multidimensional community-based programs. From a theoretical point of view, their effectiveness is synergistic (26), i.e., more than the sum of their single parts. Empirically, they have to cope with the “noise” of real life that is hard to capture (73).

Regarding the small effect sizes, the question arises whether or not the achievable weight reductions are clinically relevant. It is difficult to state why until now interventions have failed to provide convincing results in terms of effectiveness. Even a recent review on the effectiveness of conservative nonpharmacological obesity treatment studies (i.e., person-based approaches) for children reported only small effects on BMI z-scores (74), although the “dose” of intervention was much higher compared to community-based approaches. The authors argued that weight normalization in obese children cannot be expected within 2 years of treatment and suggest that future interventions should focus on improving the acceptance of overweight and on the promotion of physical and mental health irrespective of weight reduction (74). It has been pointed out that even the appropriateness of BMI to evaluate childhood obesity is controversial (75). Beyond that, it is important to distinguish between significant determinants and preventable determinants of childhood obesity (28), which are unfortunately not always the same. Assuming that factors that are closer to the individual (individual attributes, family characteristics) are the most significant for health and health-related behavior but also the most difficult to change, it is important to take aspects of the broader environment into account. From a public health perspective, even smaller effects at the population level can lead to relevant improvements because a great number of individuals can benefit (76). The beneficial effects of multidimensional environmental intervention approaches for the whole population irrespective of their sociodemographic position (14, 36, 77) have been shown to reduce socioeconomic inequalities in childhood obesity (35). Moreover, individuals are embedded in a context and “behaviour change programmes targeted at individuals do not alter the social and environmental conditions that promote and maintain the behavioural risks that are the focus of intervention” (40, p. 124).

Nevertheless, offering healthier choices may be insufficient to induce behavioral change as behavior results from combining options with subjective norms and attitudes fueled by (perceived) behavioral control (78). Norms and attitudes are influenced by one’s social context. From a psychological perspective, behavioral change models such as the theory of planned behavior (79) or social cognitive theory (80) may have the potential to explain why changing attitudes that lead to unfavorable (unhealthy) behaviors to desired (healthy) behaviors are so difficult to accomplish (81). In any case, one should not expect environmental change programs to work equally for everybody without the modification of inner views. Thus, environmental interventions need to be seen as complementary to individual approaches and not as a substitute (36). Figure 2 illustrates possible decisions before and after the installation of alternatives. First (A), there is only one option and thus one possible decision. After the installation of alternatives, the individual has to decide (according to individual and social characteristics, norms) between B and C. Consequently, no behavior change can occur without a change of choices and attitudes.

There is no behavior change without a change of choices and inner views. (A) Situation before installation of alternative choice offers only one possible decision. (B, C) After the installation of alternatives, the actual decision will depend on inner variables of the individual.
Figure 2:

There is no behavior change without a change of choices and inner views.

(A) Situation before installation of alternative choice offers only one possible decision. (B, C) After the installation of alternatives, the actual decision will depend on inner variables of the individual.

Regarding the cost-effectiveness of specific environmental interventions, studies are lacking (82). However, some multidimensional school-based studies have been shown to be cost-saving (77). In conclusion, complex interventions aiming at environmental changes and the strengthening of individuals and communities as well as macro-policy changes are promising strategies to reduce obesity in children (and adults) without increasing socioeconomic inequalities. Medical practitioners, educators, and policy makers should be aware of the complex causes of childhood obesity.

Acknowledgments

We thank Seraphim Alvanides (Northumbria University at Newcastle, UK) for commenting on the manuscript.

References

  • 1.

    Blüher S, Meigen C, Gausche R, Keller E, Pfäffle R, et al. Age-specific stabilization in obesity prevalence in German children: a cross-sectional study from 1999 to 2008. Int J Pediatr Obes 2011;6:e199.Google Scholar

  • 2.

    Martin A, Saunders DH, Shenkin SD, Sproule J. Lifestyle intervention for improving school achievement in overweight or obese children and adolescents. Cochrane Database Syst Rev 2014;3:CD009728.Google Scholar

  • 3.

    Lee YS. Genetics of nonsyndromic obesity. Curr Opin Pediatr 2013;25:666–73.CrossrefPubMedGoogle Scholar

  • 4.

    Stunkard AJ, Foch TT, Hrubec Z. A twin study of human obesity. J Am Med Assoc 1986;256:51–4.Google Scholar

  • 5.

    Stunkard AJ, Sørensen TI, Hanis C, Teasdale TW, Chakraborty R, et al. An adoption study of human obesity. N Engl J Med 1986;314:193–8.CrossrefGoogle Scholar

  • 6.

    Böttcher Y, Kovacs P. Genetics of obesity in childhood and adolescence. In: Kiess W, Wabitsch M, Maffeis C, Sharma A, editors. Metabolic syndrome and obesity in childhood and adolescence. Basel: Karger AG, 2015:31–9.Google Scholar

  • 7.

    Manco M, Dallapiccola B. Genetics of pediatric obesity. Pediatrics 2012;130:123–33.CrossrefPubMedGoogle Scholar

  • 8.

    Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010;42:937–48.Google Scholar

  • 9.

    Hardy R, Wills AK, Wong A, Elks CE, Wareham NJ, et al. Life course variations in the associations between FTO and MC4R gene variants and body size. Hum Mol Genet 2009;19:545–52.PubMedGoogle Scholar

  • 10.

    Hebebrand J. Putting the greater dimensions of obesity into perspective. Obes Facts 2010;3:341–2.PubMedCrossrefGoogle Scholar

  • 11.

    Godfrey KM, Sheppard A, Gluckman PD, Lillycrop KA, Burdge GC, et al. Epigenetic gene promoter methylation at birth is associated with child’s later adiposity. Diabetes 2011;60:1528–34.CrossrefGoogle Scholar

  • 12.

    Shrewsbury V, Wardle J. Socioeconomic status and adiposity in childhood: a systematic review of cross-sectional studies 1990–2005. Obesity (Silver Spring) 2008;16:275–84.Google Scholar

  • 13.

    Lange D, Plachta-Danielzik S, Landsberg B, Müller MJ. Soziale Ungleichheit, Migrationshintergrund, Lebenswelten und Übergewicht bei Kindern und Jugendlichen. Ergebnisse der Kieler Adipositas-Präventionsstudie (KOPS). Bundesgesundheitsbl Gesundheitsforsch Gesundheitsschutz 2010;53:707–15.Google Scholar

  • 14.

    Swinburn B, Egger G, Raza F. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med 1999;29(6 Pt 1):563–70.CrossrefPubMedGoogle Scholar

  • 15.

    Carter MA, Dubois L. Neighbourhoods and child adiposity: a critical appraisal of the literature. Health Place 2010;16:616–28.PubMedCrossrefGoogle Scholar

  • 16.

    Igel U, Baar J, Benkert, I. Brähler, E, Hochstädt E, et al. Der Einfluss von Deprivation im Ortsteil auf das Übergewicht von Vorschulkindern. Adipositas 2013;7:27–31.Google Scholar

  • 17.

    Feng J, Glass TA, Curriero FC, Stewart WF, Schwartz BS. The built environment and obesity: a systematic review of the epidemiologic evidence. Health Place 2010;16:175–90.CrossrefPubMedGoogle Scholar

  • 18.

    Dunton GF, Kaplan J, Wolch J, Jerrett M, Reynolds KD. Physical environmental correlates of childhood obesity: a systematic review. Obes Rev 2009;10:393–402.PubMedCrossrefGoogle Scholar

  • 19.

    Booth KM, Pinkston MM, Poston WS. Obesity and the built environment. J Am Diet Assoc 2005;105(5 Suppl 1):S110–7.CrossrefGoogle Scholar

  • 20.

    Brook RD, Xu X, Bard RL, Dvonch JT, Morishita M, et al. Reduced metabolic insulin sensitivity following sub-acute exposures to low levels of ambient fine particulate matter air pollution. Sci Total Environ 2013;448:66–71.Google Scholar

  • 21.

    Raaschou-Nielsen O, Sorensen M, Ketzel M, Hertel O, Loft S, et al. Long-term exposure to traffic-related air pollution and diabetes-associated mortality: a cohort study. Diabetologia 2013;56:36–46.Google Scholar

  • 22.

    Thiering E, Cyrys J, Kratzsch J, Meisinger C, Hoffmann B, et al. Long-term exposure to traffic-related air pollution and insulin resistance in children: results from the GINIplus and LISAplus birth cohorts. Diabetologia 2013;56:1696–704.CrossrefPubMedGoogle Scholar

  • 23.

    Matsui EC. Environmental exposures and asthma morbidity in children living in urban neighborhoods. Allergy 2014;69:553–8.PubMedCrossrefGoogle Scholar

  • 24.

    Wolch J, Jerrett M, Reynolds K, McConnell R, Chang R, et al. Childhood obesity and proximity to urban parks and recreational resources: a longitudinal cohort study. Health Place 2011;17:207–14.CrossrefGoogle Scholar

  • 25.

    Burgoine T, Alvanides S, Lake AA. Assessing the obesogenic environment of North East England. Health Place 2011;17: 738–47.CrossrefPubMedGoogle Scholar

  • 26.

    Sallis JF, Owen N, Fisher EB. Ecological models of health behavior. In: Glanz K, Rimer BK, Viswanath K, editors. Health behavior and health education: theory, research, and practice, 4th ed. San Francisco, CA: Jossey-Bass, 2008:465–85.Google Scholar

  • 27.

    Obesity system influence diagram. Available at: http://www.shiftn.com/obesity/Full-Map.html? Accessed on December 23, 2014.

  • 28.

    Plachta-Danielzik S, Kehden B, Landsberg B, Schaffrath Rosario A, Kurth B, et al. Attributable risks for childhood overweight: evidence for limited effectiveness of prevention. Pediatrics 2012;130:e865–71.Google Scholar

  • 29.

    Gose M, Plachta-Danielzik S, Willié B, Johannsen M, Landsberg B, et al. Longitudinal influences of neighbourhood built and social environment on children’s weight status. Int J Environ Res Public Health 2013;10:5083–96.CrossrefGoogle Scholar

  • 30.

    Jansen W, Brug J. Parents often do not recognize overweight in their child, regardless of their socio-demographic background. Eur J Public Health 2006;16:645–7.PubMedCrossrefGoogle Scholar

  • 31.

    Alff F, Markert J, Zschaler S, Gausche R, Kiess W, et al. Reasons for (non) participating in a telephone-based intervention program for families with overweight children. PLoS One 2012;7:e34580.CrossrefGoogle Scholar

  • 32.

    Rudolph H, Bluher S, Falkenberg C, Neef M, Korner A, et al. Perception of body weight status: a case control study of obese and lean children and adolescents and their parents. Obes Facts 2010;3:83–91.Google Scholar

  • 33.

    Kiess W, Sergejev E, Körner A, Hebebrand J. Ist eine Therapie der Adipositas im Kindes- und Jugendalter überhaupt möglich? Bundesgesundheitsbl Gesundheitsforsch Gesundheitsschutz 2011;54:527–32.Google Scholar

  • 34.

    Eliakim A, Friedland O, Kowen G, Wolach B, Nemet D. Parental obesity and higher pre-intervention BMI reduce the tikelihood of a multidisciplinary childhood obesity program to succeed – a clinical observation. J Pediatr Endocrinol Metab 2004;17:1055–61.Google Scholar

  • 35.

    Hillier-Brown FC, Bambra CL, Cairns J, Kasim A, Moore HJ, et al. A systematic review of the effectiveness of individual, community and societal level interventions at reducing socioeconomic inequalities in obesity amongst children. BMC Public Health 2014;14:834.CrossrefGoogle Scholar

  • 36.

    Swinburn B, Egger G. Preventive strategies against weight gain and obesity. Obes Rev 2002;3:289–301.CrossrefGoogle Scholar

  • 37.

    Sallis JF, Floyd MF, Rodriguez DA, Saelens BE. The role of built environments in physical activity, obesity, and cardiovascular disease. Circulation 2012;125:729–37.CrossrefGoogle Scholar

  • 38.

    Whitehead M. A typology of actions to tackle social inequalities in health. J Epidemiol Community Health 2007;61:473–8.CrossrefGoogle Scholar

  • 39.

    Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet 2011;378:804–14.CrossrefGoogle Scholar

  • 40.

    Swerissen H. The sustainability of health promotion interventions for different levels of social organization. Health Promot Int 2004;19:123–30.CrossrefGoogle Scholar

  • 41.

    Livingstone MB, McCaffrey TA, Rennie KL. Childhood obesity prevention studies: lessons learned and to be learned. Public Health Nutr 2006;9:1121–9.Google Scholar

  • 42.

    Bleich SN, Segal J, Wu Y, Wilson R, Wang Y. Systematic review of community-based childhood obesity prevention studies. Pediatrics 2013;132:e201–10.Google Scholar

  • 43.

    Foltz JL, May AL, Belay B, Nihiser AJ, Dooyema CA, et al. Population-level intervention strategies and examples for obesity prevention in children. Annu Rev Nutr 2012;32:391–415.CrossrefGoogle Scholar

  • 44.

    Bemelmans WJ, Wijnhoven TM, Verschuuren M, Breda, J. Overview of 71 European community-based initiatives against childhood obesity starting between 2005 and 2011: general characteristics and reported effects. BMC Public Health 2014;14:758.Google Scholar

  • 45.

    Wang Y. Childhood obesity prevention programs: comparative effectiveness review and meta-analysis; 2013, Rockville MD: AHRQ Publication No. 13-EHC081-EF.Google Scholar

  • 46.

    Vasques C, Magalhães P, Cortinhas A, Mota P, Leitão J, et al. Effects of intervention programs on child and adolescent BMI: a meta-analysis study. J Phys Act Health 2014;11:426–44.CrossrefGoogle Scholar

  • 47.

    Summerbell CD, Waters E, Edmunds LD, Kelly S, Brown T, et al. Interventions for preventing obesity in children. Cochrane Database Syst Rev 2005;3:CD001871.Google Scholar

  • 48.

    Beauchamp A, Backholer K, Magliano D, Peeters A. The effect of obesity prevention interventions according to socioeconomic position: a systematic review. Obes Rev 2014;15:541–54.CrossrefGoogle Scholar

  • 49.

    Waters E, de Silva-Sanigorski A, Hall BJ, Brown T, Campbell KJ, et al. Interventions for preventing obesity in children. Cochrane Database Syst Rev 2011;12:CD001871.Google Scholar

  • 50.

    van Sluijs, Esther M F, Kriemler S, McMinn AM. The effect of community and family interventions on young people’s physical activity levels: a review of reviews and updated systematic review. Br J Sports Med 2011;45:914–22.Google Scholar

  • 51.

    Centers for Disease Control and Prevention. CDC – Environmental Change – Communities Putting Prevention to Work. Available at: http://www.cdc.gov/nccdphp/dch/programs/communitiesputtingpreventiontowork/program/environmental_change.htm. Accessed on February 23, 2015.

  • 52.

    Boarnet MG, Anderson CL, Day K, McMillan T, Alfonzo M. Evaluation of the California safe routes to school legislation: urban form changes and children’s active transportation to school. Am J Prev Med 2005;28(2 Suppl 2):134–40.CrossrefGoogle Scholar

  • 53.

    Farley TA, Meriwether RA, Baker ET, Watkins LT, Johnson CC, et al. Safe play spaces to promote physical activity in inner-city children: results from a pilot study of an environmental intervention. Am J Public Health 2007;97:1625–31.CrossrefGoogle Scholar

  • 54.

    Tester J, Baker R. Making the playfields even: evaluating the impact of an environmental intervention on park use and physical activity. Prev Med 2009;48:316–20.CrossrefGoogle Scholar

  • 55.

    Puder JJ, Marques-Vidal P, Schindler C, Zahner L, Niederer I, et al. Effect of multidimensional lifestyle intervention on fitness and adiposity in predominantly migrant preschool children (Ballabeina): cluster randomised controlled trial. Br Med J 2011;343:d6195.Google Scholar

  • 56.

    Singh AS, Chin A Paw MJ, Kremers SP, Visscher TL, Brug J, et al. Design of the Dutch obesity intervention in teenagers (NRG-DOiT): systematic development, implementation and evaluation of a school-based intervention aimed at the prevention of excessive weight gain in adolescents. BMC Public Health 2006;6:304.CrossrefGoogle Scholar

  • 57.

    Singh AS, Chin A Paw MJ, Brug J, van Mechelen W. Dutch obesity intervention in teenagers: effectiveness of a school-based program on body composition and behavior. Arch Pediatr Adolesc Med 2009;163:309–17.CrossrefGoogle Scholar

  • 58.

    Kain J, Uauy R, Albala, Vio F, Cerda R, et al. School-based obesity prevention in Chilean primary school children: methodology and evaluation of a controlled study. Int J Obes Relat Metab Disord 2004;28:483–93.CrossrefGoogle Scholar

  • 59.

    Hollar D, Messiah SE, Lopez-Mitnik G, Hollar TL, Almon M, et al. Effect of a two-year obesity prevention intervention on percentile changes in body mass index and academic performance in low-income elementary school children. Am J Public Health 2010;100:646–53.CrossrefGoogle Scholar

  • 60.

    Simon C, Wagner A, Platat C, Arveiler D, Schweitzer B, et al. ICAPS: a multilevel program to improve physical activity in adolescents. Diabetes Metab 2006;32:41–9.CrossrefGoogle Scholar

  • 61.

    Simon C, Schweitzer B, Oujaa M, Wagner A, Arveiler D, et al. Successful overweight prevention in adolescents by increasing physical activity: a 4-year randomized controlled intervention. Int J Obes (Lond) 2008;32:1489–98.Google Scholar

  • 62.

    Economos CD, Hyatt RR, Must A, Goldberg JP, Kuder J, et al. Shape up Somerville two-year results: a community-based environmental change intervention sustains weight reduction in children. Prev Med 2013;57:322–7.CrossrefGoogle Scholar

  • 63.

    Economos CD, Hyatt RR, Goldberg JP, Must A, Naumova EN, et al. A community intervention reduces BMI z-score in children: shape up Somerville first year results. Obesity (Silver Spring) 2007;15:1325–36.Google Scholar

  • 64.

    de Silva-Sanigorski, Andrea M, Bell AC, Kremer P, Nichols M, et al. Reducing obesity in early childhood: results from Romp & Chomp, an Australian community-wide intervention program. Am J Clin Nutr 2010;91:831–40.Google Scholar

  • 65.

    Ludwig J, Sanbonmatsu L, Gennetian L, Adam E, Duncan GJ, et al. Neighborhoods, obesity, and diabetes – a randomized social experiment. N Engl J Med 2011;365:1509–19.CrossrefGoogle Scholar

  • 66.

    Ludwig J, Duncan GJ, Gennetian LA, Katz LF, Kessler RC, et al. Neighborhood effects on the long-term well-being of low-income adults. Science 2012;337:1505–10.CrossrefGoogle Scholar

  • 67.

    Romon M, Lommez A, Tafflet M, Basdevant A, Oppert JM, et al. Downward trends in the prevalence of childhood overweight in the setting of 12-year school- and community-based programmes. Public Health Nutr 2009;12:1735–42.CrossrefGoogle Scholar

  • 68.

    Borys J, Le Bodo Y, Jebb SA, Seidell JC, Summerbell C, et al. EPODE approach for childhood obesity prevention: methods, progress and international development. Obes Rev 2012;13:299–315.CrossrefGoogle Scholar

  • 69.

    Mantziki K, Vassilopoulos A, Radulian G, Borys J, Du Plessis H, et al. Promoting health equity in European children: design and methodology of the prospective EPHE (Epode for the Promotion of Health Equity) evaluation study. BMC Public Health 2014;14:303.CrossrefGoogle Scholar

  • 70.

    Mazzeschi C, Pazzagli C, Laghezza L, Battistini D, Reginato E, et al. Description of the EUROBIS program: a combination of an Epode community-based and a clinical care intervention to improve the lifestyles of children and adolescents with overweight or obesity. Biomed Res Int 2014;2014:546262.Google Scholar

  • 71.

    OTF. Reversing the Epidemic: The New York City Obesity Task Force Plan to Prevent and Control Obesity. Available at: http://www.nyc.gov/html/om/pdf/2012/otf_report.pdf. Accessed on December 31, 2014.

  • 72.

    OTF. New York City Obesity Task Force: Interim Progress Report. Available at: http://www.nyc.gov/html/nycfood/downloads/pdf/obesity-task-force-scorecard-12-31-13-final.pdf. Accessed on December 31, 2014.

  • 73.

    Hohmann AA, Shear MK. Community-based intervention research: coping with the “noise” of real life in study design. Am J Psychiatry 2002;159:201–7.Google Scholar

  • 74.

    Mühlig Y, Wabitsch M, Moss A, Hebebrand J. Weight loss in children and adolescents. Dtsch Arztebl Int 2014;111:818–24.Google Scholar

  • 75.

    Markovic-Jovanovic SR, Stolic RV, Jovanovic AN. The reliability of body mass index in the diagnosis of obesity and metabolic risk in children. J Pediatr Endocrinol Metab 2015;28:515–23.Google Scholar

  • 76.

    Igel U, Grande G. Urban living conditions: the relation between neighborhood characteristics and obesity in children and adolescents. In: Kiess W, Wabitsch M, Maffeis C, Sharma A, editors. Metabolic syndrome and obesity in childhood and adolescence. Basel: Karger AG, 2015;19:126–36.Google Scholar

  • 77.

    Gortmaker SL, Swinburn BA, Levy D, Carter R, Mabry PL, et al. Changing the future of obesity: science, policy, and action. Lancet 2011;378:838–47.CrossrefGoogle Scholar

  • 78.

    Armitage CJ, Conner M. Efficacy of the theory of planned behaviour: a meta-analytic review. Br J Soc Psychol 2001;40:471–99.CrossrefGoogle Scholar

  • 79.

    Montano DE, Kasprzyk D. Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. In: Glanz K, Rimer BK, Viswanath K, editors. Health behavior and health education: theory, research, and practice, 4th ed. San Francisco, CA: Jossey-Bass, 2008:67–96.Google Scholar

  • 80.

    McAlister AL, Perry CL, Parcel GS. How individuals, environments, and health behaviors interact: social cognitive theory. In: Glanz K, Rimer BK, Viswanath K, editors. Health behavior and health education: theory, research, and practice, 4th ed. San Francisco, CA: Jossey-Bass, 2008:169–88.Google Scholar

  • 81.

    Baranowski T, Cullen KW, Nicklas T, Thompson D, Baranowski J. Are current health behavioral change models helpful in guiding prevention of weight gain efforts? Obes Res 2003;11(Suppl):23S–43S.CrossrefGoogle Scholar

  • 82.

    Haby MM, Vos T, Carter R, Moodie M, Markwick A, et al. A new approach to assessing the health benefit from obesity interventions in children and adolescents: the assessing cost-effectiveness in obesity project. Int J Obes (Lond) 2006;30:1463–75.CrossrefGoogle Scholar

About the article

Corresponding author: Ulrike Igel, Faculty of Architecture and Social Sciences, University of Applied Sciences Leipzig (HTWK Leipzig), POB 301166, D-04251 Leipzig, Germany, E-mail:

aThese authors contributed equally to this work.


Received: 2015-03-23

Accepted: 2015-04-02

Published Online: 2015-04-30

Published in Print: 2015-05-01


Citation Information: Journal of Pediatric Endocrinology and Metabolism, ISSN (Online) 2191-0251, ISSN (Print) 0334-018X, DOI: https://doi.org/10.1515/jpem-2015-0127.

Export Citation

©2015 by De Gruyter. Copyright Clearance Center

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Meda Kondolot, Serpil Poyrazoğlu, Duygu Horoz, Arda Borlu, Canan Altunay, Elcin Balcı, Ahmet Öztürk, Mümtaz M. Mazıcıoğlu, and Selim Kurtoğlu
Journal of Pediatric Endocrinology and Metabolism, 2017, Volume 30, Number 5
[2]
Renata A. Carnauba, Daniela F. S. Chaves, Ana Beatriz Baptistella, Valéria Paschoal, Andreia Naves, and Anna Maria Buehler
International Journal of Food Sciences and Nutrition, 2017, Volume 68, Number 2, Page 158
[3]
Silvana Gaetani and Tommaso Cassano
Frontiers in Neuroscience, 2016, Volume 10
[4]
Wieland Kiess
Obesity Medicine, 2016, Volume 1, Page 21
[5]
Hermann L. Müller
Current Opinion in Endocrinology & Diabetes and Obesity, 2016, Volume 23, Number 1, Page 81
[6]
Wieland Kiess, Isabel V. Wagner, Jürgen Kratzsch, and Antje Körner
Endocrinology and Metabolism Clinics of North America, 2015, Volume 44, Number 4, Page 761

Comments (0)

Please log in or register to comment.
Log in