The present data suggest that active commuting to school per se does not affect FM or BMI until considering distance to school. Increasing walking or cycling distance results in decreasing FM. However, the everyday need to get to and from school may enhance adolescents' overall PA.

Objective: To identify lifestyle clusters in adolescents and to characterize their association with overweight and obesity. Design: Cross-sectional and longitudinal data of the Kiel Obesity Prevention Study. Setting: Schools in Kiel, Germany. Subjects and methods: Cross-sectional data of 1894 adolescents aged 14 years and 4-year longitudinal data of a subsample of 389 children aged 10 and 14 years. Selfreported data of physical activity, modes of commuting to school, media time, nutrition, alcohol consumption and smoking were used to identify lifestyle clusters with two-step cluster analysis. Obesity indices (height, weight, waist circumference and fat mass (FM)) were measured. Results: Three lifestyle clusters were identified: a 'low activity and low-risk behaviour' cluster (cluster 1: n 740, 39?1 %); a 'high media time and high-risk behaviour' cluster (cluster 2: n 498, 26?3 %); and a 'high activity and medium-risk behaviour' cluster (cluster 3: n 656, 34?6 %). Strictly speaking, none of these clusters was considered to be markedly healthy. The prevalence of overweight and obesity tended to be lower in cluster 3 (15?9 %) than in clusters 1 (20?4 %) and 2 (20?5 %; P 5 0?053). Longitudinally, 4-year changes in FM were found to be lowest in cluster 2, but the 4-year incidence rate of obesity was lowest in cluster 3. Conclusions: Explicit healthy lifestyles do not exist, but an active lifestyle reduces the incidence of obesity. In adolescents, health promotion should take into account the diversity of lifestyles and address specific lifestyle clusters.

The objective was to examine longitudinal 4-year-relationships between neighbourhood social environment and children’s body mass index-standard deviation score (BMI-SDS) taking into account the built environment. Furthermore, we have analysed the influence of potential interactions between the social environment and family/social data on children’s BMI-SDS. Between 2006–2008 and 2010–2012, anthropometric measurements were conducted among 485 children (age at baseline: 6.1 (5.8–6.4)). Socio-demographic characteristics and perception of residential environment were reported by parents. Geographic Information Systems were used to examine street length, number of food outlets and distance to the nearest playground and park/green space within an 800 m Euclidian buffer of each participant address point. Additional data on neighbourhood characteristics (e.g., traffic density, walkability, crime rates) were obtained from the State Capital of Kiel, Germany. In a multivariate model, walkability, street type, socioeconomic status of the district and perceived frequency of passing trucks/busses were associated with BMI-SDS over 4 years, but only neighbourhood SES had an effect on change in BMI-SDS. However, familial/social factors rather than neighbourhood environment (especially social environment) had an impact on children’s BMI-SDS over 4 years. Thus, social inequalities in childhood overweight are only partially explained by social neighbourhood environment.

Objective: To systematically analyse determinants of overweight prevalence and incidence in children and adolescents, as a basis of treatment and prevention. Design: Cross-sectional and longitudinal data of the Kiel Obesity Prevention Study (KOPS). Setting: Schools in Kiel, Germany. Subjects: Cross-sectional data from 6249 students aged 5-16 years and 4-year longitudinal data from 1087 children aged 5-11 years. Weight status of students was assessed and familial factors (weight status of parents and siblings, smoking habits), social factors (socio-economic status, nationality, single parenting), birth weight as well as lifestyle variables (physical activity, media time, nutrition) were considered as independent variables in multivariate logistic regression analyses to predict the likelihood of the student being overweight. Results: The cross-sectional data revealed the prevalence of overweight as 18?3 % in boys and 19?2 % in girls. In both sexes determinants of overweight prevalence were overweight and obese parents, overweight siblings, parental smoking, single parenthood and non-German nationality. High birth weight and low physical activity additionally increased the risk in boys. High media time and low parental education were significant determinants in girls. Effect of media time was mediated by maternal weight status in boys as well as by socio-economic status and age in girls. From the longitudinal data, the 4-year cumulative incidence of overweight was 10?0 % in boys and 8?2 % in girls. Parental obesity, parental smoking and low physical activity were determinants of overweight incidence in boys, whereas paternal obesity increased the risk in girls.Conclusions: Treatment and prevention should address family and social determinants with a focus on physical activity and media use.Childhood obesity is a major public health challenge. At present there is a lack of convincing evidence about suitable and effective strategies for the prevention of childhood overweight. Recently, an obesity prevention evidence framework has been proposed (1) . Key policies include: (i) building a case for action on obesity; (ii) identifying contributing factors and points of intervention; (iii) defining opportunities for action; (iv) evaluating potential interventions; and (v) selecting a portfolio of specific policies, programmes and actions. Therefore, a systematic analysis of determinants of overweight in the micro-as well as the macro-environment is necessary to provide a sound basis for developing strategies against overweight. The systematic analysis should include an analysis of the determinants of overweight prevalence as well as overweight incidence, separately. Childhood overweight (and not only obesity) is predictive for adult morbidity and mortality (2) . In addition, the life-long persistence and health consequences of overweight and obesity in many children suggest a strong need for the prevention of overweight (2) . Primary prevention strategies address the whole population, in particular normalweight sub...

The aim of the present study was to compare individual associations of BMI, triceps skinfold (TSF), waist circumference (WC) and percentage fat mass (%FM) with blood pressure (BP) and blood lipids in children and adolescents. Cross-sectional data on BMI, TSF, WC, %FM as well as on BP, TAG and HDL were analysed in 4220 (BP) and 729 (lipids) 9 -11-year-old children and 3174 (BP) and 536 (lipids) 13 -16-year-old adolescents as part of the Kiel Obesity Prevention Study. All obesity indices were similarly associated with BP and blood lipids. In girls, WC had closer correlations to BP than BMI (systolic BP: 0·27 and 0·24 for BMI, 0·34 and 0·28 for WC in 9 -11-and 13 -16-year-olds). Subjects with an obesity index $ 90th percentile had higher prevalences of elevated BP and blood lipids than subjects with a normal index. In children with normal BMI or WC, an additionally elevated second obesity index was associated with a 2·5 -7·4-fold higher prevalence of high BP when compared with children with normal indices. In adolescents, an elevated WC plus an elevated second obesity index was associated with a 2·6 -8·2-fold higher prevalence of high BP when compared with adolescents with an elevated WC plus a normal second index. We conclude that (i) both BMI and WC are appropriate to estimate CVD risk, (ii) the use of a second obesity index is recommended in children with normal BMI or normal WC as well as in adolescents with elevated WC and (iii) all obesity indices seemed to be appropriate for risk assessment. Overweight: Children: Blood lipids: Cardiovascular disease risk factorsChildhood overweight is a public health problem. Defining overweight in children and adolescents is not uniform with respect to obesity indices and cut-offs used. BMI is widely used as a measure of fat mass (FM) and international BMI reference values for children and adolescents have been published (1) . However, BMI is only an indirect parameter of total body fat and does not reflect body fat distribution (2,3) . In addition, the association between BMI and disease risk is unproven in children and adolescents. In adults, BMI cutoffs for overweight and obesity were defined according to overweight-and obesity-associated co-morbidity (4) . However, in children prospective data on the association between obesity indices and disease risk are rare; for example, longitudinal data of the Bogalusa Heart Study showed a relationship between childhood obesity and incidence of metabolic disorders in young adulthood (5) . The International Obesity Taskforce Working Group recommended that BMI cut-offs for defining overweight and obesity in children should be linked to the adult disease-related cut-off points of 25 and 30 kg/m 2(1) . In addition to BMI, triceps skinfold thickness (TSF), waist circumference (WC) and percentage FM (%FM) as derived from bioelectrical impedance analysis have been recommended to identify individuals with increased overweight-associated disease risks (3) . However, the use of these obesity indices is limited because of methodological aspe...