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.

Objective: The aim of this study was to evaluate the 8-year outcome of school-based intervention on weight status, lifestyle and blood pressure (BP) as part of the Kiel Obesity Prevention Study (KOPS). Methods: Within a quasi-randomized controlled trial, 240 intervention (I) and 952 non-intervention (NI) students at age 6 and 14 years were assessed in schools. Six nutrition units followed by 20-min running games were performed within the first year at school. Primary outcome was the 8-year change in body mass index standard deviation score (BMI-SDS) according to German references. Effective intervention was tested using multilevel linear regression analysis. Results: Eight-year changes in BMISDS were +0.18 and +0.22 with increases in prevalence of overweight from 8.3 to 10.4% and 7.0 to 11.2% in I and NI students, respectively. Cumulative 8-year incidence of overweight was 5.9% and 7.1% in I and NI students, respectively. There was no overall effect of intervention, but a significant interaction was shown between the intervention and the socio-economic status (SES), which demonstrated that in high SES, the 8-year change in BMI-SDS was in favour of I (–0.17 in I and +0.17 in NI; p < 0.01). Intervention had no measurable effects on lifestyle and BP. Conclusions: School-based health promotion has some favourable and sustained effects on 8-year changes in BMI-SDS, which are most pronounced in students of high SES families. The data argue in favour of further preventive measures.

AIMS:Prevention of obesity is a public health agenda. There are only few longitudinal studies on prevention of overweight in children. The Kiel Obesity Prevention Study (KOPS) intends to characterise the determinants of childhood overweight and the effect of preventive measures within schools as well as within families. METHODS: Between 1996 and 2005, KOPS investigated 4997 German 5-7 and 4487 9-11-y-old children or 41 and 37% of the total population of all first and fourth graders in 32 primary schools in Kiel (248 000 inhabitants), northwest Germany. Main outcome measures were nutritional status, health habits and risk factors of disease. In addition, health promotion was performed each year in three schools for all first graders and their teachers (nutrition education and active school breaks) together with a family-oriented approach in families with obese and preobese children. Up to now, the children were followed for 4 y and were reinvestigated at age 10 y. RESULTS: The KOPS population was representative for all 5-7 and 9-11-y-old children in Kiel. The prevalence of overweight/ obesity (Z90th/97th BMI reference percentile) was 7.0/5.8 and 11.3/6.3% in 5-7 and 9-11-y-old children, respectively. Parental overweight, a low socio-economic status and a high birth weight were identified as main risk factors for overweight in prepubertal children. The first results of the interventions show that obesity prevention was possible, but there were limited success rates in boys and children from low social class. CONCLUSION: Faced with the environmental contributors to the obesity problem societal rather than individual responsibilities are evident. This idea suggests that dissecting and tackling the obesogenic environment is necessary to complement school-and family-based interventions.

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.