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. Self-reported 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 = 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: 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. Self-reported 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 = 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.
Authors: A M Santaliestra-Pasías; T Mouratidou; L Reisch; I Pigeot; W Ahrens; S Mårild; D Molnár; A Siani; S Sieri; M Tornatiris; T Veidebaum; V Verbestel; I De Bourdeaudhuij; L A Moreno Journal: Eur J Clin Nutr Date: 2015-06-03 Impact factor: 4.016
Authors: Trudy M A Wijnhoven; Joop M A van Raaij; Agneta Yngve; Agneta Sjöberg; Marie Kunešová; Vesselka Duleva; Ausra Petrauskiene; Ana I Rito; João Breda Journal: Public Health Nutr Date: 2015-07-01 Impact factor: 4.022
Authors: Erik Sigmund; Petr Badura; Dagmar Sigmundová; Jaroslava Voráčová; Jiří Zacpal; Michal Kalman; Jan Pavelka; Jana Vokacová; Vladimír Hobza; Zdenek Hamrik Journal: BMC Public Health Date: 2018-01-10 Impact factor: 3.295