The European Journal of Public Health Advance Access originally published online on January 22, 2009
The European Journal of Public Health 2009 19(3):271-277; doi:10.1093/eurpub/ckn144
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Health Behaviours |
Optimising lifestyle interventions: identification of health behaviour patterns by cluster analysis in a German 50+ survey
Sven Schneider1, Christina Huy1, Marc Schuessler1, Katharina Diehl1 and Stefanie Schwarz2
1 Mannheim Institute of Public Health, Social and Preventive Medicine (MIPH), University of Heidelberg, Mannheim Medical Faculty, Mannheim, Germany
2 Faculty of Social Sciences, University of Mannheim, Mannheim, Germany
Correspondence: Sven Schneider, Mannheim Institute of Public Health, Social and Preventive Medicine (MIPH), Mannheim Medical Faculty, University of Heidelberg, Ludolf-Krehl-Straße 7-11, D-68167 Mannheim, Germany. tel: +49 621 383 99 17, fax: +49 621 383 99 20, e-mail: sven.schneider{at}medma.uni-heidelberg.de
Received July 9, 2008 , accepted December 22, 2008
Background: Many prevention and intervention measures are still targeting isolated behaviours such as tobacco use or physical inactivity. Cluster analysis enables the aggregation of single health behaviours in order to identify distinctive behaviour patterns. The purpose of this study was to group a sample of the over-50 population into clusters that exhibit specific health behaviour patterns regarding regular tobacco use, excessive alcohol consumption, unhealthy diet and physical inactivity. Methods: From the total population of the federal state of Baden-Wuerttemberg, Germany, 982 men and 1020 women aged 50–70 were randomly selected. Subjects were asked by trained interviewers in computer-assisted telephone interviews (CATI) about health behaviour and sociodemographic characteristics. Cluster analysis was conducted to identify distinct health behaviour patterns. Multinomial logistic regression was used to characterize clusters by specific social attributes. Results: Five homogeneous health behaviour clusters were identified: ‘No Risk Behaviours’ (25.3%), ‘Physically Inactives’ (21.1%), ‘Fruit and Vegetable Avoiders’ (18.2%), ‘Smokers with Risk Behaviours’ (12.7%) and ‘Drinkers with Risk Behaviours’ (22.7%). Whereas the first cluster is the ideal in terms of risk and prevention, the latter two groups include regular users of tobacco and excessive consumers of alcohol, who also engage in other risk behaviours like inactivity and maintaining an unhealthy diet. These two risk groups also exhibit specific sociodemographic attributes (male, living alone, social class affiliation). Conclusion: Unhealthy behaviours evidently occur in typical combinations. An awareness of this clustering enables prevention and intervention measures to be planned so that multiple behaviours can be modified simultaneously.
Keywords: cluster analysis, health behaviour, lifestyle, prevention, risk factors