Schneider Sven, Huy Christina, Schuessler Marc, Diehl Katharina, Schwarz Stefanie
Mannheim Institute of Public Health, Social and Preventive Medicine (MIPH), University of Heidelberg, Mannheim Medical Faculty, Mannheim, Germany.
Eur J Public Health. 2009 Jun;19(3):271-7. doi: 10.1093/eurpub/ckn144. Epub 2009 Jan 22.
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.
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.
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).
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.
许多预防和干预措施仍针对诸如吸烟或缺乏体育锻炼等孤立行为。聚类分析能够将单一健康行为汇总起来，以识别独特的行为模式。本研究的目的是将50岁以上人群的样本分组为不同的聚类，这些聚类在定期吸烟、过量饮酒、不健康饮食和缺乏体育锻炼方面呈现出特定的健康行为模式。
从德国巴登-符腾堡州的总人口中，随机选取了982名年龄在50 - 70岁之间的男性和1020名女性。由经过培训的访谈员通过计算机辅助电话访谈（CATI）询问受试者的健康行为和社会人口学特征。进行聚类分析以识别不同的健康行为模式。使用多项逻辑回归通过特定的社会属性对聚类进行特征描述。
识别出了五个同质的健康行为聚类：“无风险行为者”（25.3%）、“缺乏体育锻炼者”（21.1%）、“避免食用水果和蔬菜者”（18.2%）、“有风险行为的吸烟者”（12.7%）和“有风险行为的饮酒者”（22.7%）。虽然第一个聚类在风险和预防方面是理想的，但后两个群体包括经常吸烟的人和过量饮酒的人，他们还参与其他风险行为，如缺乏体育锻炼和保持不健康饮食。这两个风险群体也呈现出特定的社会人口学属性（男性、独居、社会阶层归属）。
不健康行为显然以典型的组合形式出现。认识到这种聚类情况有助于规划预防和干预措施，以便能同时改变多种行为。
