Reference : Types of health-related behaviours: a cluster analysis of the Luxembourgish HBSC data

	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Human health sciences : Public health, health care sciences & services
	To cite this reference:	http://hdl.handle.net/10993/47291

Title :	Types of health-related behaviours: a cluster analysis of the Luxembourgish HBSC data
Language :	English
Author, co-author :	Heinz, Andreas [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) >]
	Willems, Helmut Erich [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS) >]
	van Duin, Claire [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) >]
	Catunda, Carolina [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) >]
	Residori, Caroline [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) >]
Publication date :	Jun-2021
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	HBSC Spring Meeting 2021
Event date :	10-11 June 2021
Event organizer :	HBSC International Coordinating Centre
Keywords :	[en] HBSC ; health behaviour ; hierarchical cluster analysis ; substance use ; risk behaviour ; physical activity
Abstract :	[en] Background: Although it is known that health behaviours, socio-demographic variables and outcomes correlate, it is rarely investigated if there are typical patterns of these variables among the research subjects. Objectives: To find out whether the students can be divided into distinct groups based on their health behaviour and whether these groups differ in other ways (outcomes and socio-demographics). Method: In step 1, a hierarchical cluster analysis was carried out to determine the number of groups and to identify the cluster centres. In step 2, this information was entered as the initial values of a cluster centre analysis. In step 3, the clusters were characterised using additional variables. Results: The 8065 students surveyed could be divided into 5 distinct groups based on their data on smoking, drinking, soft drinks, exercising, fighting and bullying, with cluster 1 and cluster 5 representing the strongest contrast. Cluster 1 comprises students whose health behaviour is generally positive. It is the largest cluster with 49.5% of students. Cluster 5 comprises students whose behaviour is consistently negative. It is the smallest cluster with 7.1% of students. Students in cluster 2 are close to average on many variables, but their dental health is problematic because they frequently consume soft drinks and rarely brush their teeth. Students in cluster 3 are physically inactive, their mental health is poor, but they are also rarely injured. The students in cluster 4 stand out because of their aggressive behaviour. Conclusion: With the help of cluster analysis, it is possible to categorise the students into a small number of groups based on their health behaviour. These groups are coherent in terms of health behaviour, many outcome variables and socio-demographic variables.
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/47291

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