ABSTRACT
BACKGROUND: Lifestyle factors often co-occur in clusters. This study examines whether clusters of lifestyle risk factors, such as smoking, alcohol use, physical inactivity, poor diet, sexual risk behaviour, cannabis and other drug use, change over time in a representative sample of Dutch adults. Additionally, the association between mental health and self-reported depression of lifestyle clusters was examined. METHODS: Each year cross-sectional data of approximately 7500 individuals of 18 years and older from the annual Dutch Health Survey of 2014-2019 were used. Clusters were determined by a two-step cluster analysis. Furthermore, regression analyses determined the association between clusters of lifestyle risk factors and mental health. RESULTS: Results show six clusters composed of one, multiple or no lifestyle risk factors. The clusters remained relatively stable over time: in some clusters, the number of people slightly changed between 2014 and 2019. More specifically, clusters that increased in size were the cluster with no lifestyle risk factors and the cluster with multiple lifestyle risk factors. Furthermore, results show that clusters with none to a few lifestyle risk factors were associated with better mental health and a lower prevalence of self-reported depression compared with clusters with multiple lifestyle risk factors. CONCLUSIONS: The clustering of lifestyle risk factors remained stable over time. People with multiple lifestyle risk factors had poorer mental health than those without risk factors. These findings may emphasize the need for intervention strategies targeting this subgroup with multiple lifestyle risk factors.
Subject(s)
Life Style , Mental Health , Adult , Humans , Cross-Sectional Studies , Risk Factors , Cluster AnalysisABSTRACT
The Lifestyle Monitor (LSM) was launched in 2013 on behalf of the Ministry of Health, Welfare and Sports to reorganize the multiple data collections in the field of lifestyle and health in the Netherlands. The reorganization should enhance the efficiency and coherence of the lifestyle and health data collections and should provide unambiguous figures for policymakers. This article describes the background and content (including the lifestyle-themes) of the LSM and the tasks and roles of the collaborating parties involved. The measurement methods used and requirements for data requests are described as well. Finally, some examples of figures and trends over the period 2014-2020 to underpin the health policy are described.
