Background
Individuals with overweight/obesity are a heterogeneous population and a better understanding of factors differentiating subgroups can help deliver more targeted weight management interventions that benefit everyone equally. Previous research employed cluster analysis to understand heterogeneity within a population with obesity in one region of England, using the Yorkshire Health Study (YHS) dataset. The aim of this study is to build on that research and contribute a more detailed understanding of subgroups to support more tailored weight management strategies.
Methods
The study entailed using cluster analysis methods to identify a number of discrete subgroups characterised by demographic, health and lifestyle commonalities, using a larger Yorkshire Health Study (YHS) dataset (n = 47,080) and broader range of weight categories (healthy weight, overweight and obesity). Clustering involved using the k-prototypes method for mixed data types and the optimum number of clusters was determined by identifying the point of inflexion (elbow) on the scree plot.
Results
Six-clusters were identified as the optimum overall solution, which comprised six distinct subgroups differentiated by a range of variables related to weight status: younger, healthy, active, heavy drinking males; older with poor physical health, but good quality of life; older with poor health, quality of life and well-being; older, ex-smokers with poor health but high well-being; younger, healthy and active females; and younger with poor mental health and well-being.
Conclusions
The findings contribute additional insight on differences between specific population groups in relation to key determinants of weight. This understanding should ensure that within an overall systems based approach to tackling this major public health issue, there is adequate attention to delivering more tailored weight management strategies for different groups.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)