Abstract
Physical activity is currently commonly summarized by simple composite scores of total activity, such as total metabolic equivalent score (METS), without further information about the many specific aspects of activities. We sought to identify more comprehensive physical activity patterns, and their association with cardiovascular disease risk factors.
The Northern Manhattan Study is a multiethnic cohort of stroke-free individuals. Questionnaires were used to capture multiple dimensions of leisure-time physical activity. Participants were grouped into METS categories and also into clusters by multivariate mixture modeling of activity frequency, duration, energy expenditure, and number of activity types. Associations between clusters and risk factors were assessed using χ2 tests.
Using data available in 3293 participants, we identified six model-based clusters that were differentiated by frequency and diversity of activities, rather than activity duration. High activity clusters had lower prevalence of the risk factors compared with those with lower activity; associations with obesity and hypertension remained significant after adjusting for METS (P = .027, .043). METS and risk factors were not significantly associated after adjusting for the clusters.
Data-driven clustering method is a principled, generalizable approach to depict physical activity and form subgroups associated with cardiovascular risk factors independently of METS.
