title: Patterns of leisure-time physical activity using multivariate finite mixture modeling and cardiovascular risk factors in the Northern Manhattan Study.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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 χ(2) tests.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  Data-driven clustering method is a principled, generalizable approach to depict physical activity and form subgroups associated with cardiovascular risk factors independently of METS.
authors: Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY. Electronic address: yc632@columbia.edu.
authors: Ying Kuen
authors: YK
authors: Cheung
authors: Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY.
authors: Gary
authors: G
authors: Yu
authors: Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY.
authors: Melanie M
authors: MM
authors: Wall
authors: Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL; Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami FL.
authors: Ralph L
authors: RL
authors: Sacco
authors: Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY.
authors: Mitchell S V
authors: MS
authors: Elkind
authors: Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY.
authors: Joshua Z
authors: JZ
authors: Willey
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000428:Alcohol Drinking / Q000453:epidemiology; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000208:ethnology; D016000:Cluster Analysis; D005006:Ethnicity; D015444:Exercise; D005260:Female; D006801:Humans; D006973:Hypertension / Q000453:epidemiology; D007899:Leisure Activities; D008297:Male; D055686:Metabolic Equivalent; D008875:Middle Aged; D009765:Obesity / Q000453:epidemiology; D012307:Risk Factors; D012737:Sex Factors; D012907:Smoking / Q000453:epidemiology; D012944:Social Support; D012959:Socioeconomic Factors; D011795:Surveys and Questionnaires / Q000592:standards*; D013997:Time Factors
keywords: Cluster analysis; Exercise; Hypertension; Metabolic equivalent; Obesity; Questionnaires