Habitual physical activity patterns in a nationally representative sample of U.S. adults

Susan K. Malone, Freda Patterson, Laura Grunin, Gail D. Melkus, Barbara Riegel, Naresh Punjabi, Gary Yu, Jacek Urbanek, Ciprian Crainiceanu, Allan Pack

Research output: Contribution to journalArticlepeer-review

Abstract

Physical inactivity is a leading determinant of noncommunicable diseases. Yet, many adults remain physically inactive. Physical activity guidelines do not account for the multidimensionality of physical activity, such as the type or variety of physical activity behaviors. This study identified patterns of physical activity across multiple dimensions (e.g., frequency, duration, and variety) using a nationally representative sample of adults. Sociodemographic characteristics, health behaviors, and clinical characteristics associated with each physical activity pattern were defined. Multivariate finite mixture modeling was used to identify patterns of physical activity among 2003-2004 and 2005-2006 adult National Health and Nutrition Examination Survey participants. Chi-square tests were used to identify sociodemographic differences within each physical activity cluster and test associations between the physical activity clusters with health behaviors and clinical characteristics. Five clusters of physical activity patterns were identified: (a) low frequency, short duration (n = 730, 13%); (b) low frequency, long duration (n = 392, 7%); (c) daily frequency, short duration (n = 3,011, 55%); (d) daily frequency, long duration (n = 373, 7%); and (e) high frequency, average duration (n = 964, 18%). Walking was the most common form of activity; highly active adults engaged in more varied types of activity. High-activity clusters were comprised of a greater proportion of younger, White, nonsmoking adult men reporting moderate alcohol use without mobility problems or chronic health conditions. Active females engaged in frequent short bouts of activity. Data-driven approaches are useful for identifying clusters of physical activity that encompass multiple dimensions of activity. These activity clusters vary across sociodemographic and clinical subgroups.

Original languageEnglish (US)
Pages (from-to)332-341
Number of pages10
JournalTranslational Behavioral Medicine
Volume11
Issue number2
DOIs
StatePublished - Feb 1 2021

Keywords

  • Cluster analysis
  • Leisure activity
  • Physical activity
  • Sociodemographics

ASJC Scopus subject areas

  • Applied Psychology
  • Behavioral Neuroscience

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