TY - JOUR
T1 - Habitual physical activity patterns in a nationally representative sample of U.S. adults
AU - Malone, Susan K.
AU - Patterson, Freda
AU - Grunin, Laura
AU - Melkus, Gail D.
AU - Riegel, Barbara
AU - Punjabi, Naresh
AU - Yu, Gary
AU - Urbanek, Jacek
AU - Crainiceanu, Ciprian
AU - Pack, Allan
N1 - Funding Information:
This study was funded by the National Institute of Nursing Research (K99NR017416), grant support for Dr. Freda Patterson: National Institute on Minority Health and Health Disparities of the National Institutes of Health (R01MD012734) (FP), Institutional Development Award (IDeA) Center of Biomedical Research Excellence from the National Institute of General Medical Sciences of the National Institutes of Health (P20GM113125). Support for Dr. Susan Malone: National Heart, Lung, and Blood Institute (T32HL7953).
Publisher Copyright:
© 2020 Society of Behavioral Medicine 2020. All rights reserved.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - 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.
AB - 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.
KW - Cluster analysis
KW - Leisure activity
KW - Physical activity
KW - Sociodemographics
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U2 - 10.1093/tbm/ibaa002
DO - 10.1093/tbm/ibaa002
M3 - Article
C2 - 31985811
AN - SCOPUS:85103227725
VL - 11
SP - 332
EP - 341
JO - Translational Behavioral Medicine
JF - Translational Behavioral Medicine
SN - 1869-6716
IS - 2
ER -