Literature DB >> 31985811

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

Susan K Malone1, Freda Patterson2, Laura Grunin1, Gail D Melkus1, Barbara Riegel3, Naresh Punjabi4, Gary Yu1, Jacek Urbanek4, Ciprian Crainiceanu5, Allan Pack6.   

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. © Society of Behavioral Medicine 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  Cluster analysis; Leisure activity; Physical activity; Sociodemographics

Mesh:

Year:  2021        PMID: 31985811      PMCID: PMC7963290          DOI: 10.1093/tbm/ibaa002

Source DB:  PubMed          Journal:  Transl Behav Med        ISSN: 1613-9860            Impact factor:   3.046


  39 in total

1.  Long-term recreational physical activity and breast cancer in the National Health and Nutrition Examination Survey I epidemiologic follow-up study.

Authors:  R A Breslow; R Ballard-Barbash; K Munoz; B I Graubard
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2001-07       Impact factor: 4.254

Review 2.  Dose-response of physical activity and low back pain, osteoarthritis, and osteoporosis.

Authors:  I M Vuori
Journal:  Med Sci Sports Exerc       Date:  2001-06       Impact factor: 5.411

3.  Age and gender differences in objectively measured physical activity in youth.

Authors:  Stewart G Trost; Russell R Pate; James F Sallis; Patty S Freedson; Wendell C Taylor; Marsha Dowda; John Sirard
Journal:  Med Sci Sports Exerc       Date:  2002-02       Impact factor: 5.411

4.  Inadequate physical activity and health care expenditures in the United States.

Authors:  Susan A Carlson; Janet E Fulton; Michael Pratt; Zhou Yang; E Kathleen Adams
Journal:  Prog Cardiovasc Dis       Date:  2014-08-09       Impact factor: 8.194

5.  Physical Activity, All-Cause and Cardiovascular Mortality, and Cardiovascular Disease.

Authors:  William E Kraus; Kenneth E Powell; William L Haskell; Kathleen F Janz; Wayne W Campbell; John M Jakicic; Richard P Troiano; Kyle Sprow; Andrea Torres; Katrina L Piercy
Journal:  Med Sci Sports Exerc       Date:  2019-06       Impact factor: 5.411

6.  Leisure-Time Physical Activity and Cardiovascular Mortality in an Elderly Population in Northern Manhattan: A Prospective Cohort Study.

Authors:  Ying Kuen Cheung; Yeseon P Moon; Erin R Kulick; Ralph L Sacco; Mitchell S V Elkind; Joshua Z Willey
Journal:  J Gen Intern Med       Date:  2016-10-17       Impact factor: 5.128

7.  The benefits and challenges of multiple health behavior change in research and in practice.

Authors:  Judith J Prochaska; Claudio R Nigg; Bonnie Spring; Wayne F Velicer; James O Prochaska
Journal:  Prev Med       Date:  2009-12-04       Impact factor: 4.018

8.  Physical Activity and Incident Depression: A Meta-Analysis of Prospective Cohort Studies.

Authors:  Felipe B Schuch; Davy Vancampfort; Joseph Firth; Simon Rosenbaum; Philip B Ward; Edson S Silva; Mats Hallgren; Antonio Ponce De Leon; Andrea L Dunn; Andrea C Deslandes; Marcelo P Fleck; Andre F Carvalho; Brendon Stubbs
Journal:  Am J Psychiatry       Date:  2018-04-25       Impact factor: 18.112

9.  Smoking, Screen-Based Sedentary Behavior, and Diet Associated with Habitual Sleep Duration and Chronotype: Data from the UK Biobank.

Authors:  Freda Patterson; Susan Kohl Malone; Alicia Lozano; Michael A Grandner; Alexandra L Hanlon
Journal:  Ann Behav Med       Date:  2016-10

10.  Trends in Adherence to the Physical Activity Guidelines for Americans for Aerobic Activity and Time Spent on Sedentary Behavior Among US Adults, 2007 to 2016.

Authors:  Yang Du; Buyun Liu; Yangbo Sun; Linda G Snetselaar; Robert B Wallace; Wei Bao
Journal:  JAMA Netw Open       Date:  2019-07-03
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  2 in total

1.  Psychosocial Mediation of Light-Moderate Physical Activity and Cognitive Performance among Adults Aged 60+ in China.

Authors:  Ji Liu; Faying Qiang
Journal:  Behav Sci (Basel)       Date:  2022-06-01

Review 2.  Golf and Health, More than 18 Holes-A Bibliometric Analysis.

Authors:  María Del Mar Martín-García; José Luis Ruiz-Real; Juan Carlos Gázquez-Abad; Juan Uribe-Toril
Journal:  Healthcare (Basel)       Date:  2022-07-16
  2 in total

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