Sydney A Jones1, Fang Wen2, Amy H Herring3, Kelly R Evenson2. 1. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, USA. Electronic address: SydneyJones@unc.edu. 2. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, USA. 3. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, USA.
Abstract
OBJECTIVES: Physical activity and sedentary behavior patterns may be differentially associated with socio-demographic and health measures. We explored correlates of day-to-day patterns over a week in accelerometer measured physical activity and sedentary behavior to inform intervention development. DESIGN: Cross-sectional study. METHODS: National Health and Nutrition Examination Survey (NHANES) adult participants (≥20 years) in 2003-2006 wore an accelerometer for 1 week. Accelerometer data from 7236 participants were used to derive latent classes describing day-to-day patterns over a week of physical activity and sedentary behavior. Correlates of each pattern were identified using multinomial logistic regression from 21 potential variables grouped into four domains: socio-demographic, acculturation, cardiovascular, and health history. RESULTS: Older age, female sex, higher body mass index, and history of chronic disease were consistently associated with lower odds of being in a more active compared to the least active class. In contrast, being employed, speaking Spanish at home, and having better self-rated health were associated with higher odds of being in a more active compared to the least active class. CONCLUSIONS: Correlates of physical activity and sedentary behavior patterns were identified from all domains (socio-demographic, acculturation, cardiovascular, and health history). Most correlates that were positively associated with physical activity were negatively associated with sedentary behavior. Better understanding of the correlates of physical activity and sedentary behavior patterns can inform interventions to promote physical activity and reduce sedentary behavior. Copyright Â
OBJECTIVES: Physical activity and sedentary behavior patterns may be differentially associated with socio-demographic and health measures. We explored correlates of day-to-day patterns over a week in accelerometer measured physical activity and sedentary behavior to inform intervention development. DESIGN: Cross-sectional study. METHODS: National Health and Nutrition Examination Survey (NHANES) adult participants (≥20 years) in 2003-2006 wore an accelerometer for 1 week. Accelerometer data from 7236 participants were used to derive latent classes describing day-to-day patterns over a week of physical activity and sedentary behavior. Correlates of each pattern were identified using multinomial logistic regression from 21 potential variables grouped into four domains: socio-demographic, acculturation, cardiovascular, and health history. RESULTS: Older age, female sex, higher body mass index, and history of chronic disease were consistently associated with lower odds of being in a more active compared to the least active class. In contrast, being employed, speaking Spanish at home, and having better self-rated health were associated with higher odds of being in a more active compared to the least active class. CONCLUSIONS: Correlates of physical activity and sedentary behavior patterns were identified from all domains (socio-demographic, acculturation, cardiovascular, and health history). Most correlates that were positively associated with physical activity were negatively associated with sedentary behavior. Better understanding of the correlates of physical activity and sedentary behavior patterns can inform interventions to promote physical activity and reduce sedentary behavior. Copyright Â
Authors: Charles E Matthews; Kong Y Chen; Patty S Freedson; Maciej S Buchowski; Bettina M Beech; Russell R Pate; Richard P Troiano Journal: Am J Epidemiol Date: 2008-02-25 Impact factor: 4.897
Authors: Richard P Troiano; David Berrigan; Kevin W Dodd; Louise C Mâsse; Timothy Tilert; Margaret McDowell Journal: Med Sci Sports Exerc Date: 2008-01 Impact factor: 5.411
Authors: Marita Södergren; Wei Chun Wang; Jo Salmon; Kylie Ball; David Crawford; Sarah A McNaughton Journal: Maturitas Date: 2013-09-29 Impact factor: 4.342
Authors: Jason A Bennie; Josephine Y Chau; Hidde P van der Ploeg; Emmanuel Stamatakis; Anna Do; Adrian Bauman Journal: Int J Behav Nutr Phys Act Date: 2013-09-11 Impact factor: 6.457
Authors: Sophie Baumann; Diana Guertler; Franziska Weymar; Martin Bahls; Marcus Dörr; Neeltje van den Berg; Ulrich John; Sabina Ulbricht Journal: J Behav Med Date: 2019-06-12
Authors: Gerson Ferrari; Adilson Marques; Tiago V Barreira; Irina Kovalskys; Georgina Gómez; Attilio Rigotti; Lilia Yadira Cortés; Martha Cecilia Yépez García; Rossina G Pareja; Marianella Herrera-Cuenca; Viviana Guajardo; Ana Carolina B Leme; Juan Guzmán Habinger; Pedro Valdivia-Moral; Mónica Suárez-Reyes; Andreas Ihle; Elvio R Gouveia; Mauro Fisberg Journal: Int J Environ Res Public Health Date: 2021-04-27 Impact factor: 3.390
Authors: Pearl A McElfish; Brett Rowland; Aaron J Scott; Janine Boyers; Christopher R Long; Holly C Felix; Joseph Keawe'aimoku Kaholokula; Ka'imi Sinclair; Zoran Bursac; Sheldon Riklon Journal: J Immigr Minor Health Date: 2021-04-10
Authors: Leah M Schumacher; Stephanie G Kerrigan; Jocelyn E Remmert; Christine C Call; Fengqing Zhang; Meghan L Butryn Journal: Psychol Sport Exerc Date: 2019-01-31
Authors: Charlotte Lund Rasmussen; Dorothea Dumuid; Karel Hron; Nidhi Gupta; Marie Birk Jørgensen; Kirsten Nabe-Nielsen; Andreas Holtermann Journal: BMC Public Health Date: 2021-07-07 Impact factor: 3.295