Active transportation increases adherence to activity recommendations.
ABSTRACT Levels of physical activity (PA) contribute to health status and outcomes directly and indirectly via the effects of PA on obesity and other risk factors. Much past surveillance has focused on leisure-time physical activity (LTPA), but this may bias estimates of prevalence. This study explores inclusion of non-leisure-time walking and bicycling (NLTWB) used for transportation on the prevalence of adherence to PA recommendations and the magnitude of apparent disparities in adherence for California adults.
Results of the 2001 California Health Interview Survey, a large (n = 55,151) telephone survey were analyzed in 2005 using tabulation and logistic regression.
Higher levels of LTPA were associated with youth, males, education, income, Pacific Islanders, and non-Hispanic (NH) whites. Inclusion of NLTWB reduced these differences for all five variables. The largest decreases in disparities in adherence occurred for race/ethnicity, education, and income, with decreases in adherence differences from approximately 18% to 7% for NH white vs Latino, approximately 27% to 16% for more than high school versus less than high school, and approximately 25% to 11% for more than 300% versus less than 100% of poverty level. Logistic regression comparing adherence gives similar results. For example, in respondents with more than high school education versus less than high school education (referent), the odds ratio changed from 2.23 (95% confidence interval [CI] = 2.0-2.4) to 1.7 (1.6-1.9) after the inclusion of NLTWB.
Assessment of PA in multiple domains is required to understand differences in total levels of PA for people with different incomes, education levels, and racial/ethnic backgrounds. Inclusion of NLTWB reduces but does not eliminate disparities in adherence to recommended levels of PA.
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ABSTRACT: Physical inactivity in each domain (leisure, work, commuting, and household) is not completely independent. This study aimed to describe the clustering of physical inactivity in different domains and its association with sociodemographic factors among Brazilian industrial workers. This was a cross-sectional, population-based study using data from 23 Brazilian states and the Federal District collected via questionnaires between 2006 and 2008. Physical inactivity in each domain was defined as non-participation in specific physical activities. Clustering of physical inactivity was identified using the ratio of the observed (O) and expected (E) percentages of each combination. Multinomial logistic regression was used to identify sociodemographic factors with the outcome. Among the 44,477 interviewees, most combinations exceeded expectations, particularly the clustering of physical inactivity in all domains among men (O/E = 1.37; 95%CI: 1.30; 1.44) and women (O/E = 1.47; 95%CI: 1.36; 1.60). Physical inactivity in two or more domains was observed more frequently in women, older age groups, individuals living without a partner, and those with higher education and income levels. Physical inactivity tends to be observed in clusters regardless of gender. Women and workers with higher income levels were the main factors associated with to be physically inactive in two or more domains.Journal of Physical Activity and Health 12/2014; DOI:10.1123/jpah.2014-0309 · 1.95 Impact Factor
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ABSTRACT: We conduct a cross-sectional ecological analysis to examine environmental correlates of active commuting in 39,660 urban tracts using data from the 2010 Census, 2007–2011 American Community Survey, and other sources. The five-year average (2007–2011) prevalence is 3.05% for walking, 0.63% for biking, and 7.28% for public transportation to work, with higher prevalence for all modes in lower-income tracts. Environmental factors account for more variances in public transportation to work but economic and demographic factors account for more variances in walking and biking to work. Population density, median housing age, street connectivity, tree canopy, distance to parks, air quality, and county sprawl index are associated with active commuting, but the association can vary in size and direction for different transportation mode and for higher-income and lower-income tracts.Health & Place 10/2014; 30:242–250. DOI:10.1016/j.healthplace.2014.09.014 · 2.44 Impact Factor