Association of Self-Reported Leisure-Time Physical Inactivity with Particulate Matter 2.5 Air Pollution

By Wen, Xiao-Jun; Balluz, Lina S. et al. | Journal of Environmental Health, July 2009 | Go to article overview

Association of Self-Reported Leisure-Time Physical Inactivity with Particulate Matter 2.5 Air Pollution


Wen, Xiao-Jun, Balluz, Lina S., Shire, Jeffrey D., Mokdad, Ali H., Kohl, Harold W., Journal of Environmental Health


Introduction

Physical inactivity is a risk factor that increases the incidence of at least 17 unhealthy conditions (Booth, Gordon, Carlson, & Hamilton, 2000) and mortality from chronic diseases (Franklin, Zeka, & Schwartz, 2007), including diabetes (Klein et al., 2004), obesity (Puska, Nishida, & Porter, 2007), cardiovascular disease (Hu et al., 2005), and depression (Motl, Birnbaum, Kubik, & Dishman, 2004).

Many factors correlated with physical inactivity in the general population have been identified (Sallis & Owen, 1999), which include low socioeconomic status, low educational level, older age, race, and the presence of an underlying chronic disease. Personal barriers, such as the lack of time and exercise equipment and the fear of pain or injury (Deshpande, Lovegreen, Baker, & Brownson, 2005), and physical environmental barriers, such as lack of access to facilities, land-use mix, and the perception of safety (Brownson, Baker, Housemann, Brennan, & Bacak, 2001; Humpel, Owen, & Leslie, 2002), are associated with physical inactivity. Social environmental factors, including social support, membership in organizations, social participation, and protective social and community factors are associated positively with increased physical activity (Brennan, Baker, Haire-Joshu, & Brownson, 2003; Giles-Corti & Donovan, 2002).

Particulate matter of aerodynamic diameter less than 2.5 [micro]m ([PM.sub.2.5]) is a mixture of solid, liquid, or solid and liquid particles that are suspended in the air. [PM.sub.2.5] comes mostly from the emissions from the combustion of fossil fuels from stationary sources, such as heating and power generation, and in motor vehicles. It is well known that ambient air pollution has been associated with an increased risk of chronic diseases, including respiratory and cardiovascular diseases (Laden, Neas, Dockery, & Schwartz, 2000; Pope & Dockery, 1999; Schwartz, 1991). It is conceivable, but not yet demonstrated, that [PM.sub.2.5] may influence physical inactivity in the densely populated areas where the sources of [PM.sub.2.5] generation are abundant.

In this study, we explored the possible association between the annual average [PM.sub.2.5] concentration and self-reported leisure-time physical inactivity (LTPI) by using data from the Behavioral Risk Factor Surveillance System (BRFSS) and the U.S. Environmental Protection Agency (U.S. EPA) air quality data from 2001.

Methods

BRFSS is a standardized state-based telephone survey system that is designed to collect data on health behaviors and conditions from noninstitutionalized adults who are aged 18 years and above. The data is collected by state health departments with assistance from the Centers for Disease Control and Prevention (CDC). The advanced survey design and the characteristics of the BRFSS have been described elsewhere (Mokdad, Stroup, & Giles, 2003; Nelson, Holtzman, Waller, Leutzinger, & Condon, 1998; Remington et al., 1988).

We assessed self-reported LTPI through responses to the question, "During the past 30 days, other than your regular job, did you participate in any physical activities or exercise such as running, calisthenics, golf, gardening, or walking for exercise?" The respondents had four choices: "yes," "no," "don't know/not sure," or "refused." A "no" answer to this question is defined as LTPI.

U.S. EPA's Air Quality System (AQS) database contains measurements of concentrations of the six criteria pollutants at sites in all 50 states. U.S. EPA collects data related to the daily concentrations of the six criteria pollutants through on-site monitors. Not all six pollutants, however, are monitored year round in most locations. U.S. EPA also uses predicted concentrations of each pollutant each day to calculate air quality index (AQI) scores for public health alerts. The data we used are the measured concentrations of the [PM. …

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