Table 1.
Summary of studies (from oldest to newest) that examined impact of air pollution on physical activity in An et al. (2018) and An et al. (2019) reviews.
| Study | Country | Study design | Sample size | Age (years) | Air pollution measure | Main findings |
|---|---|---|---|---|---|---|
| Wen et al. (2009b) | U.S. | Cross-sectional | 33,888 | ≥18 | Air quality index | The prevalence of change in outdoor activity due to media alerts of AQI was 31% among adults with lifetime asthma and 16% without asthma. The prevalence of outdoor activity change increased to 75% among those with lifetime asthma and to 68% without asthma, when the combined the effects of media alerts and individual perception were examined. The odds of activity change based on the media alerts was 2.30 (OR = 2.16, 95% CI = 1.61, 2.90) among those with lifetime asthma and 1.72 (OR = 1.72, 95% CI = 1.50, 1.98) without asthma, compared to those unaware of media alerts, after adjustment for demographic variables and covariates. |
| Wen et al. (2009) Feb | U.S. | Cross-sectional | 63,290 | ≥18 | PM2.5 | A 10-unit (μg/m3) increase in county annual average PM2.5 concentration was found to be associated with an increase in the odds of physical inactivity by 16% (OR = 1.16, 95% CI = 1.06, 1.27). |
| Hankey et al. 2012 | U.S. | Cross-sectional | 30,007 | 38 (range: 21–54) | PM2.5, O3, NOx | Between-neighborhood differences in the estimated risk for ischemic heart disease (IHD) mortality from air pollution were comparable in magnitude (9 more risk for IHD deaths/100,000/year for PM2.5 and 3 fewer IHD deaths for O3 in high- vs. low-walkability neighborhoods), suggesting that population health benefits from increased physical activity in high-walkability neighborhoods may be offset by adverse effects of air pollution exposure. |
| Wells et al. 2012 | U.S. | Cross-sectional | 10,898 | 46.9 (95% CI: 46.3, 47.6) | General air quality | A total of 1305 (12.0%, 95% CI = 10.9, 13.1) individuals responded that they did something differently due to bad air quality. Among those who reported changing their activity, the most commonly reported change was to spend less time outdoors. |
| Roberts et al. 2014 | U.S. | Cross-sectional | 329,628 | ≥18 | PM2.5, PM10, O3 | A 2.4% relative increase in the odds of physical inactivity per mg/m3 increase of PM2.5 exposure among the obese respondents (OR = 1.02, 95% CI = 1.00,1.05). An increasing concentration of PM10 among the normal weight respondents was also associated with higher odds of inactivity (OR = 1.01, 95% CI = 1.00, 1.02). |
| An and Xiang 2015 | U.S. | Cross-sectional | 2,381,292 | ≥18 | PM2.5 | One unit (1 μg/m3) increase in county monthly average PM2.5 concentration was found to be associated with an increase in the odds of physical inactivity by 0.46% (OR = 1.0046, 95% CI = 1.0034, 1.0059). |
| Alahmari et al. (2015) | U.K. | Longitudinal | 73 | 71.1 ± 8.7 | PM10, O3 | Relationship between PM10 (μg/m3) and daily step count: regression coefficient = -5.4 , 95% CI = -12.2, 1.3, p-value = 0.112. Relationship between O3 (μg/m3) and daily step count: regression coefficient = -8.0, 95% CI = -13.5, −2.4, p-value = 0.005. Relationship between PM10 (μg/m3) and hours spent outdoors: regression coefficient = 2.3 × 10-3, 95% CI = -6.5 × 10-3, 1.8 × 10-3, p-value = 0.275. Relationship between O3 (μg/m3) and hours spent outdoors: regression coefficient = -9.9 × 10-3, 95% CI = -14.2 × 10-3, −5.6 × 10-3, p-value < 0.001. |
| Chen and Lin 2016 | China | Cross-sectional | 2,268 | 42–49 | General air quality | One-unit increase of perceived air quality is associated with a reduction in physical inactivity by 20% (OR = 0.80, 95% CI = 71%, 89%). |
| Yang and Zacharias 2016 | China | Cross-sectional | 852 | ≥60 | General air quality | Air pollution, traffic safety, the lack of road space, climatic disadvantages, insufficient secure parking for bicycles, and inadequate night lighting are seen as major barriers by all commuters. |
| Hu et al. 2017 | China | Cross-sectional | 153 | 36.8 ± 7.9 | General air quality | App users were less likely to participate in outdoor running, biking, and walking (F = 24.16, p < 0.01) when air pollution concentration increased. |
| Li and Kamargianni 2017 | China | Cross-sectional | 492 | All ages | General air quality | In winter, biking (Coefficient = -0.009, t-Statistic = -2.63), bike-sharing (Coefficient = -0.058, t-Statistic = -6.71), and walking (Coefficient = -0.018, t-Statistic = -5.12) were not preferred when air pollution level increased. Instead travelers switched to the use of cars (Coefficient = 0.015, t-Statistic = 5.63), buses (Coefficient = 0.0002, t-Statistic = 0.06), taxis (Coefficient = 0.003, t-Statistic = 0.65), and electric bikes (Coefficient = 0.003, t-Statistic = 1.35). In summer, air pollution was negatively correlated with walking (Coefficient = -0.001, t-Statistic = -0.13) but positively correlated with biking (Coefficient = 0.016, t-Statistic = 1.85) and bike-sharing (Coefficient = 0.017, t-Statistic = 2.20). |
| Yu et al. 2017a | China | Longitudinal | 848–890 | 66.8 (95% CI: 66.4–67.3) | PM2.5 | An increase in ambient PM2.5 concentration by 1 standard deviation (56.6 µg/m3) was associated with a reduction in weekly total hours of walking by 4.69 (95% CI = 1.30, 8.08), a reduction in leisure-time Physical Activity Scale for the Elderly (PASE) score by 71.16 (95% CI = 28.91, 113.41), and a reduction in total PASE score by 110.67 (95% CI = 59.25, 162.08). An increase in ambient PM2.5 concentration by one standard deviation was associated with an increase in daily average hours of nighttime/daytime sleeping by 1.75 (95% CI = 1.24, 2.26). |
| Yu et al. 2017b | China | Longitudinal | 3,223–3,242 | 18.2 ± 0.9 | PM2.5 | An increase in ambient PM2.5 concentration by one standard deviation (44.72 μg/m3) was associated with a reduction in 22.32 weekly minutes of vigorous physical activity (95% CI = 19.77, 24.88), a reduction in 10.63 weekly minutes of moderate physical activity (95% CI = 6.64, 14.61), a reduction in 32.45 (95% CI: 27.28, 37.63) weekly minutes of moderate to vigorous physical activity (MVPA), and a reduction in 226.14 (95% CI = 256.06, 196.21) weekly physical activity MET-minute scores. |
| An and Yu 2018 | China | Longitudinal | 12,184–12,291 | 18.1 (95% CI: 18.0–18.1) | PM2.5 | An increase in the ambient PM2.5 concentration by one standard deviation (36.5 μg/m3) was associated with a reduction in weekly total minutes of walking by 7.3 (95% CI = 5.3, 9.4), a reduction in weekly total minutes of vigorous physical activity by 10.1 (95% CI = 8.5, 11.7), a reduction in daily average hours of sedentary behavior by 0.06 (95% CI = 0.02, 0.10). |
| Li and Kamargianni 2018 | China | Cross-sectional | 4,769 | All ages | General air quality | Air pollution had significant negative effect on bike-sharing choice (Coefficient = -0.0045, t-Statistic = -8.29); Air pollution also had significant negative impact on walking (Coefficient = -0.0045, t-Statistic = -9.17), electric bike use (Coefficient = -0.0022, t-Statistic = -3.93), and bus use (Coefficient = -0.0020, t-Statistic = -2.65); Car-sharing (Coefficient = 0.0023, t-Statistic = 1.96) was the only transportation mode that had a positive correlation with air pollution level. |
| Zhang and An (2018) | China | Longitudinal | 300 | Air quality index | There was a negative non-linear relationship between air pollution level and television use. Compared to the days when air quality was good (0 ≤ AQI ≤ 50), days with fair air quality (50 < AQI ≤ 100), light air pollution (100 < AQI ≤ 150), and moderate-to-severe air pollution (AQI greater than 150) were associated with a reduction in daily average television use by 2.9 (p = 0.002), 4.6 (p < 0.001), and 1.9 (p = 0.369) minutes, respectively. | |
| Zhao et al 2018 | China | Cross-sectional | 307 | All ages | PM2.5 | Residents with lower income (Coefficient = 0.58, 95% CI = -0.00, 1.16), those over 30 years old (Coefficient = 0.67, 95% CI = 0.11, 1.22), and male respondents were more likely to continue cycling in hazy weather. |
AQI - air quality index; OR - Odds Ratio; CI - Confidence intervals.