Relationship between physical activity, fruits & vegetables, and air quality in children with asthma

Juan Aguilera1 MD, MPH;

David Perez1, BS; Alisha Redelfs1, Dr. PH, MPH, CHES;Soyoung Jeon1, Ph.D.; Amit Raysoni2, Ph.D, MPH;Wen-Whai Li1, Ph.D., P.E.; Leah Whigham1, Ph.D.

1The University of Texas at El Paso2The University of Texas Rio Grande Valley

February, 2019

Air Pollution

• 43.5 million exposed to traffic pollution in the U.S. (living within 1 block) 1

• More likely to affect those in underserved communities2

• Abundant evidence of adverse health effects 3-5

1. Li et al. 20112. Raysoni et al. 20133. Greenwald et al. 20134. Zora et al. 20135. Sarnat et al. 2012

Heavy Traffic Air Pollution

• Associations in asthmatic children 6

↑ Airway inflammation

↓ Lung function

• Schoolchildren living 30-300 meters from a

major roadway 7

↑ Arterial stiffness

↓ Academic performance

↑ Absenteeism

↑ Clinical symptoms

6. Raysoni et al. 20117. Staniswalis et al. 2009

Exposure to air pollutants and physical activity

• Physical activity: ↑ respiratory intake↑ deposition of air pollutants in the lungs14

• Exercise: exposure or inhalation to air pollutants ↓ performance15

↓ lung function16

Particulate matter images retrieved from http://aurametrix.weebly.com/topics/particulate-matter

14. Giles et al. 201415. Rundell et al. 200816. Cutrufello. 2012

Exposure to air pollutants and physical activity

• The benefits of physical activity are essential for overall health8

• Outdoor activities (walking, jogging, dancing)↓ Risk of cardiovascular disease ↓ Metabolic syndrome9

• Outdoor physical activity exposes people to air pollutants (might lead to)↑ Cardiovascular or respiratory diseases 10-13

8. Janssen et al. 20109. Raysoni et al. 201310. Greenwald et al. 201311. Zora et al. 201312. Sarnat et al. 2012

Effects on Asthma

• People with asthma may have ↓ physical activity

avoid aerobic fitness concerns of triggering asthma symptoms17-18

• In a polluted environment ↑ risk of having an asthma attack10

↑ lung pathologies14

• Health habits → young age• Emphasize physical activity with asthma

patients19

Mechanistic framework for air pollution effects in asthma retrieved fromhttps://www.sciencedirect.com/science/article/pii/S0140673614606176

17. Mälkiä et al. 199818. Garfinkel et al.199210. Sharman et al 200414. Giles et al al. 201419. Mancuso et al. 2006

Carotenoids as Antioxidants

• Carotenoids are powerful antioxidants

present in the human diets which can

protect against asthma

↓ damage caused by oxidation21

• Lycopene exerts a protective effect on

exercise-induced asthma22 and could be

used for therapeutic effects23

21. Wood et al. 200522. Nahum et al. 200023. Wood et al. 2008

USDA Database for Carotenoid content of selected foods

Data Collected for the Study

• Study period: 10 weeks • Oct - Dec 2017

• Air quality data at elementary schools• PM2.5 ,PM10, NO2 and ozone

• Health measurements: 1 day/week, ages 6-12

• F/V intake: CW & FB n=23• Physical Activity: CW n=12

CW

FB

• Carotenoids are biomarkers of dietary fruit and vegetable (F/V) intake24

• Can be assessed non-invasively by reflectance spectroscopy

F/V Intake

VEGGIEMETERTM

24. Jahns et al., …. &Whigham et al. 2014

• Movement in three axes• % time spent on moderate to vigorous

physical activity (MVPA), light activity, and sedentary activity• MVPA : brisk walking, jogging, and

playing active sports• Light : slow walking, playing

instruments• Sedentary: sitting, lying down

Physical Activity Monitor

Accelerometer

Statistical Analysis

• Summary statistics of air pollution metrics (PM2.5, PM10, NO2, O3)

• Pollutant averaged with exposure periods (24-, 48-, 72-, 96-hr)

• Summary statistics of F/V & physical activity outcomes

• Correlation analyses with air quality monitoring

• Longitudinal analyses using GEE models with assumptions of:

• subject-specific cluster

• exchangeable correlation structure for the repeated measures of data

Subject Characteristics

Variable

All (n=23) CW (n=12) FB (n=11)

p

value*

mean range mean range mean range

Age (yrs) 7.8 (5-10) 8.3 (6-10) 7.4 (5-10) 0.2156

Height

(in) 53.0 (43.3-70.0) 54.3 (46.3-70.0) 51.5 (43.3-58.3) 0.1848

Weight

(lb) 79.3 (40-152) 76.3 (45.8-134) 82.6 (40-152) 0.6685

BMI 19.2 (12.3-31.5) 17.9 (12.3-27.8) 20.7 (15.0-31.5) 0.2537

BMI (%) 63.5 (0-99.5) 49.8 (0-99.4) 78.3 (37.4-99.5) 0.0503

Variable All (n=23)

CW

(n=12) FB (n=11) p

value**n % n % n %Gender

Male 12 52% 7 58% 5 45% 0.6843

Female 11 48% 5 42% 6 55%

Race

Black 4 17% 0 0% 4 36% 0.0137

Hispanic 18 78% 12 100% 6 55%

White 1 4% 0 0% 1 9%

BMI category

Underweight 2 9% 2 17% 0 0% 0.6135

Normal 13 57% 6 50% 7 64%

Overweight 1 4% 1 8% 0 0%

Obese 7 30% 3 25% 4 36%

Associations with F/V intake

• 96-hr ambient PM concentrations at FB site were

significantly associated with decreased skin

carotenoid levels

• 14.44 ↓ F/V intake (CI: -25.53, -3.34) for PM2.5

• 13.48 ↓ F/V intake (CI: -23.31, -3.65) for PM10

PM10PM2.5

Interaction of physical activity rates per factor levelSubject-specific

Factor**

Frequency,% Physical activity(n=12) MVPA p-value* Sedentary p-value*

Health Insurance Coverage(n=11)Medicaid 6 55% 66.5% 0.003 23.9% 0.039Private 5 45% 61.2% 27.9%

Smoking (outside of household) 2 17%

59.9%0.013

29.9%0.010

No 10 83% 64.2% 25.7%Cooking FuelElectric 1 8% 68.7% 0.035 22.7% 0.127Gas 11 92% 62.9% 26.8%

Leukotrieneblockers (LB) 7 58% 66.4% < 0.001 23.7% < 0.001No 5 42% 59.4% 30.3%

Long-acting bronchodilators and inhaled corticosteroids (LABAIC) 2 17% 68.1% 0.012 22.0% 0.013

No 10 83% 62.6% 27.2%Nasal corticosteroids (NC) 4 33% 66.8% 0.003 23.4% 0.007

No 8 67% 61.7% 28.0%

Subject-specific Factor**

Frequency,% Physical activity(n=12) MVPA p-value* Sedentary p-value*

SexMale 7 58% 65.8% 0.001 24.2% 0.001Female 5 42% 60.0% 29.2%

BMI categoryUnderweight & Normal 8 67% 61.9% 0.010 28.4% < 0.001Overweight & Obese 4 33% 66.5% 22.6%

Father with Asthma 3 25% 60.9% 0.041 28.8% 0.032No 9 75% 64.3% 25.7%

Siblings with Asthma 6 50% 61.2% 0.005 28.8% 0.001No 6 50% 65.6% 24.1%

Having Eczema 3 25% 66.8% 0.012 23.2% 0.011No 9 75% 62.2% 27.7%

*p-value for mean difference in physical activity between factor levelsusing Kruskal-Wallis test.

**There were no significant interactions found for mother with asthma; father, mother, or sibling with hay fever; allergic phenotype (air or food); caretaker education level; Short Acting Beta Agonist (SABA); Inhaled Corticosteroids (IC); Systemic Corticosteroids (SC).

Associations between MVPA and sedentary activity

Pollutant

IQR

MVPA* Sedentary*% Change in

PA per IQR

change in pollutant

95% C.I.

p value

% Change in PA

per IQR change in pollutant

95% C.I.

p valuelower upper lower upperPM2.5 96-hr 4.0 -3.5% -5.0% -1.9% < 0.001 3.4% 1.8% 5.1% < 0.001

PM10 96-hr 9.6 -1.6% -2.4% -0.8% < 0.001 1.5% 0.7% 2.3% < 0.001

NO2 96-hr 5.0 -1.4% -2.6% -0.1% 0.04 1.5% 0.3% 2.8% 0.02

O3 96-hr 8.6 -0.3% -1.8% 1.2% 0.66 0.5% -1.1% 2.1% 0.53

72-hr MaxO38hr

9.9 -4.0% -6.4% -1.6% 0.001 4.6% 2.2% 7.1% < 0.001

*There were significant interactions found for 72-hr and 96-hr CAMS data for PM2.5, PM10 and 96-hr CAMS for NO2 for both MVAP and sedentary activity.

Summary

• The effects on carotenoids correlatesignificantly with increased exposure under acertain threshold of pollution levels

• 96-hr PM2.5, PM10, and NO2 negativelycorrelate with MVPA and positively correlatewith sedentary activity

• GEE models account for individual factors

• For O3 the use of maximum values had asignificant association

Discussion

• First study to characterize associations of traffic air pollutants using objective measures of physical activity and F/V intake in children with asthma

• On-site school monitoring reveals a relatively higher level of exposure than CAMS locations

• More research is needed to discern the effects of carotenoids as protective factors against pollutants in asthma and the impact of air pollutants on physical activity.

Recommendations

• Placement of natural barriers to mitigate air pollutants (intercept particulate matter)20

• Policy changes• Add on-site air quality monitoring at schools

near high-traffic roads. Use data to inform:• Outdoor activity schedule• Transportation-to-school policies

(decrease vehicle idling at drop off and pick up, increase active transportation, etc.)

• Planning of future schools away from high-traffic roads

20. Currie et al. 2008

Acknowledgements

This study was partially supported by a grant from the U.S.Department of Transportation (DOT) through the CARTEEHThe contents of this presentation are solely the responsibility of the authors and donot necessarily represent the official views of the DOT

And partially funded by the Paso del Norte Institute for Healthy Living

References1. Li W-W, Sarnat JA, Raysoni AU, Sarnat SE, Stock TH, Holguin F, Greenwald R, Olvera HA, Johnson BA, 2011. Characterization of traffic related air pollution in elementary schools and its impact on asthmatic children in El Paso, Texas.

2010. Mickey Leland National Urban Air Toxic Research Center, NUATRC Report Number 20, Houston, Texas. June 2011.2. Raysoni AU, Stock TH, Sarnat JA, Sosa TM, Sarnat SE, Holguin F, Greenwald R, Johnson B, Li W-W, 2013. Characterization of traffic-related air pollution metrics at four schools in El Paso, Texas, USA: Implications for exposure

assessment and siting schools in urban areas, Journal of the Atmospheric Environment, 80: 140-151.3. Greenwald R, Sarnat J, Li W-W, Raysoni AU, Sarnat SE, Johnson BA, Stock TH, Holguin F, Sosa T, 2013, Associations between Source-indicative Pollution Metrics and Increases in Pulmonary Inflammation and Reduced Lung Function in a

Panel of Asthmatic Children Texas, J. of Air Quality, Atmosphere and Health. 6(2):487-4994. Zora JE, Sarnat SE, Raysoni AU, Johnson BA, Li W-W, Greenwald R, Stock T, Sarnat JA, 2013. Associations between urban air pollution and pediatric asthma control in El Paso, Texas, Journal of the Science of the Total Environment,

448:56-65.5. Sarnat SE, Raysoni AU, Li W-W, Holguin F, Johnson B, Flores S, Garcia JH, Sarnat JA, 2012. Impact of traffic-related air pollution on exhaled nitric oxide in asthmatic children along the US-Mexico border, Environmental Health

Perspectives, 120: 437-440 (2012).6. Raysoni A, Sarnat JA, Sarnat SE, Garcia JH, Holguin F, Flores S, Li W-W, 2011. Binational school-based monitoring of traffic-related air pollutants in El Paso, Texas (USA) and Ciudad Juárez, Chihuahua (México), Journal of the

Environmental Pollution, 159 (10): 2476-2486.7. Staniswalis JG, Yang H, Li W-W, Kelly KE, 2009. Using a Continuous Time Lag to Determine the Association Between Ambient PM2.5 Hourly Levels and Daily Mortality: Indication of the Importance of the Total Number of Particles, J. of

AWMA, 59:1173-1185

8. Janssen, I., and A. G. LeBlanc. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. International journal of behavioral nutrition and physical activity, Vol. 7, No. 1, 2010, p. 40.

9. Chen, M., M. He, X. Min, A. Pan, X. Zhang, P. Yao, X. Li, Y. Liu, J. Yuan, W. Chen, L. Zhou, W. Fang, Y. Liang, Y. Wang, X. Miao, M. Lang, P. Zhang, D. Li, H. Guo, H. Yang, F. B. Hu, and T. Wu. Different physical activity subtypes and risk of metabolic syndrome in middle-aged and older Chinese people. PloS one, Vol. 8, No. 1, 2013, p. e53258.

10. Sharman, J., J. Cockcroft, and J. Coombes. Cardiovascular implications of exposure to traffic air pollution during exercise. Qjm, Vol. 97, No. 10, 2004, pp. 637-643.11. Le Tertre, A., S. Medina, E. Samoli, B. Forsberg, P. Michelozzi, A. Boumghar, J. Vonk, A. Bellini, R. Atkinson, and J. Ayres. Short-term effects of particulate air pollution on cardiovascular diseases in eight European

cities. Journal of Epidemiology & Community Health, Vol. 56, No. 10, 2002, pp. 773-779.12. Shah, A. S. V., J. P. Langrish, H. Nair, D. A. McAllister, A. L. Hunter, K. Donaldson, D. E. Newby, and N. L. Mills. Global association of air pollution and heart failure: A systematic review and meta-analysis. The

Lancet, Vol. 382, No. 9897, 2013, pp. 1039–1048.13. Pope III, C. A., M. Ezzati, and D. W. Dockery. Fine-particulate air pollution and life expectancy in the United States. New England Journal of Medicine, Vol. 360, No. 4, 2009, pp. 376-386.14. Giles, L. V., and M. S. Koehle. The health effects of exercising in air pollution. Sports medicine (Auckland, N.Z.), Vol. 44, No. 2, 2014, pp. 223–249.15. Rundell, K. W., J. B. Slee, R. Caviston, and A. M. Hollenbach. Decreased lung function after inhalation of ultrafine and fine particulate matter during exercise is related to decreased total nitrate in exhaled breath

condensate. Inhalation toxicology, Vol. 20, No. 1, 2008, pp. 1–9.16. Cutrufello, P. T., J. M. Smoliga, and K. W. Rundell. Small Things Make a Big Difference. Sports medicine, Vol. 42, No. 12, 2012, pp. 1041-1058.17. Mälkiä, E., and O. Impivaara. Intensity of physical activity and respiratory function in subjects with and without bronchial asthma. Scandinavian journal of medicine & science in sports, Vol. 8, No. 1, 1998, pp. 27-

32.18. Garfinkel, S., S. Kesten, K. Chapman, and A. Rebuck. Physiologic and nonphysiologic determinants of aerobic fitness in mild to moderate asthma. Am Rev Respir Dis, Vol. 145, No. 4 Pt 1, 1992, pp. 741-745.19. Mancuso, C. A., W. Sayles, L. Robbins, E. G. Phillips, K. Ravenell, C. Duffy, S. Wenderoth, and M. E. Charlson. Barriers and facilitators to healthy physical activity in asthma patients. The Journal of asthma : official

journal of the Association for the Care of Asthma, Vol. 43, No. 2, 2006, pp. 137–143.20. Currie, B. A., and B. Bass. Estimates of air pollution mitigation with green plants and green roofs using the UFORE model. Urban Ecosystems, Vol. 11, No. 4, 2008, pp. 409-422.21. Wood, L. G., Garg, M. L., Blake, R. J., Garcia-Caraballo, S., & Gibson, P. G. (2005). Airway and circulating levels of carotenoids in asthma and healthy controls. Journal of the American College of Nutrition, 24(6),

448-455.22. Wood, L. G., Garg, M. L., Blake, R. J., Garcia-Caraballo, S., & Gibson, P. G. (2005). Airway and circulating levels of carotenoids in asthma and healthy controls. Journal of the American College of Nutrition, 24(6),

448-455. 23. Neuman, I., Nahum, H., & Ben‐Amotz, A. (2000). Reduction of exercise‐induced asthma oxidative stress by lycopene, a natural antioxidant. Allergy, 55(12), 1184-1189. 24. Jahns, L., Johnson, L. K., Mayne, S. T., Cartmel, B., Picklo Sr, M. J., Ermakov, I. V., ... & Whigham, L. D. (2014). Skin and plasma carotenoid response to a provided intervention diet high in vegetables and fruit:

uptake and depletion kinetics–. The American journal of clinical nutrition, 100(3), 930-937.

Thank you !

“Health science involves devotion to lifelong learning and research is the key.

Only by working together we will find solutions to health problems and set standards of care and

disease prevention for everyone”

24-hr 48-hr 72-hr 96-hr 96-hr (CAMS)

PM2.5 (µg/m3)

Mean 12.52 11.73 11.48 12.16 10.17

SD 3.71 2.40 1.88 2.80 5.25

Median 13.15 11.13 11.35 11.27 9.75

IQR 4.91 4.14 3.12 4.07 5.22

Max 18.86 15.65 14.33 17.58 18.69

Min 6.33 8.98 8.60 8.61 3.40

PM10 (µg/m3)

Mean 45.30 43.05 42.55 44.94 36.89

SD 17.36 12.47 8.70 9.13 12.43

Median 40.30 38.47 40.32 45.84 38.67

IQR 24.57 19.06 11.93 9.56 16.84

Max 74.14 62.31 56.99 60.10 51.61

Min 24.49 25.87 31.36 28.54 13.84

NO2 (ppb)

Mean 17.63 18.20 18.40 18.94 17.90

SD 6.06 3.25 3.06 3.72 5.11

Median 19.22 18.59 18.47 19.04 16.33

IQR 7.81 4.76 2.76 4.96 5.20

Max 26.17 22.16 22.70 23.64 27.13

Min 7.21 12.20 12.17 11.62 13.02

O3 (ppb)

Mean 21.41 20.37 21.75 20.35 19.85

SD 10.51 6.66 7.25 5.47 5.08

Median 19.60 18.94 19.37 18.29 18.85

IQR 18.09 11.69 12.32 8.57 7.51

Max 38.90 31.13 34.52 29.71 28.43

Min 9.16 12.52 13.86 15.59 14.81

School and ambient pollutant metrics (additional information)

Diurnal weekday and weekend trends of each pollutant

PollutantPrimary/Secondary

Averaging Time

Level

Nitrogen Dioxide (NO2)

primary 1 hour 100 ppb

primary andsecondary

1 year 53 ppb (2)

Ozone (O3)primary andsecondary

8 hours0.070 ppm (3)

Particle Pollution (PM)

PM2.5

primary 1 year 12.0 μg/m3

secondary 1 year 15.0 μg/m3

primary andsecondary

24 hours 35 μg/m3

PM10primary andsecondary

24 hours 150 μg/m3

EPA Standards

Interaction of physical activity rates per factor level

Subject-specific FactorFrequency,% Physical activity(n=12) MVPA p-value* Sedentary p-value*

Health Insurance Coverage(n=11)Medicaid 6 55% 66.5% 0.003 23.9% 0.039Private 5 45% 61.2% 27.9%

Smoking (outside of household) 2 17% 59.9% 0.013 29.9% 0.010No 10 83% 64.2% 25.7%

Cooking FuelElectric 1 8% 68.7% 0.035 22.7% 0.127Gas 11 92% 62.9% 26.8%

Leukotrieneblockers (LB) 7 58% 66.4% < 0.001 23.7% < 0.001No 5 42% 59.4% 30.3%

Short-acting bronchodilators (SABA) 7 58% 62.8% 0.155 27.3% 0.065

No 5 42% 64.4% 25.2%Inhaled corticosteroids (IC) 6 50% 63.2% 0.894 26.1% 0.493

No 6 50% 63.6% 26.8%Long-acting bronchodilators and inhaled corticosteroids (LABAIC) 2 17% 68.1% 0.012 22.0% 0.013

No 10 83% 62.6% 27.2%Nasal corticosteroids (NC) 4 33% 66.8% 0.003 23.4% 0.007

No 8 67% 61.7% 28.0%Systemic corticosteroids (SC) 2 17% 64.6% 0.641 25.3% 0.791

No 10 83% 63.2% 26.7%

Subject-specific FactorFrequency,% Physical activity(n=12) MVPA p-value* Sedentary p-value*

SexMale 7 58% 65.8% 0.001 24.2% 0.001Female 5 42% 60.0% 29.2%

BMI category

Underweight & Normal 8 67% 61.9% 0.010 28.4% < 0.001

Overweight & Obese 4 33% 66.5% 22.6%Mother with Asthma 5 42% 63.2% 0.895 26.1% 0.503

No 7 58% 63.6% 26.7%Father with Asthma 3 25% 60.9% 0.041 28.8% 0.032

No 9 75% 64.3% 25.7%Mother with Hay Fever 8 67% 63.4% 0.944 26.3% 0.595

No 4 33% 63.5% 26.8%Father with Hay Fever 8 67% 62.7% 0.305 26.9% 0.511

No 4 33% 64.8% 25.6%Siblings with Asthma 6 50% 61.2% 0.005 28.8% 0.001

No 6 50% 65.6% 24.1%Siblings with Hay Fever 8 67% 63.0% 0.602 27.2% 0.169

No 4 33% 64.2% 25.1%Having Eczema 3 25% 66.8% 0.012 23.2% 0.011

No 9 75% 62.2% 27.7%

Allergic Phenotype (Aeroallergens) 8 67% 63.1% 0.597 26.7% 0.794No 4 33% 64.1% 26.0%

Allergic Phenotype (Food) 3 25% 61.8% 0.143 27.4% 0.366No 9 75% 64.1% 26.1%

Caretaker EducationLess than or Equal to High School 6 50% 63.8% 0.997 26.3% 0.771Greater than High School 6 50% 63.1% 26.6%

*p-value for mean difference in physical activity between factor levels using Kruskal-Wallistest.

Associations between MVPA and sedentary activity with pollutant metrics

PollutantIQR

MVPA Sedentary% Change in PA per IQR change

in pollutant

95% C.I.

p value

% Change in PA per IQR change

in pollutant

95% C.I.

p valuelower upper lower upperPM2.5 24-hr 4.91 0.47% -0.54% 1.48% 0.365 -0.96% -1.92% 0.01% 0.051

48-hr 4.13 0.80% -0.37% 1.96% 0.180 -1.53% -2.75% -0.31% 0.01472-hr 3.11 -1.71% -2.95% -0.46% 0.007 1.43% 0.24% 2.61% 0.01896-hr 4.07 -3.45% -5.00% -1.90% < 0.001 3.43% 1.78% 5.09% < 0.00196-hr CAMS 5.22 -3.86% -6.12% -1.59% 0.001 4.04% 1.71% 6.37% 0.001

PM10 24-hr 24.57 -0.43% -1.50% 0.64% 0.427 -0.06% -0.99% 0.87% 0.90248-hr 19.05 -0.58% -1.66% 0.50% 0.293 -0.17% -1.18% 0.83% 0.73572-hr 11.93 -1.32% -2.24% -0.39% 0.005 1.00% 0.09% 1.91% 0.03196-hr 9.56 -1.59% -2.37% -0.81% < 0.001 1.51% 0.69% 2.34% < 0.00196-hr CAMS 16.84 -2.87% -4.65% -1.08% 0.002 3.07% 1.19% 4.95% 0.001

NO2 24-hr 7.81 -0.45% -1.71% 0.82% 0.489 0.43% -0.62% 1.47% 0.42448-hr 4.76 -0.28% -1.41% 0.85% 0.626 0.29% -0.72% 1.30% 0.57472-hr 2.76 -0.60% -1.30% 0.11% 0.098 0.66% -0.06% 1.38% 0.07596-hr 4.96 -1.35% -2.62% -0.09% 0.036 1.52% 0.25% 2.79% 0.01996-hr CAMS 5.19 -0.78% -1.53% -0.04% 0.040 0.63% -0.12% 1.38% 0.099

O3 72-hr Max O38hr 9.94 -3.99% -6.35% -1.63% 0.001 4.62% 2.15% 7.08% < 0.001

24-hr 18.10 -0.25% -3.51% 3.01% 0.881 1.16% -2.10% 4.43% 0.48648-hr 11.69 -1.31% -4.01% 1.40% 0.344 2.07% -0.85% 4.98% 0.16472-hr 12.32 -0.66% -2.33% 1.01% 0.437 1.41% -0.37% 3.19% 0.12096-hr 8.57 -0.33% -1.81% 1.15% 0.661 0.49% -1.05% 2.04% 0.53096-hr CAMS 7.50 -0.04% -1.51% 1.43% 0.955 0.24% -1.34% 1.82% 0.766

Associations between MVPA and sedentary activity

PollutantIQR

MVPA Sedentary

% Change in PA per IQR change

in pollutant

95% C.I.

p value

% Change in PA per IQR change

in pollutant

95% C.I.

p valuelower upper lower upperPM2.5 72-hr 3.11 -1.71% -2.95% -0.46% 0.007 1.43% 0.24% 2.61% 0.018

96-hr 4.07 -3.45% -5.00% -1.90% < 0.001 3.43% 1.78% 5.09% < 0.00196-hr CAMS 5.22 -3.86% -6.12% -1.59% 0.001 4.04% 1.71% 6.37% 0.001

PM10 72-hr 11.93 -1.32% -2.24% -0.39% 0.005 1.00% 0.09% 1.91% 0.03196-hr 9.56 -1.59% -2.37% -0.81% < 0.001 1.51% 0.69% 2.34% < 0.00196-hr CAMS 16.84 -2.87% -4.65% -1.08% 0.002 3.07% 1.19% 4.95% 0.001

NO2 72-hr 2.76 -0.60% -1.30% 0.11% 0.098 0.66% -0.06% 1.38% 0.07596-hr 4.96 -1.35% -2.62% -0.09% 0.036 1.52% 0.25% 2.79% 0.01996-hr CAMS 5.19 -0.78% -1.53% -0.04% 0.040 0.63% -0.12% 1.38% 0.099

O3 72-hr Max O38hr 9.94 -3.99% -6.35% -1.63% 0.001 4.62% 2.15% 7.08% < 0.001

72-hr 12.32 -0.66% -2.33% 1.01% 0.437 1.41% -0.37% 3.19% 0.12096-hr 8.57 -0.33% -1.81% 1.15% 0.661 0.49% -1.05% 2.04% 0.53096-hr CAMS 7.50 -0.04% -1.51% 1.43% 0.955 0.24% -1.34% 1.82% 0.766