Physical Activity; COPD

Association Between Physical Activity and Inflammatory Markers Among U.S. Adults with Chronic Obstructive Pulmonary Disease

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Paul D. Loprinzi, PhD; Jerome F. Walker, EdD; Hyo Lee, PhD Abstract

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Key Words: Accelerometry, Epidemiology, Exercise, Inflammation, National Health and Nutrition Examination Survey (NHANES), Tobacco, Prevention Research. Manuscript format: research; Research purpose: modeling/relationship testing, descriptive; Study design: cross-sectional; Outcome measure: biometric; Setting: national; Health focus: physical activity, smoking control; Strategy: education, behavior change; Target population age: adults; Target population circumstances: education, race/ethnicity.

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PURPOSE

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Purpose. Chronic obstructive pulmonary disease (COPD) may cause not only inflammation in the lungs but also systemic effects. One potential strategy to reduce systemic inflammation and attenuate disease progression is physical activity (PA). However, no nationally representative studies, to our knowledge, have examined the association between objectively measured physical activity and inflammation among those with COPD. Design. Cross-sectional. Setting. National Health and Nutrition Examination Survey 2003–2006. Subjects. Two hundred thirty-eight former or current smokers with self-reported COPD who had complete data on study variables. Measures. Participants wore an accelerometer for 4 days to assess light-intensity PA (LPA), moderate-to-vigorous PA (MVPA), and total physical activity (TPA); completed questionnaires to assess self-reported COPD and smoking status; and had their blood taken to assess white blood cell (WBC) and neutrophil levels. Analysis. Multivariable linear regression analysis was used. Results. LPA (b ¼ .0004), MVPA (b ¼ .04), and TPA (b ¼ .0004) were significantly inversely associated with WBC level. Similarly, LPA (b ¼ .001) and TPA (b ¼ .001) were significantly inversely associated with neutrophils; however, MVPA was marginally associated with neutrophils (b ¼ .05; p ¼.06). Conclusion. These analyses demonstrate an inverse association between objectively measured PA and inflammation among current or former smokers with COPD. If these findings are confirmed elsewhere, then PA among those with COPD may serve as an anti-inflammatory strategy to possibly decrease cardiovascular and metabolic disease occurrence. (Am J Health Promot 0000;00[0]:000– 000.)

Globally, chronic obstructive pulmonary disease (COPD) causes over 2 million deaths annually1 and affects approximately 5% of U.S. adults.2 In 2000, COPD was responsible for over 8 million physician office and outpatient visits, 1.5 million emergency department visits, and 726,000 hospitalizations.3 The most important etiology of COPD is smoking. Chronic exposure to its noxious particles and gases is associated with an abnormal inflammatory lung response. Immune white blood cells (WBCs) in the lung are continuously activated and release inflammatory signaling molecules (e.g., tumor necrosis factor, interleukin-6) that contribute to cumulative lung tissue injury over time. Smoking and COPD also induce systemic effects (e.g., cardiovascular abnormalities)4–6 as WBC inflammatory mediators translocate into the bloodstream.7,8 In turn, these proinflammatory molecules may activate plaque tissues, inducing vascular inflammation and, ultimately, vascular events (e.g., heart attack, stroke).7 Steiropoulos et al.9 recently showed that those with COPD, compared to

Paul D. Loprinzi, PhD, is with the Department of Exercise Science, and Jerome F. Walker, EdD, is with the Department of Respiratory Therapy, Lansing School of Nursing and Health Sciences, Bellarmine University, Louisville, Kentucky. Hyo Lee, PhD, is with the Department of Sport and Health Sciences, Sangmyung University, Seoul, Korea. Send reprint requests to Paul Loprinzi, PhD, Department of Exercise Science, Lansing School of Nursing & Health Sciences, Bellarmine University, Louisville, KY 40205; [email protected]. This manuscript was submitted May 10, 2013; revisions were requested July 15 and July 18, 2013; the manuscript was accepted for publication July 19, 2013. Copyright Ó 0000 by American Journal of Health Promotion, Inc. 0890-1171/0000/$5.00 þ 0 DOI: 10.4278/ajhp.130510-QUAN-235

American Journal of Health Promotion

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those without COPD, had higher WBC levels, which is consistent with other reports showing that current and former smokers, compared to those who never smoked, have higher WBC counts.10 Taken together, these findings suggest that current smoking or past smoking behavior and COPDinduced inflammation may be putting these already ill individuals at greater risk for other chronic conditions, as elevated WBC (and neutrophils) increases the risk of not only coronary heart disease morbidity and mortality,11–14 but also diabetes,15–18 cancer,19,20 and total cancer mortality.14,21 One potential strategy to reduce inflammation is regular participation in physical activity.22,23 Recently, Johannsen et al.24 studied 390 postmenopausal women and demonstrated an inverse dose-response relationship between aerobic exercise and WBC and neutrophils. Whether a similar effect occurs among those with COPD is, at this point, unclear.25 Others have demonstrated that, compared to COPD patients engaging in more physical activity, those with lower levels of physical activity tend to have higher levels of systemic inflammation.26 Yet none of the 47 studies evaluated in the systematic review by Bossenbroek et al.26 both employed a nationally representative sample and used an objective measure of physical activity. The majority of these studies used a subjective measure of physical activity, and those employing an objective measure (e.g., accelerometry) had a relatively small sample size, with the mean number of participants across the studies being 51 (range ¼ 10–170). Consequently, generalizability of these previous studies may be limited. Additionally, self-reported physical activity is prone to considerable measurement error,27 creating bias and reducing statistical power to detect physical activity–disease associations.28 As a result, additional studies in this area of inquiry are needed, in particular those employing an objective measure of physical activity and using a representative sample of Americans to ensure the most accurate association is represented for adults in the United States. To address these gaps in the literature, our study’s purpose was to examine the association between objectively

measured physical activity (i.e., accelerometry) and WBC and neutrophil levels among a nationally representative sample of current or former smokers with self-reported COPD. We hypothesize that physical activity will be inversely associated with WBC levels and neutrophils in COPD, suggesting that physical activity may help to reduce disease progression and, ultimately, cardiovascular and metabolic disease occurrence by mitigating inflammation.

METHODS

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Measures Assessment of COPD. In the 2003–2006 NHANES cycles, objectively determined pulmonary data were not collected. As a result, COPD was assessed via self-report. Participants were asked if a doctor or health professional ever had told them that they had chronic bronchitis or emphysema. In the present study, participants who answered yes were considered to have provisional COPD. Self-reported physician diagnosis of COPD has been clinically validated (via spirometry); in an independent sample, 98% of the studied participants with self-reported physician diagnosis did indeed have spirometrically determined COPD.30

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Design Data from the 2003–2006 National Health and Nutrition Examination Survey (NHANES) were used.29 Analyses were restricted to the 2003–2006 cycles because these are the only cycles that presently have accelerometry data. NHANES is conducted by the National Center for Health Statistics (NCHS), and all procedures for data collection were approved by the NCHS ethics review board (NCHS IRB No. for 2003– 2004 cycle: 98-12; NCHS IRB No. for 2005–2006 cycle: 2005-06). NHANES participants are initially interviewed in their home and then undergo a battery of assessments (e.g., blood draws, anthropometric measurements, and completion of additional surveys) in the mobile examination center (MEC). Briefly, the MEC consists of four trailers that house medical equipment and trained medical professionals. All participants provided written informed consent prior to data collection.

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were excluded because of missing data on the study variables, there were no differences (p . .05) in any of the study variables with the exception of age. Those included were older (55.7 years) than those excluded (50.6 years) (p ¼ .049).

Sample Among the 20,470 participants in the 2003–2006 NHANES cycles, 467 participants remained after excluding those who were not current or former smokers and did not have COPD; 277 remained after excluding those with insufficient accelerometry data (i.e., ,4 days of 10þ h/d); 269 remained after excluding those with missing WBC or neutrophil data, and 238 remained after excluding those with missing data on the covariates, with these 238 participants comprising the analytic sample. With regard to the 238 participants in the present study and the 229 participants who were current or former smokers with COPD but

Assessment of Smoking Status. Those classified as current smokers indicated that they now smoke every day or some days; those classified as former smokers had smoked at least 100 cigarettes in their life, but do not currently smoke. Smoking status was biochemically assessed from cotinine levels using gender-specific cut points (1.78 ng/mL for males and 4.47 ng/mL for females).31 Serum cotinine was measured by an isotope dilution–high performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry.32 Assessment of WBC and Neutrophils. Blood samples were taken at the MEC (prior to physical activity monitoring) to assess WBC and neutrophil counts. The method used to determine WBC and neutrophil counts was based on the Beckman Coulter method of counting, in combination with an automatic diluting and mixing device for sample processing. Further details of the laboratory methodology and quality control can be found elsewhere.33 Assessment of Physical Activity. At the MEC, participants who were able to walk were asked to wear an ActiGraph 7164 accelerometer on their right hip

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Table 1 Multivariable Linear Regression Association Between Physical Activity (Independent Variable) and WBC (Dependent Variable), NHANES 2003–2006 (n ¼ 238)* WBC Variable

LPA Coefficient (95% CI)

P

Physical activity Covariates Former smoker vs. current smoker Age, 1 y older Female vs. male BMI, 1 kg/m2 higher Nonwhite vs. white Poverty-to-income ratio, 1 unit higher C-reactive protein, 1 mg/ dL higher Cotinine, 1 ng/mL higher 1þ vs. 0 comorbidities 2 vs. 0 comorbidities 3þ vs. 0 comorbidities Homocysteine, 1 lmol/L higher Accelerometer wear time, 1 h higher No functioning limitations vs. limitations Medication use vs. no medication use

0.0004 (0.001 to 0.00002)

0.04

0.10 (0.24 to 0.03) 0.002 (0.005 to 0.001) 0.008 (0.08 to 0.06) 0.003 (0.002 to 0.008) 0.05 (0.15 to 0.04)

0.13 0.12 0.81 0.22 0.24

0.002 (0.01 to 0.02)

0.87

MVPA Coefficient (95% CI)

P

TPA Coefficient (95% CI)

0.04 (0.07 to 0.003)

0.05

0.0004 (0.0007 to 0.00002)

0.03

0.10 (0.24 to 0.03) 0.002 (0.006 to 0.0003) 0.03 (0.11 to 0.04) 0.003 (0.002 to 0.008) 0.05 (0.15 to 0.03)

0.14 0.07 0.34 0.22 0.22

0.10 (0.24 to 0.03) 0.002 (0.005 to 0.006) 0.01 (0.08 to 0.06) 0.003 (0.002 to 0.007) 0.05 (0.15 to 0.04)

0.13 0.11 0.73 0.24 0.25

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0.002 (0.01 to 0.02)

0.82

0.001 (0.02 to 0.02)

0.08 (0.06 to 0.11) ,0.001 0.08 (0.06 to 0.10) ,0.001 0.08 (0.06 to 0.11) 0.00002 (0.0004 to 0.0004) 0.90 0.00002 (0.0004 to 0.0004) 0.89 0.00001 (0.0004 to 0.0004) 0.007 (0.12 to 0.13) 0.91 0.002 (0.13 to 0.13) 0.96 0.009 (0.12 to 0.14) 0.04 (0.09 to 0.17) 0.52 0.02 (0.10 to 0.16) 0.66 0.04 (0.09 to 0.17) 0.01 (0.14 to 0.12) 0.89 0.003 (0.14 to 0.13) 0.96 0.01 (0.14 to 0.13)

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0.01 (0.002 to 0.03) 0.003 (0.01 to 0.02)

0.01 (0.08 to 0.05) 0.002 (0.06 to 0.07)

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P

0.89

,0.001 0.93 0.88 0.52 0.92

0.08

0.01 (0.003 to 0.02)

0.12

0.01 (0.002 to 0.02)

0.08

0.73

0.0004 (0.02 to 0.02)

0.96

0.004 (0.01 to 0.02)

0.70

0.72

0.01 (0.08 to 0.06)

0.73

0.95

0.003 (0.07 to 0.06)

0.92

0.01 (0.08 to 0.06) 0.002 (0.06 to 0.07)

0.73 0.96

* WBC and MVPA were log transformed to improve normality. All physical activity intensities are expressed as 1-minute changes. Physical activity results controlled for smoking status, age, gender, BMI, race-ethnicity, poverty-to-income ratio, C-reactive protein, cotinine, comorbidity index, homocysteine, accelerometer wear time, physical functioning, and medication use. Three separate models were computed: one for LPA, one for MVPA, and one for TPA (LPA plus MVPA).WBC served as the dependent variable. WBC indicates white blood cells; NHANES, National Health and Nutrition Examination Survey; LPA, light-intensity PA; MVPA, moderate-to-vigorous PA; TPA, total physical activity; and BMI, body mass index.

for 7 days. Accelerometers were fastened to an elastic belt worn around the participant’s waist near the iliac crest. Participants were asked to wear the accelerometer during all activities, except during water-based activities and while sleeping. The accelerometer measured the frequency, intensity, and duration of physical activity by generating an activity count proportional to the measured acceleration. Additional details about the mechanics of accelerometry can be found elsewhere.34 Estimates for physical activity were summarized in 1-minute epochs. As established by the work of Troiano et al.,35 activity counts between 100 and 2019 counts per minute were classified as light-intensity physical activity (LPA), activity counts 2020 per minute were classified as moderate-tovigorous physical activity (MVPA) in-

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tensity,35 and activity counts 100 per minute were considered as total physical activity (TPA; LPA plus MVPA); moderate- and vigorous-intensity physical activity were combined as participants spent little time at vigorousintensity physical activity (mean: .43 min/d; SE ¼ .18). To determine the amount of time the monitor was worn, nonwear was defined by a period of a minimum of 60 consecutive minutes of zero activity counts, with the allowance of 1 to 2 minutes of activity counts between 0 and 100.35 For the analyses described here, and to represent habitual physical activity patterns, only those participants with at least 4 days with 10 or more hours per day of wear time were included35; a period of at least 4 days of valid monitoring data (i.e., 10 h/d) has been shown to

accurately predict habitual physical activity levels in adults.36,37 Assessment of Covariates. Information about age, gender, race-ethnicity, poverty-to-income ratio (PIR), and comorbidity index was obtained from a questionnaire. As a measure of socioeconomic status, PIR was determined by the NCHS. Ranging from 0 to 5, PIR was defined as the ratio of the family individual income to their poverty threshold. For example, a PIR of .5 suggests that the family income is 50% below the poverty threshold. Participants were classified as having 0, 1, 2, or 3þ comorbidities based on selfreport of the following chronic diseases/events: arthritis, coronary heart disease, stroke, congestive heart failure, cancer, and heart attack.38,39 Comorbid hypertension was also included

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Table 2 Multivariable Linear Regression Association Between Physical Activity (Independent Variable) and Neutrophils (Dependent Variable), NHANES 2003–2006 (n ¼ 238)* Neutrophils Variable

LPA Coefficient (95% CI)

P

Physical activity Covariates Former smoker vs. current smoker Age, 1 y older Female vs. male BMI, 1 kg/m2 higher Nonwhite vs. white Poverty-to-income ratio, 1 unit higher C-reactive protein, 1 mg/ dL higher Cotinine, 1 ng/mL higher 1 comorbidity vs. 0 comorbidities 2 comorbidities vs. 0 comorbidities 3þ comorbidities vs. 0 comorbidities Homocysteine, 1 lmol/L higher Accelerometer wear time, 1 h higher No functioning limitations vs. limitations Medication use vs. no medication use

0.001 (0.0009 to 0.0001)

0.02

0.04 (0.21 to 0.11) 0.002 (0.006 to 0.001) 0.01 (0.10 to 0.08) 0.004 (0.001 to 0.01) 0.13 (0.25 to 0.009) 0.008 (0.03 to 0.02)

P

TPA Coefficient (95% CI)

0.05 (0.09 to 0.004)

0.06

0.001 (0.0009 to 0.00009)

0.01

0.55 0.16 0.80 0.14 0.03

0.04 (0.21 to 0.12) 0.003 (0.007 to 0.0006) 0.04 (0.14 to 0.04) 0.004 (0.001 to 0.01) 0.13 (0.25 to 0.01)

0.58 0.10 0.31 0.14 0.03

0.05 (0.21 to 0.11) 0.002 (0.006 to 0.001) 0.01 (0.10 to 0.07) 0.004 (0.001 to 0.01) 0.13 (0.25 to 0.008)

0.54 0.14 0.72 0.16 0.03

0.54

0.008 (0.03 to 0.02)

0.12 (0.09 to 0.15) ,0.001 0.0001 (0.0003 to 0.0006) 0.56 0.01 (0.17 to 0.14)

MVPA Coefficient (95% CI)

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0.12 (0.09 to 0.14) ,0.001 0.0001 (0.0003 to 0.0006) 0.55

P

0.009 (0.03 to 0.02)

0.12 (0.09 to 0.15) 0.0001 (0.0003 to 0.0006)

,0.001 0.59

0.02 (0.17 to 0.13)

0.78

0.97

0.01 (0.17 to 0.14)

0.84

0.01 (0.17 to 0.15)

0.89

0.004 (0.17 to 0.16)

0.95

0.01 (0.17 to 0.15)

0.92

0.01 (0.01 to 0.03)

0.28

0.01 (0.01 to 0.03)

0.40

0.01 (0.01 to 0.03)

0.29

0.001 (0.03 to 0.03)

0.95

0.004 (0.03 to 0.02)

0.74

0.001 (0.03 to 0.03)

0.91

0.02 (0.05 to 0.10)

0.55

0.02 (0.06 to 0.10)

0.59

0.02 (0.05 to 0.10)

0.53

0.001 (0.09 to 0.09)

0.98

0.007 (0.09 to 0.07)

0.85

0.001 (0.09 to 0.09)

0.96

0.84

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0.002 (0.15 to 0.15)

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0.01 (0.17 to 0.14)

0.52

0.004 (0.14 to 0.15)

0.89

0.95

* Neutrophils and MVPA were log transformed to improve normality. All physical activity intensities are expressed as 1-minute changes. Physical activity results controlled for smoking status, age, gender, body mass index, race-ethnicity, poverty-to-income ratio, C-reactive protein, cotinine, comorbidity index, homocysteine, accelerometer wear time, physical functioning, and medication use. Three separate models were computed: one for LPA, one for MVPA, and one for TPA (LPA plus MVPA). Neutrophils served as the dependent variable. NHANES indicates National Health and Nutrition Examination Survey; LPA, light-intensity PA; MVPA, moderate-to-vigorous intensity PA; TPA, total physical activity; and BMI, body mass index.

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when measured systolic blood pressure was 140 mm Hg, when measured diastolic blood pressure was 90 mm Hg, or with reported use of blood pressure–lowering medication. Additionally, diabetes was an included comorbidity. Participants were considered to have evidence of diabetes if they self-reported a previous diagnosis of the disease (excluding gestation diabetes mellitus), were taking insulin or other medication (e.g., pills) to lower blood sugar, had a blood glycohemoglobin (i.e., hemoglobin A1C) of 6.5% or greater,40 or had a fasting glucose level of 126 mg/dL or higher.41 Body mass index was calculated from measured weight and height (weight in

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kilograms divided by the square of height in meters). Homocysteine, a marker of endothelial function, was measured using the fluorescence polarization immunoassay. High sensitivity C-reactive protein concentration was quantified using latex-enhanced nephelometry. Accelerometer wear time (number of hours the accelerometer was worn per day) was also included as a covariate, as accelerometer wear time can influence activity estimates.42 Participants were considered to have a functional disability if they reported special assistance for walking (e.g., cane), had limitations from keeping them from working, or reported having any difficulty in five categories of functional disability, in-

cluding lower extremity mobility, general physical activity, activities of daily living, instrumental activities of daily living, and leisure and social activities. The individual items assessing these functional disability categories can be found elsewhere.43 Lastly, a binary variable was created with participants classified as taking medications if they self-reported taking antidiabetic medications, blood pressure–lowering medication, or cholesterol-lowering medication. Analysis All statistical analyses were performed using procedures from sample survey data using STATA (version 12.0, College Station, TX) to account for the complex survey design used in

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Table 3 Characteristics of the Analyzed Sample, NHANES 2003–2006 (n ¼ 238)* Variable

Mean/Proportion (SE)

Smoking† % Current smoker Cotinine, ng/mL % Former smoker Cotinine, ng/mL Age, y % Female Body mass index, kg/m2 Race-ethnicity % Non-Hispanic white % Other race Poverty-to-income ratio‡ Comorbidity index, %§ 0 comorbidities 1 comorbidity 2 comorbidities 3þ comorbidities Homocysteine, lmol/L White blood cell count, 1000 cells/lL Segmented neutrophils, No. C-reactive protein, mg/dL % with some physical functioning limitation|| Physical activity Light intensity, min/d Moderate-to-vigorous intensity, min/d Total physical activity, min/d Accelerometer wear time, h/d

52.2 270.9 47.7 0.1 55.7 56.9 28.7

(4.2) (12.1) (4.2) (0.03) (1.1) (4.7) (0.6)

84.5 (2.5) 15.4 (2.5) 2.7 (0.1)

(2.6) (3.7) (3.1) (4.3) (0.2) (0.2) (0.1) (0.06) (4.1)

310.1 13.2 323.4 13.8

(7.4) (1.3) (8.0) (0.1)

* NHANES indicates National Health and Nutrition Examination Survey. † Those classified as current smokers indicated that they now smoke every day or some days; those classified as former smokers have smoked at least 100 cigarettes in their life, but do not currently smoke. ‡ Poverty-to-income ratio was defined as the ratio of the family individual income to their poverty threshold. § Participants were classified as having 0, 1, 2, or 3þ comorbidities based on having arthritis, coronary heart disease, stroke, congestive heart failure, cancer, heart attack, hypertension, and diabetes. || Participants were considered to have a functional disability if they required special assistance for walking (e.g., cane), had limitations from keeping them from working, or reported having any difficulty in five categories of functional disability, including lower extremity mobility, general physical activity, activities of daily living, instrumental activities of daily living, and leisure and social activities.

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NHANES. To account for oversampling and nonresponse, and to provide nationally representative estimates, all analyses included the use of survey sample weights, clustering, and primary sampling units. Means and standard errors were calculated for continuous variables and proportions were calculated for categorical variables. Multivariable linear regression analysis (Tables 1 and 2) was used to assess the association between physical activity (independent variable) and WBC and neutrophils. Separate multivari-

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RESULTS

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Demographic characteristics of the analytic sample are shown in Table 3. Predictors of WBC counts and neutrophil counts, respectively, are shown in Tables 1 and 2. LPA (b ¼ .0004), MVPA (b ¼.04) and TPA (b ¼.0004) were significantly (p  .05) inversely associated with WBC. Similarly, LPA (b ¼ .001) and TPA (b ¼ .001) were significantly (p  .05) inversely associated with neutrophils. Only MVPA was marginally associated with neutrophils (b ¼.05; p ¼ .06). All associations were adjusted for smoking status (current/ former smoker), age, gender, body mass index, race-ethnicity, PIR, C-reactive protein, cotinine, comorbidity index, homocysteine, accelerometer wear time, physical functioning, and medication use. With regard to percentage change in WBC count, a 1-minute increase in LPA was associated with a ,1% decrease in WBC count (100[e.0004  1]); expressed as a larger interval change, a 60-minute increase in LPA was associated with a 2% decrease in WBC (100[e.024  1]). Similarly, a 1minute increase in LPA was associated with a ,1% decrease in neutrophils (100[e.001  1]); expressed as a larger interval change, a 60-minute increase in LPA was associated with a 6% decrease in neutrophils (100[e.06  1]).45 A 1% increase in MVPA was associated with a ,1% decrease in the average WBC level (100[e.04ln(1.01)  1]); expressed as a larger interval change, a 50% increase in MVPA was associated with a 1.61% decrease in WBC level (100[e.04ln(1.50)  1]). A 1% increase in MVPA was associated with a ,1% decrease in the average neutrophil level (100[e.05ln(1.01)  1]); expressed as a larger interval change, a 50% increase in MVPA was associated with a 2.01% decrease in neutrophil level (100[e.05ln(1.50)  1]).45

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13.9 28.7 26.2 31.0 9.6 8.0 4.9 0.60 61.0

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factor is .6 (observed mean ¼ 1.6), if the highest individual variance inflation factor is .10 (highest observed ¼ 4.0); or if the tolerance statistic is ,.1 (all observed to be ..24).44 Statistical significance was established p  .05.

able analyses were run, in which either LPA, MVPA, or TPA was included as an independent variable, and with neutrophil or WBC cell count as the dependent variable. Prior to multivariable testing, MVPA (ln[MVPA þ 1]), WBC (ln[WBC þ 1]), and neutrophils (ln[neutrophils þ 1]) were log transformed to improve severe nonnormality. All covariates were entered into the model at the same time as there was no evidence of multicollinearity; evidence of multicollinearity is likely to be present if the mean variance inflation

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DISCUSSION The purpose of this study was to examine the association between objectively measured physical activity and WBC and neutrophil counts in a nationally representative sample of current or former smokers with selfreported COPD. Our principal findings were that objectively measured LPA, MVPA, and TPA were inversely related to WBC count, and LPA and TPA were inversely related to neutrophil count. Empirical research indicates that COPD and smoking induce not only inflammation in the lung, but systemic inflammation as well.4–6 They contribute to the risk of other chronic diseases, such as cardiovascular disease, type 2 diabetes, and cancer.11–20 Although prospective studies are needed to confirm this assertion, the present findings suggest that, among current or former smokers with self-reported COPD, regular participation in physical activity may slow the progression of cardiovascular and metabolic disorders by attenuating systemic inflammation. Several mechanisms may help to elucidate the association between physical activity and WBC/neutrophil counts. As previously addressed by Johannsen et al.,24 physical activity ‘‘may alter the trafficking of leukocyte subsets between secondary lymphoid organs and blood.’’24(p8) These researchers also indicate that physical activity may lower these inflammatory markers through its direct impact on bone marrow hematopoiesis, and through physical activity–induced genetic expression.46,47 Although the effect was relatively small, LPA was inversely associated with inflammation in current or former smokers with self-reported COPD. This is a particularly important finding of the present study. Effects of MVPA on health have been extensively investigated.48,49 Those of LPA on health outcomes in the general population, let alone those with COPD, are not as well established. Promotion of LPA in COPD may be a palatable alternative to higher intensity levels, as over 70% of those with COPD present with one or more comorbid conditions that may limit their physical activity capacity.50 Additionally, physical activity intolerance among some individuals with

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the biological plausibility of this association. Future studies, particularly prospective and experimental designs, are needed to determine directionality of influence, including whether the association is bidirectional. Another limitation of the present study is the inability to objectively confirm COPD, as objectively measured pulmonary function data were not collected in the 2003–2006 NHANES cycles, which were the cycles that collected accelerometry data. Although NHANES employs a representative sample when using sampling weights, it is possible that the 238 participants in the present study with self-reported COPD are an underrepresentation of those with COPD, as research demonstrates that physicians tend to underdiagnose COPD.61 Indeed, these 238 participants, who represent approximately 5.5 million U.S. adults, are below U.S. prevalence estimates (i.e., 13.1 million) reported elsewhere.3,62 In summary, these analyses indicate an inverse association between objectively measured physical activity (LPA, MVPA, and TPA) and systemic inflammation among current or former smokers with self-reported COPD. If this association is confirmed by future prospective and experimental studies,

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COPD may also be a result of limb muscle dysfunction, which is a common manifestation of COPD.51 Individuals with muscle dysfunction are likely to have reduced muscular strength and endurance as a result of a variety of bioenergetic changes, including, for example, fiber type redistribution,52 oxidative stress,53 and muscle atrophy.54 Although not definitive, these manifestations of COPD may be a result of increased systemic inflammation.25 Regular participation in physical activity may help to attenuate their progression in COPD by mitigating inflammation, which ultimately may help to improve exercise tolerance and peripheral muscle function.25,55,56 Importantly, we know that moderate and high intensity physical activity may induce an initial proinflammatory response in individuals with COPD by increasing the release of cytokines that may play a role in muscle degradation.57,58 As a result, LPA, at first, may be a more suitable intensity level to ensure any underlying COPD-induced systemic inflammation is not exacerbated during physical activity. Taken together, these findings suggest the importance of promoting physical activity among current or former smokers with COPD. Although most studies report fairly low levels of physical activity among this population,26,59 others have reported relatively high levels of physical activity (over 60% met physical activity guidelines) among those with COPD,60 suggesting that physical activity promotion among this population may be feasible. Limitations to the present study include the cross-sectional design, precluding any causal inferences regarding the association between physical activity and inflammation. It is possible that participants with greater systemic inflammation were less active because of their condition. Another limitation is that we were unable to control for other potential confounding variables, such as depression and sleep patterns, as these data were not collected in the 2003–2004 NHANES cycle. There is compelling evidence to suggest that regular physical activity participation may lower inflammatory markers among those with COPD given

SO WHAT? Implications for Health Promotion Practitioners and Researchers What is already known on this topic? Smaller-scale studies have shown an inverse association between physical activity and inflammation among those with chronic obstructive pulmonary disease (COPD). What does this article add? This study indicates that objectively measured light, moderate-to-vigorous, and total physical activity are inversely associated with markers of inflammation among a national sample of current or former U.S. smokers with self-reported COPD. What are the implications for health promotion practice or research? Habitual physical activity among those with COPD may serve as an anti-inflammatory strategy to attenuate disease progression and possibly cardiovascular and metabolic disease occurrence.

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then physical activity among those with COPD may serve as an anti-inflammatory strategy to attenuate disease progression and possibly cardiovascular and metabolic disease occurrence. References

14.

1. Global initiative for chronic obstructive lung disease (GOLD). Available at: http://www.goldcopd.org. Accessed May 5, 2013. 2. Coultas DB, Mapel D, Gagnon R, Lydick E. The health impact of undiagnosed airflow obstruction in a national sample of United States adults. Am J Respir Crit Care Med. 2001;164:372–377. 3. Mannino DM, Homa DM, Akinbami LJ, et al. Chronic obstructive pulmonary disease surveillance—United States, 1971–2000. MMWR Surveill Summ. 2002;51:1–16. 4. Agusti AG. Systemic effects of chronic obstructive pulmonary disease. Proc Am Thorac Soc. 2005;2:367–370; discussion 371–372. 5. Fimognari FL, Scarlata S, Conte ME, Incalzi RA. Mechanisms of atherothrombosis in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2008;3:89–96. 6. Fimognari FL, Scarlata S, Antonelli-Incalzi R. Why are people with ‘‘poor lung function’’ at increased atherothrombotic risk? A critical review with potential therapeutic indications. Curr Vasc Pharmacol. 2010;8:573–586. 7. Van Eeden S, Leipsic J, Paul Man SF, Sin DD. The relationship between lung inflammation and cardiovascular disease. Am J Respir Crit Care Med. 2012;186:11–16. 8. US Dept of Health and Human Services. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for SmokingAttributable Disease: A Report of the Surgeon General. Atlanta, Ga: US Dept of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2010. 9. Steiropoulos P, Papanas N, Nena E, et al. Mean platelet volume and platelet distribution width in patients with chronic obstructive pulmonary disease: the role of comorbidities. Angiology. In press. 10. Ishizaka N, Ishizaka Y, Toda E, et al. Association between cigarette smoking, white blood cell count, and metabolic syndrome as defined by the Japanese criteria. Intern Med. 2007;46:1167–1170. 11. Kannel WB, Anderson K, Wilson PW. White blood cell count and cardiovascular disease. Insights from the Framingham Study. JAMA. 1992;267:1253–1256. 12. Gillum RF, Ingram DD, Makuc DM. White blood cell count, coronary heart disease, and death: the NHANES I Epidemiologic Follow-up Study. Am Heart J. 1993;125:855– 863. 13. Brown DW, Giles WH, Croft JB. White blood cell count: an independent predictor of coronary heart disease

15.

16.

17.

18.

American Journal of Health Promotion

20.

21.

22.

23.

24.

25.

26.

27.

28.

29. National Health and Nutrition Examination Survey. Centers for Disease Control and Prevention. Available at: http://www.cdc.gov/nchs/nhanes.htm. Accessed May 5, 2013. 30. Radeos MS, Cydulka RK, Rowe BH, et al. Validation of self-reported chronic obstructive pulmonary disease among patients in the ED. Am J Emerg Med. 2009; 27:191–196. 31. Benowitz NL, Bernert JT, Caraballo RS, et al. Optimal serum cotinine levels for distinguishing cigarette smokers and nonsmokers within different racial/ethnic groups in the United States between 1999 and 2004. Am J Epidemiol. 2009;169:236– 248. 32. Centers for Disease Control and Prevention. Laboratory procedures manual for cotinine. Available at: http:// www.cdc.gov/NCHS/data/nhanes/ nhanes_09_10/COT_F_met.pdf. Accessed May 5, 2013. 33. National Health and Nutrition Examination Survey: laboratory procedures manual. 211–215. Available at: http://wwwn.cdc.gov/nchs/nhanes/ search/datapage.aspx?Component¼ Laboratory&CycleBeginYear¼2005. Accessed May 5, 2013. 34. Chen KY, Bassett DR Jr. The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exerc. 2005;37:S490–S500. 35. Troiano RP, Berrigan D, Dodd KW, et al. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181–188. 36. Hart TL, Swartz AM, Cashin SE, Strath SJ. How many days of monitoring predict physical activity and sedentary behaviour in older adults? Int J Behav Nutr Phys Act. 2011;8:62. 37. Matthews CE, Ainsworth BE, Thompson RW, Bassett DR Jr. Sources of variance in daily physical activity levels as measured by an accelerometer. Med Sci Sports Exerc. 2002;34:1376–1381. 38. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–383. 39. Quan H, Li B, Couris CM, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173: 676–682. 40. Summary of revisions for the 2010 Clinical Practice Recommendations. Diabetes Care. 2010;33(suppl 1):S3. 41. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33(suppl 1): S62–S69. 42. Herrmann SD, Barreira TV, Kang M, Ainsworth BE. How many hours are enough? Accelerometer wear time may provide bias in daily activity estimates. J Phys Act Health. 2013;10:742–749. 43. Kalyani RR, Saudek CD, Brancati FL, Selvin E. Association of diabetes, comorbidities, and A1C with functional

t s r

i F

e n i

l n

o

19.

mortality among a national cohort. J Clin Epidemiol. 2001;54:316–322. Grimm RH Jr, Neaton JD, Ludwig W. Prognostic importance of the white blood cell count for coronary, cancer, and allcause mortality. JAMA. 1985;254:1932– 1937. Twig G, Afek A, Shamiss A, et al. White blood cells count and incidence of type 2 diabetes in young men. Diabetes Care. 2013; 36:276–282. Hatanaka E, Monteagudo PT, Marrocos MS, Campa A. Neutrophils and monocytes as potentially important sources of proinflammatory cytokines in diabetes. Clin Exp Immunol. 2006;146:443–447. Gkrania-Klotsas E, Ye Z, Cooper AJ, et al. Differential white blood cell count and type 2 diabetes: systematic review and meta-analysis of cross-sectional and prospective studies. PLoS One. 2010;5: e13405. Vozarova B, Weyer C, Lindsay RS, et al. High white blood cell count is associated with a worsening of insulin sensitivity and predicts the development of type 2 diabetes. Diabetes. 2002;51:455–461. Coussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420:860–867. Mantovani A, Pierotti MA. Cancer and inflammation: a complex relationship. Cancer Lett. 2008;267:180–181. Erlinger TP, Muntner P, Helzlsouer KJ. WBC count and the risk of cancer mortality in a national sample of U.S. adults: results from the second National Health and Nutrition Examination Survey mortality study. Cancer Epidemiol Biomarkers Prev. 2004;13:1052–1056. van der Vlist J, Janssen TW. The potential anti-inflammatory effect of exercise in chronic obstructive pulmonary disease. Respiration. 2010;79:160–174. Garrod R, Ansley P, Canavan J, Jewell A. Exercise and the inflammatory response in chronic obstructive pulmonary disease (COPD)—does training confer antiinflammatory properties in COPD? Med Hypotheses. 2007;68:291–298. Johannsen NM, Swift DL, Johnson WD, et al. Effect of different doses of aerobic exercise on total white blood cell (WBC) and WBC subfraction number in postmenopausal women: results from DREW. PLoS One. 2012;7:e31319. Laveneziana P, Palange P. Physical activity, nutritional status and systemic inflammation in COPD. Eur Respir J. 2012; 40:522–529. Bossenbroek L, de Greef MH, Wempe JB, et al. Daily physical activity in patients with chronic obstructive pulmonary disease: a systematic review. COPD. 2011;8:306–319. Shephard RJ. Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med. 2003;37: 197–206; discussion 206. Freedman LS, Schatzkin A, Midthune D, Kipnis V. Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst. 2011;103:1086– 1092.

Month 0000, Vol. 0, No. 0

0

44.

45.

46.

47.

48.

49.

disability in older adults: results from the National Health and Nutrition Examination Survey (NHANES), 1999– 2006. Diabetes Care. 2010;33:1055–1060. UCLA Statistical Consulting Group. Assessment of multicollinearity. Available at: http://www.ats.ucla.edu/stat/stata/ faq/svycollin.htm. Accessed May 5, 2013. Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. New York, NY: Springer Science Business Media Inc; 2004:126–127. Buttner P, Mosig S, Lechtermann A, et al. Exercise affects the gene expression profiles of human white blood cells. J Appl Physiol. 2007;102:26–36. Radom-Aizik S, Zaldivar F, Leu SY, et al. Effects of 30 min of aerobic exercise on gene expression in human neutrophils. J Appl Physiol. 2008;104:236–243. Warburton DE, Charlesworth S, Ivey A, et al. A systematic review of the evidence for Canada’s Physical Activity Guidelines for Adults. Int J Behav Nutr Phys Act. 2010;7:39. Humphreys BR, McLeod L, Ruseski JE. Physical activity and health outcomes: evidence from Canada. Health Econ. In press.

0

American Journal of Health Promotion

57.

58.

59.

60.

pulmonary disease. Can Respir J. 2010;17: 219–223. Reid WD, Rurak J, Harris RL. Skeletal muscle response to inflammation— lessons for chronic obstructive pulmonary disease. Crit Care Med. 2009;37:S372–S383. van Helvoort HA, Heijdra YF, Dekhuijzen PN. Systemic immunological response to exercise in patients with chronic obstructive pulmonary disease: what does it mean? Respiration. 2006;73:255–264. Moy ML, Matthess K, Stolzmann K, et al. Free-living physical activity in COPD: assessment with accelerometer and activity checklist. J Rehabil Res Dev. 2009;46:277– 286. Donaire-Gonzalez D, Gimeno-Santos E, Balcells E, et al. Physical activity in COPD patients: patterns and bouts. Eur Respir J. In press. Chapman KR, Tashkin DP, Pye DJ. Gender bias in the diagnosis of COPD. Chest. 2001; 119:1691–1695. Centers for Disease Control and Prevention. National Center for Health Statistics: National Health Interview Survey raw data, 2008. Analysis performed by American Lung Association Research and Program Services using SPSS and SUDAAN software.

t s r

i F

e n i

l n

o

50. Crisafulli E, Costi S, Luppi F, et al. Role of comorbidities in a cohort of patients with COPD undergoing pulmonary rehabilitation. Thorax. 2008;63:487–492. 51. Skeletal muscle dysfunction in chronic obstructive pulmonary disease. A statement of the American Thoracic Society and European Respiratory Society. Am J Respir Crit Care Med. 1999;159:S1–S40. 52. Gosker HR, Zeegers MP, Wouters EF, Schols AM. Muscle fibre type shifting in the vastus lateralis of patients with COPD is associated with disease severity: a systematic review and meta-analysis. Thorax. 2007;62:944–949. 53. Barreiro E, Schols AM, Polkey MI, et al. Cytokine profile in quadriceps muscles of patients with severe COPD. Thorax. 2008; 63:100–107. 54. Debigare R, Maltais F, Cote CH, et al. Profiling of mRNA expression in quadriceps of patients with COPD and muscle wasting. COPD. 2008;5:75–84. 55. Rodriguez DA, Kalko S, Puig-Vilanova E, et al. Muscle and blood redox status after exercise training in severe COPD patients. Free Radic Biol Med. 2012;52:88–94. 56. Scott AS, Baltzan MA, Fox J, Wolkove N. Success in pulmonary rehabilitation in patients with chronic obstructive

61.

62.

Month 0000, Vol. 0, No. 0