JEPH2013-242383

Hindawi Publishing Corporation Journal of Environmental and Public Health Volume 2013, Article ID 242383, 9 pages http://dx.doi.org/10.1155/2013/242383 Research Article Health-Related Factors Associated with Mode of Travel to Work Melissa Bopp,1 Andrew T. Kaczynski,2 and Matthew E. Campbell1 1 Department of Kinesiology, The Pennsylvania State University, 268R Recreation Building, University Park, PA 16802, USA 2Department of Health Promotion, Education and Behavior, Arnold School of Public Health, Prevention Research Center, University of South Carolina, Columbia, SC 29208, USA Correspondence should be addressed to Melissa Bopp; mjb73@psu.edu Received 5 December 2012; Accepted 24 January 2013 Academic Editor: Li Ming Wen Copyright © 2013 Melissa Bopp et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Active commuting (AC) to the workplace is a potential strategy for incorporating physical activity into daily life and is associated with health benefits. This study examined the association between health-related factors and mode of travel to the workplace. Methods. A volunteer convenience sample of employed adults completed an online survey regarding demographics, health- related factors, and the number of times/week walking, biking, driving, and using public transit to work (dichotomized as no walk/bike/drive/PT and walk/bike/drive/PT 1 + x/week). Logistic regression was used to predict the likelihood of each mode of transport and meeting PA recommendations from AC according to demographics and health-related factors. Results. The sample (𝑛 = 1175) was aged 43.5 ± 11.4 years and was primarily White (92.7%) and female (67.9%). Respondents reported walking (7.3%), biking (14.4%), taking public transit (20.3%), and driving (78.3%) to work at least one time/week. Among those reporting AC, 9.6% met PA recommendations from AC alone. Mode of travel to work was associated with several demographic and health- related factors, including age, number of chronic diseases, weight status, and AC beliefs. Discussion. Mode of transportation to the workplace and health-related factors such as disease or weight status should be considered in future interventions targeting AC. 1. Introduction The economic cost of preventable chronic disease in the United States is substantial, with the direct and indirect costs associatedwith cancer, cardiovascular disease, diabetes,men- tal health disorders, and pulmonary conditions estimated at more than $1 trillion for the general population in 2003. Among employed adults, much of this economic burden is shouldered by employers in terms of private health insurance expenditures and lost productivity, with the costs associated with chronic disease nearing $465 billion [1]. The visionary initiative targeting population level health is found in the US Department of Health and Human Services’ Healthy People 2020 and includes goals of attaining high-quality, longer lives free of preventable disease and premature death [2]. This document includes goals and objectives focused on changing health behaviors that contribute to chronic disease morbid- ity and mortality, including specifically improving rates of physical activity participation along with environmental and policy approaches aimed at supporting this behavior across the lifespan. Evidence outlining the benefits of regular physical activity participation for the prevention of chronic disease and premature mortality is substantial [3, 4]. Epidemiological and clinical trials have documented the benefits of physical activity in preventing diabetes and metabolic disorders [5– 8], cardiovascular disease [9–11], certain cancers [12–16], and mental health disorders [17–19].Themajority of these studies include data on all forms of physical activity (leisuretime, occupational, and transportation related). When specifically examining the health effects of transportation-related phys- ical activity to work, known as active commuting (AC), data from epidemiological surveys have found relationships between active travel and a lesser presence of self-reported obesity [20–22] and a reduced risk of cardiovascular disease and all-causemortality [23–26].Despite these knownbenefits of active transport, in the United States, rates of AC remain low (3% reporting walking to work, <1% report biking to work), especially in comparison to other countries (e.g., The Netherlands: 25% of trips are made by bicycle) [27–29]. Recent research has addressed individual, social, and environmental factors associated with AC to work. Some 2 Journal of Environmental and Public Health documented correlates of AC include demographics (age, gender, income, and race/ethnicity) [30, 31], psychosocial (self-efficacy, behavioral beliefs, attitudes, and intention) [32– 35], and environmental influences (traffic, walkable and bike- able features, safety, and convenient public transport close to the workplace) [36–41]. However, few, if any, studies have focused in much depth on how diverse health-related factors are associatedwithAC [42], despite the acknowledgment that health is a prime determinant, motivator, and outcome of AC [25]. Moreover, the impact of health-related influences on specific modes of travel (e.g., walking, biking, transit, and driving) has received even less attention. Additionally, few studies have explored the extent to which AC provides sufficient opportunity to achieve recommended levels of physical activity that are adequate to achieve health benefits [43–45]. Moreover, to the extent that this is possible, what are the characteristics of individuals who engage in enough AC to meet physical activity recommendations? Given these considerations, the purpose of this study was twofold. Our primary aim was to examine the relationship between numerous demographic and health-related factors and mode of travel to work (walking, biking, driving, and public transit). The secondary aim of the study was to examine the health-related factors associated with achieving current public health recommended levels of physical activity [46] via AC. 2. Methods 2.1. Survey Design. This cross-sectional survey was delivered online from June to December 2011 using Qualtrics (Provo, UT) and was approved by the Pennsylvania State University Institutional Review Board. 2.2. Participants and Recruitment. To be eligible, partici- pants had to be over the age of 18 years, employed full- or part-time outside of the home, and physically able to walk or bike. Recruitment was focused in the mid-Atlantic region of the USA (PA, OH, WV, MD, NJ, and DE). The primary recruitment strategy involved visiting the websites of large employers (e.g., K-12 school districts, local/county government, private businesses, and universities/colleges) in medium to large cities for employee email addresses and subsequently contacting the employees directly with an email invitation. In cases where employee email addresses were not available, we contacted employers directly and asked them to distribute an electronic invitation to participate in the survey via listserv, e-newsletter, or mass email to their employees. Among employers contacted directly (𝑛 = 142), two employers refused to send out an email invitation, 84 did not respond in any way, and 56 sent out a recruitment invitation. Recruitment of participants is displayed in Figure 1. 2.3. Measures 2.3.1. Commuting Patterns. Participants were asked to reflect on the previous month and report the average number of times per week in the last month that they walked, biked, drove, and took public transportation (where available) to which 7 were invalid email address) Direct emails to individuals (𝑁 = 5.251 of = potential participants (𝑁 = 9766) + listserv invitations (𝑁 = 4522) Accessed the electronic survey (𝑁 = 1452) (response rate = 14.9%) Completed the survey (𝑁 = 1310) (completion rate = 90.2%) Excluded (unable to walk or bike𝑁 = 52, not employed outside the home𝑁 = 24) Final sample size (𝑁 = 1234) Figure 1: Participant recruitment. and from work. For each mode of travel, a dichotomized variablewas created to indicate no travel by themode of travel or travel via the mode one or more times per week. Public transportation ridership was only considered among those who had public transit available to them as determined by self-report of public transit availability in their community (𝑛 = 748). Respondents also indicated the perceived number of minutes it would take them to walk and bike to work using one item for each mode. 2.3.2. Demographics and Health Outcomes. Participants reported their age, sex, race/ethnicity (collapsed into non- HispanicWhite, non-Hispanic Black, and other racial/ethnic groups), and income level. Participants responded (yes/no) if they had any cardiovascular/pulmonary disease (heart disease, high blood pressure, elevated cholesterol, and chronic obstructive pulmonary disease), metabolic disease (diabetes, liver, or thyroid disease), musculoskeletal disease (arthritis, osteoporosis), or depression, and a total number of chronic diseases was calculated. Diseases were then collapsed into the four categories and dichotomized (e.g., yes/no for reporting a metabolic disease). Individuals were also asked to report their height and weight for body mass Journal of Environmental and Public Health 3 index (BMI) calculations (weight in kg/(height in meters)2). Respondents also rated their current health status from 1 (poor) to 5 (excellent). To determine if individuals were meeting current physical activity recommendations (at least 150 minutes/week of moderate intensity physical activity) [46] from their AC participation, the number of trips per week walking and biking were multiplied by the amount of time they reported for a walk or bike trip to work. The total number of minutes of AC time was calculated and was then dichotomized as meeting recommendations via active commuting (+150 minutes/week of active travel to work) or not meeting recommendations. 2.3.3. Perceived Health Benefits of AC. Respondents indicated their agreement with eight statements related to physical or mental health benefits of AC (e.g., AC helps me control my weight; AC can help me to relieve stress) using a 7-point Likert scale (1 = completely disagree to 7 = completely agree). A summed score was computed for all 8 items. This scale was based on a previously-tested measure [47] and showed excellent reliability in the present sample (𝛼 = 0.89). 2.4. Statistical Analyses. Basic descriptives and frequencies were used to describe the sample. To examine the primary aim, for each mode of travel, separate univariate logistic regressionmodelswere used to predict the likelihoodofwalk- ing, biking, driving, and public transit use at least once per week according to demographics and health-related factors (age, sex, income, race/ethnicity, chronic disease presence, perceived health status, and perceived health benefits of AC). Factors significantly associated with walking, biking, driving, and use of public transit were examined simultaneously in four multivariate logistic regression models and the Nagelk- erke 𝑅2 was calculated for each of the full models to examine the factors associated with each mode of travel. To address the secondary aim, the likelihood of meeting physical activ- ity recommendations via AC was examined via univariate logistic regression with the same demographics and health- related factors and then a full model with significant factors was performed. All analyses were performed using SPSS 20.0 (Armonk, NY) and significance levels were set at 𝑃 < 0.05. 3. Results The demographics of the sample are shown in Table 1. Par- ticipants were primarily non-HispanicWhite (92.1%), female (68.3%), and had a high (over $60,000) income level (63.2%). The mean age of respondents was 43.8 years (s.d. = 11.4) and slightly more than half of respondents were overweight (31.5%) or obese (19.5%). Most individuals (78.3%) reported driving to work one or more times/week and 20.3% reported using public transit, though relatively few reported walking (7.3%) or biking (14.4%) one or more times/week. Among those traveling using active methods, 9.6% met physical activity recommendations via AC. 3.1. Walking to Work One or More Times/Week. Univariate influences on walking to work at least once per week are Table 1: Characteristics of the sample (𝑛 = 1234). Variable 𝑛 (%) Mean (SD) Demographic Age 43.76 (11.44) Sex Male 327 (31.7) Female 706 (68.3) Income level <$30K/year 55 (5.5) $30–60K/year 309 (31.2) >$60K/year 626 (63.2) Race/ethnicity Non-Hispanic White 941 (92.1) Non-Hispanic Black 33 (3.2) All other racial/ethnic groups 48 (4.8) Health related Number of chronic disease 0.64 (1.01) Reporting chronic disease CV pulmonary disease 286 (21.8) Metabolic disease 133 (10.2) Musculoskeletal disease 120 (9.2) Depression 170 (13.0) Body mass index Normal weight 460 (49.0) Overweight 296 (31.5) Obese 183 (19.5) Psychological Perceived health status (range 1–5) 3.68 (0.81) Perceived health benefits (range 8–56) 44.04 (7.91) Mode of travel to work Walking one or more time/week 95 (7.3) Biking one or more time/week 188 (14.4) Driving one or more time/week 1026 (78.3) Public transit use one or more time/week 152 (20.3) AC: active commuting. found in the first columns of Table 2. Age was negatively related to being a walker (OR = 0.97, 95% CI = 0.95– 0.99). Those from “other” racial/ethnic groups were more likely to walk (OR = 2.99, 95% CI = 1.44–6.25) compared to non-Hispanic Whites. Better perceived health status was associatedwith being awalker (OR= 1.64, 95%CI= 1.24–2.17) and being in the obese weight category was associated with being a non-walker (OR = 0.46, 95% CI = 0.23–0.93). The full model of significant correlates resulted in a Nagelkerke 𝑅 2 of 0.07, with race (“other” racial/ethnic group OR = 2.91, 95% CI = 1.34–6.31), age (OR = 0.97, 95% CI = 0.95–0.99), 4 Journal of Environmental and Public Health Table 2: Univariate influences on walking, biking, driving, and taking public transit to work, meeting physical activity recommendations via active transport modes. Variable Walking to work at least 1 time/week Biking to work at least 1 time/week Driving to work at least 1 time/week Public transit to work at least 1 time/week Meeting physical activity recommendations via active transport modes OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI Demographic variables Age 0.97∗∗ 0.95–0.99 0.93∗∗∗ 0.92–0.95 1.04∗∗∗ 1.03–1.06 0.97∗∗ 0.95–0.99 0.93∗∗∗ 0.91–0.95 Income <$30,000/year (referent) 1 1 1 1 1 $30,000–60,000/year 0.71 0.29–1.71 0.38∗∗ 0.20–0.71 1.1 0.52–2.32 0.67 0.32–1.41 0.32∗∗∗ 0.17–0.63 >$60,000/year 0.64 0.26–1.41 0.33∗∗∗ 0.18–0.60 1.94 0.94–4.05 0.46∗ 0.23–0.93 0.24∗∗∗ 0.13–0.45 Sex: female (male referent) 0.85 0.54–1.35 0.31∗∗∗ 0.22–0.43 2.88∗∗∗ 1.98–4.19 0.60∗∗ 0.41–0.87 0.28∗∗∗ 0.19–0.41 Race Non-Hispanic White (referent) 1 1 1 1 1 Non-Hispanic Black 0.73 0.17–3.13 0.33 0.08–1.38 0.91 0.31–2.64 1.65 0.63–4.36 0.19 0.04–1.92 All other racial/ethnic groups 2.99 ∗∗ 1.44–6.25 3.04∗∗∗ 1.65–5.59 0.21∗∗∗ 0.11–0.39 2.32∗ 1.17–4.60 3.41∗∗∗ 1.77–6.57 Health-related variables Number of chronic disease 0.96 0.77–1.18 0.78∗ 0.65–0.94 2.13∗∗ 1.72–2.64 1.03 0.88–1.22 0.76∗ 0.61–0.96 Reporting chronic disease (no disease as referent) CV pulmonary disease 0.98 0.59–1.63 1.37 0.92–2.04 3.37∗∗∗ 2.20–5.16 0.88 0.58–1.33 0.58∗ 0.34–0.97 Metabolic disease 0.58 0.25–1.35 0.26∗∗∗ 0.11–0.60 4.20∗∗∗ 2.11–8.37 1.23 0.67–2.25 0.58∗ 0.35–0.97 Musculoskeletal disease 1.1 0.52-2.33 1.73 0.91–3.28 3.29∗∗∗ 1.70–6.37 1.21 0.66–2.21 0.34∗ 0.14–0.85 Depression 1.28 0.72–2.28 1.21 0.78–1.87 2.75∗∗∗ 1.64–4.63 0.68 0.44–1.09 1.14 0.67–1.93 Body mass index Normal weight 1 1 1 1 1 Overweight 0.77 0.47–1.27 0.58∗∗ 0.39–0.85 1.34 0.88–2.04 0.98 0.64–1.51 0.62∗ 0.39–0.97 Obese 0.46∗ 0.23–0.93 0.26∗∗∗ 0.14–0.47 2.59∗∗ 1.40–4.79 0.77 0.44–1.34 0.35∗∗ 0.18–0.67 Psychological variables Perceived health statusa 1.64∗∗ 1.24–2.17 1.98∗∗∗ 1.59–2.45 0.63∗∗∗ 0.49–0.80 0.87 0.69–1.11 1.64∗∗∗ 1.28–2.11 Perceived health benefits of AC 1.03 0.99–1.06 1.05 ∗∗∗ 1.03–1.08 0.98 0.95–1.01 0.99 0.97–1.02 1.04∗∗ 1.01–1.07 Note: ∗𝑃 < 0.05, ∗∗𝑃 < 0.01, ∗∗∗𝑃 < 0.001, ascale ranges 1 (poor) to 5 (excellent), and AC: active commuting. and perceived health status (OR = 1.60, 95% CI = 1.17–2.20) as significant predictors of walking to work. 3.2. Biking to Work One or More Times/Week. The univariate influences for biking to work are found in Table 2. Similar to those walking to work, age was negatively associated with being a biker (OR = 0.93, 95% CI = 0.92–0.95) and females were less likely to bike to work than males (OR = 0.31, 95% CI = 0.22–0.43). Higher income status was associated with being a non-biker, with both the $30,000–$60,000 groups (OR = 0.38, 95% CI = 0.20–0.71) and the $60,000 and up group (OR = 0.31, 95% CI = 0.18–0.60) less likely to bike to work than the lowest income group (<$30,000/year). Those from “other” racial/ethnic groups were more likely to bike (OR = 3.04, 95% CI = 1.65–5.59) compared to non-Hispanic Whites. A greater number of chronic diseases was associated with being a non-biker (OR= 0.78, 95%CI = 0.65–0.94) while better perceived health status was associated with biking (OR = 1.98, 95% CI = 1.59–2.45). Those reporting metabolic disease (OR = 0.26, 95% CI = 0.11–0.60), overweight (OR = 0.58, 95% CI = 0.39–0.85), and obese status (OR = 0.26, 95% CI = 0.14–0.47) were less likely to report biking. Those with greater perceived health benefits of AC were more likely to be bikers (OR = 1.05, 95% CI = 1.03–1.08). A full multivariatemodel revealed aNagelkerke𝑅2 of 0.27, with race (“other” racial/ethnic group OR = 2.40, 95% CI = 1.09–5.30), age (OR = 0.93, 95% CI = 0.92–0.95), income ($30,000– 60,000/year OR = 0.31, 95% CI = 0.14–0.69; <$60,000/year Journal of Environmental and Public Health 5 OR = 0.36, 95% CI = 0.16–0.78), perceived health status (OR = 1.95, 95% CI = 1.47–2.61), and AC health beliefs (OR = 1.05, 95% CI = 1.02–1.08) as significant predictors. 3.3. Driving to Work One or More Times/Week. Univariate analyses for driving to work are displayed in Table 2. Older age was associated with driving one or more times per week (OR = 1.04, 95% CI = 1.03–1.06), and those from “other” racial/ethnic groups were less likely to be drivers compared with non-Hispanic Whites (OR = 0.21, 95% CI = 0.11–0.39). Femalesweremore likely to report driving compared tomales (OR = 2.88, 95%CI = 1.98–4.19). A greater number of chronic diseases (OR=2.13, 95%CI= 1.72–2.64) andpoorer perceived health status (OR = 0.63, 95% CI = 0.49–0.80) were also associated with being a driver. Reporting cardiopulmonary disease (OR = 3.37, 95% CI = 2.20–5.16), metabolic disease (OR = 4.20, 95% CI = 2.11–8.37), musculoskeletal disease (OR = 3.29, 95% CI = 1.70–6.37), depression (OR = 2.75, 95% CI = 1.64–4.63), or being overweight (OR = 2.59, 95% CI = 1.40–4.79) was associated with being a driver. The full model resulted in a Nagelkerke 𝑅2 value of 0.12, with race (“other” racial/ethnic group OR = 0.26, 95% CI = 0.13–0.55), age (OR = 1.03, 95% CI = 1.01–1.05), and perceived health status (OR = 0.62, 95% CI = 0.46–0.84) as significant predictors. 3.4. Public Transportation One or More Times/Week. Table 2 outlines the univariate influences on public transportation use. Similar to walking and biking, younger age was asso- ciated with being a public transit rider to work (OR = 0.97, 95% CI = 0.95–0.99). Those reporting a higher income were less likely to report public transit ridership compared with those at the lowest income level (OR = 0.46, 95% CI = 0.23–0.93). Females were less likely to be transit rider