Evidence suggests that psychosocial. Built Environment and Psychosocial Factors Associated With Trail Proximity and Use

Built Environment and Psychosocial Factors Associated With Trail Proximity and Use Christiaan G. Abildso, EdM; Sam Zizzi, EdD; Laurie C. Abildso, MS Jenessa C. Steele, PhD; Paul M. Gordon, PhD, MPH, FACSM Objectives: To explore the relationships among neighborhood built environment characteristics, psychosocial factors, perceived and objective proximity assessments, and use of a community rail-trail. Method: Telephone survey data of adults (n=788) in Morgantown, WVa, were classified into one of 4 distance-perception categories based on actual (using geographic information systems technology) and perceived proximity of a community rail-trail. Results: Differences in psychosocial barriers to physical activity (P=.037) and perceived neighborhood walkability (P<.001) were associated with perceived proximity to and use of a community trail. Conclusion: Specific attention should be given to address neighborhood and psychosocial barriers when constructing and promoting community trails. Key words: proximity, trail use, neighborhood walkability, psychosocial barriers Am J Health Behav. 2007;31(4):374-383 Evidence suggests that psychosocial factors, physical environments, and individuals perceptions of environmental factors influence physical activity behavior. 1-4 The Department of Health and Human Services acknowledges that Christiaan G. Abildso, Doctoral Student, Sport and Exercise Psychology Program, West Virginia University School of Physical Education; Sam Zizzi, Assistant Professor and Program Coordinator, Sport and Exercise Psychology Program, West Virginia University School of Physical Education, Morgantown, WV. Laurie C. Abildso, Research Assistant, West Virginia University Health Research Center, Morgantown, WV. Jenessa C. Steele, Assistant Professor, Department of Psychology, Radford University, Radford, VA. Paul M. Gordon, Associate Professor, Division of Exercise Physiology; Robert C. Byrd Health Science Center, West Virginia University School of Medicine, Morgantown, WV. Address correspondence to Dr Gordon, Division of Exercise Physiology, 9227 Robert C. Byrd Health Science Center, West Virginia University School of Medicine, PO Box 9227, Morgantown, WV 26506-9227. E-mail: pgordon@hsc.wvu.edu many people must overcome a perceived lack of time and inaccessibility of convenient and safe facilities to increase physical activity. 5 This approach to understanding and promoting physical activity behaviors is rooted in ecological theory, which suggests that individuals physical activity choices are influenced by the physical and sociocultural environments in which they live and interact. 6-9 Specifically, physical activity health behaviors may be impacted by proximity to behavior settings, a concept developed in ecological psychology in the 1970s. 7 These settings (eg, multipurpose trails, neighborhood streets and sidewalks, and parks) are social and physical situations in which behaviors take place, by promoting and sometimes demanding certain actions and by discouraging or prohibiting others. 10(p155) Studies support a relationship between proximity of behavior settings and physical activity. 1-3 The presence of shops, parks, beaches, and/or paths within walking distance was positively associated with walking for exercise in a sample of Australian 374

Abildso et al adults, 1 and perceived access to recreation facilities was significantly related to physical activity levels in a sample of Sumter County, SC residents. 2 In West Virginia, a sedentary lifestyle was found to be associated with perceived limitations in opportunities to be physically active. 3 The body of evidence suggests that the convenience of behavior settings is positively associated with physical activity. Rails-to-Trails A behavior setting gaining popularity among city planners and health promotion professionals is the community trail. Many trails have been created through the conversion of abandoned rail beds to multi-use trails through the Rails-to- Trails program. These rail-trails provide level-grade surfaces for many physical activities, including walking, jogging, bicycling, and in-line skating. To date, 1359 rail-trails have been constructed for a total of 13,150 miles; and 1172 more trails consisting of 14,341 miles are in development. 11 Research indicates trail use positively impacts the frequency of physical activity 12,13 and the amount of physical activity 13,14 in a surrounding community, especially in new exercisers. 15 Community trail use studies have shown various sociodemographic factors to be significantly related to trail use. An inverse relationship between age and trail use has been found, with younger people more likely to use trails than older people are. 12,14 The relationship between gender and trail use is not as clear, however. Men were twice as likely to use a trail than women were in the Troped et al study near Boston. 12 Conversely, rural Missouri women were more likely to use a walking trail than men were (43.6% vs 29.8%). 14 In addition, education 14,16 and household income 14 have been positively associated with trail use. Prior studies have also shown a positive pattern of trail use among nearby residents. A study of the use of an established trail near Boston showed that 46.5% of community members living within an average distance of.51 miles used the trail. 12 Evenson et al sampled 366 community residents in central North Carolina living within 2 miles of a newly constructed trail. 13 Of those surveyed, 23.9% had used the trail at least once within 2 months of its construction. 13 In a sample of residents of 12 rural counties in Missouri, Brownson et al found that 38.8% of those with access to trails used them, despite the fact that 43% had to travel 15 miles or more to access them. 14 Proximity to trails appears to increase the likelihood of their usage, though the relationship cannot be explained by physical proximity alone. Physical Environment and Perceived Trail Proximity Physical walkability of neighborhood environments has been shown to significantly impact various forms of physical activity. 17 Neighborhood streets and sidewalks are behavior settings, encouraging or discouraging walking and physical activity, and users frequently access community trails at neighborhood connections. Therefore, physical neighborhood characteristics may affect community trail use by impacting individual perceptions of trail proximity regardless of actual distance to the trail. 17 Specific characteristics of neighborhood physical environments associated with community trail use are largely unknown, however. Moreover, there is a lack of consensus regarding the nature of the relationship among objective and perceived environmental assessments and trail use. In a study comparing objective and perceived environmental characteristics and their relative associations with use of a suburban trail, Troped et al found that selfreported and objective distances from a trail were both inversely related with trail use, as was a self-reported busy street barrier. 12 In comparison, Brownson et al found that rural trail users were willing to travel great distances to access a trail, 14 suggesting that perceptions of trail proximity may be impacted to varying degrees by different built environment conditions in various residential settings. Determining the factors that impact physical activity choices among community residents is critical to public health, especially in Appalachia, which has some of the highest rates of physical inactivity and resulting comorbidities. 18 Thus, we sought to further explore environmental perceptions related to trail usage in order to better understand the influence of the built environment on physical activity behaviors in an Appalachian community. Unique to this study is the utilization of objective and subjective environmental assessments to analyze differences among 4 distance-perception categories Am J Health Behav. 2007;31(4):374-383 375

Psychosocial Factors of respondents based on geographic information systems (GIS) distance (within or beyond a one-half mile corridor of a trail) and perceived trail proximity (within or beyond walking distance of home). We hypothesized the following would differ among the 4 distance-perception categories: (a) trail use, (b) perceived neighborhood walkability, (c) psychosocial barriers to physical activity, and (d) physical activity patterns. METHODS This study utilized data from a 2001 telephone survey of adults in the residential areas surrounding the recently built Caperton and Decker s Creek rail-trails in Morgantown, WVa. These trails comprise 12 miles of paved walkway that run through the town of Morgantown parallel to Decker s Creek and the Monongahela River with an additional 14 miles of unpaved walkways that extend outside the city limits. The West Virginia University Institutional Review Board approved this research project for the protection of human subjects. Survey Methods A previously designed community telephone survey used by Brownson et al 19 was employed using modified physical activity assessment items from the Behavioral Risk Factor Surveillance System (BRFSS). 20 The telephone survey (St. Louis Instrument) is used to measure environmental influences on physical activity. 21 The BRFSS is used to assist in planning, implementing, and evaluating health promotion and disease prevention programs. 22 In West Virginia, the BRFSS survey is used to measure progress in physical activity promotion programs. 23 Reliability of both instruments is reported elsewhere. 21,22 Local trail use was assessed by asking the question Do you use the Caperton/Decker s Creek trails? The survey was conducted on noninstitutionalized adults (18 years or older) geographically distributed along the trails across 4 adjoining Morgantown communities in Monongalia County, WVa. GIS Mapping All survey respondents were coded by street address prior to analyzing environmental variables. They were then examined using GIS methods and were placed on a map indicating a one-half mile corridor surrounding the trail. (Figure 1) This map identified each street or road by address and then specifically identified each residential/household phone at the address. The GIS map was linked with the phone numbers obtained through an electronic telephone directory (911 database) to identify 2 groups of residents those within a one-half mile corridor (n=7000) of the trail and those beyond that corridor (n=12,000). A half-mile corridor (one-quarter mile in each direction) was used as it approximates a 5- to 10-minute walk to access the trail. All business telephone numbers were excluded from the database. A total of 3800 telephone numbers equally representing residents within (n=1900) and beyond (n=1900) the onehalf mile trail corridor were randomly selected from the sampling frame after stratification. A random-digit-dialing technique was then used to select residences for the study interview. Based on 788 completed telephone interviews (n = 395 inside, N = 393 outside), the survey response rate was 51%. Psychosocial Barriers to Physical Activity Common psychosocial barriers to physical activity were assessed in accordance with a reliable self-assessment instrument. 19,21 Using a 5-point Likert-scale, respondents were asked to assess the frequency with which 13 common psychosocial barriers impacted physical activity. The distribution of the responses to the majority of the items was skewed. Therefore, responses never and rarely were assigned the value of one, whereas sometimes, often, or very often were assigned the value of 2. The sum of these values reflected a psychosocial barrier score ranging from 13 to 26, with higher scores indicating more frequent experience of psychosocial barriers to physical activity. Perceived Neighborhood Walkability Perceived neighborhood walkability was assessed using items from a reliable selfassessment instrument. 19,21 Respondents were asked to indicate whether or not each of 10 factors was present in their neighborhood (eg, foul air, street lights, hills, enjoyable scenery). Responses indicating a neighborhood factor prohibitive of physical activity, such as the presence of high crime, were assigned a zero value. 376

Abildso et al Figure 1 GIS Map of the One-half Mile Corridor Surrounding the Caperton and Decker s Creek Rail-Trail Conversely, responses indicating a neighborhood factor conducive to physical activity, such as the presence of sidewalks, were assigned a score of one. The sum of the scores on the 10 questions was used as a total neighborhood walkability score. The range of scores for each respondent, therefore, was 0 to 10 (least to most walkable). Distance-Perception Categories Four distance-perception categories were created by combining GIS-measured distance from the rail-trail and perceived trail proximity. GIS distance was measured linearly, and perceived trail proximity was assessed by asking 2 questions about perceived distance to the trail. Respondents were first asked, Is the trail within walking or biking distance from your home, work, or school? Those responding affirmatively (n=431, 54.7%) were then asked, Which is closer? with options of home or workplace/school. Those indicating home was within walking distance (n=310, 43.4%) were labeled as perceiving the trail to be proximal, or close. Residents responding negatively to the former question or workplace/ school to the latter were combined into the far group (n=405, 56.6%). The resulting 4 groups (Table 1) were (a) those who lived beyond the one-half mile trail corridor and perceived it to be beyond walking or biking distance ( beyond-far ; BF), (b) those who lived within the one-half mile trail corridor but perceived it to be beyond walking or biking distance ( within-far ; WF), (c) those who lived beyond the one-half mile trail corridor but perceived it to be within walking or biking distance ( beyond-close ; BC), and (d) those who lived within the onehalf mile trail corridor and perceived the trail to be within walking or biking distance ( within-close ; WC). Am J Health Behav. 2007;31(4):374-383 377

Psychosocial Factors Table 1 Distance-Perception Category Grid GIS distance from the trail a Within one-half mile trail corridor ( within ) Beyond one-half mile trail corridor ( beyond ) Perceived trail proximity b Home within walking or biking distance of the trail ( close ) Home beyond walking or biking distance of the trail ( far ) Within-close (WC) n=210 (29.4%) Within-far (WF) n=151 (21.1%) Beyond-close (BC) n=100 (14.0%) Beyond-far (BF) n=254 (35.5%) Note. a GIS distance from home to trail was measured linearly. b Perceived trail proximity was assessed by asking 2 questions about perceived distance to the trail. Respondents were first asked, Is the trail within walking or biking distance from your home, work, or school? Those responding affirmatively (n=431, 54.7%) were then asked, Which is closer? with options of home or workplace/school. Procedures The WVU Survey Research Center was subcontracted to conduct the telephone survey. During an 8-week period in the summer, randomly selected respondents were interviewed, and their responses were entered into a computer database. Interviews averaged 17 minutes. The responses were then coded and transferred to SPSS for windows statistical software package (Version 11.0, 2001) for further analysis. RESULTS The sample was predominantly white (96.6%) and female (65.7%). The racial background of participants closely matched recent census data; however, the percentage of women in the sample was higher. 24 Primary demographic characteristics and significant differences among the distanceperception categories are summarized in Table 2. In the subsequent analysis section, effect size estimates are reported as Cohen s d for t-test designs, eta-squared for ANCOVA and ANOVA designs, and phi or the contingency coefficient for chi-square designs. 25,26 Chi-square analyses revealed significant relationships among distance-perception and estimated annual income [χ 2 (18; n=615) = 47.563, P<.001 (ES=.268)] and distance-perception and employment status [χ 2 (18; n=708) = 34.619, P=.011 (ES=.216)]. WF respondents were more likely to be in the lower income brackets and/or students, whereas BF respondents were more likely to be in the highest 2 income brackets and/or homemakers. Univariate analysis of variance (ANOVA) revealed a small but statistically significant difference in mean age among the distance-perception categories (F (3,709) = 2.857, P=.036, ES=.012). Post hoc analyses showed that WC respondents were younger (M = 42.1; SD = 18.6) than BF respondents (M = 47.5; SD = 19.4). Subsequent between distance-perception groups analyses were conducted using age and BMI as covariates. Controlling for education, income, and employment status would have been redundant because the young people in this sample were largely students earning minimal income. Controlling for BMI was done to limit the potential impact of obesity on 378

Abildso et al Table 2 Demographic Characteristics of Survey Respondents (n=715) by Distance-Perception Respondent Category Distance-Perception Category (n / %) Beyond-Far Within-Far Beyond-Close Within-Close Significance (254/35.5%) (151/21.1%) (100/14.0%) (210/29.4%) (P <.05) Overall Sex NS Male 74 (10.3%) 53 (7.4%) 36 (5.0%) 82 (11.5%) 245 (34.3%) Female 180 (25.2%) 98 (13.7%) 64 (9.0%) 128 (17.9%) 470 (65.7%) Age P=.002 18-25 49 (6.9%) 47 (6.6%) 15 (2.1%) 62 (8.7%) 173 (24.2%) 26-35 33 (4.6%) 22 (3.1%) 19 (2.7%) 33 (4.6%) 107 (15.0%) 36-45 41 (5.7%) 8 (1.1%) 23 (3.2%) 29 (4.1%) 101 (14.1%) 46-55 42 (5.9%) 18 (2.5%) 15 (2.1%) 33 (4.6%) 108 (15.1%) 56-65 35 (4.9%) 17 (2.4%) 8 (1.1%) 23 (3.2%) 83 (11.6%) 66-75 26 (3.6%) 24 (3.4%) 11 (1.5%) 17 (2.4%) 78 (10.9%) 76+ 28 (3.9%) 15 (2.1%) 8 (1.1%) 13 (1.8%) 64 (9.0%) Race NS White 246 (34.4%) 147 (20.6%) 97 (13.6%) 201 (28.1%) 691 (96.6%) Other 7 (1.0%) 6 (0.8%) 2 (0.3%) 14 (2.0%) 29 (4.1%) Refused 1 (0.1%) 1 (0.1%) 2 (0.3%) 2 (0.3%) 6 (0.8%) Income P<.001 $0-$4999 16 (2.6%) 21 (3.4%) 7 (1.1%) 21 (3.4%) 65 (10.6%) $5000-$14999 32 (5.2%) 38 (6.2%) 4 (0.7%) 36 (5.9%) 110 (17.9%) $15000-$24999 27 (4.4%) 17 (2.8%) 12 (2.0%) 33 (5.4%) 89 (14.5%) $25000-$34999 18 (2.9%) 14 (2.3%) 9 (1.5%) 17 (2.8%) 58 (9.4%) $35000-$49999 25 (4.1%) 15 (2.4%) 17 (2.8%) 28 (4.6%) 85 (13.8%) $50000-$74999 46 (7.5%) 15 (2.4%) 21 (3.4%) 32 (5.2%) 114 (18.5%) > $75000 47 (7.6%) 15 (2.4%) 12 (2.0%) 20 (3.3%) 94 (15.3%) Education NS Some high school 11 (1.5%) 8 (1.1%) 7 (1.0%) 12 (1.7%) 38 (5.3%) High school graduate 50 (7.0%) 26 (3.6%) 24 (3.4%) 32 (4.5%) 132 (18.5%) Some college 77 (10.8%) 55 (7.7%) 35 (4.9%) 72 (10.1%) 239 (33.5%) College graduate 116 (16.2%) 61 (8.5%) 34 (4.8%) 94 (13.2%) 305 (42.7%) Employment P=.011 Employed full-time 82 (11.6%) 47 (6.6%) 39 (5.5%) 73 (10.3%) 241 (34.0%) Employed part-time 36 (5.1%) 18 (2.5%) 8 (1.1%) 30 (4.2%) 92 (13.0%) Self-employed 12 (1.7%) 3 (0.4%) 5 (0.7%) 7 (1.0%) 27 (3.8%) Unemployed/unable to work 8 (1.1%) 5 (0.7%) 4 (0.6%) 12 (1.7%) 29 (4.1%) Homemaker 35 (4.9%) 13 (1.8%) 13 (1.8%) 15 (2.1%) 76 (10.7%) Student 19 (2.7%) 34 (4.8%) 9 (1.3%) 32 (4.5%) 94 (13.3%) Retired 60 (8.5%) 29 (4.1%) 22 (3.1%) 38 (5.4%) 149 (21.0%) BMI NS Normal (BMI < 25 kg/m 2 ) 110 (16.5%) 73 (10.9%) 40 (6.0%) 110 (16.5%) 333 (49.9%) Overweight (25< BMI <30 kg/m 2 ) 72 (10.8%) 40 (6.0%) 34 (5.1%) 52 (7.8%) 198 (29.6%) Obese (BMI >30 kg/m 2 ) 55 (8.2%) 29 (4.3%) 18 (2.7%) 35 (5.2%) 137 (20.5%) outcome variables. Factors Associated With Trail Use Chi-square analyses revealed a significant relationship among distance-perception and trail use [χ 2 (3; n=710) = 26.790, P<.001 (ES=.191)]. WC (68%) and BC (70%) respondents were more likely to report having used the trail than were WF (44%) and BF (56%) respondents. Psychosocial Factors One-way analysis of covariance (ANCOVA) procedures revealed small but significant differences in mean psychosocial barrier scores among the 4 distance-perception subgroups after adjusting for BMI and age (F (3,659) = 2.850, P=.037, ES=.013). Results are listed in Table 3. Post hoc testing revealed that BF and WF reported greater presence of psychosocial barriers than did BC respondents. Chi-square analyses revealed significant relationships among distanceperception respondent categories and the presence of 2 barriers: I am too tired [Ç2 (3; n=711) = 10.861, P=.012 (ES=.123)] and I don t have the energy to exercise [Ç2 Am J Health Behav. 2007;31(4):374-383 379

Psychosocial Factors Table 3 Total Psychosocial Barrier and Neighborhood Walkability Scores by Distance-Perception Respondent Category Distance- Psychosocial Neighborhood Perception Barrier Score a Walkability Score b n M SD M SD Beyond-far (BF) 237 17.09 c,* 2.52 5.47 e,** 1.54 Within-far (WF) 141 17.07 c,* 2.27 5.79 d,** 1.56 Beyond-close (BC) 91 16.48 c,* 2.23 6.34 e,** 1.44 Within-close (WC) 196 16.74 2.31 6.62 d,** 1.37 Total 665 16.89 2.38 5.99 1.58 Note. *P<.05; **P<.001 a Psychosocial barrier scores reported on a scale from 13-26, with higher scores indicating the presence of more perceived barriers to physical activity. b Neighborhood scores reported on a scale from 0-10, with higher scores indicating the presence of more facilitative factors. BMI and age controlled for as covariates. c BC significantly less than BF and WF d WC significantly greater than WF e BC significantly greater than BF (3; n=710) = 9.039, P=.029 (ES=.112)]). For each barrier, BC respondents were more likely to report it never or rarely interfering with physical activity. Perceived Neighborhood Walkability ANCOVA procedures revealed significant differences in mean total neighborhood walkability scores among the 4 distance-perception categories after adjusting for BMI and age (F (3, 663) = 25.020, P<.001, ES=.102). Full results are listed in Table 3. Post hoc testing revealed a significant difference in neighborhood Table 4 Relationship Among Specific Neighborhood Factors and Distance-Perception Subcategories Neighborhood Factor df N χ 2 ES a Sidewalks in neighborhood 3 715 125.489**.386 Heavy traffic in neighborhood 3 712 4.718.081 Hills in neighborhood 3 715.408.024 Street lights in neighborhood 3 714 76.459**.311 Dogs in neighborhood 3 712 4.053.075 Foul air in neighborhood 3 715 12.141*.129 Enjoyable scenery in neighborhood 3 714 12.171*.129 Walking/jogging trails in neighborhood 3 713 76.584**.311 A lot of people exercising in neighborhood 3 699 34.424**.217 High crime in neighborhood 3 711 3.864.074 Note. * P<.01; **P<.001 a Effect size estimates are reported as contingency coefficient 380

Abildso et al Table 5 Total Self-Reported Minutes of Walking and Physical Activity for Sample Subgroups a After Adjusting for BMI and Age Minutes b of Minutes b of Minutes b of Walking per Moderate Physical Vigorous Physical Week Activity per Week Activity per Week Distance-perception M SD M SD M SD Beyond-far (BF) 162.54 151.05 201.61 182.27 175.44 109.14 Within-far (WF) 193.32 185.93 194.24 166.29 148.65 100.43 Beyond-close (BC) 211.84 198.34 256.59 225.72 153.10 90.23 Within-close (WC) 182.21 163.00 214.74 185.58 155.59 98.95 Total 181.84 169.85 212.00 187.62 160.09 101.78 Note. a Minutes of physical activity are reported only for those participants who said yes when asked if they engaged in walking (n=516), or moderate (n=561) or vigorous (n=329) activity. b Minutes per week were calculated by multiplying the self-reported days of activity per week by the self-reported average minutes per activity session. walkability score between perception categories within each distance category. For example, BC respondents reported a higher mean neighborhood walkability score than did BF respondents. Similarly, WC respondents reported a higher mean neighborhood walkability score than that of WF respondents. Chi-square analyses revealed significant relationships between distance-perception category and the presence of the following neighborhood factors: sidewalks, streetlights, walking/jogging trails, and a lot of people exercising. Full results are available in Table 4. Finally, mean comparisons of self-reported minutes of walking, and moderate (MPA) and vigorous physical activity (VPA) were conducted to determine whether distance-perception grouping was related to general activity patterns. Outliers were excluded if reporting more than 420 minutes of VPA per week or 840 minutes of walking or MPA per week (5-10% outliers). ANCOVA procedures adjusting for BMI and age revealed no significant differences across groups in the minutes of VPA, MPA, or walking per week among those that responded affirmatively to a question of whether or not they engaged in each activity. Full results are available in Table 5. DISCUSSION Results of this community telephone survey found trail use (58.9%) was greater than that found by Troped et al. 12 Of note, however, was the unexpected similarity in the percentage of residents within (58.6%) and beyond (59.2%) the one-half mile trail corridor that used the trail. Combining objective and perceived measures of trail proximity explained more variance in the data than either variable individually. Trail use differed among the 4 distance-perception respondent categories, supporting our first hypothesis. Specifically, trail use differed within objective distance categories according to perceived trail proximity, suggesting that augmenting objective trail proximity measures with a perceived measure is valuable in assessing health behavior. Further analyses revealed that residents at similar objective distances from the trail (WC vs WF, or BC vs BF) also differed in their assessment of neighborhood walkability, supporting our second hypothesis. WC respondents rated their neighborhoods more walkable than did WF respondents. BC respondents also reported greater neighborhood walkability than did BF respondents. This suggests that the presence or absence of certain neighborhood factors is related to the perception of trail proximity and use even when objective distance from the trail is comparable. Specific factors impacting these results were the presence or ab- Am J Health Behav. 2007;31(4):374-383 381

Psychosocial Factors sence of sidewalks, streetlights, walking/jogging trails, and other people exercising. City planners and community health promotion practitioners should consider these factors when promoting community trails as they may impact physical activity and trail use. Differences were found in psychosocial barriers to physical activity among the distance-perception respondent categories, supporting our third hypothesis. BC respondents seemed to be heartier than WF respondents, reporting less frequent impact of common psychosocial barriers to physical activity (ie, being too tired or having too little energy to exercise), despite living farther from the trail than WF respondents. It is important to note that BC respondents did not exercise any more than any other group. This would indicate that these individuals were no more active but reported less frequent impact of psychosocial barriers despite living farther away. The differences in psychosocial barriers to physical activity and perceived neighborhood walkability scores among these respondent categories shed light on potential reasons that some residents may be willing to travel farther to access community trails 14 and other behavior settings. It was also hypothesized that physical activity patterns would differ among the distance-perception respondent categories. This hypothesis was not supported by the data. However, it is important to note that BC respondents reported averaging nearly one hour more MPA and 40 minutes more walking per week than did BF respondents. The cumulative effect of these differences over time has practical if not statistical significance. Limitations These data do present some limitations. First, objective proximity to the trail was measured as straight-line distance. Measuring proximity in this way may not be as powerful a predictor of trail use as a functional distance measure such as that used by Troped et al. 12 Second, the current study relied on crosssectional data. Determining the true impact of a rail-trail on a community may be best assessed with longitudinal, prepost trail construction assessments of community trail use and perceptions. A pre-post trail construction research design may also limit any self-selection bias that may exist by surveying individuals that chose their residence prior to trail construction not vice versa. The data are also limited by the response rate to the telephone survey. Though comparable to the median response rate from the 2004 BRFSS (52.7%) 27 and similar trail studies, 2,13,16 the response to the current telephone survey does lag behind other trail studies. 1,14 Another limitation of this study is the reliance on self-report data. Though common in this type of research, there is potential for self-report error, especially in height, weight, and physical activity data. Nevertheless, the instrumentation utilized and the methods employed are consistent with reliable data collection procedures. 21,22 Objectively measuring physical activity with pedometers and/or accelerometers may add valuable information to our understanding of physical activity and the environment. Finally, the reader should interpret the data with caution because females were overrepresented in the sample. CONCLUSION Saelens et al 17 suggested that simultaneous examination of environmental and psychosocial variables may further our understanding of individual variation in physical activity. This study did just that, highlighting the importance that neighborhood environmental (ie, absence of lighting, sidewalks, facilities, and other exercisers) and psychosocial (ie, lack of time and energy) barriers may play in the decision to use a community trail through their potential impact on perceived trail proximity. Building a trail without addressing neighborhood physical environments conducive to physical activity and perceived trail proximity may not have the intended positive effects on physical activity. At the same time, health promotion professionals should promote trails in a way that lessens the impact of psychosocial barriers in order to positively impact community trail use and health behavior. Acknowledgment This study was funded (PI: Paul M. Gordon) by the Centers for Disease Control and Prevention and the West Virginia University Prevention Research Center Cooperative Agreement #U48/ CCU3108831. 382

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