Validation of the Neighborhood Environment Walkability Scale (NEWS) Items Using Geographic Information Systems

Transcription

1 Journal of Physical Activity and Health, 2009, 6(Suppl 1), S113 S Human Kinetics, Inc. Validation of the Neighborhood Environment Walkability Scale (NEWS) Items Using Geographic Information Systems Marc A. Adams, Sherry Ryan, Jacqueline Kerr, James F. Sallis, Kevin Patrick, Lawrence D. Frank, and Gregory J. Norman Background: Concurrent validity of Neighborhood Environment Walkability Scale (NEWS) items was evaluated with objective measures of the built environment using geographic information systems (GIS). Methods: A sample of 878 parents of children 10 to 16 years old (mean age 43.5 years, SD = 6.8, 34.8% non-white, 63.8% overweight) completed NEWS and the International Physical Activity Questionnaire. GIS was used to develop 1-mile street network buffers around participants residences. GIS measures of the built environment within participants buffers included percent of commercial and institutional land uses; number of schools and colleges, recreational facilities, parks, transit stops, and trees; land topography; and traffic congestion. Results: Except for trees and traffic, concordance between the NEWS and GIS measures were significant, with weak to moderate effect sizes (r = 0.09 to 0.36, all P 01). After participants were stratified by physical activity level, stronger concordance was observed among active participants for some measures. A sensitivity analysis of self-reported distance to 15 neighborhood destinations found a 20-minute (compared with 10- or 30-minute) walking threshold generally had the strongest correlations with GIS measures. Conclusions: These findings provide evidence of the concurrent validity of self-reported built environment items with objective measures. Adams, Patrick, and Norman are with the Dept of Family and Preventive Medicine, University of California, San Diego. Ryan is with the Dept of Public Administration and Urban Studies, San Diego State University. Kerr and Sallis are with the Dept of Psychology, Active Living Research, San Diego State University. Physically active adults may be more knowledgeable about their neighborhood characteristics. Keywords: built environment, perceived environment, active living, physical activity There has been increasing interest in measuring built environments for use in studies assessing their relation to physical activity and other health outcomes 1 4 Measurement of the built environment can be subjective or objective, where subjective measurements use selfreport surveys, 5 9 and objective measures use direct observation audits, or analyses of publicly or privately developed spatial data. Spatial data on land use, recreation facility availability, crime incidence, traffic, and land topography can be manipulated in geographic information systems (GIS) to develop indices that represent the local neighborhood, usually operationalized as within 0.5 to 1 mile radius of a participant s residence Both self-reported and objectively measured urban form have been consistently related to physical activity Surveys, such as the Neighborhood Environment Walkability Scale (NEWS), reflect urban form constructs from transportation and urban planning research believed to support walking for transport. For example, the NEWS asks participants to estimate their travel time to walk to stores and facilities representing a number of land uses in their neighborhood. Studies have demonstrated generally good test-retest reliability of the NEWS items and its subscales. 5,21 23 Individuals ability to assess their neighborhood environment was validated when residents of different neighborhood types (ie, high and low walkable) reported subscale scores that reflected variations in those environments. 5,6,22,24 Associations between NEWS subscales and physical activity have been observed for both self-report and accelerometer measures. 5,6 S113

2 S114 Adams et al Other studies have investigated the concordance between self-reports and objective measures of urban form DeBourdeaudhuij and colleagues found all subscales of the NEWS were related to observers observations, except for comparisons of reported safety to crime rates. 23 Using a different survey, Boehmer and colleagues compared self-report and audit measures of neighborhood characteristics and found low agreement (k =.04 to.17) for transit stops, fitness facilities, recreational facilities, libraries, convenience stores, schools and colleges, and bike lanes, but higher agreement (k =.33 to.47) for sidewalks, parks, post offices, and supermarkets. 28 Using self-reports and GIS measures, Kirtland (2003) found low agreement (k =.00 to.19) for schools, parks, traffic, and street lighting, but higher agreement (k =.25 to.30) for shopping malls and recreation facilities. 30 Troped et al (2001) found moderate agreement (k =.41 to.59) between self-reported and GIS measures for steep hills and traffic. 31 McGinn et al (2007) found that self-reports of hills versus GIS topographical measures showed low to fair agreement (k =.12 to.22). 27 In another publication, the same authors reported low agreement (k =.00 to.14) for self-report and GIS measures of high speed and heavy traffic. 34 Participants physical activity level may affect the concordance between self-report and objective measures, because active people may be more familiar with neighborhood attributes. Two studies found that activity level was not related to the concordance between selfreport and objective measures of the environment, 27,34 while another study found some evidence that physically active adults were more accurate reporters of environmental attributes. 29 Kirtland et al found individuals who performed any physical activity had stronger agreement with GIS measures of access to recreational facilities than inactive individuals did, with the subgroup who met national guidelines showing the strongest agreement. 30 These studies provide mixed evidence but suggest that active individuals might report certain environmental features more accurately. In this study, we measured neighborhood characteristics subjectively with a subset of the Neighborhood Environment Walkability Scale and objectively with geographic information systems (GIS) within a 1-mile road network buffer around participants residences. We hypothesized that self-reports of the physical environment would have concurrent validity with comparable GIS-measured physical environment variables in a sample of adults. Because participants were asked about destinations at various distances from their homes, a sensitivity analysis was performed to examine whether different walking distance thresholds improved correlations with GIS measures. Lastly, we hypothesized that physically active, compared with inactive, participants would have stronger concordance between self-reported and objective measures. Participants Methods The sample consisted of 878 men and women, all with children 10 to 16 years old who were participants in a randomized controlled trial to improve health behaviors. The study design and main outcomes of the adolescent interventions have been reported in detail elsewhere. 35,36 Adolescents were recruited through 45 primary care providers from 6 private clinic sites in San Diego County. The parent or guardian who accompanied the adolescent to the baseline visit completed the survey and was included in the current analysis. Adult baseline data from both intervention arms were used for the current analysis. The participants consisted of 84.5% women (mean age 43.5 years, SD = 6.8) and multiple racial/ethnic groups: 65.2% White, 5.2% Asian and Pacific Islander, 6.1% Black, 16.2% Hispanic, 4.6% multiracial, and 2.8% Other. The majority of participants were overweight (32.8%) or obese (31.0%) based on body mass index from measured height and weight. Written informed consent was obtained from the parent before the study. The participating healthcare organizations and university institutional review boards approved the study. Measures Neighborhood Environment Walkability Scale. The NEWS was developed to assess constructs from transportation and urban planning literatures and assesses several environmental characteristics believed to be related to physical activity, mostly for transportation purposes. 5 The current version of the survey includes 9 sections measuring self-reported neighborhood residential density (section A), land use mix-diversity (section B), land use mix-access (section C), street connectivity (section D), walking and cycling facilities (section E), aesthetics (section F), pedestrian/traffic safety (section G), crime safety (section H), and general neighborhood satisfaction (section I). The current study used a subset of items from an early version of the NEWS. Compared with the published version of the NEWS, participants in this study were asked fifteen items from land use mix-diversity (section B), 2 items from land use mix-access (section C), and all of sections E through H. Sections A, D, and most of section C were excluded to reduce the overall response burden of the survey, which also included other measures not used in the current study. The excluded scales were considered largely redundant with available GIS data or of lesser relevance for study aims. Not all NEWS items had corresponding objective GIS measures for comparison. As a result, no items from section E, 1 item from section F, 3 items from section G, and 1 item from section H were used in the current study. Participants were asked to estimate the distance to the nearest stores and facilities in their neighborhood

3 NEWS Validation S115 (section B). The survey item asked, About how long would it take to get from your home to the nearest businesses or facilities listed below if you walked to them? Locations included: convenience or small grocery store, supermarket, hardware store, fruit or vegetable market, laundry or dry cleaners, clothing store, other stores, post office, library, elementary school, other school, bus or trolley stop, park, recreation center, and gym or fitness facility. Response options for items from land use mixdiversity (section B) were rated on a Likert type scale with the following values: 1 (1 5 min), 2 (6 10 min), 3 (11 20 min), 4 (21 30 min), 5 (31+ min). Lower scores indicated closer proximity. Street connectivity (section C) items included: The streets in my neighborhood are hilly making my neighborhood difficult to walk or bicycle in ; There are many canyons/hillsides in my neighborhood that limit the number of routes for getting from place to place. The aesthetics (section F) item was: There are trees along the streets in my neighborhood. Traffic safety (section G) items included: There is so much traffic along the street I live on that it makes it difficult or unpleasant to walk in my neighborhood ; There is so much traffic along nearby streets that it makes it difficult or unpleasant to walk in my neighborhood and When walking in my neighborhood, there are a lot of exhaust fumes (such as from cars, buses). The crime safety (section H) item was: My neighborhood streets are well lit at night. Participants rated items from sections C to H on a 4-point Likert scale ranging from strongly disagree to strongly agree. International Physical Activity Questionnaire (IPAQ). The short version of the IPAQ was self-completed. 37 The short version asks participants to estimate the frequency and duration of time spent walking and in moderate and vigorous intensity activity during the last 7 days. Reliability and validity of the IPAQ are comparable to other self-report measures of activity for adults. 38 Objective Data Objective variables concordant with specific NEWS variables were created using ArcGIS 9.1 GIS software developed by Environmental Systems Research Institute (ESRI, Redlands, CA). Aspects of the environment were identified from various existing city, county, and national data sources. In GIS, environmental spatial attributes are represented by 3 types of geometrical forms: points, lines and polygons. Points are used to depict a single location, especially when the scale of the map is too small to depict that feature as a polygon (eg, tree, residential location, recreation facility). Lines typically represent linear features such as roadways or rivers. Polygons are used to represent areas such as parks or land parcels. Point Data Schools and Colleges, and Transit Stops. Point locations for schools, colleges, and transit stops were obtained from the San Diego Association of Governments (SANDAG), which are publicly available. The most recent available data for schools and colleges was 2005, while the most recent data for transit stops was The number of schools and colleges and transit stops within each participant s 1-mile buffer was summed. Recreational Facilities. Private and public recreational facilities datafiles were obtained from another study conducted in San Diego County. 15 Facilities data were identified from 5 County Yellow Pages. Four research assistants, including 1 author from the current study (MA), transcribed the address for each facility listed in Facilities included athletic associations, batting ranges, sport clubs and parks, bowling, campgrounds, dance facilities, exercise and fitness programs, golf courses, health clubs, martial arts instruction, racquetball courts, recreation centers, skating rinks, soccer facilities, swimming facilities, tennis courts, and youth centers, etc. Facility addresses were geocoded to the street level. A count of recreational facilities was computed by summing the number of facility points within each participant s buffer. Parks. Point locations of parks were obtained from another study conducted in San Diego County (unpublished data). Park data were identified from a comprehensive list obtained from the county and supplemented with parks found on official city websites, lists provided by multiple parks and recreation departments, and a 2006 Thomas Brothers Guide. Park addresses or crossstreets were geocoded to an address-matchable roadway shapefile, approximating a point of park entry. A count of park points was then computed by summing the number of park points within each participant s buffer. Trees and Streetlights. Publicly-available files from San Diego Geographic Information Source (SanGIS) provided a shapefile of point locations for each of these variables within the City of San Diego. Locations of trees within the public right-of-way were available for 2004 with an accuracy of ±10 feet. Street light data were available for Line Data Congested Lane Miles. SANDAG conducts periodic regional transportation surveys using travel diaries to develop a regional transportation model that allows for estimation of current traffic conditions on most regionally significant roadways. Transportation model outputs include measures of transportation facility performance (eg, congestion) such as level of service (LOS) and daily traffic volumes, which are available as attributes in a shapefile. Roadway level of service is measured as LOS A through LOS F, with LOS A representing free flow

4 S116 Adams et al traffic conditions and LOS F representing highly congested conditions in which volumes exceed capacity. Lane miles of congested roadways within study participants buffers were calculated by selecting the roadway segments estimated to be congested (LOS E and F) in the year 2000, then calculating the lane miles of each selected segment by multiplying the length of the segment by the number of lanes along each roadway segment, then summing the lane miles in each participant buffer. Land Topography. Forty-foot elevation contour lines are available from SANDAG for the entire region and were used to develop a measure of the slope within each study participant s buffer. Elevation contours were selected within each study participant s buffer, and the standard deviation of the elevation values was then calculated. Buffers with a relatively higher standard deviation in elevation would reflect a greater likelihood of steep slopes. Polygon Data Commercial Land Use. A land use shapefile was obtained from SANDAG for the year The following land use descriptions represented commercial areas: regional, community, and neighborhood shopping centers; wholesale and specialty trade locations; low- and high-rise hotels and motels; resorts; rail and transit centers; parking structures; park and ride areas; other transportation areas; marine terminals; marinas; auto dealerships; commercial store fronts; other retail trade and commercial strips; tourist attractions; stadiums and arenas; racetracks; convention center; and casinos. These land uses were aggregated into 1 commercial land use area variable which was divided by the total land area in the buffer to yield a percentage. Institutional Land Use. Institutional land use acreage was derived from the 2003 SANDAG land use shapefile using the following land use descriptions: religious facilities, cemeteries, post offices, libraries, police and fire stations, missions, other public services, hospitals, other health care, military, schools, colleges and universities, junior, middle, and high schools. These were aggregated into 1 institutional land use area variable which was divided by the total land area in the buffer to yield a percentage. Analyses Multiple NEWS items were combined to form 4 indices that corresponded to available objective data. Indices were computed by adding the values of items then dividing by the number of items included in the index. Only land use mix-diversity (section B) items were used for the indices. Cronbach s alpha was computed for each index: commercial mix index (7 items; alpha =.88), institutional mix index (2 items; alpha =.74), schools index (2 items; alpha =.63), and recreation facilities index (2 items; alpha =.73). Descriptive statistics such as means, standard deviations, and ranges were generated for each item and index. Three types of analyses were conducted: 1) correlations between the NEWS items or indices (ordinal scale) and objective variables, 2) sensitivity analysis between different NEWS walking distance cut-points (dichotomous scale) and objective variables, 3) correlations between the NEWS (ordinal scale) and objective variables stratified by participant activity level. Pearson correlations (r) were used for comparisons between continuous or ordinal variables. Because some objective variables had nonnormal distributions, Spearman s rho (r p ) was calculated. Biserial correlations (r b ) were used for comparisons between dichotomous and ordinal or continuous variables. Pearson correlations are equivalent to point biserial when comparing dichotomous with ordinal or continuous variables. All analyses were performed with SPSS version 12 (SPSS Inc., Chicago, IL). To test the sensitivity of different walking distance cut-points, the original 5-point NEWS response scale for land use mix-diversity (section B) was dichotomized to determine the presence of a facility within a 10-, 20-, or 30-minute walk. The first analysis coded the variable as 1 if participants reported a facility less than or equal to a 10 minute walk, and 0 if a facility was greater than a 10 minute walk. The second analysis coded 1 if participants reported a facility less than or equal to a 20 minute walk, and 0 if a facility was greater than a 20 minute walk. The third analysis coded the variable as 1 if participants reported a facility less than or equal to a 30 minute walk, and 0 if a facility was greater than a 30 minute walk. Participants were stratified on self-reported estimated physical activity from the IPAQ. 35 Using recommended IPAQ scoring procedures ( participants were categorized as sufficiently active by meeting either the vigorous (20 minute, >2 day/week) or moderate (30 minutes, >4 days/week) guidelines, or if they were active 5 days/week for at least 600 MET/ minutes. If none of these conditions were met, then a participant was classified as insufficiently active. Significant differences in Pearson correlations between active and inactive groups were determined using the Fisher r-to-z transformation. 39 Differences in Spearman correlations were treated as Pearson coefficients using the Fisher s r-to-z transformation. 40 Results Table 1 provides descriptive information for NEWS variables and corresponding GIS variables. All NEWS items had normal distributions. For the objective variables, the number of parks and the number of streetlights had positively skewed distributions (data not shown).

5 Table 1 Neighborhood Environment Walkability Scale (NEWS) Items and Indexes, and Corresponding GIS Data NEWS survey Objective GIS data NEWS variables n Mean SD Range Alpha Variable n Mean SD Range Commercial index % commercial land use B1. Convenience/small grocery store B2. Supermarket B3. Hardware store B4. Fruit/vegetable market B5. Laundry/dry cleaners B6. Clothing store Bx. Other Stores Institutional index % institutional land use B7. Post office B8. Library Schools index # schools and colleges B9. Elementary school B10. Other schools B20. Bus or trolley stop # transit stops B21. Park # parks Recreation center index # recreational centers B22. Recreation center B23. Gym or fitness facility C6.Hilly streets in my neighborhood topography C7. Many canyons/hillsides F1. Trees along streets # trees G1. So much traffic along street I live % congested lane miles G2. So much traffic along nearby streets G8. A lot of exhaust fumes H1. Streets are well lit at night # street lights Note. NEWS responses to section B ranged from 1 (1 5 mins) to 5 (31+ mins) and responses for section C, F, G, and H items ranged from 1 (strongly disagree) to 4 (strongly agree). GIS % land use variables with higher values indicate more coverage of the network buffer area, and GIS count variables with higher values indicate a greater number within a buffer. S117

6 S118 Adams et al Comparisons Between Self-Reported and Objective Variables Table 2 shows correlations between NEWS items and corresponding objective physical environment variables. Almost all reported items correlated significantly with objective measures. The strongest individual correlations between self-reported and objective measures were found for bus and trolley stops (r =.35), stores and facilities (r = 0.24 to 0.36), schools and colleges (r =.29 to.20), hilly streets (r =.24), recreational center items (r =.20 to.18), canyon and hillsides (r =.18), parks (r =.19), post office and library items (r =.13 to.09) and exhaust fumes (r =.09). The reported number of trees in a neighborhood, amount of local street traffic, and number of streetlights were not correlated with objective measures of the corresponding environmental attribute in GIS. The Spearman correlation for parks (r =.23, P.001) was significant, but the correlation for streetlights (r =.11) was not significant. Negative correlations between % of land use and walking time on NEWS land use mix-diversity (section B) items indicate that as the proportion of a specific land use within a buffer becomes smaller, participants report it takes a greater amount of time to walk to that type of destination. For NEWS section C to H items, positive correlations indicate that as the corresponding empirically measured frequency or numerical value of these aspects increased in a buffer, participants were more likely to agree with the corresponding NEWS item. Generally, the correlations for NEWS indices and objective measures were equal to or stronger than individual NEWS items. Correlations were strongest for the commercial index (r =.40), school index (r =.29), recreational center index (r =.21), and institutional index (r = 12). All correlations between reported items and indexes and objective variables were in the expected directions. Sensitivity Analyses of NEWS Items and Objective Measures Results of the sensitivity analysis indicated stronger correlations for 7 out of 15 items for the 20-minute walking distance compared with the 10- and 30- minute walking distances. The 10-minute walking distance revealed the strongest correlations for convenience/ small grocery stores, supermarkets, bus and trolley stops, and recreational centers with the 1-mile objective environmental measures. When the 10-minute walking distance correlation was stronger than the 20-minute walking distance correlation, differences between the 2 correlations ranged from.01 to.06. The 20-minute walking distance correlated strongest with fruit and vegetable markets, laundry/dry cleaners, other stores, post offices, elementary schools, other schools, and parks. When the 20-minute walking distance correlation was stronger than the either the 10- or 30-minute cut-points, differences ranged from.02 to.10. The 30-minute dis- tance correlated strongest with objective measures for hardware stores, clothing stores, libraries, and gym and fitness facilities. When the 30-minute distance correlation was stronger than the 20-minute distance correlation, difference between the 2 correlations ranged from.02 to.05. Stratification by Physical Activity Level Table 3 shows associations between self-reported and objective measures of the environment stratified by physical activity level. Compared with the insufficiently active group, significantly higher correlations (P <.05) were found for the sufficiently active group for supermarkets (.24 vs..37), fruit and vegetable markets (.21 vs..36), laundry and dry cleaners (.18 vs..34), other stores (.19 vs..35), bus and trolley stops (.29 vs..40), and street light (.01 vs..18) variables, and the commercial index (.31 vs..46). All other comparisons were either marginally or not statistically different. Discussion In this large and diverse sample of adults, we compared 22 items from the Neighborhood Environment Walkability Scale, along with 4 composite measures of NEWS items, to 10 physical environment characteristics determined from GIS data for each individual s neighborhood, defined as within a 1-mile buffer of the home address. Primary results were that 18 of 22 NEWS items and all 4 composite NEWS scales had significant correlations of weak to moderate strength with GIS measures (magnitude of correlations ranging from 0.09 to 0.36). Thus, adults reports of specific neighborhood built environment attributes received some support for concurrent validity using GIS-based objective measures as criteria. Correlations with GIS measures were higher for participants self-reports of proximity to destinations (eg, commercial, recreational, and institutional facilities) than for other qualities of their neighborhoods (eg, traffic, trees). Destinations may be recalled more accurately because of their utility and salience. Destinations allow for tangible transactions to take place such as purchases and use of facilities. These destinations also present the opportunity for social interactions, which may make them more memorable. The availability of destinations within walking distance is an essential component of walkable neighborhoods, 41 so it is useful to know these are among the most accurately-reported environmental attributes. The ability to closely match perceived and objective measures of specific destinations ensured a rigorous test of these variables. Adequate concordance of hilly streets, canyons and hillsides was observed, which is not surprising given the varied topography of the San Diego area. Other studies have also found low to moderate agreement between self-reported and objective measures of hills, 27,31 so the

7 Table 2 Unadjusted Correlations Between NEWS Items and Indexes and Objective Measures of the Physical Environment NEWS distance scale GIS variables with various NEWS-reported distance variables 10min (1), >10min (0) 20min (1), >20min (0) NEWS variables n (r) n (r b ) (r b ) (r b ) 30min (1), >30min (0) Commercial index *** B1. Convenience/small grocery store *** *** 0.27*** 0.17*** B2. Supermarket *** *** 0.26*** 0.25*** B3. Hardware store *** *** 0.20*** 0.22*** B4. Fruit/vegetable market *** *** 0.27*** 0.25*** B5. Laundry/dry cleaners *** *** 0.26*** 0.18*** B6. Clothing store *** *** 0.32*** 0.35*** Bx. Other Stores *** *** 0.26*** 0.23*** Institutional index *** B7. Post office ** *** 0.07*** B8. Library *** * 0.10** 0.15*** Schools Index *** B9. Elementary school *** *** 0.18*** 0.12*** B10. Other schools *** *** 0.25*** 0.24*** B20. Bus or trolley stop *** *** 0.28*** 0.22*** B21. Park a *** *** 0.18*** 0.15*** Recreation center index *** B22. Recreation center *** *** 0.16*** 0.13*** B23. Gym or fitness facility *** *** 0.17*** 0.19*** C6.Hilly streets in my neighborhood *** C7. Many canyons/hillsides *** F1. Trees along streets G1. Traffic along street I live G2. Traffic along nearby streets G8. A lot of exhaust fumes * H1. Streets are well lit at night a Note. Pearson correlations (r). Biserial correlations (r b ). Spearman correlations are presented in place of Pearson for Park and Streetlight variables ( a ). *P <.05, **P <.01, ***P <.001. S119

8 S120 Adams et al Table 3 Relationships Between Self-Reported and Objective Measures Stratified by Activity Level Unadjusted correlations (r) Insufficiently active Sufficiently active NEWS survey (self-reports) N r N r Commercial index * * B1. Convenience/small grocery store B2. Supermarket * * B3. Hardware store B4. Fruit/vegetable market * * B5. Laundry/dry cleaners * * B6. Clothing store Bx. Other Stores ** ** Institutional index B7. Post office B8. Library School Index a a B9. Elementary school B10. Other schools B20. Bus or trolley stop * * B21. Park b Recreation center index B22. Recreation center B23. Gym or fitness facility C6.Hilly streets in my neighborhood C7. Many canyons/hillsides F1. Trees along streets G1. Traffic along street I live G2. Traffic along nearby streets G8. A lot of exhaust fumes H1. Streets are well lit at night a * * Note. Pearson correlations (r). Spearman correlations are presented in place of Pearson for Park and Streetlight variables ( b ). Significant differences between active and inactive group correlations determined by the Fisher r-to-z transformation. a P <.07, *P <.05, **P <.01, ***P <.001. wide variation in San Diego topography may have provided a better test of these variables. The accuracy of reporting of trees was relatively low, which could reflect a low salience of trees as a neighborhood characteristic. For example, the size of trees was not specified, and small trees may not be noticed. Because the accuracy of tree data in GIS is not known, limitations in the criterion measure could reduce the correlation. Boehmer et al also found low agreement between self-reported and objective estimates of trees. 28 For the current study, the objective neighborhood was operationally defined as the 1-mile street network buffer around participants residences. This study found that, compared with 10- and 30-minute walking distance, the self-reported 20-minute walking distance to destinations generally had the strongest correlations with GIS measures. One mile generally corresponds to a 20-minute walk, 34 so the reports of distance to destinations on the NEWS were found to be surprisingly specific. There is no consensus on the most appropriate spatial definition of neighborhood for walkability studies, though it is likely to vary by target populations and outcome variables. Studies on the perceived environment have used various distance (eg, 0.5, 1, 2 miles) and walking time (eg, 5-, 10-, 20-minute walk) thresholds to define neighborhoods. 25,28,30,34,42 Although the current study did not support any specific definition of neighborhood, results generally supported adults estimates of walking time to destinations. Little is known about what factors influence individuals ability to accurately report about features in their neighborhood environment. 22 We hypothesized that participants who had higher levels of physical activity may have greater exposure to their neighborhoods and therefore would have stronger concordance between self-reported and GIS measures. The stratified analysis provided evidence that some of the associations between perceived and objective measures of the physical environment differed by physical activity level. No differences by activity level were found for hills or traffic, which is consistent with other studies. 27,34 Hills and traffic are likely to be salient for all

9 NEWS Validation S121 residents, so these attributes are reported with similar accuracy. The present lack of difference in concordance for parks or recreational facilities was inconsistent with other studies. 28,30 However, we found stronger relationships among physically active adults for destinations such as supermarkets, fruit and vegetable markets, laundry/dry cleaners, other store types, and bus and trolley stops. The stronger findings may be related to an active person s exposure to their neighborhood. Active individuals may be outdoors more frequently or may spend more time walking, running, or biking in their neighborhoods, allowing them to observe its characteristics more carefully and report them more accurately. Recent studies have shown that the frequency of walking for transportation is related to proximity of destinations. 43,44 It is likely the more one walks to destinations, the more likely one is to accurately recall its location and distance from home. 31 People who are walking or running in their neighborhoods are likely to be better judges of time required to walk to destinations. Indeed, a consistent positive relationship between physical activity and time outdoors has been observed. 45,46 There is a concern when using self-report measures of physical activity to study relations of behavior and neighborhood environment. Because the NEWS and self-reported physical activity measures share a common source of methodological error (ie, participants recall), while the NEWS and GIS measures do not, one cannot disentangle nature of a relationship between the selfreported environment and activity level. Is it that the physical environment features serve as barriers or facilitators of activity, or is it that physical activity level in part determines the level of awareness of one s neighborhood by virtue of spending time outdoors? Future prospective studies may help disentangle these causal relationships, and use of objective measures of both environments and physical activity will reduce opportunity for bias. Strengths and limitations of the study should be considered. The 1-mile buffer using street networks to define neighborhoods represents what is truly accessible to residents better than straight-line buffers. Previous studies have called for objective measures of traffic and trees to validate perceptions, 27,34 and the current study was able to access numerous objective measures of these neighborhood features from regional sources. This allowed the examination of concordance of specific neighborhood features to NEWS items. Prior studies show mixed results for the influence of physical activity on concordance, which could be due to different phrasing of survey items, different definitions or types of data used for objective measures, or both. One study limitation was comparing self-reported nearest distance of neighborhood features to counts of the objective measures, similar to a recent study, 28 instead of objective distance to the nearest facility. Even with this limitation, practical and statistically significant effects were found under conditions that should attenuate the observed relationships. Future studies should attempt to estimate the distance to each type of destination. The 1-mile street network definition of neighborhood may not correspond to participants perceptions of the boundaries of their neighborhood. The NEWS prefaces items in land use mix-access (section C) only with a distance boundary (eg, a 10- to 15-minute walk from your home) of which we evaluated 2 questions related to hills. This may attenuate the observed relationships for other items about neighborhoods since participants may consider their neighborhood boundaries closer or farther than our 1-mile network boundary. Additional attenuations of correlations may have occurred since we examined broad land use categories (eg, % institutional land use) to more specific self-reported variables (eg, post offices and libraries). GIS measures of trees and streetlights were only available for City of San Diego necessitating a smaller sample for some analyses. Moreover, the validity of GIS data sources is unknown. An alternative explanation for the relatively strong concordance for destinations may be that GIS data are more complete for destinations than for some other variables. Finally, because this was a convenience sample of participants and not representative of the general adult population, the findings may only be generalizable to mainly parents of adolescents. The current study adds to the already substantial evidence of the validity of the NEWS by demonstrating that reports of many specific neighborhood attributes are related to matched objective measures. Of particular interest were indications that adults were relatively accurate in their reports of distances from their homes to a variety of destinations. Proximity of destinations is an important indicator of mixed land use, which is a core component of walkable neighborhoods. The NEWS variables evaluated in the current study asked participants to report on relatively objective attributes of their neighborhood environments, not to subjectively evaluate the role of these attributes in influencing physical activity. The specificity of association between reports of specific attributes and objective indicators of the same attributes supports the accuracy of self-reports, especially for highly-salient characteristics like destinations. These findings support the use of self-reports using the NEWS to supplement or substitute for objective built environment measures. Results indicated that physically active adults may be more accurate reporters of selected built environment attributes. While this finding indicates a limitation of the validity of some NEWS items, multiple studies demonstrate substantial validity of the NEWS in general populations of adults. 5,6,22,24 Future studies might evaluate whether objectively-measured physical activity has the same moderating function on concordance between self-reported and objectively measured environments. Acknowledgments This project was supported by the National Institutes of Health-National Cancer Institute (RO1 CA113828).

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