The Effects of Neighborhood Built Environment on Walking for Leisure and for Purpose Among Older People

The Effects of Neighborhood Built Environment on Walking for Leisure and for Purpose Among Older... Abstract Background and Objectives Characteristics of a neighborhood’s built environment affect the walking behavior of older people, yet studies typically rely on small nonrepresentative samples that use either subjective reports or aggregate indicators from administrative sources to represent neighborhood characteristics. Our analyses examine the usefulness of a novel method for observing neighborhoods—virtual observations—and assess the extent to which virtual-based observations predict walking among older adults. Research Design and Methods Using Google Street View, we observed the neighborhoods of 2,224 older people and examined how characteristics of the neighborhood built environments are associated with the amount of time older people spend walking for leisure and purpose. Results Multilevel model analyses revealed that sidewalk characteristics had significant associations with both walking for purpose and leisure. Land use, including the presence of multifamily dwellings, commercial businesses, and parking lots were positively associated with walking for purpose and single-family detached homes were negatively associated with walking for purpose, but none of these characteristics were associated with leisure walking. Gardens/flowers were associated with walking for leisure but not purpose. Garbage/litter was not associated with either type of walking behavior. Discussion and Implications Virtual observations are a useful method that provides meaningful information about neighborhoods. Findings demonstrate how neighborhood characteristics assessed virtually differentially impact walking for leisure and purpose among older adults and are interpreted within a social-ecological model. Neighborhood characteristics, Walking for leisure, Walking for purpose, Google Street View, Virtual observations Regular physical activity is essential for healthy aging. Physically active people have lower rates of mortality (Patel et al., 2018; Wu et al., 2015) and are less likely to suffer hypertension, diabetes, obesity, and depression (Mammen & Faulkner, 2013; Sigal, Kenny, Wasserman, Castaneda-Sceppa, & White, 2006) than people who are not active. Although the 2018 Physical Activity Guidelines for Americans recommend that middle-aged and older adults without limiting chronic conditions get 150 min of moderate-intensity aerobic physical activity each week (https://health.gov/paguidelines/guidelines/older-adults.aspx), only 16.1% of people aged 55–64, 14.4% of people 65–74, and 7.9% of people 75+ met the guideline in 2014 (National Center for Health, 2016). Most physical activity studies focus on children and young adults (Rhodes & Nasuti, 2011; Van Cauwenberg et al., 2011). However, the projected increase in the number of people aged 65 and older from 524 million in 2010 to approximately 1.5 billion by 2050 (WHO, 2011) and the clear association between physical activity and positive outcomes make understanding factors impacting physical activity among older people critical. Globally, walking is the exercise of choice among older adults (Cohen-Mansfield, Marx, Biddison, & Guralnik, 2004; Dafna, Carmen, Kamalesh, & Adrian, 2012). Walking is a safe, practical, inexpensive, and low-impact form of physical exercise that requires no specialized equipment (Mobily, 2014). Between 2005 and 2015, the prevalence of both walking for a purpose and walking for leisure increased, yet the amount of time people spent walking decreased (Ussery et al., 2018). Elements of the environment can facilitate or constrain the walking behavior of older people (Towne et al., 2016). Barnett and colleagues (2017) found that neighborhood characteristics (i.e., access to/availability of shops/commercial destinations, greenery and aesthetically pleasing scenery, access to public transport, parks/public space, crime, residential density, and street lighting) are positively associated with walking. Moreover, research shows that the importance of neighborhood characteristics increases as people age (Buffel et al., 2012; Mahmood et al., 2012), possibly because older people have lived longer in a neighborhood (Phillipson, 2007) or because older people spend more time in their neighborhood, especially following retirement (Peace, Wahl, Mollenkopf, & Oswald, 2007). Empirical studies examining the associations between neighborhood environments and walking among adults in general, and older adults in particular, suffer from a number of limitations that may constrain understanding of the impact that neighborhoods have on walking behaviors. Past studies have relied on small samples (Barnett et al., 2017; Towne et al., 2016), aggregate indicators from administrative sources (Towne et al., 2016), qualitative data (Mitra, Siva, & Kehler, 2015), or subjective reports of neighborhood characteristics (Van Cauwenberg et al., 2014). However, newer technologies allow for more fine-grain tracking of neighborhood characteristics; namely the use of Google Street View as a virtual proxy for in situ observations can facilitate systematic assessments of neighborhood characteristics in large samples across geographic distances (Clarke, Ailshire, Melendez, Bader, & Morenoff, 2010; Kelly, Wilson, Baker, Miller, & Schootman, 2013; Odgers, Caspi, Bates, Sampson, & Moffitt, 2012; Rundle, Bader, Richards, Neckerman, & Teitler, 2011). The analyses that follow advance the current science in this area by using a novel, objective method to assess neighborhoods and examine how neighborhood characteristics influence the amount of time older people spend walking for leisure and purpose. Theory A social-ecological model guides our research. The model posits that a complex interplay among characteristics of people and neighborhoods shapes health behaviors (Alfonzo, 2005; Sallis et al., 2006; Stokols, 1996). When older people experience decreased competence, their sensitivity to environmental barriers increases (Wahl & Lang, 2004). It is possible, for example, that diminished physical capacity makes older people more sensitive to the effects of physically challenging environments such as inclines, uneven surfaces, and the absence of walk-friendly infrastructure. Empirical studies find that characteristics of the environment have stronger effects on behavior when people suffer functional limitations then when people are not impaired (Forsyth, Oakes, Lee, & Schmitz, 2009; Rantakokko et al., 2010). A study of more than 700 people aged 66 and older found that older people living in more walkable neighborhoods had more transport activity and moderate-to-vigorous physical activity compared with people living in less walkable neighborhoods and that the most mobility-impaired people living in more walkable neighborhoods reported transport activity levels that were similar to less mobility-impaired people living in less walkable neighborhoods (King et al., 2011). Alternately, a supportive physical environment, such as one with high quality paths, may encourage older people to walk more (Sugiyama, Thompson, & Alves, 2008). Social-ecological models underscore the importance of specificity of environmental correlates as a function of outcome (Alfonzo, 2005; Sallis et al., 2006). People walk for different reasons. Some walk for leisure; others walk purposefully. Purposeful walking is typically defined as walking to reach a destination (Cerin, Nathan, Van Cauwenberg, Barnett, & Barnett, 2017; Frank, Kerr, Rosenberg, & King, 2010; Hirsch, Diez Roux, Moore, Evenson, & Rodriguez, 2014; Hirsch, Moore, Evenson, Rodriguez, & Diez Roux, 2013; King et al., 2011). Walking for leisure, on the other hand, does not have a specific targeted location (Hosler, Gallant, Riley-Jacome, & Rajulu, 2014; Sallis et al., 2016; Towne et al., 2016; Van Cauwenberg et al., 2018). A social-ecological model predicts that precursors of walking for purpose and walking for leisure should differ. In fact, empirical studies distinguishing walking for leisure and walking for purpose find that neighborhood characteristics have stronger associations with walking for purpose than walking for leisure (Saelens & Handy, 2008). Hirsch and colleagues (2013), for example, found that neighborhood WalkScore, an algorithm based on population density, road metrics, and access to pathways for walking to nearby amenities, was related to walking for purpose, but not walking for leisure. Moreover, Hirsch and colleagues (2014) found that when people moved to a neighborhood with a higher WalkScore, walking for transportation (purpose), but not walking for recreation (leisure), increased. Other studies find that neighborhood characteristics, including sidewalks, infrastructure, residential density, street connectivity, land-use mix, access to amenities, and retail floor area are positively associated with walking for purpose (Cerin, Nathan, Van Cauwenberg, Barnett, & Barnett, 2017; Frank, Kerr, Rosenberg, & King, 2010; King et al., 2011). Meanwhile, littering/vandalism/decay have negative associations with walking for purpose (Cerin et al., 2017). In regard to correlates of walking for leisure, Hosler and colleagues (2014) report significant associations with overall walkability of the community, presence of sidewalks, street amenities, and traffic safety. Others have found that predictors of leisure walking include living in an area perceived as having high neighborhood cohesion and safety, walkable versus car-dependent, and areas with low-moderate or high income (vs low) median household income (Towne et al., 2016). Other factors positively associated with walking for leisure include land-use mix and aesthetically pleasing scenery (Van Cauwenberg et al., 2018). Sallis and colleagues (2016) suggest that the vibrancy of highly walkable neighborhoods with access to a variety of destinations creates opportunities for social interaction and a sense of safety, which may in turn stimulate leisure walking among older people. A limitation of previous research is that studies rely on either self-report of neighborhood characteristics, information from administrative data sets, or in situ observations made by raters. While self-perceptions of neighborhoods can provide useful information, there is concern that self-reports of neighborhoods may not accurately represent the objective environment (Pruitt, Jeffe, Yan, & Schootman, 2012; Weden, Carpiano, & Robert, 2008). Moreover, there is evidence that the associations of physical activity with objective and perceived measures of the built environment differ from one another (Barnett et al., 2017; Kerr et al., 2013). Michael, Beard, Choi, Farquhar, and Carlson (2006), in a study contrasting perceptions of older adults and environmental audit data, found poor agreement between objective and perceived measures of trails, graffiti and vandalism, sidewalk existence, and sidewalk obstructions. In terms of administrative data, conclusions are limited by the crude nature of most indicators. For example, although WalkScore is a valid and objective measure (Duncan, Aldstadt, Whalen, Melly, & Gortmaker, 2011), it is based on the availability and conditions of a limited number of amenities. Other metrics collected by the US Census and other governmental agencies are limited because they rely on data aggregated across census tracts or blocks, in which features that affect walking may be unevenly distributed or the land area covered may not be used by a specific person. Finally, in situ assessments of neighborhoods are expensive and characterized by low retest reliability and significant variability among raters (Andresen et al., 2013). The analyses that follow use a novel method that relies on Google Street View to assess neighborhood characteristics and test the hypothesis that neighborhood characteristics including sidewalks, neighborhood land use (i.e., single-family detached homes, multifamily dwellings, commercial businesses, parking lots), and neighborhood aesthetics (i.e., gardens/flowers, garbage) are associated with walking. We assess whether neighborhood characteristics assessed in this manner can effectively differentiate neighborhood attributes associated with walking for purpose versus walking for leisure. Based on the literature, we expected that the associations between neighborhood characteristics and walking would be stronger for walking for purpose than walking for leisure. Methods Participants Between 2006 and 2008, we recruited and completed baseline (Wave 1) telephone interviews with 5,688 people who were part of the ORANJ BOWL (Ongoing Research on Aging in New Jersey: Bettering Opportunities for Wellness in Life) panel. We used cold calling and list-assisted random digit dialing (RDD) procedures. Eligible participants were between the ages of 50 and 74, living in New Jersey, and able to participate in a 1-hr, English-language telephone interview. Coverage loss due to cell phone-only households was small (Blumberg & Luke, 2007). ORANJ BOWL achieved a response rate of 58.73%, using standard American Association for Public Opinion Research calculations, and a Cooperation Rate of 72.88%, consistent with or better than average RDD response rates. Details about sample development are provided in Pruchno, Wilson-Genderson, and Cartwright (2010). Participants were representative of individuals aged 50–74 living in New Jersey in 2006, except for a slightly higher rate of women and individuals with more years of education. Four subsequent waves of data have been collected. This analysis uses Wave 5 data; demographic characteristics were collected at Wave 1. Wave 5 was completed approximately 9 years after Wave 1 (2015–2017). People completing Wave 5 (N = 3,076) had higher levels of education (F = 116.38, df = 3; N = 5,671) and income (F = 148.30, df = 3; N = 5,018) than those who had died, withdrawn, or not completed Wave 5. Completers were significantly older than noncompleters and younger than people who withdrew or died (F = 120.45, df = 3; 5,684). Completers were more likely to be female than those who died (χ2 = 15.06, df = 3, N = 5,688) and less likely to be African American than those who died, withdrew, or did not complete Wave 5 (χ2 = 146.15, df = 3, N = 5,688). All data collection procedures were approved by the Rowan University Institutional Review Board. Measures Data collected from ORANJ BOWL respondents Walking for leisure and purpose At Wave 5, walking for leisure was assessed with: “Over the past 30 days, did you spend at least 10 minutes walking for leisure? Include taking a walk for pleasure or taking a dog for a walk. Do not include brisk walking, jogging, or running.” People responding affirmatively were asked: “On average, how much time would you estimate that you spend walking for leisure each week?” Walking for purpose was measured with: “Over the past 30 days, did you spend at least 10 minutes intentionally walking to get somewhere? Do not include daily walking around, brisk walking, jogging, or running. Focus on purposeful walking.” People responding affirmatively were asked: “On average, how much time would you estimate that you spend intentionally walking to get somewhere each week?” Walking for leisure and purpose were treated as continuous variables (minutes). Initial examination of descriptive data confirmed that respondents were able to distinguish walking for leisure from walking for purpose, as the correlation between the two measures was .35. Individual characteristics At Wave 1, participants self-reported sex, age, education (9-point Likert scale; 1 = not high school graduate to 9 = doctoral degree), and income (6-point Likert scale; 1 = less than $15,000 to 6 = more than $150,000). At Wave 5, respondents reported the extent of difficulty they had completing four indicators of functional limitations [walking for ¼ of a mile (M = 4.4, SD = 1.1), walking up 10 steps (M = 4.6, SD = .93), standing for 2 hr (M = 3.95, SD = 1.3), stooping (M = 3.9, SD = 1.2)] using a 5-point Likert scale ranging from 1 (can’t do it at all) to 5 (not at all difficult), with higher scores indicating fewer limitations. The mean of these four items is used as a measure of lower body function (M = 4.13, SD = .97), the ability to walk. Only n = 130 participants reported the inability to walk ¼ of a mile. Virtual observations of neighborhood built environment Residential addresses of respondents at Wave 5 were geocoded using the Texas A&M University Geocoding Desktop Client (http://geoservices.tamu.edu). Only addresses resolved at the parcel or street segment level were included. Observations of the neighborhood surrounding participants’ homes were conducted virtually using the Google Maps orthographic imagery and ground-level 360-degree “Street View” panoramic image service (Anguelov et al., 2010). “Street View” imagery refers to the omnidirectional imagery service in Google Earth and Google Maps and reflects street side conditions that would be visible from an in situ pedestrian viewpoint. Observations were included for which the distance between the participant’s residence and the observed location did not exceed 304.8 m (1,000 feet), as calculated with the NEAR function using the recorded locations in ArcGIS 10.6. We limited observations to this distance under the rationale that any further distance could result in a substantially different context between the locations of the residence and the observation. Similar methods reported by Kepper and colleagues (2017) and Wilson and colleagues (2012) reveal high reliability and strong similarity between virtual street-level observations and in-person observations. Others have used this imagery service to assess aspects of the built environment that may affect health outcomes (Clarke et al., 2010; Kelly et al., 2013; Odgers et al., 2012; Rundle et al., 2011). Ben-Joseph, Lee, Cromley, Laden, and Troped (2013) report strong agreement of virtual (web-based) audits and on-site audits of the condition of the environment, including fine-grain features like sidewalks. Curtis and colleagues (2013) indicate the importance of noting the imagery collection date, which varies across space. Virtual visits were conducted by 10 trained research assistants for each available address. We examined the distance from each participant’s home to the location of the virtual audit. We determined that of the 3,045 ORANJ BOWL participants with Wave 5 data, 2,224 had virtual audit data evaluated within 1,000 feet of the residence of record. Data used for these analyses are limited to these 2,224 observations. The State of New Jersey is subdivided into 21 counties and 565 municipalities. ORANJ BOWL participants lived in all 21 counties and in 455 of the 565 municipalities, spanning the full spectrum of rural, suburban, and urban communities. Participants were well-distributed among those municipalities represented, with 329 municipalities home to five or fewer ORANJ BOWL participants; only 15 municipalities were home to more than 20 ORANJ BOWL participants. Of the 2,224 observations in this analysis, 528 observations (23.7%) were conducted in rural municipalities (with a population density of less than 1,000 persons/square mile based on 2010 US Census), 1,580 observations were conducted in urban municipalities (1,000–10,000 persons/square mile), and 116 observations (5.2%) were conducted in highly urbanized municipalities (>10,000 persons/square mile). Of the 110 municipalities not represented by this study, 24 had fewer than 1,000 persons/square mile and 4 had greater than 10,000 persons/square mile. To conduct the virtual observation, trained observers accessed the Street View interface at the nearest location to the geocoded residence address for which imagery was available. They recorded the geographic coordinates of the site (latitude/longitude, decimal degrees) and the date of the Street View imagery. Observers panned across the imagery available and along the street segment (block) adjacent to the residence and recorded observable characteristics of the environment using a web-based Qualtrics survey instrument. Characteristics rated by observers included: (1) sidewalks, (2) neighborhood land use, and (3) neighborhood aesthetics. Sidewalks (neither side, one side, both sides) were rated for the following: presence/absence of sidewalks, continuous/gaps, paved/not paved, smooth/broken, no obstructions/obstructions, and wide enough for two people/not wide enough for two people. If a sidewalk characteristic pertained to either one or both sides, it was classified as present. A sum of the six dichotomous sidewalk variables was created (range: 0–6). To assess neighborhood land-use, the presence or absence on the block of: single, detached homes; multifamily or apartment dwellings; commercial, business, or professional buildings; and parking lots was recorded. To assess neighborhood aesthetic characteristics the presence of gardens/flowers and garbage/litter were rated as present or absent. Additional characteristics related to safety (e.g., security features on buildings) and housing conditions (e.g., composition and maintenance) were collected but lacked sufficient variability to be included in the analyses. Inter-rater reliability was calculated based on 132 sites independently observed by at least 2 observers. Reliability exceeded 0.70 except for presence of gardens (.63). Agreement was defined as exact matches for yes/no questions and adjacent entries for ordered choice lists. Analysis Plan Descriptive statistics (means, standard deviations, and frequency distributions) were computed for all variables. A bivariate correlation between the walk for purpose and walk for leisure variables was computed (Pearson) as were correlations between the virtual neighborhood variables and the individual walk variables (Spearman). Given the novelty of this approach for understanding the impact of neighborhood built environment on walking, we used multilevel modeling and modeled each virtual neighborhood characteristic individually (Level 2) to understand the effect each had on walking for leisure and purpose. All models included two levels, with random effects for neighborhood estimated by multilevel linear regression using county as the cluster effect. The models included fixed but not random effects for individual (Level 1) characteristics. We controlled for individual characteristics typically associated with walking, including the ability to walk, gender, education, income, age, and race (Forjuoh et al., 2017; Mobily, 2014; Towne et al., 2016). We created a virtual observation data lag (Level 1) variable capturing the difference in time between when the virtual images were captured and stored by Google (largely between the months of July and October, during 2012–2017; most locations viewed in 2013) and when the virtual audit was conducted by our research team (between February and June, 2018). This variable accounts for the variability in the methodology used by Google over time and the fact that the neighborhoods were assessed over an extended period of time. Preliminary data analyses revealed that the distributions of many of the virtual variables were non-normal for African American participants (N = 296). Despite efforts to develop robust models that converged with all participants, we were unsuccessful. As such the findings reported below exclude data from African American participants. Results Descriptive Data Participants reported walking for leisure an average of 94.9 min a week (SD = 147.3; median = 50 min) and for purpose an average of 79.2 min (SD = 148.9; median = 30 min). Table 1 includes descriptive information about the virtual variables. The virtual audits detected an average of 3.67 (SD = 2.8) sidewalk characteristics (out of 6). Presence of single family detached homes and flowers/gardens were most prevalent, followed by parking lots and multifamily/apartment. Table 1. Descriptive Data: Virtual Variables Variable M (SD); median Sidewalk characteristics 3.67 (2.8); 5 N (%) Neighborhood land-use  Single family detached 1,732 (88.9%)  Multifamily/apartments 340 (17.5%)  Commercial, business, professional buildings 233 (12.0%)  Parking lots 342 (17.6%) Neighborhood aesthetics  Flowers/gardens 1,612 (82.7%)  Garbage 260 (13.3%) Variable M (SD); median Sidewalk characteristics 3.67 (2.8); 5 N (%) Neighborhood land-use  Single family detached 1,732 (88.9%)  Multifamily/apartments 340 (17.5%)  Commercial, business, professional buildings 233 (12.0%)  Parking lots 342 (17.6%) Neighborhood aesthetics  Flowers/gardens 1,612 (82.7%)  Garbage 260 (13.3%) Note. N = 2,224 observations. Open in new tab Table 1. Descriptive Data: Virtual Variables Variable M (SD); median Sidewalk characteristics 3.67 (2.8); 5 N (%) Neighborhood land-use  Single family detached 1,732 (88.9%)  Multifamily/apartments 340 (17.5%)  Commercial, business, professional buildings 233 (12.0%)  Parking lots 342 (17.6%) Neighborhood aesthetics  Flowers/gardens 1,612 (82.7%)  Garbage 260 (13.3%) Variable M (SD); median Sidewalk characteristics 3.67 (2.8); 5 N (%) Neighborhood land-use  Single family detached 1,732 (88.9%)  Multifamily/apartments 340 (17.5%)  Commercial, business, professional buildings 233 (12.0%)  Parking lots 342 (17.6%) Neighborhood aesthetics  Flowers/gardens 1,612 (82.7%)  Garbage 260 (13.3%) Note. N = 2,224 observations. Open in new tab Table 2 reveals small but significant positive correlations between walking for leisure and sidewalk characteristics and gardens/flowers a significant negative correlation with presence of commercial businesses. There were small but significant positive correlations between walking for purpose and sidewalk characteristics, presence of multifamily/apartment dwellings, commercial businesses, and parking lots; the significant correlation with single family detached homes was negative. Table 2. Correlations of Virtual and Walk Variables Walking behavior Sidewalk characteristics Neighborhood land-use Neighborhood aesthetics Single family detached Multifamily/ apartments Commercial businesses Parking lots Gardens/ flowers Garbage Walk for leisure 0.05* −0.02 −0.02 −0.05* −0.04 0.04* −0.003 Walk for purpose 0.04* −0.04* 0.04* 0.05* 0.08* 0.02 0.02 Walking behavior Sidewalk characteristics Neighborhood land-use Neighborhood aesthetics Single family detached Multifamily/ apartments Commercial businesses Parking lots Gardens/ flowers Garbage Walk for leisure 0.05* −0.02 −0.02 −0.05* −0.04 0.04* −0.003 Walk for purpose 0.04* −0.04* 0.04* 0.05* 0.08* 0.02 0.02 *p < .05. Open in new tab Table 2. Correlations of Virtual and Walk Variables Walking behavior Sidewalk characteristics Neighborhood land-use Neighborhood aesthetics Single family detached Multifamily/ apartments Commercial businesses Parking lots Gardens/ flowers Garbage Walk for leisure 0.05* −0.02 −0.02 −0.05* −0.04 0.04* −0.003 Walk for purpose 0.04* −0.04* 0.04* 0.05* 0.08* 0.02 0.02 Walking behavior Sidewalk characteristics Neighborhood land-use Neighborhood aesthetics Single family detached Multifamily/ apartments Commercial businesses Parking lots Gardens/ flowers Garbage Walk for leisure 0.05* −0.02 −0.02 −0.05* −0.04 0.04* −0.003 Walk for purpose 0.04* −0.04* 0.04* 0.05* 0.08* 0.02 0.02 *p < .05. Open in new tab Multilevel Models Multilevel model results are reported in Table 3. Controlling for individual characteristics and the lag variable, sidewalk characteristics had significant positive associations with both walking for purpose and leisure. Presence of single-family detached homes was negatively associated with walking for purpose and not significantly associated with walking for leisure. Presence of multifamily dwellings, commercial business and parking lots were each positively associated with walking for purpose but not associated with walking for leisure. Presence of gardens/flowers was associated with walking for leisure but not walking for purpose. Presence of garbage was not associated with either type of walking behavior. Table 3. Multilevel Model Results: Walking for Leisure and Walking for Purpose Walking for leisure Walking for purpose Variable β (SE) β (SE) Sidewalk characteristics  Intercept 70.40 (39.5) 23.84 (40.5)  Age 0.65 (0.48) −0.25 (0.52)  Education −0.87 (1.6) −3.05 (1.8)  Sex −8.93 (6.7) 11.12 (7.2)  Income −2.08 (0.47) 1.44 (3.1)  Lower body function 8.16 (0.89)**** 3.98 (0.96)****  Lag −0.01 (0.11) 0.13 (0.12)  Sidewalk Characteristics 1.81 (0.60)* 1.47 (0.64)* Neighborhood land-use  Single family detached   Intercept 54.18 (38.6) 49.97 (41.4)   Age 0.62 (0.48) −0.24 (0.52)   Education −0.94 (1.6) −3.04 (1.8)   Sex −8.71 (6.7) 10.82 (7.2)   Income −1.82 (2.9) 1.30 (3.1)   Lower body function 8.14 (0.89)**** 3.90 (0.96)   Lag 0.01 (0.11) 0.13 (0.12)   Single family detached −10.52 (5.9) −17.51 (7.4)*  Multifamily   Intercept 63.32 (37.6) 30.42 (40.4)   Age 0.64 (0.48) −0.22 (0.52)   Education −0.82 (1.6) −2.97 (1.8)   Sex −8.85 (6.7) 10.63 (7.2)   Income −2.16 (2.9) 1.35 (3.1)   Lower body function 8.13 (0.89) 3.91 (0.96)***   Lag 0.01 (0.11) 0.13 (0.12)   Multifamily −0.69 (1.9) 11.0 (4.6)*  Commercial businesses   Intercept 59.98 (37.9) 32.69 (40.7)   Age 0.62 (0.48) −0.27 (0.52)   Education −0.89 (1.6) −2.93 (1.8)   Sex −8.86 (6.7) 11.33 (7.2)   Income −2.35 (2.9) 1.59 (3.1)   Lower body function 8.11 (0.90) 3.96 (0.96)****   Lag 0.03 (0.12) 0.14 (0.12)   Commercial businesses −6.31 (6.6) 6.40 (2.9)*  Parking lots   Intercept 64.32 (37.8) 28.81 (40.5)   Age 0.64 (0.48) −0.26 (0.52)   Education −0.83 (1.6) −3.04 (1.8)   Sex −8.87 (6.7) 11.12 (7.2)   Income −2.1 (2.9) 1.90 (3.1)   Lower body function 8.14 (0.90)**** 4.02 (0.96)   Lag 0.001 (0.11) 0.12 (0.12)   Parking lots 1.24 (4.0) 12.6 (5.8)* Neighborhood aesthetics  Gardens/flowers   Intercept 70.25 (37.92) 37.27 (40.7)   Age 0.64 (0.48) −0.28 (0.52)   Education −0.90 (1.65) −2.99 (1.7)*   Sex −9.10 (6.74) 11.35 (7.23)   Income −2.44 (2.88) 1.41 (3.1)   Lower body function 8.16 (0.89)*** 3.93 (0.96)***   Lag 0.01 (0.11) 0.15 (0.12)   Gardens/flowers 9.47 (4.4)* −1.36 (5.0)  Garbage   Intercept −1.36 (0.48) −1.46 (0.50)   Age −0.01 (0.01) 0.001 (0.01)   Education 0.03 (0.02) 0.05 (0.02)   Sex 0.06 (0.09) 0.25 (0.09)   Income 0.02 (0.04) 0.06 (0.04)   Lower body function 0.16 (0.01)*** 0.07 (0.01)***   Lag 0.001 (0.0001) 0.001 (0.001)   Garbage −0.02 (0.12) 0.05 (0.12) Walking for leisure Walking for purpose Variable β (SE) β (SE) Sidewalk characteristics  Intercept 70.40 (39.5) 23.84 (40.5)  Age 0.65 (0.48) −0.25 (0.52)  Education −0.87 (1.6) −3.05 (1.8)  Sex −8.93 (6.7) 11.12 (7.2)  Income −2.08 (0.47) 1.44 (3.1)  Lower body function 8.16 (0.89)**** 3.98 (0.96)****  Lag −0.01 (0.11) 0.13 (0.12)  Sidewalk Characteristics 1.81 (0.60)* 1.47 (0.64)* Neighborhood land-use  Single family detached   Intercept 54.18 (38.6) 49.97 (41.4)   Age 0.62 (0.48) −0.24 (0.52)   Education −0.94 (1.6) −3.04 (1.8)   Sex −8.71 (6.7) 10.82 (7.2)   Income −1.82 (2.9) 1.30 (3.1)   Lower body function 8.14 (0.89)**** 3.90 (0.96)   Lag 0.01 (0.11) 0.13 (0.12)   Single family detached −10.52 (5.9) −17.51 (7.4)*  Multifamily   Intercept 63.32 (37.6) 30.42 (40.4)   Age 0.64 (0.48) −0.22 (0.52)   Education −0.82 (1.6) −2.97 (1.8)   Sex −8.85 (6.7) 10.63 (7.2)   Income −2.16 (2.9) 1.35 (3.1)   Lower body function 8.13 (0.89) 3.91 (0.96)***   Lag 0.01 (0.11) 0.13 (0.12)   Multifamily −0.69 (1.9) 11.0 (4.6)*  Commercial businesses   Intercept 59.98 (37.9) 32.69 (40.7)   Age 0.62 (0.48) −0.27 (0.52)   Education −0.89 (1.6) −2.93 (1.8)   Sex −8.86 (6.7) 11.33 (7.2)   Income −2.35 (2.9) 1.59 (3.1)   Lower body function 8.11 (0.90) 3.96 (0.96)****   Lag 0.03 (0.12) 0.14 (0.12)   Commercial businesses −6.31 (6.6) 6.40 (2.9)*  Parking lots   Intercept 64.32 (37.8) 28.81 (40.5)   Age 0.64 (0.48) −0.26 (0.52)   Education −0.83 (1.6) −3.04 (1.8)   Sex −8.87 (6.7) 11.12 (7.2)   Income −2.1 (2.9) 1.90 (3.1)   Lower body function 8.14 (0.90)**** 4.02 (0.96)   Lag 0.001 (0.11) 0.12 (0.12)   Parking lots 1.24 (4.0) 12.6 (5.8)* Neighborhood aesthetics  Gardens/flowers   Intercept 70.25 (37.92) 37.27 (40.7)   Age 0.64 (0.48) −0.28 (0.52)   Education −0.90 (1.65) −2.99 (1.7)*   Sex −9.10 (6.74) 11.35 (7.23)   Income −2.44 (2.88) 1.41 (3.1)   Lower body function 8.16 (0.89)*** 3.93 (0.96)***   Lag 0.01 (0.11) 0.15 (0.12)   Gardens/flowers 9.47 (4.4)* −1.36 (5.0)  Garbage   Intercept −1.36 (0.48) −1.46 (0.50)   Age −0.01 (0.01) 0.001 (0.01)   Education 0.03 (0.02) 0.05 (0.02)   Sex 0.06 (0.09) 0.25 (0.09)   Income 0.02 (0.04) 0.06 (0.04)   Lower body function 0.16 (0.01)*** 0.07 (0.01)***   Lag 0.001 (0.0001) 0.001 (0.001)   Garbage −0.02 (0.12) 0.05 (0.12) *p < .05. **p < .01. ***p < .001. ****p < .0001. Open in new tab Table 3. Multilevel Model Results: Walking for Leisure and Walking for Purpose Walking for leisure Walking for purpose Variable β (SE) β (SE) Sidewalk characteristics  Intercept 70.40 (39.5) 23.84 (40.5)  Age 0.65 (0.48) −0.25 (0.52)  Education −0.87 (1.6) −3.05 (1.8)  Sex −8.93 (6.7) 11.12 (7.2)  Income −2.08 (0.47) 1.44 (3.1)  Lower body function 8.16 (0.89)**** 3.98 (0.96)****  Lag −0.01 (0.11) 0.13 (0.12)  Sidewalk Characteristics 1.81 (0.60)* 1.47 (0.64)* Neighborhood land-use  Single family detached   Intercept 54.18 (38.6) 49.97 (41.4)   Age 0.62 (0.48) −0.24 (0.52)   Education −0.94 (1.6) −3.04 (1.8)   Sex −8.71 (6.7) 10.82 (7.2)   Income −1.82 (2.9) 1.30 (3.1)   Lower body function 8.14 (0.89)**** 3.90 (0.96)   Lag 0.01 (0.11) 0.13 (0.12)   Single family detached −10.52 (5.9) −17.51 (7.4)*  Multifamily   Intercept 63.32 (37.6) 30.42 (40.4)   Age 0.64 (0.48) −0.22 (0.52)   Education −0.82 (1.6) −2.97 (1.8)   Sex −8.85 (6.7) 10.63 (7.2)   Income −2.16 (2.9) 1.35 (3.1)   Lower body function 8.13 (0.89) 3.91 (0.96)***   Lag 0.01 (0.11) 0.13 (0.12)   Multifamily −0.69 (1.9) 11.0 (4.6)*  Commercial businesses   Intercept 59.98 (37.9) 32.69 (40.7)   Age 0.62 (0.48) −0.27 (0.52)   Education −0.89 (1.6) −2.93 (1.8)   Sex −8.86 (6.7) 11.33 (7.2)   Income −2.35 (2.9) 1.59 (3.1)   Lower body function 8.11 (0.90) 3.96 (0.96)****   Lag 0.03 (0.12) 0.14 (0.12)   Commercial businesses −6.31 (6.6) 6.40 (2.9)*  Parking lots   Intercept 64.32 (37.8) 28.81 (40.5)   Age 0.64 (0.48) −0.26 (0.52)   Education −0.83 (1.6) −3.04 (1.8)   Sex −8.87 (6.7) 11.12 (7.2)   Income −2.1 (2.9) 1.90 (3.1)   Lower body function 8.14 (0.90)**** 4.02 (0.96)   Lag 0.001 (0.11) 0.12 (0.12)   Parking lots 1.24 (4.0) 12.6 (5.8)* Neighborhood aesthetics  Gardens/flowers   Intercept 70.25 (37.92) 37.27 (40.7)   Age 0.64 (0.48) −0.28 (0.52)   Education −0.90 (1.65) −2.99 (1.7)*   Sex −9.10 (6.74) 11.35 (7.23)   Income −2.44 (2.88) 1.41 (3.1)   Lower body function 8.16 (0.89)*** 3.93 (0.96)***   Lag 0.01 (0.11) 0.15 (0.12)   Gardens/flowers 9.47 (4.4)* −1.36 (5.0)  Garbage   Intercept −1.36 (0.48) −1.46 (0.50)   Age −0.01 (0.01) 0.001 (0.01)   Education 0.03 (0.02) 0.05 (0.02)   Sex 0.06 (0.09) 0.25 (0.09)   Income 0.02 (0.04) 0.06 (0.04)   Lower body function 0.16 (0.01)*** 0.07 (0.01)***   Lag 0.001 (0.0001) 0.001 (0.001)   Garbage −0.02 (0.12) 0.05 (0.12) Walking for leisure Walking for purpose Variable β (SE) β (SE) Sidewalk characteristics  Intercept 70.40 (39.5) 23.84 (40.5)  Age 0.65 (0.48) −0.25 (0.52)  Education −0.87 (1.6) −3.05 (1.8)  Sex −8.93 (6.7) 11.12 (7.2)  Income −2.08 (0.47) 1.44 (3.1)  Lower body function 8.16 (0.89)**** 3.98 (0.96)****  Lag −0.01 (0.11) 0.13 (0.12)  Sidewalk Characteristics 1.81 (0.60)* 1.47 (0.64)* Neighborhood land-use  Single family detached   Intercept 54.18 (38.6) 49.97 (41.4)   Age 0.62 (0.48) −0.24 (0.52)   Education −0.94 (1.6) −3.04 (1.8)   Sex −8.71 (6.7) 10.82 (7.2)   Income −1.82 (2.9) 1.30 (3.1)   Lower body function 8.14 (0.89)**** 3.90 (0.96)   Lag 0.01 (0.11) 0.13 (0.12)   Single family detached −10.52 (5.9) −17.51 (7.4)*  Multifamily   Intercept 63.32 (37.6) 30.42 (40.4)   Age 0.64 (0.48) −0.22 (0.52)   Education −0.82 (1.6) −2.97 (1.8)   Sex −8.85 (6.7) 10.63 (7.2)   Income −2.16 (2.9) 1.35 (3.1)   Lower body function 8.13 (0.89) 3.91 (0.96)***   Lag 0.01 (0.11) 0.13 (0.12)   Multifamily −0.69 (1.9) 11.0 (4.6)*  Commercial businesses   Intercept 59.98 (37.9) 32.69 (40.7)   Age 0.62 (0.48) −0.27 (0.52)   Education −0.89 (1.6) −2.93 (1.8)   Sex −8.86 (6.7) 11.33 (7.2)   Income −2.35 (2.9) 1.59 (3.1)   Lower body function 8.11 (0.90) 3.96 (0.96)****   Lag 0.03 (0.12) 0.14 (0.12)   Commercial businesses −6.31 (6.6) 6.40 (2.9)*  Parking lots   Intercept 64.32 (37.8) 28.81 (40.5)   Age 0.64 (0.48) −0.26 (0.52)   Education −0.83 (1.6) −3.04 (1.8)   Sex −8.87 (6.7) 11.12 (7.2)   Income −2.1 (2.9) 1.90 (3.1)   Lower body function 8.14 (0.90)**** 4.02 (0.96)   Lag 0.001 (0.11) 0.12 (0.12)   Parking lots 1.24 (4.0) 12.6 (5.8)* Neighborhood aesthetics  Gardens/flowers   Intercept 70.25 (37.92) 37.27 (40.7)   Age 0.64 (0.48) −0.28 (0.52)   Education −0.90 (1.65) −2.99 (1.7)*   Sex −9.10 (6.74) 11.35 (7.23)   Income −2.44 (2.88) 1.41 (3.1)   Lower body function 8.16 (0.89)*** 3.93 (0.96)***   Lag 0.01 (0.11) 0.15 (0.12)   Gardens/flowers 9.47 (4.4)* −1.36 (5.0)  Garbage   Intercept −1.36 (0.48) −1.46 (0.50)   Age −0.01 (0.01) 0.001 (0.01)   Education 0.03 (0.02) 0.05 (0.02)   Sex 0.06 (0.09) 0.25 (0.09)   Income 0.02 (0.04) 0.06 (0.04)   Lower body function 0.16 (0.01)*** 0.07 (0.01)***   Lag 0.001 (0.0001) 0.001 (0.001)   Garbage −0.02 (0.12) 0.05 (0.12) *p < .05. **p < .01. ***p < .001. ****p < .0001. Open in new tab Discussion Understanding the impact of the built environment on walking behavior of older people is important because diminishing physical capacity associated with the aging process can make older people more vulnerable to the effects of physically challenging environments. Informed by a social-ecological model and using a novel virtual method to objectively assess neighborhoods, we found that while sidewalk characteristics and presence of single-family detached homes, multifamily homes, commercial businesses, and parking lots were associated with walking for purpose, only sidewalk characteristics and presence of gardens/flowers were associated with walking for leisure. The most important contribution of these analyses is the methodological approach. The virtual observations used here add richness and complexity to our understanding of the ways in which neighborhood characteristics influence behavior and offer researchers a sophisticated tool that can be reliably and efficiently used to assess the effects of the environment. Virtual observations provide objective ratings of the neighborhood that avoid common pit falls of subjective rating bias (i.e., same reporter for neighborhood characteristics and outcomes). The data are also more nuanced than commonly used objective indicators, including urban planning databases (Brownson, Hoehner, Day, Forsyth, & Sallis, 2009). Additionally, the virtual observations are much less resource-intensive than traditional data audits, as they do not require observers to travel to neighborhoods (Rzotkiewicz, Pearson, Dougherty, Shortridge, & Wilson, 2018; Weiss, Maantay, & Fahs, 2010). However, virtual observations certainly have their own limitations (Rzotkiewicz et al., 2018). They depend on the timing and frequency of Google assessments, yielding a particular daytime momentary snapshot, usually in summer or early fall, which may affect what can and cannot be seen virtually and how it may be perceived by residents and pedestrians. Although Google’s Street View service which began in 2007 has expanded across the planet (Google Maps Street View 2018), with many sites revisited over time (https://googleblog.blogspot.com/2014/04/go-back-in-time-with-street-view.html), Google does not release information about restrictions on the conditions or hour of imagery collection (Rundle et al., 2011). Furthermore, many characteristics with the potential to affect walking behavior, including benches, traffic, bicycling lanes, safety, sidewalk inclines, and uneven surfaces are not consistently visible with Street View, which is generally restricted to roadside views from which the interior of parcels and park may not be visible. Over time, however, it is likely that Street View will increase in sophistication, enabling more comprehensive virtual observations. Conceptually our findings regarding the greater influence that neighborhood characteristics have on walking for purpose than leisure are consistent with those reported by others (Cerin et al., 2017; Frank et al., 2010; Saelens & Handy, 2008). These associations may be explained by the greater likelihood of walking for purpose to begin at a person’s doorstep. Walking for leisure, on the other hand, may be done in a shopping mall, recreational facility, or park outside of the immediate neighborhood. Ou and colleagues (2016) found that older people are more likely to report being active indoors than outdoors. However, our interview did not ask people where they walked and therefore could not account for this variation. We also did not ask participants to classify their walking behaviors as walking for leisure or for purpose. Future studies should ask about where people walk for leisure, as well as for purpose and whether they would consider the walking they are doing as being for leisure or for purpose. Such additional work would expand our understanding of factors associated with distinct types of walking. The finding regarding the association of walking for purpose with sidewalk characteristics and neighborhood land-use (i.e., single-family detached homes, multifamily homes, commercial businesses, and parking lots) is also consistent with prior research (Cerin et al., 2017; Frank et al., 2010; King et al., 2011). However, our work advances the science in this area by demonstrating that virtual measures are reliable and valid indicators of the neighborhood built environment. As environmental planning groups seek to establish older adult specific communities, these relationships should be considered and environments built to encourage walking for purpose. In addition, the finding that leisure walking was associated with sidewalk characteristics and neighborhood aesthetics provide further insight into neighborhood features that may encourage older adults to be more mobile. Overall, more complete understanding of how neighborhoods influence health behavior requires multifaceted assessment strategies. Social-ecological models conceptualize the environment as multidimensional, including physical and social aspects (Stokols, 1996). Future studies should use a combination of objective and subjective measures to examine the effects of aspects of the physical neighborhood (e.g., traffic density, distance to places, presence of parks, crime) and the social environment (e.g., social cohesion, social capital, and interpersonal relationships; Forrest & Kearns, 2001; McNeill, Kreuter, & Subramanian, 2006) on health behaviors, as well as comparing the observed characteristics and those perceived by the residents. In addition, examining variables concurrently to see which constructs have a greater impact on outcomes is needed, as that was beyond the scope of these analyses. Furthermore, consensus on which features are best included in neighborhood audits is also needed; Brownson and colleagues (2009) reviewed 20 objective audit tools and found that they varied in content, detail, and how features were characterized. Several environmental audit tools have been developed specifically for older adults, including the Senior Walking Environmental Audit Tool (Cunningham, Michael, Farquhar, & Lapidus, 2005) and the Healthy Aging Research Network Environmental Audit Tool (https://www.aarp.org/content/dam/aarp/livable-communities/old-plan/assessments/healthy-aging-research-network-environmental-audit-tool.pdf). To advance science and make neighborhoods more conducive for older people to walk, it is important to examine the synergy between objective and subjective neighborhood characteristics. Finally, our findings regarding the non-normality of the virtual variables for the African American participants and the failure of our models to converge when this population is included limit generalizability of our findings to minority populations and demand greater attention from future studies, especially with respect to the inequities present for walking-related amenities and neighborhood context and condition. Despite limitations, virtual observations hold the potential to yield objective data about neighborhoods using minimal resources. As immersive visualization tools become more sophisticated, they will be invaluable for researchers seeking to understand how neighborhood characteristics influence health behaviors. Here, we find evidence of this specific potential, finding associations of sidewalks, neighborhood land-use, and neighborhood aesthetics with older adult walking behaviors. Such empirical findings can further inform neighborhood development work, particularly the building of age-restricted communities seeking to support healthy lifestyles in older adulthood. Funding This work was supported by the National Institutes of Health (R01 AG046463) and the University of Medicine and Dentistry of New Jersey. Acknowledgments The following people assisted our work by collecting the virtual observation data: Diane Barsuglia, Alexander Buontempo, Christopher Campbell, Hunter Davis, Samira Davis, Justin Fraser, Gabrielle Howell, Jillian Mazurek, Christina Parrilla, and Alyssa van Doorn. Conflicts of Interest None reported. References Alfonzo , M . ( 2005 ). To walk or not to walk? The hierarchy of walking needs . Environment and Behavior , 37 , 808 – 838 . doi: https://doi.org/10.1177/0013916504274016 Google Scholar Crossref Search ADS WorldCat Andresen , E. M. , Malmstrom , T. K. , Schootman , M. , Wolinsky , F. D. , Miller , J. P. , & Miller , D. K . ( 2013 ). 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The Effects of Neighborhood Built Environment on Walking for Leisure and for Purpose Among Older People

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Oxford University Press
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© The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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0016-9013
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1758-5341
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10.1093/geront/gnz093
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Abstract

Abstract Background and Objectives Characteristics of a neighborhood’s built environment affect the walking behavior of older people, yet studies typically rely on small nonrepresentative samples that use either subjective reports or aggregate indicators from administrative sources to represent neighborhood characteristics. Our analyses examine the usefulness of a novel method for observing neighborhoods—virtual observations—and assess the extent to which virtual-based observations predict walking among older adults. Research Design and Methods Using Google Street View, we observed the neighborhoods of 2,224 older people and examined how characteristics of the neighborhood built environments are associated with the amount of time older people spend walking for leisure and purpose. Results Multilevel model analyses revealed that sidewalk characteristics had significant associations with both walking for purpose and leisure. Land use, including the presence of multifamily dwellings, commercial businesses, and parking lots were positively associated with walking for purpose and single-family detached homes were negatively associated with walking for purpose, but none of these characteristics were associated with leisure walking. Gardens/flowers were associated with walking for leisure but not purpose. Garbage/litter was not associated with either type of walking behavior. Discussion and Implications Virtual observations are a useful method that provides meaningful information about neighborhoods. Findings demonstrate how neighborhood characteristics assessed virtually differentially impact walking for leisure and purpose among older adults and are interpreted within a social-ecological model. Neighborhood characteristics, Walking for leisure, Walking for purpose, Google Street View, Virtual observations Regular physical activity is essential for healthy aging. Physically active people have lower rates of mortality (Patel et al., 2018; Wu et al., 2015) and are less likely to suffer hypertension, diabetes, obesity, and depression (Mammen & Faulkner, 2013; Sigal, Kenny, Wasserman, Castaneda-Sceppa, & White, 2006) than people who are not active. Although the 2018 Physical Activity Guidelines for Americans recommend that middle-aged and older adults without limiting chronic conditions get 150 min of moderate-intensity aerobic physical activity each week (https://health.gov/paguidelines/guidelines/older-adults.aspx), only 16.1% of people aged 55–64, 14.4% of people 65–74, and 7.9% of people 75+ met the guideline in 2014 (National Center for Health, 2016). Most physical activity studies focus on children and young adults (Rhodes & Nasuti, 2011; Van Cauwenberg et al., 2011). However, the projected increase in the number of people aged 65 and older from 524 million in 2010 to approximately 1.5 billion by 2050 (WHO, 2011) and the clear association between physical activity and positive outcomes make understanding factors impacting physical activity among older people critical. Globally, walking is the exercise of choice among older adults (Cohen-Mansfield, Marx, Biddison, & Guralnik, 2004; Dafna, Carmen, Kamalesh, & Adrian, 2012). Walking is a safe, practical, inexpensive, and low-impact form of physical exercise that requires no specialized equipment (Mobily, 2014). Between 2005 and 2015, the prevalence of both walking for a purpose and walking for leisure increased, yet the amount of time people spent walking decreased (Ussery et al., 2018). Elements of the environment can facilitate or constrain the walking behavior of older people (Towne et al., 2016). Barnett and colleagues (2017) found that neighborhood characteristics (i.e., access to/availability of shops/commercial destinations, greenery and aesthetically pleasing scenery, access to public transport, parks/public space, crime, residential density, and street lighting) are positively associated with walking. Moreover, research shows that the importance of neighborhood characteristics increases as people age (Buffel et al., 2012; Mahmood et al., 2012), possibly because older people have lived longer in a neighborhood (Phillipson, 2007) or because older people spend more time in their neighborhood, especially following retirement (Peace, Wahl, Mollenkopf, & Oswald, 2007). Empirical studies examining the associations between neighborhood environments and walking among adults in general, and older adults in particular, suffer from a number of limitations that may constrain understanding of the impact that neighborhoods have on walking behaviors. Past studies have relied on small samples (Barnett et al., 2017; Towne et al., 2016), aggregate indicators from administrative sources (Towne et al., 2016), qualitative data (Mitra, Siva, & Kehler, 2015), or subjective reports of neighborhood characteristics (Van Cauwenberg et al., 2014). However, newer technologies allow for more fine-grain tracking of neighborhood characteristics; namely the use of Google Street View as a virtual proxy for in situ observations can facilitate systematic assessments of neighborhood characteristics in large samples across geographic distances (Clarke, Ailshire, Melendez, Bader, & Morenoff, 2010; Kelly, Wilson, Baker, Miller, & Schootman, 2013; Odgers, Caspi, Bates, Sampson, & Moffitt, 2012; Rundle, Bader, Richards, Neckerman, & Teitler, 2011). The analyses that follow advance the current science in this area by using a novel, objective method to assess neighborhoods and examine how neighborhood characteristics influence the amount of time older people spend walking for leisure and purpose. Theory A social-ecological model guides our research. The model posits that a complex interplay among characteristics of people and neighborhoods shapes health behaviors (Alfonzo, 2005; Sallis et al., 2006; Stokols, 1996). When older people experience decreased competence, their sensitivity to environmental barriers increases (Wahl & Lang, 2004). It is possible, for example, that diminished physical capacity makes older people more sensitive to the effects of physically challenging environments such as inclines, uneven surfaces, and the absence of walk-friendly infrastructure. Empirical studies find that characteristics of the environment have stronger effects on behavior when people suffer functional limitations then when people are not impaired (Forsyth, Oakes, Lee, & Schmitz, 2009; Rantakokko et al., 2010). A study of more than 700 people aged 66 and older found that older people living in more walkable neighborhoods had more transport activity and moderate-to-vigorous physical activity compared with people living in less walkable neighborhoods and that the most mobility-impaired people living in more walkable neighborhoods reported transport activity levels that were similar to less mobility-impaired people living in less walkable neighborhoods (King et al., 2011). Alternately, a supportive physical environment, such as one with high quality paths, may encourage older people to walk more (Sugiyama, Thompson, & Alves, 2008). Social-ecological models underscore the importance of specificity of environmental correlates as a function of outcome (Alfonzo, 2005; Sallis et al., 2006). People walk for different reasons. Some walk for leisure; others walk purposefully. Purposeful walking is typically defined as walking to reach a destination (Cerin, Nathan, Van Cauwenberg, Barnett, & Barnett, 2017; Frank, Kerr, Rosenberg, & King, 2010; Hirsch, Diez Roux, Moore, Evenson, & Rodriguez, 2014; Hirsch, Moore, Evenson, Rodriguez, & Diez Roux, 2013; King et al., 2011). Walking for leisure, on the other hand, does not have a specific targeted location (Hosler, Gallant, Riley-Jacome, & Rajulu, 2014; Sallis et al., 2016; Towne et al., 2016; Van Cauwenberg et al., 2018). A social-ecological model predicts that precursors of walking for purpose and walking for leisure should differ. In fact, empirical studies distinguishing walking for leisure and walking for purpose find that neighborhood characteristics have stronger associations with walking for purpose than walking for leisure (Saelens & Handy, 2008). Hirsch and colleagues (2013), for example, found that neighborhood WalkScore, an algorithm based on population density, road metrics, and access to pathways for walking to nearby amenities, was related to walking for purpose, but not walking for leisure. Moreover, Hirsch and colleagues (2014) found that when people moved to a neighborhood with a higher WalkScore, walking for transportation (purpose), but not walking for recreation (leisure), increased. Other studies find that neighborhood characteristics, including sidewalks, infrastructure, residential density, street connectivity, land-use mix, access to amenities, and retail floor area are positively associated with walking for purpose (Cerin, Nathan, Van Cauwenberg, Barnett, & Barnett, 2017; Frank, Kerr, Rosenberg, & King, 2010; King et al., 2011). Meanwhile, littering/vandalism/decay have negative associations with walking for purpose (Cerin et al., 2017). In regard to correlates of walking for leisure, Hosler and colleagues (2014) report significant associations with overall walkability of the community, presence of sidewalks, street amenities, and traffic safety. Others have found that predictors of leisure walking include living in an area perceived as having high neighborhood cohesion and safety, walkable versus car-dependent, and areas with low-moderate or high income (vs low) median household income (Towne et al., 2016). Other factors positively associated with walking for leisure include land-use mix and aesthetically pleasing scenery (Van Cauwenberg et al., 2018). Sallis and colleagues (2016) suggest that the vibrancy of highly walkable neighborhoods with access to a variety of destinations creates opportunities for social interaction and a sense of safety, which may in turn stimulate leisure walking among older people. A limitation of previous research is that studies rely on either self-report of neighborhood characteristics, information from administrative data sets, or in situ observations made by raters. While self-perceptions of neighborhoods can provide useful information, there is concern that self-reports of neighborhoods may not accurately represent the objective environment (Pruitt, Jeffe, Yan, & Schootman, 2012; Weden, Carpiano, & Robert, 2008). Moreover, there is evidence that the associations of physical activity with objective and perceived measures of the built environment differ from one another (Barnett et al., 2017; Kerr et al., 2013). Michael, Beard, Choi, Farquhar, and Carlson (2006), in a study contrasting perceptions of older adults and environmental audit data, found poor agreement between objective and perceived measures of trails, graffiti and vandalism, sidewalk existence, and sidewalk obstructions. In terms of administrative data, conclusions are limited by the crude nature of most indicators. For example, although WalkScore is a valid and objective measure (Duncan, Aldstadt, Whalen, Melly, & Gortmaker, 2011), it is based on the availability and conditions of a limited number of amenities. Other metrics collected by the US Census and other governmental agencies are limited because they rely on data aggregated across census tracts or blocks, in which features that affect walking may be unevenly distributed or the land area covered may not be used by a specific person. Finally, in situ assessments of neighborhoods are expensive and characterized by low retest reliability and significant variability among raters (Andresen et al., 2013). The analyses that follow use a novel method that relies on Google Street View to assess neighborhood characteristics and test the hypothesis that neighborhood characteristics including sidewalks, neighborhood land use (i.e., single-family detached homes, multifamily dwellings, commercial businesses, parking lots), and neighborhood aesthetics (i.e., gardens/flowers, garbage) are associated with walking. We assess whether neighborhood characteristics assessed in this manner can effectively differentiate neighborhood attributes associated with walking for purpose versus walking for leisure. Based on the literature, we expected that the associations between neighborhood characteristics and walking would be stronger for walking for purpose than walking for leisure. Methods Participants Between 2006 and 2008, we recruited and completed baseline (Wave 1) telephone interviews with 5,688 people who were part of the ORANJ BOWL (Ongoing Research on Aging in New Jersey: Bettering Opportunities for Wellness in Life) panel. We used cold calling and list-assisted random digit dialing (RDD) procedures. Eligible participants were between the ages of 50 and 74, living in New Jersey, and able to participate in a 1-hr, English-language telephone interview. Coverage loss due to cell phone-only households was small (Blumberg & Luke, 2007). ORANJ BOWL achieved a response rate of 58.73%, using standard American Association for Public Opinion Research calculations, and a Cooperation Rate of 72.88%, consistent with or better than average RDD response rates. Details about sample development are provided in Pruchno, Wilson-Genderson, and Cartwright (2010). Participants were representative of individuals aged 50–74 living in New Jersey in 2006, except for a slightly higher rate of women and individuals with more years of education. Four subsequent waves of data have been collected. This analysis uses Wave 5 data; demographic characteristics were collected at Wave 1. Wave 5 was completed approximately 9 years after Wave 1 (2015–2017). People completing Wave 5 (N = 3,076) had higher levels of education (F = 116.38, df = 3; N = 5,671) and income (F = 148.30, df = 3; N = 5,018) than those who had died, withdrawn, or not completed Wave 5. Completers were significantly older than noncompleters and younger than people who withdrew or died (F = 120.45, df = 3; 5,684). Completers were more likely to be female than those who died (χ2 = 15.06, df = 3, N = 5,688) and less likely to be African American than those who died, withdrew, or did not complete Wave 5 (χ2 = 146.15, df = 3, N = 5,688). All data collection procedures were approved by the Rowan University Institutional Review Board. Measures Data collected from ORANJ BOWL respondents Walking for leisure and purpose At Wave 5, walking for leisure was assessed with: “Over the past 30 days, did you spend at least 10 minutes walking for leisure? Include taking a walk for pleasure or taking a dog for a walk. Do not include brisk walking, jogging, or running.” People responding affirmatively were asked: “On average, how much time would you estimate that you spend walking for leisure each week?” Walking for purpose was measured with: “Over the past 30 days, did you spend at least 10 minutes intentionally walking to get somewhere? Do not include daily walking around, brisk walking, jogging, or running. Focus on purposeful walking.” People responding affirmatively were asked: “On average, how much time would you estimate that you spend intentionally walking to get somewhere each week?” Walking for leisure and purpose were treated as continuous variables (minutes). Initial examination of descriptive data confirmed that respondents were able to distinguish walking for leisure from walking for purpose, as the correlation between the two measures was .35. Individual characteristics At Wave 1, participants self-reported sex, age, education (9-point Likert scale; 1 = not high school graduate to 9 = doctoral degree), and income (6-point Likert scale; 1 = less than $15,000 to 6 = more than $150,000). At Wave 5, respondents reported the extent of difficulty they had completing four indicators of functional limitations [walking for ¼ of a mile (M = 4.4, SD = 1.1), walking up 10 steps (M = 4.6, SD = .93), standing for 2 hr (M = 3.95, SD = 1.3), stooping (M = 3.9, SD = 1.2)] using a 5-point Likert scale ranging from 1 (can’t do it at all) to 5 (not at all difficult), with higher scores indicating fewer limitations. The mean of these four items is used as a measure of lower body function (M = 4.13, SD = .97), the ability to walk. Only n = 130 participants reported the inability to walk ¼ of a mile. Virtual observations of neighborhood built environment Residential addresses of respondents at Wave 5 were geocoded using the Texas A&M University Geocoding Desktop Client (http://geoservices.tamu.edu). Only addresses resolved at the parcel or street segment level were included. Observations of the neighborhood surrounding participants’ homes were conducted virtually using the Google Maps orthographic imagery and ground-level 360-degree “Street View” panoramic image service (Anguelov et al., 2010). “Street View” imagery refers to the omnidirectional imagery service in Google Earth and Google Maps and reflects street side conditions that would be visible from an in situ pedestrian viewpoint. Observations were included for which the distance between the participant’s residence and the observed location did not exceed 304.8 m (1,000 feet), as calculated with the NEAR function using the recorded locations in ArcGIS 10.6. We limited observations to this distance under the rationale that any further distance could result in a substantially different context between the locations of the residence and the observation. Similar methods reported by Kepper and colleagues (2017) and Wilson and colleagues (2012) reveal high reliability and strong similarity between virtual street-level observations and in-person observations. Others have used this imagery service to assess aspects of the built environment that may affect health outcomes (Clarke et al., 2010; Kelly et al., 2013; Odgers et al., 2012; Rundle et al., 2011). Ben-Joseph, Lee, Cromley, Laden, and Troped (2013) report strong agreement of virtual (web-based) audits and on-site audits of the condition of the environment, including fine-grain features like sidewalks. Curtis and colleagues (2013) indicate the importance of noting the imagery collection date, which varies across space. Virtual visits were conducted by 10 trained research assistants for each available address. We examined the distance from each participant’s home to the location of the virtual audit. We determined that of the 3,045 ORANJ BOWL participants with Wave 5 data, 2,224 had virtual audit data evaluated within 1,000 feet of the residence of record. Data used for these analyses are limited to these 2,224 observations. The State of New Jersey is subdivided into 21 counties and 565 municipalities. ORANJ BOWL participants lived in all 21 counties and in 455 of the 565 municipalities, spanning the full spectrum of rural, suburban, and urban communities. Participants were well-distributed among those municipalities represented, with 329 municipalities home to five or fewer ORANJ BOWL participants; only 15 municipalities were home to more than 20 ORANJ BOWL participants. Of the 2,224 observations in this analysis, 528 observations (23.7%) were conducted in rural municipalities (with a population density of less than 1,000 persons/square mile based on 2010 US Census), 1,580 observations were conducted in urban municipalities (1,000–10,000 persons/square mile), and 116 observations (5.2%) were conducted in highly urbanized municipalities (>10,000 persons/square mile). Of the 110 municipalities not represented by this study, 24 had fewer than 1,000 persons/square mile and 4 had greater than 10,000 persons/square mile. To conduct the virtual observation, trained observers accessed the Street View interface at the nearest location to the geocoded residence address for which imagery was available. They recorded the geographic coordinates of the site (latitude/longitude, decimal degrees) and the date of the Street View imagery. Observers panned across the imagery available and along the street segment (block) adjacent to the residence and recorded observable characteristics of the environment using a web-based Qualtrics survey instrument. Characteristics rated by observers included: (1) sidewalks, (2) neighborhood land use, and (3) neighborhood aesthetics. Sidewalks (neither side, one side, both sides) were rated for the following: presence/absence of sidewalks, continuous/gaps, paved/not paved, smooth/broken, no obstructions/obstructions, and wide enough for two people/not wide enough for two people. If a sidewalk characteristic pertained to either one or both sides, it was classified as present. A sum of the six dichotomous sidewalk variables was created (range: 0–6). To assess neighborhood land-use, the presence or absence on the block of: single, detached homes; multifamily or apartment dwellings; commercial, business, or professional buildings; and parking lots was recorded. To assess neighborhood aesthetic characteristics the presence of gardens/flowers and garbage/litter were rated as present or absent. Additional characteristics related to safety (e.g., security features on buildings) and housing conditions (e.g., composition and maintenance) were collected but lacked sufficient variability to be included in the analyses. Inter-rater reliability was calculated based on 132 sites independently observed by at least 2 observers. Reliability exceeded 0.70 except for presence of gardens (.63). Agreement was defined as exact matches for yes/no questions and adjacent entries for ordered choice lists. Analysis Plan Descriptive statistics (means, standard deviations, and frequency distributions) were computed for all variables. A bivariate correlation between the walk for purpose and walk for leisure variables was computed (Pearson) as were correlations between the virtual neighborhood variables and the individual walk variables (Spearman). Given the novelty of this approach for understanding the impact of neighborhood built environment on walking, we used multilevel modeling and modeled each virtual neighborhood characteristic individually (Level 2) to understand the effect each had on walking for leisure and purpose. All models included two levels, with random effects for neighborhood estimated by multilevel linear regression using county as the cluster effect. The models included fixed but not random effects for individual (Level 1) characteristics. We controlled for individual characteristics typically associated with walking, including the ability to walk, gender, education, income, age, and race (Forjuoh et al., 2017; Mobily, 2014; Towne et al., 2016). We created a virtual observation data lag (Level 1) variable capturing the difference in time between when the virtual images were captured and stored by Google (largely between the months of July and October, during 2012–2017; most locations viewed in 2013) and when the virtual audit was conducted by our research team (between February and June, 2018). This variable accounts for the variability in the methodology used by Google over time and the fact that the neighborhoods were assessed over an extended period of time. Preliminary data analyses revealed that the distributions of many of the virtual variables were non-normal for African American participants (N = 296). Despite efforts to develop robust models that converged with all participants, we were unsuccessful. As such the findings reported below exclude data from African American participants. Results Descriptive Data Participants reported walking for leisure an average of 94.9 min a week (SD = 147.3; median = 50 min) and for purpose an average of 79.2 min (SD = 148.9; median = 30 min). Table 1 includes descriptive information about the virtual variables. The virtual audits detected an average of 3.67 (SD = 2.8) sidewalk characteristics (out of 6). Presence of single family detached homes and flowers/gardens were most prevalent, followed by parking lots and multifamily/apartment. Table 1. Descriptive Data: Virtual Variables Variable M (SD); median Sidewalk characteristics 3.67 (2.8); 5 N (%) Neighborhood land-use  Single family detached 1,732 (88.9%)  Multifamily/apartments 340 (17.5%)  Commercial, business, professional buildings 233 (12.0%)  Parking lots 342 (17.6%) Neighborhood aesthetics  Flowers/gardens 1,612 (82.7%)  Garbage 260 (13.3%) Variable M (SD); median Sidewalk characteristics 3.67 (2.8); 5 N (%) Neighborhood land-use  Single family detached 1,732 (88.9%)  Multifamily/apartments 340 (17.5%)  Commercial, business, professional buildings 233 (12.0%)  Parking lots 342 (17.6%) Neighborhood aesthetics  Flowers/gardens 1,612 (82.7%)  Garbage 260 (13.3%) Note. N = 2,224 observations. Open in new tab Table 1. Descriptive Data: Virtual Variables Variable M (SD); median Sidewalk characteristics 3.67 (2.8); 5 N (%) Neighborhood land-use  Single family detached 1,732 (88.9%)  Multifamily/apartments 340 (17.5%)  Commercial, business, professional buildings 233 (12.0%)  Parking lots 342 (17.6%) Neighborhood aesthetics  Flowers/gardens 1,612 (82.7%)  Garbage 260 (13.3%) Variable M (SD); median Sidewalk characteristics 3.67 (2.8); 5 N (%) Neighborhood land-use  Single family detached 1,732 (88.9%)  Multifamily/apartments 340 (17.5%)  Commercial, business, professional buildings 233 (12.0%)  Parking lots 342 (17.6%) Neighborhood aesthetics  Flowers/gardens 1,612 (82.7%)  Garbage 260 (13.3%) Note. N = 2,224 observations. Open in new tab Table 2 reveals small but significant positive correlations between walking for leisure and sidewalk characteristics and gardens/flowers a significant negative correlation with presence of commercial businesses. There were small but significant positive correlations between walking for purpose and sidewalk characteristics, presence of multifamily/apartment dwellings, commercial businesses, and parking lots; the significant correlation with single family detached homes was negative. Table 2. Correlations of Virtual and Walk Variables Walking behavior Sidewalk characteristics Neighborhood land-use Neighborhood aesthetics Single family detached Multifamily/ apartments Commercial businesses Parking lots Gardens/ flowers Garbage Walk for leisure 0.05* −0.02 −0.02 −0.05* −0.04 0.04* −0.003 Walk for purpose 0.04* −0.04* 0.04* 0.05* 0.08* 0.02 0.02 Walking behavior Sidewalk characteristics Neighborhood land-use Neighborhood aesthetics Single family detached Multifamily/ apartments Commercial businesses Parking lots Gardens/ flowers Garbage Walk for leisure 0.05* −0.02 −0.02 −0.05* −0.04 0.04* −0.003 Walk for purpose 0.04* −0.04* 0.04* 0.05* 0.08* 0.02 0.02 *p < .05. Open in new tab Table 2. Correlations of Virtual and Walk Variables Walking behavior Sidewalk characteristics Neighborhood land-use Neighborhood aesthetics Single family detached Multifamily/ apartments Commercial businesses Parking lots Gardens/ flowers Garbage Walk for leisure 0.05* −0.02 −0.02 −0.05* −0.04 0.04* −0.003 Walk for purpose 0.04* −0.04* 0.04* 0.05* 0.08* 0.02 0.02 Walking behavior Sidewalk characteristics Neighborhood land-use Neighborhood aesthetics Single family detached Multifamily/ apartments Commercial businesses Parking lots Gardens/ flowers Garbage Walk for leisure 0.05* −0.02 −0.02 −0.05* −0.04 0.04* −0.003 Walk for purpose 0.04* −0.04* 0.04* 0.05* 0.08* 0.02 0.02 *p < .05. Open in new tab Multilevel Models Multilevel model results are reported in Table 3. Controlling for individual characteristics and the lag variable, sidewalk characteristics had significant positive associations with both walking for purpose and leisure. Presence of single-family detached homes was negatively associated with walking for purpose and not significantly associated with walking for leisure. Presence of multifamily dwellings, commercial business and parking lots were each positively associated with walking for purpose but not associated with walking for leisure. Presence of gardens/flowers was associated with walking for leisure but not walking for purpose. Presence of garbage was not associated with either type of walking behavior. Table 3. Multilevel Model Results: Walking for Leisure and Walking for Purpose Walking for leisure Walking for purpose Variable β (SE) β (SE) Sidewalk characteristics  Intercept 70.40 (39.5) 23.84 (40.5)  Age 0.65 (0.48) −0.25 (0.52)  Education −0.87 (1.6) −3.05 (1.8)  Sex −8.93 (6.7) 11.12 (7.2)  Income −2.08 (0.47) 1.44 (3.1)  Lower body function 8.16 (0.89)**** 3.98 (0.96)****  Lag −0.01 (0.11) 0.13 (0.12)  Sidewalk Characteristics 1.81 (0.60)* 1.47 (0.64)* Neighborhood land-use  Single family detached   Intercept 54.18 (38.6) 49.97 (41.4)   Age 0.62 (0.48) −0.24 (0.52)   Education −0.94 (1.6) −3.04 (1.8)   Sex −8.71 (6.7) 10.82 (7.2)   Income −1.82 (2.9) 1.30 (3.1)   Lower body function 8.14 (0.89)**** 3.90 (0.96)   Lag 0.01 (0.11) 0.13 (0.12)   Single family detached −10.52 (5.9) −17.51 (7.4)*  Multifamily   Intercept 63.32 (37.6) 30.42 (40.4)   Age 0.64 (0.48) −0.22 (0.52)   Education −0.82 (1.6) −2.97 (1.8)   Sex −8.85 (6.7) 10.63 (7.2)   Income −2.16 (2.9) 1.35 (3.1)   Lower body function 8.13 (0.89) 3.91 (0.96)***   Lag 0.01 (0.11) 0.13 (0.12)   Multifamily −0.69 (1.9) 11.0 (4.6)*  Commercial businesses   Intercept 59.98 (37.9) 32.69 (40.7)   Age 0.62 (0.48) −0.27 (0.52)   Education −0.89 (1.6) −2.93 (1.8)   Sex −8.86 (6.7) 11.33 (7.2)   Income −2.35 (2.9) 1.59 (3.1)   Lower body function 8.11 (0.90) 3.96 (0.96)****   Lag 0.03 (0.12) 0.14 (0.12)   Commercial businesses −6.31 (6.6) 6.40 (2.9)*  Parking lots   Intercept 64.32 (37.8) 28.81 (40.5)   Age 0.64 (0.48) −0.26 (0.52)   Education −0.83 (1.6) −3.04 (1.8)   Sex −8.87 (6.7) 11.12 (7.2)   Income −2.1 (2.9) 1.90 (3.1)   Lower body function 8.14 (0.90)**** 4.02 (0.96)   Lag 0.001 (0.11) 0.12 (0.12)   Parking lots 1.24 (4.0) 12.6 (5.8)* Neighborhood aesthetics  Gardens/flowers   Intercept 70.25 (37.92) 37.27 (40.7)   Age 0.64 (0.48) −0.28 (0.52)   Education −0.90 (1.65) −2.99 (1.7)*   Sex −9.10 (6.74) 11.35 (7.23)   Income −2.44 (2.88) 1.41 (3.1)   Lower body function 8.16 (0.89)*** 3.93 (0.96)***   Lag 0.01 (0.11) 0.15 (0.12)   Gardens/flowers 9.47 (4.4)* −1.36 (5.0)  Garbage   Intercept −1.36 (0.48) −1.46 (0.50)   Age −0.01 (0.01) 0.001 (0.01)   Education 0.03 (0.02) 0.05 (0.02)   Sex 0.06 (0.09) 0.25 (0.09)   Income 0.02 (0.04) 0.06 (0.04)   Lower body function 0.16 (0.01)*** 0.07 (0.01)***   Lag 0.001 (0.0001) 0.001 (0.001)   Garbage −0.02 (0.12) 0.05 (0.12) Walking for leisure Walking for purpose Variable β (SE) β (SE) Sidewalk characteristics  Intercept 70.40 (39.5) 23.84 (40.5)  Age 0.65 (0.48) −0.25 (0.52)  Education −0.87 (1.6) −3.05 (1.8)  Sex −8.93 (6.7) 11.12 (7.2)  Income −2.08 (0.47) 1.44 (3.1)  Lower body function 8.16 (0.89)**** 3.98 (0.96)****  Lag −0.01 (0.11) 0.13 (0.12)  Sidewalk Characteristics 1.81 (0.60)* 1.47 (0.64)* Neighborhood land-use  Single family detached   Intercept 54.18 (38.6) 49.97 (41.4)   Age 0.62 (0.48) −0.24 (0.52)   Education −0.94 (1.6) −3.04 (1.8)   Sex −8.71 (6.7) 10.82 (7.2)   Income −1.82 (2.9) 1.30 (3.1)   Lower body function 8.14 (0.89)**** 3.90 (0.96)   Lag 0.01 (0.11) 0.13 (0.12)   Single family detached −10.52 (5.9) −17.51 (7.4)*  Multifamily   Intercept 63.32 (37.6) 30.42 (40.4)   Age 0.64 (0.48) −0.22 (0.52)   Education −0.82 (1.6) −2.97 (1.8)   Sex −8.85 (6.7) 10.63 (7.2)   Income −2.16 (2.9) 1.35 (3.1)   Lower body function 8.13 (0.89) 3.91 (0.96)***   Lag 0.01 (0.11) 0.13 (0.12)   Multifamily −0.69 (1.9) 11.0 (4.6)*  Commercial businesses   Intercept 59.98 (37.9) 32.69 (40.7)   Age 0.62 (0.48) −0.27 (0.52)   Education −0.89 (1.6) −2.93 (1.8)   Sex −8.86 (6.7) 11.33 (7.2)   Income −2.35 (2.9) 1.59 (3.1)   Lower body function 8.11 (0.90) 3.96 (0.96)****   Lag 0.03 (0.12) 0.14 (0.12)   Commercial businesses −6.31 (6.6) 6.40 (2.9)*  Parking lots   Intercept 64.32 (37.8) 28.81 (40.5)   Age 0.64 (0.48) −0.26 (0.52)   Education −0.83 (1.6) −3.04 (1.8)   Sex −8.87 (6.7) 11.12 (7.2)   Income −2.1 (2.9) 1.90 (3.1)   Lower body function 8.14 (0.90)**** 4.02 (0.96)   Lag 0.001 (0.11) 0.12 (0.12)   Parking lots 1.24 (4.0) 12.6 (5.8)* Neighborhood aesthetics  Gardens/flowers   Intercept 70.25 (37.92) 37.27 (40.7)   Age 0.64 (0.48) −0.28 (0.52)   Education −0.90 (1.65) −2.99 (1.7)*   Sex −9.10 (6.74) 11.35 (7.23)   Income −2.44 (2.88) 1.41 (3.1)   Lower body function 8.16 (0.89)*** 3.93 (0.96)***   Lag 0.01 (0.11) 0.15 (0.12)   Gardens/flowers 9.47 (4.4)* −1.36 (5.0)  Garbage   Intercept −1.36 (0.48) −1.46 (0.50)   Age −0.01 (0.01) 0.001 (0.01)   Education 0.03 (0.02) 0.05 (0.02)   Sex 0.06 (0.09) 0.25 (0.09)   Income 0.02 (0.04) 0.06 (0.04)   Lower body function 0.16 (0.01)*** 0.07 (0.01)***   Lag 0.001 (0.0001) 0.001 (0.001)   Garbage −0.02 (0.12) 0.05 (0.12) *p < .05. **p < .01. ***p < .001. ****p < .0001. Open in new tab Table 3. Multilevel Model Results: Walking for Leisure and Walking for Purpose Walking for leisure Walking for purpose Variable β (SE) β (SE) Sidewalk characteristics  Intercept 70.40 (39.5) 23.84 (40.5)  Age 0.65 (0.48) −0.25 (0.52)  Education −0.87 (1.6) −3.05 (1.8)  Sex −8.93 (6.7) 11.12 (7.2)  Income −2.08 (0.47) 1.44 (3.1)  Lower body function 8.16 (0.89)**** 3.98 (0.96)****  Lag −0.01 (0.11) 0.13 (0.12)  Sidewalk Characteristics 1.81 (0.60)* 1.47 (0.64)* Neighborhood land-use  Single family detached   Intercept 54.18 (38.6) 49.97 (41.4)   Age 0.62 (0.48) −0.24 (0.52)   Education −0.94 (1.6) −3.04 (1.8)   Sex −8.71 (6.7) 10.82 (7.2)   Income −1.82 (2.9) 1.30 (3.1)   Lower body function 8.14 (0.89)**** 3.90 (0.96)   Lag 0.01 (0.11) 0.13 (0.12)   Single family detached −10.52 (5.9) −17.51 (7.4)*  Multifamily   Intercept 63.32 (37.6) 30.42 (40.4)   Age 0.64 (0.48) −0.22 (0.52)   Education −0.82 (1.6) −2.97 (1.8)   Sex −8.85 (6.7) 10.63 (7.2)   Income −2.16 (2.9) 1.35 (3.1)   Lower body function 8.13 (0.89) 3.91 (0.96)***   Lag 0.01 (0.11) 0.13 (0.12)   Multifamily −0.69 (1.9) 11.0 (4.6)*  Commercial businesses   Intercept 59.98 (37.9) 32.69 (40.7)   Age 0.62 (0.48) −0.27 (0.52)   Education −0.89 (1.6) −2.93 (1.8)   Sex −8.86 (6.7) 11.33 (7.2)   Income −2.35 (2.9) 1.59 (3.1)   Lower body function 8.11 (0.90) 3.96 (0.96)****   Lag 0.03 (0.12) 0.14 (0.12)   Commercial businesses −6.31 (6.6) 6.40 (2.9)*  Parking lots   Intercept 64.32 (37.8) 28.81 (40.5)   Age 0.64 (0.48) −0.26 (0.52)   Education −0.83 (1.6) −3.04 (1.8)   Sex −8.87 (6.7) 11.12 (7.2)   Income −2.1 (2.9) 1.90 (3.1)   Lower body function 8.14 (0.90)**** 4.02 (0.96)   Lag 0.001 (0.11) 0.12 (0.12)   Parking lots 1.24 (4.0) 12.6 (5.8)* Neighborhood aesthetics  Gardens/flowers   Intercept 70.25 (37.92) 37.27 (40.7)   Age 0.64 (0.48) −0.28 (0.52)   Education −0.90 (1.65) −2.99 (1.7)*   Sex −9.10 (6.74) 11.35 (7.23)   Income −2.44 (2.88) 1.41 (3.1)   Lower body function 8.16 (0.89)*** 3.93 (0.96)***   Lag 0.01 (0.11) 0.15 (0.12)   Gardens/flowers 9.47 (4.4)* −1.36 (5.0)  Garbage   Intercept −1.36 (0.48) −1.46 (0.50)   Age −0.01 (0.01) 0.001 (0.01)   Education 0.03 (0.02) 0.05 (0.02)   Sex 0.06 (0.09) 0.25 (0.09)   Income 0.02 (0.04) 0.06 (0.04)   Lower body function 0.16 (0.01)*** 0.07 (0.01)***   Lag 0.001 (0.0001) 0.001 (0.001)   Garbage −0.02 (0.12) 0.05 (0.12) Walking for leisure Walking for purpose Variable β (SE) β (SE) Sidewalk characteristics  Intercept 70.40 (39.5) 23.84 (40.5)  Age 0.65 (0.48) −0.25 (0.52)  Education −0.87 (1.6) −3.05 (1.8)  Sex −8.93 (6.7) 11.12 (7.2)  Income −2.08 (0.47) 1.44 (3.1)  Lower body function 8.16 (0.89)**** 3.98 (0.96)****  Lag −0.01 (0.11) 0.13 (0.12)  Sidewalk Characteristics 1.81 (0.60)* 1.47 (0.64)* Neighborhood land-use  Single family detached   Intercept 54.18 (38.6) 49.97 (41.4)   Age 0.62 (0.48) −0.24 (0.52)   Education −0.94 (1.6) −3.04 (1.8)   Sex −8.71 (6.7) 10.82 (7.2)   Income −1.82 (2.9) 1.30 (3.1)   Lower body function 8.14 (0.89)**** 3.90 (0.96)   Lag 0.01 (0.11) 0.13 (0.12)   Single family detached −10.52 (5.9) −17.51 (7.4)*  Multifamily   Intercept 63.32 (37.6) 30.42 (40.4)   Age 0.64 (0.48) −0.22 (0.52)   Education −0.82 (1.6) −2.97 (1.8)   Sex −8.85 (6.7) 10.63 (7.2)   Income −2.16 (2.9) 1.35 (3.1)   Lower body function 8.13 (0.89) 3.91 (0.96)***   Lag 0.01 (0.11) 0.13 (0.12)   Multifamily −0.69 (1.9) 11.0 (4.6)*  Commercial businesses   Intercept 59.98 (37.9) 32.69 (40.7)   Age 0.62 (0.48) −0.27 (0.52)   Education −0.89 (1.6) −2.93 (1.8)   Sex −8.86 (6.7) 11.33 (7.2)   Income −2.35 (2.9) 1.59 (3.1)   Lower body function 8.11 (0.90) 3.96 (0.96)****   Lag 0.03 (0.12) 0.14 (0.12)   Commercial businesses −6.31 (6.6) 6.40 (2.9)*  Parking lots   Intercept 64.32 (37.8) 28.81 (40.5)   Age 0.64 (0.48) −0.26 (0.52)   Education −0.83 (1.6) −3.04 (1.8)   Sex −8.87 (6.7) 11.12 (7.2)   Income −2.1 (2.9) 1.90 (3.1)   Lower body function 8.14 (0.90)**** 4.02 (0.96)   Lag 0.001 (0.11) 0.12 (0.12)   Parking lots 1.24 (4.0) 12.6 (5.8)* Neighborhood aesthetics  Gardens/flowers   Intercept 70.25 (37.92) 37.27 (40.7)   Age 0.64 (0.48) −0.28 (0.52)   Education −0.90 (1.65) −2.99 (1.7)*   Sex −9.10 (6.74) 11.35 (7.23)   Income −2.44 (2.88) 1.41 (3.1)   Lower body function 8.16 (0.89)*** 3.93 (0.96)***   Lag 0.01 (0.11) 0.15 (0.12)   Gardens/flowers 9.47 (4.4)* −1.36 (5.0)  Garbage   Intercept −1.36 (0.48) −1.46 (0.50)   Age −0.01 (0.01) 0.001 (0.01)   Education 0.03 (0.02) 0.05 (0.02)   Sex 0.06 (0.09) 0.25 (0.09)   Income 0.02 (0.04) 0.06 (0.04)   Lower body function 0.16 (0.01)*** 0.07 (0.01)***   Lag 0.001 (0.0001) 0.001 (0.001)   Garbage −0.02 (0.12) 0.05 (0.12) *p < .05. **p < .01. ***p < .001. ****p < .0001. Open in new tab Discussion Understanding the impact of the built environment on walking behavior of older people is important because diminishing physical capacity associated with the aging process can make older people more vulnerable to the effects of physically challenging environments. Informed by a social-ecological model and using a novel virtual method to objectively assess neighborhoods, we found that while sidewalk characteristics and presence of single-family detached homes, multifamily homes, commercial businesses, and parking lots were associated with walking for purpose, only sidewalk characteristics and presence of gardens/flowers were associated with walking for leisure. The most important contribution of these analyses is the methodological approach. The virtual observations used here add richness and complexity to our understanding of the ways in which neighborhood characteristics influence behavior and offer researchers a sophisticated tool that can be reliably and efficiently used to assess the effects of the environment. Virtual observations provide objective ratings of the neighborhood that avoid common pit falls of subjective rating bias (i.e., same reporter for neighborhood characteristics and outcomes). The data are also more nuanced than commonly used objective indicators, including urban planning databases (Brownson, Hoehner, Day, Forsyth, & Sallis, 2009). Additionally, the virtual observations are much less resource-intensive than traditional data audits, as they do not require observers to travel to neighborhoods (Rzotkiewicz, Pearson, Dougherty, Shortridge, & Wilson, 2018; Weiss, Maantay, & Fahs, 2010). However, virtual observations certainly have their own limitations (Rzotkiewicz et al., 2018). They depend on the timing and frequency of Google assessments, yielding a particular daytime momentary snapshot, usually in summer or early fall, which may affect what can and cannot be seen virtually and how it may be perceived by residents and pedestrians. Although Google’s Street View service which began in 2007 has expanded across the planet (Google Maps Street View 2018), with many sites revisited over time (https://googleblog.blogspot.com/2014/04/go-back-in-time-with-street-view.html), Google does not release information about restrictions on the conditions or hour of imagery collection (Rundle et al., 2011). Furthermore, many characteristics with the potential to affect walking behavior, including benches, traffic, bicycling lanes, safety, sidewalk inclines, and uneven surfaces are not consistently visible with Street View, which is generally restricted to roadside views from which the interior of parcels and park may not be visible. Over time, however, it is likely that Street View will increase in sophistication, enabling more comprehensive virtual observations. Conceptually our findings regarding the greater influence that neighborhood characteristics have on walking for purpose than leisure are consistent with those reported by others (Cerin et al., 2017; Frank et al., 2010; Saelens & Handy, 2008). These associations may be explained by the greater likelihood of walking for purpose to begin at a person’s doorstep. Walking for leisure, on the other hand, may be done in a shopping mall, recreational facility, or park outside of the immediate neighborhood. Ou and colleagues (2016) found that older people are more likely to report being active indoors than outdoors. However, our interview did not ask people where they walked and therefore could not account for this variation. We also did not ask participants to classify their walking behaviors as walking for leisure or for purpose. Future studies should ask about where people walk for leisure, as well as for purpose and whether they would consider the walking they are doing as being for leisure or for purpose. Such additional work would expand our understanding of factors associated with distinct types of walking. The finding regarding the association of walking for purpose with sidewalk characteristics and neighborhood land-use (i.e., single-family detached homes, multifamily homes, commercial businesses, and parking lots) is also consistent with prior research (Cerin et al., 2017; Frank et al., 2010; King et al., 2011). However, our work advances the science in this area by demonstrating that virtual measures are reliable and valid indicators of the neighborhood built environment. As environmental planning groups seek to establish older adult specific communities, these relationships should be considered and environments built to encourage walking for purpose. In addition, the finding that leisure walking was associated with sidewalk characteristics and neighborhood aesthetics provide further insight into neighborhood features that may encourage older adults to be more mobile. Overall, more complete understanding of how neighborhoods influence health behavior requires multifaceted assessment strategies. Social-ecological models conceptualize the environment as multidimensional, including physical and social aspects (Stokols, 1996). Future studies should use a combination of objective and subjective measures to examine the effects of aspects of the physical neighborhood (e.g., traffic density, distance to places, presence of parks, crime) and the social environment (e.g., social cohesion, social capital, and interpersonal relationships; Forrest & Kearns, 2001; McNeill, Kreuter, & Subramanian, 2006) on health behaviors, as well as comparing the observed characteristics and those perceived by the residents. In addition, examining variables concurrently to see which constructs have a greater impact on outcomes is needed, as that was beyond the scope of these analyses. Furthermore, consensus on which features are best included in neighborhood audits is also needed; Brownson and colleagues (2009) reviewed 20 objective audit tools and found that they varied in content, detail, and how features were characterized. Several environmental audit tools have been developed specifically for older adults, including the Senior Walking Environmental Audit Tool (Cunningham, Michael, Farquhar, & Lapidus, 2005) and the Healthy Aging Research Network Environmental Audit Tool (https://www.aarp.org/content/dam/aarp/livable-communities/old-plan/assessments/healthy-aging-research-network-environmental-audit-tool.pdf). To advance science and make neighborhoods more conducive for older people to walk, it is important to examine the synergy between objective and subjective neighborhood characteristics. Finally, our findings regarding the non-normality of the virtual variables for the African American participants and the failure of our models to converge when this population is included limit generalizability of our findings to minority populations and demand greater attention from future studies, especially with respect to the inequities present for walking-related amenities and neighborhood context and condition. Despite limitations, virtual observations hold the potential to yield objective data about neighborhoods using minimal resources. As immersive visualization tools become more sophisticated, they will be invaluable for researchers seeking to understand how neighborhood characteristics influence health behaviors. Here, we find evidence of this specific potential, finding associations of sidewalks, neighborhood land-use, and neighborhood aesthetics with older adult walking behaviors. Such empirical findings can further inform neighborhood development work, particularly the building of age-restricted communities seeking to support healthy lifestyles in older adulthood. Funding This work was supported by the National Institutes of Health (R01 AG046463) and the University of Medicine and Dentistry of New Jersey. Acknowledgments The following people assisted our work by collecting the virtual observation data: Diane Barsuglia, Alexander Buontempo, Christopher Campbell, Hunter Davis, Samira Davis, Justin Fraser, Gabrielle Howell, Jillian Mazurek, Christina Parrilla, and Alyssa van Doorn. Conflicts of Interest None reported. References Alfonzo , M . ( 2005 ). To walk or not to walk? The hierarchy of walking needs . Environment and Behavior , 37 , 808 – 838 . doi: https://doi.org/10.1177/0013916504274016 Google Scholar Crossref Search ADS WorldCat Andresen , E. M. , Malmstrom , T. K. , Schootman , M. , Wolinsky , F. D. , Miller , J. P. , & Miller , D. K . ( 2013 ). 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Journal

The GerontologistOxford University Press

Published: Mar 12, 18

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