Perceptions of the Neighborhood Built Environment for Walking Behavior in Older Adults Living in Close Proximity

Abstract

Past research documents a discordance between perceived and objectively assessed neighborhood environmental features on walking behavior. Therefore, we examined differences in the perception of the same neighborhood built environment. Participants were grouped if they lived 400 m or closer to each other. The perception of the pedestrian infrastructure, neighborhood aesthetics, safety from crime, and safety from traffic was derived from a telephone survey from two North American metropolitan areas; 173 individuals were clustered into 42 groups. Older adults who walked for transport in their neighborhood experienced the same neighborhood as more walkable (β = .19; p = .011) with better pedestrian infrastructure (β = .16; p = .037). Older adults with physical limitations experienced the same neighborhood as less safe from crime (β = −.17; p = .030) and traffic (β = −.20; p = .009). The study supports the notion that individual behavior and physical restrictions alter the environment’s perception and explains part of the discordance between objective and subjective assessment of the neighborhood environment.

Keywords

GIS built environment perception physical activity walking

Introduction

Research on neighborhood built environment supports that environmental characteristics have far-reaching consequences on daily physical activity patterns and health (Borst et al., 2008; Duncan et al., 2005). The residential neighborhood becomes even more relevant to older adults’ physical activity and well-being because mobility limitations rise with advancing age. Therefore, it is crucial to understand how perceptions and objective measures of the neighborhood built environment are associated with physical activity. Both measures correspond to a low to moderate level. It is essential to identify factors that impact the perception of the built environment (Baldock et al., 2019; Gebel et al., 2009; Hajna et al., 2013; McCormack et al., 2008; Roda et al., 2016). Therefore, this study aims to provide insights into the mechanism underlying the perception of older adults’ built environment by comparing individuals living in the same neighborhood.

Research has attempted to understand the relevance of the residential neighborhood for being physically active by using subjective perceptions and objective measures of the environment to identify the most relevant features supporting physical activity (e.g., Bailey et al., 2014; Chan et al., 2019; Gebel et al., 2009; Portegijs et al.,2017, 2020) However, the conclusions of various studies on the built environment and physical activity depended on the method of assessment leading to mixed findings. A recent review revealed that the relationship between the built environment and physical activity varied depending on the usage of the objective or perceptional environmental measures (Orstad et al., 2017). Perceived supportive environmental features (e.g., safety, public transport, parks/green space, availability of physical activity facilities) appeared to be associated with physical activity at higher rates than objectively retrieved characteristics using the Geographic Information System (20.1% vs. 13.7%; Orstad et al., 2017; Yi et al., 2016). Outdoor recreational physical activity was stronger correlated with the perceived built environment than with objective measures of the environment, which means that both measurements may assess the same environmental characteristics as distinct constructs.

One explanation for the low concordance between objective and subjective measures of the built environment is that physical activity patterns might not be exclusively a result of the built environment. The actual use of the built environment (e.g., physical activity in the neighborhood) might alter the built environment’s perception as it relates to physical activity (Wallmann-Sperlich et al., 2014). Perceptions of the environment depend on an individual’s interaction with the environment, involving awareness of the surroundings (Sherrington, 1947). Perceptions of the environment can be best understood as an ongoing evaluative, interactive process that is social, cognitive, and/or affective (Bandura, 1978). From an environmental gerontology theory perspective, this has been addressed most recently in concepts dealing with Bandura’s idea of “agency” (Bandura, 2006). Agency is defined as “[. . .] the process of becoming a change agent in one’s own life by means of intentional and proactive behaviors imposed on the physical-social environment” (Chaudhury & Oswald, 2019, p. 4). However, there is little knowledge of which factors primarily alter the environment’s perception and whether they explain the discordance between subjective and objective measures. These factors might be physical characteristics of the surroundings, but also personal factors such as sex, values, attachment, past behavior, experiences, and cognitive representations of the environment (Gifford, 2007; Sallis et al., 2006).

Whether an older adult perceives the neighborhood environment as unsupportive for being physically active may hinge on how the person can move freely in the neighborhood. Places that are frequently used might be evaluated more positively, or features of the built environment might stay hidden for physically inactive persons. Overall, people tend to develop more favorable environmental features evaluations if they are more familiar with them. Repeated exposure increases the familiarity—a phenomenon known as the mere exposure effect (Zajonc, 1968). Physically active individuals might be more familiar with their built environment and report lower discordance between objective and subjective measures (Adams et al., 2009; Gebel et al., 2009; Kirtland et al., 2003), which indicates that activity patterns might enhance familiarity with environmental features and increase the awareness of physical activity-friendly environmental features.

Discrepancies between perceived and objective built environment characteristics have been associated with health outcomes such as changes in body weight (Gebel et al., 2011) and physical limitations (Portegijs et al., 2017). Thereby, the associations between perceived environmental facilitators and self-reported moderate physical activity were especially strong in participants with lower extremity physical limitations. Physical limitations might alter the built environment’s perception because no association was found for the objectively assessed walkability index. As known in the Ecological Theory of Aging, persons with lower competence (i.e., more physical limitations) face a higher level of the environmental press, potentially causing mal-adaptation and negative affect (Lawton & Nahemow, 1973). The neighborhood built environment might be experienced as less walkable, and the traffic on the street as more threatening for those older persons with lower competence. On the other hand, older adults with fewer physical limitations may perceive the same built environment as walkable and the traffic as safe.

Therefore, people living in the same neighborhood, belonging to the same socio-demographic group, having similar physical, functional, and health characteristics, might form varying perceptions of the same built environment (Ewing & Handy, 2009). Following these reflections, a few articles have conceptualized perceptions as a mediator linking the objective environment and physical activity (walking and cycling) (Ewing & Handy, 2009; Ma & Cao, 2019), assuming that people might form different mental representations, emotional place attachment or familiarity of the same neighborhood and consequently behave differently. Thereby, the duration of residence has been associated with environmental cognitions and familiarity with the neighborhood (Ball et al., 2008). These studies provide a useful starting point for understanding the bidirectional relationship between the perceived built environment and older adults’ physical activity.

However, the existing literature on this particular topic is mostly cross-sectional without theory-driven assumptions, making the direction of the effects arbitrary. Most studies are cross-sectional, indicating a statistical association between environmental factors and physical activity, which cannot be interpreted as a causal relationship (Bauman et al., 2012). Still, most models only provide a one-directional view of how the environment is associated with physical activity by investigating environmental correlates (Duncan et al., 2005; Sallis et al., 2009; Van Holle et al., 2012). Thereby, most studies made the underlying assumption that the neighborhood features’ perception is independent of the participants’ physical activity levels and health status.

Based on the literature, we hypothesize that (a) the perception of the neighborhood built environment is more positively evaluated the more often it was used and (b) older adults with physical limitations will have a more negative representation of the neighborhood built environment because it causes increased levels of difficulty to use the close neighborhood.

Methods

Data collection took place in four neighborhoods in Metro Vancouver, Canada, and four neighborhoods in Metro Portland, United States. Census tract data were used to select eight neighborhoods based on neighborhood density and average neighborhood income to ensure variation in the physical environment features, which are essential for physical activity (Chaudhury et al., 2011). A detailed description of the larger three-phase and mixed-methods research project is published elsewhere (Chaudhury et al., 2016). Briefly, the larger project aimed to examine the physical neighborhood environment, older adults’ perceptions of the built environment, and physical activity levels. The following analyses utilized a cross-sectional telephone survey conducted with a random sample of older adults from the eight neighborhoods. The local ethics committee approved the study (Number 38156). Verbal consent was obtained at the start of the telephone interview and was documented on a web-based survey platform.

Study Population

In all, 434 older adults completed the telephone survey. Eligibility criteria for participants were (a) being 60 years of age and older at the time of the study, (b) living in one of the selected neighborhoods, and (c) being able to understand English. A total of 317 participants were included in the following analyses if they reported their full postal address (N_Canada = 101; N_USA = 118). Canadian participants who reported only their postal codes (N = 98) were also included because of small-sized postal code areas in Vancouver’s metropolitan region. In a second step, we clustered participants into groups based on their geospatial distances between their homes using the Geographic Information System. We applied a geospatial linear measure between the home addresses setting up the criteria of a minimum group size of three participants living closer to each other than 400 m. A 400 m (a quarter-mile) radial buffer around an individual’s home is a standard distance to define the neighborhood in older adults (Gong et al., 2014). Canadian participants who only reported postal codes were placed in the center of the postal code area.

We were able to identify a total of 173 individuals distributed in 42 groups, with an average of 4.1 persons (SD = 1.6) per group and a mean distance between clustered participants of 197.8 m (SD = 114.6). These older adults resided across eight neighborhoods: Mount Tabor, OR (n = 14 participants clustered into n = 4 groups), Clackamas, OR (n = 11; n_groups = 3), Lake Oswego, OR (n = 28; n_groups = 7), Milwaukie, OR (n = 3; n_groups = 1), Vancouver, BC (n = 33; n_groups = 8), Burnaby, BC (n = 37; n_groups = 7), South Surrey, BC (n = 33; n_groups = 8), and Maple Ridge, BC (n = 14; n_groups = 4).

Measures

Survey measures included perceptions of neighborhood environment, walking patterns in the neighborhood, demographic factors, and health status.

Neighborhood perception scales

The perception of the physical environment served as the primary outcome measure. The neighborhood perception was derived from five sub-scales of the widely used Neighborhood Environment Walkability Scale (NEWS; Fisher et al., 2004; Sallis et al., 1997). Additional items were added to the five perception scales, informed by an earlier qualitative study phase. Participants were asked on a four-point Likert-type-scale if they strongly agree (3), somewhat agree (2), somewhat disagree (1), or strongly disagree (0). Average scores were calculated, ranging from 0 to 3, with higher scores indicating better and more favorable perceptions of the built environment. The perception scales were set to missing if more than half of the items were missing. Two cases were set to missing on the infrastructure for walking scale. As a result, (a) walkability was assessed with five items (Cronbach’s α = 0.72), (b) the infrastructure for walking with seven (α = 0.72), (c) neighborhood aesthetics with four (α = 0.67), (d) neighborhood safety from crime with four (α = 0.86), and (e) safety from traffic with four questions (α = 0.65). The wording of the single items and the factor loading is reported in Supplement 1. The internal validity of the sub-scales was good, except for the aesthetic and the safety from traffic subscales.

Walking

To assess different kinds of physical activities, the study asked, “Which physical activities have you participated in during the last 4 (4) weeks (please indicate all that apply)?”. Participants answered a list of checkboxes asking for physical activities: sports, walking for transport, walking for recreation, gardening, and housework in the previous 4 weeks. In the following analyses, walking for transport and walking for recreation in the past 4 weeks was operationalized as dichotomous (yes/no) outcomes.

Physical limitations

Two physical limitations items asked about crucial limitations in their daily life “Does your health now limit you in . . .” The questions were adapted from the Short-Form-Health Survey (SF 12) (Ware et al., 1996), namely: (a) moderate activities, such as moving a table, pushing a vacuum, bowling, playing golf, and dancing and (b) climbing several flights of stairs. Responses ranged from 0 “No, not at all,” 1 “Yes, a little” to 2 “Yes, a lot.” Physical limitations ranged from 0 “having no limitation” to 4 “having severe limitations.”

Covariates

Sex, age (in years), education, length of residence (in years), and self-rated health were considered as potential confounders. Self-rated health was measured with one item from the 12-item Short-Form Health Survey: “In general would you say your health is” including poor (0), fair (1), good (2), very good (3), and excellent (4) (Ware et al., 1996). Length of residence was used as a proxy of attachment to the neighborhood.

Statistical Analysis

Descriptive statistics are presented as percentages or mean values and their standard deviations (SD). Differences in characteristics between included and excluded participants were reported using independent t-tests for continuous data and chi-square tests for categorical data. For skewed continuous variables, a Mann–Whitney U test was applied. For each perception scale, we calculated the divergence between the group mean perception and the individual’s perception. The difference from the group mean was taken as the outcome in linear regression models to assess the discrepancy between the individual’s perception and the perception of persons living in close proximity. All models were adjusted for sex, age, and education. All analyses were conducted using STATA 15 (StataCorp LP, College Station, TX).

Results

Table 1 presents the characteristics of the study population. More than half of the 173 respondents were female (61.9%), and most had received less than a college education (57.8%). The mean age was 72.4 years (SD = 8.1 years; range = 62–88 years). Participants who lived in low-dense districts were more likely to be excluded from the analyses because clustering was not possible, having not enough participants living within a 400-m radius. Excluded participants walked less likely for transport in their neighborhood (p < .001) and perceived their environment as more pedestrian-unfriendly with fewer aesthetic appealing features (p = .017).

Table 1.

Participant’s Characteristics.

Characteristic	Included (N = 173)	Excluded (N = 261)	p value
Female (%)	61.85	66.67	.304
Age, M (SD)	72.41 (8.08)	71.00 (8.03)	.076
Education (%)
High school or less	24.86	23.75
Some post-secondary	32.95	31.03
Completed college degree	42.20	45.21	.824
Length of residency (years), M (SD)	20.00 (13.87)	23.20 (16.22)	.031
Self-rated health^a, M (SD)	2.69 (1.00)	2.78 (0.99)	.368
Functional limitation^a, M (SD)	0.88 (1.24)	0.73 (1.16)	.198
Functional limitation (%)	44.51	36.40	.091
Walking for transport (%)	60.69	41.76	<.001
Walking for recreation (%)	79.20	70.88	.053
Perception scales^b
Walkability, M (SD)	2.08 (0.54)	1.65 (0.53)	<.001
Infrastructure for walking, M (SD)	1.87 (0.47)	1.56 (0.51)	<.001
Aesthetics, M (SD)	2.13 (0.45)	2.03 (0.40)	.017
Safety from traffic, M (SD)	1.81 (0.50)	1.72 (0.49)	.088
Safety from crime, M (SD)	2.25 (0.54)	2.17 (0.49)	.115

Note. Persons were excluded if they could not be clustered into groups with ≥3 persons living closer than 400 m from each other.

Possible ranges from 0 to 4, higher values indicate more severe limitations or better self-rated health. ^bScales ranged from 0 to 3, higher scores represent more favorable evaluations.

In general, participants rated their neighborhood environmental features above the scale mean on all five perception scales (Figure 1). Older adults who reported some or severe physical limitation scored significantly lower on walkability (M_limitations = 1.97, M_{No limitations}=2.16; p = .018), aesthetics (M_limitations = 2.02, M_{No limitations} = 2.22; p = .004), safety from traffic (M_limitations = 1.71, M_{No limitations} = 1.88; p = .025), and safety from crime (M_limitations = 2.12, M_{No limitations} = 2.35; p = .004) than individuals without limitations.

Figure 1.

Mean comparison of perceived neighborhood features (t-tests) between persons with limitations and without limitations; *p <.05; **p <.01.

In the next step, we analyzed the discordance between the individual’s perception and the perception of participants living nearby. The pedestrian infrastructure was equally perceived across the group members (absolute mean discordance from the group M = 0.26), which can be explained by the fact that it is the most concrete scale asking about neighborhood features, for example: “There are sidewalks on most of the streets in my neighborhood.” We observed a higher discordance when it came to more subjective evaluations of the neighborhood, like safety from traffic (M = 0.33) and safety from crime (M = 0.33).

Table 2 presents the linear regression models’ results with the discordance between the individual and the group-wise perception as the outcome measure. Walking for recreation, self-rated health, and length of residency were dropped after the initial analyses because we found no association with the outcome in any of the five perceptions scales. Confirming the first hypothesis that a more significant usage of the neighborhood environment is associated with more positive evaluations, Table 2 shows that older adults who walk for transport in the neighborhood perceive their neighborhood as more walkable (β = .19; p = .011), having a better pedestrian infrastructure (β = .16; p = .037), and felt to be safe from traffic hazards (β = .19; p = .010).

Table 2.

Linear Regressions Based on Differences in Perceptions Form the Group Mean and Individual Characteristics (N = 173; N_groups = 42).

Variable	Walkability			Infrastructure for walking^a			Aesthetics			Safety from traffic			Safety from crime
Variable	β	(95 % CI)	p	β	(95 % CI)	p	β	(95 % CI)	p	β	(95 % CI)	p	β	(95 % CI)	p
Physical limitations^b	–.21	[−0.36, −0.06]	.006	−.05	[–0.21, 0.10]	.493	−.15	[–0.31, –0.01]	.059	−.20	[−0.35, −0.05]	.009	–.17	[−0.33, –0.02]	.030
Walking for transport^c	.19	[0.04, 0.33]	.011	.16	[0.01, 0.31]	.037	.11	[–0.05, 0.26]	.166	.19	[0.05, 0.34]	.010	.03	[−0.12, 0.18]	.688
adj. R²			.116			.034			.012			.096			.018

Note. Standardized coefficients and 95% confidence intervals (CI) are presented; All models were based on linear regression models; Persons were grouped if they lived fewer than 400 m from each other. Bold values indicate significance at p < .05.

Based on 171 cases. ^branging from 0 to 4. ^cWalking in the neighborhood for transport in the past 4 weeks (1 = yes; 0 = no); all models were adjusted for age, sex, and education.

The results were also consistent with the second hypothesis assuming that older adults with physical limitations have a more negative representation of the built neighborhood environment. Older adults with physical limitations felt more insecure from crime (β = −.17; p = .030) and traffic (β = −.20; p = .009). However, having physical limitations was not associated with a discrepant perception of the walking infrastructure. The best-fitting model was the perceived walkability of the neighborhood model (adjusted R² = .116). We could not explain differences in the aesthetic aspects of the neighborhood, which might also be due to the insufficient internal consistency of the scale.

Sensitivity analyses showed similar effects when Canadian participants were deleted from the analyses if they only reported postal codes and no home addresses. However, the effect estimates were attenuated, and some did not reach significance because of the reduced sample size (Supplement 2).

Discussion

This study examined different perceptions of the neighborhood in older adults in pre-set 400 m clusters. We found that the perception of neighborhoods varied between persons living in close proximity and depended on individual characteristics. The same built environment was perceived differently if physical limitations were present or if older adults walked in their neighborhood for transport. Perceived environmental factors that are more subjective, like safety concerns, were stronger related to physical limitations. Objective features such as amenities and street characteristics were positively associated with walking in the neighborhood for transport.

We found that the built environment was more favorable perceived if it was used. These results correspond with findings from a cross-sectional study in Australia, which found that the perception of distances to destinations was associated with walking behavior (McCormack et al., 2008). A physical activity intervention study revealed that participants with enhanced physical activity levels at follow-up also perceived their neighborhood more favorable (Ries et al., 2009; Wallmann et al., 2012; Wallmann-Sperlich et al., 2014). Participating in the physical activity intervention (i.e., no change of the neighborhood environment) made the participants perceive the built environment to be safer from traffic, with better-maintained infrastructure, and a lower distance to local facilities (Wallmann et al., 2012). These results indicate that walking may lead to a different perception of the neighborhood. Thus, in our study, residents who walk for transport may have higher frequencies of contact with their neighborhood features, making them more aware of the existing features, which may lead to a more positive perception of their neighborhood.

Older adults with physical limitations might feel more vulnerable and had a more negative representation of the built environment. One recent study aimed to understand the discordance between objective information and subjective reflection of the built environment in persons with mobility limitations. It highlighted that this group has unique needs, reflected in the individual’s perception and preference of places (Mahmood et al., 2020). It is well documented that fear of crime includes some vulnerability (Hale, 1996), which is consistent with our findings.

Our study did not find any significant associations between differences in the built neighborhood environment’s perception and walking for recreation. This is in line with previous research that reported a strong association between walking for transport and the neighborhood environment but relatively few associations with recreational walking in older adults (Forsyth et al., 2008; Procter-Gray et al., 2015; Yun, 2019) and in the general population (Saelens & Handy, 2008).

This study’s results contribute to a better understanding of the poor to medium correlations between subjective and objectively assessed built environment features (Gebel et al., 2009; Hajna et al., 2013). Studies using self-reported perceptions of the built environment alone to predict self-reported physical activity outcomes have been potentially biased (Humpel et al., 2002). It has been argued that the perception may not correspond to the objective reality or that the perception might be a misperception of the objectively measured walkability. "However, digital geographic information systems data from city agencies or commercial sources, often labeled as “objective,” can also have error and bias" (Blacksher & Lovasi, 2012, p. 174). Measures of the perceived environment are higher correlated with physical activity than corresponding objective measures because physical activity shaped the perception (Cerin et al., 2017). This makes environmental perceptions more proximal to health behavior (Caspi et al., 2012) than the objectively measured environment. Therefore, it is crucial to better understand this discordance to plan and develop effective built environmental interventions and physical activity promotion programs.

Our study’s strength is that the participants were sampled from neighborhoods with diverse urban environments with narrow districts that made a comparison between participants possible. However, the efforts to define groups living nearby also resulted in small sample sizes in the subgroups. The conceptualization of the groups biased the sample toward higher-density neighborhoods because fewer older adults fulfilled the inclusion criterion of a distance equal to or smaller than 400 m in the low-density neighborhoods. This may limit the generalizability of our findings. A second limitation is that self-reported walking activity may have been problematic because older participants might have difficulties accurately recalling their daily activities (Herbolsheimer et al., 2018). Accordingly, self-reports are likely to have a degree of measurement error (Ferrari et al., 2007). An additional limitation of our study was that we applied the difference from the mean group perception as a proxy for the discordance between objective and subjective assessments of environmental features. However, our approach avoided the problem that discordances between perceived and objective measures resulted from the fact that the perceived and objective property of an environmental feature did not adequately overlap. In future research, it would be useful to examine if a threshold of 400 m is reasonable assuming equivalent exposure or if a smaller threshold would be more appropriate. Last, causality cannot be inferred from cross-sectional data.

It is crucial in future studies to propose a theoretical model explaining how the subjective and objective built environment contributes to walking or other physical activities taking reverse causality into account. Theoretical models will help explain the associations with health outcomes and develop potentially more effective evidence-based interventions that take into consideration both objective and subjective perceptions. Our study attempted to address this gap by examining predictors of discrepancy in more depth.

The present study supports the idea that walking in the neighborhood for transport or the absence of physical limitations results in a more positive perception of the built neighborhood environmental features. Attention to different ways older adults use and perceive their environments can help to determine which characteristics are most crucial to measure and intervene in future studies.

Supplemental Material

sj-pdf-1-jag-10.1177_07334648209792587 – Supplemental material for Perceptions of the Neighborhood Built Environment for Walking Behavior in Older Adults Living in Close Proximity

Supplemental material, sj-pdf-1-jag-10.1177_07334648209792587 for Perceptions of the Neighborhood Built Environment for Walking Behavior in Older Adults Living in Close Proximity by Florian Herbolsheimer, Atiya Mahmood, Nadine Ungar, Yvonne L. Michael, Frank Oswald and Habib Chaudhury in Journal of Applied Gerontology

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Canadian Institutes of Health Research (CIHR). The first and the third authors were supported by a scholarship of the German Research Foundation (No. 398948032 and No. 396651852).

Ethics

The study was approved by the Simon Fraser University Office of Research Ethics (Approval Number 38156; Dated March 2, 2007).

ORCID iDs

Florian Herbolsheimer

Atiya Mahmood

Supplemental Material

Supplemental material for this article is available online.

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