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Smart growth is an approach to urban planning that provides a framework for making community development decisions. Despite its growing use, it is not known whether smart growth can impact physical activity. This review utilizes existing built environment research on factors that have been used in smart growth planning to determine whether they are associated with physical activity or body mass.
Searching the MEDLINE, Psycinfo and Web-of-Knowledge databases, 204 articles were identified for descriptive review, and 44 for a more in-depth review of studies that evaluated four or more smart growth planning principles.
Five smart growth factors (diverse housing types, mixed land use, housing density, compact development patterns, and levels of open space) were associated with increased levels of physical activity and walking. Results varied by gender and method of environmental assessment. Body mass was largely unaffected.
This review suggests that several features of the built environment that are typically associated with smart growth planning may promote physical activity. Future smart growth community planning could be modified to include a dedicated focus on health, and future research should explore whether combinations or a critical mass of smart growth features is associated with better population health outcomes.
Research into the effects of the built environment on health has increased dramatically in recent years (1)(2). Data from cross-sectional studies and recent reviews have shown positive associations between environmental features, such as presence of sidewalks, proximity to parks and presence of certain types of food outlets, and outcomes such as physical activity and body mass(3)(4)(5)(6)(7)(8). While more definitive results from controlled, longitudinal studies are lacking, public health advocates are already calling for policy and legal changes to assist in creating environments that are more conducive to physical activity, healthy eating, and a healthier lifestyle in general (9)(10).
Though not necessarily linked, a parallel trend is occurring in parts of the world, including the United States, in which communities are implementing urban planning policies designed to shift growth in their jurisdictions away from current patterns characterized by low density, automobile-oriented greenfield development (11). This comes in reaction to a growing perception that these patterns, which have dominated development in the United States since the end of World War II, are detrimental to existing urban areas, city cores, public infrastructure, and environmental quality (12)(13). One example of this trend is called smart growth, a set of broad principles that provides a framework for making development decisions that result in vibrant, diverse, economically healthy communities which have a strong sense of place (14). The principles have guided the development of newly built communities, as well as planning revisions for existing communities (15). The Smart Growth Network, a national umbrella organization, conceives of smart growth as 10 principles, from public input into development decisions, to mixing land use, to providing multiple transportation and housing options (see Table 1)(16).
Researchers have begun to see the utility of these planning principles as a way to achieve the policy-level goals arising from public health research on the built environment(17)(18). Smart growth may have particular purchase in affecting development due to support provided by both non-profit organizations and the federal government, including model codes, policy evaluation toolkits, development scorecards, and case studies (14)(19). That smart growth-style modifications to codes and policies can lead to the infrastructure necessary for healthier communities makes theoretical sense, but there is virtually no empirical work linking smart growth planning principles to health outcomes (20). While there are numerous studies on concepts that can be linked to smart growth principles, such as walkability and mixed land use, an explicit connection between smart growth and improved health has not yet been demonstrated. This is an important gap in the literature because as more and more communities implement smart growth principles, they need to be able to say with greater certainty whether a possible benefit of smart growth may be an improvement in overall levels of a community's health. Further, if indeed there is an association between smart growth planning approaches and health, this information can be used to build on existing knowledge and resources to better integrate a strong public health component into smart growth, something it currently lacks.
In an attempt to draw some initial conclusions about what impact smart growth may have, this review takes existing built environment research, maps the variables studied onto the 10 smart growth principles proposed by the Smart Growth Network, and identifies relationships they have with physical activity and body mass. As a result of the large number of articles identified, there are two components to this review. First, a descriptive section using data from all included articles provides information on the frequency with which the different smart growth principles are studied in order to identify well-researched areas, as well as those in which research is lacking. This is followed by a full review of a sub-sample of articles, which contains a more in-depth examination of sets of variables that are typically associated with smart growth principles and their relationship to physical activity and body mass.
The review began with a systematic search of the literature. The MEDLINE (OvidSP interface), Psycinfo, and Web of Knowledge databases were chosen for our search because of the breadth of fields they cover, including biomedical, land planning, civil engineering and transportation research. After searching these databases, bibliographies from similar prior reviews were hand searched to identify additional articles. The initial search included entries available from 1990 to the second week of March 2009. Because of the low numbers of articles published between 1990 and 2000, the lower bound was modified to be the year 2000 after completing the entire article screening process. Only English language articles were selected.
Keywords for the MEDLINE search were: obesity or overweight or body mass index or walking or exercise or physical activity AND residence characteristics or housing or built environment or smart growth. Psycinfo keywords were: physical activity or obesity or overweight or body mass index or walk* or exercise AND smart growth or environment* or neighborhood* or urban* or community*. Web of Knowledge keywords were: physical activity or obesity or overweight or body mass index or walk* or exercise AND built environment or smart growth or new urbanism. Terms followed by a ‘*’ pick up any words or phrases which begin with that term.
One author did an initial screen of titles and abstracts to eliminate obviously irrelevant articles. Examples of eliminated articles include those focused on animal behavior, cell biology and biomedical engineering.
The next step was to screen titles and abstracts to determine which ones to accept for full article screening. This was accomplished using two reviewers who rated each article for inclusion or exclusion based on predefined criteria. Disagreements among raters were settled by a third reviewer. Inclusion criteria were as follows: 1) Objective or self-report measurement of physical activity or body mass (e.g. height and weight, skinfold, or waist circumference); 2) measurement, either perceived (e.g. participant self-report) or objective (e.g. geographic information systems (GIS) mapping of objective environmental data or neighborhood audits) of at least one of the 10 smart growth principles; and 3) publication in a peer-reviewed journal. Exclusion criteria were: 1) Papers that focused primarily on socioeconomic characteristics of a geographic area, neighborhood problems, social cohesion, social capital, or total city or town size; 2) a target population consisting mostly of senior citizens (due to functional limitations that may limit their physical activity); 3) instrument validation studies; 4) papers that were reviews, case reports, editorials, commentaries, discussions or letters; and 5) behavioral interventions without an environmental component (e.g. walking programs, fitness education classes, etc.). Full articles of the accepted titles and abstracts were then screened using the same dual rater system and against the same criteria.
In order to complete the descriptive aspect of this review, an initial round of data was abstracted from all studies retained after full article screening. This included the study authors, year of publication, age or grade of subjects, gender, smart growth principles studied and outcomes measured. Given the significant overlap between the environmental features measured in the existing literature and the smart growth principles, a decision was made to map, or link, what is measured in the literature onto the individual principles. For example, a measure of proximity to public transit was mapped onto principle eight (variety of transportation choices); articles that assessed sidewalk availability were mapped onto principle two (create walkable neighborhoods); and measures such as number of or distance to parks were mapped onto principle four (distinctive, attractive communities). In cases where a study's independent variable was an index which combined multiple aspects of the built environment into a single score (for example, a walkability index which included sidewalk condition, land use mix, and residential density), the index was broken up into the component parts, and mapped those parts onto the appropriate principles; in these cases, all components had the same outcome data.
Only those articles which included at least four unique smart growth-style independent variables were included in the full review. Four was chosen because among those studies which examined one, two or three principles, at least half the principles studied in each category were either number two (walkable neighbohoods) or four (distinctive, attractive communities). As there are already a number of reviews addressing these two topics by themselves, and because the intent of this review is to evaluate the effectiveness of individual smart growth principles in the context of other principles, those with three or fewer were considered insufficient to capture the possibly multiplicative effect of multiple principles. It is only once articles assess 4 or more principles that there is sufficient diversity among measured principles to confidently say they are beginning to look at smart growth-style variables the way they were intended to be implemented, which is as a set. Data were then abstracted from this sub-sample of articles. These data included the same information from the first abstraction, plus study type, sample size, race/ethnicity breakdown, measurement methods for the environment and outcomes, covariates, and results for the association of smart growth principles with physical activity and obesity, broken down by smart growth principle.
The initial search yielded 2435 articles: 1179 from MEDLINE, 738 from Psycinfo, 476 from Web of Knowledge, and 42 from the bibliography search. After the initial screening, 979 were accepted for title and abstract screening. From this, 363 advanced to full article screening, and 204 were ultimately accepted. These studies were the basis for the descriptive aspect of this review, while a subset of 44 studies (those which measured at least four principles) made up the full review.
Table 2 presents results from the descriptive review of 204 studies. The most frequently studied principles were numbers two, four and six. Principle two, create walkable neighborhoods, was studied 150 times; principle four, foster distinctive, attractive communities with a strong sense of place, was studied 128 times; and principle six, mix land uses, was studied 107 times. Two principles were not studied at all: number three (encourage community and stakeholder collaboration), and number five (make development decisions predictable, fair and cost effective). The remaining five principles were studied between 11 and 42 times each. These principles relate to diversity of housing types and housing density, open space preservation, public transportation and development directed toward existing communities. When looking at the total number measured, it was most common to have between one and three principles per study (approximately 25% each), followed by four principles (15%), five (6%), and six (1%). No studies contained between seven and 10 total principles.
Detailed characteristics of studies selected for full review are found in supplemental Table 1 (online only content). In terms of race/ethnicity, only 32% (n=14) of studies reported the racial composition of their sample. Of those, 64% (n=9) reported a majority white/Caucasian population. Among the studies which did not report race, 43% (n= 13) were conducted in Australia, Canada or Belgium, making it likely that those too were majority white, given the ethnic/racial makeup of those countries. Other races included in study samples include Asian, Black/African-American, Hispanic, Native American, and mixed/other. Two studies had samples where the majority was non-white: Wells and Yang (72% black), and Rutt and Coleman (79% Hispanic). The vast majority of studies, 72% (n=32), had a sample population consisting of adults age 18 and up; children were the exclusive focus in five studies. There were seven studies with a mixture of adults and children; six of those were mostly adults, while one was mostly children. Though 40 studies reported the gender makeup of their sample, a detailed breakdown was only given in 29 articles. Females were the majority in 79% (n=23) of those. A range of geographic areas were represented in the studies. North America was the most frequent location with 28 studies (25 in the United States, 3 in Canada), followed by Australia (9), and Europe (5).
Thirty-nine studies (89%) utilized a cross-sectional design; the remaining five were either longitudinal or quasi-longitudinal. Physical activity alone was assessed in 37 studies, body mass alone was measured in three, and four studies examined both. Measurements of the environment and outcomes were done in a variety of ways. Objective instruments were the most common method to assess the environment, with 20 studies (45%) reporting the use of only geographic information systems (GIS) and/or neighborhood audits. Self-report of environmental characteristics alone was used in 11 studies (25%), and 13 (30%) used both self-report and objective methods. The two outcomes which were our focus, physical activity and body mass, were also assessed with both self-report and objective measures. Self-report alone was used in 35 studies, objective methods alone (accelerometers, clinical measurement of height and weight) were used in five studies, and four used both.
Results will be discussed with respect to the outcome and the smart growth principle under review (see Table 3). Note that because of the different methods used to assess outcomes, results from individual studies are interpreted in terms of whether they occurred in the expected direction (smart growth principle associated with an improvement in health), the unexpected direction (smart growth principle associated with a decline in health), or had no statistically significant effect, based on the stated significance level within each study. Also, because of the results stratification used, some studies contributed multiple measures to a given principle-outcome combination. Principles three and five are not discussed here due to the absence of studies which measured them.
Principle one was largely found to have no association with physical activity, with 80% (n=12) of the outcomes (i.e. moderate, vigorous, moderate-to-vigorous or total physical activity) being non-significant; 13% (n=2) were in the expected direction, and one was in the unexpected direction. In terms of walking (i.e. recreational, transport or total walking/biking), more results were found in the expected direction than were non-significant. 55% (n=5) of results were in the expected direction; 44% (n=4) results were non-significant. No significant effect was found with respect to body mass (n=1).
The second principle was found to have no significant association with 80% (n=28) of physical activity measures, an association in the expected direction in 17% (n=6), and in the unexpected direction once. One study found results for vigorous activity which varied by whether the outcome was measured objectively (expected direction) or subjectively (non-significant). Another study found results for moderate to vigorous physical activity where females were in the expected direction and males were non-significant; an additional study found results which were the reverse for total physical activity. Measures of walking found 50% (n=30) non-significant outcomes, 47% (n=28) of results in the expected direction, and two which were in the unexpected direction. One study each in the total and recreational walking categories had results in which females were in the expected direction and males were non-significant; these results were reversed for an additional recreational walking study. Results in one study in the transport category varied by measurement method: the objective measure was in the unexpected direction, while the subjective measure was non-significant. This principle had no effect for 80% (n=8) of measures of body mass, and an effect in the expected direction twice.
No significant effect was found in 66% (n=21) of the measures of physical activity and principle four, a result in the expected direction was found in 28% (n=9) of the measures, and two results were seen in the unexpected direction. The moderate and vigorous levels both had one result which was in the expected direction for the self-report measure, but non-significant when measured objectively. Also, there were three studies between total and MVPA which varied by gender. For the two in MVPA, females were in the expected direction, and males were non-significant. The opposite was true for the total physical activity results. Of studies measuring some form of walking, 33% (n=15) of outcomes were in the expected direction. However, over twice as many, 67% (n=31), were non-significant. There were two studies in the combined walking categories which found variations by gender. For total walking, males were in the expected direction and females were non-significant; results were reversed for the one measuring recreational walking. In the recreational category one study found results which were in the expected direction for subjective measures of the principle, but non-significant results when measured objectively. For body mass, there were a total of seven measures, each of which found no effect.
The effect of principle six on physical activity was found to have no effect on 87% (n=26) of measures, an effect in the expected direction on 10% (n=3) of measures, and an effect in the unexpected direction on one measure. One study each in the moderate and MVPA physical activity categories found results which varied by gender. In both cases, the results for females were in the expected direction, while for males they were non-significant. In studies which measured walking, 52% (n=32) of outcomes were in the expected direction, 47% (n=29) were non-significant, and one was in the unexpected direction. Two studies in total walking and one in recreational walking varied by gender. The two in total walking found conflicting results, where for one, females were in the expected direction, and for the other, results were the opposite. Females were in the expected direction and males were non-significant for the one in recreational walking. For transport walking, subjective measures were in the expected direction twice and objective measures were in the expected direction once. One study also varied by whether the outcome was framed as any amount of transport walking (expected direction) or the amount recommended for health benefits (non-significant). With respect to body mass, 80% (n= 8) of measures found no effect, and one each was in the expected and unexpected direction.
Principle seven in the context of physical activity had three outcomes (50%) in the expected direction and three which were non-significant (50%). In terms of walking measures, 82% (n=14) were non-significant, 12% (n= 2) were in the unexpected direction, and one was in the expected direction. One longitudinal study found results for total walking which depended on whether the outcome was an increase of at least 60 minutes (unexpected direction) or if it was an increase of less than that (non-significant). This principle as it relates to body mass was not measured.
Results of the eighth principle and its association with physical activity found 89% (n=16) of measures had no significant effect, and 11% (n=2) were in the unexpected direction. When walking was measured, there were 56% (n=23) non-significant results, 37% (n=15) in the expected direction, and 7% (n=3) in the unexpected direction. Among the studies measuring total walking, two found variations by gender. Females were in the expected direction and males were non-significant for one study, and for the other, those results were reversed. In the recreational walking category, females were in the expected direction and males were non-significant for one study. For transport walking, a study found that when the principle was measured objectively, results were in the expected direction, and when measured subjectively, results were non-significant. In studies measuring body mass, three measures showed no effect and one was in the expected direction.
All five measures of principle nine found no association with physical activity. Walking measures found 50% (n=7) of results in the expected direction, 14% (n=2) in the unexpected direction, and 36% (n=5) which were non-significant. This principle and body mass were assessed in three studies, two of which found no effect, with the other one finding results in the expected direction.
One measure of principle 10 found physical activity results in the expected direction, while 88% (n=7) found non-significant results. One study in the moderate to vigorous category found results where males were in the expected direction and females were non-significant. When walking was measured, 56% (n=10) of the outcomes were in the expected direction, while 44% (n=8) were non-significant. All six measures of body mass found that this principle had no effect.
The goal of this review was to determine whether smart growth principles are associated with either physical activity (general and walking-specific) or body mass. Few studies reported significant associations between smart growth principles and physical activity or body mass. The exceptions were for physical activity and principle seven (open space preservation), and walking and principles one (range of housing choices), six (mixed land use), nine (development toward existing communities) and ten (compact building design).
Body mass was the least measured outcome among the studies examined. This is often an imperfect outcome to choose when measurement is cross-sectional, as it is the furthest downstream from the predictors, and so there are likely to be many factors which affect it that were unmeasured, such as eating behaviors. All of the studies in this review which examined body mass are cross-sectional. Thus, the almost exclusively non-significant results found here are not surprising. Body mass might be a more informative outcome of the effect of smart growth principles on obesity risk if it were measured longitudinally utilizing a long-term follow-up, for example, a period greater than one year(21)(22). It is also possible that rather than working directly, smart growth's impact on body mass operates through mediation or moderation, mechanisms which are not addressed in the studies reviewed. Owing to a lack of studies which have this as an outcome, it is still not known whether smart growth can yield changes in body mass.
It is difficult to infer from this body of literature whether the objective or perceived environment is a more important determinant. It is also unclear whether there was objective evidence of the implementation of smart growth principles as part of a deliberate planning process. For the studies reviewed, evidence of planning was inferred either from observational or archival data (e.g. neighborhood audits, GIS) on geographic and structural features, such as the proximity of green spaces, or from self-reported perceptions of residents. None of the studies referred to the use of planning department data, such as development codes or general plans, as evidence of smart growth principles. If perceptions of the environment contribute to the impact of smart growth principles on health, then future interventions may focus on education or marketing campaigns to make residents aware of resources which are available to them, such as parks, greenbelts, or essential services in local shopping areas. If objective environmental features are the more important predictors of activity, then modifications would likely be necessary. This could take the form of sidewalk construction/rehabilitation and improved park playground equipment, or policy changes, such as reduced speed limits and denser housing requirements in the development code. However, it is likely that both presence of environmental features and subjective awareness of those features are important to creating behavior change, and any intervention will need to account for each of them.
There are several implications based on the findings of this review. First, communities which have diverse housing types, mixed land use, increased housing density, development which is directed toward existing communities, and increased levels of or access to natural space and critical environmental areas may show increases in walking and physical activity among residents who are exposed to these characteristics. The second is that if a community has decided to make these priorities, established smart growth literature and materials can guide adjustments to development codes, general plans, tax incentives and other policies designed to foster the above changes. Third, smart growth planning as it currently exists could incorporate a dedicated public health component (17). With a more conscious focus on health as part of the community planning process, associations between implementation of smart growth principles and health outcomes could increase. A health component could be mapped onto or operationalized for each of the 10 existing principles rather than constitute an additional new principle. Communities could decide what health issues are most important to them, and then tailor each of the principles to address those health goals. In practice this could take the form of requiring health impact assessments for any new development projects, dedicating a portion of transportation funding to bike and pedestrian projects, or the integration of public health officials into decisions about park location, transit services, housing density, etc.
Systematic inclusion of a health component in planning also has ramifications for evolving planning standards that are incorporating smart growth, including the relatively recent U.S. Green Building Council LEED (Leadership in Energy and Environmental Design) standards for neighborhood development (ND) (23). LEED-ND certifies neighborhoods on a credit system based on their inclusion of features that promote greater walkability, mixed use, public transportation alternatives, and orientation towards the existing community; reduction of vehicle miles traveled; and greater energy efficiency. LEED certification carries incentives in the form of both sales and property tax credits, which means that in the future, communities may be reinforced for following smart growth principles.
Future research should address the limitations discussed in this review. As mentioned, studies should include a wider distribution of races and genders to increase population representativeness as well as external generalizability. Second, more studies should include both subjective and objective measures of the environment in order to increase both the validity and reliability of results. Third, more controlled, longitudinal studies are needed. A major issue in this line of research is the effect of self selection into neighborhoods more conducive to physical activity by people who already lead active lifestyles (24). This can bias the outcomes and produce inflated effect sizes. Such bias can only be ruled out if an appropriate control group is utilized that represents randomization or relevant demographic matching. Longitudinal studies will allow us to determine whether there is a time threshold, as far as how long someone must live in an environment in which they are exposed to smart growth principle operationalizations before changes in physical activity are evident. They will also allow us to see whether there are effects on more distal outcomes such as body mass which require longer observation periods to determine if there is truly an effect. Fourth, more research is needed which incorporates the upstream development process, including the topics covered by principles three (encourage community and stakeholder collaboration) and five (make development decisions predictable and fair), which were found to not have been studied at all, at least insofar as they impact our chosen outcomes. Community input into development projects can take several forms, including open town hall meetings, city council meetings with public input, as well as the more specialized charrette process, by which community leaders, administrators, and organizational stakeholders can discuss and give feedback to planners before build out has taken place (25). Increasingly, stakeholders include health agency directors and community health advocates who are sensitive to planning efforts that could affect air quality, traffic noise and accidents, and other health-related factors (11). New research could examine whether the inclusion of these health-oriented stakeholders is associated with the incorporation of smart growth principles into a community's development code or general plan, and whether their inclusion is associated with the direct application of these principles to improving targeted health outcomes in the community. Finally, future studies should investigate the complete set of smart growth principles in order to gain a better sense of whether there is a particular set or combination of certain principles, a minimum number or critical mass which need to be deployed, or if there are certain key principles which must be incorporated in order to achieve beneficial health outcomes. Related to this, an understanding of what constitutes a sufficient level or threshold of each principle to modify behaviors is needed. To be sure, the studies reviewed are not the result of deliberate smart growth research. Instead, these studies measure aspects of the built environment that smart growth planning efforts seeks to modify, in order to achieve their goals. These variables vary in value along a continuum and do not necessarily represent the ideal built environment as envisioned by smart growth advocates. As a result of the vague and somewhat arbitrary terms which currently exist in the literature, such as ‘high’ mixed land use versus ‘low’ mixed land use, this review is unable to quantify, for example, exactly how many units of commercial land use per acre are necessary to cause meaningful shifts in physical activity levels. Future research will be more informative in developing policy and behavioral interventions if it is able to establish these minimum thresholds.
There are several limitations to our current review. The first is the inherent difficulty in mapping the data from the studies identified onto the 10 smart growth principles. Ultimately, these were subjective judgments based on existing definitions of each principle that were not as concrete as might be hoped for. To account for this, a multi rater system was used, and it is expected that this minimized the subjectivity to the greatest extent possible. Also, as in any literature search, there is the possibility that relevant studies were missed, either in the initial search, or in the subsequent three-step screening process. However, unintended exclusion was minimized through use of a multi-screener process and by searching diverse databases that encompass a variety of disciplines which relate to the smart growth planning process, including architecture, landscape architecture, urban planning, economics and law. Third, while a number of studies utilized objective methods to assess the built environment, none of them verified the presence of these principles in city planning records or documents. This would have allowed us to determine whether the principles observed were implemented as a direct result of smart growth planning or were pre-existing features of the built environment. Thus, the findings from this review represent conclusions about the potential effectiveness of types and sets of features that are represented in smart growth planning and which could be included in future planning decisions in order to maximize health benefits of community residents.
A caveat to the study of smart growth as a mechanism for promoting healthier communities is that development is a complex process which is shaped by forces beyond the control of any one group, including planners themselves. These include political leadership and will, market demand and the economics associated with developing a tract of land. It would be naive to think that simply shifting urban planning codes and regulations will be enough to alter entrenched development practices and achieve behavior change. The reality is that these legal and policy modifications are likely to be necessary but not sufficient components of a broader campaign to change the way communities are built and re-developed. With acknowledgement of this complexity, the findings from this review suggest that smart growth planning principles hold promise for promoting physical activity, especially walking. Furthermore, it is suggested that the potential impact of smart growth principles on health could be increased by the systematic inclusion of a health component to community planning.
Supported by National Cancer Institute grant #R01-CA-123243 and #5 T32 CA 009492-25 (Mary Ann Pentz, PI)
No conflicts of interest