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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
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6
Current State of Knowledge

Understanding how the built environment may affect physical activity is a relatively new but rapidly expanding field of inquiry. The literature comes primarily from two fields: urban planning (travel behavior) and public health (physical activity).1 The former has focused largely on automobile travel but has also explored walking and cycling as modes of travel. The physical activity literature has focused on the personal and social determinants of physically active behavior and on the intensity and the amount of physical activity, with less attention to the type or location of that activity (Handy 2004). Neither field has had a long history of examining the role of the built environment as a determinant of physical activity (Handy 2004).

In this chapter, the empirical evidence on the relationship between the built environment and physical activity is reviewed. The chapter begins with an overview of the literature and then summarizes the evidence by drawing on studies from both the travel behavior and physical activity fields. Where possible, the results are further analyzed to highlight the role of sociodemographic factors, geographic scale, and such mediating variables as safety and security and time. The studies reviewed are primarily cross-sectional, but the results of a few studies whose research designs are more conducive to drawing causal inferences are also discussed. The final section summarizes knowledge gaps revealed by this review.

1

This chapter draws heavily on a literature review and paper commissioned by the committee (Handy 2004).

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
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OVERVIEW OF THE LITERATURE

The literature review conducted for this study encompassed 22 studies from the fields of urban planning/travel behavior and 28 studies from the fields of public health/physical activity (Handy 2004). It drew heavily on recently published reviews of studies supplemented with additional studies known to the committee or Handy or published more recently. International studies were included, although the committee recognizes that the social and environmental determinants of physically active behavior may not be fully comparable with nor the results transferable to the situation in the United States. The committee acknowledges the contribution of international scholars and the importance of international collaboration on research linking the built environment and physical activity. At the same time, it cautions the reader that the policy relevance of the experience in other countries for the United States should be examined with care. Differences in land use and transportation patterns (e.g., lower densities, lower transit use, and greater reliance on the automobile in most U.S. metropolitan areas) and dissimilar regulatory and institutional arrangements (e.g., local rather than central control over land use and zoning policies) may limit the applicability of international experience to the United States (TRB 2001). For example, the experience of Australia and Canada, where land use densities and travel patterns are more similar to those of the United States, may have more relevance and transferability than the experience of many more densely populated and transit-oriented European countries.

Handy’s (2004) selection of studies for review in her commissioned paper reflects her subjective assessment of the suitability and relevance of the research. She notes that a detailed evaluation of the quality of execution of each study, using criteria such as those employed by the Task Force on Community Preventive Services, was beyond the scope and resources of the review (Handy 2004).2

2

The reader is directed to the following references for a thorough discussion of the evidence-based methods used in preparation of the task force’s Guide to Community Preventive Services: Briss et al. 2000 and Carande-Kulis et al. 2000.

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
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In fact, a task force review of environmental interventions to promote physical activity is under way but has not yet been completed.3 The findings and conclusions presented in this chapter reflect the committee’s judgment, although, with few exceptions, that judgment agrees with Handy’s assessment.

The vast majority of the studies reviewed use a cross-sectional design; that is, they examine outcomes (i.e., levels of physical activity) at a particular point in time as a function of explanatory variables (i.e., characteristics of the built environment that vary by neighborhood or region). As discussed in Chapter 5, this design enables researchers to draw correlations between variables of interest and isolate those that are statistically significant but not to demonstrate causality.

The review in this chapter should not be viewed as exhaustive but as illustrative of the research completed and under way to date. The field is growing rapidly, more interdisciplinary work is being conducted, and new studies and research results are emerging.

REVIEW OF FINDINGS

Findings from the Travel Behavior Literature

The focus of these studies is on destination-oriented walking trips and nonmotorized travel rather than on walking and cycling for recreation and exercise. As noted, nearly all the studies are cross-sectional, and many control for socioeconomic variables—household size, income, automobile ownership, age, gender, race, employment status—drawn primarily from travel diary data. Two studies incorporate attitudinal factors as control variables, including attitudes about transportation and lifestyle preferences (Kitamura et al. 1997; Bagley and Mokhtarian 2002). Measures of the built environment include population and employment density,

3

Part of the review that deals primarily with work site interventions (e.g., industrial plants, universities, and federal agencies) and related informational outreach programs has been completed (Kahn et al. 2002), and the results are discussed in the subsequent section under “Building or Site Level.”

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land use mix or diversity of land uses, and design (e.g., shade, scenery, presence of attractive stores and houses), features that have been characterized as the three D’s of land use—density, diversity, and design (Cervero and Kockelman 1997). Other measures include transportation infrastructure (e.g., presence and continuity of sidewalks), street pattern (e.g., grid, cul-de-sac) and connectivity, presence of bicycle paths, neighborhood type (e.g., traditional versus suburban planned unit development), and accessibility (e.g., distances to destinations or numbers of destinations within a specified distance).

Although it is difficult to summarize the results of these studies in view of the breadth of measures considered, inspection of the study findings (see Handy 2004, Table 3-4, and Table 6-1, pp. 174–189 in this report) suggests that certain measures are positively (or negatively) correlated with walking or cycling for travel.4 Land use correlates include a few density measures—population, employment, and retail density—and diversity of land uses. All are positively correlated with nonmotorized travel (i.e., the greater the density of population, employment, stores, and mix of land uses, the greater is the number of walking and other nonmotorized trips). Predictably, access (i.e., distance to nearest destination), another land use measure, is negatively correlated with nonmotorized travel in several studies. A grid street network and presence and extent of sidewalks are the primary transportation-related correlates, both being positively correlated with nonmotorized travel. Design features, with the exception of those of commercial areas, are insignificant, but only four studies examine the effect of such features. Certain neighborhood types—traditional,5 transit-served, and walkable—are positively correlated with walking and nonmotorized travel. The results are difficult to interpret, however, because of the lack of specificity about the characteristics of these neighborhoods.

4

The pluses and minuses in the tables represent results that are statistically significant. The level of significance (e.g., 5 percent, 1 percent) varies from study to study and is not noted.

5

Traditional neighborhoods are characterized by a people-oriented, small-town scale with such features as sidewalks and front porches, which have been emulated in neotraditional or new-urbanist developments.

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
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Findings from the Physical Activity Literature

The focus of these studies is on walking, primarily for exercise and recreation; other types of physical activity (e.g., vigorous or moderate-intensity exercise, leisure-time physical activity other than walking); and total physical activity (distinguishing between those who are active and inactive or who do and do not meet recommended levels of physical activity). Measures of the built environment used in these studies cover a breadth of variables that differ considerably from those used in the travel behavior literature. They often include perceptual characteristics—perceived safety, aesthetics and other neighborhood characteristics, and accessibility—rather than objective measures. Where objective measures are used, they fall into many of the same categories as in the travel behavior literature, such as accessibility, design, neighborhood type, and infrastructure for nonmotorized transportation. However, the destinations are most often trails, bicycle paths, or recreation centers rather than the more utilitarian destinations in the travel behavior literature (e.g., shopping, transit stations).

Drawing on ecological models, many of these studies include information about individual attitudes and intentions regarding physical activity (e.g., self-consciousness about appearance) and about the social as well as the physical environment (e.g., club membership, engaging in physical activity with another). Thus, these studies are able to assess the relative importance of all these factors in the decision to be physically active. Most of the studies also control for more typical socioeconomic variables, such as age, gender, race, ethnicity, educational level, marital status, employment status, and income level.

With regard to effects of the built environment, the study results reveal that a few measures are significantly correlated with physical activity (see Handy 2004, Table 3-7, and Table 6-2, pp. 190–209 in this report). For example, subjective measures of accessibility are positively correlated with several types of physical activity in a number of studies. Likewise, neighborhood characteristics, identified by both subjective and objective measures such as presence of sidewalks, enjoyable scenery, and seeing others exercising, are

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positively correlated with walking and total physical activity. The importance of subjective perceptions of neighborhood characteristics is not surprising in view of trip purpose in these studies, which is often for leisure or recreation rather than for destination-oriented travel. Notably absent, however, are strong associations between measures of perceived safety, design, and diversity of land uses and physical activity.

Summary Assessment

The existing literature approaches the relationship between the built environment and physical activity from a broad range of perspectives, areas of expertise, and measures of the variables of interest. The study results provide a growing body of evidence that shows an association between the built environment and physical activity. That having been said, it is difficult—perhaps because of the diversity of the literature—to sort out which characteristics of the built environment have the strongest association. Nevertheless, the study results reveal some patterns that suggest opportunities for further investigation.

Land Use

Population, employment, and land use density and mix/diversity are positively correlated with walking in the transportation literature. In the physical activity literature, fewer studies were found that examine land use measures. Land use diversity (one study) was positively correlated with walking, and density of pay and free facilities (one study) was positively correlated with total physical activity levels.

The characteristic of land use density is a good example of the complexities involved in linking the design of the built environment to travel behavior, such as walking in the neighborhood or walking to access transit. Several studies, for example, explored the link between transit use, development density, and urban design (Pushkarev and Zupan 1977; Messenger and Ewing 1996; Frank and Pivo 1994). They found that as density increased at both trip origin and destination, transit use rose, access by walking increased, and automobile use declined. Other analyses have shown that, although

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
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more compact development supports more walking and transit use, automobile ownership and travel patterns also reflect differences in the household characteristics and income of persons living at different density levels (Dunphy and Fisher 1996; Schimek 1996). When these factors are controlled for, the independent effect of density becomes far less robust. Moreover, the density thresholds needed to support transit are reached only in the most heavily populated central cities of U.S. metropolitan areas (TRB 1995). Density may well be a proxy for other variables, such as demographics, distance, car ownership levels, and transit service quality (Boarnet and Crane 2001). In her literature review for the committee, Handy (2004) notes that in studies that tested the significance of measures of both density and accessibility, the latter were significant, while the former were not. Indeed, density may serve as a proxy for accessibility, which provides a more direct explanation for travel behavior.

Accessibility

Typically measured as distance from destinations or facilities, accessibility is significantly correlated with physical activity in studies from both the travel behavior and physical activity literatures. In the former, distance from the nearest destinations, such as stores, bus stops, and parks, emerges as a significant correlate of non-motorized trips in general and of shopping and school trips in particular. Longer distances discourage all travel, but especially nonmotorized. In the physical activity literature, both perceived and objective measures of proximity and convenience of facilities, ranging from exercise equipment at home, to bicycle paths and trails, to parks, to local shopping and transit stops, are significantly and positively correlated with walking, other forms of exercise and recreation, and total physical activity.

The importance of good access to and convenience of facilities and destinations in the decision to be physically active is certainly plausible from a theoretical perspective. As discussed in Chapter 4, reducing the cost of a desired behavior—in this case by increasing the closeness and convenience of trip destinations—helps encourage the desired behavior.

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Design

The evidence for a correlation of design features and aesthetic characteristics of neighborhoods with physical activity is more limited. Design variables, such as neighborhood aesthetics and enjoyable scenery, emerge most strongly in the physical activity literature as significant correlates of physical activity, particularly walking. The one statistically significant result in the travel behavior literature is the positive correlation of design variables with walking trips for shopping. Handy et al. (1998) found that positive perceptions about shade, scenery, traffic, people, safety, and walking incentive and comfort were positively correlated with numbers of walking trips to neighborhood commercial areas.

These limited findings about the importance of design could reflect either the small number of studies that examined these variables, particularly in the travel behavior literature, or poorly specified measures of design. They could also signal the lack of a significant relationship between design and physical activity, or a relationship that may depend on the particular type of physical activity involved. Handy (2004) suggests the latter and concludes that design measures may be a more important influence on walking for recreation and exercise than on destination-oriented travel. Indeed, another review of the literature, drawing on a different set of studies, arrived at much the same conclusion (Humpel et al. 2002). Both reviewers, however, conclude that more research is needed to determine which aspects of design may matter and how they are related to different types of physical activity.

Transportation Infrastructure

The presence of sidewalks emerges in both literatures as a significant correlate of walking and nonmotorized travel. Other correlated transportation infrastructure measures include the proportion of streets with sidewalks and the percentage of the road network having a grid pattern. Some additional evidence exists that the condition of sidewalks is important to physical activity (Sharpe et al. 2004; De Bourdeaudhuij et al. 2003; Hoechner et al., in press). Senior citizens, in particular, may find uneven and cracked sidewalks barriers to walking because of the risk of falls (Loukaitou-Sideris 2004).

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
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Attitudes and Motivation

The limited number of studies that included individual and interpersonal factors found them to be more important than the physical environment in explaining levels of walking and other forms of physical activity (Kitamura et al. 1997; Bagley and Mokhtarian 2002; Giles-Corti and Donovan 2002b). For example, Handy (1996a) and Moudon et al. (1997) found high levels of walking in suburban areas even though these areas had been rated relatively low in terms of walkability. Thus the built environment may not be that important to those who are highly motivated to walk (Handy 2004). At the other end of the spectrum, a more appealing physical environment may not make a difference to those who have little motivation to walk or engage in other forms of physical activity. For many who fall between these two extremes, however, the built environment can facilitate or constrain physical activity. Handy (2004) concludes, and the committee concurs, that a supportive built environment alone is not sufficient to influence physical activity; nevertheless, it can play a facilitating role.

ANALYSIS OF FINDINGS

Effects of the Built Environment on Different Socioeconomic Groups

Data on physical activity levels of the adult population from the large public health surveys discussed in Chapter 2 indicate that activity levels decrease with age and are lower among women, ethnic and racial minorities, those with less education and low income levels, the disabled, and those living in the southeastern region of the United States (CDC 2003). The committee had hoped to examine the results of the literature review conducted for this study according to various socioeconomic groups to understand how characteristics of the built environment may affect the propensity of these groups to be physically active. Very little could be gleaned on this question, however. This is not surprising given that the results for the general population, with some exceptions, show little consistency in the effects of the various environmental characteristics studied.

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An examination of the results of the physical activity literature from the perspective of demographic differences is a good case in point. Of the 28 studies in this literature reviewed by Handy (2004), only nine describe the relationship between the built environment and physical activity separately for some demographic characteristic. Few coherent patterns emerge from the analyses. Seeing others exercising was positively associated with physical activity for African Americans, Hispanics, and rural women (King et al. 2000; Eyler et al. 2003; Wilcox et al. 2000). Physical activity was lower among racial and ethnic minorities who perceived their neighborhood to be unsafe and among older men and women (aged ≥65 years) (CDC 1999). Gender differences are more difficult to interpret. For example, walking and moderate activity among women were positively correlated with diversity of land use, ease of walking to a transit stop, access to local shopping, and emotional satisfaction with a neighborhood, but not with presence of sidewalks or satisfaction with neighborhood services (De Bourdeaudhuij et al. 2003).

Effects of the Built Environment at Different Geographic Scales

The role of the built environment can affect the propensity to be physically active at many geographic scales (the building or site level, the neighborhood, and the region) (see Figure 1-2 in Chapter 1). In general, the issue of geographic scale is underexamined in the recent literature (Boarnet 2004).

Building or Site Level

Little is known about how the design of buildings and their sites may influence physical activity (Zimring et al. 2004), which is why the committee did not focus more of its investigation at this scale. Yet most Americans spend the majority of their day in and around buildings—at home, work, or school. This suggests that these locations can provide important opportunities to be physically active.

The form of buildings and sites is thought to affect physical activity at several spatial scales. These include building elements, such as the layout of stairs and exercise rooms; overall building design; and site selection and design, which comprise connectivity be-

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tween buildings, connectivity of buildings to the edge of the site, and proximity to off-site amenities (Zimring et al. 2004).

Very limited data could be found on physical activity at home. Two studies included in Handy’s (2004) review found that having home exercise equipment was positively correlated with vigorous exercise (De Bourdeaudhuij et al. 2003; Sallis et al. 1989). Workplaces are another important but understudied location for physical activity. Stair use provides a low-cost way to integrate physical activity into the daily routine, and there is some limited evidence that interventions to increase workplace stair use (e.g., motivational signs and music in the stairwell) can be effective, although the duration of the effect is unclear (Kerr et al. 2004). Other, more costly interventions, mainly at work sites (e.g., equipment in fitness centers or community centers, creation of walking trails), in conjunction with informational programs, were found to be effective in increasing physical activity (Kahn et al. 2002). The workplace can also be an important base for walking trips, depending on the location of the building and the site layout. An analysis of trip linkage patterns, for example, found that the highest percentage of non-work-related trips involving physical activity are accounted for by walking to and from the workplace before, during, and after work (Wegmann and Jang 1998). Connectivity between buildings and shelter from the elements, placement of parking, and availability of amenities (walking or running trails in suburban, campuslike office complexes and presence of stores and other desirable destinations near urban office buildings) could encourage more such walking trips (Zimring et al. 2004). Company interest in promoting physical activity can pay off because a healthy workforce reduces health care costs (see Chapter 2).

Neighborhood Level

To date, most of the literature has focused on environmental determinants of physical activity at the neighborhood level, and this is appropriate. The neighborhood provides opportunities for all types of physical activity. Indeed, in a recent survey of U.S. adults—the U.S. Physical Activity Study—approximately two-thirds of respondents identified neighborhood streets as the setting where they

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engage in physical activity (Brownson et al. 2001). Walking is identified as the most common physical activity, reported by 20 percent of Americans (Ross 2000).

The attributes of the neighborhood built environment that may affect walking and other forms of physical activity have already been discussed. Many of these characteristics, such as the presence of sidewalks, aesthetics and other design features, and convenient access to local shopping and parks, are related primarily to physical activity within the neighborhood, including both destination-oriented travel and physical activity for exercise and recreation. These characteristics are unlikely to affect mode choice for many trips out of the neighborhood, such as commuting or traveling to a regional shopping center.

Regional Level

Another notable gap in the literature is consideration of the effect of the built environment on physical activity at scales larger than the neighborhood. Although many of the micro-scale characteristics of the neighborhood would not matter at the regional level, at least one characteristic—accessibility—would. As discussed above, accessibility can influence mode choice. Longer distances between destinations, for example, often tip the balance in favor of the speed and convenience of automobile travel. Good accessibility can also affect destination choice by drawing residents out of the neighborhood and potentially providing other opportunities for recreation and exercise. Such trade-offs and possible substitutions between physical activity within and outside of neighborhoods can be studied only at a regional scale.

Role of Mediating Variables

The relationship between the built environment and physical activity operates through many mediating variables, including socioeconomic characteristics, personal attitudes and motivation, cognitive and behavioral skills, safety and security, and time. Individual characteristics, attitudes, and skills have been discussed earlier in this chapter and in Chapter 4; the effects of safety and security and time are discussed here.

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Safety and Security

Intuitively, it is expected that if individuals perceived their environment to be unsafe, they would not be inclined to risk exposure to harm by walking or cycling or would do so only for necessary trips. Because of the likely influence of safety and security on physical activity levels, the committee commissioned a separate paper to review the literature on this topic (Loukaitou-Sideris 2004).

The paper begins by distinguishing among the primary sources of danger for pedestrians and cyclists. The main human-caused sources are crime and vehicular traffic, while the main environmental sources are roadway design (wide, heavily trafficked streets with limited or no accommodation for pedestrians and cyclists), infrastructure condition (e.g., broken and uneven sidewalks), and unattended dogs. These distinctions are important because each source of danger is related to different safety concerns, which may in turn determine which characteristics of the built environment act to inhibit or encourage physically active behavior.

Despite the presumed importance of safety and security to levels of physical activity, neither Loukaitou-Sideris (2004) nor Handy (2004), who also examined safety as a perceived neighborhood characteristic by drawing on a somewhat different group of studies, found evidence for a strong correlation. Loukaitou-Sideris (2004) suggests several reasons for this. First, a number of studies combined safety with other physical attributes in a composite measure that may have obscured the independent effect of safety. In other studies, there was little variation in the environment in which safety effects were examined; these studies were conducted in either unsafe neighborhoods or neighborhoods where safety was not a major concern and thus did not involve a mix of neighborhoods from a safety perspective. Finally, the studies did not always distinguish among different types of safety (e.g., crime, traffic), which, as discussed above, can obscure significant findings.


Personal Safety The results of studies that focused on subpopulations of women, children, and older adults or did a better job of identifying neighborhood environmental characteristics associated with safety and physical activity show a stronger positive correlation between real and perceived dangers to personal safety and sedentary

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lifestyles (Loukaitou-Sideris 2004). For example, several studies have found that crime and fear of crime are barriers to exercising and being physically active outdoors for women, particularly minority women (CDC 2003; King et al. 2000). These results have been confirmed in numerous focus groups for urban African American women, American Indian women, and Latina immigrant women (Eyler et al. 1998; Wilbur et al. 2002; Young et al. 2002; Thompson et al. 2002; Evenson et al. 2002). Likewise, parental concerns about safety curtail children’s activity levels, from use of public spaces such as parks and other play spaces (Valentine and McKendrick 1997; Sallis, McKenzie, et al. 1997; Sallis et al. 1998) to participation in nonschool sports programs (Seefeldt et al. 2002). Older adults are another vulnerable group. Numerous studies have found that older adults may restrict their activity because of concern about personal safety (CDC 1999; Booth et al. 2000). The impact of safety on the behavior of the elderly was dramatically illustrated in a comparative study of mortality rates in two Chicago neighborhoods during the July 1995 heat wave (Klinenberg 2002). The author attributes the higher mortality rates in the neighborhood with abandoned buildings, high rates of violent crime, and limited social support systems in part to the physical characteristics of that community: elderly residents were isolated and afraid to venture forth to seek cooler shelter.

A recent study of urban youth that includes carefully collected data on local socioeconomic and physical characteristics for 80 Chicago neighborhoods found that lack of community safety and measures of social (public intoxication, selling drugs, prostitution) but not physical (graffiti, abandoned cars, needles and syringes) disorder were associated with lower levels of recreational physical activity. These effects remained significant after differences in neighborhood socioeconomic characteristics where taken into account (Molnar et al. 2004).


Traffic Safety Approximately 5,400 pedestrians and cyclists were killed in the United States in 2003, and an additional 116,000 were injured, although these numbers have been declining over time (NCSA 2003a; NCSA 2003b). Children and the elderly are the most vulnerable to pedestrian–automobile collisions—children in terms of in-

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juries and older adults in terms of fatalities (Loukaitou-Sideris 2004). In 2003, children under the age of 16 made up 22 percent of the population, and although they represented only 9 percent of pedestrians killed in crashes, they accounted for 27 percent of pedestrians injured. Older adults (aged ≥70) represented 9 percent of the population but accounted for 16 percent of pedestrian fatalities (NCSA 2003b). With respect to cyclists, the most vulnerable age group was 10- to 15-year-olds, who accounted for less than 9 percent of the population but 16 percent of those killed in crashes (NCSA 2003a).

Although pedestrian and cyclist fatalities are the result of many factors (e.g., motorist behavior; alcohol involvement of drivers, pedestrians, and cyclists), characteristics of the built environment and the transportation infrastructure are part of the story. Most pedestrian–automobile collisions involving children happen in residential areas near a child’s home (Sharples et al. 1990) or on the journey to school because exposure is higher in these locations. Child pedestrian injuries appear to be higher in poor neighborhoods, for example, where children play in the streets, often because they lack access to other safe play spaces (Corless and Ohland 1999).

Lack of sidewalks and protected areas for walking and cycling to school can contribute to high levels of pedestrian collisions. Although walking and cycling represent a small fraction of all school trips (less than 20 percent), these modes have the highest fatality and injury rates on a per mile basis (TRB 2002).6 The safety of older adult pedestrians is also compromised by short traffic signal timing and wide streets with inadequate median “safe havens” (Dorfman 1997). The safety of the pedestrian population of all ages appears to be inversely associated with high traffic speeds (Jacobsen et al. 2000), number of miles of major arterial streets in a neighborhood (Levine et al. 1994), poorly located bus stops and crosswalks (Walgren 1998), and poor lighting.

Few studies were found that directly examine the effect of traffic, either real or perceived, on levels of walking and cycling. Handy

6

A study by the Santa Ana Unified School District in California, cited by Loukaitou-Sideris (2004), found that more than half the city’s 72 pedestrian–automobile collisions during the first 6 months of 1998 involved children walking near schools (Los Angeles Times 1999).

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(1996b) found that the perception of safety, which, as noted, is associated with the decision to walk, is influenced by the speed of automobile traffic. In the surveys reviewed in Chapter 4, parents mentioned traffic danger as a barrier to their children’s walking or cycling more to school (Dellinger and Staunton 2002; BR&S 2003). Clearly, efforts to increase physical activity by encouraging more walking and cycling need to be undertaken with care. Raising exposure levels can increase the risk of injury from traffic unless mitigation measures are taken.7

Time

The role of time in total physical activity levels is potentially important but poorly understood. Lack of time is often cited as a reason for not being more physically active, and modern life is time-pressured for many. Yet the time-use data cited in Chapter 3 suggest that labor-saving devices, particularly in the home, have freed up more discretionary time for many Americans. The figures on television watching alone—about 3 hours a day for the average U.S. adult—suggest that making time for physical activity is a matter of choice for many Americans.

Recognizing how time affects activity choices should be helpful in understanding the decisions and trade-offs individuals make with regard to physical activity. Individuals may have a time budget for active pursuits and may substitute one type of physical activity for another (e.g., cycling to work rather than exercising at home or at a gym).

Understanding how individuals allocate their time over the course of a day is also important in considering possible interventions. Opportunities for encouraging physical activity exist in many settings—at home, at work, at school, in travel, and in leisure. Rebuilding physical activity into the daily routine may not be so difficult if the goal is to ensure that Americans accumulate at least

7

A recent study, however, provides a counterexample. Researchers analyzed the relationship between the rates of pedestrian and bicycle activity and the number of times pedestrians or cyclists were hit by cars. They found that, in most cases, the risk of collision went down as pedestrian and bicycle activity increased. The author hypothesizes that motorists may drive more carefully in the presence of large numbers of pedestrians and cyclists (Jacobsen 2003).

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30 minutes a day of moderate-intensity activity in increments of 10 minutes on most days of the week. Small changes in activity levels in a number of different locations could probably accomplish this objective.

Evidence for Causality

The correlation between certain characteristics of the built environment and higher levels of physical activity does not prove that the built environment caused the physical activity. As noted in Chapter 5, for example, the issue of self-selection bias must be addressed: individual preferences for being physically active may determine the decision to live in a walking- and cycling-friendly environment and account for some or all of the higher levels of physical activity often observed in these neighborhoods. The research designs of a handful of studies enable some analysis of the complex relationships among individual preferences, the built environment, and physical activity levels. Various possible research designs that can lay the foundation for treating the complexities of cause-and-effect relationships are outlined in Chapter 5. They include longitudinal studies using time-series data, case-control cross-sectional studies, and other natural experiments.

Several researchers have used instrumental variable techniques to examine the potential effect of self-selection bias.8 Boarnet and Sarmiento (1998) and Greenwald and Boarnet (2001) used such

8

In technical terms, the self-selection issue is a manifestation of “endogeneity bias.” Ordinary least-squares regression analysis requires that observed explanatory variables be deterministic (not random) and uncorrelated with any unobserved explanatory variables (captured by the error term of the equation). When that requirement is violated, as it is when an explanatory variable itself is a nondeterministic function of other variables in the model, the resulting coefficient estimates are biased. In the present case, the explanatory variable residential location is apt to be determined partly by variables such as attitudes toward travel—variables that are also likely to be observed or unobserved influences on travel behavior itself. Thus, residential location is endogenous. The instrumental variables technique treats this problem by purging the endogenous variable (residential location) of its correlation with other variables in the equation for travel behavior. It does so by first estimating residential location as a function of variables not expected to be associated with travel behavior. The estimated value of residential location then meets the requirements for unbiased ordinary least-squares estimation of the equation for travel behavior. See Chapter 5 for more detailed discussion of research methods.

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techniques to control for choice of residential location in studying how neotraditional neighborhoods affected nonwork automobile and pedestrian travel, respectively. Their work tested the hypothesis that the land use characteristics of neotraditional developments (e.g., grid street patterns, higher-density housing, mixed uses) would encourage more walking and other types of non-motorized travel. In both cases, the researchers found that when variables associated with residential location preferences were identified and examined separately from variables associated with the built environment, some, but not all, of the environmental variables ceased to be significantly correlated with nonmotorized travel. For example, Greenwald and Boarnet (2001) found that population density at the block level and a combined measure of pedestrian-friendly characteristics (ease of street crossing, sidewalk continuity, street connectivity, and topography) remained significant predictors of nonwork pedestrian travel after location preferences were taken into account.

Cervero and Duncan (2002) examined mode choice among residents of transit-oriented developments by using nested logit techniques. In this analysis, mode choice was expressed hierarchically as a function of residential location, which in turn was expressed as a function of workplace location. On the basis of conditional probabilities from the nested logit output, an estimated 40 percent of transit mode choice among station-area residents was explained by the decision to reside near transit in the first place.

In one study using the structural equations modeling approach, researchers attempted to separate out the effects of attitudes, residential location choice, and travel behavior in urban and suburban neighborhoods (Bagley and Mokhtarian 2002).9 A structural equations model of residential location and travel demand that included attitudinal and lifestyle explanatory variables enabled the researchers to analyze both direct and indirect effects, as well as the

9

Structural equations modeling recognizes that causal influences may work in more than one direction; therefore, multiple equations reflecting these causal linkages are simultaneously modeled (hence using a “structural model” rather than a single equation).

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possibility of multiple directions of causality.10 The authors conclude that attitudinal and lifestyle variables had the greatest impact on travel demand among all the explanatory variables; land use characteristics had little independent effect.

Another study, discussed in Chapter 4, compared the commuting patterns of matched and mismatched residents in three neighborhoods in the San Francisco Bay Area (one urban and two suburban) and attempted to separate the effects of household location preferences from those of the spatial characteristics of residential neighborhoods (Schwanen and Mokhtarian 2004).11 As in the work of Bagley and Mokhtarian (2002), the researchers found that attitudes and lifestyle variables influenced commuting behavior: suburban-minded residents of the urban neighborhood commuted by private vehicle more often than their urban-minded neighbors (although less often than suburban-minded suburban residents). However, the authors did find some evidence that neighborhood structure itself has an autonomous effect on commuting choices. In both studies, the authors acknowledge the limitations of a cross-sectional approach, which prevented their capturing changes in behavior over time.

Krizek (2003) attempted to address such longitudinal changes by examining modifications in travel behavior among those who move from one neighborhood to another. On the basis of data from the Puget Sound Transportation Panel, he examined the travel behavior of a sample of households that had moved between 1989 and 1997 to neighborhoods with higher local accessibility. Regression models were used to predict changes in travel behavior as a function of changes in neighborhood accessibility, while changes in life cycle and regional and workplace accessibility were held constant. Krizek found that residents who moved to

10

The researchers estimated a nine-equation model for residential location (traditional or suburban), attitudes (pro–high density, pro-driving, pro-transit), and travel demand (vehicle miles, transit miles, and walk/bicycle miles) as endogenous variables for a sample of 515 residents of five neighborhoods in the San Francisco Bay Area.

11

The data for this study came from responses to a mailed questionnaire that solicited information on a variety of travel and related issues. A multinomial logit analysis model was used to analyze the results (Schwanen and Mokhtarian 2004).

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neighborhoods with better local accessibility, all else being equal, had significantly reduced vehicle and person miles traveled (VMT and PMT) and number of trips per tour but increased average number of trips. Difference in neighborhood accessibility, however, was not a significant predictor of changes in walking, cycling, and transit use, which suggested that household travel preferences remained fixed despite changes in residential location. The author hypothesizes that households having moved to an area with greater neighborhood accessibility took more trips but reduced their overall VMT and PMT because their destinations were closer to home. The impact on physical activity levels, which is not discussed by the author, is unknown. The author cautions that, although his longitudinal approach represents an advance in understanding the dynamic effects on travel behavior of a change in urban form (i.e., moving from a low- to a high-accessibility neighborhood), it was limited because no attempt was made to control for possible self-selection bias.

Another longitudinal study of the effect of changes in the built environment on nonmotorized travel—a natural-experimental study of the impact of improvements in traffic safety on children walking and cycling to school—was summarized in Chapter 4. The authors (Boarnet et al. 2004) surveyed parents of children and made independent observations of traffic volumes, speeds, and numbers of pedestrians and cyclists to examine the impact of California’s Safe Routes to School Program. They found mixed results: improvements in safety conditions had increased the numbers of children walking and cycling to school at some, but not all, sites. The authors conclude that limitations in the before-and-after study design and the relatively short time frame of the study precluded a more definitive assessment but also discuss the merits of natural-experimental research designs (Boarnet et al. 2004).12

12

Ideally, for example, baseline activity data should be collected before the intervention occurs. Respondents would then be questioned about changes in activity after the construction projects had been implemented. Boarnet et al. (2004) used a “second-best” retrospective approach. They asked respondents to recall and compare activity levels before and after project implementation (Boarnet 2004).

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Another cross-sectional study also followed a natural-experimental research design. The researchers paired two distinctly designed neighborhoods—one a neotraditional neighborhood and the other a conventional suburban development—to observe differences in physical activity behavior (Rodriguez et al. 2005). Household heads were asked to keep a travel diary and complete a written survey. The researchers isolated the variables of interest—the effect of neighborhood form on various measures of physical activity—by matching the neighborhoods on various other characteristics (regional and freeway access, property values, and age of development) and adjusting for individual and household characteristics.13 The researchers found that levels of walking and cycling were indeed greater in the neotraditional neighborhood, primarily as a result of more in-neighborhood utilitarian trips by non-motorized means. Total levels of physical activity were also greater in the neotraditional neighborhood, but the differences were not statistically significant. The authors note that more walking and cycling among residents of the neotraditional neighborhood appeared to substitute for their physical activity at other locations, because the total levels of physical activity for surveyed households in both neighborhoods were not significantly different. The authors acknowledge the limitations of relying on self-reports of the frequency and location of physical activity, a relatively low response rate that could have biased the results, and limitations on the generalizability of their results to other neighborhoods. However, they raise important issues that merit further investigation, such as the possibility of substitution effects among different types of physical activity and the fact that different attributes of the built environment may be important for different types of physical activity.

Together, the few studies reviewed here provide limited but provocative results concerning the complexity of causal connections between the built environment and physical activity levels.

13

The researchers examined the total amount of physical activity, the location of that activity (i.e., at home, in the neighborhood, outside the neighborhood), and the frequency and duration of all physical activity trips, and they examined recreational and utilitarian physical activity trips separately (Rodriguez et al. 2005).

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Those results suggest a far more nuanced relationship than has been investigated in much of the empirical work to date.

KNOWLEDGE GAPS

In its assessment of the empirical evidence to date, the committee identified several areas in which it believes knowledge gaps currently exist. The following discussion of such gaps is not exhaustive, but it covers many of the critical areas in which further research would help clarify the complex relationships between the built environment and physical activity.

Nonresidential Settings

Most empirical research to date on the influences of the built environment on physical activity has focused on residential neighborhoods. Relatively few studies have examined relationships for nonresidential settings. In the transportation field, this is partly because travel surveys are normally conducted at the household level. Given the importance of studying total physical activity levels across all settings, it would be useful to know more about physical activity in such settings as employment centers, shopping malls, mixed-use projects, and schools. Are campus-style office parks (which are a far cry from neotraditional designs) conducive to physical activity? Even though most people reach enclosed shopping malls by car and vast expanses of surface parking are provided, do these facilities promote walking, especially for certain subgroups of the population such as senior citizens? Are the mixed-use profiles of edge cities as inducements to walking offset by the absence of continuous sidewalk networks and pedestrian-unfriendly designs? Do buildings with prominent, well-lit, open staircases encourage physical activity? To what extent do signage, location of common areas, and availability and location of specific services within building complexes influence physical activity levels at work or at school? Expanding research to nonresidential settings would broaden the understanding of relationships over a wide array of built environ-

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ments and provide important insights into the totality of physically active behaviors.

Self-Selection

Some progress has been made in understanding the effects of self-selection on physical activity, but greater attention needs to be given to designing research that accounts for such influences as peoples’ lifestyle preferences and attitudes. The physical activity benefits of pedestrian-friendly designs need to be understood relative to, for example, the benefits of removing barriers to residential self-selection, such as exclusionary zoning or community resistance to infill housing construction. In addition, are opportunities to sort oneself into physical activity–friendly communities similar across socioeconomic groups? Might low-income and minority households face greater barriers to residential self-selection?

Interactive and Mediating Effects

Little research has been conducted to examine how built environments may interact with other policy interventions, such as road user pricing or flexible parking standards, to influence physical activity. Possible synergistic effects need to be explored to provide a stronger foundation for informing public policy. The role of time is another understudied variable that warrants greater attention. To what extent do individuals substitute one type of physical activity for another (e.g., exercise at work for exercise at home or in the neighborhood)? What is the effect on total physical activity levels?

Stratification

Relationships among built environments, policy interventions, and physical activity outcomes likely vary by subpopulation, urban setting, climate, and other contextual factors. Future research needs to use study designs and populations suitable for examining differences in various subgroupings. It is important to determine which environmental design strategies are most beneficial in different settings and in different types of communities, from well-to-do to low-income.

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TABLE 6-1 Summary of Existing Research—Travel Behavior Literature

Study

Samplinga

Survey

Active Travel Variable

Bagley and Mokhtarian 2002

515 individuals in five neighborhoods in San Francisco Bay Area

1992 three-day travel diary survey

Natural log of walk/bike miles

Black et al. 2001

4,214 parents at 51 selected infant schools in two regions in the United Kingdom

1996 recall survey distributed through schools

Percent walking as usual mode to school

Cervero 1996c

42,200 housing units in 11 metropolitan statistical areas; trips as unit of analysis

1985 American Housing Survey, questionnaire on commuting, cross-sectional survey

Choice of walk or bike as principal commute mode

Cervero and Duncan 2003

7,889 trips, trips as unit of analysis

2000 Bay Area Travel Survey, 2-day activity diary survey, cross-sectional

Choice of walking or biking (with variables for weekend trip, recreation/entertainment, eating/meal, social, shopping purposes)

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Controls/Confounders

Built Environment Variableb

Results

Age

Gender

Household size

Number of children under 16

Number of vehicles

Years lived in Bay Area

Lifestyle factors (seven factors)

Attitudes (10 factors)

Suburban factor

Traditional factor

Not significant

Full-time homemaker

Only one car

Southern county

Distance to school

Percent walking:

–distance (<0.5 mile—89.5% walk, 0.3% bike; 0.5 to 1 mile—66.4% walk, 1.2% bike; 1.1 to 2 miles—27.7% walk, 2.0% bike; >2 miles—5.5% walk, 0.8% bike) (not statistically tested)

Residence in central city (y/n)

Number of autos

Household income

Highway or railroad or airport within 300 ft (y/n)

Public transit adequate in neighborhood (y/n)

Distance from home to work

Single-family housing within 300 ft (y/n)

Low-rise multifamily housing within 300 ft (y/n)

Mid-rise multifamily housing within 300 ft (y/n)

High-rise multifamily housing within 300 ft (y/n)

Commercial or non-residential building within 300 ft (y/n)

Grocery or drugstore between 300 ft and 1 mile (y/n)

Walk/bike choice:

–single-family

–ratio of single-family to multi-family low-rise

+mid-rise multifamily

+high-rise multifamily

+commercial nearby

–grocery or drug between 300 ft and 1 mile (logit model)

Disability

Gender

Race

Auto ownership

Constraints

Deterrents:

  • Trip distance

  • Slope

  • Rainfall day of trip

  • Dark at time of trip

  • Low-income neighborhood

Choice of walking:

–distance

–slope

–rainfall

+land use diversity—origin

+weekend trip, recreation/ entertainment, eating/meal, social, or shopping purpose

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Study

Samplinga

Survey

Active Travel Variable

Cervero and Gorham 1995c

26 neighborhoods, 14 pairs in San Francisco Bay Area, 12 pairs in Los Angeles region

1990 U.S. Census, cross-sectional

Number of walk trips to work

Percent walk trips to work

Cervero and Radisch 1996c

620 households for nonwork survey, 840 households for work survey in six census tracts in two neighborhoods in East Bay in San Francisco Bay Area

1994 recall mail surveys, one for work trips, one for non-work trips, cross-sectional

Choice of nonauto mode for non-work trips

Choice of nonauto mode for work trips

EPA 2003

709 trips to K-12 school, trips as unit of analysis

2001 Gainesville Metropolitan Transportation Planning Organization Survey and 2000 Florida Department

Choice of walking to school

Choice of biking to school

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Controls/Confounders

Built Environment Variableb

Results

 

Characteristics:

  • Employment accessibility

  • Ped/bike design at origin

  • Ped/bike design at destination

  • Land use diversity—origin

  • Land use diversity—destination

Choice of biking:

–distance

–dark

+recreation/entertainment or social purpose (logit model)

Neighborhoods matched for income

Transit versus automobile neighborhood

Number of walk trips:

+transit neighborhoods (23 to 142 more walk trips per 1,000 households in San Francisco Bay Area; from 1 to 179 more walk trips per 1,000 households in Los Angeles region)

Percent walk trips:

+transit neighborhoods (1.2 to 13.4 percentage points more walk trips in San Francisco Bay Area, from 1.7 to 24.6 percentage points more walk trips in Los Angeles region)

Household size

Vehicles per household

Annual salary of respondent

Pedestrian versus automobile neighborhood

Choice of nonauto mode for nonwork trips:

+traditional

Choice of nonauto mode for work trips:

not significant (logit model)

Income

Cars per household

Driver’s license

Overall density (jobs and employment)

Commercial floor area ratio

Percent of streets with sidewalks

Average sidewalk width

Choice of walking:

–walk time

+sidewalk coverage

Choice of biking:a

–bike time (logit model)

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Study

Samplinga

Survey

Active Travel Variable

 

 

of Transportation Survey, 1-day travel diary survey, cross-sectional

 

Ewing et al. 1994c

163 households from six communities in Palm Beach County, Florida

c. 1994 Palm Beach County, Florida, Travel Survey, 2-day travel diary survey, cross-sectional

Percent walk or bike trips of all trips

Frank and Pivo 1994c

1,680 households, weighted to regional total in Puget Sound, Washington, region; census tract as unit of analysis

1989 Puget Sound Transportation Panel survey, 2-day travel diary survey, cross-sectional

Percent walk trips for work trips for census tract

Percent walk trips for shopping trips for census tract

Friedman et al. 1994c

Selected zones from 550 zones in San Francisco Bay Area

1980 Bay Area Travel Survey, 1-day travel diary survey, cross-sectional

Average number of walk trips per day per household

Average number of bicycle trips

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Controls/Confounders

Built Environment Variableb

Results

 

Street density

Pedestrian environment factor

Walk time to school

Bike time to school

 

None

Neighborhood

Percent walk or bike trips: not significant (ANOVA)

Mean age for residents of tract

Household type (defined by number of adults and age, share for tract)

Driver’s license (share for tract)

Trips made by employed resident (share of trip ends in tract)

Trips made by residents with bus pass (share of trip ends in tract)

Trips made by residents with access to less than one vehicle (share of trip ends in tract), mean number of vehicles available per participant ending trip in tract

Gross population density at origin

Gross population density at destination

Gross employment density at origin

Gross employment density at destination

Land use mix at origin (entropy measure)

Land use mix at destination (entropy measure)

Percent walk trips for work:

+employment density at origin

+population density at origin

+population density at destination

+land use mix at origin

+land use mix at destination

Percent walk trips for shopping:

+employment density at trip destination

+population density at trip origin

+population density at destination (linear regression; 31% and 35% of variation explained)

None

Traditional versus standard suburban communities

Number of walk trips:

+traditional (1.06 versus 0.83)

Number of bike trips:

+traditional (0.35 versus 0.24)

Percent walk trips:

+traditional (12% versus 8%)

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Study

Samplinga

Survey

Active Travel Variable

 

 

 

per day per household

Percent walk trips for zone

Percent bike trips for zone (all by purpose)

Greenwald and Boarnet 2001

1,091 residents from Portland, Oregon, region

1994 Portland Travel Survey, 2-day travel diary survey, cross-sectional

Number of walk trips in 2 days

Handy 1996ac

400 residents in four neighborhoods in San Francisco Bay Area

1992 recall phone survey, cross-sectional

Number of strolling trips per month

Percent of residents strolling at least once per month

Number of walking trips to commercial area per month

Percent of residents walking to commercial area at least once per month

Handy et al. 1998 and Handy and Clifton 2001c

1,368 residents in six neighborhoods in Austin, Texas

1994 recall mail survey, cross-sectional

Number of strolling trips per month

Number of walking trips to commer

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Controls/Confounders

Built Environment Variableb

Results

 

 

Percent bike trips:

+traditional (4% versus 2%)

(no statistical testing)

Age

Gender

Race

Income

Square of income

Number of children under 16

Cars per driver

Employees per household

Workday

Population density in block group

Population density in zip code

Retail density in 1-mile grid cell

Retail density in zip code

Percent of network that is a grid

Pedestrian environment factor (three-point scale): ease of street crossing, sidewalk continuity, street connectivity, topography

Median walk distance

Median walk speed

Number of walk trips:

+population density

+retail density

+percent of network that is a grid

+pedestrian environment factor

+median walk distance

+median walk speed (ordered probit model)

Household type (defined by number of adults, work status)

Traditional versus suburban neighborhood

Average strolling frequency:

not significant

Percent of residents strolling:

not significant

Walking trips to commercial area:

+traditional (4.8 to 5.7 versus 1.0 to 2.8 walks per month)

Percent walking to commercial areas:

+traditional (56% to 64% versus 33% to 48%)

(ANOVA)

Age

Gender

Employment status

Network distance to nearest commercial area (using GIS)

Number of strolling trips:

+perceived safety

+perceived shade

+perceived people

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Study

Samplinga

Survey

Active Travel Variable

 

 

 

cial area per month

Hanson and Schwab 1987

278 households stratified by life cycle stage in Uppsala, Sweden

1971 Uppsala 35-day travel diary survey, cross-sectional

Percent of all stops by nonmotorized modes

Percent of work stops by nonmotorized modes

Kitamura et al. 1997c

1,380 individuals in five neighborhoods in San Francisco Bay Area

1992 three-day travel diary survey

Number of nonmotorized trips

Percent nonmotorized trips for all trips

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Controls/Confounders

Built Environment Variableb

Results

Presence of children under age 5

Income

Pet to walk

Perceptual factors related to safety, shade, houses, scenery, traffic, people, stores, walking incentive, walking comfort

+Old West Austin neighborhood

Number of walking trips to commercial areas:

–distance +perceived stores

+perceived walking incentive

+perceived walking comfort

+Old West Austin neighborhood

+strolling frequency (linear regression, 15% and 29% of variation explained)

Gender

Employment status

Automobile availability

Home-based accessibility

Work-based accessibility (number of establishments by 0.5-km intervals, weighted by distance, using Euclidean distance)

Percent of all stops:

+home-based accessibility

Percent of work stops:

+home-based accessibility

+work-based accessibility (correlation coefficients)

Age

Gender

Education level

Employment status

Homemaker (y/n)

Student (y/n)

Professional (y/n)

Driver’s license (y/n)

Household size

Number of persons over 16 years

Number of autos

Household income

Number of years in Bay Area

Apartment/single-family home

Attitudes (nine factors)

Study area

Macro-scale descriptors (y/n): BART access, mixed land use, high density

Pedestrian/bicycle facility indicators (y/n): sidewalk, bike path

Micro-scale accessibility indicators: distance to nearest bus stop, rail station, grocery store, gas station, park

Perceptions of quality of residential neighborhood: no reason to move, street pleasant for walking, cycling pleasant, good local transit service,

Number of nonmotorized trips:

+North San Francisco neighborhood

+BART access

+sidewalk

Share of nonmotorized trips:

+high density

–distance to nearest bus stop

–distance to nearest park (linear regression)

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Study

Samplinga

Survey

Active Travel Variable

Kockelman 1997c

9,000 households; trips as unit of analysis

1990 Bay Area Travel Survey, 1-day travel diary survey, cross-sectional

Choice of walk or bike for all trips by adults

Krizek 2000

550 households that moved between 1989 and 1997 in Puget Sound, Washington, region

1989 and 1997 Puget Sound Transportation Panel, 2-day travel diary survey, longitudinal

Percent of trips by alternative mode (transit, walk, bike)

Change in percent of trips by alternative mode (transit, walk, bike)

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Controls/Confounders

Built Environment Variableb

Results

 

enough parking, problems of traffic congestion

 

Age

Gender

Race

Household size (members over age 5)

Auto ownership

Income per household member

Driver’s license

Employment status

Professional job

Population density in origin zone, destination zone

Employment density in origin zone, destination zone

Accessibility (gravity measure, sales and service jobs within 30 minutes by walk mode) in origin zone, destination zone

Land use balance (entropy index, six land use types) for zone, mean for all zones within 0.5 mile

General land use mix (dissimilarity index, four land use types)

Detailed land use mix (dissimilarity index, 11 land use types)

Choice of walk or bike:

+accessibility in origin zone

+accessibility in destination zone (+0.22 elasticity)

+mean nonwork entropy in origin zone

+mean entropy in destination zone (+0.23 elasticity)

(logit model)

None

LADUF rating: land use mix (number of employees of selected types), density (housing units and persons per square mile), urban form rating (average block area per grid cell); measured for 150-m grid cells, averaged over all grid cells within 0.4 km

Change in LADUF rating

Percent of trips by alternative mode:

+LADUF (29% in high, 14% in medium, 6% in low LADUF)

Change in percent of trips by alternative mode:

–move from high to medium LADUF (9.9 percentage points)

(t-tests)

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Study

Samplinga

Survey

Active Travel Variable

Krizek 2003

550 households that moved between 1989 and 1997 in Puget Sound, Washington, region

1989 and 1997 Puget Sound Transportation Panel, 2-day travel diary survey, longitudinal

Percent of trips by walking

McCormack et al. 2001

663 households from throughout region, split into three zones; 300 households in each of three mixed land use neighborhoods, neighborhood as unit of analysis

1989 Puget Sound Transportation Panel, 2-day travel diary survey, 1992 same survey implemented in three selected neighborhoods, cross-sectional

Percent walk trips for shopping trips for neighborhood

Percent walk trips for all trips for neighborhood (only walk trips longer than 5 minutes)

McNally and Kulkarni 1997c

20 neighborhoods in Orange County, California, neighborhood as unit of analysis

1991 Southern California Association of Governments, 1-day activity diary survey, cross-sectional

Number of walk trips, percent walk trips

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Controls/Confounders

Built Environment Variableb

Results

Number of vehicles

Number of adults

Number of children

Number of workers

Income

Neighborhood accessibility: density (housing units per acre), land use mix (number of employees of selected types), average block area; measured for 150-m grid cells, averaged over all grid cells within 0.4 km

Regional accessibility (gravity measure)

Percent of trips by walking:

not significant

None

Straight-line distance to nearest commercial street

Neighborhood type

Percent walk trips for shopping trips:

–distance to nearest commercial street

Percent walk trips for all trips:

+walkable neighborhood (17.7 to 18.1 versus 2.0 to 2.8)

(no statistical testing)

Income

Traditional neighborhood development, planned unit development, and mixed (classified on the basis of ratio of cul-de-sacs to total intersections, ratio of four-way to total intersections, intersections/acre, ratio of access points to development perimeter, commercial area to total area, population density)

Number of walk trips:

not significant

Percent walk trips:

not significant

(ANOVA)

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Study

Samplinga

Survey

Active Travel Variable

Moudon et al. 1997 and Hess et al. 1999

12 sites in Seattle, Washington, area, controlled for density, site as unit of analysis

c. 1996 observations for 16 hours at entry points across cordons for sites, cross-sectional

Number of pedestrians

Parsons Brinckerhoff Quade and Douglas, Inc., 1993c

400 zones in Portland, Oregon

1985 Portland Metro travel survey, 1-day travel diary survey, cross-sectional

Percent walk or bicycle trips (for trips longer than six blocks) for zone

NOTE: ANOVA = analysis of variance; BART = Bay Area Rapid Transit; LADUF = less auto-dependent urban form; PEF = pedestrian environment factor.

aUnit of analysis is individual unless otherwise noted.

bBuilt environment variables are objectively measured unless otherwise noted.

cIncluded in Saelens, Sallis, and Frank 2003.

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Controls/Confounders

Built Environment Variableb

Results

None

Urban versus suburban neighborhood

Number of pedestrians:

+urban neighborhood (38 versus 12 pedestrians per hour per 1,000 residents)

(not statistically tested)

None

Pedestrian environment factor (3-point scale): ease of street crossing, sidewalk continuity, street connectivity, topography

Residential density

Transit access to employment (number of jobs within 30 minutes by transit)

Percent walk or bike trips:

+PEF (from 1.4% in low PEF to 9.6% in high PEF to 18.6% in central business district)

+residential density (from 2.0% at zero to two households per acre to 10.4% at five or more households per acre)

+transit access (from 2.0% at low access to 13.5% at high access)

(no statistical testing)

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

TABLE 6-2 Summary of Existing Research—Physical Activity Literature

Study

Sampling

Survey

Physical Activity Variable

Ball et al. 2001c

3,392 adults in Australia

1996 Physical Activity Survey for state of New South Wales, cross-sectional survey

Walking versus not walking for exercise in last 2 weeks

Berrigan and Troiano 2002

14,827 adults 20 years old or older in United States

NHANES III, cross-sectional survey

Walk 1 or more miles 20 or more times per month (y/n)

Leisure-time physical activity other than walking 20 or more times per month (y/n)

Booth et al. 2000c

402 adults 60 years old and older in Australia

1995 Supplement to the Population Survey Monitor by the Australian Bureau of Statistics, cross-sectional survey

Sufficiently active versus inactive (based on vigorous activities, moderate activities, and walking for exercise, leisure, or recreation)

Brownson et al. 2000

1,269 adults in 17 communities in 12 rural counties in Missouri, modified BRFSS method

1998 cross-sectional phone survey

Used walking trails (y/n, for those with access)

Increased walking since using trails (y/n, for those with access)

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Controls/Confoundersa

Built Environment Variables

Resultsb

Age

Gender

Education level

Neighborhood aesthetics (5-point scales): neighborhood friendly, local area attractive, pleasant walking near home

Convenience to facilities (5-point scales): park/beach within walking distance, cycle path accessible, shops within walking distance

Walking:

+neighborhood aesthetics (high 41% more likely to walk than low)

+convenience to facilities (high 36% more likely to walk than low)

(logistic regression)

Age

Gender

Race/ethnicity

Household income

Education

Health-related activity limitation

Region

Year when home built (<1946, 1946 to 1973, 1974 to present)

Walking:

+age of house (<1946 43% more than 1974 to present house; 1946–1973 house 36% more than 1974 to present house)

Leisure time physical activity:

not significant

(logistic regression)

Age

Gender

Country of birth

Marital status

Employment status

Living situation

Attitudes

Exercise equipment at home (y/n)

Feel safe walking during day (y/n)

Footpaths safe for walking (y/n)

Access to facilities (y/n): local exercise hall, recreation center, cycle paths, golf course, gym, park, swimming pool, tennis course, bowling green

Active:

+footpaths safe for walking

+access to recreation center

+access to cycle track

+access to golf course

+access to park

+access to swimming pool

(logistic regression)

Age

Gender

Race/ethnicity

Marital status

Education level

Income

Population of community (<5,500, 5,500 to 10,000, more than 10,000)

Trail length (<¼ mile, ¼ to ½ mile, >½ mile)

Trail surface (asphalt, chat, woodchips)

Distance to trail (<5 miles, 5–10 miles,

Used walking trails:

+5,500 to 10,000 population

+¼ to ½ mile length

–chat surface (versus asphalt)

–woodchips surface (versus asphalt)

Increased use:

–population

+trail length

–chat surface (versus asphalt)

Page 192
Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Study

Sampling

Survey

Physical Activity Variable

Brownson et al. 2001

1,818 adults, United States, modified BRFSS sampling plan, oversampling of lower-income individuals

1999–2000 cross-sectional phone survey, questions based on BRFSS, NHI, other surveys

Meeting recommendations for moderate or vigorous activity (y/n)

CDC 1999c

12,767 adults in Maryland, Montana, Ohio, Pennsylvania, Virginia

1996 BRFSS, cross-sectional phone survey

Active versus inactive (based on walking, moderate activity, and vigorous activity)

Craig et al. 2002

27 neighborhoods in Canada (totaling 10,983 residents)

1996 Canadian census, cross-sectional survey

Percent of residents walking to work

De Bourdeaudhuij et al. 2003

521 adults in Ghent, Belgium

c. 2002 cross-sectional mail survey using International Physical Activity Questionnaire

Minutes of sitting, walking, moderate-intensity activities, vigorous-intensity activities in last 7 days

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Controls/Confoundersa

Built Environment Variables

Resultsb

 

11–29 miles, 30 or more miles)

–distance to trail (20% to 30% less if 5 or more miles)

(logistic regression)

Age

Gender

Race/ethnicity

Household income

Education

Places to exercise (y/n): indoors, outdoors

Specific access variables (y/n): walk/jog trail, neighborhood streets, park, shopping mall, indoor gym, treadmill

Neighborhood characteristics (y/n): sidewalks, enjoyable scenery, heavy traffic, hills, streetlights, unattended dogs, foul air from cars/factories

Personal barriers (y/n): no safe place, bad weather

Meeting recommendations:

+places to exercise indoors or outdoors

+places to exercise outdoors only

+walking/jogging trail

+park

+indoors gym

+treadmill

+sidewalks present

+enjoyable scenery

+heavy traffic

+hills

(logistic regression)

Gender

Race/ethnicity

Education level

Income

Perception of safety from crime in neighborhood (4-point scale)

Active:

+perceived safe from crime in neighborhood

Income

University education

Poverty

Degree of urbanization (urban, suburban, small urban)

Observations of 18 characteristics on 10-point scales; hierarchical linear modeling to create ecologic score for each neighborhood

Percent walking to work:

+ecologic score (1-unit increase in score associated with 25 percentage point increase in walking)

Age

Gender

Education level

Employment status

Occupation

Living situation

BMI

Neighborhood variables (3-, 5-, or 7-point scales): residential density (3 items), land use mix/diversity (13), access to local shopping (2), ease of walking to transit stop (1), availability of sidewalks (1), availability of bike lanes (2), neighborhood aesthetics (4),

Women

Sitting:

–perceived safety from crime

+land use mix/diversity

Walking:

+availability of sidewalks

Moderate activity:

+physical activity equipment in home

+satisfaction with neighborhood services

Page 194
Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Study

Sampling

Survey Variable

Physical Activity

Eyler et al. 2003

4,122 women 20 to 50 years old from diverse racial/ethnic groups (white, African American, Latina, and Native American)

2001–2002 Women and Physical Activity Survey, cross-sectional phone and interview survey

Meets recommendations for moderate or vigorous activity versus does not meet

Does any physical activity versus does none

Giles-Corti and Donovan 2002a

1,803 adults 18 to 59 years old in Perth, Australia; excluded from study: unemployed, resident

1995–1996 cross-sectional inperson survey

Walking for transport in past 2 weeks (y/n)

Walking for recreation in past 2 weeks (y/n)

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Controls/Confoundersa

Built Environment Variables

Resultsb

 

perceived safety from crime (2), perceived safety from traffic (2), connectivity (2), satisfaction with neighborhood services (2), emotional satisfaction with neighborhood (4)

Recreational variables (7-point scales or y/n): work site environment (10 items), physical activity equipment at home (13), convenience of physical activity facilities (18)

Vigorous activity:

+physical activity equipment in home

+convenience of physical activity facilities

Men Sitting:

–emotional satisfaction with neighborhood

Walking:

+land use mix/diversity

+ease of walking to transit stop Moderate activity:

+access to local shopping

+emotional satisfaction with neighborhood

Vigorous activity:

+physical activity equipment in home

+convenience of physical activity facilities

+work site environment

Racial/ethnic group

Urban, rural, mixed settings

Traffic (3-point scale)

Presence of sidewalks (y/n)

Street lighting at night (3-point scale)

Presence of unattended dogs (problem/not a problem)

Safety from crime (y/n)

Places within walking distance (y/n)

Places to exercise (y/n)

Meets recommendations:

African American, urban:

–fair lighting (versus poor)

African American, mixed:

+presence of sidewalks

Native American, mixed:

+unattended dogs not a problem

White, rural:

–fair lighting (versus poor)

Does any activity:

African American, rural:

−very good/good lighting (versus poor)

Latina, urban:

−light traffic (versus heavy)

Age

Gender

Number of children under 18

Household income

Education level

Access to built facilities (gravity measures by quartile, from GIS): sport and recreation centers, gyms, swimming pools, tennis courts, golf courses

Walking for transport:

–high access to beach (38% less)

+high perception that neighborhood has lots of traffic (26% more)

+sidewalks (65% more)

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Study

Sampling

Survey

Physical Activity Variable

 

in suburb for less than 1 year, exercised as recommended at work, medical condition likely to affect physical activity, not proficient in English

 

Walking at recommended levels (y/n, based on 12 or more sessions in 2 weeks totaling 360 minutes or more)

Giles-Corti and Donovan 2002bc

1,803 adults 18 to 59 years old in Perth, Australia

1995–1996 cross-sectional in-person survey

Exercising as recommended (y/n, based on walking for recreation and transportation, light-moderate physical activity, vigorous physical activity)

Use of facilities (y/n)

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×

Controls/ Confoundersa

Built Environment Variables

Resultsb

Work outside home (y/n)

Personal access to car (y/n)

SES of area of residence

Access to natural facilities (gravity measures by quartile, from GIS): attractive public open space, beach, river

Physical environment determinant score (sum of three measures, divided into thirds)

Perceptions of neighborhood (5-point scale, 11 items, 3 factors): neighborhood attractiveness, safety and interest; social support for walking locally; traffic and traffic hazards

Perceptions of (y/n): side-walks, streets well lit, public transit within walking distance, park within walking distance, shop within walking distance

+shops within walking distance (3 times)

+sometimes access to motor vehicle (3.46 times)

+no access to motor vehicle (4.13 times)

Walking for recreation:

+high access to beach (49% more than lower)

+perception neighborhood attractive, safe, interesting (49% more)

+sidewalks (41% more)

Walking as recommended:

+high access to public open space (43% more)

+perception neighborhood attractive, safe, interesting (50% more)

+sidewalks (65% more)

+no access to motor vehicle (2.87 times)

(logistic regression)

Age

Gender

Number of children under 18

Household income

Education level

Functional (y/n, observed): sidewalk, shop

Appeal: type of street, trees (y/n, observed), extent of tree coverage

Access to built facilities (gravity measures by quartile, from GIS): sport and recreation centers, gyms, swimming pools, tennis courts, golf courses

Access to natural facilities (gravity measures by quartile, from GIS): attractive public open space, beach, river

Physical environment determinant score (sum of three measures, divided into thirds)

Exercising as recommended:

?second third of access to built facilities relative to top third (29% less likely)

+high physical environment score relative to low (43% more likely)

Use of attractive open space:

+access

Use of river:

+access

Use of swimming pool:

+access

(logistic regression)

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Study

Sampling

Survey

Physical Activity Variable

Giles-Corti and Donovan 2003

1,803 adults 18 to 59 years old in Perth, Australia

1995–1996 cross-sectional in-person survey

Walking at recommended levels (y/n, based on 12 or more sessions in 2 weeks totaling 360 minutes or more)

Hovell et al. 1989c

2,053 adults in San Diego

1986 cross-sectional mail survey

Walking for exercise (number of minutes per week)

King et al. 2000c

2,912 women 40 years old and older in United States, modified BRFSS approach

1996–1997 U.S. Women’s Determinant Study, cross-sectional survey

Active versus underactive versus sedentary over last 2 weeks (based on moderate activity and vigorous activity)

Page 199
Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Controls/Confoundersa

Built Environment Variables

Resultsb

Age

Gender

Number of children under 18

Household income

Education level

Functional (y/n, observed): sidewalk, shop

Appeal: type of street, trees (y/n, observed), extent of tree coverage

Access to built facilities (gravity measures by quartile, from GIS): sport and recreation centers, gyms, swimming pools, tennis courts, golf courses

Access to natural facilities (gravity measures by quartile, from GIS): attractive public open space, beach, river

Physical environment determinant score (sum of three measures, divided into thirds)

Walking at recommended levels:

+high physical environment score (2.13 times as likely as low score)

+high access to attractive open space (1.47 times as likely as low access)

(logistic regression)

Age

Gender

Education level

Smoking

Alcohol

Diet

Number of exercise-related items at home (10 items, y/n)

Number of exercise facilities perceived as convenient (15 items, y/n)

Neighborhood environment (scale): safety of exercising in neighborhood, ease of exercising in neighborhood, frequency of seeing others exercising

Walking:

+neighborhood environment

(linear regression, 12% of variance explained)

Age

Race/ethnicity

Employment status

Marital status

Education level

Residence (urban/ rural/other)

Presence of (y/n): side-walks, heavy traffic, hills, streetlights, unattended dogs, enjoyable scenery, frequently see others exercising, high levels of crime

Safe to walk or jog alone during the day (5-point scale)

Barriers (5-point scale): lack a safe place to exercise, poor weather

Active:

+hills

+unattended dogs

+enjoyable scenery

+frequently see others exercising

(logistic regression)

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Study

Sampling

Survey

Physical Activity Variable

MacDougall et al. 1997c

1,765 adults in Adelaide, Australia

1987 cross-sectional mail survey by the South Australia Community Health Research Unit

Moderately active versus inactive (based on moderate activity, vigorous sport, walking for exercise)

Parks et al. 2003

1,818 adults, United States, modified BRFSS sampling plan, oversampling of lower-income individuals

1999–2000 cross-sectional phone survey, questions based on BRFSS, NHI, other surveys

Meets public health recommendations versus insufficient activity or inactive

Powell et al. 2003

4,532 adults in Georgia

2001 Georgia BRFSS

Meets physical activity recommendations

Ross 2000

2,482 adults in Illinois

1995 Survey of Community, Crime and Health, cross-sectional phone survey

Number of days walking per week

Number of days of strenuous exercise per week

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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Controls/Confoundersa

Built Environment Variables

Resultsb

Age

Education

General health

Social connections

Satisfaction with recreation facilities (5-point scale)

Satisfaction with living environment (5-point scale)

Moderately active:

+satisfied with recreation facilities

(logistic regression)

Age

Race

Gender

Stratified by urban, suburban, rural and by high and low income

Places to exercise (y/n): walk/jog trail, neighborhood streets, park, shopping mall, indoor gym, treadmill

Number of places to exercise (0 to 4)

Personal barriers (y/n): no safe place, bad weather

Meets for urban:

+walking/jogging trails

+park

+indoor gym

+treadmill

+other equipment

+number of places

Meets for suburban:

+walking/jogging trails

+indoor gym

Meets for rural:

+indoor gym

+4 places to exercise

None

Some place to walk (y/n):

Not home based: public park, school track, gym or fitness center, walking or jogging trail, shopping mall, other place

Home based: neighborhood streets or roads, neighborhood sidewalk, treadmill at home

Meeting recommendations:

+public park

+school track

+gym or fitness center

+walking or jogging trail

+other place

+neighborhood streets or roads

+neighborhood sidewalk

Age

Gender

Race

Ethnicity

Marital status

Education

Household income

Below poverty line

Neighborhood poverty, race, ethnicity, and education characteristics

City of Chicago versus suburb of Chicago versus small city versus small town or rural area

Walking:

+Chicago versus small town or rural area

Page 202
Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Study

Sampling

Survey

Physical Activity Variable

Rutten et al. 2001

3,343 adults, six European countries (Belgium, Finland, Germany, Netherlands, Spain, Switzerland)

MAREPS study, 1997–1998 cross-sectional phone survey

Level of vigorous activity (sedentary, not/somewhat vigorous, vigorous, very vigorous)

Saelens, Sallis, Black, and Chen 2003

107 adults, two neighborhoods in San Diego

c. 2002 cross-sectional mail survey and accelerometers

Moderate-intensity physical activity (minutes during last 7 days)

Vigorous-intensity physical activity (minutes during last 7 days)

Total physical activity (minutes during last 7 days)

Sallis et al. 1989c

1,789 adults in San Diego

c. 1988 cross-sectional mail survey

Frequency of vigorous exercise (times per week for at least 20 minutes with increase in heart rate or breathing)

Page 203
Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Controls/Confoundersa

Built Environment Variables

Resultsb

None

Perceived physical activity–related opportunities:

Residential area offers many opportunities to be physically active (5-point scale)

Local clubs and other providers in community offer many opportunities (5-point scale)

Community does not do enough for citizens and their physical activity (5-point scale)

From sedentary to not/somewhat vigorous:

+perceived physical activity– related opportunities

(ANOVA)

Age

Education level

High-walkability versus low-walkability neighborhood

Moderate-intensity:

+high-walkability (194.8 versus 130.7 minutes)

Total physical activity:

+high-walkability (210.5 versus 139.9 minutes)

Age

Gender

Education level

Smoking

Alcohol

Diet

Number of exercise-related items at home (10 items, y/n)

Number of exercise facilities perceived as convenient (15 items, y/n)

Neighborhood environment (scale): safety of exercising in neighborhood, ease of exercising in neighborhood, frequency of seeing others exercising

Barriers (5-point frequency scale): lack of equipment, lack of facilities, lack of good weather

Vigorous exercise:

+home equipment

(linear regression; 27% of variation explained with all variables included)

Page 204
Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Study

Sampling

Survey

Physical Activity Variable

Sallis et al. 1990c

2,053 adults with mean of 48 years in San Diego

 

Sedentary versus exerciser (based on three or more exercise sessions per week)

Sallis et al. 1992c

1,719 adults in San Diego

Prospective study: followup to Sallis et al. 1989; mail survey

Frequency of vigorous exercise (times per week for at least 20 minutes with increase in heart rate or breathing)

Change in vigorous exercise over 24 months

Sallis, Johnson et al. 1997c

110 college students with mean age 20.6 in San Diego

c. 1996 survey administered through college class

Walking for exercise (minutes/week)

Strength exercise (days/week)

Vigorous exercise (days/week)

Page 205
Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Controls/Confoundersa

Built Environment Variables

Resultsb

Age

Education level

Income

Density of pay and free facilities

Exerciser:

+density of pay facilities

Age

Gender

Education level

Income

Race/ethnicity

Marital status

Smoking

Number of exercise-related items at home (10 items, y/n)

Number of exercise facilities perceived as convenient (15 items, y/n)

Neighborhood environment (scale): safety of exercising in neighborhood, ease of exercising in neighborhood, frequency of seeing others exercising

Barriers (5-point frequency scale): lack of equipment, lack of facilities, lack of good weather

Change in vigorous activity in sedentary men:

–neighborhood environment

(linear regression)

 

Exercise facilities in home (y/n, 15 items)

Neighborhood environment (sum of three items): presence of (y/n): sidewalks, heavy traffic, hills, street-lights, dogs unattended, enjoyable scenery, crime; rating neighborhood as residential, commercial, or mixed; safe for walking during day (5-point scale)

Convenient facilities: places to exercise on a frequently traveled route or within 5-minute walk (y/n, 18 places)

Walking for exercise:

not significant

Strength exercise:

+home equipment

Vigorous exercise:

not significant

(linear regression)

Page 206
Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Study

Sampling

Survey

Physical Activity Variable

Shaw et al. 1991c

14,674 adults 18 to 69 years old in Canada who wished to participate in more physical activity

1983 Canada Fitness Survey, cross-sectional survey

Participation in 35 recreational activities (hours per week)

Stahl et al. 2001c

3,343 adults, 6 European countries (Belgium, Finland, Germany, Netherlands, Spain, Switzerland)

MAREPS study, 1997–1998 cross-sectional phone survey

Active versus inactive (based on participation in any gymnastics, physical activity, or sports)

Troped et al. 2001c

413 adults with mean age 51 years in Arlington, Massachusetts

1998 cross-sectional mail survey

Use versus nonuse of bikeway

Wilcox et al. 2000c

2,912 women 40 years old and older in United States, modified

1996–1997 U.S. Women’s Determinant Study, cross-

Active versus underactive versus sedentary over last 2 weeks

Page 207
Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Controls/Confoundersa

Built Environment Variables

Resultsb

Gender

No facilities nearby (y/n)

Available facilities are inadequate (y/n)

Participation for women:

+no facilities nearby

+available facilities inadequate

Participation for men:

+available facilities inadequate

(ANOVA)

Age

Gender

Education level

Country

Local opportunity scale (5-point scales): area offers many opportunities to be active, local clubs and other providers offer many opportunities, community does not do enough for citizens and their physical activity

Active:

+local opportunities

(logistic regression)

Age

Gender

Physical activity limitation

Education level

Neighborhood features (y/n): sidewalks, heavy traffic, hills, street-lights, unattended dogs, enjoyable scenery, frequently see others exercising, high levels of crime

Neighborhood character (3-point scale): rating of neighborhood as residential, mixed, or commercial

Neighborhood safety (5-point scales): how safe walking during day

Reported distance from bikeway

Reported steep hill on way to bikeway

Reported cross busy street to access bikeway

Bikeway use:

–reported distance (.65 times as likely for every 0.25 increase in distance)

–busy street to cross (2 times as likely if no street to cross)

(logistic regression)

Age

Race

Education level

Geographic region

Presence of (y/n): side-walks, heavy traffic, hills, streetlights, unattended dogs, enjoy-

Not sedentary in rural women:

+lack of scenery

+frequency of seeing others exercising

Page 208
Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Study

Sampling

Survey

Physical Activity Variable

 

BRFSS sampling plan

sectional survey

(based on moderate activity and vigorous activity)

NOTE: ANOVA = analysis of variance; BMI = body mass index; BRFSS = Behavioral Risk Factor Surveillance System; MAREPS = Methodology for the Analysis of the Rationality and Effectiveness of Prevention and Health Promotion Strategies; NHANES = National Health and Nutrition Examination Survey; SES = socioeconomic status.

aSociodemographic and geographic variables only; many studies include other individual measures and social environment measures.

bResults of multivariate analyses reported when available.

cIncluded in Humpel et al. 2002.

Page 209
Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
×

Controls/Confoundersa

Built Environment Variables

Resultsb

 

able scenery, frequently see others exercising, high levels of crime

Safe to walk or jog alone during the day (5-point scale)

Barriers (5-point scale): lack a safe place to exercise, poor weather

 

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×

REFERENCES

Abbreviations

BR&S Belden, Russonello & Stewart

CDC Centers for Disease Control and Prevention

EPA U.S. Environmental Protection Agency

NCSA National Center for Statistics and Analysis

NHTSA National Highway Traffic Safety Administration

TRB Transportation Research Board


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Suggested Citation:"6 Current State of Knowledge." Transportation Research Board and Institute of Medicine. 2005. Does the Built Environment Influence Physical Activity?: Examining the Evidence -- Special Report 282. Washington, DC: The National Academies Press. doi: 10.17226/11203.
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TRB Special Report 282: Does the Built Environment Influence Physical Activity? Examining the Evidence reviews the broad trends affecting the relationships among physical activity, health, transportation, and land use; summarizes what is known about these relationships, including the strength and magnitude of any causal connections; examines implications for policy; and recommends priorities for future research.

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