Physical Activity and Environment Research in the Health Field

10.1177/0885412204267680ARTICLEJournal of Planning LiteraturePhysical Activity

Physical Activity and EnvironmentResearch in the Health Field:Implications for Urban andTransportation PlanningPractice and Research

Chanam LeeAnne Vernez Moudon

This article reviews literature from the health field investigat-ing the characteristics of environments that support or hinderphysical activity. This literature shows that physical activityis associated with objective and subjective measures of acces-sibility to recreational facilities and local destinations, as wellas with neighborhood safety and visual quality. Walking andbiking emerge as prominent forms of physical activity andoccur primarily in neighborhood streets and public facilities,suggesting that building walkable and bikable communitiescan address health as well as transportation concerns. Thestudies help advance environment-behavior research relatedto urban and transportation planning. They identify behav-ioral and environmental determinants of physical activityand employ rigorous data collection methods and theoreticalframeworks that are new to the planning field. The articleconcludes that multidisciplinary research will likely yieldpromising results in identifying the aspects of environmentsthat can be modified to encourage physical activity and physi-cally active travel.

Keywords: physical activity; walking; biking; environmentaldeterminants; transportation

This article introduces urban and transportationplanning audiences to a body of literature originatingfrom the public health field. The literature consists oftwenty recently published empirical studies address-ing the environmental characteristics that influencephysical activity, including walking and biking.

Understanding and promoting physical activitydemand multidisciplinary approaches (Sallis, Bauman,and Pratt 1998; King et al. 2002). This article relies on a

CHANAM LEE is an assistant professor at Texas A&M Univer-sity, College Station. This work was conducted during her doctoralstudies at the University of Washington, Seattle. Her research areasare physical activity, health, urban form, and nonmotorized trans-portation. She has worked professionally as a land planning consul-tant, landscape architect, and urban planner.

ANNE VERNEZ MOUDONIS, Dr. ès. Sc., is a professor of archi-tecture, landscape architecture, and urban design and planning atthe University of Washington, Seattle. She is president of the Inter-national Seminar on Urban Morphology, a faculty associate at theLincoln Institute of Land Policy, a fellow of the Urban Land Insti-tute, and a national adviser to the Robert Wood Johnson FoundationProgram on Active Living Research. Her books include Built forChange: Neighborhood Architecture in San Francisco (MITPress, 1986), Public Streets for Public Use (Columbia UniversityPress, 1991), and Monitoring Land Supply With GeographicInformation Systems (with M. Hubner, John Wiley, 2000).

Journal of Planning Literature,Vol.19,No.2(November2004).DOI: 10.1177/0885412204267680Copyright © 2004 by Sage Publications

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multidisciplinary framework that connects physicalactivity from the health perspective to the transporta-tion perspective. Also, the importance of physical envi-ronments in supporting walking and biking brings in athird player, the urban design and planning profes-sions, that have the capability to intervene in the envi-ronment (Figure 1) (Lee and Moudon 2001).

A few recently published review articles have takena similar multidisciplinary approach to physical activ-ity. Frank and Engelke (2001) connect public health andurban planning. They review selected empirical evi-dence showing the health benefits of physical activityand environmental influences on physical activity andon modes of travel. Handy et al. (2002) bring the urbanand transportation planning literature to the publichealth audience. Saelens, Sallis, and Frank (2003)address the same audience with a focus on empiricalstudies from transportation planning that analyze theimpact of environments on walking and biking. Miss-ing is a systematic review for planning audiences of thepublic health literature dealing with the environmentaldeterminants of physical activity. This article fills thegap and examines lessons for future practice andresearch.

Public health research sorts physical activity intofour purpose-related categories: (1) recreational or lei-sure time activity, (2) work-related activity, (3) house-

hold-related activity, and (4) transportation-relatedactivity (Centers for Disease Control and Prevention[CDC] 1996). Roberts et al. (1996) state that walking andbiking are unique forms of physical activity becausethey transcend these traditional physical activity classi-fications. Walking and biking figure prominently aspopular forms of physical activity, as they are accessi-ble, affordable, and readily incorporated into one’sdaily routine. They also begin to address challengesthat both health and transportation professionals face,namely, the preponderance of sedentary life styles andthe increased dependence on automobile travel. At thesame time, any effort to promote walking and biking asmeans of active transportation must take into accountthe impediments to walking and biking brought byenvironments built after World War II, which have beenshaped primarily for and by automobiles. As the major-ity of the country’s population now lives in postwar,automobile-oriented environments (Pendall, Fulton,and Harrison 2000), health and transportation profes-sionals need to work closely with urban designers andplanners to address environmental factors that supportor hinder physically active travel.

The studies reviewed in this article have alreadyshaped large research- and community-based pro-grams promoting active living environments.1 Theyprovide insights into the relationship between, and the

148 Journal of Planning Literature

FIGURE 1. Conceptual Framework for Multidisciplinary Research and Polity for Physical Activity Promotion



methodological challenges of research on, physicalactivity and environment.

SIGNIFICANCE AND PURPOSE

Urban and transportation planning professionalshave paid a considerable amount of attention to theimpact of the built environment on travel patterns (e.g.,Ewing and Cervero 2001; Boarnet and Crane 2001;Handy 1996; Steiner 1994). The effects of the built envi-ronment on travel mode choice and the potential for theenvironment to be modified to reduce automobile usehave been extensively studied. However, research find-ings still remain tentative especially on nonmotorizedtravel behaviors and, therefore, the subject of continueddebates (Crane 1996). Methodological problemsgeneric to this type of study include (1) complex andinterrelated variables that are often spatially clusteredand/or nested, (2) numerous confounding factors, (3)limited data availability on nonautomobile travel andenvironments, (4) difficulty in effectively quantifyingthe built environment, (5) the use of large spatial andanalytic units of analyses, and (6) difficulties in estab-lishing causality (Federal Highway Administration[FHWA] 1999; U.S. Department of Transportation[USDOT] 2000). Also, parallel transportation researchfocuses on environmental variables limited to roadwayconditions. Personal and social determinants ofwalking and biking are rarely addressed (Moudon andLee 2003).

A few reviews of the public health research on thedeterminants of physical activity exist already (e.g.,Humpel, Owen, and Eva 2002; National Public HealthPartnership [NPHP] 2001; Sallis, Bauman and Pratt1998: Seefeldt, Malina, and Clark 2002). These reviewsare written primarily for the health audience, and, withthe exception of Humpel et al., they tend to focus onstudies dealing with personal and social determinantsof physical activity. Their discussion of physical envi-ronmental determinants is limited, possibly due to abroad and often loose definition of environments in thepublic health research published to date (Saelens, Sallis,and Frank 2003). In contrast, this article provides astructured review of empirical studies concerned withcommunity-based, physical environmental determi-nants of physical activity. These studies contribute tourban and transportation planning in the followingways: (1) they further the testing of specific physicalenvironmental variables that are associated with physi-cal activity, including walking and biking; (2) they pointto neighborhood places where people are engaged inphysical activity; (3) they identify barriers perceived tobe present in their environment discouraging peoplefrom being more active; and (4) they introduce method-

ological and theoretical approaches that can be usefulfor planning research. In addition, this article employs aconceptual framework that can facilitate theclassif ication and evaluation process of theenvironmental variables used in the studies reviewed.

This review has three purposes. The first is to high-light the studies’ key findings confirming walking asthe most common type of physical activity and identifypreferred places for, and perceived barriers to, physicalactivity. The review proceeds to examine the environ-mental variables used, acknowledging those withstrong empirical evidence for supporting physicalactivity (see the appendix for a classification of the stud-ies based on the type of measures, objective and/or sub-jective, used for the independent variables capturingenvironments). Third, lessons are drawn from both thefindings and the theoretical and methodological frame-works of the reviewed studies. The article concludeswith a discussion of the findings’ implications for prac-tice and research in promoting active living, possiblyfilling gaps or strengthening existing knowledge inurban and transportation planning fields.

METHODOLOGICAL FRAMEWORK OF THIS REVIEW

The structure of this review is based on the criteriaemployed for the literature selection and the BehavioralModel of Environment (BME) as a conceptual constructto evaluate the chosen studies’ environmental variablesand key findings.

Criteria for Literature Selection

Twenty public health studies are chosen for thisreview, based on their contribution to building empiri-cal evidence of community-based physical environ-mental determinants of physical activity. The studiesfocus on outdoor environments and lifestyle-basedphysical activities. They consider various types ofphysical activity, including, but not limited to, walkingand biking. Excluded is research dealing solely withprivate and indoor environments (e.g., home, school orwork-site environment, interior building design), smallenvironmental cues (e.g., signs next to elevators), orsocial and policy-related environmental factors (e.g.,advocacy efforts, school-based programs, and laws andregulations).

Keyword searches of several computerized data-bases, including MedLINE, PsycINFO, and Web of Sci-ence, and publication searches from the federal andlocal public health agencies identified the twenty stud-ies. Keywords included walk, bike, bicycle, cycle, physicalactivity, environment, community, determinant, environ-mental determinant, environmental factor, facilitator,enabler, barrier, correlate, neighborhood, neighborhood factor,

Physical Activity 149



and neighborhood effect. The initial literature search wasconducted during September 2002 and periodicallyupdated until June 2003.

Use of the Behavioral Model of Environment

Physical environmental factors influencing physicalactivity are numerous and subject to complex interac-tions among themselves. A theoretical model canbecome useful, as it serves to conceptualize andoperationalize environmental factors and their rela-tionships (Saelens, Sallis, and Frank 2003). This reviewemploys a BME (Moudon and Lee 2003), which identi-fies the generic parts of environments affecting outdoorphysical activity, specifically walking and biking. TheBME also points to areas where interventions can be

made to better support these activities (Figure 2). Themodel organizes classes of variables characterizing thethree components of the environment for promotingwalking and biking: origin/destination (OD), route (R),and area (A).

THE ORIGIN AND DESTINATION OF THE WALKING

OR BIKING TRIP (ORIGIN/DESTINATION OR OD)

Any given walking and biking trip starts and ends atcertain points. The types and locations of origins anddestinations play a determinant role in one’s decision towalk or bike (e.g., Goldsmith 1992; USDOT 1995;Steiner 1998; Rutherford et al. 1995; Handy 1996). Tripdestinations also relate to trip purpose. Regular com-mute-trip destinations include work site and school,


FIGURE 2. Behavioral Model of Environment: Three Components of Origin/Destination, Route, and Area.SOURCE: Moudon and Lee (2003, 23). Used with permission.NOTE: R1 = airline route to destination; R2 = street network route to destination; R3 = recreational route.



and social or shopping-trip destinations include gro-cery stores, malls, restaurants, coffee shops, parks, andso on. Trip origins can vary depending on how an indi-vidual trip or a trip chain is defined, but home or worklocations generally serve as common origins. Destina-tions must be relatively proximate to origins in order toallow for the option to walk or bike. Points of origin anddestination are spatially different for transportation butmay be the same for recreation or exercise (e.g., walkingaround the neighborhood).

THE CHARACTERISTICS OF THE ROUTE

TAKEN FOR THESE TRIPS (ROUTE OR R)

The characteristics of the route between origin anddestination consist of not only the physical conditionsof, and along, the roadway but also the quality influenc-ing the safety, convenience, comfort, and enjoyment ofwalkers and bikers. The combination of these character-istics affects one’s decision to walk or bike and howlong one is willing to walk or bike (e.g., Rapoport 1987;Corti, Donovan, and Holman 1997). Roadway charac-teristics are commonly measured as the number of vehi-cle lanes, vehicular speed, slope, and presence of side-walks and bike lanes, as well as the number of cars,bikers, or people on the roadway. Route qualities areoften measured subjectively as the users’ rating of per-ceived safety, convenience, and visual quality of theroadway and roadside environments.

THE CHARACTERISTICS OF THE AREAS AROUND

ORIGIN AND DESTINATION PLACES (AREA OR A)

Area characteristics consist of social and behavioralaspects of the physical environment, such as the uses ofland, activities that take place, and the intensity of theseuses and activities. Population density, floor area ofcommercial buildings, street block size, and number ofstreet intersections are a few common examples of vari-ables used. They are often measured objectively, usingpublicly available spatial and/or tabular databases(Ewing and Cervero 2001). Subjective measures of thearea component include people’s perception of the areaor neighborhood quality, such as safety from crime,friendliness, and enjoyable scenery.

The area component concerns the volumes of, andthe choices of, routes and activities available for walk-ers and bikers. The intensity and mix of land uses in anarea affect how much potential and actual walking orbiking activities the area will generate or attract (FHWA1999; Moudon et al. 2001). The overall patterns of streetnetworks (e.g., small grid, large grid, culs-de-sac,loops) are considered as an area component in thismodel, while the characteristics of individual street seg-ments belong to the route component. The types ofstreet networks along with the land uses patterns affect

the level of choices that people can have in the area. Forexample, small gridlike streets and mixed land usesprovide more alternative routes and often varioustravel modes, such as transit, when moving from theorigin to destination.

Considerations of environment from all three com-ponents of the BME are important, and these compo-nents are not mutually exclusive of each other. Manyvariables address more than one component of the BME(Figure 3). For example, measures of accessibility todestinations often overlap with both the OD and the Rcomponents, and both aspects of accessibility influenceone’s decision to walk or bike. Having a destinationlocated within a walkable or bikable distance fromhome (OD) allows for the option to walk or bike. At thesame time, the route quality (R), such as sidewalk orbike lane connectivity, quality of the roadside environ-ment, and street-crossing conditions, influence one’sactual decision to engage in walking or biking.

This model serves as a conceptual framework fordiscussing the findings of this review, especially in thereview of environmental variables tested in the studiesto influence physical activity, walking, and biking.

METHODS USED IN THE STUDIES

Tables A1 through A4 in the appendix provide anoverview of the methodologies used in the chosen stud-ies. Table columns are lettered (Athrough I) to guide thediscussion of each element of the methods. Included inthis section are the study population and sample; theo-retical framework; dependent variables capturingdimensions of physical activity, including walking andbiking; independent variables classified into objectiveand/or subjective measures; confounding variablescontrolled for; data collection methods; and statisticaltechniques for data analyses.

Study Population (A) and Sample (B)

Study populations are mainly adults in general butalso include children, older adults, minorities, stu-dents, and women. Sample sizes for the quantitativestudies (Tables A1 through A3) are large, ranging froma few hundreds to more than one hundred thousand.Several studies use existing population-based surveys,resulting in relatively large sample sizes. The surveysinclude the Behavioral Risk Factors Surveillance Sys-tem (BRFSS) (CDC n.d.) and the National Health andNutrition Examination Survey (NHNES) from theUnited States and the Australian Activity Survey (AAS)from Australia.

A majority of the studies employ probability (or ran-dom) sampling techniques, often incorporating cluster-ing and stratification strategies. Probability sampling




ensures the generalizability of study findings to largepopulations. The technique is less common in the plan-ning field where primary data collection efforts are lessfrequent and limited.

Theoretical Framework (C)

The social ecological perspective provides a broadframe of reference for studies reviewed here, emphasiz-ing the dynamic interplay between the personal behav-ioral and environmental factors (Sallis and Owen 1997;Stokols 1992). In contrast to traditional health behaviortheories that focus on the role of personal factors onbehavior, this perspective stresses the importance ofboth sociocultural and physical environmental factorsin behavior change.

The social ecological approach has a foundation insocial cognitive theory (Bandura 1986). First introducedas social learning theory by Bandura (1977), social cog-nitive theory is based on the assumption that individu-als are generally motivated to engage in behaviors thatwill result in rewards and to avoid punishments(Bandura 2001). The theory focuses on motivational fac-tors and self-regulatory mechanisms that contribute toa person’s behavior, in addition to environmental fac-tors. It explains human behavior in terms of a continu-ous reciprocal interaction between individual,behavioral, and environmental influences.

Social cognitive theory has been widely adopted inthe area of health promotion (Seefeldt, Malina, andClark 2002). The same concepts of multi-level, interac-tive influences on behavior change are used in the socialecological perspective (e.g., Baker et al. 2000; McLeroyet al. 1988; Sallis and Owen 1997; Stokols 1992). The lat-

ter views behavior as determined by personal, social,organizational, community and policy factors, andemphasizes the need for the environmental interven-tions in health promotion programs (McLeroy et al.1988).

Additional theories contribute to the construction ofreviewed studies: the theory of planned behavior,emphasizing the role of intention to perform the behav-ior and perceived behavior control to influence actualbehavior (Ajzen 1988, 1991); the theory of trying, focus-ing on the conscious process of forming the intention tobehave before performing the behavior (Bagozzi andWarshaw 1990); and the theory of behavior setting,emphasizing the importance of dynamic and interac-tive real-life settings in which human behaviors takeplace (Baker 1968).

Many theories share core constructs that are appliedin the studies as attitude toward physical activity, socialenvironment, perception of neighborhood resources,opportunities and benefits of physical activity, skillsneeded to perform physical activity, and so on. Six stud-ies have a basis in social cognitive theory, three of which(King et al. 2000; Booth et al. 2000; Hovell et al. 1992)derive their independent variables directly from itsconstructs. Another study (Giles-Corti and Donovan2002) selects individual factors associated with physi-cal activity based on the theory of planned behavior andthe theory of trying. Sallis et al. (1997) consider the the-ory of behavior setting by focusing on two commonbehavior settings, home and neighborhood, to investi-gate the influences of perceived environmental factorson physical activity.


FIGURE 3. Behavioral Model of Environment: Conceptual Structure, and Examples of Variables



Dependent Variables (D)

Dependent variables in public health studies ofteninclude engagement in, or total amount of, physicalactivity. While all types of physical activity are consid-ered, most studies focus on unstructured, moderateactivities such as walking and biking, with seven stud-ies using walking specifically as their dependentvariable.

Dependent variables are often self-reported anddichotomized or categorized for analysis. They includeengagement in (1) overall physical activity, (2) sufficientlevel of physical activity for health benefits2—based onenergy expenditure or total amount of physical activity,(3) leisure or exercise physical activity, and (4) specifictypes of physical activity such as walking, jogging,swimming, and so on. Other dichotomous variablesinclude whether participants use specific types of recre-ational facilities, such as gyms, parks, sidewalks, bikelanes, trails, swimming pools, tennis courts, healthclubs, open spaces, golf courses, and so on.

Continuous dependent variables considered includetotal amount (frequency and duration combined) of (1)physical activity, (2) leisure time physical activity, (3)household-related physical activity, (4) walking, and(5) vigorous activities. Also studied is the prevalence ofwalking to work. These continuous variables are some-times transformed into categorical or dichotomizedvariables for analyses (e.g., Berrigan and Troiano 2002;Booth et al. 2000; Wilcox et al. 2000; Bauman et al. 1999).

Independent Variables (E, F)

A large number of independent variables measurepersonal and social determinants of physical activity,some of which are considered also as control variables(see G below). Environmental factors as independentvariables, the focus of this review, are classified intoobjective and subjective variables. Out of the totaltwenty studies, only three include both objective andsubjective measures (Table A1), four use objective mea-sures only (Table A2), and ten use subjective measuresonly (Table A3). The remaining three studies are explor-atory, and no independent variables are specified.

Objective measures cover the spatial characteristicsof residential locations in terms of accessibility, densityof people or development, and geographic locations(e.g., urban or costal location). These variables arederived from maps and measurements using the Geo-graphic Information System (GIS). One study of pre-schoolers uses a direct observation method to measureboth dependent and independent variables (Klesgeset al. 1990). Two studies identify road network dis-tances to recreational facilities, one of which also con-siders the presence of barriers (hills and heavy traffic)

along the route (Troped et al. 2001). An early studybased on objective measures (Sallis et al. 1990) uses thetotal number and density of pay and free exercise facili-ties near home to estimate accessibility to these facili-ties. Berrigan and Troiano (2002) use housing stock ageas a proxy for the neighborhood’s urban formcharacteristics.

Subjective measures of physical environmental fac-tors address perception of safety, convenience, comfort,visual quality, neighborhood character, and presence ofor proximity to exercise facilities and shops.

Confounding Variables (G)

Most studies control for one or more of the con-founding factors, such as age, sex, education, andincome. However, reporting of their relationship withthe dependent variable is often brief and vague. Suchfactors as transit service and objectively measured traf-fic conditions, which often confound the relationshipbetween physical activity and environment, are notaddressed in any of the studies. Neither are socioeco-nomic factors specific to transportation behaviors, suchas car ownership.

Data Collection (H)

Data are collected specifically for the individualstudy (13 out of the 20 studies) or come from reliablesecondary sources. Primary self-reported data comefrom telephone interviews, or less frequently, mail sur-veys. Many of the surveys use questions from the exist-ing questionnaires mentioned earlier (e.g., Berriganand Troiano 2002; Ball et al. 2001; CDC 1998, 1999). Thethree exploratory studies (Table A4) use focus-groupmethods to generate the data. Primary data sources forobjective variables include observations (Giles-Cortiand Donovan 2002; Klesges et al. 1990), mappings(Sallis et al. 1990), and GIS-based measurements(Troped et al. 2001).

Statistical Analyses (I)

Descriptive statistics and correlation analyses areoften complemented by logistic regression analysis,which is a common choice with a dichotomizeddependent variable. Almost half of the studies uselogistic regression alone or combined with other analy-ses. The popularity of the logistic regression methodlikely comes from the health field’s traditional interestin achieving a sufficient level of physical activity forhealth purposes. This method is effective in explainingthe likelihood of achieving a threshold. Yet, thedichotomization may result in loss of information thatcan only be examined at a more fine-grained scale.Three studies (Craig et al. 2002; Rutten et al. 2001;Hovell et al. 1992) use hierarchical regression models to




consider nested data structures, as they include vari-ables at multiple levels (e.g., individual- and neighbor-hood-level variables). Ball et al. (2001) use a structuralequation model, which is a multivariate analysis forinvestigating the underlying structure of usually alarge number of variables, to analyze perceived envi-ronmental factors. All but Hovell et al.’s study (1992)are cross-sectional, and therefore causality assumptionof the statistical analyses may not hold.

FINDINGS FROM THE STUDIES

The studies together yield general findings about (1)walking as the most common type of physical activity,(2) preferred places for physical activity, and (3) barriersto physical activity. An inventory of empirically testedenvironmental variables associated with physical activ-ity is included and discussed in terms of the BME.

Walking as the Most CommonType of Physical Activity

Four studies (Ball et al. 2001; Booth et al. 1997; Giles-Corti and Donovan 2002; Troped et al. 2001) reportwalking as the most frequently engaged physical activ-ity. Walking is confirmed to be a preferred form of phys-ical activity by an overwhelming majority of study pop-ulations across different gender, age, and incomegroups (Table 1). However, the degrees of popularityvary across subgroups (Table 1). Findings show thatwalking is more prevalent among women and olderadults (Booth et al. 1997). Stephens et al. (1985) reportthat walking is more popular among typically inactivesegments of the population, such as ethnic minoritiesand the elders. Gardening, swimming, and jogging areamong other frequently reported activities.

The preference of walking is further supported instudies outside the twenty selected articles includedhere (e.g., Bull et al. 2000; Siegel, Brackbill, and Heath1995; Statistics Canada 1998-99; Go for Green 1998;CDC 2000a, 2000b). These studies together help affirmthat promoting walking is the most practical way toachieve healthful levels of physical activity. U.S., Aus-tralian, and Canadian populations appear to practicewalking and biking for recreation more so than fortransportation. The CDC (2000b) found that most peo-ple when they walk for recreation walk at least 30 min-utes at a time, reaching a sufficient threshold of dailyactivity for health benefits. Yet walking for utilitarianpurposes may be shorter: for example, the NationwidePersonal Transportation Survey (USDOT 1995) reportsthat most walking trips last only about five to ten min-utes. Additional research will help sort out thepurposes behind the total amount of walking peopledo.

Preferred Places for Physical Activity

Outdoor and freely available neighborhood facilitiesare most frequently used for physical activity (Table 2).Neighborhood streets are most commonly used placesin the Giles-Corti and Donovan (2002) and Troped et al.(2001) studies. Giles-Corti and Donovan (2002) findthat 46 percent of the respondents use their neighbor-hood streets for exercise, compared with only 11 per-cent using gyms, health clubs, or exercise centers, and 9percent using sport or recreation centers. There arestudies outside the twenty articles that further demon-strate the popularity of neighborhood streets as placesfor physical activity (Brownson et al. 2001; Bull et al.2000). Brownson et al. (2001) report that neighborhoodstreets are used by more than 66 percent of the respon-dents who reported some degree of physical activity,while only 21 percent used an indoor gym and 25 per-cent used a treadmill. Other freely available publicopen spaces, such as parks and trails, are also commonplaces for exercise (e.g., Giles-Corti and Donovan 2002).

The popularity of neighborhood streets may beexplained in part by their easy accessibility from homeand potential to serve a dual purpose: in BME terms,they are both destinations for recreational activities androutes to get to places. The prevalence of walking asphysical activity also explains the attractiveness ofstreets that are natural venues for walkers. This findingpoints to opportunities for increasing walking and bik-ing for transportation purposes.

Barriers to Physical Activity

The studies also show that people feel the built envi-ronment is not supportive enough to induce physicalactivity. Long distances separating places, lack of safeplaces and facilities for recreation, and poor accessibil-ity to recreational facilities are among the common bar-riers people perceive exist in their environment (Table3). While some environmental barriers are difficult tomodify, such as bad weather conditions (e.g., Brownsonet al. 2000) and short daylight hours (Hahn andCraythorn 1994), many can be modified or eliminated.

Table 3 summarizes the barriers into four broad cate-gories, including opportunity, distance, access, and safetybarriers. Both opportunity and distance barriers relate tothe OD component of BME, as they concern availabilityof proximate destinations from origins. Access barriersinclude both OD and R components and focus on lack ofhigh-quality, route-related facilities for walkers andbikers. Safety barriers involve unsafe roadway condi-tions often due to poor maintenance and perceived fearof crime, traffic, accident, injury, dogs, and people.Access and safety barriers include perhaps some of theeasiest ones to lift to support physical activity.





TABLE 1. Preferred Type of Physical Activity

Preferred Type of 1st Author andPhysical Activity Level of Preference Subjects Year Published

Walking forrecreation

86.1% in previous 4 weeks (1) American adults living inArlington, MA

Troped 2001

38% of young and 68% of older adults (1) inprevious 2 weeks

Young and older sedentaryAustralian adults

Booth 1997

68.5% in the previous 2 weeks (1) Australian adults Giles-Corti 200238-52% males and 41-64% females (depending

on sex, age, education, and environmentalfactors) in the past 2 weeks

Australian adults Ball 2001

Walking fortransportation

72.1% in the past 2 weeks Australian adults Giles-Corti 2002

Other Studies (outside the 20 selected articles)



80.6% of females and 73.4% of males duringa week (1)

Western Australian adults Bull 2000

50.4% of males and 69.2% of females duringa week (1)


44.1% during a week (1) American adults CDC 199642% (walking was the only leisure time

physical activity for 21% of them) duringa week

American adults CDC 2000b

37.7% of males and 52.5% of females duringa week

Overweight American adults CDC 2000a

69% (75% females and 64% males) (1) Canadian adults Statistics Canada1998-99

85% at least sometimes (1) Canadian adults Go for Green 199835.6% (1) American adults Siegel 1995


58% at least some times Canadian adults Go for Green 1998

42% in previous 2 weeks Western Australian adults Bauman 199625% during a week Western Australian adults Bull 200021% to local facilities Western Australian adults

living in PerthSeaton 2001

4% to work (8% used public transport, ofwhich 55% walked 15+ minutes as part oftrip)

Western Australian adultsliving in Perth

Seaton 2001



Biking forrecreation

48% biked for leisure or recreation Canadian adults Go for Green 1998



15.4% during a week (4) American adults CDC 199624% (19% females and 28% males) (4) Canadian adults Statistics Canada

1998-99

(continued)



Personal and social factors are also reported to hin-der physical activity. Lack of time (e.g., Brownson et al.2001; King et al. 2000; Eyler et al. 1998) is the leading fac-tor that discourages physical activity. This finding sug-gests that supporting walking for the dual purpose ofexercise and transportation may help increase levels ofphysical activity. Other personal barriers include poorhealth (Eyler et al. 1998; Owen and Bauman 1992), childcare responsibility (Hahn and Craythorn 1994), andlack of energy (King et al. 2000). Personal safety con-cerns raised pertain to injuries, falls, traffic accidents,and so on. Common social barriers include not havingcompany (Hahn and Craythorn 1994) and not seeingother people exercising (Wilcox et al. 2000). Studies out-side the twenty articles report additional deterrents ofphysical activity, including lack of interest (Vuori, Oja,and Paronen 1994; Owen and Bauman 1992), self-consciousness about one’s appearance (Brownson et al.2001), and costs of structured physical activity pro-grams (Booth et al. 1997). However, it must be notedthat these findings still remain inconclusive, and sev-eral variables have shown both positive and negative

impact on physical activity (i.e., hills, self-conscious-ness about physical appearance, and unattended dogs).These mixed findings may be partly due to lack of rep-resentativeness in the studies’ participants and theirenvironmental conditions (e.g. Eyler et al. 1998; Hahnand Craythorn 1994; King et al. 2000). Because theimpact of these variables on physical activity differsdepending on age, gender, and ethnic background,intervention strategies tai lored to thesociodemographic composition of the specific commu-nity are likely to be effective in promoting both physicalactivity and nonmotorized travel.

Environmental Variables Empirically Testedto Influence Physical Activity

The twenty studies address the three components ofthe BME in varying degrees. Individual studies typi-cally consider only one or two of the components, andvariables included often do not capture comprehen-sively the forms and characteristics of the built environ-ment. As a result, the relative strength of associationbetween individual environmental variables and phys-


Biking fortransportation

26% at least sometimes Canadian adults Go for Green 1998

2% to local facilities Western Australian adultsliving in Perth

Seaton 2001

1% to work Western Australian adultsliving in Perth

Seaton 2001

4.9% of males and 2.6% of femalesduring a week (12)


Gardening/yardwork

37.0% males and 38.2% females duringa week (2)


29.4% during a week (2) American adults CDC 199648% (45% females and 51% males) Canadian adults Statistics Canada

1998-998.2% during a week Overweight American adults CDC 2000a

Swimming/surfing

13.4% of males and 11.2% of femalesduring a week (4)


6.5% during a week (9) American adults CDC 199624% (24% females and 24% males) (4) Canadian adults Statistics Canada

1998-9919% in previous 2 weeks (2) Young and older sedentary

Australian adultsBooth 1997

Jogging/running 12.1% of males and 6.1% of femalesduring a week (6)


9.1% during a week (7) American adults CDC 19969.6% of males during a week Overweight American adults CDC 2000a

NOTE: Numbers in parentheses show the rank based on the activity’s level of prevalence reported in the study: recall period (orfrequency of engagement in the activity) and the rank based on the prevalence of activity are reported only when the informationis available from the corresponding study. CDC = Centers for Disease Control and Prevention.

TABLE 1 (continued)




ical activity is not systematically examined, nor is theissue of likely covariance between environmental vari-

ables assessed. Nonetheless, significant correlations arefound between some of the individual environmental


TABLE 2. Preferred Place for Physical Activity

Preferred Placeto Do Physical Type of 1st Author andActivity Physical Activity Level of Preference Subjects Year Published

Neighborhoodstreets

Physical activity 45.6% (1) Western Australian adults Giles-Corti 2002

Physical activity forrecreation

79.1% (2 among the bikewayusers after the bikeway);64.1% (1 among the nonusers)

American adults living inArlington, MA

Troped 2001

Parks Physical activity forrecreation

20.7% Parks and recreationfacilities (2 among thenonusers of the MinutemanBikeway, parks and recre-ational facilities combined)

American adults living inArlington, MA

Troped 2001

Public open space Physical activity 28.8% (2) Western Australian adults Giles-Corti 2002Beach Physical activity 22.7% (3) Western Australian adults Giles-Corti 2002


Preferred Placeto Do Physical Type of 1st Author andActivity Physical Activity Level of Preference Subjects Year Published

Neighborhoodstreets

Physical activity 66.1% of the respondents whoreported some degree ofphysical activity (1)

American adults Brownson 2001


52.3% (1) Western Australian adults Bull 2000

Running/joggingfor recreation


Shopping malls Physical activityfor recreation

37.0% of the respondentswho reported some degreeof physical activity (2)


Parks Physical activityfor recreation

29.6% of the respondentswho reported some degreeof physical activity (3)



12% (2) Western Australian adults Bull 2000

Running/jogging 18.5% (2) Western Australian adults Bull 2000Walking and jog-

ging trailsPhysical activity

for recreation24.8% of the respondents

who reported some degreeof physical activity (4)


Cycle paths Walking forrecreation


Running/jogging 8.4% (4) Western Australian adults Bull 2000Beach Walking for

recreation9.9% (3) Western Australian adults Bull 2000

Running/jogging 16.5% (3) Western Australian adults Bull 2000

NOTE: Numbers in parentheses show the rank based on the place’s level of prevalence for the particular physical activityreported in the study: level of preference is reported based on the entire population, not on those who are active only, unlessnoted.




TABLE 3. Physical Environmental Barriers to Physical Activity

BME Perceived Type of 1st Author andComponent Criteria Environmental Barriers Physical Activity Subjects Year Published

OD Opportunitybarrier

No facilities Physical activity Older Australian Booth 2000

Lack of land for recreation(unstructuredand passive activities)

Physical activity Focus group ofAustralian adults

Hahn 1994

Distancebarrier

Travel distance Physical activity Focus group ofAustralian adults

Hahn 1994

OD/R Accessbarrier

Access (cost, lack of transporta-tion and programs)

Physical activity Focus group of olderminority Americanadults

Eyler 1998

(Rural residents) Lack of awalking trail or malls


Eyler 1998

Limited footpaths andcycle ways


Hahn 1994

Access difficulties include badlymaintained or unsafe foot orcycle paths, unsafe pedestriancrossings, and road safety forpedestrians and cyclists


Hahn 1994

Lack or poor access to facilities(lack of pedestrian or bikeroutes)

Walking American adults Brownson 2000

OD/R/A Safetybarrier

Unsafe footpaths andcycle ways


Hahn 1994

Safety (traffic, people, dogs) Physical activity Focus group of olderminority Americanadults

Eyler 1998

(Rural, suburban and urban)Fear of the surroundings andcrime


Eyler 1998

Lack of safe places to exercise Walking American adults Brownson 2000Lack of safe places to exercise Physical activity for

recreationOlder female minority

American adultsKing 2000

Fear of injury Walking American adults Brownson 2000Fear of injury Physical activity Older Australian Booth 2000Fear of injury Physical activity for

recreationOlder female minority

American adultsKing 2000

Fear for personal safety Physical activity Focus group ofAustralian adults

Hahn 1994


BME Perceived Type of 1st Author andComponent Criteria Environmental Barriers Perceived Activity Subjects Year Published

OD Distancebarrier

Distance Walking and bikingfor transportation

Canadian adults Go for Green1998

Distance too far to get to placeson foot


West Australian adults James 2001

OD/R Accessbarrier

Too uncomfortable to walk Walking fortransportation

West Australian adults James 2001

(continued)



variables and physical activity, after controlling forsociodemographic factors. Tables 4 and 5 list the vari-ables as objective and subjective measures, respectively,classifying them according to the components of theBME, and specifying the type of physical activity envi-ronmental variable associated with them, along withthe direction of the association.

Generally, objective measures emphasize accessibil-ity to destinations in the residential neighborhood envi-ronment, corresponding mainly to the OD and Routecomponents of the BME. Subjective measures include awide range of variables considering all three compo-nents of the BME. Most route- and area-based attributesare measured subjectively.

OD-related variables, such as the presence of, andproximity to, exercise facilities in the neighborhood,whether perceived or actual, play a role in people’s lev-els of physical activity. This is consistent with findingsin the planning literature that distance to destinations isa determinant factor for transportation mode choice(USDOT 1995). Most destinations included in the stud-ies are considered for their recreational opportunities.As a result, some of the typical route-related variables inthe planning research, such as neighborhood streetsand walking trails, are often treated as destination. Thisreflects the health research’s focus on engagement inphysical activity itself, rather than on means of travel.Most studies emphasize residential location as the ori-gin for physical activity and travel. Specific destinationfacilities found to foster physical activity include publicfacilities such as footpaths, trails, parks, public openspaces, and cycle tracks, as well as private facilities suchas gyms, health clubs, recreation centers, and swim-ming pools.

A relatively small number of studies show associa-tions between route-related variables and levels ofphysical activity (e.g., Corti, Donovan, and Holman1997; Craig et al. 2002; Troped et al. 2001). In Craig et al.(2002), subjectively measured variables, including con-tinuity and choices of walking route, as well as trafficthreats and other obstacles along the route, contributeto explain variations in the composite environmentalscore used to evaluate the routes. The scores are foundto be associated with levels of walking to work. Tropedet al. (2001), on one hand, find the objectively measuredpresence of hills to negatively influence the use of alocal bikeway. King et al. (2000), on the other hand, findthe perceived presence of hills to be positively relatedwith physical activity. This apparent discrepancy islikely due to the different measurement types (objectiveversus subjective) and different dependent variablesused by the studies (i.e., respondents seeking to reach abikeway perceive hills as a barrier to accessing the bike-way, but respondents seeking leisure time activity orhousehold-related physical activity perceive hills as anattractor, possibly because they afford good views).Positive associations are also reported between levels ofphysical activity and the perception of tamed trafficconditions, pedestrian-friendly facilities (e.g., foot-paths, signage, street lights, etc.), and effective trafficcontrol measures, as well as with increased visualquality, perceived safety, and convenience.

Important objective area-based variables includesteep terrain, home age, and costal and urban residen-tial locations. The latter three variables are used as prox-ies for general urban form characteristics. While prox-ies may be an efficient means to address the multiple,highly interrelated variables that represent the built


Lack of pleasant route Walking and bikingfor transportation


Lack or poor access to facilities Walking and bikingto work

Finnish adult workers Vuori 1994

R/A Safetybarrier

Fear of accident Walking and bikingto work


Fear of injury Walking and bikingto work


Traffic safety/bad road Walking and bikingfor transportation


NOTE: BME = Behavioral Model of Environment; OD = origin/destination; R = route; A = area.

TABLE 3 (continued)


BME Perceived Type of 1st Author andComponent Criteria Environmental Barriers Perceived Activity Subjects Year Published



environment, they may not pass the test of externalvalidity if they fail to show any effect after controllingfor other sociodemographic factors and/or more exten-sively studied individual environmental factors such asdensity or land use mix. Subjectively measured area-

based variables, such as the perception of enjoyablescenery, are also found to influence physical activity(King et al. 2000). Amenities and aesthetic features areshown to increase the use of local parks (Corti, Dono-van, and Holman 1997). Perception of environmental


TABLE 4. Objective Measures Influencing Physical Activity

BME Objective Measures of Relationship with 1st Author andComponent Criteria Physical Environmental Variables Dependent Variable Found Year Published

OD Destinationquality

Park size 1 Use of local parks Corti 1997

Other amenities available at thefacility

1 Use of pay exercise facility Corti 1997

OD/A Availability ofdestinations

Number of local shops 1 Walking around their neighborhood Corti 1997

Number of destinations Contribute to explain variations ofenvironment score amongneighborhood

Craig 2002

OD/R Accessibility todestinations

Access to facilities (this relationshipstronger for beach, river, golfcourses, and tennis courts; less clearfor other facilities)

1 Use of facilities Giles-Corti2002

Access to exercise facilities 1 Likelihood of achieving physicalactivity as recommended

Giles-Corti2002

GIS road network distance to trail 2 Bikeway use Troped 2001Distance from home to pay facilities Contribute to explain difference

between sedentary and exercisergroups

Sallis 1990

Convenience ofdestinations

Rating of perceived convenience ofspecific facilities

Contribute to explain differencebetween sedentary and exercisergroups

Sallis 1990

R Route quality Steep hill barrier along the route tothe facility

2 Bikeway use Troped 2001

A Density Density of total exercise facilitieswithin 1 km

Contribute to explain differencebetween sedentary and exercisergroups

Sallis 1990

Density of pay facility 1 Exercise Sallis 1990House age Living in housing built before 1973

(as a proxy for the residentialneighborhood’s urban form)

1 Likelihood of walking 20+ times/week (only in urban/suburban area);this relationship did not hold forrural area

Berrigan2002

Geographiclocation

Costal residential location 2 Likelihood of being sedentary Bauman1999

Costal residential location 1 Likelihood of being adequatelyactive

Bauman1999

Costal residential location 1 Likelihood of being vigorouslyactive

Bauman1999

Neighborhoodcharacteristics

Degree of urbanization 1 Physical activity CDC 1998

Degree of urbanization 2 Physical inactivity (strongest inSouth region: 12.3% higher prevalenceof physical inactivity in rural area)

CDC 1998

NOTE: 1 or 2 sign shows the direction of association that the independent variable has with the dependent variable (1 = positive,2 = negative). BME = Behavioral Model of Environment; OD = origin/destination; R = route; A= area; GIS = Geographic Informa-tion System; CDC = Centers for Disease Control and Prevention.




TABLE 5. Subjective Measures Influencing Physical Activity

BME Subjective Measures of Relationship with 1st Author andComponent Criteria Physical Environmental Variables Dependent Variable Found Year Published

OD Availability ofdestinations

Perceived presence of park 1 Likelihood of being sufficientlyactive

Booth 2000

Perceived presence of recreationcenter (correl)*

1 Likelihood of being sufficientlyactive

Booth 2000

Perceived presence of cycle track(correl)


Booth 2000

Perceived presence of golf course(correl)


Booth 2000

Perceived presence of swimmingpool (correl)


Booth 2000

Self-reported distance to trail—trailas destination


Perceived opportunities 1 Physical activity (weak butsignificant)

Rutten 2001

OD/R Proximity/accessibility

Perceived proximity and accessibility 1 Use of local parks Corti 1997

Facility’s accessibility and proximityto home or work, or facility’slocation along the route to work

1 Use of pay exercise facility Corti 1997

OD/A Mix ofdestinations

Variety of destinations Contribute to explain variations ofenvironment score among neighbor-hood*

Craig 2002

OD/R/A Accessibility todestinations

Baseline number of convenientfacilities (number of exercisefacilities, such as aerobic dancestudios, bike lanes, and runningtracks, perceived as convenient)

1 Walking Hovell 1992

R Walking routeavailability

Presence of footpath 1 Walking around their neighborhood Corti 1997

Presence of walking paths 1 Walking around their neighborhood Corti 1997Availability of walking routes

(sidewalks, paths)Contribute to explain variations of envi-

ronment score among neighborhood*Craig 2002

Walking routequality

Safe footpath for walking 1 Likelihood of being sufficiently active Booth 2000

Traffic control measures 1 Walking around their neighborhood Corti 1997Little difficulty using footpaths

(correl)1 Likelihood of being sufficiently active Booth 2000

R/A Walking systemquality

Inclusive of pedestrian (people-oriented buildings, signage,amenities)

Contribute to explain variations of envi-ronment score among neighborhood*

Craig 2002

Meets pedestrian’s need (routecontinuity, route choices, crossinglights, topography, traffic, obstacles)


Craig 2002

Traffic threats (amount, speed,separation from traffic)


Craig 2002

Obstacles (debris, construction,maintenance)


Craig 2002

Time and effort required to walk—more specific (route directness,topography, obstacles,characteristics of intersections)


Craig 2002

(continued)



aesthetics and convenience are associated withincreased level of walking for exercise (Ball et al. 2001).

Overall, the studies use relatively specific objectivemeasures of route and destination related to recreationalfacilities, but general and aggregated objective area-based measures. Compared to urban and transporta-tion planning research, considerations of land use pat-terns, such as density, mix, and route characteristics,remain limited in the health field. Only Craig et al.(2002) find density and variety of destinations to be sig-nificant contributors to the composite neighborhoodscore, which is correlated with walking to work. Also,measures to capture proximity, accessibility, or conve-

nience remain loosely specified; clear definitions of,and distinctions between, these terms will be needed infuture research.

LESSONS FOR FUTURE PRACTICE ANDRESEARCH IN PROMOTING ACTIVE LIVING

This review suggests the development of comple-mentary knowledge bases in health and urban/trans-portation planning and unveils new promising ave-nues for urban and transportation practice and researchon the relationship between land use and transporta-tion behavior. At the level of professional practice, envi-


A Walking systemquality

Walking system (continuity) Contribute to explain variations of envi-ronment score among neighborhood*

Craig 2002

Transportationsystem quality

Transportation system (connection toother modes of transportation, bikeparking, benches at transit stops)


Craig 2002

Neighborhoodcharacteristics

Neighborhood character perceivedas residential compared to mixedor commercial


Availability of amenities 1 Use of local parks Corti 1997Visual quality Aesthetic features including lakes

and bird life1 Use of local parks Corti 1997

Complexity of stimulus (amountand variety of visual and auditorystimuli)


Craig 2002

Potential overload of stimulus(amount and variety of visual andauditory stimuli)


Craig 2002

Perceived presence of enjoyablescenery (total sample)

1 Physical activity King 2000

Perception of environmental aesthetics 1 Walking for exercise Ball 2001Area quality Perceived presence of hills (total

sample)1 Physical activity King 2000

Perception of convenience ofenvironment

1 Walking for exercise Ball 2001

Perceivedsafety

Safety from crime (lighting, frontporches, escape routes, peoplearound, street type, etc.)


Craig 2002

Potential for crime (graffiti,vandalism, disrepair, streetlighting, etc.)


Craig 2002

Perceived safety 2 Physical inactivity (this relationshipstrongest among persons aged 65+years and minorities) (correl)**

CDC 1999

Perceived safety 2 Physical inactivity among older adults(controlling for race, education, age, sex)

CDC 1999

NOTE: 1 or 2 sign shows the direction of association that the independent variable has with the dependent variable (1 = positive,2 = negative). *(correl) means that the associations are tested as bivariate relations only, without controlling for confounding fac-

TABLE 5 (continued)

BME Subjective Measures of Relationship with 1st Author andComponent Criteria Physical Environmental Variables Dependent Variable Found Year Published



ronmental interventions involving the design of streetsand the location of destinations in neighborhoods showpotential to support increased levels of physical activityand physically active travel. At the level of research, thepublic health field offers advanced theoretical andmethodological perspectives on human behavior andprovides useful insights into conceptual frameworksthat can guide further empirical testing of therelationship between behavior and environment.

Lessons for Practice: Areas of Possible Intervention

EVIDENCE SUPPORTING LATENT DEMAND FOR

WALKING AND BIKING AS MEANS OF TRAVEL

Consistent evidence of people’s predilection forwalking and, to a lesser degree, biking, suggests thatwalking and biking can become more common forms ofboth exercise and transportation in the future. Furthersupporting the potential of walking and biking asmeans of achieving high active living standards is theseemingly unfailing predominance of neighborhoodstreets as popular places for exercise. Given that currentstreet environments often poorly accommodate theseactivities, the large reported amounts of recreationalwalking and biking on streets suggests that providingappropriate street design and proximate routine desti-nations (e.g., retail shops and service facilities) willlikely increase levels of walking and biking for travel.

Transportation behavior research has long pointed toa latent demand for walking and biking trips. Manyautomobile trips are short enough to be substituted bywalking or biking. In the United States, 27 percent ofautomobile trips are shorter than 1 mile, and 40 percentare shorter than 2 miles (USDOT 1990). These distancesare well within the reported walkable and bikableranges of 0.74 mile to 2 miles, as conditioned by peo-ple’s general health, perception, and attitude (Bernhoft1998; USDOT 1995; Puget Sound Regional Council[PSRC] 2001). In addition, the majority of vehiculartrips are made for nonwork purposes, some of whichcould feasibly be replaced by slower travel modes—38percent of total trips are for social and recreation pur-poses, and another 35 percent are for family and per-sonal businesses (USDOT 1995). In Canada, peoplereport that they not only can but also want to increasetheir participation in walking and biking for transpor-tation purposes (Go for Green 1998, 17). Most peoplealso recognize the health value of walking (NPHP 2001,7). As a result, the potential to convert latent demandfor walking and biking into actual travel behaviorchange seems high; one of the key approaches to thischange will likely be through changes in the builtenvironment.

PROMOTING LAND USE INTENSITY AND

MIX, AND INVESTING IN PEDESTRIAN

AND BICYCLE FACILITIES

The studies confirm the importance of proximateand attractive destinations to support walking and bik-ing. They strengthen and complement existing evi-dence in urban and transportation planning research,where such route-oriented variables as the presence ofpedestrian and bicycle infrastructure (e.g., sidewalks,bike lanes, etc.), and area-related ones, such as density,land use mix, and street types, can be associated withincreased levels of walking and biking (e.g., Cerveroand Kockelman 1997; Ewing, Deanna, and Li 1996;Frank and Pivo 1994; Handy 1996; Hess et al. 1999;Kitamura, Mokhtarian, and Laidet 1997; Moudon et al.1997).

During the past several decades, the lack of sufficientcoordination between land use and transportationplanning and the limited public expenditures innonmotorized facilities—less than 2 percent of totalfederal transportation budgets are allocated for pedes-trian and bicycle facilities and programs (FHWA2002)—have contributed to creating urban environ-ments where walking and biking are marginalized ordisregarded as transportation modes. The studies’ find-ings imply that, to enhance the health and well-being ofthe population, infrastructure for walking and bikingneeds to become an integral part of public transporta-tion systems and services. Mixing land uses withinshort distances of each other must also be actively pur-sued to entice increases in walking and biking for trans-portation (Rutherford et al. 1995; Hess et al. 1999;Moudon and Hess 2000).

TARGETING ENVIRONMENTAL ENABLERS

AND BARRIERS TO PHYSICAL ACTIVITY,

WALKING, AND BIKING

Tables 4 and 5 point to specific physical environmen-tal enablers of physical activity. Perceived area-relatedenablers encompass various visual characteristics ofthe neighborhood, such as presence of aesthetic fea-tures, appropriate levels of visual stimuli, and enjoy-able scenery. Also positively associated with physicalactivity are such objective measures of neighborhood asurban and costal locations, older housing, and mixed orcommercial-dominant neighborhoods.

Because neighborhood streets are found to be themost frequently used places for physical activity, inter-ventions involving maintenance, comfort, connectivity,continuity, and safety of the transportation infrastruc-ture, and especially route-oriented components such assidewalks and bike lanes, will likely serve as effective




facilitators of walking and biking. Provision andenhancement of trails also seem to increase activitylevels (Brownson et al. 2001).

Environmental barriers to physical activity encom-pass various negative qualities of neighborhoods—mostly area-related safety factors including perceivedfear of crime, personal injury, traffic, and dogs. Interest-ingly, the presence of other people in the neighborhoodand in places of exercise is found to be a source of bothfear and social support, an indicator of the complexinfluences of environment on perceptions. Removingimpediments to physical activity that exist in the physi-cal environment will be a logical first step in promotingactive living. Examples of environmental modificationtoward creating activity-friendly neighborhoods are (1)providing safe places for exercise near homes; (2) locat-ing attractive, routine destinations near homes; (3) con-necting destinations with safe, convenient, and pleas-ant transportation systems; and (4) providing well-maintained, well-lit, and continuous sidewalks andbike lanes.

A socially supportive atmosphere (e.g., the presenceof other people exercising in the neighborhood and theopportunity to be physically active with friends or fam-ily) will also help remove some of the barriers or furtherbolster physical environmental enablers of physicalactivity. Brownson et al. (2001) note that an easy accessto supportive environments is a necessary, but not a suf-ficient, condition to promote physical activity and pointto the importance of personal and social factors, such astime, motivation, encouragement, and social support.The concept of reciprocal determinism drawn fromsocial cognitive theory helps explain how personal bar-riers to physical activity may interact with environmen-tal factors. For example, while it may seem difficult toovercome the fact that people have insufficient time tobe active, providing them with an environment thatencourages integrating walking/biking with otherdaily activities such as work, commuting and childcare, may get them to walk regularly (Booth et al. 1997,135). Furthermore, implementing physical environ-ments for active living likely will interact positivelywith improvements in the social environment, offeringpeople new opportunities to meet with others. As such,reciprocal determinism invites further research inneighborhood environment and behavior to developeffective approaches to modifying environments.

Lessons for Research: Theories and Methods

CONSIDERATION OF ENVIRONMENTS

WITHIN SOCIAL ECOLOGICAL THEORIES

As discussed earlier, multiple theories guide healthresearch, and complex theoretical frameworks focusing

on behavior and behavior change direct the classifica-tion and selection of variables, their interdependencies,and the identification of thresholds related to statedgoals for behavior change. These theories have also pro-vided a natural link between research findings and edu-cational programs promoting public awareness of thehealth benefits of physical activity, as well as its socialand psychological rewards at the personal and commu-nity levels. They can also add to urban and transporta-tion planning research, which has traditionally beenfocused on economics, and in which location theory(Alonso 1964), consumer choice, and random utilitytheory (McFadden 2001) have dominated as explicitlystated research frameworks.

The social ecological model highlights the social,physical, and policy or institutional dimensions of envi-ronments (McLeroy et al. 1988). Theoretically groundedsocial environmental variables included in thereviewed studies rely on social modeling (e.g., Boothet al. 2000; Giles-Corti and Donovan 2002; Hovell et al.1992), social support (e.g., Ball et al. 2001; Booth et al.2000; Wilcox et al. 2000), and social reinforcement(Booth et al. 2000). Those are interpersonal variablescapturing the relationships between persons and werefirst addressed in Bandura’s (1977) Social Learning The-ory. Social modeling refers to people’s capability tolearn a new behavior from observing others (Bandura1977, 1989). For example, one can be stimulated to walkor bike after observing others in the neighborhoodwalking or biking. Similarly, social support puts for-ward the role of one’s “significant other” in influencingbehavior by doing certain activities together. Socialreinforcement can take the form of verbalencouragement to do these activities.

The physical environmental dimension captured insome of the studies also adds such variables as accessi-bility to recreational facilities (e.g., Giles-Corti andDonovan 2002; Sallis et al. 1990; Troped et al. 2001) andthe presence of supportive physical facilities (e.g., Salliset al. 1997; Wilcox et al. 2000).

Associations between these theory-driven variablesand physical activity appear to hold in the studiesreviewed. The primary concern at this point is to matchthe highly developed scope of social environmentaltheoretical frameworks with similarly sophisticatedand rigorous constructs of the physical environment inorder to successfully identify physical environmentalvariables associated with physical activity. Stokols(1992) and Sallis and Owen (1997) call for futureresearch to consider explicitly community-based, phys-ical environmental influences on physical activity. Kinget al. (2002) propose to place theoretical perspectivesalong a continuum of personal choice—including thecognitive and behavioral factors affecting physical




activity—and, on the other end of the spectrum, activ-ity-related choice, which is shaped by physicalenvironments and related policies.

BEHAVIOR MODEL OF ENVIRONMENT

AS A CONCEPTUAL FRAMEWORK FOR

UNDERSTANDING THE BUILT ENVIRONMENT

The BME or a version of the BME, offers a theoreticalframework for better understanding the physical envi-ronment and, thus, complementing the social ecologi-cal approach. The BME helps define environmentalvariables characterizing activity settings, as they mayshape the behavior change. Future research needs toassess systematically which of the BME componentshas the strongest effect on behavior or which of the vari-ables defining each BME components affect behavior.Such variables as length of route, route attributes (e.g.,sidewalks, lighting, etc.), and area characteristics (e.g.,number of residents, number of destinations) need to befurther investigated to establish their association withlevels of physical activity. The influence of the BMEcomponents and variables on different types of behav-iors, such as walking versus biking, and on differentpurposes of behavior, such as recreational versustransportation activities, also requires attention.

APPROACHES TO RESEARCH DESIGN,

SAMPLING, AND DATA COLLECTION

The studies employ rigorous research design andmethods on the behavioral and psychological compo-nents of physical activity by (1) ensuring randomness inthe sample populations, (2) using tested or validatedinstruments for data collection, (3) employing adisaggregated approach to data analysis, and (4) con-sidering a broad range of theory-driven psychosocialconfounders. Furthermore, the common use of primarydata provides targeted and high-quality informationtailored to answer specific research questions. It alsohelps control for confounding factors. Elaborate testingand validation processes for data collection are wellestablished. For example, telephone interviews followstrict protocols to ensure a high response rate and valid-ity of responses. Questionnaires are tested for the ques-tion order, wording, recall period (e.g., frequency ofwalking during the past week vs. past month), andappropriate use of closed- and open-ended questions.Clearly, approaches to collect behavioral data are farmore advanced than the approaches to deal with physi-cal environmental conditions. For example, the currentresearch emphasizes a statistically rigorous sampling ofthe participants but disregards the need to appropri-ately select the types of environments. To date, manystudies rely on an imprecisely measured, or an insuffi-cient range of variations in, the environment, which

may limit the ability to detect associations with physicalactivity.

The idea that people choose to live in an environmentthat meets their behavioral inclination, commonlycalled self-selection, can weaken some of the findingson the environmental determinants of physical activity.While none of the reviewed studies address this issueexplicitly, they indirectly approach this issue by consid-ering various demographic and psychological factorsthat underlie the self-selection issue. Further attentionis still required to determine the nature and extent of theself-selection problem itself, and the specific factorsleading to household location choice.

The timing of data collection is also important to con-sider for further methodological improvements.Because levels of physical activity and walking and bik-ing vary by season, day of the week, and time of the day(e.g., Vuori, Oja, and Paronen 1994), data collectiontimes must account for these variations. Furthermore,times for behavioral and environmental data collectionmust be coordinated, especially when using secondarydata, which is common in planning research. Other-wise, the findings are subject to misinterpretation orovergeneralization. Longitudinal studies (e.g., beforeand after intervention study including both case andcontrol samples) should be considered to help establishcausality of the environment-behavior relationship.However, direct causality may never be established dueto the time, cost, and technical difficulties involvingfree-living individuals in ever-changing, dynamicenvironments.

CONSIDERATION OF BOTH OBJECTIVE

AND SUBJECTIVE DATA

Objective and subjective measures of environmentalfactors tend to be correlated, yet differences existbetween the two (Sallis et al. 1990). Both types of mea-sures have strengths and weaknesses when used to cap-ture the environmental conditions for walking and bik-ing. The advantages of objective measures may include(1) reduced measurement errors, (2) easy quantificationand standardization, and (3) easy translation into pol-icy implications. At the same time, theories of behaviorchange commonly employed in physical activity pro-motion suggest that the perceptual characteristics ofenvironment may be more closely related to actualbehavior outcomes than the objective characteristics ofenvironment. According to these theories, changes inbehavior involve an internalization process assessingthe environmental information. Further studies areneeded regarding the relative influence of objective andsubjective measures on levels of physical activity.




IMPORTANCE OF SPATIAL SCALE

AND SPATIAL DEPENDENCY

Many of the studies use the individual respondent asa unit of analysis, creating consistency among the vari-ables. This disaggregated approach allows consider-ation of individual-level extraneous factors. Neighbor-hood effects are less well addressed, however, and onlya few studies employ hierarchical modeling techniquesto deal statistically with multi-level effects, such as indi-vidual- versus neighborhood-level effects (Klesgeset al. 1990; Craig et al. 2002; Rutten et al. 2001). Also,because most objective measures of neighborhoodcharacteristics come from large aggregated spatialunits, such as zip codes, counties, or even larger geo-graphic regions (e.g., Bauman et al. 1999; CDC 1998;Wilcox et al. 2000), the many fine-grained variations inenvironments that matter for walking and biking areevened out (Hess 2001; Krizek 2001). As a result, associ-ations between physical activity and environment,which may be present at small-area scales, can get lostwhen data are aggregated to large areas. Potentialeffects of aggregation and disaggregation of data onbehavior need to be investigated.

In addition, spatial dependency or autocorrelations(i.e., people living nearby share similar environmentalconditions) are largely overlooked. Measures of envi-ronmental variables are known to covary spatially.However, most statistical analyses falsely assume theirindependency. The simple hierarchical analyses used inseveral of the studies reviewed offer only limited solu-tions to this problem. The nature of spatial dependencyremains to be understood, and the utility of such tech-niques as spatial statistics and hierarchical modelingshould be examined further (Miller 2001).

MEASUREMENT ISSUES IN BEHAVIORAL

AND ENVIRONMENTAL FACTORS

The studies’ dependent variables are commonlydichotomized, begging further examination of thepotential dilution of patterns of associations that couldbe observed at a more fine-grained or continuous scale.Systematically comparing associations that the envi-ronmental factors have with different measures ofdependent variables, such as total amounts, frequen-cies, and temporal distribution (daily, weekly, season-ally, etc.) of walking and biking, will likely improve ourunderstanding of how supportive environments canhelp achieve specific types of behaviors.

In most studies, measurements of environmentalfactors focus on the OD component of the BME (e.g.,recreational destinations). Specific area-based mea-sures, along with objective measures of traffic condi-tions, quality of transit service, and the characteristics

of street networks, and so forth, will need to be includedin future research (Moudon and Lee 2003).

CONCLUSION

Public health research on the physical environmen-tal determinants of physical activity identifies walkingand biking as popular and desirable means of beingactive. The studies provide evidence that creating activ-ity-friendly communities will increase levels of recre-ational physical activity. Effective strategies for pro-moting walking and biking likely will involve adding atransportation function to the existing popularity ofrecreational walking and biking. Combining recre-ational and travel-based walking and biking will cir-cumvent the issue of limited time, which people oftenreport as a major barrier to physical activity, andtherefore may increase the frequency of physicallyactive travel.

The popularity of neighborhood streets as places foractive living further reinforces the potential for walkingand biking as means of travel that can contribute toincreased levels of physical activity. Yet, multipurposewalking and biking will require environmental inter-ventions to ensure easy, safe, and pleasant access to rou-tine destinations. Intervention strategies must begrounded on empirical evidence, and their successfulimplementation is conditioned by various factors,including the responsiveness to the existing local envi-ronments, resources available, and ease and cost ofintervention. Strategies likely to be successful includethose targeting specific types of community, such asschools and work sites, and specific groups of people,such as the elders, children, and female, minority, andlow-income groups. Consideration of both incentive-based and regulatory interventions seems promising.Corresponding changes will be necessary in currenttransportation investment procedures that continue tofavor vehicular, rather than nonmotorized, modes oftravel. Also, the current dependence on automobiletravel suggests the need for reevaluating people’s life-styles and preferences. Only by reducing automobileuse can walking and biking become widely accepted,readily achievable, habitual, and routine in people’sdaily life.

Aspects of public health research methods are worthemulating in urban and transportation planning fields.The social ecological model provides a rich theoreticalframework to understand the multi-level (social, physi-cal environmental as well as psychological) influenceson behavior. Also, systematic validation and testing ofsurvey instruments, and careful consideration of con-founding factors and multi-level variables can serve tostrengthen urban and transportation planning




research. Published literature in the health field pro-vides exacting details about research protocols, includ-ing study limitations, potential bias , andgeneralizability of the findings. These strict publicationstandards establish connections between research find-ings from different projects and help build a systematicand collective research agenda.

The studies point to the need for a theoretical frame-work to conceptualize and measure physical environ-ments comprehensively. The BME used in this articlebegins to provide such a conceptual setting. The modelindicates that so far, health studies have limited envi-ronmental factors to a few of the route and destinationcomponents of environments. Future research needs toaddress the characteristics of areas (neighborhoods anddistricts) where active living can take place as well asconsider correlations between environmental vari-ables. Theoretical frameworks of the environmentalconditions will also help sample the full range ofvariability in environmental factors.

Research in health and urban/transportation fieldsis complementary. Future multidisciplinary research islikely to promise a better understanding of both thebehavioral and environmental aspects of physicalactivity and physically active travel.

ACKNOWLEDGMENTS

This work is part of a project supported by the Cen-ters for Disease Control and Prevention (CDC) and car-ried out through the University of Washington HealthPromotion Research Center (HPRC) to develop validprospective environmental audit instruments to beused for communities and professionals to supportneighborhood walking and biking. The project officerat the CDC is Dr. Thomas Schmid. Co-Principal Investi-gators are Drs. Allen Cheadle, Cheza Collier, andDonna Johnson, at the University of Washington, andRobert Weathers, at Seattle Pacific University.




168

APP

EN

DIX

Rev

iew

of S

tud

y M

etho

dol

ogy

TAB

LE

A1.

Stud

ies

Usi

ng B

oth

Obj

ecti

ve a

nd S

ubje

ctiv

e M

easu

res

of In

dep

end

ent V

aria

bles

AB

CD

EF

GH

I

Con

foun

din

g1s

t Aut

hor

Sam

ple

Obj

ecti

veSu

bjec

tive

Fact

ors

Dat

aSt

atis

tica

lSo

urce

and

Yea

rSt

udy

Type

/T

heor

etic

alD

epen

den

tIn

dep

end

ent

Ind

epen

den

tC

ontr

olle

d/

Col

lect

ion

Ana

lysi

sK

eyPu

blis

hed

Popu

lati

onSi

zeFr

amew

ork

Var

iabl

esV

aria

bles

Var

iabl

esC

onsi

der

edM

etho

dPe

rfor

med

1G

iles-

Cor

ti20

02

Mid

dle

-age

dad

ults

livin

g in

Pert

h m

etro

-po

litan

are

a,W

este

rnA

ustr

alia

Prob

abili

tycl

uste

rsa

mpl

e of

1803

Yes:

theo

ry o

fpl

anne

dbe

havi

or,

theo

ry o

f try

ing

(soc

ioec

olog

ical

fram

ewor

k)

Use

of f

acili

ties

Exe

rcis

e as

reco

mm

ende

d—30

+ m

inut

eson

mos

t day

sof

the

wee

k

Shor

test

rou

te fr

om h

ome

to r

ecre

atio

nal f

acili

ties

incl

udin

g:1.

Gol

f cou

rse

2. G

ym/

heal

th c

lub/

exer

cise

cen

ter

3. S

port

and

rec

reat

ion

cent

ers

4. S

wim

min

g po

ols

5. T

enni

s co

urts

6. P

ublic

ope

n sp

ace

7. R

iver

8. B

each

9. O

ther

faci

litie

s

Dis

tanc

es a

re r

ecor

ded

in50

0 m

cat

egor

ies

(500

m o

rle

ss to

20

km o

r m

ore)

and

then

agg

rega

ted

to c

reat

eov

eral

l spa

tial

acc

ess

scal

es (4

leve

ls)

Phys

ical

env

iron

men

tal

det

erm

inan

ts m

easu

red

as d

irec

t obs

erva

tion

of:

1. P

rese

nce

of fo

otpa

th2.

Vis

ible

sho

ps in

the

stre

et3.

Typ

e of

str

eet

4. S

tree

t tre

es5.

Lev

el o

f tre

e co

vera

ge6.

Tot

al s

pati

al a

cces

s to

exer

cise

faci

litie

s7.

Tot

al s

pati

al a

cces

s to

natu

ral r

ecre

atio

nfa

cilit

ies

(riv

er, b

each

)

Ind

ivid

ual d

eter

min

ants

incl

ude:

1. A

ttit

ude

tow

ard

tryi

ng e

xerc

ise

2. A

ttit

ude

tow

ard

the

proc

ess

of tr

ying

3. S

elec

tive

nor

m4.

Fre

quen

cy o

f try

ing

inth

e la

st 3

mon

ths

5. P

erce

ived

beh

avio

ral

cont

rol

6. B

ehav

iora

l ski

lls u

sed

last

mon

th7.

Inte

ntio

n to

try

in th

ene

xt 2

wee

ks

Soci

al d

eter

min

ants

incl

ude:

1. C

lub

mem

bers

hip

2. F

requ

ency

of

part

icip

atio

n in

phy

sica

lac

tivi

ty b

y 5

sign

ific

ant

othe

rs3.

Fre

quen

cy o

f asi

gnif

ican

tot

her

doi

ng p

hysi

cal

acti

vity

wit

h re

spon

den

tAge

Sex

Ed

ucat

ion

Hou

seho

ldin

com

eM

arit

al s

tatu

sN

umbe

r of

child

ren

age

18 in

hous

ehol

d

Tele

phon

esu

rvey

Geo

grap

hic

Info

rmat

ion

Syst

em (G

IS)

Obs

erva

tion

Unc

ond

itio

nal

logi

stic

regr

essi

onM

ulti

vari

ate

sum

mar

izat

ion

scor

e



169

2Sa

llis

1990

Ad

ults

livin

g in

the

city

of

San

Die

go,

Cal

ifor

nia

Ran

dom

sam

ple

of 2

,053

No

Freq

uenc

y(t

imes

/w

eek)

of v

igor

ous

recr

eati

onal

phys

ical

acti

vity

Eng

agem

ent

of a

ny o

f 24

acti

viti

es d

ur-

ing

the

prec

ed-

ing

2 w

eeks

(e.g

., ae

robi

cd

ance

, jog

ging

,te

nnis

, sw

im-

min

g, w

eigh

tlif

ting

, bas

ket -

ball,

soc

cer,

racq

uetb

all,

etc.

)

Num

ber

of to

tal f

ree

exer

-ci

se fa

cilit

ies

wit

hin

1, 2

, 3,

4, 5

km

from

hom

e (p

ublic

park

, spo

rts

fiel

ds,

pub

licre

crea

tion

cen

ters

, col

lege

san

d u

nive

rsit

ies,

pub

licsc

hool

)N

umbe

r of

tota

l pay

exe

r -ci

se fa

cilit

ies

wit

hin

1, 2

, 3,

4, 5

km

from

hom

e (t

enni

san

d r

acqu

et c

lubs

, aer

obic

and

dan

ce s

tud

ios,

mem

-be

rshi

p sw

imm

ing

pool

s,he

alth

and

fitn

ess

club

s,Y

MC

As

and

YW

CA

s,sk

atin

g ri

nks)

Num

ber

of to

tal f

ree

and

pay

exer

cise

faci

litie

sw

ithi

n 1,

2, 3

, 4, 5

km

from

hom

e

Perc

eive

d c

onve

nien

ce o

f15

type

s of

exe

rcis

efa

cilit

ies

(rat

ed)

Perc

eive

d b

arri

ers

toex

erci

se

Age

Ed

ucat

ion

Inco

me

Mai

l sur

vey

Fiel

d m

appi

ngD

escr

ipti

vest

atis

tics

F-te

st fo

rtw

o gr

oup

dif

fere

nces

3Tr

oped

2001

Ad

ults

livin

g in

Arl

ingt

on,

MA

Ran

dom

sam

ple

of 4

13

No

Use

of t

heM

inut

eman

Bik

eway

Rec

reat

iona

lph

ysic

al a

ctiv

-it

y d

urin

g th

epa

st 4

wee

ksTy

pe o

fph

ysic

alac

tivi

tyL

ocat

ions

for

phys

ical

acti

vity

Roa

d n

etw

ork

dis

tanc

e to

the

bike

way

from

hom

e(c

lose

st o

ffic

ial a

cces

spo

int)

Bus

y st

reet

bar

rier

alo

ngth

e sh

orte

st n

etw

ork

rout

eto

acc

ess

the

bike

way

Stee

p hi

ll ba

rrie

r al

ong

the

shor

test

net

wor

k ro

ute

acce

ss to

the

bike

way

(10+

% s

lope

for

at le

ast

100

m—

visu

alex

amin

atio

n)

Nei

ghbo

rhoo

d fe

atur

es(i

nclu

din

g si

dew

alk,

hill

,cr

ime)

Perc

eive

d s

afet

yN

eigh

borh

ood

cha

ract

er(r

esid

enti

al, m

ixed

,co

mm

erci

al)

Dis

tanc

e to

bik

eway

Stee

p hi

ll ba

rrie

rB

usy

stre

et b

arri

er

Age

Sex

Ed

ucat

ion

Phys

ical

acti

vity

limit

atio

n

Mai

l sur

vey

GIS

Pear

son’

sco

rrel

atio

nL

ogis

tic

regr

essi

onM

ulti

ple

logi

stic

regr

essi

onL

ikel

ihoo

d r

atio

test

ing (con

tinu

ed)



170

APP

EN

DIX

(con

tinu

ed)

TAB

LE

A2.

Stud

ies

Usi

ng O

bjec

tive

Mea

sure

s O

nly

AB

CD

EG

HI

Con

foun

din

g1s

t Aut

hor

Obj

ecti

veFa

ctor

sD

ata

Stat

isti

cal

Sour

cean

d Y

ear

Stud

ySa

mpl

eT

heor

etic

alD

epen

den

tIn

dep

end

ent

Con

trol

led

/C

olle

ctio

nA

naly

sis

Key

Publ

ishe

dPo

pula

tion

Type

/Si

zeFr

amew

ork

Var

iabl

esV

aria

bles

Con

sid

ered

Met

hod

Perf

orm

ed

4B

erri

gan

2002

U.S

. ad

ults

aged

20+

Stra

tifi

edm

ulti

stag

epr

obab

ility

sam

plin

g of

14,8

27 a

dul

ts(w

ith

over

sam

plin

gof

Afr

ican

and

Mex

ican

Am

eric

ans)

Yes:

loos

ely

base

d o

nec

olog

ical

mod

el

Wal

king

freq

uenc

yca

tego

rize

d a

s:1.

Non

e/m

onth

2. 1

-19

tim

es/

mon

th3.

20+

tim

es/

mon

th

Hou

sing

age

cat

egor

ized

as:

1. B

efor

e 19

462.

194

6-19

733.

Aft

er 1

973

Subu

rban

/ur

ban

vs. r

ural

area

Age

Sex

Rac

e/et

hnic

ity

Ed

ucat

ion

Hou

seho

ldin

com

eA

ctiv

ity

limit

atio

n

Use

d th

e th

ird

Nat

iona

lH

ealt

h an

dN

utri

tion

Exa

min

atio

nSu

rvey

Cen

susL

ogis

tic

regr

essi

on

5B

aum

an19

99A

dul

ts a

ged

18+

yea

rsliv

ing

insi

xtee

nhe

alth

ser

vice

regi

ons

inN

ew S

outh

Wal

es

Stra

tifi

edra

ndom

sam

ple

of1,

000

No

3 va

riab

les

cons

truc

ted

(bas

ed o

n en

ergy

expe

ndit

ure

dra

wn

from

the

self

-rep

orte

dac

tivi

ty a

nd w

eigh

t):

1. B

eing

tota

llyse

den

tary

(50–

kca

l/w

eek)

2. B

eing

ad

equa

tely

acti

ve fo

r he

alth

(800

+ k

cal/

wee

k)3.

Bei

ng v

igor

ousl

yac

tive

(1,6

00+

kca

l/w

eek)

Loc

atio

n of

res

iden

ce—

cost

al v

ersu

s in

land

base

d o

n po

stco

de

Gen

der

Age

Cou

ntry

of

birt

hE

duc

atio

nE

mpl

oym

ent

Use

d 1

6,17

8-re

spon

den

tte

leph

one

sur-

vey

of N

ewSo

uth

Wal

esre

sid

ents

Post

cod

e

Log

isti

cre

gres

sion

6C

DC

1998

Ad

ults

age

d18

+ y

ears

livin

g in

the

Uni

ted

Stat

es

Popu

lati

on-

base

d r

and

omsa

mpl

e of

118,

778

No

Eng

agem

ent i

n ex

erci

se,

recr

eati

on, o

r ph

ysic

alac

tivi

ty o

ther

than

thei

r re

gula

r jo

bd

utie

s d

urin

g th

epa

st m

onth

Deg

ree

of u

rban

izat

ion

clas

sifi

ed b

y us

ing

the

U.S

. Dep

artm

ent o

fA

gric

ultu

re’s

rur

al-

urba

n co

ntin

uum

cod

es(p

opul

atio

n ba

sed

)—te

nco

des

col

laps

ed in

to fi

veca

tego

ries

(spa

tial

uni

t:co

unty

)

Age

Sex

Ed

ucat

ion

Hou

seho

ldin

com

e

Use

d B

ehav

-io

ral R

isk

Fac-

tors

Sur

veil-

lanc

e Sy

stem

(BR

FSS)

GIS

Log

isti

cre

gres

sion

Cor

rela

tion

anal

ysis



171

7K

lesg

es19

90Se

lf-s

elec

ted

resp

ond

ents

recr

uite

d b

yre

spon

sefo

rms

dis

trib

uted

tolo

cal

ped

iatr

icia

ns’

offi

ces,

day

care

cen

ters

,an

d c

hurc

hes

222

pre-

scho

oler

sN

oA

ctiv

ity

leve

l of t

hech

ild (s

tati

onar

y,m

inim

al a

ctiv

ity,

slow

mov

emen

t,ra

pid

mov

emen

t)

Dir

ect o

bser

vati

ons

on:

1. T

ype

of p

hysi

cal

envi

ronm

ent i

n w

hich

the

acti

vity

was

occu

rrin

g (h

ome,

ow

nya

rd, p

ublic

pla

ygro

und

,st

reet

/si

dew

alk)

2. T

he p

erso

ns w

ho w

ere

pres

ent d

urin

g th

eac

tivi

ty3.

The

type

of i

nter

acti

onbe

twee

n th

e ch

ild a

ndth

e pe

rson

s pr

esen

t in

the

envi

ronm

ent

Age

Sex

Rac

e (a

ll w

hite

)W

eigh

tW

eath

erco

ndit

ions

allo

win

g fo

rou

tdoo

rac

tivi

tyd

urin

gob

serv

atio

nti

me

Obs

erva

tion

Cor

rela

tion

anal

ysis

Hie

rarc

hica

llin

ear

regr

essi

on

(con

tinu

ed)



172

APP

EN

DIX

(con

tinu

ed)

TAB

LE

A3.

Stud

ies

Usi

ng S

ubje

ctiv

e M

easu

res

Onl

y

AB

CD

FG

HI

Con

foun

din

g1s

t Aut

hor

Fact

ors

Dat

aSt

atis

tica

lSo

urce

and

Yea

rSt

udy

Sam

ple

The

oret

ical

Dep

end

ent

Subj

ecti

veC

ontr

olle

d/

Col

lect

ion

Ana

lysi

sK

eyPu

blis

hed

Popu

lati

onTy

pe/

Size

Fram

ewor

kV

aria

bles

Ind

epen

den

t Var

iabl

esC

onsi

der

edM

etho

dPe

rfor

med

8B

all

2001

Ad

ults

livi

ngin

Sou

thW

ales

,A

ustr

alia

Ran

dom

sam

ple

of 3

,392

Yes:

soc

ial

ecol

ogic

alfr

amew

ork

Freq

uenc

y an

d d

urat

ion

of w

alki

ng fo

r ex

erci

sein

the

past

2 w

eeks

(con

sid

er w

alki

ng o

f10

+ m

inut

es o

nly)

—d

icho

tom

ized

into

any

or n

o w

alki

ng

Aes

thet

ic s

core

mea

sure

das

rat

ings

(Lik

ert-

type

scal

e) o

f:1.

Nei

ghbo

rhoo

dfr

iend

lines

s2.

Nei

ghbo

rhoo

dat

trac

tive

ness

3. P

leas

antn

ess

in w

alki

ng

Con

veni

ence

sco

re m

easu

red

by r

atin

g of

:1.

Sho

ps w

ithi

n w

alki

ngd

ista

nce

2. P

ark

or b

each

wit

hin

wal

king

dis

tanc

e3.

Cyc

le p

ath

acce

ssib

le

Soci

al e

nvir

onm

ent (

com

-pa

ny) m

easu

red

as:

Hav

ing

som

eone

to w

alk

wit

h (y

es/

no)

Age

Sex

Ed

ucat

ion

Use

d d

ata

from

the

1996

Aus

tral

ian

Act

ivit

ySu

rvey

for

the

stat

e of

New

Sout

h W

ales

(tel

epho

nesu

rvey

)

LIS

RE

L(c

on-

firm

ator

ym

odel

—fo

r th

e 3

perc

eive

den

viro

nmen

tva

riab

lesc

ores

)



173

9B

ooth

2000

Ad

ults

old

erth

an 6

0 ye

ars

livin

g in

com

mun

itie

sin

Aus

tral

ia

Syst

emat

icra

ndom

ized

sam

ple

of 4

49

Yes:

Soc

ial

cogn

itiv

eth

eory

Phys

ical

act

ivit

ypa

rtic

ipat

ion

mea

-su

red

as

freq

uenc

y(c

ount

ing

wal

king

of

10+

min

utes

onl

y) a

ndam

ount

of:

1. V

igor

ous

acti

viti

es2.

Wal

king

for

exer

-ci

se,

leis

ure,

or

rec -

reat

ion

3. M

oder

ate-

inte

nsit

yac

tivi

ties

suc

h as

gard

enin

g

Ene

rgy

expe

ndit

ure

isca

lcul

ated

bas

ed o

nth

e pr

evio

us ty

pes

ofac

tivi

ties

, and

fina

lou

tcom

e va

riab

les

are

dic

hoto

miz

ed b

ased

on to

tal e

nerg

yex

pend

itur

e of

800

kcal

/w

eek

(suf

fi-

cien

tly

acti

ve v

s.in

suff

icie

ntly

act

ive)

Env

iron

men

tal i

nflu

ence

—m

easu

red

as

pres

ence

of:

1. E

xerc

ise

equi

pmen

t at

hom

e2.

Saf

ety

or d

iffic

ulty

of

wal

king

in th

ene

ighb

orho

od d

urin

gth

e d

ay3.

Acc

ess

to fa

cilit

ies

(exe

rcis

e ha

ll, r

ecre

atio

nce

nter

, cyc

le p

ath,

gol

fco

urse

, gym

, par

k,sw

imm

ing

pool

, ten

nis

cour

t, or

bow

ling

gree

n)4.

Soc

ial e

nvir

onm

ent

(fri

end

and

fam

ilyen

cour

agem

ent,

etc.

)

Oth

er n

onen

viro

nmen

tal

vari

able

s in

clud

ed:

1. S

ocio

dem

ogra

phic

mea

sure

s2.

Att

itud

e3.

Soc

ial r

einf

orce

men

t4.

Soc

ial m

odel

ing

Gen

der

Age

Mar

ital

sta

tus

Cou

ntry

of

birt

hL

ivin

gsi

tuat

ion

(liv

ing

alon

evs

. oth

er)

Em

ploy

men

tst

atus

Use

d th

ePo

pula

tion

Surv

eyM

onit

or d

ata

(fac

e-to

-fac

ein

terv

iew

)

Chi

-squ

are

anal

ysis

Log

isti

cre

gres

sion

10B

row

n-so

n20

00

Ad

ults

livin

g in

the

12 r

ural

com

mun

itie

sin

Mis

sour

i,U

nite

d S

tate

s

Popu

lati

on-

base

d s

ampl

eof

1,2

69

No:

onl

ym

enti

onen

viro

n-m

enta

lan

d p

olic

yap

proa

ch

Trai

l use

Incr

ease

in w

alki

ngsi

nce

usin

g tr

ail

Wal

king

for

exer

cise

in th

e pa

st m

onth

:w

alke

rR

egul

ar w

alki

ng(5

+ ti

mes

/w

eek

and

30+

min

/ti

me)

:re

gula

r w

alke

r

Pres

ence

of w

alki

ng tr

ail i

nar

eaD

ista

nce

to tr

ail

Trai

l sur

face

Trai

l len

gth

wit

hin

each

com

mun

ity

Acc

ess

to in

doo

r ex

erci

sefa

cilit

ies

Age

Sex

Rac

e/et

hnic

grou

pM

arit

al s

tatu

sE

duc

atio

nH

ouse

hold

inco

me

Popu

lati

onof

the

com

mun

ity

Tele

phon

esu

rvey

on

wal

king

beha

vior

,kn

owle

dge

,an

d a

ttit

ude

(que

stio

nnai

rein

clud

es s

tan-

dar

d it

ems

from

the

Mis

-so

uri B

RFS

San

d a

dd

itio

nal

item

s)

Prev

alen

ceod

ds

rati

o

11C

DC

1999

Ad

ults

age

d18

+ y

ears

livin

g in

the

five

sel

ecte

dst

ates

of t

heU

nite

d S

tate

s

Popu

lati

on-

base

d r

and

omsa

mpl

e of

12,7

67

No

Phys

ical

inac

tivi

ty(r

epor

ting

no

acti

vity

or e

xerc

ise

dur

ing

the

past

mon

th)

Rat

ings

of p

erce

ived

safe

ty fr

om c

rim

e in

the

neig

hbor

hood

Rac

e (w

hite

,no

nwhi

te)

Use

d B

RFS

SL

ogis

tic

regr

essi

onC

orre

lati

onan

alys

is

(con

tinu

ed)



174

12C

raig

2002

All

citi

zens

of

the

Uni

ted

Stat

es

Popu

lati

on(2

0% s

ampl

efo

r th

e lo

ngfo

rm in

clud

ing

ques

tion

s on

educ

atio

n,in

com

e, a

ndus

ual m

ode

oftr

ansp

orta

tion

to w

ork)

No

% w

alki

ng to

wor

kE

nvir

onm

enta

l sco

re fo

rea

ch n

eigh

borh

ood

calc

ulat

ed b

ased

on

the

obse

rver

rat

ings

of

envi

ronm

enta

l ite

ms

incl

udin

g (r

ated

):1.

Num

ber

of d

esti

nati

ons

2. V

arie

ty o

f des

tina

tion

s3.

Incl

usiv

e of

ped

estr

ian

4. S

ocia

l dyn

amic

s5.

Wal

king

rou

tes

6. M

eets

ped

estr

ian’

s ne

ed7.

Wal

king

sys

tem

8. T

rans

port

atio

n sy

stem

9. C

ompl

exit

y of

sti

mul

us10

. Pot

enti

al o

verl

oad

of

stim

ulus

11. V

isua

l int

eres

t12

. Vis

ual a

esth

etic

s13

. Tim

e an

d e

ffor

t req

uire

dto

wal

k14

. Tra

ffic

thre

ats

15. O

bsta

cles

16. S

afet

y fr

om c

rim

e17

. Pot

enti

al fo

r cr

ime

Deg

ree

ofur

bani

zati

onIn

com

eU

nive

rsit

yed

ucat

ion

% li

ving

inpo

vert

y

Cen

sus

Fiel

dob

serv

atio

n

Hie

rarc

hica

llin

ear

mod

el(3

leve

ls)

APP

EN

DIX

(con

tinu

ed)

TAB

LE

A3

(con

tinu

ed) A

BC

DF

GH

I

Con

foun

din

g1s

t Aut

hor

Fact

ors

Dat

aSt

atis

tica

lSo

urce

and

Yea

rSt

udy

Sam

ple

The

oret

ical

Dep

end

ent

Subj

ecti

veC

ontr

olle

d/

Col

lect

ion

Ana

lysi

sK

eyPu

blis

hed

Popu

lati

onTy

pe/

Size

Fram

ewor

kV

aria

bles

Ind

epen

den

t Var

iabl

esC

onsi

der

edM

etho

dPe

rfor

med



175

13H

ovel

l19

92A

dul

ts li

ving

in S

an D

iego

,C

alif

orni

a,U

nite

d S

tate

s

Ran

dom

sam

ple

of 1

,739

Yes: ind

epen

-d

ent

vari

able

sd

eriv

edfr

omle

arni

ng th

e -or

yan

d s

ocia

lco

gnit

ive

theo

ry

Cha

nge

in to

tal m

inut

espe

r w

eek

of w

alki

ngfo

r ex

erci

se b

etw

een

base

line

and

follo

w-u

p

Nei

ghbo

rhoo

d s

afet

yE

ase

of e

xerc

isin

g in

the

neig

hbor

hood

Freq

uenc

y of

see

ing

othe

rsex

erci

seN

umbe

r of

exe

rcis

e fa

cilit

ies

(aer

obic

dan

ce s

tud

ios,

bik

ela

nes,

run

ning

trac

ks, e

tc.)

perc

eive

d a

s co

nven

ient

Soci

odem

ogra

phic

var

iabl

esd

raw

n fr

om le

arni

ng th

eory

are

also

incl

uded

Age

Sex

Ed

ucat

ion

Inco

me

Mai

l sur

vey

Biv

aria

teco

rrel

atio

nH

iera

rchi

cal

mul

tipl

ere

gres

sion

AN

OV

A

14K

ing

2000

Min

orit

yw

omen

40

year

s of

age

or o

lder

Mul

tist

age

clus

ter

sam

ple

by z

ip;

nati

onw

ide

repr

esen

tati

vesa

mpl

e of

2,91

2

Yes: ind

epen

-d

ent

vari

able

sd

eriv

edfr

om s

ocia

lco

gnit

ive

theo

ry

Lev

el o

f lei

sure

tim

ean

d h

ouse

hold

-rel

ated

phys

ical

act

ivit

yd

urin

g th

e pa

st2

wee

ks

Soci

odem

ogra

phic

var

iabl

esH

ealt

h-re

late

d v

aria

bles

Psyc

hoso

cial

var

iabl

esPr

ogra

m-b

ased

var

iabl

esE

nvir

onm

enta

l var

iabl

es—

mea

sure

d a

s pr

esen

ce o

f:1.

Sid

ewal

k2.

Hea

vy tr

affi

c3.

Hill

s4.

Str

eetl

ight

s5.

Una

tten

ded

dog

s6.

Enj

oyab

le s

cene

ry7.

Fre

quen

t obs

erva

tion

of

othe

rs e

xerc

isin

g8.

Hig

h le

vels

of c

rim

e9.

Lev

el o

f saf

ety

inw

alki

ng/

jogg

ing

alon

e

Seas

onal

vari

atio

n in

phys

ical

acti

vity

(by

doi

ngth

e su

rvey

dur

ing

a1-

year

peri

od)

Soci

odem

o-gr

aphi

cva

riab

les

Tele

phon

esu

rvey

Des

crip

tive

stat

isti

csPe

arso

npr

oduc

t-m

omen

tco

rrel

atio

nsL

ogis

tic

regr

essi

on

15R

utte

n20

01A

dul

ts fr

omth

e 6

Eur

opea

nco

untr

ies

Ran

dom

sam

ple

of 3

,343

No

Eng

agem

ent i

n ph

ysic

alac

tivi

tyH

ealt

h st

atus

Opp

ortu

niti

es in

thei

r re

si-

den

tial

are

a fo

r ph

ysic

alac

tivi

tyL

ocal

clu

bs a

nd p

rovi

der

sof

feri

ng p

hysi

cal a

ctiv

ity

oppo

rtun

itie

sC

omm

unit

y ac

tion

s to

sup

-po

rt p

hysi

cal a

ctiv

ity

Inco

me

Tele

phon

esu

rvey

Prin

cipa

lco

mpo

nent

anal

yses

(for

que

s-ti

onna

ire

test

ing)

Des

crip

tive

stat

isti

csC

orre

lati

onan

alys

esA

NO

VA

Hie

rarc

hica

lre

gres

sion

(con

tinu

ed)



176

16Sa

lllis

1997

Intr

oduc

tory

psyc

holo

gyst

uden

tsfr

om S

anD

iego

Sta

teU

nive

rsit

y,U

nite

d S

tate

s

110

colle

gest

uden

ts in

psyc

holo

gy

No:

con

cept

of b

ehav

ior

sett

ing

(men

tion

ecol

ogic

alm

odel

and

soci

alco

gnit

ive

theo

ry)

Min

utes

of w

alki

ngpe

r w

eek

Day

s of

vig

orou

sex

erci

se p

er w

eek

Day

s of

str

engt

hex

erci

se p

er w

eek

Hom

e E

nvir

onm

ent S

cale

mea

sure

d a

s av

aila

bilit

yof

15

supp

lies

or p

iece

s of

equi

pmen

t at h

ome

that

can

be u

sed

for

phys

ical

acti

vity

Nei

ghbo

rhoo

d E

nvir

onm

ent

Scal

e in

clud

ing:

1. N

eigh

borh

ood

feat

ures

(sid

ewal

k, h

ill, e

njoy

able

scen

ery,

hig

h cr

ime

rate

[and

4 o

ther

var

iabl

es])

2. P

erce

ived

saf

ety,

mea

sure

d a

s sa

fety

inw

alki

ng in

the

neig

hbor

hood

dur

ing

the

day

3. N

eigh

borh

ood

perc

epti

on a

s re

sid

enti

al,

mix

ed, o

r co

mm

erci

al

Con

veni

ent F

acili

ties

Sca

le,

mea

sure

d a

s pr

esen

ce o

f 18

faci

litie

s th

at c

an b

e us

edfo

r ph

ysic

al a

ctiv

ity

Age

Sex

Eth

nici

ty

Not

spe

cifi

edPe

arso

nco

rrel

atio

n

APP

EN

DIX

(con

tinu

ed)

TAB

LE

A3

(con

tinu

ed) A

BC

DF

GH

I

Con

foun

din

g1s

t Aut

hor

Fact

ors

Dat

aSt

atis

tica

lSo

urce

and

Yea

rSt

udy

Sam

ple

The

oret

ical

Dep

end

ent

Subj

ecti

veC

ontr

olle

d/

Col

lect

ion

Ana

lysi

sK

eyPu

blis

hed

Popu

lati

onTy

pe/

Size

Fram

ewor

kV

aria

bles

Ind

epen

den

t Var

iabl

esC

onsi

der

edM

etho

dPe

rfor

med



177

17W

ilcox

2000

Wom

en a

ged

40 o

r ol

der

livin

g in

zip

cod

e ar

eas

wit

h 20

+ %

of e

thni

cm

inor

itie

san

d u

rban

/ru

ral a

rea

ofth

e U

nite

dSt

ates

Mul

tist

age

clus

ter

sam

plin

gof

2,9

12

No

Am

ount

of l

eisu

re ti

me

phys

ical

act

ivit

y in

the

past

2 w

eeks

(inc

lud

ing

wal

king

)—ca

tego

rize

d in

toac

tive

, und

erac

tive

,an

d s

eden

tary

Env

iron

men

tal

det

erm

inan

ts m

easu

red

as p

erce

ived

pre

senc

e of

:1.

Sid

ewal

k2.

Hea

vy tr

affi

c3.

Hill

s4.

Str

eet l

ight

s5.

Una

tten

ded

dog

s6.

Enj

oyab

le s

cene

ry7.

Fre

quen

tly

obse

rve

othe

rex

erci

se8.

Hig

h le

vel o

f cri

me

9. E

asy

acce

ss to

wal

king

trai

l, sw

imm

ing

pool

s,re

crea

tion

cen

ters

, or

bicy

cle

path

Soci

odem

ogra

phic

,ps

ycho

soci

al, a

nd h

ealt

h-re

late

d v

aria

bles

are

als

oin

clud

ed

Rac

eA

geE

duc

atio

nG

eogr

aphi

cre

gion

(Nor

thea

st,

Mid

wes

t,So

uth,

Wes

t)H

ealt

h st

atus

Tele

phon

esu

rvey

—m

odif

ied

vers

ion

ofB

RFS

S

Des

crip

tive

stat

isti

csPe

arso

nco

rrel

atio

nsL

ogis

tic

regr

essi

on



178

APP

EN

DIX

(con

tinu

ed)

TAB

LE

A4

Oth

er E

xplo

rati

ve S

tud

ies

AB

CD

E/

FG

HI

Con

foun

din

g1s

t Aut

hor

Fact

ors

Dat

aSt

atis

tica

lSo

urce

and

Yea

rSt

udy

Sam

ple

The

oret

ical

Dep

end

ent

Con

trol

led

/C

olle

ctio

nA

naly

sis

Key

Publ

ishe

dPo

pula

tion

Type

/Si

zeFr

amew

ork

Var

iabl

esK

ey R

esea

rch

Que

stio

nsC

onsi

der

edM

etho

dPe

rfor

med

18C

orti

1997

Ad

ults

age

dbe

twee

n 25

and

67

24 (6

-8 p

ergr

oup)

No:

onl

ym

enti

onso

cial

ecol

ogic

alpe

rspe

ctiv

e

Phys

ical

Act

ivit

yU

se o

f loc

al p

arks

Wal

king

aro

und

thei

rlo

cal n

eigh

borh

ood

Use

of p

ay fa

cilit

y(g

yms,

hea

lth

club

s,an

d r

ecre

atio

nce

nter

s)

NA

Soci

oeco

nom

icst

atus

of t

hepa

rtic

ipan

tsst

rati

fied

4 Fo

cus

grou

psR

ole-

play

Non

e

19E

yler

1998

Vol

unte

ers

old

er th

an 4

0,an

d e

ithe

rA

sian

Am

eric

an/

Paci

fic

Isla

nder

,B

lack

,H

ispa

nic,

or

Am

eric

anIn

dia

n liv

ing

in C

alif

orni

aor

Mis

sour

i,U

nite

d S

tate

s

Self

-sel

ecte

dgr

oup

of80

-100

(exa

ctnu

mbe

r no

tsp

ecif

ied

)

No

Phys

ical

act

ivit

yB

arri

ers

Ena

bler

sB

enef

its

Act

ivit

ies

Sour

ces

Rec

omm

end

atio

ns

NA

10 fo

cus

grou

ps(8

-10

each

grou

p)

NU

D*I

STqu

alit

ativ

ean

alys

ispr

ogra

m

20H

ahn

1994

5 co

mm

unit

ygr

oups

from

Dub

bo a

ndW

ellin

gton

,N

ew S

outh

Wal

es

Self

-sel

ecte

dgr

oup

of 3

0-50

(exa

ct n

umbe

rno

t spe

cifi

ed)

No

Act

ivit

y le

vel

Att

itud

e an

d k

now

led

geab

out a

deq

uate

phy

sica

lac

tivi

tyTy

pe o

f phy

sica

l act

ivit

yB

arri

ers

to m

ore

acti

vity

Exp

erie

nces

wal

king

and

biki

ng a

roun

d th

eir

tow

n

NA

Zon

ing

onco

unci

l map

s(r

ecre

atio

nal

faci

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NOTES

1. The programs are Active Community Environments by the Cen-ters for Disease Control and Prevention and Active Living Policy andEnvironmental Studies by the Robert Wood Johnson Foundation.

2. Generally, thirty minutes or more per day and five or more daysper week of physical activity or an energy expenditure of 800 kcal ormore per week are considered to be sufficient for health benefits.

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