The built environment, walking, and physical activity: Is the environment more important to some people than others?

Abstract

We examine whether specific types of people are more sensitive to the built environment when making a decision to walk or engage in other physical activity. Over 700 participants from 36 environmentally diverse, but equivalent-sized neighborhoods or focus areas responded to a survey, kept a travel diary, and wore an accelerometer for seven days. Subgroups defined by demographic and socioeconomic variables, as well as self reported health and weight status demonstrate that most subgroups of people walk more for transportation in high density areas. However, only the less healthy walked more overall in high density areas after controlling for sociodemographic characteristics and physical activity was remarkably similar among the groups and across different kinds of environments. While environmental interventions may not increase physical activity population wide, some populations – including some for whom interventions may be important such as the less healthy and the unemployed or retired – are more affected by these neighborhood environmental characteristics.

Full text

This article appeared in a journal published by Elsevier. The attached
copy is furnished to the author for internal non-commercial research
and education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
article (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
http://www.elsevier.com/copyright

Author's personal copy
The built environment, walking, and physical activity: Is the
environment more important to some people than others?
Ann Forsyth
a,*
, J. Michael Oakes
b
, Brian Lee
b
, Kathryn H. Schmitz
c
a
Department of City and Regional Planning, 106 West Sibley Hall, Cornell University, Ithaca, NY 14853, USA
b
Division of Epidemiology and Community Health, 1300 South Second Street, Suite 300, Minneapolis, MN 55454, University of Minnesota, Minneapolis, MN, USA
c
Division of Clinical Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, 921 Blockley Hall,
423 Guardian Dr., Philadelphia, PA 19104-6021, USA
article info
Keywords:
Walking
Physical activity
Neighborhood
Built environment
abstract
We examine whether specific types of people are more sensitive to the built environment
when making a decision to walk or engage in other physical activity. Over 700 participants
from 36 environmentally diverse, but equivalent-sized neighborhoods or focus areas
responded to a survey, kept a travel diary, and wore an accelerometer for seven days. Sub-
groups defined by demographic and socioeconomic variables, as well as self reported
health and weight status demonstrate that most subgroups of people walk more for trans-
portation in high density areas. However, only the less healthy walked more overall in high
density areas after controlling for sociodemographic characteristics and physical activity
was remarkably similar among the groups and across different kinds of environments.
While environmental interventions may not increase physical activity population wide,
some populations – including some for whom interventions may be important such as
the less healthy and the unemployed or retired – are more affected by these neighborhood
environmental characteristics.
Ó2008 Elsevier Ltd. All rights reserved.
1. Background
How do density and street pattern affect travel walking, leisure walking, total walking and physical activity for different
types of people? Work in the field of transportation has found people living in certain types of places walk more for travel
(Pucher and Renne, 2003; Transportation Research Board, 2005). In addition, there has been a great deal of recent interest in
the potential to leverage this situation to try to increase overall physical activity, an important public health aim (Handy
et al., 2002; Sallis et al., 2004).
This paper examines whether specific types of people are more sensitive to the built environment when making a deci-
sion to walk or engage in other physical activity. As non-motorized transportation receives a higher profile due to concerns
over issues such as energy and equity, such questions about the environmental supports for non-motorized modes are of
increasing interest to transportation professionals. The paper draws on the data set of the uniquely designed Twin Cities
Walking Study (TCWS) conducted in Minnesota, US. The study found that the built environment affected travel walking
but it did not clearly cause an increase in physical activity in the general population (Oakes et al., 2007; Forsyth et al.,
2007, 2008). However these general findings might mask important differences among groups of people. While not statis-
tically powered for extensive subgroup analyses, this paper uses these data to examine subgroups by: race, education,
sex, self reported health, work status, presence of children in the household, car ownership, and obesity.
1361-9209/$ - see front matter Ó2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.trd.2008.10.003
*Corresponding author.
E-mail address: forsyth@cornell.edu (A. Forsyth).
Transportation Research Part D 14 (2009) 42–49
Contents lists available at ScienceDirect
Transportation Research Part D
journal homepage: www.elsevier.com/locate/trd

Author's personal copy
Many earlier studies have examined the physical activity or walking behavior of different subpopulations distinguished
by age, ethnicity, income, sex, car ownership, or household size (Agrawal and Schimek, 2007; Bates et al., 2005; Plaut, 2005;
Simpson et al., 2003). Others have examined the effects of the built environment on walking and physical activity for par-
ticular populations such as all residents of sample neighborhoods or children. For adults these studies have typically but not
uniformly found that higher population densities, more connected street patterns, attractive destinations, and some pedes-
trian amenities support travel walking; effects on leisure and walking are more mixed (Badland and Schofield, 2005; Lee and
Moudon, 2006; McGinn et al., 2007; Plotnikoff et al., 2004).
However, few studies have examined these two issues together, that is whether measured local built environmental
features have different effects on different subgroups of adults living within the same neighborhoods (Papas et al., 2007;
Wendel-Vos et al., 2007). In addition, studies have often focused on a small subset of physical activity or walking behavior,
for example, on trips to work or on recreational walking.
There are some exceptions. A study of walking by 2650 Australian adults, using the International Physical Activity Ques-
tionnaire (IPAQ), found that education moderated the effects of environment on walking for transportation—those with 12 or
more years of education walked more for transportation in areas scoring high on a walkability index composed of housing
density, street pattern, land use, and retail dimensions; those with 10 or fewer years did not (Owen et al., 2007). An earlier
Australian study with 800 responses had found men more likely to walk in their neighborhood if they lived in a coastal area
but women were not so affected (Humpel et al., 2004). A study of over 3000 children and youth in Atlanta (ages 5–18), using
a two-day dairy to record if these youth had walked at all, found that white, high-income, and high-car households were
more affected by the built environment (Kerr et al., 2007). This paper expands this work.
2. Methods
The TCWS involved 716 participants sampled from the 36 environmentally diverse, but equivalent-sized neighborhoods
or focus areas, with approximately equal numbers in each area. Focus areas were 805 805 m and were selected from part of
the Twin Cities metropolitan area for which particularly good GIS data were available. Within the target region, 130 such
focus areas were identified and stratified into high (>24.7 persons/ha or 10/acre), medium, or low (<12.4 persons/ha or
5/acre) gross population density categories and high (>3.2 ha or 8 acres), medium, and low (<2 ha or 5 acres) block size.
The extreme types were then identified – high density large block (HDLB), high density small block (HDSB), low density large
block (LDLB), and low density small block (LDSB). Nine of each type were randomly selected, and in each area approximately
20 participants were recruited. This was done in four equal waves over an eight month period of warmer weather from April
to November. Of respondents, 74% were randomly selected and the rest recruited through other means; overall demograph-
ics matched the census profiles of these areas (Oakes et al., 2008). Height and weight were measured in person; surveys were
conducted by phone with the respondent following a printed copy of the survey (Twin Cities Walking Study, 2005; Forsyth
et al., 2009).
The study was innovative in sample design and in using both ‘‘objective” or GIS-based measures of the built environment
along with survey-based data on environments, self reported and objective measures of physical activity, measured height
and weight, and survey measures of a number of social and psychological variables. Travel walking, leisure walking, and
walking were measured for a seven day period by IPAQ and a version of the National Household Travel Survey diary modified
to include leisure walking. Physical activity was measured by IPAQ and accelerometer (motion detector). This paper uses the
measures the authors considered to be most valid. While the IPAQ had limitations it clearly distinguished between travel or
leisure walking, measured in MET minutes or intensity by duration; this distinction between travel and leisure was more
complex in the diaries. Diaries were used to analyze total walking, measured in distance, and accelerometers for total phys-
ical activity measured in accelerometer counts.
Environmental features were measured at many geographical scales including the 36 focus areas, and individual circular
and street network buffers around all participants ranging from 200 m to 1600 m. While providing rich data, earlier work
found little variation in environmental effects by geography; here we present analysis in terms of the 805 805 m square
focus areas (Forsyth et al., 2007, 2008). While the larger study focused on many environmental variables, here we examine
the sampling variables—gross density and median block size. Data were collected in 2004 and analysis has been ongoing.
Given the highly skewed distributions of outcome variables, two approaches were employed: negative binomial (nbreg)
and ordinal logistic regression (ologit) although only the latter are reported.
3. Results
Table 1 reports the sample characteristics of subpopulations by race, education, sex, self reported health, presence of chil-
dren in the household, employment vs. unemployment or retirement, car ownership, and obesity. All were self reported with
the exception of obesity (body mass index > 30) – height and weight were measured in person and the index calculated from
those figures. Self reported health was assessed through an answer to: in general, would you say your health is: Excellent,
Very Good, Good, Fair, Poor. The first two were classed as healthy and the last three as not.
Tables 2a–d tabulate mean values of the outcome walking and activity variables by environmental and subgroup
characteristics. We do not present statistical tests of difference due to concerns about multiple comparisons and confounding.
A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49 43

Author's personal copy
Table 2a reports results for mean travel walking measured by International Physical Activity Questionnaire. Some populations
appear to be more affected by the built environment in terms of walking. Most obvious is the situation that people without cars
walk a great deal for travel. Nonwhites walk a great deal for travel in areas with large blocks.
Table 2b presents results for the same populations and areas for leisure walking measured by IPAQ. There is generally
more leisure walking in low density areas, particularly for the college-educated, men, healthy, retired, and non-obese. Table
2c presents results for total walking measured by travel diary in miles. Again, those without cars stand out as walking more
as do a number of groups in high density, small block areas including whites, those with college degrees, males, the healthy,
those without children, the unemployed or retired, and the non-obese.
Table 2d then shows the results for the same areas and groups for total physical activity measured by accelerometer.
What is striking is the lack of difference between subpopulation groups. In addition, the high density small block areas
do not have higher total physical activity; those without cars do not have higher activity either though they had high levels
of walking.
Multivariate regression models suggest a more complex picture. These models were adjusted for age, self reported health,
measured body mass index (BMI), ethnicity, education, and household income. We examined two groups for each variable
Table 1
Sample characteristics of sub-populations.
NPercent
Race White 579 82.0
Non-white 127 18.0
College degree Yes 319 45.2
No 387 54.8
Sex Male 247 35.2
Female 455 61.8
Self reported health Less than Healthy 294 41.7
Healthy 411 58.3
Have children Yes 309 44.3
No 389 55.7
Unemployed or retired Yes 208 29.5
No 497 70.5
Has a car in household Yes 660 93.75
No 44 6.25
Obese Yes 221 31.84
No 473 68.16
Table 2a
Bivariate results of travel walking by population group with travel walking measured via IPAQ in mean MET minutes per week
a
.
Overall High density
large block
High density
small block
Low density
large block
Low density
small block
Race White 224.77 239.21 302.47 130.10 252.26
Non-white 433.25 551.63 449.38 676.50 114.18
College degree Yes 258.65 305.61 321.34 153.96 279.75
No 266.06 324.07 359.79 156.26 183.99
Sex Male 319.47 347.30 407.18 207.61 327.82
Female 231.95 300.47 306.75 122.85 192.23
Self reported health Less than healthy 237.03 233.55 379.10 117.03 147.54
Healthy 281.78 384.05 312.14 172.06 288.20
Have children Yes 252.68 271.97 291.08 161.47 279.54
No 271.56 366.45 388.15 152.09 202.17
Unemployed or retired Yes 319.49 404.90 368.60 147.42 329.68
No 239.12 276.14 335.74 157.81 190.97
Has a car Yes 219.04 255.70 282.11 155.08 193.61
No 916.50 986.70 741.81 – 1544.40
Obese (BMI > 30) Yes 270.35 345.28 398.42 133.53 177.99
No 266.91 311.86 326.40 168.56 262.09
a
1 MET (metabolic equivalent) is the energy expenditure for sitting quietly, so MET minutes are a measure of intensity by duration. The Centers for
Disease Control and Prevention recommendation of 30 min of moderate physical activity (3–6 METS), five times per week is approximately 450–900 MET
minutes per week (Centers for Disease Control, 2006a,b).
44 A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49

Author's personal copy
e.g. men and women separately, car owners and car free, etc. Table 3a presents significant results for travel walking
measured by IPAQ. As would be expected, basically all groups walk more for travel in high density areas—meaning that
the environment affects different groups in similar ways. Statistically significant odds ratios for density were in the range
of 1.78–2.45 meaning that groups were 1.78–2.45 times as likely to walk in higher density areas including: whites, males,
those without a college degree, the less healthy, those without children in the household, the unemployed and retired, those
with a car, those with a BMI under 30, and the obese. Block size was only significant for non whites with those in large block
areas walking more for travel. Odds ratios for all other groups were in the same direction (more density, more travel
walking) and several were close to significance including females, those with a college degree, and those with children.
There were no statistically significant results for leisure walking. This is in line with mixed findings in other studies and
likely reflects the strong role of social and psychological factors in leisure activity.
Table 3b presents results for walking measured by diary. Here the less healthy walk more in higher density areas, and the
unemployed and retirees walk more in large block areas. A number of other findings were close to significance with several
groups walking more in high density areas including whites, those without a college degree, the employed, those with cars,
and the obese.
Table 2c
Bivariate results of total miles by population group with total walking measured via seven day diary in mean miles walked per day.
Overall High density
large block
High density
small block
Low density
large block
Low density
small block
Race White 0.89 0.76 1.22 0.85 0.81
Non-white 0.68 0.72 0.75 0.41 0.56
College degree Yes 1.02 1.01 1.39 0.90 0.89
No 0.73 0.58 0.90 0.76 0.65
Sex Male 0.85 0.75 1.16 0.84 0.61
Female 0.85 0.74 1.01 0.83 0.85
Self reported health Less than healthy 0.72 0.65 0.94 0.55 0.64
Healthy 0.96 0.83 1.24 0.95 0.86
Have children Yes 0.77 0.70 0.82 0.84 0.73
No 0.91 0.81 1.27 0.82 0.79
Unemployed or retired Yes 0.92 0.87 1.14 0.87 0.77
No 0.83 0.69 1.06 0.81 0.78
Has a car Yes 0.79 0.65 0.95 0.83 0.75
No 1.81 1.80 1.85 – 1.62
Obese (BMI > 30) Yes 0.74 0.65 1.08 0.58 0.60
No 0.92 0.79 1.14 0.93 0.84
Table 2b
Bivariate results of leisure walking by population group with leisure walking measured via IPAQ in mean MET minutes per week
*
.
Overall High density
large block
High density
small block
Low density
large block
Low density
small block
Race White 345.44 287.67 282.73 397.27 392.68
Non-white 216.69 145.28 252.22 305.25 241.56
College degree Yes 373.83 297.05 267.83 442.57 448.50
No 279.67 223.58 277.84 341.18 291.63
Sex Male 362.68 298.36 268.26 421.24 474.45
Female 302.13 227.33 279.42 378.53 328.35
Self reported health Less than healthy 249.94 175.56 270.09 325.21 251.80
Healthy 374.40 315.25 281.28 422.75 449.27
Have children Yes 293.65 191.62 261.80 358.03 393.13
No 345.29 314.63 288.14 417.91 353.71
Unemployed or retired Yes 409.38 302.50 306.96 550.29 508.96
No 285.54 228.90 259.65 338.19 312.15
Has a car Yes 322.72 255.10 267.32 393.04 364.95
No 306.75 211.20 310.75 – 574.20
Obese (BMI > 30) Yes 243.05 173.68 285.45 324.99 174.48
No 363.28 286.44 279.84 428.02 454.36
*
See note in Table 2a defining METs.
A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49 45

Author's personal copy
Table 3c presents significant results for physical activity measured by accelerometer. Whites and the non-obese were less
physically active overall in high density area, and men were less physically active in large block areas. This runs counter to
the hope that higher density areas have higher physical activity; instead results are insignificant or in the opposite direction.
Only men followed the pattern of being less active in less connected street patterns.
Table 3a
Populations with significant effects of built environment in mean travel walking (MET minutes per week)
a
.
White OR Pvalue 95% CI No children OR Pvalue 95% CI
Lower Upper Lower Upper
High density 1.97 0.02 1.14 3.43 High density 2.17 0.02 1.14 4.15
Large block 0.81 0.42 0.47 1.37 Large block 0.74 0.36 0.39 1.41
Interaction 0.64 0.17 0.34 1.21 Interaction 0.75 0.46 0.34 1.63
N566 N375
BIC
b
1209.4 BIC 822.5
Non-white Unemployed or retired
High density 1.99 0.3 0.54 7.34 High density 1.81 0.03 1.07 3.08
Large block 6.81 0.03 1.21 38.26 Large block 0.78 0.38 0.44 1.36
Interaction 0.13 0.06 0.01 1.12 Interaction 0.69 0.29 0.34 1.38
N119 N487
BIC 280.4 BIC 1056.7
Male Has car
High density 1.78 0.02 1.12 2.83 High density 1.91 0.01 1.19 3.08
Large block 0.95 0.88 0.51 1.77 Large block 0.94 0.81 0.59 1.52
Interaction 0.7 0.38 0.32 1.54 Interaction 0.6 0.09 0.33 1.08
N239 N641
BIC 503.0 BIC 1362.1
No college degree Not obese
High density 2.07 0.03 1.06 4.05 High density 1.93 0.02 1.13 3.3
Large block 1.12 0.73 0.6 2.09 Large block 0.91 0.76 0.51 1.63
Interaction 0.44 0.04 0.2 0.95 Interaction 0.7 0.31 0.35 1.39
N373 N455
BIC 814.6 BIC 988.15
Less than healthy Obese
High density 2.45 0.05 1 5.97 High density 2.22 0.02 1.15 4.3
Large block 1.3 0.55 0.55 3.09 Large block 1.18 0.64 0.6 2.31
Interaction 0.37 0.06 0.13 1.03 Interaction 0.38 0.03 0.16 0.89
N284 N207
BIC 620.1 BIC 483.3
a
Significant results are in bold.
b
BIC = The Bayesian Information Criterion—a measure of model fit where lower is better.
Table 2d
Bivariate results of total physical activity by population group with total PA measured via accelerometer in mean thousands of counts per valid day
a
.
Overall High density
large block
High density
small block
Low density
large block
Low density
small block
Race White 224.57 228.08 210.10 217.75 241.50
Non-white 222.51 229.33 219.36 207.87 221.65
College degree Yes 227.58 229.80 209.27 228.93 238.88
No 221.43 227.45 215.01 205.00 238.37
Sex Male 217.82 218.66 213.69 209.16 232.91
Female 227.87 233.80 212.02 223.68 240.96
Self reported health Less than healthy 226.19 234.13 212.13 221.28 239.45
Healthy 222.69 223.64 212.91 215.55 238.09
Have children Yes 229.20 240.71 225.09 208.13 241.11
No 220.28 214.85 202.90 223.41 238.00
Unemployed or retired Yes 220.55 215.65 201.33 216.17 249.37
No 225.72 234.33 217.58 217.70 234.01
Has a car Yes 224.80 226.63 216.14 217.30 238.76
No 215.41 252.73 188.21 – 234.01
Obese (BMI > 30) Yes 226.60 223.09 217.86 228.48 239.61
No 222.87 231.74 208.56 209.44 240.34
a
Accelerometer total counts per valid day where counts are movement detected by the device and invalid days are those with <2 h of counts above
minimum threshold.
46 A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49

Author's personal copy
Table 3c
Populations with significant environmental differences in mean accelerometer counts per valid day (P< 05)
a
.
White OR Pvalue 95% CI
Lower Upper
High density 0.66 0.04 0.45 0.98
Large block 0.75 0.16 0.51 1.12
Interaction 1.62 0.12 0.88 2.95
N569
BIC
b
1652.1
Male
High density 0.69 0.3 0.35 1.38
Large block 0.53 0.02 0.31 0.91
Interaction 2.16 0.12 0.82 5.7
N242
BIC 731.51
Not obese
High density 0.63 0.04 0.41 0.99
Large block 0.68 0.13 0.42 1.12
Interaction 2.04 0.04 1.03 4.04
N457
BIC 1340.2
a
Significant results are in bold.
b
BIC = The Bayesian Information Criterion—a measure of model fit where lower is better.
Table 3b
Populations with significant effects of built environment on total walking (in miles) (P< 05)
a
.
Less than healthy 95% CI
OR Pvalue Lower Upper
High density 2.26 0.03 1.1 4.61
Large block 1.13 0.75 0.54 2.36
Interaction 0.56 0.21 0.23 1.38
N252
BIC
b
591.96
Unemployed/retired
High density 1.16 0.74 0.49 2.72
Large block 2.28 0.02 1.12 4.66
Interaction 0.52 0.21 0.19 1.45
N177
BIC 428.74
a
Significant results are in bold.
b
BIC = The Bayesian Information Criterion—a measure of model fit where lower is better.
Fig. 1. Odds ratio by sub-population for density. Note: travel and leisure walk values for respondents with no car in the household not listed due to small
sample size.
A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49 47

Author's personal copy
Figs. 1 and 2 present the results of Tables 3a–c in a graphical format, demonstrating higher odds ratios for travel walking.
Overall, while some populations appear quite sensitive to different environmental features in terms of walking for travel, a
finding of interest in the transportation field, these relationships were muted for other kinds of walking and overall physical
activity.
4. Conclusions
While the built environment alone is not an answer to the problem of obesity and related health concerns, the hypothesis
that it plays an important role in prevention remains appealing to scholars and policymakers. Overall, as predicted by trans-
portation research most subgroups of people in this study walked more for travel in high density areas. In the adjusted mod-
el, there were no significant relationships for leisure walking and the environmental features analyzed. Further, the
relationship between high density and total walking was significant only for the less healthy. In terms of physical activity,
the results were weak and mixed; whites and the non-obese was less physically active overall in such high density areas,
counter to proposals in the literature, and while men were less active in large block areas all other results were not statis-
tically significant.
The study has a number of limitations – a cross-sectional observational design, sample size limitations for subpopula-
tions, and analysis of only density and block size variables, measured at one geography, the focus area, rather than various
buffers. More work could investigate destinations and pedestrian infrastructure that had not proved significant for the gen-
eral population in being associated with overall physical activity but might prove important for subpopulations, as well as
other populations such as different age groups and dog walkers (Forsyth et al., 2007, 2008). It also does not account for self-
selection of those liking to walk into more walkable areas – however, accounting for this would have only reduced the mod-
est findings still further.
The results may point to the importance of the social environment over the built environment (Wendel-Vos et al., 2007).
Earlier bivariate analyses with the same data set had shown that questions about social life, though not about social cohe-
sion, were positively and significantly associated with total physical activity. Social life was measured in terms of ‘‘how many
days in the last month people had waved, said hello, stopped and talked to a neighbor, gone to a neighbor’s house, had a
neighbor over to socialize, gone somewhere with a neighbor, asked them for help, or sought advice” (Forsyth et al., 2008).
While representing only one study, these findings lend further support to the idea that individuals have a physical activity
budget and if they walk more they do less of other things (Rodríguez et al., 2006; Krizek et al., 2004; Forsyth et al., 2008).
However, the results indicate that certain populations are more affected by particular characteristics than others these in-
clude some populations for whom interventions may be important such as the less healthy. It follows that possible points of
intervention to promote increased overall physical activity for subgroups remain an important area of inquiry and policy
analysis.
Acknowledgements
Thanks to the Active Living Research program of the Robert Wood Johnson Foundation that funded this study. We thank
Joel Koepp for valuable assistance.
References
Agrawal, A.W., Schimek, P., 2007. Extent and correlates of walking in the USA. Transportation Research Part D 12 (8), 548–563.
Badland, H., Schofield, G., 2005. Transport, urban design, and physical activity: an evidence-based update. Transportation Research Part D 10 (3), 177–196.
Fig. 2. Odds ratio by sub-population for block size. Note: travel walking not listed for black (non-white) since OR was over 6; travel values for no car not
listed due to small sample size.
48 A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49

Author's personal copy
Bates, J., Serdula, M., Kahn, L., Jones, D., Gillespie, C., Ainsworth, B., 2005. Total and leisure-time walking among US adults: should every step count?
American Journal of Preventive Medicine 29 (1), 46–50.
Centers for Disease Control and Prevention, 2006a. Physical Activity for Everyone: Measuring Physical Activity Intensity: Metabolic Equivalent (MET) Level.
<http://www.cdc.gov/nccdphp/dnpa/physical/measuring/met.htm>.
Centers for Disease Control and Prevention, 2006b. US Physical Activity Statistics: Definitions. <http://www.cdc.gov/nccdphp/dnpa/physical/measuring/
met.htm>.
Forsyth, A., Oakes, J.M., Schmitz, K.H., Hearst, M., 2007. Does residential density increase walking and other physical activity? Urban Studies 44 (4), 679–
697.
Forsyth, A., Hearst, M., Oakes, J.M., Schmitz, M.K., 2008. Design and destinations: factors influencing walking and total physical activity. Urban Studies 45
(9), 1973–1996.
Forsyth, A., Oakes, J.M., Schmitz, K.H., 2009. Test–retest reliability of the Twin Cities walking survey. Journal of Physical Activity and Health.
Handy, S., Boarnet, M.G., Ewing, R., Killingsworth, R.E., 2002. How the built environment affects physical activity: views from urban planning. American
Journal of Preventive Medicine 23 (2S), 64–79.
Humpel, N., Owen, N., Leslie, E., Marshall, A.L., Bauman, A., Sallis, J.F., 2004. Associations of location and perceived environmental attributes with walking in
neighborhoods. American Journal of Health Promotion 18 (3), 239–242.
Kerr, J., Frank, L., Sallis, J.F., Chapman, J., 2007. Urban form correlates of pedestrian travel in youth: differences by gender, race-ethnicity, and household
attributes. Transportation Research Part D 12, 177–182.
Krizek, K., Birnbaum, A., Levinson, D., 2004. A schematic for focusing on youth in investigations of community design and physical activity. American Journal
of Health Promotion 191, 33–38.
Lee, C., Moudon, A.V., 2006. The 3Ds + R: quantifying land use and urban form correlates of walking. Transportation Research Part D 11, 204–215.
McGinn, A.P., Evenson, K.R., Herring, A.H., Huston, S.L., 2007. The relationship between leisure, walking, and transportation activity with the natural
environment. Health and Place 13 (3), 588–602.
Oakes, J.M., Forsyth A., Schmitz, K.H., 2007. The effect of neighborhood density and street connectivity on walking behavior: the Twin Cities walking study.
Epidemiologic Perspectives and Innovations 4, p. 16. <http://www.epi-perspectives.com/content/4/1/16/>.
Oakes, J.M., Forsyth, A., Hearst, M., Schmitz, K.H., forthcoming. Recruiting a representative sample for neighborhood effects research: strategies and
outcomes of the Twin Cities walking study. Environment and Behavior.
Owen, N., Cerin, E., Leslie, E., duToit, L., Coffee, L., Frank, L.D., Bauman, L.D., Hugo, G., Saelens, B.E., Sallis, J.F., 2007. Neighborhood walkability and the walking
behavior of Australian adults. American Journal of Preventive Medicine 33 (5), 387–395.
Papas, M., Allberg, A., Ewing, R., Helzlsouer, K., Gary, T., Klassen, A., 2007. The built environment and obesity. Epidemiologic Reviews 29, 129–143.
Plaut, P., 2005. Non-motorized commuting in the US. Transportation Research Part D 10, 347–356.
Plotnikoff, R.C., Mayhew, A., Birkett, N., Loucaides, C.A., Fodor, G., 2004. Age, gender, and urban rural differences in correlates of physical activity. Preventive
Medicine 39, 1115–1125.
Pucher, J., Renne, J., 2003. Socioeconomics of urban travel: evidence from the 2001 NHTS. Transportation Quarterly 57 (3), 49–77.
Rodríguez, D., Khattak, K., Evenson, K., 2006. Can new urbanism encourage physical activity? Comparing a new urbanist neighborhood with conventional
suburbs. Journal of the American Planning Association 72 (1), 43–54.
Sallis, J.F., Frank, L., Saelens, B., Kraft, M.K., 2004. Active transportation and physical activity: opportunities for collaboration on transportation and public
health research. Transportation Research Part A 38, 249–268.
Simpson, M.E., Serdula, M., Galuska, D., Gillespie, C., Donehoo, R., Macera, C., Mack, K., 2003. Walking trends among US adults: the behaviors risk factor
surveillance system 1987–2000. American Journal of Preventive Medicine 25 (2), 95–100.
Transportation Research Board, 2005. Does the Built Environment Influence Physical Activity: Examining the Evidence. Transportation Research Board,
Committee on Physical Activity, Health, Transportation and Land Use, Washington, DC.
Twin Cities Walking Study, 2005. Twin Cities Walking Survey. <http://www.activelivingresearch.org/index.php/Twin_Cities_Walking_Survey/328>.
Wendel-Vos, W., Droomers, M., Kremers, S., Brug, J., van Lenthe, F., 2007. Potential environmental determinants of physical activity in adults: a systematic
review. Obesity Reviews 8, 425–440.
A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49 49