Original Research
Journal of Physical Activity and Health, 2010, 7, 423-431
© 2010 Human Kinetics, Inc.
Associations Between Intrapersonal and Neighborhood
Environmental Characteristics and Cycling
for Transport and Recreation in Adults:
Baseline Results From the RESIDE Study
Sylvia Titze, Billie Giles-Corti, Matthew W. Knuiman, Terri J. Pikora, Anna Timperio,
Fiona C. Bull, and Kimberly van Niel
Background: This study investigated the relationship between individual and neighborhood environmental
factors and cycling for transport and for recreation among adults living in Perth, Western Australia. Methods:
Baseline cross-sectional data from 1813 participants (40.5% male; age range 18 to 78 years) in the RESIDential
Environment (RESIDE) project were analyzed. The questionnaire included information on cycling behavior
and on cycling-specific individual, social environmental, and neighborhood environmental attributes. Cycling
for transport and recreation were dichotomized as whether or not individuals cycled in a usual week. Results:
Among the individual factors, positive attitudes toward cycling and perceived behavioral control increased the
odds of cycling for transport and for recreation. Among the neighborhood environmental attributes, leafy and
attractive neighborhoods, access to bicycle/walking paths, the presence of traffic slowing devices and having
many 4-way street intersections were positively associated with cycling for transport. Many alternative routes
in the local area increased the odds of cycling for recreation. Conclusions: Effective strategies for increasing
cycling (particularly cycling for transport) may include incorporating supportive environments such as creating leafy and attractive neighborhood surroundings, low traffic speed, and increased street connectivity, in
addition to campaigns aimed at strengthening positive attitudes and confidence to cycle.
Keywords: bicycling, built environment, ecological model, public health
Numerous studies have established that moderateintensity physical activity enhances physical and mental
well-being.1–3 In most Western countries, however,
sedentariness continues to be a public health problem4,5
and strategies to promote and maintain regular physical
activity remain a public health priority.
Several studies have shown that cycling for transport
is a physiologically effective physical activity behavior
to improve cardiorespiratory function.6–8 Furthermore,
cycling for transport has been shown to produce positive
health outcomes. Among a Danish population cycling
to work was associated with a reduced relative risk for
all-cause mortality of 0.729 and a meta-analytic review3
showed that active transport (walking and cycling) was
Titze is with the Dept of Physical Activity and Health, Institute
of Sport Science, Graz, Austria. Giles-Corti is with the Centre
for the Built Environment and Health, University of Western
Australia, Perth, Australia. Knuiman, Pikora, and Bull are with
the School of Population Health, University of Western Australia, Perth, Australia. Timperio is with the Centre for Physical
Activity & Nutrition Research, Deakin University, Burwood,
Australia. van Niel is with the School of Earth and Geographical Sciences, University of Western Australia, Perth, Australia.
associated with an overall 11% reduction in cardiovascular risk. Based on the compendium by Ainsworth and
colleagues10 cycling for recreation meets at least the
moderate-intensity level of physical activity and is thus
also likely to be beneficial for function and health.
To successfully promote cycling for transport and
recreation at the population level it is necessary to identify
the specific modifiable factors that are related to cycling
behavior. Over the past decade studies have investigated
the effect of the built environment on physical activity
in general, on walking only, or on walking and cycling
in combination.11–13 One study tested the association
between neighborhood features and transportation- and
recreation-based activity.14 It found that activities for
transportation and recreation were associated with different environmental characteristics. The authors concluded
that the physical environment may affect transportation
activity more so than recreational physical activity. There
is a paucity of publications whether this is also true for
cycling for transport and recreation.
Several authors highlight that the capacity to predict a behavior is enhanced when there is correspondence between a specific behavior outcome measure
and the environmental variables hypothesized to be
associated with that particular behavior.15–17 Despite
423
424 Titze et al
this recommendation few studies have simultaneously
assessed intrapersonal and built-environment factors
and their association with specific physical activity
behaviors.
This study used constructs of the Theory of Planned
Behavior18 and the Social Cognitive Theory19 specifically
worded for cycling. These constructs predicted cycling
and physical activity behavior20,21 in previous studies The
selected built environmental attributes, are based on a
framework by Pikora16 which was specifically developed
for walking and cycling for transport and recreation and
on the Neighborhood Environment Walkability Scale
(NEWS)22 which is to date among the best developed
questionnaire to assess the local environment.
This paper investigated the relationship of perceived
neighborhood environmental attributes and intrapersonal
factors to cycling for transport and recreation among
adults living in Perth, Western Australia. We hypothesized
that intrapersonal and built environmental characteristics
would be related to both transportation and recreational
cycling, and that different built environmental factors
would be related to the different types of cycling.
Methods
Sample and Procedure
Participants were 1813 adults (age range 18 to 78 years)
who participated in the baseline survey of RESIDE, a
quasi-experimental 5-year longitudinal study. The study
protocol was approved by the Human Research Ethics
Committee at The University of Western Australia and all
participants provided written consent. The study design
and selection of participants has been reported elsewhere.23 Briefly, from September 2003, Perth residents
who were building new houses in 74 new developments
and planned to move into them by December 2005 were
eligible for inclusion in the study. Both telephone and
postal recruitment strategies were used and 1 person from
each household was selected randomly to participate.
Response rate was 33.4%.
Sociodemographic Variables
Among other sociodemographic variables participants
self-reported gender, age (year of birth), education (8
brackets), and availability of motor vehicle with 4 answering options: yes, always; yes, sometimes; no; do not drive.
Intrapersonal Attributes
Seven major psychosocial constructs related to cycling
were included in the questionnaire: attitude, perceived
behavioral control, self-efficacy, enjoyment, behavioral
skills, family support, and friend support. Because the
questions about self-efficacy, enjoyment, behavioral
skills, family and friend support were only relevant for
cyclists (eg, “Went cycling with my friends” or “When I
cycle in my neighborhood I enjoy it”), we only included
attitude toward cycling and perceived behavioral control
in the multivariate model. Both constructs were scored on
a 7-point rating scale. Attitude was assessed by: “Regardless of whether you succeed or fail, would you say that
going through the actual process of trying to cycle for
recreation or transport on most days in your neighborhood in the next month, would be: pleasant-unpleasant,
easy-difficult, positive-negative. Cronbach’s alpha for the
attitude scale was 0.91. Perceived behavioral control for
cycling was assessed by 1 question: “Assuming that you
tried to walk or cycle for recreation or transport on most
days in your neighborhood in the next month, how likely
is it that you would actually stick to regular cycling? In
the dichotomization process negative to neutral categories
were compiled vs. positive categories. The questions were
used in a previous study24 but were specifically worded
for cycling in this study.
Neighborhood Characteristics
Out of all questions of the Neighborhood Environment
Walkability Scale (NEWS) 19 environmental variables
would have been eligible for the inclusion into the
multivariate logistic regression model. These were land
use mix-access (based on NEWS),25 neighborhood surroundings (ie, aesthetics), perceived traffic volume, and
perceived neighborhood crime (all based on NEWS-A).25
Cronbach’s alpha for all scales except the perceived
traffic volume scale (α = 0.61) were above 0.74 indicating good internal consistency. The reproduction of the
NEWS-scales “street connectivity” failed because of
low Cronbach`s alpha (α = 0.21).26 Therefore the items
for this proposed scale were included as single items in
subsequent analyses. The model’s single items were:
access to bicycle or walking paths, number of destinations
for transport cycling (eg, local shops, post office, video
outlet, job etc.), number of destinations for recreational
cycling (eg, natural bushland, sports field, beach etc.),
presence of many traffic slowing devices, presence of
many 4-way intersections, presence of many alternative
routes for getting from place to place when walking, presence of major barriers to walking (eg, freeways, major
roads), car parking access in local shopping areas, streets
do not have many cul-de-sacs, presence of hilly streets,
slow traffic speed on most nearby streets, busy streets
have pedestrian crossings and traffic lights, streets are
well lit at night, presence of walkways that connect culde-sacs, and usually short distance between intersections.
All characteristics were assessed on a 5-point rating scale
(strongly disagree, disagree, neither agree or disagree,
agree, strongly disagree plus the option “does not apply
to me”). The variables were dichotomized into negative
to neutral categories vs. the positive categories.
Cycling Behavior Measures
Participants were asked how many times they cycled as
a mode of transport over a usual week within (within a
10 to 15 minute walk of their home) and outside their
Bicycling for Transport and Recreation 425
neighborhood. The same questions were asked with
respect to cycling for recreation. Cycling behavior within
and outside the neighborhood was combined and finally,
participants were dichotomized according to whether or
not they cycled for transport/ cycled for recreation over
a usual week.
Statistical Analysis
Analyses were carried out using SPSS 15.0 software in
2007. Principal components analysis was used to test the
internal consistency of the neighborhood environmental
and the intrapersonal constructs.
All independent variables were dichotomized as
shown in Table 1. Pearson chi-square tests were used to
examine the association between cycling and each independent variable. All variables with p-values ≤ 0.10 were
included into the logistic regression analyses (to protect
against potential type II error and to avoid overfitting).
Logistic regression modeling involved forced entry of
gender, age (4 categories), and education (3 categories) in
the first block. In the second block, a stepwise backward
elimination procedure was used to identify the significant
and independent individual and environmental predictors of cycling for transport and cycling for recreation,
respectively. Due to missing values (eg, destinations for
transport cycling has 139 missing values) the logistic
regression model of cycling for transport included 1653
participants and the model of cycling for recreation 1656
participants. Variables with P < .05 remained in the final
model. We also tested for interactions between gender
and the other independent variables as well as between
age and the other independent variables but as only one
out of the many interactions tested reached statistical
significance it could easily have arisen by chance and so
we have not made specific mention of this interaction.
Results
Among those who answered the transport cycling questions (N = 1795) 105 (5.8%) participants cycled for transport. Among those who answered the recreational cycling
question (N = 1798) 175 (9.7%) cycled for recreation. In
a usual week the median of the cycling frequency inside
the neighborhood is 1 for both groups. Sixty eight cyclists
cycled for transport and for recreational purposes. Table 1
shows the frequency distributions for those variables that
were associated (P ≤ .10) with cycling in the bivariate
analyses and were therefore entered into the logistic
regression models.
The study sample included more women (58.5%)
than men, the average age was 40 years (SD = 11.9 years),
and about 40% of the study participants had secondary
high school education or less. Given the large amount of
suburban sprawl in Perth, almost 93% of the study participants reported always having access to a motor vehicle.
Two-thirds of the participants had a positive attitude
toward cycling but only one-third perceived it likely
that they would cycle under current circumstances. Furthermore, two-thirds of the participants agreed that the
neighborhood provides good access to services, that the
neighborhood surroundings were leafy and attractive, that
there was access to cycling/walking paths, and that the
traffic volume was low. About one-half perceived more
than 6 businesses and more than 2 recreation facilities
within 10 to 15 min walking distance of their home. Twofifths perceived there were many traffic slowing devices
present. About one-third agreed that they had many 4-way
intersections in the local area and 70% agreed that there
are many alternative routes for getting from place-toplace. Most perceived few major barriers to walking in
their neighborhood and more than two-thirds perceived
cul-de-sacs in their neighborhood.
Table 2 shows the odds ratios for demographic
variables and the adjusted odds ratios for intrapersonal
and neighborhood-environment characteristics of cycling
for transport and recreation compared with noncycling.
Being a woman decreased the odds of cycling for transportation and being older than 30 years increased the odds
of cycling for recreation. Education was not associated
with cycling for transport.
Having a car permanently available, decreased the
odds of cycling for transport and for recreation compared with the category “never/sometimes, don’t drive.”
Among the intrapersonal attributes a positive attitude
toward cycling as well as perceived behavioral control
for cycling substantially increased the odds of cycling
compared with a negative or neutral attitude and a low
perceived behavioral control, respectively.
Among the neighborhood-environment characteristics, a leafy and attractive neighborhood, access to
bicycle or walking paths, the presence of traffic slowing
devices and having many 4-way street intersections all
significantly increased the odds of cycling for transport
compared with a low level of these attributes. The perception of alternative routes for getting from place-to-place
significantly increased the odds of cycling for recreation
compared with low perceived prevalence of alternative
routes.
Discussion
In Perth Western Australia, cycling for transport was
associated with gender, 2 intrapersonal (attitude toward
and behavioral control for cycling), and 4 environmental
characteristics (neighborhood surroundings leafy and
attractive, access to bicycle/walking paths, presence of
many slowing devices, presence of many 4-way intersections). However, for recreational cycling, only 1 environmental characteristic was significant (ie, the presence of
many alternative routes).
Consistent with earlier conceptual frameworks16
there were differences in the association between the
environmental characteristics and the 2 cycling behaviors.
This could be due to the scale at which the environment
was assessed (ie, local neighborhood or a 15 minute walk
Table 1 Distribution of Sociodemographic Characteristics,
Intraindividual, and Neighborhood Environmental Attributes Which
Were Entered Into the Final Logistic Regression Model (N = 1656)
Characteristics
Gender
Men
Women
Age (years)
≤29
30–40
41–55
56+
Education
Secondary level or less
Trade/apprenticeship/certificate
Bachelor or higher
Car availability
Never/sometimes/don’t drive
Always
Attitude toward cycling
Negative to neutral
Positive
Perceived behavioral control for cycling
Unlikely to neutral
Likely
Access to services (land use mix-access)*
Disagree to neutral
Agree
Neighborhood surroundings green and attractive
Disagree to neutral
Agree
Bicycle/walking paths accessible
Disagree to neutral
Agree
Traffic volume
High
Low
Number of destinations for transport cycling
0–6 destinations
>6 destinations
Number of destinations for recreational cycling
0–2 destinations
>2 destinations
Many traffic slowing devices*
Disagree to neutral
Agree
Presence of many 4-way intersections
Low to neutral
High
Presence of many alternative routes
Low to neutral
High
Major barriers in local area
High
Low to neutral
Car parking is difficult*
Difficult to neutral
Not difficult
Absence of cul-de-sacs*
Disagree to neutral
Agree
Note. Percentages do not always add up to 100% because of rounding errors.
* Based on N = 1653.
426
Total %
41.5
58.5
20.7
37.9
28.7
12.7
38.7
37.4
24.0
7.2
92.8
32.3
67.7
67.6
32.4
34.6
65.4
31.6
68.4
33.4
66.6
35.5
64.5
52.5
47.5
50.2
49.8
59.3
40.7
70.2
29.8
29.4
70.6
17.8
82.2
14.3
85.7
72.1
27.9
Table 2 Adjusted Odds Ratios (Cyclists vs. Noncyclists) for Individual and Neighborhood
Environmental Characteristics for Cycling for Transport (N = 1653) and for Cycling for Recreation
(N = 1656)
Cycling for transport
Variable
OR
Cycling for recreation
95% CI
P
OR
0.30–0.76
0.002
0.74
95% CI
P
0.51–1.06
0.104
Gender
Men
1.00
Women
0.48
1.00
Age (years)
≤29
1.00
0.021
1.00
30-40
1.37
0.78–2.43
0.277
2.08
1.22–3.55
0.007
41-55
0.58
0.29–1.15
0.117
1.73
0.98–3.04
0.058
56+
0.67
0.26–1.70
0.396
2.12
1.06–4.24
0.034
0.370
1.00
0.051
Education
Secondary level or less
1.00
0.149
Trade/apprenticeship/certificate
1.38
0.82–2.33
0.220
1.10
0.74–1.65
0.646
Bachelor or higher
1.01
0.55–1.85
0.981
0.68
0.41–1.13
0.136
0.26–0.86
0.014
1.76–6.94
<0.001
7.73
5.06–11.79
<0.001
1.00
1.72
1.11–2.67
0.016
Car availability
Never/sometimes/don’t drive
1.00
Always
0.27
1.00
0.14–0.52
<0.001
0.47
Attitude toward cycling
Negative or neutral
1.00
Positive
3.07
1.00
1.28–7.39
0.012
3.72–10.60
<0.001
1.11–3.49
0.021
1.01–3.08
0.045
1.04–2.55
0.033
1.12–2.76
0.014
3.50
Perceived behavioral control for cycling
Unlikely to neutral
1.00
Likely
6.28
1.00
Neighborhood surroundings leafy and attractive
Disagree to neutral
1.00
Agree
1.97
Bicycle/walking paths accessible
Disagree to neutral
1.00
Agree
1.77
Presence of many traffic slowing devices
Disagree to neutral
1.00
Agree
1.63
Presence of many 4-way intersections
Low to neutral
1.00
High
1.76
Presence of many alternative routes
Low to neutral
High
427
428 Titze et al
from home) and possibly that the local environment is
less important for recreational cyclists. However, additional analyses (not reported) showed that the majority
of recreational cyclists cycled within the neighborhood
(62%) and only 13% cycled exclusively outside the
neighborhood. It is possible that respondents could not
differentiate between local neighborhood (10 to 15 min
walk) and outside the neighborhood. However, more
likely, there are different correlates for the recreational
and cycling-related behaviors and, as with walking27 the
behaviors should be studied separately.15
In our sample 5.8% of participants cycled at least
once each week for transport and 9.7% for recreation.
This result is comparable with the 2006 Western Australian Adult Physical Activity Survey (ie, 3.8% and 9.3%,
respectively).28 but lower than the prevalence of transport
cycling reported in other countries (eg, Canada, 7.9%29;
Denmark 18%; the Netherlands, 27%).30
In countries with a low modal share of cycling,
men are consistently more likely to cycle for transport
and recreation than women,31,32 findings consistent with
our own. However, in places with higher modal shares
of cycling (eg, Denmark and the Netherlands) there is
almost no difference in the prevalence of female and
male cyclists.33 In Denmark, women take 45% of all bike
trips and 55% in the Netherlands.30 Similarly, in Graz,
a midsized Austrian city (250,000 inhabitants, cycling
modal split 14%) there was no gender difference in the
prevalence of adult transport cyclists.34
The model showed a positive association between
age and recreational cycling. From a biological point of
view, cycling is an activity that can be easily performed
by older individuals. Furthermore, older retired people
have more time for recreational cycling. In contrast, a
U.S. study found younger age to be positively associated with the use of a community rail-trail32 and the participants of a mass community cycling event in Sydney
(Australia) were mostly younger than 50 years of age.35
As recreational cycling can range from being a member
of a cycling club36 to relaxed sightseeing, more research
is needed to examine the influence of age on participation
in different forms of recreational cycling.
Our findings showed that permanent availability of a
car is negatively associated with bicycle use. In contrast,
in a U.S. study37 the proportion of people owning 1 or
more cars was higher for cyclists than for noncyclists.
With rising fuel prices, it is possible that in the future,
motor vehicle ownership may be less influential.
Overall, the pattern of associations between sociodemographic variables and cycling for transport and recreation appears inconsistent in the literature, perhaps due
to differences in the measures of cycling behavior and
sociodemographic variables. There may also be culturally dependent differences (eg, social norms related to
transport cycling) in these relationships, thus the location
of studies may be important.
Our final models did not adjust for the participants’
physical activity level. A separate analysis additionally
adjusted for physical activity (physically active <30 min
vs. ≥30 min) showed no effect.
In this present study the bicycling infrastructure,
namely access to bicycle and walking paths, was positively associated with transport cycling. This finding
is congruent with other investigations.31,38 Pucher and
Buehler30 argued that in Germany, the Netherlands and
Denmark the high level of cycling is—among other
factors—due to the expansion of the bikeway network.
They concluded that an improvement of the cycling
infrastructure is a major approach to increase cycling
participation levels.
Having a positive attitude and perceived behavioral control toward cycling was positively related
with transport and recreational cycling. This result
is congruent with the Theory of Planned Behavior
and has been shown consistently with other healthrelated behaviors39 and with specific physical activity
behaviors such as swimming, aerobics, and cycling.20
One implication of this result is that cycling promotion should include both personal messages about the
positive aspects of cycling and the ease of adopting
cycling behavior. However, to be effective, efforts to
develop bicycle-friendly environments would increase
confidence of new cyclists and assist in maintaining the
behavior once adopted.
Our study suggest that green and attractive environments are positively associated with cycling for
transport. These environmental characteristics were
derived from the NEWS22 which has been mostly
applied for walking rather than cycling behavior. In a
study among Belgian and Portuguese adults an aesthetic
environment was not associated with either cycling or
walking for transport.40 Among Dutch adults a positive
relationship was found between the presence of sports
grounds in the neighborhood (defined as a 300-m radius
around the postal codes of participants) and recreational
cycling as well as between parks and sport grounds
in the neighborhood (300-m radius) and cycling for
transport.41 However, the authors argued that it is very
likely that these results reflect the association of living
in the outskirts of town where recreational cycling is
more likely and public open space is present, rather
than the presence of a sport grounds or parks close to
home per se. In Austria attractiveness was associated
with irregular cycling among students but not with
regular cycling among adults.34,42 Apart from the different instruments which were applied in these studies
one possible reason for inconsistent results may be the
way how attractiveness is measured. In the current study
“the attractiveness of the neighborhood” was assessed,
while in the Austrian studies “the attractiveness along
the commuting route” was rated by the study participants. Future efforts to develop a NEWS-like scale for
cycling may wish to consider the cycling route, rather
than the local neighborhood as the ‘scale’ at which
perceptions are measured.
Bicycling for Transport and Recreation 429
We found that the presence of many traffic-calming
devices was positively associated with cycling for transport. Morrison and colleagues43 studied the effect of a
neighborhood traffic-calming scheme on walking and
cycling in the main road of a deprived urban housing
estate in Glasgow, Scotland. After the installation of
the new scheme respondents said that they would allow
children to cycle more in the neighborhood. We are not
aware of other studies reporting results about the effects
of traffic-calming devices on walking and cycling, but
these types of studies are warranted.
We also found that the presence of 4-way intersections was positively related with cycling for transport.
With respect to the NEWS this characteristic is part of
the street connectivity factor,25 because many 4-way intersections allow a more direct route to the destination. The
positive effect of a direct route on cycling for transportation has also been found in other studies conducted in
Austria, Canada, and U.S.42,44,45 Another street connectivity item, namely the presence of many alternative routes,
was positively associated with recreational cycling. This
result is supported by another Australian study conducted
in Melbourne.46 The perceived presence of many alternative routes might indicate that people perceive a choice
of possible routes for recreational cycling. The choice
of different routes might also include a choice between
different trip lengths and different grades along the route.
It is assumed that a variety of routes make recreational
cycling attractive.
Our study has a number of limitations and strengths.
Although causality cannot be implied from the crosssectional data, the results help to better understand both
the intrapersonal and neighborhood environmental characteristics that may be associated with different kinds
of cycling behavior. Self-reported data were used for
the assessment of transport and recreational bicycling
and related characteristics. Especially with respect to
measuring physical activity behavior there is a tendency
for over-reporting47 which might weaken the associations
with the independent variables. In our study we combined cycling in- and outside the neighborhood although
participants were only asked about the perception of the
local neighborhood. Although the analyses showed that
the majority cycled from home the results do not allow us
to answer questions about the effect of the environment
outside the local neighborhood. Another limitation is the
relatively low proportion of cyclists. As a consequence
we were only able to analyze factors associated with
any cycling and not factors associated with amount of
cycling which might differ. However, with a total sample
size of about 1600, our study did have adequate statistical power (more than 90%) to detect factors associated
any cycling that had odds ratios of 2 or greater. Another
consequence of the low prevalence of cycling is that our
findings therefore may not generalize to populations and
environments where cycling is more common. Authors of
another Australian study faced the problem of low proportion of transport cyclists,46 and this problem is likely to
be relevant for most North American and British studies
given the low prevalence of cycling in these countries.
In our study we relied on people’s perception of the
environment on the basis that perception is also important
in influencing behavior. The objective assessment of the
environment applying the methodology of Geographic
Information Systems or auditing would be useful to include
area level factors into the model. At the time of writing
these data were not available. Due to the wording of the
questions related to the social environment it was not possible to include these characteristics into our model as they
were only relevant to cyclists. Social Cognitive Theory and
the Theory of Planned Behavior suggest that the social
environment affects the physical activity behavior. The
inclusion of the social environmental characteristics is
important and is warranted in future studies because of the
likely impact on the other independent variables on cycling.
Despite the limitations, this study has a number of
strengths. First we had a substantially large populationbased sample of adults with prevalences of cycling
comparable to estimates from a recent statewide survey27
Second, we applied behavior-specific questions, which
we have previously recommended 15 to improve the
capacity to predict a behavior. Third, we differentiated
between cycling for transport and recreation. Fourth, the
questions about the intrapersonal and built-environmental
characteristics were based on health behavior theories (eg,
Theory of Planned Behavior), on a theoretical framework
for the assessment of the environmental determinants of
walking and cycling,16 and on the well tested NEWS.25
This approach is consistent with an ecological model
which for more than a decade, has been recommended
for physical activity research.48
Conclusions
The findings suggest that several intrapersonal and
neighborhood environment variables may affect cycling
for transport and recreation, different neighborhood characteristics are associated with cycling for transport and
for recreation, and the built environment may be more
important for cycling for transport. The results suggest
that creating cycling-friendly environments that include
cycling paths, are attractive, with low traffic speed, with
connected street networks, and strengthening individuals’
positive attitudes and confidence to cycling, may increase
the effectiveness of transport-related physical activity
promotion strategies.
Acknowledgments
Funding from the Western Australian Health Promotion Foundation (Healthway) is gratefully acknowledged (Grant No.
11828). The University of Graz financially supported the first
author. The second author is supported by a NHMRC Senior
Research Fellowship (#503712) and the fifth, by a VicHealth
Public Health Fellowship (2004 0536). We thank Pekka Oja for
the helpful comments to the manuscript.
430 Titze et al
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