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. Author manuscript; available in PMC: 2016 Nov 1.
Published in final edited form as: Health Place. 2015 Oct 27;36:65–73. doi: 10.1016/j.healthplace.2015.09.007

Moderating effects of age, gender and education on the associations of perceived neighborhood environment attributes with accelerometer-based physical activity: the IPEN Adult study Moderating effects of age, gender and education on the associations of perceived neighborhood environment attributes with accelerometer-based physical activity: the IPEN Adult study

Delfien Van Dyck 1,2, Ester Cerin 3,4, Ilse De Bourdeaudhuij 2, Deborah Salvo 5,6,7, Lars B Christiansen 8, Duncan Macfarlane 9, Neville Owen 10, Josef Mitas 11, Jens Troelsen 8, Ines Aguinaga-Ontoso 12, Rachel Davey 13, Rodrigo Reis 14,15, Olga L Sarmiento 16, Grant Schofield 17, Terry L Conway 18, James F Sallis 18
PMCID: PMC4679587  NIHMSID: NIHMS726866  PMID: 26454247

Abstract

The study's purpose was to examine age, gender, and education as potential moderators of the associations of perceived neighborhood environment variables with accelerometer-based moderate-to-vigorous physical activity (MVPA). Data were from 7273 adults from 16 sites (11 countries) that were part of a coordinated multi-country cross-sectional study. Age moderated the associations of perceived crime safety, and perceiving no major physical barriers to walking, with MVPA: positive associations were only found in older adults. Perceived land use mix-access was linearly (positive) associated with MVPA in men, and curvilinearly in women. Perceived crime safety was related to MVPA only in women. No moderating relationships were found for education. Overall the associations of adults’ perceptions of environmental attributes with MVPA were largely independent of the socio-demographic factors examined. These findings are encouraging, suggesting that efforts to optimize the perceived built and social environment may act in a socially-equitable manner to facilitate MVPA.

INTRODUCTION

Many single-country studies have examined the association between the perceived and objective built and social environment and adults’ physical activity (PA) (Arango et al, 2013; Ding & Gebel, 2012; Van Holle et al, 2012). These findings are encouraging, showing consistent associations of some environmental attributes (e.g. walkability, access to services, environmental quality) with PA including active transportation, leisure-time walking and accelerometer-based moderate-to-vigorous PA (MVPA; Ding & Gebel, 2012). Nonetheless, for most other environmental attributes (e.g. aesthetics, safety, proximity to recreation facilities) associations with PA are inconsistent across studies (Bauman et al, 2012). There are many possible explanations for such inconsistencies, such as measurement differences, country-specificity of findings, analytic differences, and failure to account for population-specific effects (Bauman et al, 2012; Sallis et al, 2011). Consequently, there is a strong value in conducting multi-country studies adopting a common protocol, to avoid several of these threats to validity.

Overall, the strength of the contributions of neighborhood built and social environmental attributes to explain PA was modest in previous studies (Bauman et al, 2012). This could be due to the presence of moderating effects: some of the associations between the built environment and PA may differ systematically across socio-demographic groups. Socio-ecological models of health behavior support this rationale, as they posit that behaviors are influenced by an interaction between intrapersonal, socio-cultural, policy and environmental factors (Sallis et al, 2008). Consequently, they suggest that it is important to focus not only on the built and social environment when examining correlates of PA, but also on the interplay with individual-level (e.g. sociodemographics) and socio-cultural (e.g. including various countries/cultures) factors. Previous studies have examined potential moderating effects of socio-demographic factors to establish whether neighborhood environment improvements, or improving residents’ environmental perceptions, could lead to sustainable and evenly distributed effects on PA across population subgroups (Gordon-Larsen & Popkin, 2011). Findings have been inconsistent; Forsyth and colleagues (2009) determined the relationship between objective residential density and transport-related walking was strongest in men, lower-educated, unemployed adults and those without children. Similarly, a Canadian study suggested that everyone may benefit from living in an objectively-assessed high walkable neighborhood, but associations with total PA seemed stronger among lower-educated adults (McCormack et al, 2014). In contrast, an Australian study showed objective walkability to be related to walking for transport only in highly-educated adults (Owen et al, 2007). Foster & Giles-Corti (2008) reported positive relationships between perceived crime safety and PA that were stronger among lower-educated, women and older adults. Another study in young adults showed that the association between perceived safety from crime and MVPA was not age- or gender-dependent (Boone-Heinonen & Gordon-Larsen, 2011). Finally, Villanueva and colleagues (2014) concluded that objective neighborhood walkability was supportive of walking, regardless of age.

The available evidence on environment - PA associations identifies possible moderating effects of socio-demographic attributes, but the existence and direction of these relationships may be dependent on the environmental attributes and types of PA studied, and whether objective or perceived environmental factors were included. In some studies, the absence of moderating effects might have been due to insufficient power. Furthermore, although socio-ecological models emphasize the importance of including the interaction with socio-cultural factors in research, no studies previously examined if these moderating effects may be dependent on the country/city one lives in.

In conclusion, there are limitations to what may be inferred from the findings of previous studies examining socio-demographic moderators of environment-PA associations and there is a strong value in conducting multi-country studies. The purpose of the present study was to examine potential moderating effects of gender, education and age on the associations of perceived neighborhood environment attributes with accelerometer-based MVPA (including meeting PA guidelines for weight gain/cancer prevention) in a multi-country study. We also examined whether such moderating effects might vary by study site and estimated the associations of socio-demographic factors with PA outcomes.

METHODS AND MATERIALS

Study design

For this paper, data of the International Physical Environment Network (IPEN) Adult Study were used. IPEN Adult is an observational epidemiologic multi-country cross-sectional study examining associations between the built environment and PA across 17 city-regions (sites) from 12 countries: Australia (Adelaide), Belgium (Ghent), Brazil (Curitiba), Colombia (Bogota), Czech Republic (Olomouc, Hradec Kralove), Denmark (Aarhus), China (Hong Kong), Mexico (Cuernavaca), New Zealand (North Shore, Waitakere, Wellington, Christchurch), Spain (Pamplona), United Kingdom (Stoke-on-Trent), and the USA (Seattle, Baltimore). Data collection dates ranged from 2002 to 2011. For the present analyses, 11 countries (16 sites) with objective accelerometer-based PA data were included (Adelaide, Australia was excluded).

All participants were from neighborhoods stratified into four quadrants: high walkable/high socioeconomic status (SES), high walkable/low SES, low walkable/high SES, and low walkable/low SES. All countries but Spain used an objectively defined walkability index using Geographic Information Systems (GIS) data and census-level SES indicators to select neighborhoods (Kerr et al, 2013). The GIS-based walkability index was computed for all areas across each study site’s entire region, using the smallest administrative unit available, then neighborhoods were selected (for details, see Frank et al, 2010; Kerr et al, 2013). Spain used ‘construction date’ as a parameter for neighborhood selection, which has been associated with walkability (Berrigan & Troiano, 2002).

Ethical approval was obtained from each local institutional review board, and participants’ informed consent obtained prior to data collection.

Recruitment and participants

The IPEN-required recruitment strategy systematically selected residents in the selected neighborhoods to participate by completing surveys on their PA and perceptions of their neighborhood environment, and by wearing an accelerometer to objectively assess PA (some countries collected accelerometry only on a subsample). Details about participant recruitment/response rates have been published elsewhere (Kerr et al, 2013). Recruitment age ranged from 15 to 84 years, but only adult participants aged 18-66 years were included. Data from 16 sites in 11 countries (11,572 participants) were analyzed. Not all participants wore an accelerometer, due to no consent (Belgium, Czech Republic, New Zealand and USA) or budget-related inability to collect data from all participants (Brazil, Colombia, Denmark, China, Spain and UK). For the sites aiming to collect accelerometer data from all participants, 86.5% to 100% of participants consented. Compared to those who did not wear accelerometers (n=3304) or had less than four valid days of accelerometer data (n=502), those who had ≥4 valid days of wearing time (n=7,273) were more likely to be older (p<.001), married (p=.012), employed (p=.005), tertiary educated (p=.001), and live in perceived crime-safe neighborhoods (p=.025) with high pedestrian infrastructure/safety (p=.043). No significant differences were found for gender, neighborhood SES, objectively-assessed neighborhood walkability, and the remaining nine perceived neighborhood characteristics. The socio-demographics of the sample with valid accelerometer data are presented in Table 1.

Table 1.

Sample characteristics: socio-demographic information, accelerometer-based PA outcomes and perceived environmental attributes
ALL
SITES		 BEL1 BRA2 COL2 CZE1
 DEN2 HK2 MEX1 NZ1
 SP2 UK2 USA1
Site A Site
B		 Site C Site
D		 Site E Site
F		 Site
G		 Site
H
N with ≥4
day valid
PA data
(%
sample)		 7,273
(51)		 1,050
(99)		 330
(47)		 223
(23)		 258
(78)		 122
(73)		 272
(42)		 269
(56)		 656
(97)		 373
(73)		 399
(78)		 416
(84)		 373
(75)		 329
(36)		 135
(16)		 119
8
(93)		 870
(95)
Age, years
Mean
(SD) 		43 (12) 43 (13) 42
(13)		 46
(12)		 39
(14)		 36
(14)		 40
(14)		 42
(13)		 42
(13)		 43
(12)		 42
(11)		 40
(12)		 43
(12)		 39
(13)		 44
(13)		 44
(11)		 47
(11)
Gender,
%men 		45.9 48.5 48.5 31.8 36.0 38.
6		 39.
0		 40.5 45.7 37.4 40.4 47.6 45.
6		 39.5 46.
7		 55.
0		 48.
7
Education
, %
Less than
HS
HS
graduate
College or
more 		12.1
38.4
49.5		 4.3
32.7
62.9		 27.9
31.2
40.9		 46.6
36.3
17.0		 23.0
43.5
33.5		 15.
7
56.
5
27.
8		 7.4
42.
3
50.
4		 36.4
23.1
40.5		 43.9
28.8
27.3		 2.4
58.3
39.3		 3.8
64.7
31.6		 0.5
45.0
54.6		 8.6
57.
0
34.
4		 4.3
32.7
63.0		 38.
8
46.
3
14.
9		 1.1
34.
9
64.
0		 1.8
29.
6
68.
5
Work
status,
%working 		78.8 80.3 79.4 60.5 77.9 82.
8		 75.
4		 62.7 71.5 76.4 86.2 87.5 85.
5		 76.3 64.
4		 81.
4		 83.
0
Marital
status,
%couple 		64.3 73.4 60.3 61.4 60.3 52.
6		 69.
1		 56.1 64.8 71.1 76.1 60.1 57.
1		 57.3 45.
9		 64.
1		 61.
1
Valid days
of
accelero
meter
wear
time,
Mean
(SD) 		6.5
(1.1)		 6.7
(1.1)		 6.7
(1.0)		 6.6
(1.0)		 6.2
(1.2)		 6.2
(1.4
)		 7.0
(0.8
)		 5.9
(1.0)		 5.7
(1.0)		 6.4
(1.3)		 6.4
(1.3)		 6.7
(1.3)		 6.5
(1.3
)		 6.5
(0.8)		 6.6
(1.0
)		 6.7
(0.8
)		 6.7
(1.2
)
Accelero
meter
wear time
(hrs/day)
Mean
(SD) 		14.4
(1.3)		 14.7
(1.3)		 14.0
(1.3)		 13.9
(1.2)		 13.9
(1.4)		 14.
2
(1.3
)		 14.
9
(1.1
)		 14.4
(1.4)		 14.0
(1.4)		 14.2
(1.2)		 14.1
(1.3)		 14.0
(1.2)		 14.
0
(1.2
)		 15.0
(1.1)		 14.
6
(1.2
)		 14.
7
(1.3
)		 14.
8
(1.4
)
MVPA
(min/day)
Mean
(SD) 		38.0
(26.8)		 35.5
(23.5)		 31.5
(24.6
)		 37.0
(26.4
)		 47.1
(27.7
)		 45.
1
(25.
9)		 39.
7
(23.
2)		 44.9
(25.3)		 31.2
(25.2
)		 45.7
(28.4
)		 37.2
(29.2
)		 50.1
(31.0
)		 44.
0
(32.
5)		 51.0
(29.
5)		 36.
7
(27.
3)		 36.
3
(24.
9)		 29.
2
(22.
0)
PAG for
cancer
and
weight
gain
preventio
n, % 		19.7 15.5 13.9 16.1 29.8 29.
5		 24.
3		 27.5 12.3 27.9 16.3 30.0 25.
2		 31.3 19.
3		 18.
9		 12.
9
Residenti
al density
(0-1000),
Mean
(SD) 		74.6
(112.7
)		 82.6
(72.6)		 99.7
(123.
6)		 51.7
(59.6
)		 89.1
(68.6
)		 85.
1
(68.
8)		 83.
5
(63.
4)		 443.8
(216.2
)		 38.1
(40.9
)		 30.0
(49.9
)		 19.1
(26.4
)		 45.5
(65.5
)		 22.
7
(26.
7)		 187.
0
(102
.3)		 36.
2
(32.
5)		 37.
5
(53.
9)		 59.
9
(79.
4)
Land use
mix –
access
(1-4),
Mean
(SD) 		3.3
(0.7)		 3.3
(0.6)		 3.6
(0.5)		 3.4
(0.4)		 3.5
(0.6)		 3.4
(0.6
)		 3.6
(0.6
)		 3.5
(0.7)		 3.3
(0.5)		 3.2
(0.6)		 3.1
(0.5)		 3.4
(0.5)		 3.4
(0.5
)		 3.7
(0.5)		 3.4
(0.7
)		 3.2
(0.8
)		 3.0
(0.8
)
Land use
mix –
diversity
(1-5),
Mean
(SD) 		3.8
(0.8)		 3.6
(0.9)		 4.1
(0.5)		 4.2
(0.4)		 3.9
(0.6)		 4.0
(0.6
)		 4.2
(0.6
)		 4.1
(0.7)		 3.7
(0.6)		 3.8
(0.6)		 3.7
(0.7)		 4.1
(0.6)		 3.9
(0.6
)		 4.5
(0.4)		 3.7
(0.5
)		 3.8
(0.8
)		 3.6
(0.9
)
Street
connectiv
ity
(1-4),
Mean
(SD) 		3.0
(0.7)		 2.7
(0.7)		 3.3
(0.7)		 3.1
(0.6)		 3.0
(0.7)		 3.0
(0.6
)		 3.1
(0.6
)		 3.2
(0.8)		 2.9
(0.5)		 2.7
(0.5)		 2.7
(0.4)		 2.8
(0.5)		 3.0
(0.5
)		 3.3
(0.7)		 3.1
(0.7
)		 3.0
(0.8
)		 3.0
(0.8
)
Infrastruc
ture and
safety
(1-4),
Mean
(SD) 		2.9
(0.6)		 2.8
(0.5)		 2.8
(0.8)		 2.8
(0.5)		 3.1
(0.5)		 3.2
(0.5
)		 3.1
(0.5
)		 3.4
(0.6)		 2.6
(0.4)		 2.8
(0.3)		 2.8
(0.4)		 2.9
(0.4)		 3.0
(0.4
)		 3.4
(0.5)		 3.2
(0.5
)		 3.0
(0.6
)		 3.1
(0.6
)
Aesthetic
s
(1-4),
Mean
(SD) 		2.8
(0.7)		 2.6
(0.6)		 2.9
(0.8)		 2.4
(0.5)		 2.4
(0.6)		 2.6
(0.5
)		 2.7
(0.6
)		 2.8
(0.7)		 2.6
(0.5)		 2.8
(0.5)		 2.9
(0.5)		 2.8
(0.5)		 2.8
(0.6
)		 2.7
(0.7)		 2.3
(0.8
)		 3.1
(0.7
)		 3.1
(0.6
)
Traffic
safety
(1-4),
Mean
(SD) 		2.6
(0.6)		 2.4
(0.6)		 2.4
(0.8)		 2.4
(0.5)		 2.9
(0.6)		 3.1
(0.5
)		 2.9
(0.5
)		 2.9
(0.6)		 2.4
(0.5)		 2.6
(0.5)		 2.6
(0.5)		 2.8
(0.4)		 2.7
(0.5
)		 2.5
(0.7)		 2.5
(0.7
)		 2.7
(0.7
)		 2.7
(0.7
)
Crime
safety
(1-4),
Mean
(SD) 		3.1
(0.7)		 3.2
(0.5)		 2.3
(0.5)		 1.9
(0.6)		 3.2
(0.6)		 3.4
(0.5
)		 3.3
(0.6
)		 3.4
(0.6)		 2.2
(0.7)		 3.1
(0.5)		 2.9
(0.4)		 3.1
(0.4)		 2.9
(0.6
)		 3.6
(0.6)		 3.0
(0.7
)		 3.4
(0.6
)		 3.4
(0.7
)
Few cul-
de-sacs
(1-4),
Mean
(SD) 		2.8
(1.0)		 3.0
(0.8)		 2.9
(1.1)		 2.7
(0.8)		 2.9
(1.0)		 3.0
(0.9
)		 2.8
(0.9
)		 3.5
(0.8)		 2.6
(0.7)		 2.3
(0.6)		 2.3
(0.6)		 2.5
(0.7)		 2.5
(0.8
)		 3.6
(0.9)		 2.3
(1.0
)		 2.8
(1.1
)		 2.8
(1.2
)
No major
barriers
to
walking
(1-4),
Mean
(SD) 		3.3
(0.8)		 3.3
(0.7)		 3.1
(1.1)		 2.9
(0.7)		 3.4
(0.8)		 3.5
(0.8
)		 3.7
(0.6
)		 3.3
(1.0)		 2.8
(0.7)		 3.3
(0.6)		 3.2
(0.6)		 3.3
(0.5)		 3.5
(0.7
)		 3.6
(0.8)		 3.4
(0.8
)		 3.2
(1.0
)		 3.8
(0.6
)
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Notes: Site A: Olomouc, B: Hradec Kralove, C: North Shore, D: Waitakere, E: Wellington, F: Christchurch, G: Seattle, H: Baltimore; HS=high school; PA=physical activity; MVPA = moderate-to-vigorous physical activity; PAG = physical activity guidelines for weight gain/cancer prevention; SD = standard deviation; valid days of accelerometer wear are those with 10+ valid hours of wear; for the perceived environmental attributes, the theoretical range of answers is reported between brackets.

1

Study site aimed to collect accelerometer data in the total sample

2

Study site aimed to collect accelerometer data in a fixed proportion of the total sample

Measures

Neighborhood Environment Walkability Scale (NEWS)

The Neighborhood Environment Walkability Scale (NEWS; Saelens et al, 2003) or NEWS-Abbreviated (Cerin et al, 2006) collected information on built environment perceptions. Confirmatory factor analysis maximized cross-country comparability of sub-scale responses across the 12 IPEN countries (Cerin et al, 2013). The resulting 10 NEWS measures constructed for the IPEN Adult study gauged (1) Residential density; (2) Land use mix – diversity; (3) Land use mix – access; (4) Street connectivity; (5) Infrastructure/safety for walking; (6) Aesthetics; (7) Traffic safety; (8) Safety from crime; (9) Streets having few cul-de-sacs; and (10) No physical barriers to walking.

The Residential density subscale is a weighted sum of items reflecting perceived density of housing, ranging from predominantly single-family dwellings to high-rise buildings of ≥20 stories. The Land use mix – diversity scale reflects average perceived walking proximity (i.e., average of five-point ratings ranging from ≤5 to >30 minutes walking: (1) ≤5 minutes, (2) 6-10 minutes, (3) 11-20 minutes, (4) 21-30 minutes, (5) >30 minutes) from home to 9 destinations (supermarket, small grocery/similar stores, post office, schools, transit stop, restaurants, park, gym/fitness facility, and other stores/services). The remaining eight scales were average ratings of items answered on a four-point Likert scale (1= strongly disagree to 4 = strongly agree). Scales were scored in a direction consistent with higher scores for more favorable responses, with individual items reversed when necessary. For detailed items and scoring for each country’s scales see Cerin et al (2013).

Accelerometer-measured PA

Mean minutes/day of MVPA were assessed objectively using valid and reliable accelerometers. (Freedson & Miller, 2000; Welk, 2002). Twelve sites used an ActiGraph device (Pensacola, FL), whilst New Zealand sites used the Actical (Philips Respironics, Bend, OR). Data were collected with or aggregated to 1-minute epochs. Non-wear time was defined as ≥60 minutes of consecutive zero counts. Participants were included in analyses if they had ≥4 valid wearing days each with ≥10 valid wearing hours. For Actigraph data, Freedson cut points were used (Freedson et al, 1998). For the Actical data new moderate (730-3399 cpm) and vigorous (≥3400 cpm) intensity cut points were developed to enable comparison with the ActiGraph-Freedson estimates (see Cain 2013). For details on accelerometer data collection and reduction see Cerin et al, (in press).

Daily minutes in each PA intensity were summed across valid wearing days and divided by the number of valid days to compute the average daily minutes of MVPA. An additional binary PA outcome was created corresponding to meeting the PA guidelines for cancer/weight gain prevention of ≥420 min/week of moderate or ≥210 min/week of vigorous PA (World Cancer Research Fund and American Institute of Cancer Research Guidelines, 2007, Lee et al, 2010, Institute of Medicine, 2002).

Socio-demographic characteristics

Age, gender, education, employment status and marital status were self-reported. As classification of education varied by country, all data were categorized into ‘less than secondary school degree’, ‘secondary school degree’ and ‘college degree (i.e. 3 or 4 year Bachelor’s Degree) or higher (i.e. Master’s Degree or PhD)’. Marital status was dichotomized as married/living with a partner versus not. Employment status was recoded as having a paid job: yes or no.

Data Analytic Plan

Descriptive statistics were computed for the whole sample with valid accelerometer data and by study site. Independent associations of perceived environmental variables with PA outcomes and moderating effects of age, education and gender were estimated using generalized additive mixed models (GAMMs; Wood, 2006) accounting for clustering effects at the administrative unit level (Cerin et al., 2014). GAMMs are very flexible regression models that can be used for outcomes with various distributional assumptions (e.g., normally-distributed, binary or positively skewed outcomes) when data are correlated (i.e., collected in participants living in specific neighborhoods). They can also model curvilinear relationships of unknown form via smoothing terms. In this study, GAMMS with Gamma variance and logarithmic link functions were used for the continuous PA measure (daily minutes of MVPA). GAMMs with binomial variance and logit link functions were used for the dichotomous PA measure (meeting the PA guidelines). The reported antilogarithms of the regression coefficients of these two sets of models represent the proportional increase in daily minutes of MVPA associated with a 1 unit increase in the predictor (risk ratio), and the odds of meeting vs. not meeting the guidelines (odds ratios), respectively.

Main-effect GAMMs estimated the dose-response relationships of all perceived environmental attributes with the continuous and categorical PA outcomes, adjusting for study site, sociodemographics, and unit-level SES. Fully-adjusted (all environmental variables entered) GAMMs were estimated. For all main effects, a two-tailed probability level of 0.05 was adopted. Curvilinear relationships of environmental attributes with outcomes were estimated using non-parametric thin-plate splines in GAMMs (Wood, 2006). Smooth terms failing to provide sufficient evidence of a curvilinear relationship (based on quasi-Akaike Information Criterion; qAIC) were replaced by simpler linear terms (Woods, 2006). Separate GAMMs were run to estimate environmental attributes by socio-demographics (age, education and gender) interaction effects by adding a single two-way interaction term to the main effects models. Another set of models estimated whether moderating effects of socio-demographics by perceived environmental attribute on physical activity outcomes varied by study site. This was done by adding three-way site by socio-demographics by environmental attribute interaction terms to the simpler models with two-way interactions. The significance of the interaction effect was evaluated by comparing qAIC values of models with and without a specific interaction term. An interaction effect was deemed significant if it yielded a qAIC 10 or more units smaller than the main effect model, indicating no support for the simpler main-effect model (Burnham and Anderson, 2002). All significant interaction effects from the single-interaction models were included in final interaction-effect GAMMs (one for daily MVPA and other for the odds of meeting the PA guidelines for weight gain/cancer prevention). These analyses tested for the presence of moderating effects on the multiplicative scale (relative risk and odds ratio scales).

Significant interaction effects were probed by computing gender-, education-, or age-specific associations by study site (as appropriate) using linear combinations of regression coefficients based on the pooled data. Age-specific associations were estimated at average, 1 standard deviation (SD) below, and 1 SD above values of age. Continuous predictors were centered around their mean. As only 305 cases (4.19%) had missing data, data analyses were performed on complete cases (Cerin et al., 2014). All analyses were conducted in R (R Development Core Team, 2013) using the packages ‘car’ (Fox and Weisberg, 2011), ‘mgcv’ (Wood, 2006), and ‘gmodels’ (Warnes, 2012).

RESULTS

Table 1 shows the overall and site-specific descriptive statistics for socio-demographics, accelerometer-based PA outcomes and perceived environmental attributes. The sample consisted of 7273 participants; 54% were women, 50% had a college/university degree, 79% worked, 64% lived with a partner and 20% met the PA guidelines for cancer/weight gain prevention. Mean age was 43 years (SD=12), with mean 38 min/day of MVPA (SD=26.8).

Associations of age, education and gender with PA outcomes

Age was negatively associated with accelerometer-based MVPA and the odds of meeting the PA guidelines for weight gain/cancer prevention (Table 2). For example, a one-year increment in age was associated with a decrease of 0.8% (95% CI: 0.6%, 0.9%) in daily minutes of MVPA and 1.7% (95% CI: 1.2%, 2.2%) lower odds of meeting the PA guidelines. Educational attainment was negatively associated with daily minutes of MVPA only. Women accumulated fewer min/day of MVPA than men and were less likely to meet the PA guidelines (Table 2). The associations of sociodemographics with the PA outcomes did not differ significantly across study sites.

Table 2.

Associations of age, education and gender with PA outcomes

Socio-demographic factor Moderate-to-vigorous PA
(min/day)a 		Meeting the PA guidelines for
weight gain / cancer
preventionb
exp(b) exp(95% CI) p OR (95% CI) P
Age 0.992 (0.991, 0.994) <.001 0.983 (0.978, 0.988) <.001
Education (reference: less than high
school graduate)
 High school graduate and/or some
college		 0.916 (0.863, 0.970) .003 0.861 (0.685, 1,083) .203
 College degree or higher 0.924 (0.870, 0.981) .010 0.809 (0.640, 1.023 .078
Gender (reference: men)
 Women 0.795 (0.770, 0.821) <.001 0.527 (0.466, 0.596) <.001
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Notes. OR = odds ratio; PA=physical activity; 95% CI = 95% confidence interval; exp(b) antilogarithm of regression coefficient; exp(95% CI) = antilogarithm of confidence interval. All regression coefficients are adjusted for respondents’ age, gender, marital status, education, employment status, administrative-unit socio-economic status, and accelerometer wear time.

a

generalized additive mixed model (GAMM) with Gamma variance and logarithmic link functions, for which exp(b) is interpreted as the proportional increase in PA associated with a 1 unit increase in the predictor.

b

GAMM with binomial variance and logit link functions.

Moderating effects of age, education, and gender on the associations of perceived environmental attributes with PA outcomes

Age moderated the associations of perceived crime safety and having no major barriers to walking (Tables 3 and 4). Specifically, no significant associations with MVPA were found among respondents with a below average and average age, while those with 1 SD above the sample mean showed positive associations (Table 4). No significant evidence was found for moderating effects of age with respect to meeting the weight gain/cancer prevention PA guidelines, nor for moderating effects of education on either of the PA outcomes (Table 3).

Table 3.

Summary results of moderating effects of age, education and gender on the associations of perceived neighborhood environment attributes and PA outcomesa

Environmental attribute Physical activity
outcome		 Age Education Gender
Residential density MVPA (min/day) −4.64 −1.39 −1.40
Meeting PA
guidelines		 −17.32 −3.10 5.04
Land use mix – access MVPA (min/day) 2.88 −10.58 11.12 *
Meeting PA
guidelines		 −36.17 −8.17 13.79 *
Land use mix – diversity MVPA (min/day) 4.42 −3.87 −0.88
Meeting PA
guidelines		 −15.09 1.60 −4.64
Street connectivity MVPA (min/day) 0.95 −4.23 3.61
Meeting PA
guidelines		 −21.26 −11.52 10.00 *
Pedestrian infrastructure
and safety		 MVPA (min/day) 7.45 −3.17 −2.34
Meeting PA
guidelines		 −9.00 −11.49 2.17
Aesthetics MVPA (min/day) 8.94 −2.64 7.48
Meeting PA
guidelines		 −9.09 −12.32 −4.52
Traffic safety MVPA (min/day) −2.10 9.48 9.48
Meeting PA
guidelines		 −4.09 −1.03 −7.54
Safety from crime MVPA (min/day) 14.70 * 1.45 25.66 *
Meeting PA
guidelines		 −20.10 −21.41 −13.60
Few cul-de-sacs MVPA (min/day) −2.43 −1.60 4.38
Meeting PA
guidelines		 −4.09 −2.54 −0.08
No major barriers to
walking		 MVPA (min/day) 17.28 * −3.94 3.30
Meeting PA
guidelines		 −15.15 −10.74 −10.27
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Notes. MVPA=moderate-to-vigorous physical activity; PA=physical activity;

a

Values represent differences between quasi-Akaike Information Criterion values of main- and interaction-effect models.

*

Values equal to or greater than 10 are indicative of no support for the simpler, main-effect model and support for the interaction effect.

Table 4.

Age- and gender-specific associations of perceived environmental attributes with the PA outcomes

Moderate-to-vigorous PA
(min/day)		 Meeting the PA guidelines for
weight gain / cancer
prevention
Moderator : Environmental
attribute		 exp(b) exp(95%
CI)		 p OR 95% CI p
Age : Safety from crime
 Association at −1 SD below
mean age		 0.985 0.948,
1.022		 .422 - - -
 Association at mean age 1.017 0.987,
1.049		 .266 - - -
 Association at 1 SD above
mean age		 1.053 1.015,
1.094 		.007 - - -
Age : No major barriers to
waking
 Association at −1 SD below
mean age		 0.987 0.957,
1.018		 .420 - - -
 Association at mean age 1.016 0.994,
1.038		 .158 - - -
 Association at 1 SD above
mean age		 1.040 1.010,
1.017 		.009 - - -
Gender : Land use mix - access
 Association in men (Linear) 1.038 1.011,
1.065 		.006 1.186 1.015,
1.385 		.032
 Association in women (Linear) 1.009 0.937,
1.086		 .819 1.149 0.965,
1.368		 .120
 Associations in women
(Curvilinear; see Figure 1)		 F (2.39, 2.39) = 7.06 <.001 - - -
Gender : Street connectivity
 Association in men - - - 1.156 1.005,
1.328 		.042
 Association in women - - - 1.026 0.882,
1.192		 .741
Gender : Safety from crime
 Association in men 0.982 0.944,
1.020		 .350 - - -
 Association in women 1.050 1.013,
1.088 		.009 - - -
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Notes. PA=physical activity. All regression coefficients are adjusted for respondents’ age, gender, marital status, education, employment status, administrative-unit (neighborhood) socio-economic status and perceived environmental attributes. Linear = linear regression term. Curvilinear = curvilinear regression term. exp(b) = antilogarithm of regression coefficient, to be interpreted as the proportional increase in the outcome with a 1 unit increase on the predictor; exp(95% CI) = antilogarithm of confidence intervals; OR = odds ratio; 95% CI: 95% confidence intervals; - = not applicable.

Gender moderated the associations of perceived land use mix–access and crime safety with the daily minutes of MVPA and of land use mix–access and street connectivity with the odds of meeting the PA guidelines (Tables 3 and 4). In men, perceived land use mix–access was linearly and positively associated with MVPA, while, in women, this association was curvilinear and positive only at mid-to-high levels of land use mix–access (Figure 1). Perceived safety from crime was positively associated with MVPA only in women (Table 4). Perceived land use mix-access and street connectivity were positively associated with the odds of meeting the weight gain/cancer prevention PA guidelines in men only (Table 4). Moderating effects of socio-demographics and perceived environmental variables on PA outcomes did not vary significantly across study sites and hence, site-specific effects are not reported.

Figure 1. Relationships between perceived land use mix – access and average daily minutes of accelerometer-based moderate-to-vigorous PA in men and women.

Figure 1

Open in a new tab

Note. The solid line represents point estimates (and dashed line their 95% confidence intervals) of average daily minutes of moderate-to-vigorous physical activity at various levels of perceived land use mix – access. These estimates were computed at average values of other environmental variables and covariates.

DISCUSSION

These are the first multi-country study findings examining moderating effects of socio-demographic characteristics (age, gender, education) on the relationship between the perceived neighborhood environment and adults’ PA. Recently, findings from the IPEN Adult study have indicated that at the individual (within-site) level, the strength of the main associations of perceived environmental attributes with accelerometer-based MVPA was modest (explained 1.2% of the variance in MVPA), with significant effects observed for land use mix-access, aesthetics and safety from crime (Cerin et al, 2014). The present analyses showed that a limited number of moderating effects were present. Thus, most associations of neighborhood built and social environmental attributes with accelerometer-derived MVPA and meeting the weight gain/cancer prevention PA guidelines are generalizable not only across numerous countries (Cerin et al, 2014), but also across socio-demographic subgroups. This illustrates that relatively few of the significant findings varied by demographic subgroup. This novel finding in the context of identifying potential environmental and policy interventions suggests that optimizing perceptions of neighborhood aesthetics and land use mix–access (Cerin et al, 2014) may be effective for adult populations in general. However, this also means that the previously identified ‘modest’ contributions of perceived environmental attributes explaining MVPA (Cerin et al, 2014) remain modest across the socio-demographic groups examined. Two of the associations between environmental perceptions and MVPA were age-dependent. The positive relationship of perceived safety from crime and perceiving no major barriers to walking with MVPA was only significant in ‘older’ adults (1SD above the sample mean, i.e. approximately 55 years). Furthermore, perceptions of crime safety were only positively related to MVPA in women. Previously, it has been argued that women and older adults, who are more physically vulnerable, have more concerns about personal safety (Foster & Giles-Corti, 2008; Roman & Chalfin, 2008); hence this may explain why positive associations were only found in this subgroup. Some studies found safety concerns restricted PA in both men and women, as well as younger and older adults, while others did not find any associations (Foster & Giles-Corti, 2008). These mixed results may be due to the fact that crime safety is often not clearly defined in questionnaires. In IPEN, the 11 countries showed large variability in perceived crime safety (e.g. low in Brazil and Colombia; high in Denmark and New Zealand), possibly providing a more complete picture of the true associations and moderating effects in comparison with single-country studies.

Gender also moderated associations between perceived land use mix-access and both outcome measures, and between perceived connectivity and the odds of reaching the weight gain/cancer prevention PA guidelines. Street connectivity was only positively related to the odds of reaching the PA guidelines in men. Land use mix-access was linearly and positively related to both outcomes in men, and curvilinearly (only positive at mid-to-high levels of land use mix-access) to min/day of MVPA in women. There is no simple explanation for these findings. Boone-Heinonen et al (2011) suggested that perceptions of high connectivity may induce more heavy traffic, and that women rather than men may perceive this as a barrier for PA, possibly explaining the non-significant association with MVPA in women. Furthermore, the two built environment variables may be mainly related to meeting weight gain/cancer prevention PA guidelines in men because too few women meet these high guidelines, reducing power. No previous studies described a curvilinear association between land-use-mix access and MVPA in women, possibly because the statistical techniques used in other studies did not allow the detection of curvilinear associations, whilst in this analysis the GAMMs/regression methods were able to describe (curvi)linear associations. Future studies should consider this method, as our findings show that the associations between the perceived built environment and PA can be curvilinear. However, the curvilinear association identified here should be interpreted with caution: very few women reported low perceived access to services (mean scale score <2.0 out of 4.0), resulting in large confidence intervals at the lower end of the curve. Educational attainment did not moderate any of the associations between the perceived environment and MVPA or reaching the PA guidelines for weight gain/cancer prevention. Previous single-country studies have reported mixed results on the moderating effects of education: some reporting stronger associations in less-educated adults (Forsyth et al, 2009; McCormack et al, 2014; Pearce & Maddison, 2011), while others finding stronger relationships of neighborhood walkability and pedestrian safety with walking for transport (Owen et al, 2007) or total MVPA (Carlson et al, 2014) in more highly-educated adults. However, all but one (Carlson et al, 2014) of these previous studies used objective measures to assess the built environment. As it has been shown that the agreement between the objective and perceived environment is limited and both can be related differently to PA (McCormack et al, 2008; McGinn et al, 2007), it might be the case that mainly the relationship between objective environmental factors and PA is moderated by educational level. To draw definite conclusions on the role of education in moderating the relationship between the physical environment and PA, more large-scale multi-country studies, preferably with a prospective design, are needed.

The heterogeneity in moderating effects found here, and in previous studies might be due to methodological and cultural factors, differences in outcome measures, measurement methods and survey item interpretations, or response biases. Nonetheless, the absence of moderating effects of education indicates that lower-educated adults, who are difficult to reach through individual interventions and are more susceptible to being insufficiently active (Sallis et al, 2009; Trost et al, 2002), might benefit from initiatives targeting improvements in perceptions of neighborhood environment characteristics such as aesthetics, land use mix-access and safety from crime (Cerin et al, 2014) as much as their higher-educated counterparts.

As a secondary aim, we examined the country-specificity of the associations of gender, age and education with the PA outcomes and of the moderating effects of these socio-demographic factors. Regarding the main associations between the socio-demographics and the PA outcomes, the findings were generally in line with previous studies (Trost et al, 2002), with older adults and women being less active than younger adults and men. More highly-educated adults accumulated fewer min/day of MVPA than lower-educated adults, possibly due to work-related MVPA being higher in less-educated adults, as they are more likely to do manual work. Concerning the moderating effects of gender, age and education on the relationship between the perceived environment and PA, no country-specific findings were revealed: all results were generalizable across the 11 participating countries. The examination of the country-specificity of these associations and moderations is a very innovative part of the current analyses, taking into account the ‘socio-cultural layer’ of socioecological models (Sallis et al, 2008): because no country-specific findings were discovered this suggests that cultural differences between countries did not affect the relationship between socio-demographic factors and accelerometer-based MVPA nor the moderating effects of sociodemographics on the association between the perceived environment and MVPA. Although previous analyses using the same data (Cerin et al, 2014) showed some differences in the main associations between the perceived environment and accelerometer-based MVPA by study site (e.g. aesthetics were positively related to MVPA in the USA, but negatively in Belgium), the moderation of such relationships by sociodemographic factors is not different across countries.

Although the present study had several strengths, including the large sample size, comparable data collection protocols across 11 countries’ cities/regions, objective measures of MVPA, use of a valid questionnaire to assess environmental perceptions, and application of advanced statistical models that allowed for curvilinear associations, some limitations are acknowledged. First, the results may not be generalizable to the total population in the participating countries, as participants were recruited from specific neighborhoods based on their walkability and income levels. Second, response rates, survey methods and accelerometer models used varied across study sites. This may imply sampling biases or other methodological biases across study sites. Third, accelerometers do not take into account context-specific information of PA, which would have helped to better understand the moderating effects that were identified, nor do they accurately measure all activities (e.g. cycling, swimming, resistance training). Fourth, only perceived environmental attributes were included in this paper; within IPEN Adult objective GIS-based measures are also available, but they measure fewer environmental variables.

In summary, present findings from an 11-country study add important knowledge about the possible moderating effects of socio-demographic factors on the relationship between the perceived built environment and accelerometer-based PA. Some moderating effects of age and gender were present, but overall the associations between environmental perceptions and accelerometer-based MVPA, expressed as odds ratios and risk ratios, were independent of age, sex, and education. Future studies should focus on other potential moderators such as psychosocial factors (Van Dyck et al, 2009). The novel findings presented here are encouraging, and suggest that international efforts to optimize the perceptions residents have of their built and social environments (mainly land use mix-access and aesthetics) may facilitate engagement in MVPA in men and women, younger and older adults, and higher- and lower-educated adults worldwide. Nonetheless, confirmatory prospective studies are needed to elicit stronger recommendations.

Highlights.

  • Previous studies showed inconsistent associations of the environment with MVPA

  • Moderating effects may explain these inconsistent associations

  • The moderating effects of age, gender and education were rather limited

  • Optimizing land use mix and aesthetics may facilitate MVPA in whole adult populations

Acknowledgments

FUNDING

All authors declare financial support for the submitted work from the National Cancer Institute of the National Institutes of Health. US data collection and Coordinating Center processing was supported by the following NIH grants: R01 HL67350 (NHLBI) and R01 CA127296 (NCI). Data collection in Hong Kong was supported by the grants (#HKU740907H and #747807H) and the HKU URC Strategic Research Theme (Public Health). The study conducted in Bogota was funded by Colciencias grant 519 2010, Fogarty and CeiBA (Center in Complex-Systems, Basic and Applied Research at the Universidad de los Andes). The contributions of Neville Owen were supported by a NHMRC Program Grant (#569940), a NHMRC Senior Principal Research Fellowship (#1003960), and by the Victorian Government’s Operational Infrastructure Support Program. The Danish study was partly funded by the Municipality of Aarhus. Data collection in the Czech Republic was supported by the grant MEYS (# MSM 6198959221). Data collection in New Zealand was supported by the Health Research Council of New Zealand grant # 07/356. Data collection in Mexico was supported by the CDC Foundation which received an unrestricted training grant from The Coca-Cola Company. Data collected for the UK was in part funded by the Medical Research Council (NPRI Initiative), grant number: 75376. Olga L Sarmiento received a research grant from the Coca Cola Company outside of submitted work. Delfien Van Dyck is funded by the Research Foundation Flanders, outside of submitted work. James F Sallis received grants and personal fees from the Robert Wood Johnson Foundation outside of submitted work, grants and non-financial support from Nick, Inc. outside of submitted work, is a Santech, Inc. shareholder and is a consultant and receiver of royalties from SPARK Programs of School Specialty, Inc. The results of the present study do not constitute endorsement by the ACSM. The funding bodies had no input in study design and the collection, analysis and interpretation of data and the writing of the article and the decision to submit it for publication. All authors are independent from the funding bodies.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ETHICAL APPROVAL

Dr James Sallis is the principal investigator on the IPEN coordinating grant funded by the National Institutes of Health, National Cancer Institute. Ethics Committee approval for the lead grant was obtained from Dr Sallis’ institution, the University of California, San Diego, Institutional Review Board (Biomedical Committee) and San Diego State University. Additionally, as a requirement for participation in the IPEN study, all 12 countries providing data obtained approval from the Ethics Committee at each principal investigator’s home institution.

CONFLICT OF INTEREST STATEMENT

All authors declare that they have no competing interests.

REFERENCES

  1. Arango CM, Paez DC, Reis RS, Brownson RC, Parra DC. Association between the perceived environment and physical activity among adults in Latin America: a systematic review. International Journal of Behavioral Nutrition and Physical Activity. 2013;10:122. doi: 10.1186/1479-5868-10-122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RF, Martin BW, for the Lancet Physical Activity Series Working Group Correlates of physical ativity: why are some people physically active and others not? Lancet. 2012;380:258–271. doi: 10.1016/S0140-6736(12)60735-1. [DOI] [PubMed] [Google Scholar]
  3. Berrigan D, Troiano RP. The association between urban form and physical activity in U.S. adults. American Journal of Preventive Medicine. 2002;23(2 Suppl):74–79. doi: 10.1016/s0749-3797(02)00476-2. [DOI] [PubMed] [Google Scholar]
  4. Boone-Heinonen J, Gordon-Larsen P. Life stage and sex specificity in relationships between the built and socioeconomic environments and physical activity. Journal of Epidemiology and Community Health. 2011;65:847–852. doi: 10.1136/jech.2009.105064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed Springer; New York, NY: 2002. [Google Scholar]
  6. Cain K. Accelerometer scoring protocol for the IPEN-adult study. University of California; San Diego, CA: 2013. Available for download at: http://www.ipenproject.org/documents/methods_docs/IPEN_Protocol.pdf. [Google Scholar]
  7. Carlson JA, Bracy NL, Sallis JF, Millstein RA, Saelens BE, Kerr J, et al. Sociodemographic moderators of relations of neighborhood safety to physical activity. Medicine and Science in Sports and Exercise. 2014;46:1554–1563. doi: 10.1249/MSS.0000000000000274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cerin E, Cain KL, Conway TL, Van Dyck D, Hinckson E, Schipperijn J, et al. Neighborhood environments and objectively measured physical activity in 11 countries. Medicine and Science in Sports and Exercise. 2014;46:2253–2264. doi: 10.1249/MSS.0000000000000367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cerin E, Conway TL, Cain KL, Kerr J, De Bourdeaudhuij I, Owen N, et al. Sharing good NEWS across the world: developing comparable scores across 12 countries for the Neighborhood Environment Walkability Scale (NEWS) BMC Public Health. 2013;13:309. doi: 10.1186/1471-2458-13-309. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cerin E, Saelens BE, Sallis JF, Frank LD. Neighborhood environment walkability scale: validity and development of a short form. Medicine and Science in Sports and Exercise. 2006;38:1682–1691. doi: 10.1249/01.mss.0000227639.83607.4d. [DOI] [PubMed] [Google Scholar]
  11. Ding D, Gebel K. Built environment, physical activity, and obesity: what have we learned from reviewing the literature? Health & Place. 2012;18:100–105. doi: 10.1016/j.healthplace.2011.08.021. [DOI] [PubMed] [Google Scholar]
  12. Forsyth A, Oakes JM, Lee B, Schmitz KH. The built environment, walking, and physical activity: Is the environment more important to some people than others? Transportation Research Part D. 2009;14:42–49. [Google Scholar]
  13. Foster S, Giles-Corti B. The built environment, neighborhood crime and constrained physical activity: an exploration of inconsistent findings. Preventive Medicine. 2008;47:241–251. doi: 10.1016/j.ypmed.2008.03.017. [DOI] [PubMed] [Google Scholar]
  14. Fox J, Weisberg S. An R companion to applied regression. 2nd ed Sage; Thousand Oaks, CA: 2011. [Google Scholar]
  15. Frank LD, Sallis JF, Saelens BE, Leary L, Cain K, Conway TL, et al. The development of a walkability index: application to the neighborhood quality of life study. British Journal of Sports Medicine. 2010;44:924–933. doi: 10.1136/bjsm.2009.058701. [DOI] [PubMed] [Google Scholar]
  16. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Medicine and Science in Sports and Exercise. 1998;30:777–781. doi: 10.1097/00005768-199805000-00021. [DOI] [PubMed] [Google Scholar]
  17. Gordon-Larsen P, Popkin B. Understanding socioeconomic and racial/ethnic status disparities in diet, exercise, and weight: underlying contextual factors and pathways. Journal of the American Dietetic Association. 2011;111:1816–1819. doi: 10.1016/j.jada.2011.09.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Institute of Medicine . Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids (macronutrients) National Academies Press; Washington, DC: 2002. Food and Nutrition Board, Institute of Medicine. [Google Scholar]
  19. Kerr J, Sallis JF, Owen N, De Bourdeaudhuij I, Cerin E, Sugiyama T, et al. Advancing science and policy through a coordinated international study of physical activity and built environments: IPEN Adult methods. Journal of Physical Activity and Health. 2013;10:581–601. doi: 10.1123/jpah.10.4.581. [DOI] [PubMed] [Google Scholar]
  20. Lee IM, Djoussé L, Sesso HD, Wang L, Buring JE. Physical activity and weight gain prevention. Journal of the American Medical Association. 2010;303:1173–1179. doi: 10.1001/jama.2010.312. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. McCormack GR, Shiell A, Doyle-Baker PK, Friedenreich CM, Sandalack BA. Subpopulation differences in the association between neighborhood urban form and neighborhood-based physical activity. Health & Place. 2014;28:109–115. doi: 10.1016/j.healthplace.2014.04.001. [DOI] [PubMed] [Google Scholar]
  22. McCormack GR, Cerin E, Leslie E, Du Toit L, Owen N. Objective versus perceived distances to destinations: correspondence and predictive validity. Environment and Behavior. 2008;40:401–425. [Google Scholar]
  23. McGinn AP, Evenson KR, Herrin AH, Huston SL, Rodriguez DA. Exploring associations between physical activity and perceived and objective measures of the built environment. Journal of Urban Health. 2007;84:162–184. doi: 10.1007/s11524-006-9136-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Owen N, Cerin E, Leslie E, duToit L, Coffee N, Frank LD, et al. Neighborhood walkability and the walking behavior of Australian adults. American Journal of Preventive Medicine. 2007;33:387–395. doi: 10.1016/j.amepre.2007.07.025. [DOI] [PubMed] [Google Scholar]
  25. Pearce JR, Maddison R. Do enhancements to the urban built environment improve physical activity levels among socially disadvantaged populations? International Journal of Equity in Health. 2011;10:28. doi: 10.1186/1475-9276-10-28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. R Core Team . R: A language and environment for statistical computing. R Foundation for Statistical Computing; Vienna, Austria: 2013. ISBN 3-900051-07-0, URL http://www.R-project.org/ [Google Scholar]
  27. Roman CG, Chalfin A. Fear of walking outdoors: a multilevel ecologic analysis of crime and disorder. American Journal of Preventive Medicine. 2008;34:306–312. doi: 10.1016/j.amepre.2008.01.017. [DOI] [PubMed] [Google Scholar]
  28. Saelens BE, Sallis JF, Black JB, Chen D. Neigborhood-based differences in physical activity: an environment scale evaluation. American Journal of Public Health. 2003;93:1552–1558. doi: 10.2105/ajph.93.9.1552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Sallis JF. Environmental and policy research on physical activity is going global. Research in Exercise Epidemiology. 2011;13:111–117. [Google Scholar]
  30. Sallis JF, Owen N, Fisher EB. Ecological models of health behavior. In: Glanz K, Rimer BK, Viswanath K, editors. Health Behavior and Health Education: Theory, Research and Practice. fourth ed Jossey-Bass; San Francisco: 2008. pp. 465–486. [Google Scholar]
  31. Sallis JF, Saelens BE, Frank LD, Conway TL, Slymen DJ, Cain K, et al. Neighborhood built environment and income: examining multiple health outcomes. Social Science & Medicine. 2009;68:1285–1293. doi: 10.1016/j.socscimed.2009.01.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Trost SG, Owen N, Bauman AE, Sallis JF, Brown W. Correlates of adults’ participation in physical activity: review and update. Medicine and Science in Sports and Exercise. 2002;34:1996–2001. doi: 10.1097/00005768-200212000-00020. [DOI] [PubMed] [Google Scholar]
  33. Van Dyck D, Deforche B, Cardon G, De Bourdeaudhuij I. Neighbourhood walkability and its particular importance for adults with a preference for passive transport. Health & Place. 2009;15:496–504. doi: 10.1016/j.healthplace.2008.08.010. [DOI] [PubMed] [Google Scholar]
  34. Van Holle V, Deforche B, Van Cauwenberg J, Goubert L, Maes L, Van de Weghe N, et al. Relationship between the physical environment and different domains of physical activity in European adults: a systematic review. BMC Public Health. 2012;12:807. doi: 10.1186/1471-2458-12-807. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Villanueva K, Knuiman M, Nathan A, Giles-Corti B, Christian H, Foster S, et al. The impact of neighborhood walkability on walking: does it differ across adult life stage and does neighborhood buffer size matter? Health & Place. 2008;25:43–46. doi: 10.1016/j.healthplace.2013.10.005. 2008. [DOI] [PubMed] [Google Scholar]
  36. Warnes GR. Gmodels: Various R programming tools for model fitting. (R package version 2.15.3) 2012 http://CRAN.R-project.org/package=gmodels.
  37. Welk GJ. Use of accelerometry-based activity monitors to assess physical activity. In: Welk GJ, editor. Physical activity assessments for health-related research. Human Kinetics; Champaign, IL: 2002. pp. 125–141. [Google Scholar]
  38. Wood SN. Generalized additive models: an introduction with R. Chapman & Hall; Boca Raton, FL: 2006. [Google Scholar]
  39. World Cancer Research Fund and American Institute for Cancer Research . Food, nutrition, physical activity and the prevention of cancer: a global perspective. American Institute for Cancer Research; Washington, DC: 2007. [Google Scholar]

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