International Studies of Physical
Activity & Built Environment
James Sallis
San Diego State University
www.drjamessallis.sdsu.edu
www.activelivingresearch.org
www.activelivingresearch.org
Deaths attributed to 19 leading factors,
by country income level, 2004
Health Statistics and Informatics
Why Environment & Physical Activity?
Broad societal & technological developments are
believed to be reducing PA in work, transport, &
household settings
These developments are happening in all
countries
Ecological models of behavior teach that policy &
environmental factors have the broadest & longestlasting impacts
Research on environment & policy aspects of PA
is limited in all countries and absent in most
www.activelivingresearch.org
Need for Research on Environment + PA
WHO global strategy on diet and physical
activity emphasizes environment & policy
change
National PA plans call for environment &
policy change
Country-specific research is needed to
inform policy & planning decisions
www.activelivingresearch.org
Environments
Differ!!!!
Ghent, Belgium
Atlanta, USA
6
7
“Walkable”: Mixed use, connected, dense
8
Not “walkable”
street connectivity and
mixed land use
9
Funded by NIH/NHLBI 2001-2005
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www.nqls.org
Neighborhood Walkability and
Income Are Related to Physical
Activity & BMI
James F. Sallis, Brian E. Saelens, Lawrence D.
Frank, Donald J. Slymen, Terry L. Conway, Kelli
Cain, & James C. Chapman.
San Diego State University; University of
Washington; University of British Columbia
11
Primary Aim
 Investigate whether people who live in
“walkable” communities are more active,
after adjusting for SES, than people who live
in less walkable communities.

12
This study was the methodological template
for other studies so comparisons could be
made
NQLS Neighborhood Categories
Walkability
Low
High
4 per region
4 per region
4 per region
4 per region
13
GIS-Based Walkability Index
Net Residential Density
Intersection Density (intersections per acre)
Retail floor area ratio (FAR): ratio of retail building square
footage to land area
Land Use Mix: evenness of mix across 4 types of uses.
Walkability = sum of z-scores of components
14
Calculated for census block groups to select N-hoods
Participant Selection & Recruitment




15
Adults aged 20-65 recruited from randomly selected
households in target neighborhoods
Recruitment by mail & phone
2100 available for analyses; 30% response rate
48% female; 25% non-white
Key Measures
 Actigraph accelerometer
–
–


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Worn for up to 14 days
Outcome is mean daily minutes of MVPA
BMI, based on self-report height & weight
NEWS: Neighborhood Environment
Walkability Scale
Accelerometer-based MVPA Min/day
in Walkability-by-Income Quadrants
Walkability: p =.0002
Income: p =.36
Walkability X Income: p =.57
35
35.7
30
(Mean *)
MVPA minutes per day
40
25
33.4
28.5
Low Walk
High Walk
29.0
20
15
10
5
0
Low Income
High Income
* Adjusted for neighborhood clustering, gender, age, education, ethnicity, # motor vehicles/adult in household, site,
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marital status, number of people in household, and length of time at current address.
Percent Overweight or Obese (BMI>25)
in Walkability-by-Income Quadrants
Walkability: p =.007
Income: p =.081
Walkability X Income: p =.26
% Overweight or Obese
70
60
63.1
50
56.8
Low Walk
High Walk
60.4
48.2
40
30
20
10
0
Low Income
High Income
* Adjusted for neighborhood clustering, gender, age, education, ethnicity, # motor vehicles/adult in household, site,
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marital status, number of people in household, and length of time at current address.
National Centre for Social Applications of GIS
19
PLACE Study
Physical activity in Localities And Community Environments
Neville Owen, Adrian Bauman, Graeme Hugo, James F
Sallis, Eva Leslie, Jo Salmon, Ester Cerin,Tim Armstrong
National Health and Medical Research Council of Australia, 2002–2004
Primary Aim:
to investigate whether people who live in ‘walkable’ communities
are more physically active, after adjusting for socio-economic
status
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“Walkable”
density, street connectivity, and
mixed land use
21
Not “walkable”
mixed land use
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Walking for Transport and Recreation in
Low- and High-Walkable Communities*
Weekly minutes (median)
Transport
130
110
90
70
50
30
10
Recreation
130
110
90
70
50
30
10
Low-walkable High-walkable
High SES
Low SES
Low-walkable High-walkable
High SES
* Preliminary Analyses: unadjusted for confounders
Low SES
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Differences in PA behaviour in
Belgian adults living in ‘high
walkable’ versus ‘low walkable’
neighbourhoods. Belgian
Environmental Physical Activity
Study (BEPAS)
Delfien Van Dyck
Ilse De Bourdeaudhuij
Ghent University – BELGIUM
Faculty of Medicine and Health Sciences
Department of Movement and Sports Sciences
Greet Cardon
Benedicte Deforche
Preventive Medicine, 2010
24
BEPAS: Accelerometer-based MVPA Min/day
in Walkability-by-Income Quadrants
Walkability: β(SE)= .095(.030) p <.001
Income: β(SE)= -.026(.029) p =0.18
Walkability X Income: β(SE)= -.014(.040) p =.36
45
CSA MVPA min/day
40
41,13
35
30
36,14
32,95
30,78
25
20
low walk
high walk
15
10
5
0
low income
high income
Adjusted for neighborhood clustering, gender, age, education, working status
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BEPAS: Transport Walking Min/week
in Walkability-by-Income Quadrants
Walkability: β(SE)= .746(.157) p <.001
Income: β(SE)= -.360 (.155) p <.05
Walkability X Income: β(SE)= .027(.220) p =.45
min/week transport walking
160
151,16
140
120
100
low walk
80
83,85
60
40
high walk
50,3
20
25,27
0
low income
high income
Adjusted for neighborhood clustering, gender, age, education, working status
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BEPAS: Transport Cycling Min/week
in Walkability-by-Income Quadrants
Walkability: β(SE)= .447(.105) p <.001
Income: β(SE)= .029(.102) p =.39
Walkability X Income: β(SE)= -.051(.144) p =.36
min/week transport cycling
90
80
83,67
80,95
70
60
low walk
50
40
40,56
47,25
high walk
30
20
10
0
low income
high income
Adjusted for neighborhood clustering, gender, age, education, working status
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BEPAS: Percent Overweight or Obese (BMI>25)
in Walkability-by-Income Quadrants
Walkability: β(SE)= -.870(.182) p <.001
Income: β(SE)= -.197(.167) p =.12
Walkability X Income: β(SE)= .910(249) p <.001
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% overweight or obese
40
44,7
39,4 39,7
35
30
25
low walk
24,8
20
high walk
15
10
5
0
low income
high income
Adjusted for neighborhood clustering, gender, age, education, working status
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BEPAS: Conclusions
• Living in high walkable neighbourhoods:
•
•
•
•
•
80 min/week more walking for transport
40 min/week more cycling for transport
20 min/week more walking for recreation
35 min/week less motor transport
50 min/week more MVPA (accelerometer)
– Very similar to US results
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Relationships between environmental
attributes and walking for various
purposes among Japanese adults
Shigeru Inoue, MD, PhD
Department of Preventive Medicine
and Public Health
Tokyo Medical University
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Nakano area of Tokyo, Japan
Residential area
Shopping street
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Methods
 Study design
A Cross-sectional mail survey in
four Japanese cities
(Tsukuba, Koganei,
Shizuoka and Kagoshima).
Tsukuba
Area: 284km2
Population: 208,985
Density: 736 /km2
Kagoshima
547km2
604,431
1,105 /km2
Koganei
Shizuoka
1,388km2
710,854
512 /km2
11km2
113,433
10,312 /km2
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Summary of the results from NEWS
All
R es idential dens ity
L and us e mix-divers ity
L and us e mix-acces s
S treet connectivity
Walking/cycling facilities
Aes thetics
T raffic s afety
C rime s afety
total
leis ure
○
○
○
‐
○
○
○
‐
‐
‐
‐
‐
○
○
○
‐
Men
daily
errands
○
○
○
○
○
○
‐
‐
total
leis ure
○
○
○
‐
○
‐
‐
‐
‐
‐
‐
‐
○
○
‐
‐
Women
daily
errands
○
‐
○
‐
‐
‐
‐
‐
total
leis ure
○
‐
○
‐
○
○
‐
‐
‐
‐
‐
‐
‐
○
‐
‐
daily
errands
○
○
○
○
○
○
‐
○
○：Significant relationship
• All environmental variables were related to specific types
of walking behavior in expected direction.
• Environmental variables related to walking for leisure and
walking for daily errands were different
• Relationship between walking and environment was
especially strong in women’s walking for daily errands 33
Built environment correlates of physical activity
behaviours in a developing city:
The case of Bogota, Colombia
Olga Lucia Sarmiento and team
Universidade de los Andes
34
35
photo: O.L. Sarmiento
Main Results
• Walking for transport (30 min/day for at least 5
days/week) was positively associated with:
– Street density (POR 1.71, 95% CI 1.19-2.46)
– Street connectivity (POR 2.21, 95% CI 1.40-3.49)
– Bus Rapid Transit stations in the neighborhood (POR
1.71, 95% CI 1.19-3.47)
• Biking for transport was positively associated with:
– Street density (POR 1.99, 95% CI 1.24-3.19)
• Leisure time physical activity (30 min/day for at least
5 days/week) was positively associated with:
– Park density (POR 2.05, 95%CI 1.13-3.72)
– Bus Rapid Transit stations in the neighbohood (POR
1.27, 95% CI 1.07-1.50)
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Built Environments & Physical
Activity: An 11-Country Study
James F. Sallis, USA
Heather Bowles, Australia
Adrian Bauman, Australia
Barbara E. Ainsworth, USA
Fiona C. Bull, UK
Michael Sjostrom, Sweden
Cora Craig, Canada
Et al.
Am J Prev Med, 2009
37
Rationale
• Each country has limited range of variation
in built environment variables, so multicountry studies are needed to understand
full impact of environments.
• Most studies of environmental correlates of
PA analyze each environment variable
separately, so the cumulative effects are not
clear.
• Built environment survey measure was
added to International PA Prevalence Study
38
PANES: Physical Activity
Neighborhood Environment Survey
• Perceived social & physical environment
items adapted from published surveys
(Kirtland, 2003; Saelens, 2003).
• Standard methods of translation and
cultural adaptation.
• Reliability confirmed in Sweden, Nigeria,
US
• Attributes rated on 4-point scale, but
dichotomized as “agree” vs “disagree”
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Sample Sizes by Country
•
•
•
•
•
•
Belgium, 1425
Brazil, 951
Canada, 856
Colombia, 2699
Hong Kong, 1225
Japan, 1001
•
•
•
•
•
Lithuania, 2099
New Zealand, 1298
Norway, 1131
Sweden, 998
United States, 4711
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Associations Between Individual Environmental Characteristics and HEPA/Minimal
Activity Among Respondents who Live in Cities with Population ≥ 30,000
Odds Ratio
HEPA/Minimal Activity
1.8
1.6
1.4
1.2
1.0
0.8
0.6
Single Family
Houses
Shops Near
Home
T ransit Stop
Near Home
Sidewalks
Present
Facilities to
Bicycle
Low Cost Rec
Facilities
Unsafe to Walk
due to Crime
'Agree' with Environmental Characteristic
('Disagree' is referent)
41
Dose Response between Number of Environmental
Characteristics and HEPA/Minimal Activity
(Pooled City Sample)
Odds Ratio
HEPA/Minimally Active
3.00
2.60
2.20
1.80
1.40
1.00
0.60
1
2
3
4
5
Total Number of Environmental Characteristics
(Zero is referent)
6
42
Started at ICBM in Mainz Germany in 2004 by:
Sallis & Kerr, US
Owen, Australia
DeBourdeaudhuij, Belgium
Studies in 3 countries indicated that a common study
design and measures were feasible, so the goal was to
apply methods to other countries, improving on IPS study43
Why do we need the IPEN study?
• National organizations and WHO
recommend environment & policy changes
to increase PA
– Need international evidence
• Full variability in environments requires
research in multiple countries
• If data are to be pooled, common
measures & design are needed
• More detailed measures will provide more
specific policy guidance
44
Maximizing within and between
country variance (illustration)
HK
UK
Japan
Walking
Belgium
Czech
Sweden
Columbia
Aus
US
BUT relationship between
walking and walkability may
not be linear
NZ
Walkability
45
Main NCI Study Aims
• IPEN study funded by NCI 2009—2013
• Main aims:
1. Support countries to collect or enhance data
according to common protocol
2. Transfer data to central dataset
3. Study co-ordination, quality control, & pooled
analyses
4. Support the network more widely
5. Advance science through pooled analyses
6. Use results to inform policy internationally
46
IPEN participating countries (so far)
–Australia
–Belgium
–Brazil
–Canada (2)
–Denmark
–Columbia
–Czech Republic
–Hong Kong
–Mexico
–Spain
–Sweden
–UK
–New Zealand
–USA
47
Policy relevance
• If studies show stronger relationship between
activity & environment, then policy makers more
likely to support & fund environmental change
• Examples from other countries with unique
environments can inform built environment
changes (without expensive experiments)
• National data needed in each country to
convince national policy makers
48
IPEN investigators in Toronto 2010: www.ipenproject.org
49
Conclusions
• Environments Seem to
Matter Around the World
• The international database
Is expanding rapidly
• We need to use research to
Drive policy change
www.ipenproject.org
50