Transcript Walkability - James Sallis

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
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“Walkable”: Mixed use, connected, dense
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Not “walkable”
street connectivity and
mixed land use
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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
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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.

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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
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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
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Calculated for census block groups to select N-hoods
Participant Selection & Recruitment
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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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–
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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
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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
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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
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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
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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
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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:
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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
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Men
daily
errands
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total
leis ure
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Women
daily
errands
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total
leis ure
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daily
errands
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○：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
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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
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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
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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
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Belgium, 1425
Brazil, 951
Canada, 856
Colombia, 2699
Hong Kong, 1225
Japan, 1001
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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)
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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
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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
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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
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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
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IPEN participating countries (so far)
–Australia
–Belgium
–Brazil
–Canada (2)
–Denmark
–Columbia
–Czech Republic
–Hong Kong
–Mexico
–Spain
–Sweden
–UK
–New Zealand
–USA
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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
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IPEN investigators in Toronto 2010: www.ipenproject.org
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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
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