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International Studies of Physical Activity & Built Environment James Sallis San Diego State University www.drjamessallis.sdsu.edu. Deaths attributed to 19 leading factors, by country income level, 2004. Why Environment & Physical Activity?. - PowerPoint PPT Presentation
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www.activelivingresearch.org
International Studies of Physical International Studies of Physical Activity & Built EnvironmentActivity & Built Environment
James SallisJames SallisSan Diego State UniversitySan Diego State Universitywww.drjamessallis.sdsu.eduwww.drjamessallis.sdsu.edu
www.activelivingresearch.org
Health Statistics and Informatics
Deaths attributed to 19 leading factors,by country income level, 2004
www.activelivingresearch.org
Why Environment & Physical Why Environment & Physical Activity?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 & longest-lasting 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 Need for Research on Environment + PA+ 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
66
EnvironmentsDiffer!!!!
Atlanta, USA
Ghent, Belgium
7
Adelaide, Australia
88“Walkable”: Mixed use, connected, dense
99
Not “walkable”Not “walkable”
street connectivity and mixed land usestreet connectivity and mixed land use
10
Funded by NIH/NHLBI 2001-2005
www.nqls.org
11
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
12
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.
This study was the methodological template for other studies so comparisons could be made
13
NQLS Neighborhood Categories
Walkability
Soc
ioec
onom
ic S
tatu
s Low High
Hig
hLo
w 4 per region
4 per region 4 per region
4 per region
14
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
Calculated for census block groups to select N-hoods
15
Participant Selection & Recruitment 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
16
Key Measures Actigraph accelerometer
– 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
17
Accelerometer-based MVPA Min/day in Walkability-by-Income Quadrants
28.5
33.4
29.0
35.7
0
5
10
15
20
25
30
35
40
MV
PA
min
ute
s p
er d
ay(M
ea
n *
)
Low Income High Income
Low Walk
High Walk
Walkability: p =.0002
Income: p =.36
Walkability X Income: p =.57
* Adjusted for neighborhood clustering, gender, age, education, ethnicity, # motor vehicles/adult in household, site, marital status, number of people in household, and length of time at current address.
18
Percent Overweight or Obese (BMI>25) in Walkability-by-Income Quadrants
63.156.8
60.4
48.2
0
10
20
30
40
50
60
70
% O
verw
eig
ht
or
Ob
ese
Low Income High Income
Low Walk
High Walk
Walkability: p =.007
Income: p =.081
Walkability X Income: p =.26
* Adjusted for neighborhood clustering, gender, age, education, ethnicity, # motor vehicles/adult in household, site, marital status, number of people in household, and length of time at current address.
19 National Centre for Social Applications of GIS
20
PLACE StudyPhysical 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
21
“Walkable”
density, street connectivity, and mixed land use
22
Not “walkable” mixed land use
23
Transport
1030507090
110130
Low-walkable High-walkable
Wee
kly m
inut
es (m
edia
n)
High SES Low SES
Recreation
1030507090
110130
Low-walkable High-walkable
High SES Low SES
Walking for Transport and Recreation in Low- and High-Walkable Communities*
* Preliminary Analyses: unadjusted for confounders
24
Differences in PA behaviour in Belgian adults living in ‘high
walkable’ versus ‘low walkable’
neighbourhoods. Belgian Environmental Physical Activity
Study (BEPAS)
Ghent University – BELGIUMFaculty of Medicine and Health SciencesDepartment of Movement and Sports Sciences
Delfien Van Dyck
Ilse De Bourdeaudhuij
Greet Cardon
Benedicte Deforche
Preventive Medicine, 2010
25
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
0
5
10
15
20
25
30
35
40
45
low income high income
32,9530,78
41,13
36,14
CSA
MV
PA m
in/d
ay
low walk
high walk
Adjusted for neighborhood clustering, gender, age, education, working status
26
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
0
20
40
60
80
100
120
140
160
low income high income
50,3
25,27
151,16
83,85
min
/wee
k tr
ansp
ort
wal
king
low walk
high walk
Adjusted for neighborhood clustering, gender, age, education, working status
27
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
0
10
20
30
40
50
60
70
80
90
low income high income
40,5647,25
80,95 83,67
min
/wee
k tr
ansp
ort
cycl
ing
low walk
high walk
Adjusted for neighborhood clustering, gender, age, education, working status
28
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
Adjusted for neighborhood clustering, gender, age, education, working status
0
5
10
15
20
25
30
35
40
45
low income high income
44,7
39,4
24,8
39,7
% o
verw
eigh
t or
obe
se
low walk
high walk
29
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
30
Relationships between environmental attributes and walking for various
purposes among Japanese adults
Shigeru Inoue, MD, PhDDepartment of Preventive Medicine
and Public HealthTokyo Medical University
31
Residential area Shopping street
Nakano area of Tokyo, Japan
32
Methods Study designA Cross-sectional mail survey in
four Japanese cities (Tsukuba, Koganei, Shizuoka and Kagoshima).
TsukubaArea: 284km2
Population: 208,985Density: 736 /km2
Shizuoka1,388km2
710,854512 /km2
Koganei11km2
113,43310,312 /km2
Kagoshima547km2
604,4311,105 /km2
33
Summary of the results from NEWS
• 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
○ : Significant relationship
total leisure dailyerrands
total leisure dailyerrands
total leisure dailyerrands
R es idential dens ity ○ ‐ ○ ○ ‐ ○ ○ ‐ ○L and use mix-divers ity ○ ‐ ○ ○ ‐ ‐ ‐ ‐ ○L and use mix-access ○ ‐ ○ ○ ‐ ○ ○ ‐ ○S treet connectivity ‐ ‐ ○ ‐ ‐ ‐ ‐ ‐ ○Walking/cycling facilities ○ ○ ○ ○ ○ ‐ ○ ‐ ○Aesthetics ○ ○ ○ ‐ ○ ‐ ○ ○ ○T raffic safety ○ ○ ‐ ‐ ‐ ‐ ‐ ‐ ‐C rime safety ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ○
All Men Women
34
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
35photo: O.L. Sarmiento
36
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)
37
Built Environments & Physical Activity: An 11-Country Study
James F. Sallis, USAHeather Bowles, AustraliaAdrian Bauman, Australia Barbara E. Ainsworth, USA
Fiona C. Bull, UKMichael Sjostrom, Sweden
Cora Craig, CanadaEt al.
Am J Prev Med, 2009
38
Rationale• Each country has limited range of variation
in built environment variables, so multi-country 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
39
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”
40
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
41
Associations Between Individual Environmental Characteristics and HEPA/Minimal Activity Among Respondents who Live in Cities with Population ≥ 30,000
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Single FamilyHouses
Shops NearHome
T ransit StopNear Home
SidewalksP resent
Facilit ies toBicycle
Low Cost RecFacilit ies
Unsafe to Walkdue to Crime
'Agre e ' wi th Environm e ntal C haracte ristic('Disagre e ' i s re fe re nt)
Odd
s R
atio
HE
PA
/Min
imal
Act
ivit
y
42
Dose Response between Number of Environmental Characteristics and HEPA/Minimal Activity
(Pooled City Sample)
0.60
1.00
1.40
1.80
2.20
2.60
3.00
1 2 3 4 5 6
Total Num be r of Environm e ntal C haracte ristics(Ze ro i s re fe re nt)
Odd
s R
atio
HE
PA
/Min
imal
ly A
ctiv
e
43
Started at ICBM in Mainz Germany in 2004 by:Sallis & Kerr, USOwen, AustraliaDeBourdeaudhuij, 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 study
44
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
45
Maximizing within and between country variance (illustration)
USAus
HKJapan
UK
NZ
Sweden
Belgium
Czech
Columbia
Walkability
Wal
kin
g
BUT relationship between walking and walkability may not be linear
46
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
47
IPEN participating countries (so far)
–Australia–Belgium–Brazil–Canada (2)–Denmark–Columbia–Czech Republic
–Hong Kong
–Mexico
–Spain
–Sweden
–UK
–New Zealand
–USA
48
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
49
IPEN investigators in Toronto 2010: www.ipenproject.org
50
Conclusions• Environments Seem to Matter Around the World• The international databaseIs expanding rapidly• We need to use research to Drive policy change
www.ipenproject.org