Active and Green: Healthy Communities Are Sustainable Communities James Sallis, PhD San Diego State University www.drjamessallis.sdsu.edu For LISC Webinar May 4, 2011
Goals of talk Physical inactivity is a major health challenge We know a lot about creating environments that support physical activity Getting people active can reduce GHG in several ways We are unlikely to meet GHG goals unless we get people more active, especially for transportation
Physical Activity Snapshot Physical inactivity is the fourth leading cause of cause of death in the US Due to its role in heart disease, diabetes, cancers, depression, etc Who meets PA guidelines? Fewer than 50% of elementary age children Fewer than 10% of middle & high school youth Fewer than 5% of adults
Increase in percentage of children and youth ages 2 to 19 who are obese since the 1970s % obese 20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% 5% 15% 19% 6% 6% 17% All children African American Low income Anderson & Butcher, The Future of Children: Childhood Obesity, 2006 1971-1974 1999-2002
SLOTH Model of Physical Activity Sleep Leisure Occupation Transportation Household
Emphasizing Environmental & Policy Solutions Affect large populations Likely to have long-term or permanent effects Make the healthy choice easier Remove/reduce barriers Provide incentives Make educational & motivational interventions more effective
Elements of An Active Living Community Design Destinations Community Transportation System Home School & Worksite Park & Rec
Walkable : Mixed use, connected, dense
Not walkable street connectivity and mixed land use
The Neighborhood Quality of Life (NQLS) Study: The Link Between Neighborhood Design and Physical Activity James Sallis Brian Saelens Lawrence Frank And team Results published March 2009 in Social Science and Medicine
NQLS Neighborhood Categories Socioeconomic Status High Low Walkability Low High 4 per city 4 per city 4 per city 4 per city
Accelerometer-based MVPA Min/day in Walkability-by-Income Quadrants Walkability: p =.0002 Income: p =.36 Walkability X Income: p =.57 40 MVPA minutes per day (Mean *) 35 30 25 20 15 10 5 28.5 33.4 29.0 35.7 Low Walk High Walk 0 Low Income High Income * 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.
Percent Overweight or Obese (BMI>25) in Walkability-by-Income Quadrants 70 Walkability: p =.007 Income: p =.081 Walkability X Income: p =.26 % Overweight or Obese 60 50 40 30 20 10 63.1 56.8 60.4 48.2 Low Walk High Walk 0 Low Income High Income * 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.
Driving Minutes Per Week in Walkability-by-Education Quadrants Walkability: p =.001 Education: p =.86 Walkability X Educ: p =.35 300 D r i v i n g m i n u t e s / w e e k 250 200 150 100 50 262 126.0 242.0 133.0 Low Walk High Walk 0 Low Education High Education * Adjusted for age, sex, ethnicity, whether or not the participant had a child living in the home
NEWS Walking/Cycling Facilities in Walkability-by-Income Quadrants Income: p =.029 Walkability X Income: p =.89 3.5 NEWS Walking/Cycling Facilities (Mean *) 3 2.5 2 1.5 1 0.5 2.64 2.94 2.87 3.19 Low Walk High Walk 0 Low Income High Income *All models adjusted for gender, age, education, ethnicity, # motor vehicles/adult in household, site, marital status, number of people in household, and length of time at current address. Neighborhood was included as a random effect to adjust for clustering.
NEWS Pedestrian/Traffic Safety in Walkability-by-Income Quadrants Income: p = <.0001 Walkability X Income: p =.48 3.20 NEWS Pedestrian/Traffic Safety (Mean *) 3.10 3.00 2.90 2.80 2.70 2.60 2.50 2.40 3.12 2.87 2.87 2.70 Low Income High Income Low Walk High Walk *All models adjusted for gender, age, education, ethnicity, # motor vehicles/adult in household, site, marital status, number of people in household, and length of time at current address. Neighborhood was included as a random effect to adjust for clustering.
Multiple Pathways from Land Use to Health: Walkability Associations With Active Transportation, Body Mass Index, and Air Quality. Frank et al. JAPA 2007 5% increase in walkability associated with: 32% increase in walking for transport ¼ point decrease in BMI (about 1.25 pounds) 6.5% decrease in vehicle miles traveled 5.6% decrease in oxides of nitrogen (NOx) grams 5.5% decrease in volatile organic compounds (VOC) grams County government is acting on results
The Green Connection Walkable neighborhoods support More activity Less obesity Less driving Less GHG Target improvements in low income neighborhoods Change zoning codes to favor/require mixed use, denser development
People with access to parks & recreation Facilities are more likely to be active
A national study of US adolescents (N=20,745)* found a greater number of physical activity facilities is directly related to physical activity and inversely related to risk of overweight 1.5 Odds ratio 1.25 Referent 1 0.75 Odds of having 5 or more bouts of MVPA Odds of being overweight 1.26.68 0.5 *using Add Health data Gordon-Larsen et al, Pediatrics, 2006 http://www.pediatrics.org/cgi/content/full/117/2/417 One Two Three Four Five Six Seven Number of facilities per block group
% with no recreation facility 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Percent of census tracts without a recreational facility by race/ethnicity 70% 81% 38% African American Hispanic White Moore, Am J Prev Med, 2007
The Green Connection Parks, trails, & green spaces Support physical activity Are less common in low-income areas Absorb CO2 Trails can be used for recreation & transportation Aesthetics, including street trees, attract people to walk for recreation Planting more trees & creating more urban parks will help health & environment Target changes in low-income areas
Different environments----different congestion
Walkability > Driving > Obesity? The more miles a person travels by vehicle, the more likely they are to be obese 30% 25% 27.08 20% 15% 10% 9.5 14.3 18.05 5% 0% Q1 Q2 Q3 Q4 Quartiles of vehicle miles traveled (VMT) Lopez Zetina 2006
Does car dependence make us fat? Obesity falls sharply with increased walking, cycling, and transit use! 30 60 25 20 15 10 Percent of Obesity Percent Walk, Bike,Transit 5 0 50 40 30 20 10 0 USA New Zealand Australia Canada Ireland France Finland Italy Spain Germany Sweden Austria Netherlands Switzerland Denmark Credit: John Pucher Obesity Walk, Bike, Transit
Daily steps are higher among adults who commute by train instead of car Average Daily Steps (pedometer) 10000 9000 8000 7000 9500 7500 6000 Train Commuting Mode Car Wener & Evans, Environment and Behavior, 2007
Where do people bicycle in Portland, OR? Based on GPS. Type of road Without bicycle facilities % of bicycle miles 51 92 % of road miles With bicycle facilities (lane, separate path, bike boulevard 49 8 Jennifer Dill. J Public Health Policy. 2008.
Increase in Bike Share of Trips in Cities Around the World Source: Pucher, Dill, and Handy, Infrastructure, Programs, and Policies to Increase Bicycling, Preventive Medicine, Jan 2010, Vol. 50, S.1, pp. S106 S125.
NYC s Active Design Guidelines
The Green Connection Carbonless Footprints: Promoting health & climate stabilization through active transportation Lawrence Frank et al. Preventive Medicine 2010 Active transport uses human energy, not fossil fuel energy Related to land use and transportation policies
Resources from www.activelivingresearch.org