mmuting mode to school (active versus passive). Independent variables included: 1) parents' outcome expectations (from Social Cognitive Theory: the expected risks/benefits for their child doing ACS), 2) distance to school, 3) participation in an adult-led walk to school group, 4) temperature, and 5) child demographics. Generalized mixed-models estimated odds ratios for ACS (n=369 or 49.7% of Time 1 respondents).
- ACS was associated with the following:
- males (OR=2.59, 95% CI [1.57-4.30])
- adult-led walk to school group participation (OR=1.80, 95% CI [1.14-2.86])
- parents' outcome expectations (OR=1.26, 95% CI [1.14-1.39])
- temperature (OR=1.03, 95% CI [1.01-1.07)
- distance to school (OR=0.23, 95% CI [0.14-0.37])
- Latino ethnicity (OR=0.28, 95% CI [0.12-0.65]).
- CONCLUSIONS: Programs and policies sensitive to parents' concerns, e.g. adult-led walk to school groups, and targeting Latinos and girls appear promising for increasing ACS.
Mendoza JA, Cowan D, Liu Y. (2013). Predictors of Children's Active Commuting to School: An Observational Evaluation in Five US Communities. J Phys Act Health. Epublished.
- We assessed changes in transit-associated walking in the United States from 2001 to 2009 and documented their importance to public health.
- We examined transit walk times using the National Household Travel Survey, a telephone survey administered by the US Department of Transportation to examine travel behavior in the United States.
- People are more likely to transit walk if they are from lower income households, are non-White, and live in large urban areas with access to rail systems. Transit walkers in large urban areas with a rail system were 72% more likely to transit walk 30 minutes or more per day than were those without a rail system. From 2001 to 2009, the estimated number of transit walkers rose from 7.5 million to 9.6 million (a 28% increase); those whose transit-associated walking time was 30 minutes or more increased from approximately 2.6 million to 3.4 million (a 31% increase).
- Transit walking contributes to meeting physical activity recommendations. Study results may contribute to transportation-related health impact assessment studies evaluating the impact of proposed transit systems on physical activity, potentially influencing transportation planning decisions.
Freeland AL, Banerjee SN, Dannenberg AL, Wendel AM. (2013). Walking associated with public transit: moving toward increased physical activity in the United States. Am J Public Health, 103(3), 536-542. doi: 10.2105/AJPH.2012.300912.
- This study developed and tested an algorithm to classify accelerometer data as walking or non-walking using either GPS or travel diary data within a large sample of adults under free-living conditions.
- Participants wore an accelerometer and a GPS unit, and concurrently completed a travel diary for 7 consecutive days. Physical activity (PA) bouts were identified using accelerometry count sequences. PA bouts were then classified as walking or non-walking based on a decision-tree algorithm consisting of 7 classification scenarios. Algorithm reliability was examined relative to two independent analysts' classification of a 100-bout verification sample. The algorithm was then applied to the entire set of PA bouts.
- The 706 participants' (mean age 51 years, 62% female, 80% non-Hispanic white, 70% college graduate or higher) yielded 4,702 person-days of data and had a total of 13,971 PA bouts. The algorithm showed a mean agreement of 95% with the independent analysts. It classified physical activity into 8,170 (58.5 %) walking bouts and 5,337 (38.2%) non-walking bouts; 464 (3.3%) bouts were not classified for lack of GPS and diary data. Nearly 70% of the walking bouts and 68% of the non-walking bouts were classified using only the objective accelerometer and GPS data. Travel diary data helped classify 30% of all bouts with no GPS data. The mean duration of PA bouts classified as walking was 15.2 min (SD=12.9). On average, participants had 1.7 walking bouts and 25.4 total walking minutes per day.
- GPS and trave