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 travel diary information can be helpful in classifying most accelerometer-derived PA bouts into walking or non-walking behavior.
Kang B, Moudon AV, Hurvitz PM, et al. (2013). Walking Objectively Measured: Classifying Accelerometer Data with GPS and Travel Diaries. Med Sci Sports Exerc. (e-published).
- Given the health benefits of walking, there is interest in understanding how physical environments favor walking. Although GIS-derived measures of land-use mix, street connectivity, and residential density are commonly combined into indices to assess how conducive neighborhoods are to walking, field validation of these measures is limited.
- To assess the relationship between audit- and GIS-derived measures of overall neighborhood walkability and between objective (audit- and GIS-derived) and participant-reported measures of walkability.
- Walkability assessments were conducted in 2009. Street-level audits were conducted using a modified version of the Pedestrian Environmental Data Scan. GIS analyses were used to de