Physical inactivity is prevalent and a worldwide public health concern.1 Increasing active transport is an appealing strategy to increase overall physical activity, although more clarity is needed about the amount of physical activity directly attributable to transportation choices. Users of public transit (e.g., bus, train) engage in more overall physical activity than do nonusers and more often meet daily physical activity recommendations ( ‡ 30 min/d on most days), likely from the active transport involved with accessing transit (e.g., walking to a bus stop). 2---5Reported total walking time is also higher among transit users than among nonusers. 4,6 National travel diary data indicate that the average American transit user (approximately 2%---3% of adults) walks 19 minutes per day to and from transit, and approximately one third of these transit users attain recommended levels of physical activity based solely on the amount of walking related to their transit use. 2,3 Better public transit access and more use appear related to more overall physical activity and specifically walking. However, many previous studies about the relation between transit use and physical activity fail to address the critical issues of possible confounding and potential substitution. Regarding confounding, examining only overall physical activity or total walking among transit users versus nonusers without disaggregating physical activity into constituent types and purposes of walking makes it difficult to determine how much physical activity is directly related to using public transit (i.e., walking or biking to and from public transit stops), separate from other types of utilitarian (e.g., walking to the store) or recreational physical activity. This is problematic because other factors could readily account for the relation between transit use and higher overall physical activity and walking. For example, built environment factors such as residential density and land use mix are related to transit use because transit access is higher in more dense and higher mix areas; however, these built environment variables are also related to walking to and from other nonresidential locations (e.g., stores, restaurants).8 Thus, without more precision, it is not possible to rule out a spurious relation (e.g., through built environment or other shared variables) between transit access and use and physical activity. The issue of substitution is also critical to measuring the health effect of transit use on physical activity. If transit users decrease the time spent in other activities in lieu of the time spent in transit-related walking, attempts to increase transit use would not yield increases in physical activity but merely shift from one form to another form of physical activity. Studies that provide more precise estimates of walking to and from transit use have not examined whether such substitution occurs. A recent US time use study suggested that some substitution may be happening as individuals with longer commutes, which are often char...
Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Second, data streams are integrated using common timestamps into a single data structure, the “LifeLog.” A graphic interface tool, “LifeLog View,” enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors.
Objectives Utilitarian and recreational walking both contribute to physical activity. Yet walking for these two purposes may be different behaviors. We sought to provide operational definitions of utilitarian and recreational walking and to objectively measure their behavioral, spatial, and temporal differences in order to inform transportation and public health policies and interventions. Methods Data were collected 2008–2009 from 651 Seattle-King County residents, wearing an accelerometer and a GPS unit, and filling-in a travel diary for 7 days. Walking activity bouts were classified as utilitarian or recreational based on whether walking had a destination or not. Differences between the two walking purposes were analyzed, adjusting for the nested structure of walking activity within participants. Results Of the 4,905 observed walking bouts, 87.4% were utilitarian and 12.6% recreational walking. Utilitarian walking bouts were 45% shorter in duration (−12.1 min) and 9% faster in speed (+0.3km/h) than recreational walking bouts. Recreational walking occurred more frequently in the home neighborhood and was not associated with recreational land uses. Utilitarian walking occurred in areas having higher residential, employment, and street density, lower residential property value, higher area percentage of mixed-use neighborhood destinations, lower percentage of parks/trails, and lower average topographic slope than recreational walking. Conclusion Utilitarian and recreational walking are substantially different in terms of frequency, speed, duration, location, and related built environment. Policies that promote walking should adopt type-specific strategies. The high occurrence of recreational walking near home highlights the importance of the home neighborhood for this activity.
Little is known about where physical activity (PA) occurs, or whether different demographic groups accumulate PA in different locations. 1. Method Objective data on PA and location from 611 adults over 7 days were collected in King County, WA in 2008-2009. The relative amounts of time spent in sedentary-to-low and moderate-to-vigorous PA (MVPA) were quantified at three locations: “home” (<125 m from geocoded home locations); “near” home (125 - 1,666 m, defining the home neighborhood); and “away” from home (> 1,666 m). Differences in MVPA by demographics and location were examined. The percent of daily time in MVPA was estimated using a mixed model adjusted for location, sex, age, race/ethnicity, employment, education, BMI, and income. 2. Results Most MVPA time occurred in nonhome locations, and disproportionately “near” home; this location was associated with 16.46% greater time in MVPA, compared to at-home activity (p<0.001), whereas more time spent at “away” locations was associated with 3.74% greater time in MVPA (p<0.001). Location was found to be a predictor of MVPA independent of demographic factors. 3. Conclusion A large proportion of MVPA time is spent at “near” locations, corresponding to the home neighborhood studied in previous PA research. “Away” locations also host time spent in MVPA and should be the focus of future research.
Purpose 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. Methods 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. Results 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. Conclusions GPS and travel diary information can be helpful in classifying most accelerometer-derived PA bouts into walking or non-walking behavior.
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