The Built Environment and Location-Based Physical Activity

Abstract

Background Studies of the built environment and physical activity have implicitly assumed that a substantial amount of activity occurs near home, but in fact the location is unknown. Purpose Examine associations between built environment variables within home and work buffers and moderate-vigorous physical activity occurring within these locations. Methods Adults (n= 148) from Massachusetts wore an accelerometer and GPS unit for up to four days. Moderate and vigorous intensity activity was quantified within 50 m and 1 km home and work buffers. Multiple regression models were used to examine associations between five objective built environment variables within 1 km home and work buffers (intersection density, land use mix, population and housing unit density, vegetation index) and moderate-vigorous physical activity within those areas. Results The mean daily minutes of moderate-vigorous physical activity accumulated in all locations = 61.1 ± 32.8, while duration within 1 km home buffers = 14.0 ± 16.4 min. Intersection density, land use mix, and population and housing unit density within 1 km home buffers were positively associated with moderate-vigorous physical activity in the buffer, while a vegetation index showed an inverse relationship (all p< 0.05). None of these variables showed associations with total moderate-vigorous activity. Within 1 km of work, only population and housing unit density were significantly associated with moderate-vigorous physical activity within the buffer. Conclusions Findings are consistent with studies showing that certain attributes of the built environment around homes are positively related to physical activity, but in this case only when the outcome was “location-based”. Simultaneous accelerometer-GPS monitoring shows promise as a method to improve understanding of how the built environment influences physical activity behaviors by allowing activity to be quantified in a range of physical contexts and thereby provide a more explicit link between physical activity outcomes and built environment exposures.

Workplace Neighborhoods, Walking, Physical Activity, Weight Status, and Perceived Health

“…There are some suggestive findings about mixed use areas near the workplace and increased walking, akin to those in Troped et al (13) and compatible with conventional wisdom about supports for travel walking (22). However, this study does not support claims of very strong physical workplace neighborhood effects on total physical activity or BMI/obesity.…”

Associations between time spent in green areas and physical activity among late middle-aged adults

“…This made it possible to attribute PA levels to each GPS data point, which cannot be done when using only selfreported data, where for example only binary data (participant walked or did not walk today) is extracted (Clark et al, 2014). Third, the combined use with GPS data offers detailed insights in where active behaviour is done (Duncan et al, 2009;Troped et al, 2010;Oreskovic et al, 2012;Kerr et al, 2012). In contrast to previous studies, where only the home neighbourhood is considered, we additionally gathered information on the specific context of where PA is carried out.…”

Where Are Adults Active? An Examination of Physical Activity Locations Using GPS in Five US Cities

“…S6). Park use increased with age (9.2% of bout minutes for ages [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]15.2% for ages 18.8% for ages 60-85) ( Table 3 and Table/ Fig. S2) was greater for healthy and overweight participants (17.7 and 13.7% of bout minutes vs 4.1% for obese; Table 3 and Table/ Fig.…”

“…Many studies also solicited a binary yes/no response in regards to use of a particular location type for PA [9][10][11][12][13][16][17][18][19][20][21][22][23][24][25], preventing examination of the percent of PA time completed at a specific location. Studies that did use GPS to aid in location assessment typically lacked specificity of location types, for example by simply reporting the location as inside/outside the home neighborhood [27,28]. Indeed, lack of specificity of location types is one of the main weaknesses of contemporary automated GIS approaches to examining PA locations, which typically either rely on identifying locations relative to key participant addresses (e.g., within a distance of home, work, or school) or GIS databases that have variable availability and comparability across large study areas.…”