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Discussion
A major challenge to the validity of studies on the effect of environment on physical activity is determining whether residents with a predisposition to be active choose a neighborhood that is more supportive of physical activities. This study was among the first to investigate residential preferences and physical activity behaviors.
The neighborhood features associated with the most significant change scores were those associated with greater walkability, such as closeness to open spaces, parks, recreational facilities, and shops; ease of walking; and sense of community. Similarly, Mumford et al found that residents who moved to a development similar to Mueller, in Atlanta, Georgia, selected the neighborhood for reasons associated with walkability. Giles-Corti et al also found that more than half of respondents rated aspects of neighborhood walkability as important factors in their decisions to move to a new location.
Although respondents may have decided to move to Mueller partly on the basis of their desired level of physical activity, the environment itself also seemed to play a role in their decision. The biggest increase in physical activity was seen among those who were the least active before moving to Mueller. More specifically, the low-activity group had the most significant increase in walking and biking for recreation inside the neighborhood and was the only group to increase transport-related physical activity after moving to Mueller. However, the high-activity group reported attaching a higher level of importance to neighborhood characteristics that are supportive of these behaviors.
Although Mueller's master plan calls for a town center with restaurants and shops, the closest amenities were between one-half and 1 mile away from the developed houses at the time of data collection. Access to and from Mueller was also limited because the surrounding arterial roadways were not pedestrian-friendly. Although respondents may have considered closeness to restaurants and shops to be important, the data suggest that the limited amenities within close distance may explain the low level of walking and biking for transport among all groups.
Several limitations of this study should be noted. This was a cross-sectional study that retrospectively measured self-reported pre-and post-move neighborhood preferences and physical activity. However, because individuals were used as their own controls, confounding lifestyle variables were less of a problem than in cross-sectional studies comparing individuals living in different neighborhoods. Moreover, it is not feasible to randomly assign people to live in different neighborhoods. Our design allowed for the identification of specific behavioral changes and neighborhood preferences pre- to post-move.
Self-report measures have well-known challenges, including the unknown validity of the NPAQ. However, NPAQ was based on the validated IPAQ, and the results are systematically reliable across subjects; therefore, there should not be intergroup measurement errors. The use of recall-based, pre-move data is also a limitation. Residents had lived in Mueller an average of 10 months at the time of our study, and, therefore, we cannot rule out the possibility that recollection of previous neighborhood preferences and behaviors may be inaccurate. This problem may be particularly evident among respondents who had lived in Mueller for longer periods of time. However, we did not find significant differences in physical activity among those who had lived in Mueller for less than 6 months, for 6 to 11 months, or for more than 1 year. Moreover, the subjective self-report data found in this study are consistent with objective accelerometer values found in a study that compared high- and low-walkability neighborhoods. Given the challenges to conducting longitudinal studies, more research is needed on the validity of these retrospective self-reports.
Overall, our study reported high levels of physical activity. This finding could be explained in several ways. First, respondents may have chosen to move to Mueller because it supports their desire to be active. The high-activity group did report attaching a significantly higher level of importance on closeness to open space and parks and a higher level of importance on ease of walking and closeness to recreational facilities than the low- and middle-activity groups. However, the low- and middle-activity groups had an increase in reported total physical activity unlike the high-activity group, which suggests that the environment itself, rather than personal preferences, could have had an effect. Another explanation for the high levels of physical activity is that 1 adult from each household was asked to complete the survey. Seeing that the survey concerned physical activity, the more active adult in the household may have opted to respond. Regardless, there were substantial increases in activity within the low-activity group.
Respondents may have thought it was socially desirable to represent their current physical activity more favorably. Although we cannot rule this out, we did not see behavior-specific increases across the board nor did we see increases in total physical activity for the high-activity group. The high levels of activity might also reflect a response bias, namely, that households with active adults may be more likely to return surveys than households with inactive adults. Although the influence of the 36% nonresponders is unknown, a 63.4% response rate suggests our results are generalizable to Mueller. Although the demographic characteristics of Mueller residents are similar to those of residents living in other new urbanist-designed communities (29,31,34), the generalizability is unknown.
Regression to the mean could partially explain the greater increases in physical activity among the low-activity group and the decreases among the high-activity group. Studies that measure change are frequently at risk of being affected by regression to the mean. However, this study found an overall reported increase in physical activity per week after residents moved to Mueller (66.4 min; 95% CI [32.8, 100.1], results fully described elsewhere). Thus, regression to the mean cannot fully explain the observed changes in physical activity for several reasons. First, there were significant increases in physical activity of the middle-activity group. If regression to the mean were fully operating, we would not see these increased levels of physical activity. Second, the decrease in physical activity reported by the high-activity group was only about one-third the magnitude of the increase reported by the low-activity group. If the observed changes were only a result of the regression to the mean, the increase within the low-activity group would have been about the same as the decrease in the high-activity group.
Notwithstanding these limitations, this study appears to be 1 of the few to examine both pre- and post-move preferences in neighborhood features and physical activity. It helps to advance the research toward a better understanding of efforts to alter environments to promote population shifts in physical activity. Public health professionals can use data such as these to execute environmental changes that are supportive of physical activity and to justify relevant policies to work with departments of planning, transportation, public works, and economic growth and development. Our study data provide some support for the possible role the environment plays in physical activity. Future research should feature prospective study designs, investigate individual neighborhood characteristics that promote greater physical activity among new residents, and include more diverse samples.
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