Neighborhood environments that may encourage or discourage physical activity are complex and multidimensional, but most existing research examines single or only a few aspects of the environment. Our study shows inter-relatedness of environmental characteristics in a nationally representative adolescent population and reveals several patterns of built and SES environments reflecting constructs consistent with research in adult populations. Further, correlations among environment characteristics resulted in confounding to estimated associations with MVPA, demonstrating the complexity of potential environmental influences on physical activity.
Insights about the environment gained from pattern analysis
Our factor analysis identified inter-relationships among environmental measures too tightly correlated to analyze simultaneously as individual measures, while less inter-correlated environmental characteristics can be analyzed using traditional multivariate methods.
Inseparability of environmental features
In existing research, single environment measures are often examined as indicators of isolated environment characteristics. For example, intersection density is a common measure of street connectivity [18
], and facilities counts are often used to indicate access to resources. However, dense, gridded streets are common in city centers [37
], which represent a multitude of built, socioeconomic, and other features, and it is intuitive that more physical activity facilities are located in otherwise densely developed areas. Indeed, Cervero and colleagues [16
] introduced the concept of intensity, representing dense population and resources and interpreted as a measure of density. Consistent with this conceptualization, our study demonstrated that counts of physical activity facilities were strongly linked with population and intersection density, suggesting that it is important to adjust for density in estimation of physical activity facilities' effects. Yet statistical adjustment may be inappropriate due to strong correlation between density measures and facilities counts. Instead, we found that ratios of physical activity facilities per 1,000 population was a useful strategy for separating density from count of facilities, similar to Diez Roux and colleagues [2
In contrast, other street connectivity measures did not load onto factors in our study, indicating that they were not strongly correlated with each other or with other aspects of the built environment. Our results contrast with other studies showing constructs with multiple connectivity index indicators [12
]. This discrepancy may be explained by the national scope of Add Health as opposed to one or more metropolitan areas in the studies noted. Connectivity indices are ratios of various components such as number of intersections, street segments, and route alternatives, so they may reflect different constructs in areas with high versus low component values. Likewise, Ewing et al [17
] reported a single principal component representing urban sprawl characterized by residential density, land use mix, and street accessibility in a national sample, but their study was also limited to metropolitan areas and used block size measures rather than connectivity indices to represent street accessibility. Alternatively, our buffer-defined areas may influence intersection and street segment counts, particularly in rural areas with few streets, altering the meaning of the connectivity indices.
Dimensionality of environmental constructs
Factor solutions distinguished dimensions of similar constructs, which in turn were differentially related to MVPA. Factor analysis identified two types of facilities which were related to MVPA in different ways. For example, in females, MVPA was negatively associated with intensity (pay facilities) but marginally positively associated with intensity (public facilities) in fully adjusted models. Likewise, two SES environment factors emerged, one reflecting economic and education characteristics, the other reflecting social characteristics. These factors were correlated but appear to be differentially related to the built environment and MVPA.
Importance of incorporating many aspects of the environment when estimating neighborhood effects on physical activity
Factors allowed a wide range of environmental measures to be simultaneously incorporated into the analysis, revealing confounding by SES and built environment characteristics:
Confounding by SES environment characteristics
In particular, built and SES factors were strongly associated, and adjustment for SES environment factor(s) resulted in changes to several built environment-MVPA associations. Further, the 2-dimensional SES environment construct was a stronger confounder of associations between MVPA and intensity (public facilities) and, to a lesser extent, street connectivity, compared to the 1-dimensional construct. Such confounding could reflect placement of public facilities in areas of greatest need. Likewise, high street connectedness is common in poor inner-city areas where physical activity may be influenced by social contexts particularly relevant to females such as crime [38
], which is better captured by the 2-dimensional SES environment construct.
These results support our conceptualization of the SES environment as a confounder of the built environment-MVPA association. However, relationships between the built and SES environments may be bidirectional and dynamic. For example, crime may mediate, rather than confound, relationships between built and SES environment measures and physical activity. Furthermore, the social and economic resources of a community may influence where built environment features are situated, social norms with regard to health behavior [39
], and perceived and objective safety; ultimately, the SES environment measures may be surrogates for a multitude of influences on MVPA. While future research should investigate and account for these complexities, examination of the SES and built environments as independent influences on MVPA is valuable for documenting SES disparities and investigating the potential benefits of modifying the built environment while accounting for inter-correlation with the less modifiable SES environment.
Confounding by built environment characteristics
While built environment characteristics met our objective definition of confounding, absolute changes to estimates were small and did not change study conclusions regarding the relationship between the built environment and MVPA. One possible explanation for weak confounding is that our built environment factors were multidimensional and account for correlations between built environment measures; individual built environment measures may confound other measures loading onto the same factor. However, strong correlations preclude formal testing of this hypothesis. Additionally, the degree of confounding by built and SES environment characteristics in our study may have been minimized by weak built environment-MVPA relationships.
These findings suggest that failure to adjust for both economic and social aspects of the SES environment may lead to biased estimates of some built environment-MVPA associations. Fortunately, census variables are readily available. In contrast, relatively weak confounding by other built environment characteristics is encouraging for studies without the wide range of measures used in this study. However, in several cases, simultaneously adjusting for multiple built environment measures magnified the associations. Furthermore, in studies showing stronger associations or examining one-dimensional built environment measures, omission of additional built environment characteristics may lead to more substantial underestimation of effects. Finally, even small degrees of confounding may influence conclusions drawn from generally weak associations in the extant literature.
Forging ahead with replicable measures into longitudinal settings and external populations
Multidimensional built and SES environment constructs identified from factor analysis allowed us to simultaneously examine a large set of measures with respect to MVPA. In a next step, we used the knowledge gained from factor analysis to create simplified measures (Tables &) that incorporate inter-relationships, yet are more easily replicable in future studies. We emphasize that our simplified measures represent the set of variables identified using factor analysis and should be interpreted as such. In fact, replication of regression results with single indicators demonstrates that these measures, which are often analyzed on their own, may act as proxies for underlying environmental constructs.
Two branches of investigation are needed to better understand the potential causal effects of these measures. First, these simplified measures can be used in longitudinal analyses and examination in external populations. As opposed to other strategies such as scale measures, they are readily understandable and examined in prior research, and selection of single indicators reduces the number of measures needed to replicate findings in other studies.
Second, investigation of mechanisms leading to the observed associations will help to distinguish between proxies and policy-relevant determinants of physical activity. For example, crime replicated associations between the disadvantageous social environment factor and MVPA, but how crime might influence physical activity, or if yet another characteristic is the causal agent, is unknown. Research incorporating psychological measures (e.g., self-efficacy and perceived barriers) or detailed audit-based environment data (e.g., aesthetics and quality of facilities) can improve understanding of behavioral mechanisms. Such research may reveal additional layers, possibly showing our multidimensional environment constructs as proxies for more qualitative inter-personal or cultural aspects of the environment.
Determining whether patterning of environmental measures is similar in other populations is an important next step. If patterning in other age groups differs substantially from our nationally representative sample of adolescents, our simple measures may have limited ability to represent the constructs identified in this study and thus must be tested before applying them in other populations.
We found differences in built environment-MVPA associations by sex, which is consistent with previous studies examining walkability and physical activity resources [32
]. Homogenous landscape appears to be a negative correlate of MVPA for males but not females, possibly because males may be more likely to be active outdoors [42
] with less regard to safety or other concerns. Intensity (pay facilities) was associated with lower MVPA in females but not males. On the other hand, count of public facilities corrected for population was associated with higher MVPA in females but not males, perhaps also due to safety concerns addressed by access to facilities. Such differences by sex may shift as adolescents age into adulthood, when overall physical activity levels are lower [19
], or decrease among adolescents over time as physical activity promotion efforts in recent decades may have addressed barriers such as safety or provided additional sex-neutral activity opportunities.
Further investigation of the dose-response relationship between the built environment and MVPA is another opportunity for future research. We found non-linear associations between four aspects of the built environment and MVPA. The strongest associations were generally observed for the largest quartile, which, due to data skewness, contained very large factor score or measure values. Using quartile measures allowed comparability between associations with factors versus single indicators, but closer examination of dose-response and shape of the relationship is warranted. Shifts in the shape of the dose-response relationships - sometimes alternating between monotonic and U-shaped - with additional covariates add complexity and should be further examined.
Limitations and Strengths
Limitations include cross-sectional study design, which does not imply causality. Yet, we identified replicable measures that set the stage for longitudinal analyses, which can establish temporality and better address bias due to residential self-selection [43
]. Second, there was some temporal mismatch between individual-level interviews (1995-96) and GIS data sources (e.g., StreetMap 2000, 1992 land cover dataset), but our GIS is unique in providing historical data approximately contemporaneous with multiple survey waves. Our county-level crime measure was crude, yet it provided an objective measure of safety available across the US that was strongly associated with MVPA. Third, while we analyzed an extensive number of environmental variables, we did not consider quality of facilities, perceived environment measures, or other potential psychological mediators.
Fourth, we examined overall leisure time MVPA frequency, which does not distinguish between possible behavior-specific effects [45
] or incorporate physical activity duration or intensity. Our built and SES environment measures may show stronger relationships with specific types of physical activity. For example, stronger relationships may be present between alpha street connectivity and active transportation behaviors, or between pay or public facilities and team sports or exercise. Clearly, future research should examine behavior-specific associations while accounting for complex patterning of the environment.
Finally, we did not address urbanicity, which may be an important moderator [46
] of built environment-MVPA relationships. However, our study informs a growing body of work using national datasets by addressing environment patterning and confounding in broad range of neighborhood environments as well as examining measures applicable longitudinally during periods in which individuals may move in or out of urban areas. Additionally, the wide range of existing urbanicity measures are generally based on environment characteristics of interest (e.g., population density), thereby obscuring practical applications such as modification of the built environment in suburban areas to more closely resemble urban areas. Nevertheless, analogous analysis stratified by some measure of urbanicity is an important next step.
Additional strengths include examination of a wide range of environment measures in a nationally representative sample of adolescents, an understudied population. We explicitly examined and compared built and SES environment characteristics, which were strongly related. Finally, we used pattern analysis methods to not only investigate inter-relationships, but also to inform the creation of replicable measures.