Elsevier

Health & Place

Volume 34, July 2015, Pages 164-170
Health & Place

Patterns of neighborhood environment attributes in relation to children's physical activity

https://doi.org/10.1016/j.healthplace.2015.05.006Get rights and content

Abstract

Characterizing neighborhood environments in relation to physical activity is complex. Latent profiles of parents’ perceptions of neighborhood characteristics were examined in relation to accelerometer-measured moderate-to-vigorous physical activity (MVPA) among 678 children (ages 6–12) in two US regions. Neighborhood environment profiles derived from walkability, transit access, aesthetics, crime and traffic safety, pedestrian infrastructure, and recreation/park access were created for each region. The San Diego County profile lowest on walkability and recreation/park access was associated with an average of 13 fewer min/day of children's out-of-school MVPA compared to profiles higher on walkability and recreation/park access. Seattle/King County profiles did not differ on children's MVPA. Neighborhood environment profiles were associated with children's MVPA in one region, but results were inconsistent across regions.

Introduction

Regular physical activity reduces risk for developing adverse health outcomes and improves cardiovascular, muscular, and metabolic health. For children, the Physical Activity Guidelines for Americans recommends acquiring 60 or more minutes of aerobic physical activity per day, most of which should be moderate-to-vigorous intensity (MVPA) (“Physical Activity Guidelines for Americans,” 2008) to realize health-enhancing benefits. Long et al. (2013) reported that children 6–11 years old on average exceeded daily recommendations with the largest proportion of MVPA time occurring outside of school hours (Long et al., 2013), though the proportion meeting guidelines drops dramatically during adolescence (Troiano et al., 2008). Thus, increased emphasis on identifying promising strategies for increasing youth out-of-school MVPA is warranted.

Ecological models propose that features of built environments influence physical activity and may be especially important for out-of-school physical activity (Saelens et al., 2003). Research supports the important role of neighborhood environments in children's physical activity (Bauman et al., 2012, Kneeshaw-Price et al., 2013). A central tenet of ecological models is that multiple factors within and across levels of influence should explain physical activity better than a single factor (Sallis et al., 2009). This principle provides a rationale for simultaneously considering multiple factors when explaining MVPA. Limitations arise when analyzing individual attributes separately, as an individual's residential environment is composed of combinations of physical and social neighborhood features, many of which co-occur. For children, individual features such as recreation facility access, traffic safety, crime safety, and access to public transportation have been associated with children's physical activity in multiple studies while other features, such as street connectivity, residential density, and greater land-use mix have yielded mixed findings (Ding et al., 2011).

Unique combinations of neighborhood features may be associated with MVPA in different ways (Adams et al., 2011). For example, one neighborhood may exhibit high land-use mix and high street connectivity and good access to transit and parks while another neighborhood within the same urban area may exhibit a different combination of features. Examining combinations of neighborhood features in relation to MVPA may account for more variation in prediction models, thereby strengthening results. The difficulty lies in how best to account for the numerous combinations and complex patterns of built environment features. Different a priori (e.g. indices) and data-driven (e.g. factor analysis) approaches have merits. For example, Tappe et al. explored relationships between perceived built environment features and objective MVPA using a backwards stepwise regression approach. Though this approach considered multiple aspects of built environment features on physical activity while controlling for the other features, the combination of individual features could not be ascertained (Tappe et al., 2013).

The use of latent profile analysis (LPA) to examine built environment features is a relatively new data-driven approach that recognizes the natural co-occurrence of features and seeks to consider this co-occurring impact on outcomes. LPA has shown promise identifying complex patterns of built environment features among adults and older adults with resultant profiles associated with MVPA in these populations (Adams et al., 2012, Adams et al., 2013).

Parent reports of neighborhood characteristics, such as neighborhood safety and proximity to parks, have been associated with greater levels of children's physical activity (Rosenberg et al., 2009). Parents may limit their child's outdoor activity due to negatively perceived access to recreation facilities or poor safety features and crime (Brownson et al., 2009). Therefore, parents' perceived built environment features may be an important measure when examining children's out-of-school MVPA, as combinations of built environment features may be revealed that are not evident in objectively measured neighborhood features.

The first purpose of the present analysis was to explore whether latent profile analysis could derive unique combinations among 11 perceived neighborhood environment features using a validated self-report measure of the built environment. Second, we explored whether objectively measured children's total and out-of-school MVPA differed across derived latent profiles. We expected that combinations of built environment features would result in unique patterns, termed profiles, and that children in physical activity-supportive neighborhood profiles would have more total and out-of-school MVPA. The present analysis was conducted separately in two U.S. metropolitan regions to examine the consistency in profile derivation and relation to children's MVPA.

Section snippets

Design and sampling

This secondary analysis used baseline data from the Neighborhood Impact on Kids Study (NIK), including parent surveys, objectively measured physical activity, and measured anthropometrics of participating children. NIK is a longitudinal cohort study in Seattle/King County, WA and San Diego County, CA examining neighborhood environment characteristics in relation to child and parent overweight and obesity, with baseline measures (2007) and follow-up measures collected two years apart (Saelens et

Results

LPA 2-, 3-, and 4-profile models were identified for both regions. For the Seattle/King County region, model fit estimates indicated better fit for the 4-profile solution (AIC=10458.78, BIC=10685.14) compared to the 3-profile (AIC=10653.01, BIC=10832.54) and 2-profile (AIC=10787.95, BIC=11011.64) solutions, but the 3-profile solution was chosen because the 4th profile had a small sample size (< 5% of sample). The San Diego County 4-profile solution was chosen due to better model fit estimates

Discussion

Children's out-of-school MVPA minutes per day differed between distinct neighborhood profiles in the San Diego region. Children living in neighborhoods perceived as less walkable and not proximal to transit and recreation spaces engaged in about 13 min less daily out-of-school MVPA than children in the other three San Diego profiles. Other profiles were perceived as having better pedestrian facilities and access to recreation facilities and parks that were positively associated with children's

Conclusion

This secondary analysis of the Neighborhood Impact on Kids study examined LPA-derived neighborhood environment profiles in relation to young children's out-of-school MVPA. Neighborhood profiles indicative of areas supportive and unsupportive of children's physical activity based on parent's perceived neighborhood features were uncovered. The addition of transit access, aesthetics, traffic safety, crime safety, pedestrian and recreation facilities to the analysis of neighborhood walkability

Conflicts of interest

None to report.

Acknowledgements

This work was supported by in part by the American Heart Association's Beginning Grant in Aid (#12BGIA9280017), the NIH National Institute of Environmental Health Sciences (ES014240), USDA2007-55215-17924, and by Grants to the Seattle Children's Pediatric Clinical Research Center, which is supported by Grants UL1 RR025014, KL2 RR025015, and TL1 RR025016 from the NIH National Center for Research Resources.

References (26)

  • M.A. Adams et al.

    Validation of the Neighborhood Environment Walkability Scale (NEWS) items using geographic information systems

    J. Phys. Act. Health

    (2009)
  • M.A. Adams et al.

    Neighborhood environment profiles for physical activity among older adults

    Am. J. Health Behav.

    (2012)
  • R.C. Brownson et al.

    Measuring the Built Environment for Physical Activity

    Am. J. Prev. Med.

    (2009)
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