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Pediatric Weight Management

Child Nutrition and Environment


Nelson MC, Gordon-Larsen P, Song Y, Popkin BM. Built and Social Environments: Associations with Adolescent Overweight and Activity. American Journal of Preventative Medicine, 2006; 31 (2): 109-117.

Study Design:
Cross-Sectional Study
D - Click here for explanation of classification scheme.
Quality Rating:
Positive POSITIVE: See Quality Criteria Checklist below.
Research Purpose:

Using data from a nationally-representative, ethnically-diverse sample of adolescents, the aims of this study were to

  1. Identify meaningful patterns of socio-demographic and built features in neighborhood environments that have been identified as potentially important determinants of physical activity
  2. Describe the cross-sectional associations between these neighborhood patterns and adolescent resident's physical activity and weight status.
Inclusion Criteria:
  • The Wave-I in-home survey (1994 to 1995) included 20,745 adolescent participants
  • Analyses were conducted in 2005 and 2006
  • Add Health is a school-based longitudinal survey of youths, Grades Seven through 12. A random sample of 80 high schools and 52 junior high feeder schools was selected.
  • The Add Health sample was designed to be nationally-representative of students in Grades Seven through 12 in 1995 in the United States.
Exclusion Criteria:
  • Observations with missing covariate or outcome data were excluded
  • Participants who were severely disabled or pregnant were also excluded.
Description of Study Protocol:


  • Add Health is a school-based longitudinal survey of youths, Grades Seven through 12. A random sample of 80 high schools and 52 junior high feeder schools was selected.
  • The Add Health sample was designed to be nationally-representative of students in Grades Seven through 12 in 1995 in the United States
  • The Wave-I in-home survey (1994 to 1995) included 20,745 adolescent particpants.


  • Weight, height, physical activity (PA) and sedentary behavior were self-reported
  • Using diverse measures of the participants’ residential neighborhoods (e.g., socio-economic status, crime, road type, street connectivity, physical activitiy recreation facilities), cluster analyses identified homogeneous groups of adolescents sharing neighborhood characteristics
  • Poisson regression predicted relative risk (RR) of being physically active (five or more bouts per week of moderate-to-vigorous PA) and overweight (body mass index at or above the 95th percentile, Centers for Disease Control and Prevention/National Center for Health Statistics growth curves).

Statistical Analysis

Cluster analyses were used to identify patterns of environmental characteristics and to specify homogeneous, non-overlapping clusters (or patterns) of neighborhoods sharing various meaningful characteristics. Multiple cluster analyses were conducted partitioning data into different numbers of clusters by Euclidean distances between observations that were weighted for national representation, using SAS FASTCLUS, SAS version 9 (Research Triangle Institute, Research Triangle Park NC, 2004). Representing different constructs of the neighborhood, 19 variables were used. Z-score transformations of variables were used to generate clusters, allowing for the appropriate weighing of variables with different scales.

To identify intitial cluster centers (i.e., seed values), 1,000 iterations of each cluster procedure were conducted. The initail group center for each iteration was randomly generated. The iteration with the largest overall R2 value, which allowed for the evaluation of relative heterogeneity between clusters (vs. homogeneity within clusters), was identified. Clusters best fitting the data maximized this inter- to intra-variability ratio, yielding a higher R2. [For the six-cluster solution series (i.e., the final cluster solution), the maximum R2 value identified through this iterative process was 0.41.] Results of these numerous analyses were assessed to identify common patterns appearing across various procedures. The final presented clusters were those representing the most robust data patterns. 

To demonstrate meaningful variability between patterns and to validate these findings, neighborhood clusters were assessed as independent variables in generalized linear models predicting adolescent PA, sedentary behavior and overweight. As another tool for comparison, broad neighborhood characteristics were examined (e.g., broad urbanicity classifications of urban, suburban and rural; median household income; percentage college-educated population; percentage minority population), which have been used extensively in previous literature.

Data Collection Summary:

Timing of Measurements

The measurements were colllected during 1994 and 1995.

Dependent Variables

  • Residential location (home street addresses of most participants were idenfitied and geocoded)
  • Buffers for respondent locations (a three-km buffer was drawn around each respondent's residental location)
  • Plysical activity facilities within three km [detailed characterization from the commercially-available, retrospective (1995) digitized Yellow Pages were obtained]
  • Walkability within three km (neighborhood street networks that are continuous, integrated and maximize linkages between starting points and destinations)
  • Road type within three km (road networks wre mapped using retrospective US Census line files)
  • Census measures
  • Crime (reported by the 1995 US Federal Bureau of Investigation Uniform Crime Reporting county-level data from the National Archive of Criminal Justic Data).

Independent Variables

  • Physical activity and sedentary behaviors (self-reported using standard epidemilogic seven-day recall methodology)
  • Weight status (self-reported height and weight to calculate body mass index).

Control Variables

Age, race or ethnicity and parent education and income were controlled for.

Description of Actual Data Sample:
  • Initial N: 20,745
  • Attrition (final N): 20,745 (50.1% males)
  • Age: 15.4±0.12 years
  • Ethnicity: 68.5% white, 15.2 % black, 11.4 % Hispanic, 4.0% Asian
  • Other relevant demographics: Approximately 14.7% of participants’ parents had less than a high school education, 32.5% had graduated from high school (or had a general equivalency diploma), 27.8% had some college and 25.0% had a college degree or higher
  • Anthropometrics (e.g., were groups same or different on important measures): See below
  • Location: United States.
Summary of Results:

Key Findings

Six robust neighborhood patterns were identified:

  1. Rural working class
  2. Exurban
  3. Newer suburban
  4. Upper-middle class, older suburban
  5. Mixed-race urban
  6. Low-socioeconomic-status (SES) inner-city areas.

These neighborhood patterns were distinguished by important differences in the 19 neighborhood attributes used to generate the final cluster soluction, inclusing SES, race or ethnicity, socio-environment, crime, road type, street connectivity and recreation facilities. 

Compared to adolescents living in newer suburbs, those in rural working-class [adjusted RR (ARR), 1.38; 95% confidence interval (CI), 1.13 to 1.69), exurban (ARR, 1.30; CI, 1.04 to 1.64) and mixed-race urban (ARR, 1.31; CI, 1.05 to 1.64) neighborhoods were more likely to be overweight, independent of individual SES, age and race or ethnicity. Adolescents living in older suburban areas were more likely to be physically active than residents of newer suburbs (ARR, 1.1; CI, 1.04 to 1.18). Those living in low-SES inner-city neighborhoods were more likely to be active (though not significantly so), compared to mixed-race urban residents (ARR, 1.09; CI, 1.00 to 1.18).

Author Conclusion:


These findings demonstrate disadvantageous associations between specific rural and urban  environments and behavior, illustrating important effects of the neighborhood on health and the inherent complexity of assessing residential landscapes across the United States.Simple classical urban-suburban-rural measures mask these important complexities.


Funding Source:
Government: NIH, CDC
Robert Wood Johnson Foundation's Active Living Research Program
Other non-profit:
Reviewer Comments:

Large study examining the association between adolescent overweight, activity and the built environment.

Quality Criteria Checklist: Primary Research
Relevance Questions
  1. Would implementing the studied intervention or procedure (if found successful) result in improved outcomes for the patients/clients/population group? (Not Applicable for some epidemiological studies) Yes
  2. Did the authors study an outcome (dependent variable) or topic that the patients/clients/population group would care about? Yes
  3. Is the focus of the intervention or procedure (independent variable) or topic of study a common issue of concern to dieteticspractice? Yes
  4. Is the intervention or procedure feasible? (NA for some epidemiological studies) Yes
Validity Questions
1. Was the research question clearly stated? Yes
  1.1. Was (were) the specific intervention(s) or procedure(s) [independent variable(s)] identified? Yes
  1.2. Was (were) the outcome(s) [dependent variable(s)] clearly indicated? Yes
  1.3. Were the target population and setting specified? Yes
2. Was the selection of study subjects/patients free from bias? Yes
  2.1. Were inclusion/exclusion criteria specified (e.g., risk, point in disease progression, diagnostic or prognosis criteria), and with sufficient detail and without omitting criteria critical to the study? Yes
  2.2. Were criteria applied equally to all study groups? Yes
  2.3. Were health, demographics, and other characteristics of subjects described? Yes
  2.4. Were the subjects/patients a representative sample of the relevant population? Yes
3. Were study groups comparable? Yes
  3.1. Was the method of assigning subjects/patients to groups described and unbiased? (Method of randomization identified if RCT) N/A
  3.2. Were distribution of disease status, prognostic factors, and other factors (e.g., demographics) similar across study groups at baseline? N/A
  3.3. Were concurrent controls or comparisons used? (Concurrent preferred over historical control or comparison groups.) N/A
  3.4. If cohort study or cross-sectional study, were groups comparable on important confounding factors and/or were preexisting differences accounted for by using appropriate adjustments in statistical analysis? Yes
  3.5. If case control study, were potential confounding factors comparable for cases and controls? (If case series or trial with subjects serving as own control, this criterion is not applicable.) Yes
  3.6. If diagnostic test, was there an independent blind comparison with an appropriate reference standard (e.g., "gold standard")? N/A
4. Was method of handling withdrawals described? Yes
  4.1. Were follow-up methods described and the same for all groups? N/A
  4.2. Was the number, characteristics of withdrawals (i.e., dropouts, lost to follow up, attrition rate) and/or response rate (cross-sectional studies) described for each group? (Follow up goal for a strong study is 80%.) Yes
  4.3. Were all enrolled subjects/patients (in the original sample) accounted for? Yes
  4.4. Were reasons for withdrawals similar across groups? N/A
  4.5. If diagnostic test, was decision to perform reference test not dependent on results of test under study? N/A
5. Was blinding used to prevent introduction of bias? Yes
  5.1. In intervention study, were subjects, clinicians/practitioners, and investigators blinded to treatment group, as appropriate? N/A
  5.2. Were data collectors blinded for outcomes assessment? (If outcome is measured using an objective test, such as a lab value, this criterion is assumed to be met.) Yes
  5.3. In cohort study or cross-sectional study, were measurements of outcomes and risk factors blinded? ???
  5.4. In case control study, was case definition explicit and case ascertainment not influenced by exposure status? N/A
  5.5. In diagnostic study, were test results blinded to patient history and other test results? N/A
6. Were intervention/therapeutic regimens/exposure factor or procedure and any comparison(s) described in detail? Were interveningfactors described? Yes
  6.1. In RCT or other intervention trial, were protocols described for all regimens studied? N/A
  6.2. In observational study, were interventions, study settings, and clinicians/provider described? N/A
  6.3. Was the intensity and duration of the intervention or exposure factor sufficient to produce a meaningful effect? N/A
  6.4. Was the amount of exposure and, if relevant, subject/patient compliance measured? Yes
  6.5. Were co-interventions (e.g., ancillary treatments, other therapies) described? N/A
  6.6. Were extra or unplanned treatments described? N/A
  6.7. Was the information for 6.4, 6.5, and 6.6 assessed the same way for all groups? N/A
  6.8. In diagnostic study, were details of test administration and replication sufficient? N/A
7. Were outcomes clearly defined and the measurements valid and reliable? Yes
  7.1. Were primary and secondary endpoints described and relevant to the question? Yes
  7.2. Were nutrition measures appropriate to question and outcomes of concern? Yes
  7.3. Was the period of follow-up long enough for important outcome(s) to occur? N/A
  7.4. Were the observations and measurements based on standard, valid, and reliable data collection instruments/tests/procedures? Yes
  7.5. Was the measurement of effect at an appropriate level of precision? Yes
  7.6. Were other factors accounted for (measured) that could affect outcomes? Yes
  7.7. Were the measurements conducted consistently across groups? Yes
8. Was the statistical analysis appropriate for the study design and type of outcome indicators? Yes
  8.1. Were statistical analyses adequately described and the results reported appropriately? Yes
  8.2. Were correct statistical tests used and assumptions of test not violated? Yes
  8.3. Were statistics reported with levels of significance and/or confidence intervals? Yes
  8.4. Was "intent to treat" analysis of outcomes done (and as appropriate, was there an analysis of outcomes for those maximally exposed or a dose-response analysis)? N/A
  8.5. Were adequate adjustments made for effects of confounding factors that might have affected the outcomes (e.g., multivariate analyses)? Yes
  8.6. Was clinical significance as well as statistical significance reported? Yes
  8.7. If negative findings, was a power calculation reported to address type 2 error? N/A
9. Are conclusions supported by results with biases and limitations taken into consideration? Yes
  9.1. Is there a discussion of findings? Yes
  9.2. Are biases and study limitations identified and discussed? Yes
10. Is bias due to study's funding or sponsorship unlikely? Yes
  10.1. Were sources of funding and investigators' affiliations described? Yes
  10.2. Was the study free from apparent conflict of interest? Yes