The objective of this study was to examine weight status among southern Appalachian adolescents and to identify risk factors for obesity. We analyzed baseline data from the Team Up for Healthy Living study in 2012. Overall, 19.8% of the sample was overweight, and 26.6% was obese. Boys had higher rates of overweight/obesity than girls (50.5% vs 42.3%). Being male (odds ratio [OR] = 1.79; 95% confidence interval [CI], 1.39–2.29), having a mother with a high school education or less (OR = 1.39; 95% CI, 1.05–1.83), or having a father with a high school education or less (OR = 1.57; 95% CI, 1.17–2.09) was associated with a higher prevalence of obesity and a higher body mass index
The Southern Appalachian region has one of the highest rates of obesity in the United States (
We used baseline data from the first wave (n = 544) and second wave (n = 965) of Team Up for Healthy Living (
Study exclusion criteria included the following: enrollment in another weight-management program; having a diagnosed eating disorder such as anorexia nervosa or bulimia nervosa; having an underlying condition affecting weight status, such as hypothyroidism, Cushing’s syndrome, or chronic steroid use; having dietary or physical activity restrictions, such as those recommended for adolescents who have hypertension, diabetes, or severe orthopedic problems; and pregnancy.
The overall participation rate for the study was 91.2% (1,509/1,654). The study was approved by the institutional review board at East Tennessee State University. All students provided assent, and parents provided passive consent.
We determined weight status by using the 2000 Centers for Disease Control and Prevention growth charts and data based on direct measurement of height and weight: overweight was defined as a body mass index (BMI) in the 85th to 95th percentile; obesity was defined as a BMI greater than the 95th percentile (
Linear and logistic mixed models were fit for identifying risk factors, with BMI
Most (93.4%) study participants were white (
| Characteristic | Overall | Boys (n = 765) | Girls (n = 744) |
|---|---|---|---|
|
| 14.9 (0.7) | 14.9 (0.8) | 14.8 (0.7) |
|
| |||
| Male | 49.3 | — | — |
| Female | 50.7 | — | — |
|
| |||
| American Indian or Alaska Native | 1.0 | 1.1 | 0.8 |
| Asian | 0.3 | 0.4 | 0.1 |
| Black or African American | 0.8 | 0.8 | 0.7 |
| Hispanic or Latino | 2.7 | 2.7 | 2.7 |
| Native Hawaiian or Other Pacific Islander | 0.1 | 0 | 0.1 |
| White | 93.4 | 92.9 | 93.9 |
| Other | 1.9 | 2.2 | 1.7 |
|
| |||
| Less than high school | 6.0 | 5.3 | 6.8 |
| High school graduate or GED | 29.1 | 30.1 | 28.1 |
| Some college | 21.5 | 21.2 | 21.9 |
| College degree | 27.1 | 26.8 | 27.4 |
| Do not know | 16.3 | 16.6 | 15.9 |
|
| |||
| Less than high school | 8.5 | 8.2 | 8.8 |
| High school graduate or GED | 33.3 | 34.1 | 32.5 |
| Some college | 15.2 | 16.8 | 13.6 |
| College degree | 20.9 | 21.1 | 20.7 |
| Do not know | 22.1 | 19.8 | 24.4 |
|
| |||
| <$20,000 | 3.9 | 4.1 | 3.6 |
| $20,000–$44,999 | 7.6 | 9.7 | 5.5 |
| $45,000–$74,999 | 7.7 | 8.9 | 6.5 |
| ≥$75,000 | 8.8 | 11.5 | 6.1 |
| Do not know | 72.0 | 65.8 | 78.3 |
|
| |||
| Weight | 0.012 (.09) | 0.005 (.35) | <0.001 (.48) |
| Height | 0.022 (.03) | 0.029 (.08) | 0.012 (.26) |
| BMI | 0.008 (.36) | 0.010 (.24) | 0 |
|
| |||
| BMI | 24.5 (5.7) | 24.8 (5.9) | 24.2 (5.5) |
| Standardized BMI | 0.87 (1.04) | 0.94 (1.12) | 0.81 (0.95) |
| BMI percentile | 73.0 (26.5) | 73.5 (27.7) | 72.4 (25.3) |
|
| |||
| Underweight | 1.1 | 1.5 | 0.8 |
| Healthy weight | 52.5 | 48.1 | 56.9 |
| Overweight | 19.8 | 18.2 | 21.5 |
| Obese | 26.6 | 32.3 | 20.8 |
Abbreviations:—, not available; BMI, body mass index; GED, General Education Development; ICC, intraclass correlation coefficient; SD, standard deviation.
Data were missing in overall sample for the following: grade (n = 46), race/ethnicity (n = 48), mother’s education (n = 79), father’s education (n = 85), annual family household income (n = 54), and measured weight (n = 18).
ICC for classes nested within schools; ICC defined as σ2B/(σ2W + σ2B), where σ2B is the between-subjects (classes) variance and σW2 is the within-subjects variance. Variance components were estimated by a mixed linear model in SAS version 9.2 (SAS Institute Inc).
Not estimable; BMIs varied randomly between students’ classes.
Calculated according to Centers for Disease Control and Prevention 2000 growth charts (
Measured weight status categories were assigned via age- and sex-specific BMI percentile scores based on the Centers for Disease Control and Prevention 2000 growth charts (
Being male, having a mother with a high school education or less, or having a father with a high school education or less was associated with a higher likelihood of obesity (being male, OR = 1.79 [95% CI, 1.39–2.29]; mother’s education, OR = 1.39 [95% CI, 1.05–1.83]; father’s education (OR = 1.57 [95% CI, 1.17–2.09]) and a higher BMI
| Characteristic | Overall | Boys | Girls | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Overweight | Obesity | BMI z Score | Overweight | Obesity | BMI z Score | Overweight | Obesity | BMI z Score | |
|
| 0.99 (0.98–1.01) | 1.01 (1.00–1.03) | 0.004 | 0.99 (0.85–1.15) | 1.01 (0.99–1.03) | 0.006 | 1.00 (0.97–1.02) | 1.01 (.99–1.03) | −0.001 |
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| |||||||||
| Girls | 1.0 [Referent] | — | — | ||||||
| Boys | 0.98 (0.75–1.29) | 1.79 (1.39–2.29) | 0.131 | — | — | ||||
|
| |||||||||
| Nonwhite | 1.0 [Referent] | 1.0 [Referent] | 1.0 [Referent] | ||||||
| White | 1.37 (0.83–2.24) | 0.96 (0.64–1.44) | −0.002 | 2.16 (0.01–4.97) | 1.01 (0.59–1.73) | 0.133 | 1.01 (0.54–1.89) | 0.89 (0.48–1.65) | −0.142 |
|
| |||||||||
| ≥$45,000 | 1.0 [Referent] | 1.0 [Referent] | 1.0 [Referent] | ||||||
| Unknown | 1.05 (0.72–1.52) | 0.94 (0.67–1.32) | −0.040 | 1.28 (0.76–2.15) | 1.17 (0.76–1.80) | 0.030 | 0.81 (0.47–1.41) | 0.83 (0.47–1.49) | −0.095 |
| <$45,000 | 0.83 (0.47–1.45) | 1.36 (0.86–2.15) | 0.029 | 1.03 (0.49–2.16) | 1.38 (0.78–2.46) | 0.009 | 0.62 (0.26–1.47) | 1.35 (0.62–2.91) | 0.055 |
|
| |||||||||
| Some college or more | 1.0 [Referent] | 1.0 [Referent] | 1.0 [Referent] | ||||||
| Unknown | 0.81 (0.56–1.17) | 0.94 (0.67–1.31) | −0.006 | 0.87 (0.52–1.46) | 1.12 (0.72–1.73) | −0.018 | 0.75 (0.45–1.26) | 0.72 (0.43–1.23) | 0.001 |
| High school or less | 1.12 (0.82–1.52) | 1.39 (1.05–1.83) | 0.160 | 0.99 (0.63–1.55) | 1.51 (1.03–2.21) | 0.158 | 1.24 (0.82–1.88) | 1.29 (0.85–1.96) | 0.171 |
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| |||||||||
| Some college or more | 1.0 [Referent] | 1.0 [Referent] | 1.0 [Referent] | ||||||
| Unknown | 0.99 (0.70–1.40) | 1.04 (0.75–1.45) | 0.786 | 1.13 (0.68–1.87) | 1.16 (0.75–1.82) | 0.065 | 0.88 (0.55–1.42) | 1.04 (0.63–1.71) | 0.032 |
| High school or less | 1.12 (0.82–1.54) | 1.57 (1.17–2.09) | 0.043 | 1.10 (0.70–1.74) | 1.70 (1.16–2.50) | 0.265 | 1.14 (0.74–1.75) | 1.51 (0.96–2.38) | 0.117 |
Abbreviations: BMI, body mass index; — , not available.
Binary mixed model logistic regression was used, with overweight (excluding obesity) relative to normal weight as an outcome variable.
Binary mixed model logistic regression was used, with obesity relative to normal/underweight as an outcome variable.
Linear mixed model was used, with BMI
We found similar results for the overall sample when we combined data for overweight and obesity. When stratified by sex, only lower levels of maternal and paternal education were associated with overweight and obesity among boys.
Consistent with a previous study (
Among adolescent boys, lower levels of education among mothers and fathers predicted a higher likelihood of obesity, whereas among girls, only a lower level of education among mothers was predictive. In contrast to the findings of our study, other studies have reported that the father’s level of education was a better predictor of obesity among adolescents than the mother’s education (
Appalachia has a population of approximately 25 million, 42% of whom live in rural areas; only 20% of the national population lives in rural areas (
This study has limitations. The large percentage of “do not know” responses to the question on annual family income level decreases the power of the study to detect possible differences. Second, our study sample is not representative of the entire Appalachian region. Despite these limitations, our study adds important findings to the literature on adolescent obesity in an understudied population. It suggests that parental education could be used to help identify adolescents at high risk for obesity in the target population. Future research using longitudinal data are warranted.
This project was funded by the National Institutes of Health (R01MD006200); part of Dr Youfa Wang’s effort is supported by research grants from the National Institutes of Health (R01HD064685-01A1, R01DK81335-01A1).
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.