Excess body weight is a significant public health concern in the USA with approximately 70% of adults being overweight or obese1. Several investigative groups have documented a rural-urban obesity disparity with 36-40% of rural residents being obese compared to 30-33% of urban residents2,3. Excess body weight is associated with numerous chronic conditions including cardiovascular disease (CVD), cancer, diabetes, hypertension, arthritis, and asthma4-12, which may contribute to other rural health disparities such as reduced life expectancy and higher CVD mortality13-15.
Despite being socioeconomically disadvantaged and encountering numerous barriers to living healthy lives16-20, rural residents are more likely to be obese than urban residents, independent of diet, physical activity, and sociodemographic factors2,3,21,22. These studies support a growing body of research suggesting that broader neighborhood characteristics are associated with poor health outcomes independent of individual-level factors. Living in a disadvantaged neighborhood, for example, has been associated with a greater likelihood of developing coronary heart disease independent of personal socioeconomic status and risk factors23. Rural residents living in economically disadvantaged areas may therefore experience the greatest risk for excess body weight.
Appalachia is a 531 000 km2 region in the USA encompassing 13 states following the Appalachian Mountains from northern Mississippi to southern New York24. Many counties within rural Appalachian Kentucky are economically among the worst 10% in the nation and are considered distressed by the Appalachian Regional Commission based on three indicators compared with national averages: 3-year average unemployment, per capita market income, and poverty25. Schoenberg and colleagues26 recently reported that adults living in Appalachian Kentucky were more likely to be obese compared to those living in non-Appalachian Kentucky adjusting for sociodemographics, days of poor mental health, and smoking status. However, it's currently unknown whether living in distressed rural Appalachia is associated with excess body weight, adjusting for other weight-related behaviors. A greater understanding of independent community-level associations with excess body weight would be beneficial for policy makers and community leaders in considering comprehensive strategies to reduce weight-related chronic disease risk in this region.
With evidence that the rural-urban obesity disparity is greatest among younger adults2, the college years are an important time to examine the relationship between geographic residence and excess body weight. Many college students frequently consume fast food, do not consume the recommended amount of fruits and vegetables, and are physically inactive27-31, contributing to weight gain during the college years27,29,32,33. In addition to engaging in unhealthy behaviors, many college students also suffer from psychological distress34,35, which is associated with weight gain in this population36. College students living in economically distressed rural Appalachia may be particularly at risk for excess body weight given broader neighborhood-level effects on health outcomes. Thus, the purpose of this study was to determine if living in economically distressed rural Appalachia is independently associated with excess body weight among college students.
Design, setting, and sample
Data from 54 students from urban Central Kentucky and 55 students from rural Eastern Appalachia Kentucky were included in this cross-sectional study. Students were recruited using purposive sampling from a community and a 4-year college in urban central Kentucky and a community college in rural Eastern Appalachian Kentucky. Students were eligible if they were 18-25 years of age, enrolled in college, and a lifetime resident of either rural Eastern Appalachian Kentucky or urban Fayette County, Kentucky. Lifetime residency of these counties was a criterion to minimize residential self-selection bias where people may choose their residence based upon environments that support their lifestyle preferences37-39. Students with a self-reported CVD diagnosis including hypertension, hyperlipidemia, and diabetes were excluded because they might be engaged in healthier lifestyle behaviors as part of their self-management. Pregnant students were excluded due to changes in body weight that occur during this period.
Rural Appalachian/urban non-Appalachian status: Rural status was defined according to Rural-Urban Continuum Codes provided by the US Department of Agriculture40. Rural counties in this study had Rural-Urban Continuum Codes ranging from 7 to 9, indicating their status as non-metropolitan counties. These counties were defined as 'distressed' according to the Appalachian Regional Commission, indicating they rank economically in the bottom 10% of all counties nationwide41. The eight rural Eastern Appalachian counties where data were collected have some of the highest rates of poverty and unemployment in Kentucky. Residents in these counties also have some of the worst overall health in the state42, with rates of heart disease mortality significantly higher than the national average43. In contrast, urban Fayette County in Central Kentucky, with a Rural-Urban Continuum Code of 2 (indicating metropolitan status) is a non-Appalachian county and has among the lowest rates of poverty and unemployment in the state. The Kentucky Institute of Medicine42 ranked Fayette County as sixth best in the state for general health.
Depressive symptoms: Depressive symptoms were measured using the nine-item Patient Health Questionnaire (PHQ-9), which assessed the frequency of depressive symptoms over the previous 2 weeks and ranging from 0 (not at all) to 3 (nearly every day)44. Total score range was 0-27 with a score of 0-4 representing minimal symptoms, 5-9 mild symptoms, 10-14 moderate symptoms, 15-19 moderately severe symptoms and ≥20 severe symptoms. Cronbach's alpha range was 0.79-0.89 among individuals in primary care44,45.
Fruit and vegetable consumption: Fruit and vegetable consumption was measured using questions from the Behavioral Risk Factor Surveillance System46 regarding the frequency of consuming 100% pure fruit juices, fruit, cooked or canned beans, dark green vegetables, orange-colored vegetables, and other vegetables. Participants could list the daily, weekly, or monthly frequency of consumption of each item during the past 30 days. Fruits and vegetables consumed daily were multiplied by 30 and those consumed weekly were multiplied by four to compute a monthly total. Daily fruit and vegetable intake was calculated by summing the monthly consumption of each item and dividing by 30 days.
Smoking: Smoking was self-reported from questions asking participants to indicate their smoking status, with options including 'currently smoking (smoking within 1 month of today)', 'recently smoked (stopped between 1 month and 1 year before today)', 'formerly smoked (stopped more than 1 year ago)', and 'never smoked'. Smoking status was dichotomized into 'current or recently smoked' and 'formerly or never smoked'.
Physical activity: Vigorous physical activity was measured by the International Physical Activity Questionnaire short form (IPAQ-SF), which was designed to be used by adults aged 15-69 years47. The IPAQ-SF contains seven questions regarding the frequency and time spent in vigorous and moderate intensity activity, walking, and sitting. Vigorous physical activity was used in this analysis as it has the highest convergent validity with other measures of physical activity48. Weekly time spent in vigorous physical activity was calculated by multiplying the self-reported number of days engaged in vigorous physical activity by the self-reported time spent engaged in vigorous physical activity on one of those days.
Excess body weight: Participants were defined as underweight, normal weight, overweight, or obese class I-III according to standard BMI cut-points (<18.5, 18.5-24.9, 25-29.9, 30-34.9, 35-39.9, and ≥40 kg/m2, respectively)49. Body weight was measured using a portable, mechanical digital scale to the nearest 0.05 kg. Height was measured using a portable stadiometer to the nearest 0.1 cm with participants wearing no shoes and light clothing. Excess body weight was defined as a BMI≥25kg/m2.
Other variables of interest: A self-administered sociodemographic questionnaire was used to gather data on age, sex, race/ethnicity, household income, insurance status, having a primary care provider, and living arrangements.
Institutional Review Board approval was obtained prior to study initiation. Students were predominantly recruited on college campuses using recruitment tables staffed with study personnel. Flyers were emailed to students at each site and were posted at high traffic locations. The Center for Clinical and Translational Sciences at the 4-year college site provided recruitment assistance through a clinical research opportunities database.
Students who were interested and eligible scheduled a meeting with the principal investigator at a mutually agreed upon date and time to review the study, provide written informed consent, complete study questionnaires, and undergo a brief health examination where height and weight were measured.
A small number of participants had missing or provided unusable data for some activity and dietary variables used in the analysis. In these cases, the means from each geographic area were used accordingly. Mean adjusted values for time spent in vigorous activity (n=2, urban; n=1, rural) were used because some participants provided unusable data on the IPAQ-SF by checking 'not sure/don't know.' Mean adjusted values for other vegetables (n=1, urban), daily fruit (n=1, rural), and total fruit and vegetables (n=1, rural) were also used for missing values.
Sociodemographics, depressive symptoms, health behaviors, and overweight or obesity were summarized using means with standard deviations, percentages, or medians with interquartile ranges as appropriate, and groups were compared using independent sample tests, χ2 , or Mann-Whitney U-tests. Normality of the data were examined using the Kolmogorov-Smirnov and Shapiro-Wilk tests. Depressive symptoms, fruit and vegetable intake, time spent in vigorous physical activity, and BMI were non-normally distributed. Binary logistic regression was used to determine whether living in rural Eastern Appalachian Kentucky was associated with excess body weight, controlling for sociodemographics, depressive symptoms, and health behaviors. The assumption of linearity between continuous variables and log of the outcome variable was tested by examining the interaction between each continuous variable and its log. There were no significant interactions, indicating that the assumption of linearity was met. The overall model χ was used to assess the significance of the model, and the Hosmer-Lemeshow test was used to assess model fit. An alpha level of 0.05 was used to determine statistical significance for all analyses. All statistical analyses were performed using the Statistical Package for the Social Sciences v23 (IBM; http://www-01.ibm.com/software/analytics/spss/)
This study was approved by the University of Kentucky Institutional Review Board (12-0927).
Sociodemographic characteristics of participants
Table 1 shows differences in sociodemographic characteristics between the groups. The mean age of the rural Eastern Appalachian Kentucky group was 1 year younger and that group completed approximately 1 less year of school than the urban Central Kentucky group. A greater percentage of the rural Eastern Appalachian Kentucky group was Caucasian/White, lived with family, reported lower household income, and fewer currently had insurance or a primary care provider.
Table 1: Sociodemographic characteristics of college students in rural Appalachian Kentucky and urban Central Kentucky
Differences in depressive symptoms, health behaviors, and body weight
Table 2 shows differences in depressive symptoms, health behaviors, and overweight and obesity between the groups. Approximately 60% of students in the rural Appalachian group had mild to moderate depressive symptoms compared to 26% in the urban Central Kentucky group. More than triple the number of students (42% vs 13%, p=0.001) in the rural Appalachian group were current or recent smokers, and the median BMI (25.4 vs 23.2, p=0.037) and proportion of students who were class II and class III obese (25% vs 0%, p<0.001) were greater in this group. There were no differences in self-reported fruit and vegetable intake and vigorous physical activity between the groups.
Table 2: Differences in depressive symptoms, health behaviors, and body weight between college students in rural Appalachian Kentucky and urban Central Kentucky
Association between rural Appalachian/urban status and excess body weight
Table 3 shows the binary logistic regression model (Cox and Snell R2=0.214; Nagelkerke R2=0.291; omnibus test of model χ2=26.231, p=0.002) of the association of rural Appalachian/urban residency and excess body weight controlling for sociodemographics, depressive symptoms, and health behaviors. Living in rural Eastern Appalachian Kentucky was independently associated with more than a six-fold increase in odds of having excess body weight (OR=6.36, 95%CI=1.97-20.48, p=0.002). Being male was also associated with higher odds of having excess body weight (OR=3.91, 95%CI=1.51-10.18, p=0.005). Sociodemographic factors, depressive symptoms, and health behaviors were not associated with excess body weight.
Table 3: Factors associated with excess body weight in college students in rural Appalachian Kentucky and urban Central Kentucky
College students who were lifelong residents of rural Appalachian Kentucky were at greater risk for having excess body weight than those living in urban, central Kentucky. Greater risk for excess body weight was independent of sociodemographic factors, depressive symptoms, and health behaviors. This is the first study to highlight the important impact that economically distressed rural environments have on the risk for excess body weight in college students who are already at high risk for weight gain.
This study adds to the growing body of knowledge regarding the influence of broader neighborhood and community-level factors on health outcomes and complements previous studies linking poor community-level socioeconomic conditions with unfavorable health outcomes. Neighborhood poverty has been associated with remaining obese during the transition from adolescence to young adulthood50, and living in a disadvantaged neighborhood has been associated with greater risk of coronary heart disease independent of individual socioeconomic status and cardiovascular risk factors23. Among children aged 10-17 years, 18% of state-level obesity prevalence has been attributed to area poverty after adjusting for individual socioeconomic and behavioral factors51. Policy-level strategies that improve economic conditions of deprived rural areas may be effective in improving health outcomes and reducing rural health disparities.
Similar to the current study, living in distressed Appalachian counties has been independently associated with a greater likelihood of developing diabetes52, and diabetes is diagnosed earlier among residents living in these areas53. Findings from this study also complement those of Schoenberg and colleagues26 who reported that obesity risk is greater among those residing in Appalachian Kentucky, controlling for sociodemographics, days of poor mental health, and smoking. Although the specific mechanisms responsible for these associations have yet to be elucidated, these collective findings demonstrate the impact of poor community-level socioeconomic conditions and health outcomes independent of individual-level factors.
Rural-urban disparities in weight status of children and adolescents have been well documented, with findings indicating greater risk for overweight and obesity among those living in rural areas22,54-56. Data from a recent meta-analysis of five studies indicate that children and adolescents living in rural areas have 26% greater odds of obesity compared to those in urban areas57. Findings from the current study fill an important gap in the literature by providing additional evidence that rural residency is an independent risk factor for excess body weight among college students.
College students are commonly known for engaging in unhealthy weight-related behaviors such as low fruit and vegetable intake, frequent fast food consumption, and sedentary lifestyles27-29,31. Unhealthy behaviors often worsen as younger adults transition further into adulthood58,59, making this a key population to examine factors associated with excess body weight. Although health behaviors were not associated with excess body weight in the current study, other investigative groups using large, nationally representative surveys have noted an increased obesity risk with unhealthy behaviors2,3. Thus, the current study may have been underpowered to detect significant associations between excess body weight and health behaviors.
In addition to demonstrating that living in rural Eastern Appalachian Kentucky was independently associated with excess body weight, class III extreme obesity (BMI>40 kg/m2) was more prevalent in this group compared to those living in urban Central Kentucky. This finding is alarming and indicates greater risk for obesity-related comorbidities that may develop in this population. Given the widespread agreement among rural health stakeholders that heart disease, stroke, and diabetes are high priority considerations60, researchers should collaborate with community partners to design interventions to reduce excess body weight in younger adults as a strategy to reduce chronic disease risk in rural areas.
Given the importance of social determinants of cardiovascular health that have been recently highlighted by the American Heart Association61 and that Appalachian adults are often socioeconomically disadvantaged62, college students were recruited to partially control for the impact of socioeconomic status on excess body weight. However, increasing education attainment is reported to be more strongly associated with declining obesity prevalence in urban compared to rural areas2,63. This may be due to challenges faced by rural residents to access affordable, high-quality healthy foods and opportunities for physical activity17,64-66. Higher education may therefore be overshadowed by financial constraints and limited opportunities to engage in healthy behaviors among rural residents.
Although adults living in rural areas are less likely to consume five or more daily servings of fruits and vegetables compared to those in non-rural areas67, fruit and vegetable intake did not differ between rural and urban samples in the current study. This finding was somewhat surprising given the low household incomes reported by the rural Appalachian sample. Factors other than access to affordable healthy fruits and vegetables may therefore play a larger role in whether college students consume the recommended amounts of fruits and vegetables and subsequently reduce their risk for excess body weight. Boone-Heinonen and colleagues68 reported that supermarket and grocery store availability were generally not associated with meeting fruit and vegetable recommendations among young adults participating in the Coronary Artery Risk Development In Young Adults (CARDIA) study.
Being male was associated with more than an approximately four-fold increased risk for having excess body weight. Although the magnitude of this association was somewhat surprising, this finding is consistent with existing research that has demonstrated greater weight gain and risk of weight gain in college males than females32,69,70. Males in college have described numerous barriers to engaging in healthy behaviors including a perceived abundance of unhealthy foods and ignoring long-term consequences of unhealthy behaviors71, fewer males than females considering the nutritional content of food31,72,73, and males being less worried about their weight32. Therefore, it is possible that differences in unmeasured dietary and activity habits contributed to the greater risk for excess body weight among men in the current study.
Given the relationship between living in a disadvantaged neighborhood and poor health outcomes, depressive symptoms were speculated to be a contributing factor to the rural-urban disparity in excess body weight. Although students in the rural Appalachian group experienced more depressive symptoms, this was not a predictor of excess body weight. Additional research is needed to determine if other psychosocial determinants contribute to the increased risk of excess body weight and other health outcomes among those living in disadvantaged regions.
Several limitations to this study should be recognized. The fruit and vegetable questions used in this study measured the frequency of consumption rather than serving sizes and were comparable with large epidemiological studies focused on eating behaviors74,75. A more comprehensive dietary assessment is needed to provide a better understanding of how dietary practices contribute to body weight among college students living in rural versus urban areas. Inaccuracies in self-reported vigorous physical activity could have also influenced the current study's findings. However, this was deemed unlikely because the IPAQ's assessment of vigorous physical activity has the highest concurrent validity with other forms of activity assessment48. Using objective measures of physical activity would offer additional insight regarding how activity frequency, intensity, and duration influence rural-urban disparities in excess body weight. Although consistent with the demographics of rural Eastern Appalachian Kentucky, this study's predominantly Caucasian sample limits the ability to examine racial differences, which is an important factor in considering risk for obesity by rural-urban status76. Finally, results should be interpreted with caution given the sample size. However, assuming that the probability of excess body weight among younger adults in the general population is 0.61, this study was powered at 86% (alpha=0.05) given the observed odds ratio of 6.36 (G*Power 18.104.22.168)77.Additional research is needed to validate the extent to which college students living in economically distressed rural areas are at greater risk for excess body weight than their urban counterparts.
This study has several strengths. First, only college students who were lifelong residents of their respective rural or urban counties were recruited to minimize residential self-selection bias. Controlling for residential self-selection was important because people may choose to live in environments that support their lifestyle preferences, which could confound the relationship between geographic residence and body weight. Second, recruiting students without diagnosed CVD risk factors reduced the possibility that students were purposely engaging in healthy behaviors to manage medical conditions that would affect body weight. Consequently, this study provides a greater understanding of the obesogenic nature of rural environments.
Excess body weight is a significant public health concern in the USA that disproportionately affects rural residents. Findings from this study suggest that living in economically distressed rural Appalachian Kentucky is associated with excess body weight in college students independent of individual-level socioeconomic factors, depressive symptoms, and health behaviors. Determining other factors contributing to excess body weight among people living in rural areas is needed to better inform interventions that address the obesity epidemic.
1. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. Journal of the American Medical Association 2014; 311(8): 806-814.
2. Befort CA, Nazir N, Perri MG. Prevalence of obesity among adults from rural and urban areas of the United States: findings from NHANES (2005-2008). Journal of Rural Health 2012; 28(4): 392-397.
3. Trivedi T, Liu J, Probst J, Merchant A, Jhones S, Martin AB. Obesity and obesity-related behaviors among rural and urban adults in the USA. Rural and Remote Health (Internet) 2015; 15(4): 3267. Available: www.rrh.org.au (Accessed 12 April 2016).
4. Joshy G, Korda RJ, Attia J, Liu B, Bauman AE, Banks E. Body mass index and incident hospitalisation for cardiovascular disease in 158 546 participants from the 45 and Up Study. International Journal of Obesity 2014; 38(6): 848-856.
5. Wilson PW, Bozeman SR, Burton TM, Hoaglin DC, Ben-Joseph R, Pashos CL. Prediction of first events of coronary heart disease and stroke with consideration of adiposity. Circulation 2008; 118(2): 124-130.
6. Wilson PW, D'Agostino RB, Sullivan L, Parise H, Kannel WB. Overweight and obesity as determinants of cardiovascular risk: the Framingham experience. Archives of Internal Medicine 2002; 162(16): 1867-1872.
7. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health (Internet) 2009; 9: 88. Available: http://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-9-88 (Accessed 12 April 2016).
8. Canoy D, Cairns BJ, Balkwill A, Wright FL, Green J, Reeves G, et al. Body mass index and incident coronary heart disease in women: a population-based prospective study. BMC Medicine (Internet) 2013; 11: 87. Available: http://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-11-87 (Accessed 12 April 2016).
9. Hotchkiss JW, Davies CA, Leyland AH. Adiposity has differing associations with incident coronary heart disease and mortality in the Scottish population: cross-sectional surveys with follow-up. International Journal of Obesity 2013; 37(5): 732-739.
10. De Pergola G, Silvestris F. Obesity as a major risk factor for cancer. Journal of Obesity 2013; 2013: 291546.
11. Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet 2008; 371(9612): 569-578.
12. Silverwood V, Blagojevic-Bucknall M, Jinks C, Jordan JL, Protheroe J, Jordan KP. Current evidence on risk factors for knee osteoarthritis in older adults: a systematic review and meta-analysis. Osteoarthritis and Cartilage 2015; 23(4): 507-515.
13. Singh GK, Siahpush M. Widening rural-urban disparities in life expectancy, U.S., 1969-2009. American Journal of Preventive Medicine 2014; 46(2): e19-29.
14. Singh GK, Siahpush M. Widening rural-urban disparities in all-cause mortality and mortality from major causes of death in the USA, 1969-2009. Journal of Urban Health 2014; 91(2): 272-292.
15. Kulshreshtha A, Goyal A, Dabhadkar K, Veledar E, Vaccarino V. Urban-rural differences in coronary heart disease mortality in the United States: 1999-2009. Public Health Reports 2014; 129(1): 19-29.
16. Bellamy GR, Bolin JN, Gamm LD. Rural Healthy People 2010, 2020, and beyond: the need goes on. Family & Community Health 2011; 34(2): 182-188.
17. Mudd-Martin G, Biddle MJ, Chung ML, Lennie TA, Bailey AL, Casey BR, et al. Rural Appalachian perspectives on heart health: social ecological contexts. American Journal of Health Behavior 2014; 38(1): 134-143.
18. Seguin R, Connor L, Nelson M, LaCroix A, Eldridge G. Understanding barriers and facilitators to healthy eating and active living in rural communities. Journal of Nutrition and Metabolism 2014; 2014: 146502.
19. Frost SS, Goins RT, Hunter RH, Hooker SP, Bryant LL, Kruger J, et al. Effects of the built environment on physical activity of adults living in rural settings. American Journal of Health Promotion 2010; 24(4): 267-283.
20. Larson NI, Story MT, Nelson MC. Neighborhood environments: disparities in access to healthy foods in the U.S. American Journal of Preventive Medicine 2009; 36(1): 74-81.
21. Patterson PD, Moore CG, Probst JC, Shinogle JA. Obesity and physical inactivity in rural America. Journal of Rural Health 2004; 20(2): 151-159.
22. Liu J, Bennett KJ, Harun N, Probst JC. Urban-rural differences in overweight status and physical inactivity among US children aged 10-17 years. Journal of Rural Health 2008; 24(4): 407-415.
23. Diez Roux AV, Merkin SS, Arnett D, Chambless L, Massing M, Nieto FJ, et al. Neighborhood of residence and incidence of coronary heart disease. New England Journal of Medicine 2001; 345(2): 99-106.
24. Appalachian Regional Commission. The Appalachian Region. (Online). Available: http://www.arc.gov/appalachian_region/TheAppalachianRegion.asp (Accessed 12 April 2016).
25. Appalachian Regional Commission. County economic status, fiscal year 2015: Appalachian Kentucky. (Internet). Available: http://www.arc.gov/reports/region_report.asp?FIPS=21999&REPORT_ID=52 (Accessed 12 April 2016).
26. Schoenberg NE, Huang B, Seshadri S, Tucker TC. Trends in cigarette smoking and obesity in Appalachian Kentucky. Southern Medical Journal 2015; 108(3): 170-177.
27. Racette SB, Deusinger SS, Strube MJ, Highstein GR, Deusinger RH. Weight changes, exercise, and dietary patterns during freshman and sophomore years of college. Journal of American College Health 2005; 53(6): 245-251.
28. American College Health Association. The American College Health Association National College Health Assessment (ACHA-NCHA) spring 2008 reference group data report (abridged). Journal of American College Health 2009; 57(5): 477-488.
29. Racette SB, Deusinger SS, Strube MJ, Highstein GR, Deusinger RH. Changes in weight and health behaviors from freshman through senior year of college. Journal of Nutrition Education and Behavior 2008; 40(1): 39-42.
30. Dinger MK. Physical activity and dietary intake among college students. American Journal of Health Studies 1999; 15(3): 139-148.
31. Morse KL, Driskell JA. Observed sex differences in fast-food consumption and nutrition self-assessments and beliefs of college students. Nutrition Research 2009; 29(3): 173-179.
32. Cluskey M, Grobe D. College weight gain and behavior transitions: male and female differences. Journal of the American Dietetic Association 2009; 109(2): 325-329.
33. Wengreen HJ, Moncur C. Change in diet, physical activity, and body weight among young-adults during the transition from high school to college. Nutrition Journal 2009; 8: 32.
34. American College Health Association. American College Health Association-National College Health Assessment (ACHA-NCHA-II) Reference Group data report -spring 2013. (Internet) 2013. Available: http://www.acha-ncha.org/docs/ACHA-NCHA-II_ReferenceGroup_DataReport_Spring2013.pdf (Accessed 12 April 2016).
35. Spencer L. Results of a heart disease risk-factor screening among traditional college students. Journal of American College Health 2002; 50(6): 291-296.
36. Vella-Zarb RA, Elgar FJ. The 'freshman 5': a meta-analysis of weight gain in the freshman year of college. Journal of American College Health 2009; 58(2): 161-166.
37. Boone-Heinonen J, Guilkey DK, Evenson KR, Gordon-Larsen P. Residential self-selection bias in the estimation of built environment effects on physical activity between adolescence and young adulthood. International Journal of Behavioral Nutrition and Physical Activity 2010; 7: 70.
38. Boone-Heinonen J, Gordon-Larsen P, Guilkey DK, Jacobs DR, Jr., Popkin BM. Environment and physical activity dynamics: the role of residential self-selection. Psychology of Sport and Exercise 2011; 12(1): 54-60.
39. Diez Roux AV. Estimating neighborhood health effects: the challenges of causal inference in a complex world. Social Science & Medicine 2004; 58(10): 1953-1960.
40. U.S. Department of Agriculture. Rural-Urban Continuum Codes 2013. (Internet). Available: http://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx (Accessed 25 August 2015).
41. Appalachian Regional Commission. Source and methodology: Distressed Designation and County Economic Status Classification System, FY 2007 - FY 2015 (Internet). Available: http://www.arc.gov/research/SourceandMethodologyCountyEconomicStatusFY2007FY2015.asp (Accessed 12 April 2016).
44. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine 2001; 16(9): 606-613.
45. Huang FY, Chung H, Kroenke K, Delucchi KL, Spitzer RL. Using the Patient Health Questionnaire-9 to measure depression among racially and ethnically diverse primary care patients. Journal of General Internal Medicine 2006; 21(6): 547-552.
46. Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Questionnaires. (Internet). Available: http://www.cdc.gov/brfss/questionnaires (Accessed 16 October 2012).
48. Kim Y, Park I, Kang M. Convergent validity of the international physical activity questionnaire (IPAQ): meta-analysis. Public Health Nutrition 2013; 16(3): 440-452.
49. National Institutes of Health, National Heart Lung and Blood Institute, North American Association for the Study of Obesity. The practical guide: identification, evaluation, and treatment of overweight and obesity in adults. (Online). 2000. Available: http://www.nhlbi.nih.gov/files/docs/guidelines/prctgd_c.pdf (Accessed 12 April 2016).
50. Lee H, Harris KM, Gordon-Larsen P. Life course perspectives on the links between poverty and obesity during the transition to young adulthood. Population Research and Policy Review 2009; 28(4): 505-532.
51. Singh GK, Kogan MD, van Dyck PC. A multilevel analysis of state and regional disparities in childhood and adolescent obesity in the United States. Journal of Community Health 2008; 33(2): 90-102.
52. Barker L, Crespo R, Gerzoff RB, Denham S, Shrewsberry M, Cornelius-Averhart D. Residence in a distressed county in Appalachia as a risk factor for diabetes, Behavioral Risk Factor Surveillance System, 2006-2007. Preventing Chronic Disease 2010; 7(5): A104.
53. Barker L, Gerzoff R, Crespo R, Shrewsberry M. Age at diagnosis of diabetes in Appalachia. Population Health Metrics 2011; 9: 54.
54. Liu JH, Jones SJ, Sun H, Probst JC, Merchant AT, Cavicchia P. Diet, physical activity, and sedentary behaviors as risk factors for childhood obesity: an urban and rural comparison. Childhood Obesity 2012; 8(5): 440-448.
55. Lutfiyya MN, Lipsky MS, Wisdom-Behounek J, Inpanbutr-Martinkus M. Is rural residency a risk factor for overweight and obesity for U.S. children? Obesity 2007; 15(9): 2348-2356.
56. Joens-Matre RR, Welk GJ, Calabro MA, Russell DW, Nicklay E, Hensley LD. Rural-urban differences in physical activity, physical fitness, and overweight prevalence of children. Journal of Rural Health 2008; 24(1): 49-54.
57. Johnson JA, 3rd, Johnson AM. Urban-rural differences in childhood and adolescent obesity in the United States: a systematic review and meta-analysis. Childhood Obesity 2015; 11(3): 233-241.
58. Gordon-Larsen P, The NS, Adair LS. Longitudinal trends in obesity in the United States from adolescence to the third decade of life. Obesity 2010; 18(9): 1801-1804.
59. Gordon-Larsen P, Nelson MC, Popkin BM. Longitudinal physical activity and sedentary behavior trends: adolescence to adulthood. American Journal of Preventive Medicine 2004; 27(4): 277-283.
60. Gamm L, Hutchison L. Rural health priorities in America: where you stand depends on where you sit. Journal of Rural Health 2003; 19(3): 209-213.
61. Havranek EP, Mujahid MS, Barr DA, Blair IV, Cohen MS, Cruz-Flores S, et al. Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. Circulation 2015; 132(9): 873-898.
62. McGarvey EL, Leon-Verdin M, Killos LF, Guterbock T, Cohn WF. Health disparities between Appalachian and non-Appalachian counties in Virginia USA. Journal of Community Health 2011; 36(3): 348-356.
63. Jackson JE, Doescher MP, Jerant AF, Hart LG. A national study of obesity prevalence and trends by type of rural county. Journal of Rural Health 2005; 21(2): 140-148.
65. Kruger TM, Swanson M, Davis RE, Wright S, Dollarhide K, Schoenberg NE. Formative research conducted in rural Appalachia to inform a community physical activity intervention. American Journal of Health Promotion 2012; 26(3): 143-151.
66. Swanson M, Schoenberg NE, Erwin H, Davis RE. Perspectives on physical activity and exercise among Appalachian youth. Journal of Physical Activity & Health 2013; 10(1): 42-47.
67. Lutfiyya MN, Chang LF, Lipsky MS. A cross-sectional study of US rural adults' consumption of fruits and vegetables: do they consume at least five servings daily? BMC Public Health 2012; 12: 280.
68. Boone-Heinonen J, Gordon-Larsen P, Kiefe CI, Shikany JM, Lewis CE, Popkin BM. Fast food restaurants and food stores: longitudinal associations with diet in young to middle-aged adults: the CARDIA study. Archives of Internal Medicine 2011; 171(13): 1162-1170.
69. Deforche B, Van Dyck D, Deliens T, De Bourdeaudhuij I. Changes in weight, physical activity, sedentary behaviour and dietary intake during the transition to higher education: a prospective study. International Journal of Behavioral Nutrition and Physical Activity 2015; 12: 16.
70. Deliens T, Clarys P, Van Hecke L, De Bourdeaudhuij I, Deforche B. Changes in weight and body composition during the first semester at university. A prospective explanatory study. Appetite 2013; 65: 111-116.
71. Abshire DA, Lennie TA, Moser DK, Mudd-Martin GT. Perceptions related to cardiovascular disease risk in Caucasian college males. American Journal of Men's Health 2016; 10(6): NP136-NP144.
72. Davy SR, Benes BA, Driskell JA. Sex differences in dieting trends, eating habits, and nutrition beliefs of a group of midwestern college students. Journal of the American Dietetic Association 2006; 106(10): 1673-1677.
73. Levi A, Chan KK, Pence D. Real men do not read labels: the effects of masculinity and involvement on college students' food decisions. Journal of American College Health 2006; 55(2): 91-98.
74. Grimm KA, Foltz JL, Blanck HM, Scanlon KS. Household income disparities in fruit and vegetable consumption by state and territory: results of the 2009 Behavioral Risk Factor Surveillance System. Journal of the Academy of Nutrition and Dietetics 2012; 112(12): 2014-2021.
75. Fang J, Yang Q, Hong Y, Loustalot F. Status of cardiovascular health among adult Americans in the 50 States and the District of Columbia, 2009. Journal of the American Heart Association 2012; 1(6): e005371.
76. Kenney MK, Wang J, Iannotti R. Residency and racial/ethnic differences in weight status and lifestyle behaviors among US youth. Journal of Rural Health 2014; 30(1): 89-100.
77. Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behavior Research Methods 2009; 41(4): 1149-1160.