PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of fnrJournal home pagePublisher home pageThis JournalSubmit a manuscripte-alert
 
Food Nutr Res. 2011; 55: 10.3402/fnr.v55i0.5819.
Published online Apr 19, 2011. doi:  10.3402/fnr.v55i0.5819
PMCID: PMC3153312
Less-healthy eating behaviors have a greater association with a high level of sugar-sweetened beverage consumption among rural adults than among urban adults
Joseph R. Sharkey,* Cassandra M. Johnson, and Wesley R. Dean
Program for Research in Nutrition and Health Disparities, Department of Social and Behavioral Health, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX, USA
*Joseph R. Sharkey, School of Rural Public Health, MS 1266, College Station, TX 77843-1266 USA. Tel: +(979) 458 4268. Fax: +(979) 458 4264. Email: jrsharkey/at/srph.tamhsc.edu
Received November 19, 2010; Revised March 17, 2011; Accepted March 29, 2011.
Background
Sugar-sweetened beverage (SSB) consumption is associated with the increasing prevalence of overweight and obesity in the United States; however, little is known about how less-healthy eating behaviors influence high levels of SSB consumption among rural adults.
Objective
We assessed the frequency of SSB consumption among rural and urban adults, examined the correlates of frequent SSB consumption, and determined difference in correlates between rural and urban adults in a large region of Texas.
Design
A cross-sectional study using data on 1,878 adult participants (urban=734 and rural=1,144), who were recruited by random digit dialing to participate in the seven-county 2006 Brazos Valley Community Health Assessment. Data included demographic characteristics, eating behaviors (SSB consumption, frequency of fast-food meals, frequency of breakfast meals, and daily fruit and vegetable intake), and household food insecurity.
Results
The prevalence of any consumption of SSB and the prevalence of high consumption of SSB were significantly higher among rural adults compared with urban counterparts. The multivariable logistic regression models indicated that a high level of SSB consumption (≥3 cans or glasses SSB/day) was associated with demographic characteristics (poverty-level income and children in the home), frequent consumption of fast-food meals, infrequent breakfast meals, low fruit and vegetable intake, and household food insecurity especially among rural adults.
Conclusions
This study provides impetus for understanding associations among multiple eating behaviors, especially among economically and geographically disadvantaged adults. New strategies are needed for educating consumers, not only about how to moderate their SSB intake, but also how to simultaneously disrupt the co-occurrence of undesirable eating and promote healthful eating.
Keywords: sugar-sweetened beverages, household food insecurity, fast-food consumption, sugar drinks, rural
In the United States, trends of increasing obesity have been paralleled by increasing consumption of energy-dense and nutrient-poor sugar-sweetened beverages (SSBs) including soft drinks or soda, sport drinks, fruit drinks and punches, low-calorie drinks, and sweetened tea (1, 2). The SSBs are the most commonly consumed caloric beverage and a leading source of added sugars (13). Several studies have demonstrated that SSB consumption is associated with higher intake of energy, added sugars, lower intake of fiber, and displacement of more healthful food and beverages (1, 35). Identified determinants of frequent SSB consumption among adults include low income, limited education, being black and male, younger age, consumption of fast-food meals, and food availability, preferences, and culture (3, 4, 68). While the results are mixed (911), reviews and meta-analyses have found a positive association between SSB and obesity, increased risk for type 2 diabetes, cardiovascular disease, and metabolic syndrome for adults (1, 2, 12). Rural residents have several of the characteristics including widespread socioeconomic disadvantage and worse access to local sources of healthier foods that increase their risk for chronic diseases, food insecurity, poor dietary behaviors, and higher intakes of SSBs (1316). Still there has been limited work on factors associated with SSB consumption among rural populations in the United States and very little on the behavioral context of SSB consumption for rural adults (1719). Moreover, there are apparently no publications describing rural–urban differences for US adults’ SSB consumption (2023). Considering the role of SSB consumption in reducing risk for chronic disease, it is critical to understand the correlates of increased SSB consumption for at-risk populations such as residents living in rural areas (2, 3). The current study seeks to assess the relations between SSB consumption and specific eating-related behaviors among rural adults by (1) assessing the frequency of SSB consumption among rural and urban adults, (2) examining the correlates of frequent SSB consumption, and (3) determining the difference in correlates between rural and urban adults in a large region of Texas.
Sample and study design
We used data from the 2006 Brazos Valley Community Health Assessment (BVHA), which was developed by a collaboration of local and regional academic and community-based organizations in the Brazos Valley of central Texas. Participants were recruited from adult community residents who resided in one of six rural and one urban county by a professional independent survey research firm that identified 9,940 valid telephone numbers through random digit dialing. Of these telephone numbers, 3,501 households were contacted on initial contact and agreed to participate. Further details of the sampling frame have been reported elsewhere (24). More than 2,500 adults (19.4% minority, 71% female, and 61% rural residents) who resided in the seven counties returned the mailed survey; the response rate was 73.8% (25). This study used data from 1,878 adult participants in the BVHA who had complete responses for demographic characteristics, eating behaviors, and household food-related hardship (experience of running out of food, without money to obtain more) (26, 27); 649 participants (25.7%) were excluded due to missing data. There were no statistically significant differences between included and excluded participants with regards to demographic characteristics or rural residence. The Texas A&M University Institutional Review board approved the study protocol and all participants provided informed consent.
Measures
Demographic characteristics
Demographic characteristics included age (18–44 years, 45–64 years, and ≥65 years), race/ethnicity (non-Hispanic white vs. all others), household income (poverty: ≤100% FPL [Federal Poverty Level], low income: 101–199% FPL, and above low income: ≥200% FPL), employment status (employed full-time outside the home for wages vs. not employed full-time outside the home), marital status (married vs. not married), ≥1 child under the age of 18 years living in the household, and body mass index (BMI), which was calculated from self-reported height and weight (kg/m2). The BMI was categorized as normal (BMI <25 kg/m2), overweight (BMI 25–29.9 kg/m2), and obese (BMI ≥30 kg/m2).
Eating behaviors
Eating behaviors were selected based on prior community-based work in North Carolina and included prevalence and consumption of SSBs, frequency of fast food meals, frequency of eating a regular breakfast meal, and daily intake of fruit and vegetables (25, 28, 29). SSB consumption was assessed with the following question: ‘How many cans of regular soda (not diet) or glasses of sweet tea do you drink on an average day?’ Six response categories included 0, 1, 2, 3, 4, 5, or 6; and more than 6. The prevalence of SSB consumption was defined as the proportion of adults who reported any consumption of SSB (≥1 can or glass per day). Based on a distribution of responses, a dichotomized variable for a high level of SSB consumption was defined as ≥3 cans or glasses per day versus <3 cans or glasses. Frequency of fast food meals was determined from the question: ‘How many times a week do you eat fast food meals?’ The same six response categories were provided as above; and a similar approach for a dichotomized variable for frequent fast food meal consumption was defined (≥3 times/week vs. <3 times/week). The following question was used to describe breakfast meals frequency: ‘How many days a week do you eat a regular breakfast meal?’ From the six possible responses, a dichotomized breakfast meal variable was constructed as <3 days/week versus ≥3 days/week. Two questions from a validated, self-reported two-item screener were combined to describe fruit and vegetable intake: (1) How many servings of fruit do you usually eat each day (a serving=½ cup of fruit or ¾ cup of fruit juice)? and (2) How many servings of vegetables do you usually eat each day (a serving=½ cup of cooked or one cup raw vegetables)? (30, 31). A three-category variable was constructed for total daily intake of fruit and vegetables: 0–2 servings, 3–4 servings, and ≥5 servings.
Household food-related hardship
The first quantitative food depletion item in the household hunger dimension of the Radimer-Cornell measure of hunger and food insecurity was used to determine the presence of household food insecurity in the past 30 days (27, 3235). Respondents were asked to choose the frequency (often true, sometimes true, or never true) that the following occurred for their household in the past 30 days: ‘The food that we bought didn't last and we didn't have enough money to buy more.’ Responses of often true and sometimes true were combined to indicate food-related hardship (often true or sometimes true) versus no food-related hardship (never true). This measure describes the household experience of running out of food without money to obtain more (26, 27).
Statistical analyses
Release 11 of Stata Statistical Software was used for all statistical analyses; p<0.05 was considered statistically significant. Descriptive statistics were estimated for demographic characteristics, eating behaviors, and food-related hardship. The difference between rural and urban adults was assessed with contingency tables by using the χ2 statistic. Bivariate correlations between theoretically selected variables (demographic characteristics, eating behaviors, and food-related hardship shown in Table 1) and SSB intake were estimated. Correlations at p<0.10 were retained for inclusion in the logistic regression model that included rural and urban respondents; excluded variables included sex, overweight, ages 45–64 years, and employment status. Using backward elimination of all variables with p>0.05, a combined multivariable logistic regression model (n=1,878) was estimated for high level of SSB consumption (≥3 cans/glasses per day vs. <3 cans/glasses). Using the final model for the combined sample, separate multivariable logistic regression models were estimated for the 734 urban respondents and the 1,144 rural respondents.
Table. 1
Table. 1
Difference in demographic characteristics, eating behaviors, and household food-related hardship between urban and rural adults (n=1,878)a
Sample characteristics for urban and rural respondents are shown in Table 1. Rural respondents were older than urban counterparts; a larger proportion were women, reported a household income 101–199% FPL, and were obese; and a smaller proportion were employed full-time outside the home for wages or had at least one child under the age of 18 years living in the household. Compared with urban respondents, the prevalence and high level of SSB consumption (≥3 cans or glasses of SSB/day) was greater among rural adults. A greater proportion of rural adults ate a regular breakfast meal less than three times a week and consumed fewer servings of fruit and vegetables. On the other hand, a larger proportion of urban adults ate fast food meals at least three times a week. Finally, a larger proportion of rural adults reported household food-related hardship than urban counterparts (23.7% vs. 17.2%). Several differences between urban and rural adults remained significant after correcting for multiple comparisons with a Bonferroni-corrected level of statistical significance.
Several demographic variables were not correlated with SSB consumption; namely sex, overweight status (BMI 25–29.9 kg/m2), age category of participants 45–64 years, and employment status. Although statistically significant, the strength of individual correlations was weak (r ≤ 0.15). Age category of participants 18–44 years (r=0.11, p<0.001), minority status (r=0.10, p<0.001), poverty-level household income (r=0.15, p<0.001), presence of ≥1 child in the household (r=0.14, p<0.001), and obesity (r=0.07, p=0.005) were positively correlated with SSB consumption; older age category (≥65 years) was negatively correlated. Among the variables for eating behaviors, frequency of fast food meals (r=0.09, p<0.001), low fruit and vegetable intake (r=0.15, p<0.001), and consuming <3 breakfast meals/week (r=0.17, p<0.001) were positively correlated with SSB consumption; high fruit and vegetable intake of ≥5 servings/day was negatively correlated with SSB consumption (r=–0.12, p<0.001). Food-related hardship was positively associated with SSB consumption (r=0.21, p<0.001).
Minority status (p=0.64), employment status (p=0.57), age (p=0.19), and obesity (p=0.17) were sequentially removed from the final model for the combined rural and urban sample, which adjusted for demographic characteristics, eating behavior, and household food-related hardship. Independent of demographic characteristics, eating behaviors, and food-related hardship, rural residence was associated with greater odds for reporting a high level consumption of SSBs (OR 1.8; 95% CI 1.3, 2.4; p<0.001) than urban residence. Among all adults having a poverty-level household income (OR 2.2; 95% CI 1.6, 3.1), children in the household (1.8; 95% CI 1.3, 2.3), frequent consumption of fast-food meals (1.6; 95% CI 1.2, 2.2), infrequent breakfast meals (1.7; 95% CI 1.3, 2.3), low fruit and vegetable intake (OR 2.1; 95% CI 1.4, 3.3), and food-related hardship (OR 1.9; 95% CI 1.4, 2.6) increased the odds for a high-level consumption of SSB.
Table 2 shows the results from the multivariable regression model for rural adults. Among rural adults, a higher level of SSB consumption was associated with greater odds for respondents with poverty-level household income, presence of child in the household, frequent consumption of fast-food meals, infrequent consumption of regular breakfast, low fruit and vegetable intake, and food-related hardship. Among urban adults (Table 3), one eating behavior (infrequent consumption of a regular breakfast meal), household food-related hardship, and one demographic characteristic (children in the home) were associated with SSB consumption. Interestingly, frequency of fast-food meals and low fruit and vegetable intake were not associated with a high level of SSB consumption among urban adults.
Table. 2
Table. 2
Odds ratios and 95% CI from multiple variable logistic regression models correlating demographic characteristics, eating behaviors, and household food-related hardship with consumption of sugar-sweetened beverages among 1,144 rural adultsa
Table. 3
Table. 3
Odds ratios and 95% CI from multiple variable logistic regression models correlating demographic characteristics, eating behaviors, and household food-related hardship with consumption of sugar-sweetened beverages among 734 urban adultsa
Although research findings suggest a link between consumption of SSBs and health outcomes (1, 2, 12), there are few studies that have examined the influence of less-healthy eating behaviors and food-related hardship on the consumption of high levels of SSB, especially among rural adults. This is critical considering the dramatic increase in prevalence of overweight and obesity (36, 37), SSB consumption (3, 9, 3840), frequency of fast-food meal consumption (41), and nutrition and health disparities associated with rural residence (25, 4246). However, studies of SSB consumption rarely have considered eating behaviors and adequacy of household food supplies as contributing factors. Findings from this study of 1,878 rural and urban adults extend our understanding of the influence of less-healthy eating behaviors and household food-related hardship on higher levels of SSB consumption. There are two major findings of this study. First, the prevalence and high level of consumption of SSB were significantly greater among rural adults compared with urban counterparts. Second, a high level of SSB consumption was associated with less-healthy eating behaviors, especially among rural adults. To our knowledge, this is apparently the first study that simultaneously evaluated the association of multiple eating behaviors and household food-related hardship among a large sample of rural adults. Several findings require further discussion.
Unlike primarily urban studies that used a single definition of SSB consumption such as once or more a week (17), ≥one 12-ounce serving of sugar-sweetened soda per day (6), ≥1 SSB/day (47), and >1 bottle/day (48), this study considered prevalence (≥1 can or glass of SSB/day) and a high level of SSB consumption (≥3 cans or glasses of SSB/day). More than 52% of rural adults, compared with 43.7% of urban adults, consumed at least one SSB per day. This appears to be higher than a similar size study of rural adults (n=1,817) in Wyoming, Montana, and Idaho that defined SSB consumption as less than once/week versus once or more per week (17) or the large, primarily urban Nurses’ Health Study II that found that 9.5% of the sample consumed ≥1 SSB/day (47). Compared with previous studies of SSB consumption, our finding that rural adults consumed higher levels of SSB than urban adults is apparently new. One possible explanation may be that previous studies did not attempt to examine high levels of SSB consumption; but chose lower levels of consumption, such as at least one SSB per day or week (6, 17, 47, 48). Another explanation may be that rural residents have greater access to convenience and non-traditional food stores and fast-food opportunities where SSB are more available and affordable (43, 45, 4951). Preference and greater household availability for SSB such as regular soft drinks or sugar-sweet tea, which has been identified through household food inventories, may provide another explanation for high levels of SSB consumption (52, 53).
In addition to consumption of SSB, three additional less-healthy eating behaviors that are associated with poor diet quality were examined; namely, infrequent breakfast meals (28, 54, 55), frequent consumption of fast-food meals (56), and fewer portions of fruit and vegetables (57, 58). Rural adults compared less favorably with urban adults in two of these three eating behaviors. A greater proportion of rural adults infrequently consumed a regular breakfast meal and ate less than three daily servings of fruit and vegetables. Lower fruit and vegetable intake among rural adults may be the result of limited access to food stores that market fruit and vegetables – store availability and transportation infrastructure (46, 59, 60). In the United States, there has been an overall decline in breakfast consumption (61). One explanation for less frequent breakfast meal consumption among rural adults may be that rural adults travel a greater distance in the morning to work and do not have the time for a regular breakfast. In both urban and rural areas, there are increased opportunities for fast food through traditional fast-food restaurants and marketing of fast food through convenience and other retail stores, often referred to as ‘channel blurring’ (49, 50, 62). An explanation for greater utilization of fast-food meals by urban adults may be greater accessibility and availability.
Inadequate household food supplies or household food-related hardship are known to influence food choice and dietary intake (46, 51, 63). We identified great nutritional disparity between rural and urban adults, which has been absent from the literature. More than 23% of rural adults compared with 17.2% of urban adults reported that in the past 30 days purchased food did not last and there was no money to buy more, which is supported by secondary analysis of national surveys (64). One explanation for the higher prevalence of both household food-related hardship and SSB consumption for rural adults may be related to the coping strategies food-insecure individuals employ to mitigate the consequences of food-related hardship (6568) such as consuming inexpensive and inflationary-resistant energy-dense foods (69).
Findings from multiple variable regression models confirmed geographic differences and similarities in the association of demographic characteristics, eating behaviors, and food-related hardship with high levels of SSB consumption. Although poverty-level household income increased the odds for SSB consumption among rural adults and not urban adults, in both geographic groups the presence of a child in the household was associated with a high level of SSB consumption. All three eating behaviors – frequent fast-food meals, infrequent breakfast, and low intakes of fruit and vegetables – were associated with SSB consumption among rural adults, but only infrequent consumption was significant among urban residents. Food-related hardship was associated with SSB consumption among both rural and urban adults; the effect size was greater among the urban sample. Thus, multiple less-healthy eating behaviors have a greater association with SSB consumption among rural adults than among urban adults. Interestingly, two less-healthy eating behaviors were not independently associated with SSB consumption in our urban subsample.
A prior rural study found an increased likelihood of overweight or obesity associated with greater frequency of SSB and fast food (17). Thus, it is critically important to understand individual and household contextual influences on high levels of SSB consumption. Our findings revealed linkages among multiple less-healthy eating behaviors, which enhance results from a similar study of rural adults (25). Adults, especially rural adults who frequently ate fast-food meals, infrequently consumed a breakfast meal, or had fewer daily servings of fruit and vegetables were also more likely to consume high levels of SSB. Just as healthier food patterns are associated with healthier beverage patterns (70), the present study shows that consumption of SSB appears to be closely linked to less-healthy eating patterns (71, 72). As such, SSB consumption may serve as a marker of other less-healthy eating behaviors and overall poor nutrition.
There are several limitations to this study that warrant mention. First, the self-reported measure of SSB consumption may understate actual frequency and amount of SSB consumed on a usual day. Future work will include specific prompts for calorically sweetened beverages to include carbonated and non-carbonated soft drinks, fruit punch, fruit drinks, lemonade, sweetened powder drinks, bottled coffees, and coffees or teas with added sugar (73). Second, data did not provide information on seasonal variation. Third, data were not available on the type and amount of fast-food items consumed or the source of fast-food meals or SSB. Finally, measures on sedentary behaviors (e.g. television viewing, computer use, video gaming) should be included (74).
Despite these limitations, this study advances our knowledge about less-healthy eating behaviors and household food-related hardship. Results from this study provide impetus for understanding interactions among multiple eating behaviors especially among economically and geographically disadvantaged adults. Considering that Americans are consuming more total calories per day, with much coming from SSB and fast food (75), new strategies are needed for educating consumers not only about how to moderate their SSB intake, but also how to simultaneously disrupt the co-occurrence of undesirable eating behaviors (e.g. fast-food consumption and skipping breakfast) and promote healthful behaviors (e.g. eating a regular breakfast and increasing fruit and vegetable intake). Challenges include the perception and observation that SSB are priced and promoted preferentially with meal deals at fast-food outlets and other venues that market fast-food items (49, 76), and that energy-dense foods are not only least expensive but also most resistant to inflation (69). Given the economic disincentive for consumers to make healthier selections at fast-food restaurants and other venues (49, 76, 77) and the reality of low-cost accessible energy-dense foods, strategies must consider convenience and cost (69) especially for low-income and/or rural families (51).
Statement of authors’ contributions to manuscript
J.R.S. designed the research; J.R.S. analyzed the data; J.R.S., C.M.J., and W.R.D. wrote the paper; J.R.S. had primary responsibility for final content. All authors read and approved the final manuscript.
Conflict of interest and funding
Supported in part by the National Institutes of Health (NIH)/National Center on Minority Health and Health Disparities (#5P20MD002295) and by Cooperative Agreement #1U48DP001924 from the Centers for Disease Control and Prevention, Prevention Research Centers Program through Core Research Project and Special Interest Project Nutrition and Obesity Policy Research and Evaluation Network. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH and CDC.
1. Hu FB, Malik VS. Sugar-sweetened beverages and risk of obesity and type 2 diabetes: epidemiologic evidence. Physiol Behav. 2010;100:47–54. [PMC free article] [PubMed]
2. Malik VS, Popkin BM, Bray GA, Despres J-P, Hu FB. Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk. Circulation. 2010;121:1356–64. [PMC free article] [PubMed]
3. Bleich SN, Wang YC, Wang Y, Gortmaker SL. Increasing consumption of sugar-sweetened beverages among US adults: 1988–1994 to 1999–2004. Am J Clin Nutr. 2009;89:372–81. [PubMed]
4. Duffey KJ, Gordon-Larsen P, Steffen LM, David R, Jacobs J, Popkin BM. Drinking caloric beverages increases the risk of adverse cardiometabolic outcomes in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Am J Clin Nutr. 2010;92:954–59. [PubMed]
5. Bermudez OI, Gao X. Greater consumption of sweetened beverages and added sugars is associated with obesity among U.S. young adults. Am J Clin Nutr. 2011;89:372–81.
6. Rehm CD, Matte TD, Wye GV, Young C, Frieden TR. Demographic and behavioral factors associated with daily sugar-sweetened soda consumption in New York City adults. J Urban Health. 2008;85:375–85. [PMC free article] [PubMed]
7. Ayala GX, Rogers M, Arredondo EM, Campbell NR, Baquero B, Duerksen SC, et al. Away-from-home food intake and risk for obesity: examining the influence of context. Obesity. 2008;16:1002–8. [PubMed]
8. Kuczmarski MF, Mason MA, Schwenk EA, Evans MK, Zonderman AB. Beverage consumption patterns of a low-income population. Top Clin Nutr. 2010;25:191–201. [PMC free article] [PubMed]
9. Vartanian LR, Schwartz MB, Brownell KD. Effects of soft drink consumption on nutrition and health: a systematic review and meta-analysis. Am J Public Health. 2007;97:667–75. [PubMed]
10. Gibson S. Sugar-sweetened soft drinks and obesity: a systematic review of the evidence from observational studies and interventions. Nutr Res Rev. 2008;21:134–47. [PubMed]
11. Mattes RD, Shikany JM, Kaiser KA, Allison DB. Nutritively sweetened beverage consumption and body weight: a systematic review and meta-analysis of randomized experiments; Obesity Rev; 2010. [PMC free article] [PubMed] [Cross Ref]
12. Malik V, Popkin B, Bray G, Després JP, Willett WC, Hu FB. Sugar sweetened beverages and risk of metabolic syndrome and type 2 diabetes: a meta-analysis. Diabetes Care. 2010;33:2477–83. [PMC free article] [PubMed]
13. Sharkey JR. Measuring potential access to food stores and food service places in rural areas in the United States. Am J Prev Med. 2009;36:S151–5. [PubMed]
14. Nord M, Coleman-Jensen A, Andrews M, Carlson S. Washington, DC: U.S. Department of Agriculture, Economic Research Service; 2010. Household food security in the United States, 2009. ERR-108.
15. Blanchard TC, Matthews TL. Retail concentration, food deserts, and food-disadvantaged communities in rural America. In: Hinrichs CC, Lyson TA, editors. Remaking the North America food system. Lincoln, NE: University of Nebraska Press; 2007. pp. 201–15.
16. Ricketts TC, editor. Rural health in the United States. Oxford: Oxford University Press; 1999.
17. Liebman M, Pelican S, Moore SA, Holmes B, Wardlaw MK, Melcher LM, et al. Dietary intake, eating behavior, and physical activity-related determiants of high body mass index in rural communities in Wyoming, Montana, and Idaho. Int J Obesity. 2003;27:684–92. [PubMed]
18. Stroehla BC, Malcoe LH, Velie EM. Dietary sources of nutrients among rural Native American and white children. J Am Diet Assoc. 2005;105:1908–16. [PubMed]
19. Liebman M, Pelican S, Moore SA, Holmes B, Wardlaw MK, Melcher LM, et al. Dietary intake-, eating behavior-, and physical activity-related determinants of high body mass index in the 2003 Wellness IN the Rockies Cross-Sectional Study. Nutr Res. 2006;26:111–17.
20. Adair LS, Popkin BM. Are child eating patterns being transformed globally? Obesity. 2005;13:1281–99. [PubMed]
21. Savige G, Ball K, Worsley A, Crawford D. Food intake patterns among Australian adolescents. Asia Pacific J Clin Nutr. 2007;16:738–47. [PubMed]
22. Torun B, Stein AD, Schroeder D, Grajeda R, Conlisk A, Rodriguez M, et al. Rural-to-urban migration and cardiovascular disease risk factors in young Guatemalan adults. Int J Epidemiol. 2002;31:218–26. [PubMed]
23. Jimenez-Aguilar A, Flores M, Shamah-Levy T. Sugar-sweetened beverages consumption and BMI in Mexican adolescents: Mexican National Health and Nutrition Survey. Salud Publica de Mexico. 2009;51:604–12. [PubMed]
24. Prochaska J, Sharkey JR, Ory MG, Burdine JN. Assessing healthful eating among community dwelling rural older adults using self-reported fruit and vegetable consumption via a community-wide mail-out health status assessment. J Nutr Elder. 2006;25:101–12. [PubMed]
25. Johnson CM, Sharkey JR, Dean WR. Eating behaviors and social capital are associated with fruit and vegetable intake among rural adults. J Hunger Environ Nutr. 2010;5:302–15. [PMC free article] [PubMed]
26. Bickel G, Nord M, Price C, Hamilton W, Cook J. Guide to measuring household food security, 2000. Washington, DC: USDA; http://www.fns.usda.gov/fsec/files/fsguide.pdf [cited 6 April 2011]
27. Campbell CC. Food insecurity: a nutritional outcome or a predictor variable. J Nutr. 1991;121:408–15. [PubMed]
28. Sharkey JR, Branch LG, Zohoori N, Giuliani C, Busby-Whitehead J, Haines PS. Inadequate nutrient intake among homebound older persons in the community and its correlation with individual characteristics and health-related factors. Am J Clin Nutr. 2002;76:1435–45. [PubMed]
29. Partners NCP. Healthy eating: starting the conversation. http://www.ncpreventionpartners.org/dnn/LinkClick.aspx?fileticket=pFBoCKAlWvM=&tabid=82 [cited 23 May 2010]
30. Resnicow K, Odom E, Wang T, Dudley WN, Mitchell D, Vaughan R, et al. Validation of three food frequency questionnaires and 24-hour recalls with serum carotenoid levels in a sample of African American adults. Am J Epidemiol. 2000;152:1072–80. [PubMed]
31. Campbell M, Carr C, Devellis B, Switzer B, Biddle A, Amamoo MA, et al. A randomized trial of tailoring and motivational interviewing to promote fruit and vegetable consumption for cancer prevention and control. Ann Behav Med. 2009;38:71–85. [PMC free article] [PubMed]
32. Kendall A, Olson CM, Frongillo EA. Validation of the Radimer/Cornell measures of hunger and food insecurity. J Nutr. 1995;125:2793–2801. [PubMed]
33. Radimer K, Olson C, Campbell C. Development of indicators to assess hunger. J Nutr. 1990;120:1544–8. [PubMed]
34. Seefeldt KS, Castelli T. Low-income women's experiences with food programs, food spending, and food-related hardships: evidence from qualitative data. Washington, DC: Economic Research Service: Food and Nutrition Assistance Program; 2009.
35. Dean WR, Sharkey JR. Food insecurity, social capital and perceived personal disparity in a predominately rural region of Texas: an inidividual-level analysis; Soc Sci Med; 2011. [e-pub March 30, 2011] [PMC free article] [PubMed]
36. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 2002;288:1723–7. [PubMed]
37. Ogden C, Caroll M, Curtin L, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999–2004. JAMA. 2006;295:1549–55. [PubMed]
38. Briefel RR, Johnson CL. Secular trends in dietary intake in the United States. Ann Rev Nutr. 2004;24:401–31. [PubMed]
39. Nielsen SJ, Siega-Riz AM, Popkin BM. Trends in energy intake in U.S. between 1977 and 1996: similar shifts seen across age groups. Obesity Res. 2002;10:370–8. [PubMed]
40. Nielsen SJ, Popkin BM. Changes in beverage intake between 1977 and 2001. Am J Prev Med. 2004;27:205–10. [PubMed]
41. Guthrie JF, Lin B-H, Frazao E. Role of food prepared away from home in the American diet, 1977–78 versus 1994–96: changes and consequences. J Nutr Educ Behav. 2002;34:140–50. [PubMed]
42. Bennett KJ, Olatosi B, Probst JC. Health disparities: a rural-urban chartbook. Columbia, SC: South Carolina Rural Health Research Center; 2008.
43. Sharkey J, Horel S. Neighborhood socioeconomic deprivation and minority composition are associated with better potential spatial access to the food environment in a large rural area. J Nutr. 2008;138:620–7. [PubMed]
44. Sharkey JR, Horel S, Dean WR. Neighborhood deprivation, vehicle ownership, and potential spatial access to a variety of fruits and vegetables in a large rural area in Texas. Int J Health Geograph. 2010;9:26. [PMC free article] [PubMed]
45. Sharkey JR, Horel S, Han D, Huber JC. Association between neighborhood need and spatial access to food stores and fast food restaurants in neighborhoods of Colonias. Int J Health Geogr. 2009;8:9. [PMC free article] [PubMed]
46. Dean WR, Sharkey JR. Rural and urban differences in the associations between characteristics of the community food environment and fruit and vegetable intake; J Nutr Edu Behav; 2011. in press. [PMC free article] [PubMed]
47. Schulze MB, Manson JE, Ludwig DS, Colditz GA, Stampfer MJ, Willett WC, et al. Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and middle-aged women. JAMA. 2004;292:927–34. [PubMed]
48. Francis DK, Van den Broeck J, Younger N, McFarlane S, Rudder K, Gordon-Strachan G, et al. Fast-food and sweetened beverage consumption: association with overweight and high waist circumference in adolescents. Public Health Nutr. 2009;12:1106–14. [PubMed]
49. Creel JS, Sharkey JR, McIntosh A, Anding J, Huber JC. Availability of healthier options in traditional and nontraditional rural fast-food outlets. BMC Public Health. 2008;8:395. [PMC free article] [PubMed]
50. Sharkey JR, Johnson CM, Dean WR, Horel SA. Focusing on fast food restaurants alone underestimates exposure to fast food in a large rural area. Nutr J. 2011;10:10. [PMC free article] [PubMed]
51. Drewnowski A. Obesity and the food environment. Am J Prev Med. 2004;27:154–62. [PubMed]
52. Sisk C, Sharkey JR, McIntosh A, Anding J. Using multiple household food inventories to measure food availability in the home over 30 days: a pilot study. Nutr J. 2010;9:19. [PMC free article] [PubMed]
53. Sharkey JR, Dean WR, St John JA, Huber JC., Jr Using direct observations on multiple occasions to measure household food availability among low-income Mexicano residents in Texas colonias. BMC Public Health. 2010;10:445. [PMC free article] [PubMed]
54. Nicklas TA, Myers L, Reger C, Beech B, Berenson GS. Impact of breakfast consumption on nutritional adequacy of the diets of young adults in Bogalusa, Lousiana: ethnic and gender contrasts. J Am Diet Assoc. 1998;98:1432–8. [PubMed]
55. Williams P. Breakfast and the diets of Australian adults: an analysis of data from the 1995 National Nutrition Survey. Int J Food Sci Nutr. 2005;56:65–79. [PubMed]
56. Todd JE, Mancino L, Lin B-H. Washington, DC: U.S. Department of Agriculture, Economic Research Service; 2010. The impact of food away from home on adult diet quality. ERR-90. [PMC free article] [PubMed]
57. VanDuyn MA, Pivonka E. Overview of the health benefits of fruit and vegetable consumption for the dietetics professional: selected literature. J Am Diet Assoc. 2000;100:1511–21. [PubMed]
58. Serdula MK, Byers T, Mokdad AH, Simoes E, Mendlein JM, Coates RJ. The association between fruit and vegetable intake and chronic disease risk factors. Epidemiology. 1996;7:161–5. [PubMed]
59. Sharkey JR, Johnson CM, Dean WR. Food access and perceptions of the community and household food environment as correlates of fruit and vegetable intake among rural seniors. BMC Geriatr. 2010;10:32. [PMC free article] [PubMed]
60. Ver Ploeg M, Breneman V, Farrigan T, Hamrick K, Hopkins D, Kaufman P, et al. Economic Research Service. Washington, DC: USDA; 2009. Access to affordable and nutritious food: measuring and understanding food deserts and their consequences: report to congress. http://www.ers.usda.gov/Publications/ap/ap036/
61. Haines PS, Gulkey DK, Popkin BM. Trends in breakfast consumption of US adults between 1965 and 1991. J Am Diet Assoc. 1996;96:464–70. [PubMed]
62. Stewart H, Blisard N, Bhuyan S, Nayga RM., Jr Agricultural Economic Report No. 829. Economic Research Service. Washington, DC: USDA; 2004. The demand for food away from home: full-service or fast food?
63. Kendall A, Olson CM, Frongillo EA. Relationship of hunger and food insecurity to food availability and consumption. J Am Diet Assoc. 1996;96:1019–24. [PubMed]
64. Nord M, Andrews M, Carlson S. Economic Research Service, No. 83. Washington, DC: USDA; 2009. Household food security in the United States, 2008.
65. Bove CF, Olson CM. Obesity in low-income rural women: qualitative insights about physical activity and eating patterns. Women Health. 2006;44:57–78. [PubMed]
66. Mammen S, Bauer JW, Richards L. Understanding persistent food insecurity: a paradox of place and circumstance. Soc Indic Res. 2009;92:151–68.
67. Olson CM. Food insecurity in women: a recipe for unhealthy trade-offs. Top Clin Nutr. 2005;20:321–28.
68. Olson CM, Anderson K, Kiss E, Lawrence FC, Seiling SB. Factors protecting against and contributing to food insecurity among rural families. Fam Econ Nutr Rev. 2004;16:12–20.
69. Monsivais P, Drewnowski A. The rising cost of low-energy-density foods. J Am Diet Assoc. 2007;107:2071–76. [PubMed]
70. Duffey KJ, Popkin BM. Adults with healthier dietary patterns have healthier beverage patterns. J Nutr. 2006;136:2901–7. [PubMed]
71. Lin B, Frazao E, Guthrie J. Contribution of away-from-home foods to American diet quality. Fam Econ Nutr Rev. 1999;12:85–9.
72. Lin B-H, Guthrie J, Frazão E. Agriculture Information Bulletin No. 749. Washington, DC: U.S. Department of Agriculture; 1999. Away-from-home foods increasingly important to quality of American diet.
73. Popkin BM, Armstrong LE, Bray GM, Caballero B, Frei B, Willett WC. A new proposed guidance system for beverage consumption in the United States. Am J Clin Nutr. 2006;83:529–42. [PubMed]
74. Andaya AA, Arredondo EM, Alcaraz JE, Lindsay SP, Elder JP. The association between family meals, tv viewing during meals, and fruit, vegetables, soda, and chips intake among Latino children; J Nutr Educ Behav; 2010. [E-pub October 22, 2010] [PMC free article] [PubMed]
75. Paeratakul S, Ferdinand DP, Champagne CM, Ryan DH, Bray GA. Fast-food consumption among US adults and children: dietary and nutrient intake profile. J Am Diet Assoc. 2003;103:1332–8. [PubMed]
76. Hattersley L, Irwin M, King L, Allman-Farinelli M. Determinants and patterns of soft drink consumption in young adults: a qualitative study. Public Health Nutr. 2009;12:1816–22. [PubMed]
77. Dumanovsky T, Nonas CA, Huang CY, Silver LD, Bassett MT. What people buy from fast-food restaurants: caloric content and menu selection, New York City 2007. Obesity. 2007;17:1369–74. [PubMed]
Articles from Food & Nutrition Research are provided here courtesy of
Co-Action Publishing