Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Am J Public Health. Author manuscript; available in PMC 2010 October 1.
Published in final edited form as:
PMCID: PMC2741505

Differential Trends in Weight-Related Health Behaviors Among American Young Adults by Gender, Race/Ethnicity, and Socioeconomic Status: 1984–2006



We investigated temporal patterns from 1984 to 2006 in 6 weight-related health behaviors by using longitudinal data for multiple cohorts of young adults (aged 19–26 years) from the nationally representative Monitoring the Future Study.


We used growth curve models to examine historical trends in 6 health behaviors: frequency of eating breakfast, eating green vegetables, eating fruit, exercising, watching television, and sleeping 7 hours each night. Variations across gender, race/ethnicity, and socioeconomic status were investigated.


Frequency of exercising was consistently lower among young adult women than young adult men over this 23-year period. Compared with White women, Hispanic women, and women from other race/ethnic groups, Black women showed declines in the frequency of exercise since 1984. In general, young adult women showed a marked increase in the frequency of eating breakfast over this period, although Black women did not show any net gains.


Social disparities in body weight may increase because Black women, Hispanic women, and men with lower socioeconomic status show declining trends in positive weight-related health behaviors compared with White young adults with higher socioeconomic status.

As the prevalence of obesity and overweight rises in the United States,15 researchers continue to investigate a range of mechanisms by which people attain excessive body weight.610 Agreement is growing that the source of the obesity epidemic lies in an environment that produces an energy gap,1115 where energy intake exceeds energy expenditure even by as little as 100 excess calories per day.12,13 Yet, it is unclear whether this 100-calorie excess is a function of increased intake or decreased output (or some combination of both) in American activity and consumption behaviors over time.

Limited data exist on trends in energy intake and energy expenditure among Americans over the past 3 decades, but the data that are available are nonetheless consistent with the rise in obesity observed over the same period. Between 1977 and 1996, Americans increased their total energy consumption by about 200 kcal/day.16 This was largely a result of increased consumption of snacks and soft drinks, particularly among young adults,16,17 while vegetable and fruit consumption remained low.1821 These consumption behaviors have all been linked to excess weight gain.2225 The increased availability of inexpensive, energy-dense food and beverages2630 coupled with a lack of access to fresh fruits and vegetables31,32 are some of the environmental factors that may contribute to these trends. The US population has also adopted an increasingly sedentary lifestyle3336 in an environment that is associated with a reduction in energy expenditure, including car-dependent neighborhoods that discourage walking and biking3741 and limited physical activity in schools.28,42 On average, American youth spend over 30 hours per week watching television,43 which is positively associated with being overweight, either through sedentary activity or through exposure to the marketing of poor-quality foods.4346 Modern lifestyles are increasingly characterized by skipping breakfast and sleeping less,36,4749 which have also been linked to energy imbalance.5056

These reported trends in health behaviors, however, are based on data from repeated cross-sectional surveys18,21,57 that were often conducted up to 5 years or more apart,16,21,57 the results of which are typically reported in aggregate across a time span of 4 to 7 years.20,57 Moreover, published articles frequently focus on trends in only 1 health behavior (e.g., fruit and vegetable consumption18,20) and not the relative practice of energy consumption and expenditure behaviors among individuals over time. As a result, more detailed trends in health behaviors, particularly as they illustrate subtle changes in the balance of energy intake and output occurring annually among American young adults over the last quarter century, are poorly understood.

Also, differences in these behaviors and their trends by gender, socioeconomic status (SES), and racial/ethnic background are not well described at a population level, even though well-documented health disparities in obesity by social position exist.4,5861 Using data from the National Health and Nutrition Examination Survey (1988–2002), one study found that non-Hispanic Blacks, persons in poverty, and those with less than a high school education were less likely to meet US Department of Agriculture fruit and vegetable guidelines than were non-Hispanic Whites and socioeconomically advantaged individuals.57 However, these results were based on 2 cross-sectional data sets collected 5 years apart. Delva et al.10 used repeat cross-sectional data collected annually from secondary school students between 1986 and 2003 to report declining trends in the proportion of adolescents who ate breakfast or exercised regularly, with a lower prevalence among women, racial/ethnic minorities, and those with low SES. Trends in the frequency of these behaviors beyond the secondary school setting, however, remain largely undocumented.

The purpose of our study was, first, to investigate long-term patterns in weight-related health behaviors among young adults (aged 19–26 years) over the past 23 years (1984–2006) and, second, to assess how these patterns varied by social position (race/ethnicity, gender, and SES). Analyses were based on longitudinal data for multiple cohorts of individuals with frequent repeat measures to better track historical changes in weight-related health behaviors over time. By focusing on young adults, we aimed to better understand how weight-related health behaviors have changed in this early period of the adult life course, when many adult health behavior patterns show their formative roots. The transition to adulthood (sometimes referred to as emerging or early adulthood) is a period when individuals are on their own typically for the first time, when life plans are put into action, and when distinctive life paths become more manifest.62 We hypothesized that the frequency of healthy behaviors would generally decline among young adults over this period, and that the rate of decline would be greater among those in disadvantaged social positions (women, Blacks, Hispanics, and those of lower SES).


Data were drawn from the Monitoring the Future project, a nationwide school-based survey of drug, alcohol, and tobacco use that has been conducted annually in the United States since 1976.62 The survey also monitors lifestyles and behaviors (including a limited number of health behaviors). The study design (described in more detail elsewhere6365) involves a nationally representative sample of high school seniors surveyed in the spring of each year (approximately 15000 per year). A 16% representative subsample (approximately 2400 students per year) is randomly selected from each cohort for 6 biennial follow-ups (to the age of 30 years) carried out with self-completed mailed questionnaires. Drug users (regular users of marijuana or any use of illicit drugs in the previous 30 days) were oversampled for the panel component by a ratio of 3 to 1 and were then reweighted in the analyses as discussed in more detail below.

Study Population

We focused on historical trends in early adult health behaviors in the years immediately following high school, when respondents were aged 19 to 26 years (the first 4 follow-ups in the Monitoring the Future panel). To keep the age range consistent each year, we based our analyses on only the 23-year period (1984–2006) for which data were available for respondents aged 19 to 26 years (inclusive). To ease respondent burden in the Monitoring the Future panel study, multiple forms are used, with some questions appearing on only some of the forms. A random one sixth of respondents received a questionnaire that included items about eating habits, exercise, and sleeping habits; a random one sixth completed a questionnaire that included an item pertaining to sedentary behavior. Thus, the final sample available for analysis consisted of 17314 young adults (9631 women and 7683 men) who were aged 19 to 26 years between 1984 and 2006; each young adult provided up to 4 questionnaires at 2-year intervals.


We examined historical trends in 6 health behaviors. Three questions were asked about eating habits: “How often do you eat break-fast?”; “How often do you eat at least some green vegetables?”; and “How often do you eat at least some fruit?” with a 6-level response scale (never, seldom, sometimes, most days, nearly every day, and every day) coded 1 to 6, respectively.

Respondents were asked 1 question about how often they exercised vigorously (jogging, swimming, calisthenics, or any other active sports), and responses were again scored on a 6-level response scale (never to every day). Respondents were asked 1 question about how often they get at least 7 hours of sleep (6-level response scale ranging from never to every day).

Respondents were asked 1 question about television viewing: “How much TV do you estimate you watch on an average weekday?” Responses were measured in hours, ranging from none (0 hours) to 5 or more hours per weekday.

Three variables captured key indicators of social position that have been found to be related to obesity and overweight: (1) gender, (2) race/ethnicity, and (3) SES. Gender was captured with a dummy variable coded 1 for women and 0 for men. Race/ethnicity was represented by 4 dummy variables in the analyses: White, Black, Hispanic, and other racial or ethnic group (including Asians and Native Americans). Because respondents were in various stages of educational attainment, we used an indicator of SES constructed from the parents’ education (maximum of the respondent’s parents’ education), which was dichotomized as less than a high school education or a high school degree and higher.

Statistical Analyses

Growth curve models66 were used to examine patterns of health behaviors in young adults over time. Growth curve models belong to a general class of mixed models that take into consideration the clustering of observations within persons and also can handle unbalanced designs (inconsistent number of observations per person).67 Although the frequency of each health behavior was assessed on a 6-level ordinal scale, a linear mixed model was used (assuming an underlying normal distribution based on an examination of descriptive histograms and the shape of the residuals). A 2-level linear model was analyzed, with multiple observations nested within persons over time. To facilitate parameter interpretation, the intercept was centered at the earliest year (1984). The structure of this model can be expressed by equations at 2 levels. At level1(within-personmodel), exercise frequency, for example, at time t is nested within individuals (i):


where π0i is the expected exercise frequency score for person i in 1984 (because year is centered), πli captures the rate of change in exercise frequency over the 23-year study period, and rti is the within-person residual.

The level-1 parameters were then modeled as a function of individual characteristics (at level 2). The level 2 (between-person) submodel assumed that health behavior trajectories vary across individuals, and we explicitly modeled these differences as follows:



Here, for example, the intercept and time slope from equation 1 were modeled as a function of SES, where β01 represents the difference in the initial exercise frequency score in 1984 for someone from low SES compared with a person from higher SES, and β11 captures the difference in the rate of change in exercise behavior over time for low SES compared with high SES persons. The residual errors (e0i, eli) capture random variance in the intercept and slope values across persons, which we attempted to capture through the incorporation of additional person-level variables (e.g., race or ethnicity).

The MIXED procedure in SAS version 9.2 (SAS Institute, Inc., Cary, NC) was used to estimate all models by using full maximum likelihood. Separate models were run for each of the 6 health behaviors, and each model was run separately for men and women to capture gender differences. By examining young adults over a narrow age range (aged 19–26 years), the analyses focused on historical trends in health behaviors, not age differences in these behaviors. Specifically testing for age effects by use of dummy variables for younger (aged 19–22 years) and older (aged 23–26 years) subgroups yielded no statistically significant differences in intercept or slope coefficients.

Retention rates in the panel respondents were highest in the first follow-up after high school (averaging 70% of the original cohort), and fell to an average of 64% in the biennial follow-ups through to age 26. To account for nonrandom attrition in the analyses, the probability of participation at each wave was modeled by using logistic regression analysis (with backward elimination) according to a broad array of baseline sociodemographic and academic characteristics. Consistent with previous work,68 6 variables consistently predicted participation at each follow-up: being female, being White, coming from a 2-parent family, enrollment in an academic or college prep program in high school, and higher average grades in high school were positively associated with participation, and living in the southern region of the US at baseline was negatively associated with participation at each wave. By deriving the predicted probability of participation from these 6 variables, each observation at each wave was weighted to account for attrition based on the inverse probability of participation. Observations were also weighted to adjust for the oversampling of drug users in the panel design.


Approximately 3 in 4 persons in the analytic sample were White, and about 1 in 10 had low parental educational attainment (Table 1). (The base growth curve models (modeling the effects of time only) for each of the 6 health behaviors since 1984 are presented in a table available as a supplement to the online version of this article at Most behaviors followed a linear rate of change (although quadratic effects were found for the frequency of eating breakfast). Over this period, young adult men exercised more frequently than did young adult women, but there was a significant decline in the frequency of exercise among men since 1984. The frequency of eating fruit and vegetables remained relatively stable among young adult women but declined significantly among young men since 1984. Among both young adult men and women, the frequency of getting at least 7 hours of sleep each night declined steadily over this 23-year period. Despite the focus on television as an important determinant for obesity, television viewing remained stable in the young adult population since 1984, at least in terms of weekday viewing. The frequency of eating breakfast increased in recent years among young adult women but decreased among young adult men.

Descriptive Statistics for Young Adults Aged 19–26 Years, by Gender: Monitoring the Future Study, 1984–2006

Variations in the practice of healthy behaviors by social position were modeled by sequentially including the race/ethnicity and SES variables to the base growth curve models. The results indicated racial/ethnic differences in the frequency of eating breakfast among young adult women (Table 2, first column of model A). The predicted trends since 1984 are shown in Figure 1a. Although White women showed a steady increase in the frequency of eating breakfast over the past 23 years (increasing from more than “sometimes” in 1984 [score of 3] to eating breakfast “most days” in 2006 [score of 4]; Figure 1a), the trajectory for non-Hispanic Black women declined through 1996 and only began to increase around the year 2000. Racial/ethnic differences in break-fast trajectories among women were not explained by SES (effects did not change after we adjusted for SES; second column for model A in Table 2), and significant negative effects of low SES on the trajectory slope persisted above and beyond the effects of race/ethnicity. There were no social disparities in the frequency of eating breakfast among young adult men over this period (Table 3).

Predicted frequency among young adults by race/ethnicity of (a) women eating breakfast, (b) women exercising, (c) women sleeping 7 hours a night, (d) men sleeping 7 hours a night, (e) women eating fruit, and (f) men eating green vegetables: Monitoring ...
Growth Curve Models for Health Behaviors Among Young Adult Women Aged 19–26 Years, by Race/Ethnicity and Socioeconomic Status: Monitoring the Future Study, 1984–2006
Growth Curve Models for Health Behaviors in Young Adult Men Aged 19–26 Years By Race/Ethnicity and Socioeconomic Status, Monitoring the Future Study, 1984–2006

Although fruit and vegetable consumption changed little among young adults over this 23-year period, consumption of both was consistently lower among African American and Hispanic men and women at any given year (models B and C in Table 2 and Table 3; Figure 1e and 1f). Moreover, young adult Hispanic women showed a trend of declining fruit consumption since 1984 (model C in Table 2; Figure 1e). This was only partly a function of lower SES (the effects were only slightly attenuated after we adjusted for SES).

Although the frequency of exercise remained relatively stable among young adult women on average (model D in Table 2), adjustment for race/ethnicity indicated that this stability was observed only among non-Black women. Among Black women, the frequency of exercising declined steadily since 1984 (Figure 1b).

Although the results showed declines in the frequency of sleep among all young adults over this 23-year period (model E in Table 2 and Table 3), both Black and Hispanic women showed significantly greater declines in the frequency of getting at least 7 hours of sleep each night than did White women. For men, dramatic declines in sleep were observed among young adults from lower SES after we adjusted for race/ethnicity. Figure 1 presents a visual illustration of the predicted trends in sleep by race/ethnicity (Figure 1c, for women) and SES (Figure 1d, for men) since 1984.

As shown in model F in Table 2 and Table 3, for both young adult men and women, the frequency of watching television remained consistently higher among minority racial/ethnic groups than among White young adults over this period (about a 1-hour difference per average weekday). Moreover, young adult women from a lower SES reported a greater frequency of television viewing than did women from higher SES over and above the effects of race/ethnicity (model F in Table 2).


We investigated historical patterns in 6 weight-related health behaviors by using longitudinal data for multiple cohorts of young adults (aged 19–26 years) who had been followed repeatedly over 23 years (1984–2006). Observed disparities in health behaviors by gender, race/ethnicity, and SES were consistent with reported disparities in obesity,4,5861 particularly among women. The results of this study indicated that young adult women have consistently exercised less frequently than men since 1984, which suggests that differences in energy expenditure could be playing a role in the gender disparities in obesity and overweight. In addition, long-term declines in the frequency of exercise were observed only among Black women, which may account for some of the racial disparities in overweight and obesity observed among young women over this period. Black young adults also tended to watch more television than did White young adults over this period.

Consistent with other findings,57 the frequency of eating green vegetables was lower among young adult Blacks and Hispanics than among young adult Whites over this period. Young adult Black women also reported a lower frequency of eating fruit than did White women, and Hispanic women showed a trend of declining fruit consumption since 1984. Because low fruit and vegetable intake is associated with energy-dense, high-calorie diets,69 racial/ethnic differences in fruit and vegetable consumption may account for some of the racial/ethnic patterns in obesity. Although young adult women showed a marked improvement in the frequency of eating breakfast over this period, Black women were the only racial/ethnic group that did not show any overall gains. The effects of race/ethnicity were not explained by SES, which suggests that the observed racial and ethnic disparities in healthy behaviors were not a function of socioeconomic resources as conferred by parental education.

The association between sleep and obesity is increasingly being documented,50,51,5456 and the overall declines in sleep observed among all young adults since 1984 are provocative. Inadequate sleep may have some role in the increasing rates of obesity and overweight observed over this period.

Socioeconomic and racial/ethnic differences in the trajectories of these 6 weight-related health behaviors have implications for future trends in social disparities in health. For example, health disparities among women may continue to increase if socioeconomically advantaged White women exhibit a trend toward increases in certain healthy behaviors (i.e., exercising, eating breakfast), whereas African American and Hispanic women follow a declining trend in the frequency of exercise, with little improvement in the frequency of consuming fruit and vegetables or eating breakfast. These patterns support a “fundamental cause” hypothesis of poor health,70 which is based on the premise that socially advantaged groups have more access to knowledge, money, power, prestige, and beneficial social connections, which allows them to make healthy food choices31,32 (by having greater awareness of, access to, and resources to purchase healthy foods) or provides them with opportunities for exercise and physical activity.35,71 If young adults continue to differentially adopt more healthy behaviors as a function of their social position, health disparities in obesity and obesity-related illnesses may very well increase in future cohorts of young adults.

Because of a reliance on existing long-term survey data that were collected primarily for the purpose of understanding drug use, this analysis was constrained to a limited number of health behaviors. The findings are further restricted by the use of self-reports, with the consequence that healthy behaviors, such as exercise, may be overreported. However, we do not expect any over reporting to vary markedly across time, nor across gender, race/ethnic, and socioeconomic subgroups, which are the comparative groups of interest. The measure of television viewing was limited to weekday viewing and therefore underestimated viewing on a weekly basis. Other limitations included nonrandom attrition in the panel data, but this was to some degree statistically adjusted for in the analyses. Because the study participants were selected in their senior year of high school, high school dropouts were not represented.64 Because dropout rates differ by race/ethnicity, with Hispanics having higher rates, this could have some effect on race/ethnic comparisons.

Nevertheless, these results help to put in place an evidence base that will help to identify the best strategies to address the obesity epidemic.72,73 Primary prevention policy must focus on the behaviors and practices that contribute to an “energy gap” and excess weight, especially among youth and young adults. Our findings suggest that interventions (at both the individual and environmental levels) targeted toward increasing exercise may hold promise, particularly for Black women, who have experienced the most notable declines in exercise over this period. Policies and interventions should also be comprehensive, addressing both exercise as well as dietary intake behaviors from a multisectoral perspective.74 This includes educational and other types of interventions aimed at intra-and interpersonal factors that contribute to obesity, as well as policies and programs aimed at families, at institutions such as schools, businesses, and worksites, and at physical and social environments.75

Our results also highlight the importance of understanding healthy eating and physical activity behaviors from a life course perspective, in which nutrition and exercise practices start in childhood and carry through school and subsequent early adult and midlife environments.74 Without tailoring any policies or interventions toward the needs and circumstances of the “poorest communities and population groups,”74 (p135) health disparities may very well increase if socially disadvantaged young adults continue to lag behind their more advantaged counterparts in the practice of healthy eating and exercise behaviors.


This work was funded by the Robert Wood Johnson Foundation (grant 64703), as part of the Youth, Education, and Society (YES) Project. YES is an integral part of a larger research initiative of the Robert Wood Johnson Foundation entitled Bridging the Gap: Research Informing Practice and Policy for Healthy Youth Behavior. Bridging the Gap utilizes data from the Monitoring the Future study, which is funded by the National Institute on Drug Abuse, a part of the National Institutes of Health. This work was also supported by the University of Michigan Center for Social Inequality Mind and Body (National Institutes of Health/National Institutes of Child Health and Human Development grant R24HD04786), as part of the Life Course core.

Note. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the sponsors.


Reprints can be ordered at by clicking the “Reprints/Eprints” link.

Human Participant Protection

All procedures are reviewed and approved on an annual basis by the University of Michigan’s institutional review board for compliance with federal guidelines for the treatment of human participants.


1. Levi J, Segal LM, Gadola E. F as in Fat: How Obesity Policies are Failing in America. Washington, DC: Trust for America’s Health; 2007.
2. Flegal KM, Carroll MD, Kuczmarski RJ, Johson CL. Overweight and obesity in the United States: prevalence and trends, 1960–1994. Int J Obes Relat Metab Disord. 1998;22:39–47. [PubMed]
3. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LER, Flegal KM. Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA. 2004;291(23):2847–2850. [PubMed]
4. Lewis CE, Jacobs DR, McCreath H, et al. Weight gain continues in the 1990s: 10 year trends in weight and overweight from the CARDIA study. Am J Epidemiol. 2000;151:1172–1181. [PubMed]
5. Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP. The continuing epidemics of obesity and diabetes in the United States. JAMA. 2001;286:1195–1200. [PubMed]
6. McLaren L. Socioeconomic status and obesity. Epidemiol Rev. 2007;29:29–48. [PubMed]
7. Yang W, Kelly T, He J. Genetic epidemiology of obesity. Epidemiol Rev. 2007;29:49–61. [PubMed]
8. Papas MA, Alberg AJ, Ewing R, Helzlsouer KJ, Gary TL, Klassen AC. The built environment and obesity. Epidemiol Rev. 2007;29:129–143. [PubMed]
9. O’Malley PM, Johnston LD, Delva J, Bachman JG, Schulenberg JE. Variation in obesity among American secondary school students by school and school characteristics. Am J Prev Med. 2007;33:S187–S194. [PubMed]
10. Delva J, O’Malley PM, Johnston LD. Racial/ethnic and socioeconomic status differences in overweight and health-related behaviors among American students: National trends 1986–2003. J Adolesc Health. 2006;39:536–545. [PubMed]
11. Hill JO, Peters JC. Environmental contributions to the obesity epidemic. Science. 1998;280:1371–1374. [PubMed]
12. Hill JO, Wyatt HR, Reed GW, Peters JC. Obesity and the environment: where do we go from here? Science. 2003;299:853–855. [PubMed]
13. Wang YC, Gortmaker SL, Sobol AM, Kuntz KM. Estimating the energy gap among US children: a counterfactual approach. Pediatrics. 2006;118:e1721–e1733. [PubMed]
14. Caballero B. The global epidemic of obesity: where do we go from here? Epidemiol Rev. 2007;29:1–5. [PubMed]
15. Egger G, Swinburn B. An ‘ecological’ approach to the obesity pandemic. BMJ. 1997;315:477–480. [PMC free article] [PubMed]
16. Nielsen SJ, Siega-Riz AM, Popkin BM. Trends in energy intake in US between 1997 and 1996: similar shifts seen across age groups. Obes Res. 2002;10:370–378. [PubMed]
17. Nielsen SJ, Popkin BM. Changes in beverage intake between 1977 and 2001. Am J Prev Med. 2004;27:205–210. [PubMed]
18. Li R, Serdula M, Bland S, Mokdad A, Bowman B, Nelson D. Trends in fruit and vegetable consumption among adults in 16 US states: Behavioral Risk Factor Surveillance System, 1990–1996. Am J Public Health. 2000;90:777–781. [PubMed]
19. Patterson BH, Block G, Rosenberger WF, Pee D, Kahle LL. Fruit and vegetables in the American diet: data from the NHANES II survey. Am J Public Health. 1990;80:1443–1449. [PubMed]
20. Serdula MK, Gillespie C, Kettel-Khan L, Farris R, Seymour J, Denny C. Trends in fruit and vegetable consumption among adults in the United States: Behavioral Risk Factor Surveillance System, 1994–2000. Am J Public Health. 2004;94:1014–1018. [PubMed]
21. Breslow RA, Subar AF, Patterson BH, Block G. Trends in food intake: the 1987 and 1992 National Health Interview Surveys. Nutr Cancer. 1997;28:86–92. [PubMed]
22. Ludwig DS, Majzoub JA, Al-Zahrani A, Dallal GE, Blanco I, Roberts SB. High glycemic index foods, over-eating, and obesity. Pediatrics. 1999;103:E26. [PubMed]
23. Danforth E. Diet and obesity. Am J Clin Nutr. 1985;41:1132–1145. [PubMed]
24. Miller WC, Linderman AK, Wallace J, Niederpruem M. Diet composition, energy intake and exercise in relation to body fat in men and women. Am J Clin Nutr. 1990;52:426–430. [PubMed]
25. Tohill BC, Seymour J, Serdula M, Kettel-Khan L, Rolls BJ. What epidemiologic studies tell us about the relationship between fruit and vegetable consumption and body weight. Nutr Rev. 2004;62:365–374. [PubMed]
26. Drewnowski A, Specter SE. Poverty and obesity: the role of energy density and energy costs. Am J Clin Nutr. 2004;79:6–16. [PubMed]
27. French SA, Harnack L, Jeffrey RW. Fast food restaurant use among women in the Pound of Prevention study: dietary, behavioral and demographic correlates. Int J Obes Relat Metab Disord. 2000;24:1353–1359. [PubMed]
28. Rajeshwari R, Yang SJ, Nicklas TA, et al. Secular trends in children’s sweetened-beverage consumption (1973–1994): the Bogalusa Heart Study. J Am Diet Assoc. 2005;105:208–214. [PubMed]
29. Striegel-Moore RH, Thompson D, Affenito SG, et al. Correlates of beverage intake in adolescent girls: The National Heart, Lung, and Blood Institute Growth and Health Study. J Pediatr. 2006;148:183–187. [PubMed]
30. Johnston LD, Delva J, O’Malley PM. Soft drink availability, contracts, and revenues in American secondary schools. Am J Prev Med. 2007;33:S209–S225. [PubMed]
31. Morland K, Wing S, Diez Roux A, Poole C. Neighborhood characteristics associated with the location of food stores and food service places. Am J Prev Med. 2002;22:23–29. [PubMed]
32. Powell LM, Auld MC, Chaloupka FJ, O’Malley PM, Johnston LD. Associations between access to food stores and adolescent body mass index. Am J Prev Med. 2007;33:S301–S307. [PubMed]
33. Centers for Disease Control. Prevalence of physical activity, including lifestyle activities, among adults – United States, 2000–2001. MMWR Morb Mortal Wkly Rep. 2003;52:764–769. [PubMed]
34. Kimm SY, Glynn NW, Kriska AM. Decline in physical activity in black girls and white girls during adolescence. N Engl J Med. 2002;347:709–715. [PubMed]
35. Johnston LD, Delva J, O’Malley PM. Sports participation and physical education in American secondary schools: current levels and racial/ethnic and socioeconomic disparities. Am J Prev Med. 2007;33:S195–S208. [PubMed]
36. Johnston LD, O’Malley PM. Obesity Among American Adolescents: Tracking the Problem and Searching for Causes. Ann Arbor, MI: Institute for Social Research; 2003. Youth, Education, & Society Occasional Paper no. 3.
37. Cervero R, Duncan M. Walking, bicycling, and urban landscapes: evidence from the San Francisco Bay area. Am J Public Health. 2003;93:1478–1483. [PubMed]
38. Frank L, Pivo G. The impacts of mixed use and density on the utilization of three modes of travel: the single occupant vehicle, transit, and walking. Transportation Research Record. 1994;1466:44–52.
39. Lopez R. Urban sprawl and risk for being over-weight or obese. Am J Public Health. 2004;94:1574–1579. [PubMed]
40. Ewing R, Schmid T, Killingsworth R, Zlot A, Raudenbush S. Relationship between urban sprawl and physical activity, obesity, and morbidity. Am J Health Promot. 2003;18:47–57. [PubMed]
41. Frank LD, Sallis JF, Conway TL, Chapman JE, Saelens BE, Bachman W. Many pathways from land use to health associations between neighborhood walkability and active transportation, body mass index, and air quality. J Am Plann Assoc. 2006;72:75–87.
42. Kaestner R, Xu X. Effects of Title IX and Sports Participation on Girls’ Physical Activity and Weight. Adv Health Econ Health Serv Res. 2007;17:79–111. [PubMed]
43. Andersen RE, Crespo CJ, Bartlett SJ, Cheskin LJ, Pratt M. Relationship of physical activity and television viewing with body weight and level of fatness among children: results from the Third National Health and Nutrition Examination Survey. JAMA. 1998;279:938–942. [PubMed]
44. Powell LM, Szczypka G, Chaloupka FJ, Braunschweig CL. Nutritional content of television food advertisements seen by children and adolescents in the United States. Pediatrics. 2007;120:576–583. [PubMed]
45. Wiecha JL, Peterson KE, Ludwig DS, Kim J, Sobol A, Gortmaker SL. When children eat what they watch: impact of television viewing on dietary intake in youth. Arch Pediatr Adolesc Med. 2006;160:436–442. [PubMed]
46. Gable S, Chang Y, Krull JL. Television watching and frequency of family meals are predictive of overweight onset and persistence in a national sample of school-aged children. J Am Diet Assoc. 2007;107:53–61. [PubMed]
47. Delva J, Johnston LD, O’Malley PM. Obesity and Lifestyle Habits Among American Adolescents: A Study of SES, Gender, and Racial/Ethnic Differences 1986–2003. Ann Arbor, MI: Institute for Social Research; 2005. Youth, Education, and Society Occasional Paper no. 6.
48. Siega-Riz AM, Popkin BM, Carson T. Trends in breakfast consumption for children in the US from 1965–1991. Am J Clin Nutr. 1998;67:748S–756S. [PubMed]
49. Webb WB, Agnew HW. Are we chronically sleep deprived? Bull Psychon Soc. 1975;6:47–48.
50. Al Mamum A, Lawlor DA, Cramb S, O’Callaghan M, Williams G, Najman J. Do childhood sleeping problems predict obesity in young adulthood? Evidence from a prospective birth cohort study. Am J Epidemiol. 2007;166:1368–1373. [PubMed]
51. Taheri S. The link between short sleep duration and obesity: we should recommend more sleep to prevent obesity. Arch Dis Child. 2006;91:881–884. [PMC free article] [PubMed]
52. Berkey CS, Rockett HR, Gillman MW, Field AE, Colditz GA. Longitudinal study of skipping breakfast and weight change in adolescents. Int J Obes. 2003;27:1258–1266. [PubMed]
53. Wyatt HR, Grunwald GK, Mosca CL, Klem JL, Wing RR, Hill JO. Long term weight loss and breakfast in subjects in the national weight control registry. Obes Res. 2002;10:78–82. [PubMed]
54. Vorona RD, Winn MP, Babineau TW, Eng BP, Feldman HR, Ware JC. Overweight and obese patients in a primary care population report less sleep than patients with a normal body mass index. Arch Intern Med. 2005;165:25–30. [PubMed]
55. Fogelholm M, Kronholm E, Kukkonen-Harjula K, Partonen T, Partinen M, Harma M. Sleep-related disturbances and physical inactivity are independently associated with obesity in adults. Int J Obes (Lond) 2007;31:1713–1721. [PubMed]
56. Sekine M, Yamagami T, Handa K, et al. A dose-response relationship between short sleeping hours and childhood obesity: Results of the Toyama Birth Cohort Study. Child Care Health Dev. 2002;28:163–170. [PubMed]
57. Casagrande SS, Wang Y, Anderson C, Gary TL. Have Americans increased their fruit and vegetable intake? The trends between 1988 and 2002. Am J Prev Med. 2007;32:257–263. [PubMed]
58. Sobal J, Stunkard AJ. Socioeconomic status and obesity: a review of the literature. Psychol Bull. 1989;105(2):260–275. [PubMed]
59. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 2002;288:1723–1727. [PubMed]
60. Kuczmarski RJ, Flegal KM, Campbell SM, Johnson CL. Increasing prevalence of overweight among US adults: the National Health and Nutrition Examination Surveys, 1960–1991. JAMA. 1994;272:205–211. [PubMed]
61. Paeratakul S, Lovejoy JC, Ryan DH, Bray GA. The relation of gender, race, and socioeconomic status to obesity and obesity comorbidities in a national sample of US adults. Int J Obes Relat Metab Disord. 2002;26:1205–1210. [PubMed]
62. Schulenberg J, O’Malley PM, Bachman JG, Johnston LD. Early adult transitions and their relation to well-being and substance use. In: Settersten RA Jr, Furstenberg FF Jr, Rumbaut RG, editors. On the Frontier of Adulthood: Theory, Research, and Public Policy. Chicago, IL: University of Chicago Press; 2005. pp. 417–453.
63. Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE. Monitoring the Future National Survey Results on Drug Use, 1975–2006. Secondary School Student. Vol 1. Bethesda, MD: National Institute on Drug Abuse; 2007. NIH Publication no. 07-6205.
64. Bachman JG, Johnston LD, O’Malley PM. The Monitoring the Future Project after Thirty-two Years: Design and Procedures. Ann Arbor, MI: Institute for Social Research; 2006. Monitoring the Future Occasional Paper no. 64.
65. Chaloupka FJ, Johnston LD. Bridging the gap: research informing practice and policy for healthy youth behavior. Am J Prev Med. 2007;33:S147–S161. [PubMed]
66. Singer JD, Willet JB. Applied Longitudinal Data Analysis. New York, NY: Oxford University Press; 2003.
67. Raudenbush SW, Chan WS. Application of a hierarchical linear model to the study of adolescent deviance in an overlapping cohort design. J Consult Clin Psychol. 1993;61:941–951. [PubMed]
68. Schulenberg JE, Merline AC, Johnston LD, O’Malley PM, Bachman JG, Laetz VB. Trajectories of marijuana use during the transition to adulthood: the big picture based on national panel data. J Drug Issues. 2005;35:255–279. [PMC free article] [PubMed]
69. Ledikwe JH, Blanck HM, Khan LK, et al. Dietary energy density is associated with energy intake and weight status in US adults. Am J Clin Nutr. 2006;83:1362–1368. [PubMed]
70. Link BG, Phelan JC. Social conditions as fundamental causes of disease. J Health Soc Behav. 1995:80–94. (Spec No) [PubMed]
71. Powell LM, Slater S, Chaloupka FJ. The relationship between community physical activity settings and race, ethnicity, and socioeconomic status. Evidence-Based Prev Med. 2004;1(2):135–144.
72. Foxhall K. Beginning to begin: reports from the battle on obesity. Am J Public Health. 2006;96:2106–2112. [PubMed]
73. Sallis JF, Story M, Orleans CT. A research perspective on findings from Bridging the Gap. Am J Prev Med. 2007;33:S169–S171. [PubMed]
74. World Health Organization. Diet, nutrition, and the prevention of chronic diseases. World Health Organ Tech Rep Ser. 2003:916. [PubMed]
75. Story M, Kaphingst KM, Robinson-Obrien R, Glanz K. Creating healthy food and eating environments: policy and environmental approaches. Annu Rev Public Health. 2008;29:253–272. [PubMed]