Abstract
Recent epidemic increases in the U.S. prevalence of obesity and diabetes are a consequence of widespread environmental changes affecting energy balance and its regulation. These environmental changes range from exposure to endocrine disrupting pollutants to shortened sleep duration to physical inactivity to excess caloric intake. Overall, we need a better understanding of the factors affecting individual susceptibility and resistance to adverse exposures and behaviors and of determinants of individual response to treatment. Obesity and diabetes prevention will require responding to two primary behavioral risk factors: excess energy intake and insufficient energy expenditure. Adverse food environments (external, nonphysiological influences on eating behaviors) contribute to excess caloric intake but can be countered through behavioral and economic approaches. Adverse built environments, which can be modified to foster more physical activity, are promising venues for community-level intervention. Techniques to help people to modulate energy intake and increase energy expenditure must address their personal situations: health literacy, psychological factors, and social relationships. Behaviorally oriented translational research can help in developing useful interventions and environmental modifications that are tailored to individual needs.
Keywords: behavior, diabetes, diet, environment, obesity
Introduction: Environmental Influences on Diabetes and Obesity
Societal changes occurring since 1985 have led to a remarkable increase in the prevalence of obesity among adults and overweight/obesity in children.1 By 2007, 25.6% of U.S. adults were obese by self-report.2 The highest regional prevalence (27%) was in the South, exceeding 30% in three states (Alabama, Mississippi, and Tennessee).2 Correspondingly, in 2005–2006, 16.3% of children and adolescents 2–19 years of age were overweight or obese.3 On the heels of this rise in obesity, there has been a near doubling of the incidence rate for newly diagnosed cases of diabetes among adults, from 4.8 cases/1000 population to 9.1/1000.4 Among the states, there is nearly a three-fold range, from a low in Minnesota (5.0/1000) to a high in Puerto Rico (12.8/1000).4 Native Americans and Alaska natives are at especially high risk; in adults, the prevalence of obesity is 34% and overall diabetes prevalence is conservatively estimated at 13% (2–3 times the U.S. national average for U.S. whites).5 There also is great variation among tribes, with the highest diabetes prevalence (>60%) observed in the Arizona Pima Indians.6 Demographic estimates project a U.S. national population prevalence of diabetes as high as 12.0% by 2050, a societal burden of huge potential cost.7 Type 2 diabetes is rare in youth but is also suspected to be increasing, especially among Native Americans and other ethnic minority groups with a high prevalence of obesity.8–10
This article will consider the meaning of “environment” in the context of diabetes and obesity risk. How can a broad concept of environment help us to develop improved approaches for prevention and management? The concept of “personalized medicine” refers to the individual genetic/genomic and psychosocial determinants of risk and of response to treatment or intervention. Individuals are exposed to and interact with multiple environments: the physical environment (chemical exposures and daily light/dark cycles), the “built” environment, and, of course, the food environment. People also exist within a less tangible but no less important psychosocial and socio-economic environment, which includes their internal psychological state, their social interactions, and their capacity to process the information to which they are exposed. Personally and locally tailored solutions will be needed to help people grapple with the specific environments and risk factors to which they are exposed.
Endocrine Disrupters
The natural, physical world first comes to mind with the term “environment.” The concept of environmental exposures connotes pollution of air and water, as well as other passive chemical exposures. Endocrine-disrupting chemicals in the environment first drew public health attention with regard to their possible effects on human fertility and reproductive tissues.11 Endocrine disrupters are chemically diverse and include estrogen receptor agonists (such as diethylstilbestrol, bisphenol A [BPA], and the phytoestrogen, genistein), androgen receptor antagonists (such as phthalates), and aryl hydrocarbon receptor agonists (such as dioxins).12 The estrogen mimetic, BPA, is used to make polycarbonate plastics for coatings and food containers (including baby bottles). Bisphenol A has been implicated in the development of obesity due to disrupted patterns of hormone regulation and adipocyte differentiation that could affect growth, food intake, adipose tissue distribution and function, and insulin sensitivity.12,13 Bisphenol A has long been known to leach from plastics and to have deleterious health effects at low levels in experimental animal models. Of particular concern is the possibility that in utero and early childhood exposure can lead to lifelong deleterious epigenetic changes that can affect adult health and be passed on to offspring. These concerns have been strengthened by reports based of the ubiquitous presence of BPA in human urine14 and positive associations of urinary levels with risk of diabetes and cardiovascular disease.15 Legislative and manufacturing solutions have already been attempted, focusing primarily on eliminating BPA from baby bottles.16 Research needs, as detailed in an expert panel report, include clarifying biological mechanisms, developing methods for mitigating exposures, characterizing the etiologic relationships of BPA with human health across the lifespan, and developing markers of risk and exposure to better identify genetically and phenotypically susceptible individuals.17
Circadian Rhythms and Sleep
All living things evolved in synchrony with Earth's cycles of light and dark, and every cell in the body has molecular clocks that regulate and synchronize myriad physiological functions, including hormone metabolism.18 The daily experience of sleep is critical to “setting” these clocks. Therefore, the impact of inadequate sleep time is of public health concern. In 2009, 20% of Americans reported sleeping <6 h/night (versus 13% in 2001), and 28% reported sleeping >8 h/night (versus 38% in 2001).19 Sleep deprivation is known to have behavioral consequences such as decreased alertness, accidents, and emotional disturbances.20 Short sleep duration is associated with overweight and obesity and with risk of developing diabetes as well.21,22 Obstructive sleep apnea and other forms of sleep-disordered breathing are well-known comorbidities of overweight, obesity, and type 2 diabetes, requiring treatment to minimize cardiovascular risk.23 However, recent hypothesis-testing research in humans and animals indicates that sleep disturbances also play a causal role in generating or exacerbating problems of energy balance and insulin function.22–25Misalignment of sleep/wake cycles and eating behavior (for example, eating late at night) is thought to distort the normal synchronization of metabolic gene transcription.26 Conversely, eating behavior and food ingestion appear capable of entraining hypothalamic oscillators affecting multiple physiologic systems.26 These findings are intriguing given the importance of timing and composition of meals in controlling blood sugar in type 2 diabetes. Also, work in animal models suggests that metabolomic profiling may have the potential to identify internal clocks, suggesting that therapy could be personalized to the individual's body clock (“chronotherapy”) once this technology becomes available for humans.27 Thus, with further basic and applied research,28 personal and public health-level sleep interventions might have the potential to complement other management approaches to weight and diabetes.
Environmental Influences on Food Intake and Physical Activity
The obesity epidemic is an unintended biological consequence of powerful economic forces, which have led to a wide-spread imbalance between energy expenditure and energy intake. Labor and time have a relatively high value, promoting the use of labor-saving and time-saving technologies in the home and workplace. Physical activity has declined steadily since 1960, reflecting declining work-related activity, increasing sedentary activity (e.g., time spent using computers or watching television), increased automobile travel time from home to work, and increases in automobile use versus walking or public transit.29 Also, surprisingly large decrements in at-home energy expenditure (>100 kcal/d) can be attributed to transition to labor-efficient technologies.30 Use of food prepared outside the home has risen such that it now accounts for approximately half of food expenditures.31 These commercially prepared foods tend to have increasingly large portion sizes, as food costs are relatively low compared to labor costs.32
Environmental drivers of overeating include not only the presentation of large portions, but also the ubiquitous availability of food, social acceptance of frequent eating, little or confusing information about the energy content of foods, and susceptibility to food marketing tools such as packaging, image identity, and indirect product enhancements.33,34 Perception of such external influences can be quite inaccurate. Eating larger quantities in the presence of other eaters may be attributed to physiologic sensations (such as hunger) but actually is due to social mirroring35 or other complex decision-making phenomena.36 These external influences on eating behavior need to be understood in order to develop effective interventions. Conversely, ingenious applications of marketing research also may suggest useful interventions, such as switching to smaller plate sizes to encourage lower calorie intake.33,37
The “built environment” is emerging as an important dimension of environmental risk for higher rates of obesity.29 “Built environment” is a relatively new term that refers to the patterns of human behavior within the physical world, encompassing urban design (arrangement, appearance, and function of physical elements and public spaces in cities), land use (location and density of residential, commercial, and recreational structures and activities), and transportation systems (usage patterns and physical infrastructure).38 Research on the built environment uses methodology drawn from the disciplines of urban planning and architecture. Buildings are evaluated for the presence of well-lit, safe staircases, recreational space, office layouts that encourage walking around, and recreational facilities. Schools are assessed for classroom space for movement versus sitting, access via walking, the presence of safe playgrounds, and proximity to fast-food outlets. Neighborhoods and communities are studied using global positioning technologies for land-use mix, distribution of fast-food outlets, street connectivity, presence of green/open space, access to public transportation, density of public transit stations, street crime rate, availability of standard grocery stores, and presence of recreational facilities. Although causality has not been proven between weight gain and features of built environment, recent research is confirming dose-response associations. For example, in Portland, Oregon, a 10% increase in land-use mix has been associated with a 25% reduction in the prevalence of overweight/obesity, and a one standard deviation increase in the density of fast-food outlets has been associated with a 7% increase in overweight/obesity.39
Environmental inequity, i.e., living in an especially adverse physical and social environment, is thought to contribute to the higher risk of developing obesity and diabetes and of suffering complications of disease that is experienced by members of minority and economically disadvantaged demographic groups.40 Individuals living in low-income neighborhoods or who are at social disadvantage frequently live with many of the environmental and social issues discussed in this essay. These include exposure to circadian stress (shift work, double shifts) and living in an unhealthful built environment with poor access to physical activity venues (few recreational facilities, safety issues), healthful food supply, and health providers. Literacy, numeracy (numerical literacy), and health literacy are typically weak in these communities, and people may have a poor sense of autonomy and control over their environments and low self-efficacy for behavior change.
Information Environment: Nutrition and Diabetes Aspects
Guidance for lifestyle modification, including dietary change, presumes that the individual can absorb the concepts of what makes a better diet and then will make better decisions about what to eat. Nutrition information is complex, however, and includes choice of foods, portion sizes, nutrient content information for multiple nutrients, and relative rankings of nutrient content among foods. We ask patients to integrate multiple simultaneous variables and to choose foods that meet guidelines for calories (weight); for sodium, cholesterol, saturated fat, and trans fat (blood pressure and plasma lipids); and for type, amount, and timing of carbohydrate (diabetes, especially when using insulin). This is a daunting task. Food labels make this information readily available, but reading, understanding, and utilizing Nutrition Facts Panel data requires literacy and numeracy skills at approximately the 6–9th grade level. In fact, many adult Americans do not have these skills; the U.S. Department of Education's 2003 National Assessment of Adult Literacy found that 35% of surveyed adults had “basic” or “below basic” skills for document literacy (necessary to understand text) and that 55% had basic or below basic skills for quantitative literacy (necessary to do simple arithmetical calculations).41 These common problems with literacy, and especially with numeracy, have been observed in primary care patient populations.42 In fact, it has been suggested that the ability to understand and use food label information be used as a quick test of literacy skills for following primary care information.43 With aging, the difficulties are compounded. There often is an erosion of skills (such as short-term memory and working memory) needed for cognitive processing of food label information.44
Individuals with obesity and/or diabetes are more likely to be older and to have lower educational attainment and lower English proficiency. It is worrisome that many educational materials provided by the American Diabetes Association and American Heart Association materials are unsuitable for low-literacy populations.45 The cognitive processing demand of even the simplest diabetes education information is intimidating, and we must develop new ways to enhance competency and self-confidence of patients. In addition, the elderly and those with diabetes often suffer from impaired vision, which will further compound their difficulties with printed materials. An effective information environment for diabetes and obesity prevention and treatment for adults and children will require individualized assessment, perhaps based on diagnostic tests, and a suitable educational approach that provides print and nonprint educational materials matched to the patient's skill level and learning style.46
The Need for Translational Research
A useful model for describing the stages of prevention and better management of obesity and diabetes can be drawn from the world of cardiovascular disease prevention.47 Primordial prevention seeks to prevent risk factors from developing especially in children, youth, and young adults (e.g., maintain healthy weight and prevent development of overweight and obesity). Primary prevention seeks to prevent risk factors from causing disease (e.g., reduce likelihood of transitioning from prediabetes to diabetes). Secondary prevention seeks to prevent adverse disease outcomes (e.g., optimize diabetes management to prevent complications).
Understanding the capacity of comprehensive environmental change to reduce individual and population risk of obesity and diabetes will require what is now being termed “type 2 translational research”, the “bedside-to-curbside” complement of “bench-to-bedside” translational research that brings basic science findings to the clinic.48 Well-designed randomized trials and studies taking advantage of natural experiments are needed to estimate the effects of multiple environmental changes. Research and practical approaches for prevention and treatment must be directed at different groups within society depending on their needs. These needs will vary with age (children, adolescents, retirees) and risk level (primordial and primary prevention in the general population, secondary prevention in high risk populations). Studies must be designed to compare effectiveness of interventions in a variety of settings (health care environments, communities, schools) with public and private partners. Identifying and then implementing effective techniques at the individual and community levels will require the tools of behaviorally based “implementation science” so that basic science and clinical knowledge can be translated into public health programs and thence into improved public health.
Interventions: Potential for Change
Eating and physical activity behaviors are known to have shifted in response to powerful social and economic forces, with deleterious effects on body weight and diabetes. How are individuals to push back or grapple with these forces, either individually or collectively? It is useful to have a framework for understanding what is involved for individuals to change their eating, activity, and other habits.
There is ample literature on the factors involved with health behavior change and disease management behavior, especially in terms of achieving adherence to treatment recommendations. Numerous theories and models have been developed to design research, to account for observational data, to predict what people will do, and to develop effective therapeutic, counseling, and other practical approaches.49,50 Many models focus on personal determinants of individual behavior (classic learning theories, health belief model, transtheoretical model, relapse prevention model) while others more fully incorporate the role of interpersonal relationships (social cognitive theory, planned behavior theory) and social supports.50
There also is growing interest in ecological models of behavior change50,51 that accommodate the social environ-ment in which the person lives, such as institutional factors, public policy, and interpersonal and group relationships. The potential of these models was reinforced by a social network analysis based on the Framingham Heart Study cohort, which found that the probability of becoming obese was higher among individuals who associated with other obese people.52 A parallel study in adolescents also identified peer influences on body mass index (BMI).53 A vigorous debate has ensued about whether this type of modeling is a valid way of inferring shared behaviors or standards of reference (i.e., is a heavy body weight acceptable?) or whether it actually is assessing contextual influences (i.e., access to shared environment, such as fast-food restaurants or walkable communities).54, 55 Either way, these findings imply that community-level interventions will be critical for achieving progress in reducing the prevalence of obesity.
The 2008 Physical Activity Guidelines for Americans point out that moderate levels of activity, however achieved, are associated with a 30–40% lower risk of developing type 2 diabetes and metabolic syndrome.56 Indeed, lifestyle intervention programs focusing on individual behavior modification have been proven effective in improving cardiovascular and diabetes risk factors in adults. For example, the Diabetes Prevention Program (DPP) found that modest weight loss (7%) and increased physical activity (walking 150 min/week) reduced the 3 y incidence of diabetes in individuals with impaired glucose tolerance (prediabetes) by nearly 60%.57 Similarly, the PREMIER trial showed that improved diet and exercise habits, reinforced by counseling for goal-setting, self-monitoring, and problem-solving, were effective in promoting weight loss and lowering blood pressure in individuals with prehypertension or stage 1 hypertension.58 The DPP program has been successfully implemented in other settings for adults,59 but for youth at high risk of prediabetes, more research is needed to determine effective interventions.60, 61 Nevertheless, these randomized counseling-oriented interventions are very labor-intensive and would be hard to implement under many situations, especially for children. School-based or multicomponent designs (e.g., diet, exercise, reduced screen time) that include a focus on parental involvement62–65 can be effective approaches to obesity prevention and treatment in children and adolescents. Community-level interventions for helping youth and families maintain a healthy weight (such as the We Can! Program) also hold great promise.51, 66 Diabetes self-management training, including approaches that engage family, can be effective in improving glycemic control,67, 68 but research that better addresses environmental and community setting issues is needed.69, 70
Conclusion: Moving to “Diabetesville”
Imagine this family: two kids (inactive elementary and middle school students, overweight by 10–20 lb), their mom (BMI = 35, with a history of gestational diabetes), and their dad (BMI = 32, with metabolic syndrome and a family history of type 2 diabetes). This hypothetical family has multiple behavioral risk factors and possibly a social network of similarly overweight friends, neighbors, and relatives. Their propensity to obesity and diabetes likely has a genetic component that would not have manifested itself 30 years ago when the food environment and daily physical activity levels were more favorable.
Now imagine that, one evening, the parents come home from work, roust the children from watching TV, and, as they eat their fast-food dinner, announce, “Kids, we're moving to Diabetesville. It's a community not far from here where everything is set up to help us improve our habits. We're going to buy a house there, you're going to go to school there, and we'll all do better.”
What would Diabetesville be like? Of course, there must be adequate attention to ethical and legal concerns, such as protection of free speech, free trade, and the opportunity to exercise free will (i.e., avoiding a “nutrition police” atmosphere). However, many aspects of the food environment, the built environment, and the social environment could be favorably altered:
grocery stores (accessible locations, more nutritious food items, single-serving or limited-calorie packaging, nutrition information, checkout register comparisons against nutrition guidelines, menu planning services)
restaurants (smaller servings, more nutritious recipes, nutrition information for menu items)
consumer goods (smart clothing to monitor metabolic status, such as shirts to detect hemoglobin A1c levels, shoes to monitor skin oxygenation in diabetic feet, wrist devices to monitor physical activity and outdoor recreation; furniture to detect inactivity)
neighborhood design (presence of sidewalks, access to public transportation, safe environment, parks, presence of recreational facilities, stair-friendly public buildings)
public schools (cafeteria and vending machine offerings, classroom food policies, protected physical education time, sleep-friendly start times, nutrition/physical activity curriculum modules, movement-friendly building layout, fitness/health/BMI reports)
satisfactory access to health services and health education
community-level social marketing to encourage healthful behaviors
Natural economic pressures toward convenience and time saving are at the heart of the obesity epidemic. Conversely, interventions perceived as personally inconvenient, difficult, or costly will not succeed. Weight loss and physical activity interventions must address genuine time and economic costs, such as taking time to exercise and to prepare more nutritious meals, and the price of more nutritious foodstuffs or having access to recreational facilities.
In theory, more movement and a higher level of everyday or leisure-time physical activity might lead to improved strength, balance, aerobic capacity, and improved insulin sensitivity. Lower caloric intake, in conjunction with higher activity level, might lead to modest but medically useful weight loss. A better balanced diet, higher in nutrient density, lower in salt, and possibly with a lower glycemic load, might lead to improved glycemic control and better micronutrient status and blood pressure control. A community atmosphere that facilitates adherence to preventive and therapeutic recommendations and provides the means of achieving them, might promote confidence and self-efficacy.71
Would moving to Diabetesville really “work” for our hypothetical high-risk family? The answer is we don't really know. Too little is known about the potential impact of environmental change and how best to evaluate it.72 We also don't know enough about genetic, genomic, and psychological propensities that mediate the individual's risk from environmental factors, nor how to tailor dietary and other interventions for individual benefit.73 However, in the meantime, given that two-thirds of the population is now affected by diabetes or obesity or their risk factors, there likely are few U.S. families who would not benefit from living in Diabetesville.
Acknowledgments
Thanks to colleagues who provided suggestions and background material for this essay: Susan Czajkowski, Ph.D.; Suzanne Goldberg, M.S.N., R.N.; Jared Jobe, Ph.D.; Peter Kaufmann, Ph.D.; Charlotte Pratt, Ph.D.; Denise Simons-Morton, M.D., Ph.D.; Pothur Srinivas, Ph.D.; Kate Stoney, Ph.D.; and Michael Twery, Ph.D.
Abbreviations
- BMI
body mass index [weight (kg)/height (m2)]
- BPA
bisphenol A
- DPP
Diabetes Prevention Program
References
- 1.Centers for Disease Control and Prevention. U.S. obesity trends, 1985–2007. Accessed March 30, 2009 http://www.cdc.gov/nccdphp/dnpa/obesity/trend/maps/index.htm.
- 2.Centers for Disease Control and Prevention. State-specific prevalence of obesity among adults: United States, 2007. MMWR Morb Mortal Wkly Rep. 2008;57(28):765–768. [PubMed] [Google Scholar]
- 3.Ogden CL, Carroll MD, Flegal KM. High body mass index for age among US children and adolescents, 2003–2006. JAMA. 2008;299(20):2401–2405. doi: 10.1001/jama.299.20.2401. [DOI] [PubMed] [Google Scholar]
- 4.Centers for Disease Control and Prevention. State-specific incidence of diabetes among adults: participating states, 1995–1997 and 2005–2007. MMWR Morb Mortal Wkly Rep. 2008;57(43):1169–1173. [PubMed] [Google Scholar]
- 5.Barnes PM, Adams PF, Powell-Griner E. Health characteristics of the American Indian and Alaska Native adult population: United States, 1999–2003. [Advance data from Vital and Health Statistics, Number 356] April 27. 2005. http://www.cdc.gov/nchs/data/ad/ad356.pdf. Accessed April 2, 2009.
- 6.Welty TK, Lee ET, Yeh J, Cowan LD, Go O, Fabsitz RR, Le NA, Oopik AJ, Robbins DC, Howard BV. Cardiovascular disease risk factors among American Indians. The Strong Heart Study. Am J Epidemiol. 1995;142(3):269–287. doi: 10.1093/oxfordjournals.aje.a117633. [DOI] [PubMed] [Google Scholar]
- 7.Narayan KM, Boyle JP, Geiss LS, Saaddine JB, Thompson TJ. Impact of recent increase in incidence on future diabetes burden: U.S. 2005–2050. Diabetes Care. 2006;29(9):2114–2116. doi: 10.2337/dc06-1136. [DOI] [PubMed] [Google Scholar]
- 8.Story M, Stevens J, Himes J, Stone E, Rock BH, Ethelbah B, Davis S. Obesity in American-Indian children: prevalence, consequences, and prevention. Prev Med. 2003;37(6 Pt 2):S3–S12. doi: 10.1016/j.ypmed.2003.08.008. [DOI] [PubMed] [Google Scholar]
- 9.Pinhas-Hamiel O, Zeitler P. The global spread of type 2 diabetes mellitus in children and adolescents. J Pediatr. 2005;146(5):693–700. doi: 10.1016/j.jpeds.2004.12.042. [DOI] [PubMed] [Google Scholar]
- 10.Dabelea D, Bell RA, D'Agostino RB, Jr, Imperatore G, Johansen JM, Linder B, Liu LL, Loots B, Marcovina S, Mayer-Davis EJ, Pettitt DJ, Waitzfelder B Writing Group for the SEARCH for Diabetes in Youth Study Group. Incidence of diabetes in youth in the United States. JAMA. 2007;297(24):2716–2724. doi: 10.1001/jama.297.24.2716. [DOI] [PubMed] [Google Scholar]
- 11.Harrison PT. Endocrine disrupters and human health. BMJ. 2001;323(7325):1317–1318. doi: 10.1136/bmj.323.7325.1317. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Phillips KP, Foster WG. Key developments in endocrine disrupter research and human health. J Toxicol Environ Health B Crit Rev. 2008;11(3-4):322–344. doi: 10.1080/10937400701876194. [DOI] [PubMed] [Google Scholar]
- 13.Vom Saal FS, Myers JP. Bisphenol A and risk of metabolic disorders. JAMA. 2008;300(11):1353–1355. doi: 10.1001/jama.300.11.1353. [DOI] [PubMed] [Google Scholar]
- 14.Calafat AM, Ye X, Wong LY, Reidy JA, Needham LL. Exposure of the U.S. population to bisphenol A and 4-tertiary-octylphenol: 2003–2004. Environ Health Perspect. 2008;116(1):39–44. doi: 10.1289/ehp.10753. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Lang IA, Galloway TS, Scarlett A, Henley WE, Depledge M, Wallace RB, Melzer D. Association of urinary bisphenol A concentration with medical disorders and laboratory abnormalities in adults. JAMA. 2008;300(11):1303–1310. doi: 10.1001/jama.300.11.1303. [DOI] [PubMed] [Google Scholar]
- 16.Erickson BE, McCoy M. Congress, companies target chemicals: bisphenol A and phthalates are in the crosshairs. Chem Eng News. 2009;87(12):9. [Google Scholar]
- 17.vom Saal FS, Akingbemi BT, Belcher SM, Birnbaum LS, Crain DA, Eriksen M, Farabollini F, Guillette LJ, Jr, Hauser R, Heindel JJ, Ho SM, Hunt PA, Iguchi T, Jobling S, Kanno J, Keri RA, Knudsen KE, Laufer H, LeBlanc GA, Marcus M, McLachlan JA, Myers JP, Nadal A, Newbold RR, Olea N, Prins GS, Richter CA, Rubin BS, Sonnenschein C, Soto AM, Talsness CE, Vandenbergh JG, Vandenberg LN, Walser-Kuntz DR, Watson CS, Welshons WV, Wetherill Y, Zoeller RT. Chapel Hill Bisphenol A Expert Panel consensus statement: integration of mechanisms, effects in animals and potential to impact human health at current levels of exposure. Reprod Toxicol. 2007;24(2):131–138. doi: 10.1016/j.reprotox.2007.07.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Gillette MU, Sejnowski TJ. Physiology. Biological clocks coordinately keep life on time. Science. 2005;309(5738):1196–1198. doi: 10.1126/science.1111420. [DOI] [PubMed] [Google Scholar]
- 19.National Sleep Foundation. 2009 Sleep in America poll: highlights and key findings. 2009. Mar 2, http://www.sleepfoundation.org/sites/default/files/2009%20sleep%20America%20SOF%20embargoed.pdf. Accessed June 19, 2009.
- 20.Minkel JD, Banks S, Dinges DF. Sleep deprivation: neurobehavioral changes. Encycl Neurosci. 2009;8:1007–1014. [Google Scholar]
- 21.Gangwisch JE, Heymsfield SB, Boden-Albala B, Buijs RM, Kreier F, Pickering TG, Rundle AG, Zammit GK, Malaspina D. Sleep duration as a risk factor for diabetes incidence in a large US sample. Sleep. 2007;30(12):1667–1673. doi: 10.1093/sleep/30.12.1667. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Knutson KL, Van Cauter E. Associations between sleep loss and increased risk of obesity and diabetes. Ann N Y Acad Sci. 2008;1129:287–304. doi: 10.1196/annals.1417.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Somers VK, White DP, Amin R, Abraham WT, Costa F, Culebras A, Daniels S, Floras JS, Hunt CE, Olson LJ, Pickering TG, Russell R, Woo M, Young T. Sleep apnea and cardiovascular disease: an American Heart Association/American College of Cardiology Foundation Scientific Statement from the American Heart Association Council for High Blood Pressure Research Professional Education Committee, Council on Clinical Cardiology, Stroke Council, and Council on Cardiovascular Nursing. J Am Coll Cardiol. 2008;52(8):686–717. doi: 10.1016/j.jacc.2008.05.002. [DOI] [PubMed] [Google Scholar]
- 24.Scheer FA, Hilton MF, Mantzoros CS, Shea SA. Adverse metabolic and cardiovascular consequences of circadian misalignment. Proc Natl Acad Sci U S A. 2009;106(11):4453–4458. doi: 10.1073/pnas.0808180106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Turek FW, Joshu C, Kohsaka A, Lin E, Ivanova G, McDearmon E, Laposky A, Losee-Olson S, Easton A, Jensen DR, Eckel RH, Takahashi JS, Bass J. Obesity and metabolic syndrome in circadian Clock mutant mice. Science. 2005;308(5724):1043–1045. doi: 10.1126/science.1108750. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Green CB, Takahashi JS, Bass J. The meter of metabolism. Cell. 2008;134(5):728–742. doi: 10.1016/j.cell.2008.08.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Minami Y, Kasukawa T, Kakazu Y, Iigo M, Sugimoto M, Ikeda S, Yasui A, van der Horst GT, Soga T, Ueda HR. Measurement of internal body time by blood metabolomics. Proc Nat Acad Sci U S A. 2009;106(24):9890–9895. doi: 10.1073/pnas.0900617106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.National Center on Sleep Disorders Research. [National Sleep Disorders Research Plan] National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services. NIH Publication No. 03-5209. July 2003. [Google Scholar]
- 29.Brownson RC, Boehmer TK, Luke DA. Declining rates of physical activity in the United States: what are the contributors? Annu Rev Public Health. 2005;26:421–443. doi: 10.1146/annurev.publhealth.26.021304.144437. [DOI] [PubMed] [Google Scholar]
- 30.Lanningham-Foster L, Nysse LJ, Levine JA. Labor saved, calories lost: the energetic impact of domestic labor-saving devices. Obes Res. 2003;11(10):1178–1181. doi: 10.1038/oby.2003.162. [DOI] [PubMed] [Google Scholar]
- 31.U.S. Department of Agriculture, Economic Research Service. Food marketing system in the U.S.: food retailing. http://www.ers.usda.gov/Briefing/FoodMarketingSystem/foodretailing.htm. Accessed April 2, 2009.
- 32.Young LR, Nestle M. Expanding portion sizes in the US marketplace: implications for nutrition counseling. J Am Diet Assoc. 2003;103(2):231–234. doi: 10.1053/jada.2003.50027. [DOI] [PubMed] [Google Scholar]
- 33.Wansink B. Environmental factors that increase the food intake and consumption volume of unknowing consumers. Annu Rev Nutr. 2004;24:455–479. doi: 10.1146/annurev.nutr.24.012003.132140. [DOI] [PubMed] [Google Scholar]
- 34.Cohen DA. Neurophysiological pathways to obesity: below awareness and beyond individual control. Diabetes. 2008;57(7):1768–1773. doi: 10.2337/db08-0163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Vartanian LR, Herman CP, Wansink B. Are we aware of the external factors that influence our food intake? Health Psychol. 2008;27(5):533–538. doi: 10.1037/0278-6133.27.5.533. [DOI] [PubMed] [Google Scholar]
- 36.National Institute of Diabetes and Digestive and Kidney Diseases. Decision making in eating behavior: interacting perspectives from the individual, family, and environment. http://www3.niddk.nih.gov/fund/other/decision2008/. Accessed April 3, 2009. [DOI] [PubMed]
- 37.Small Plate Movement. www.smallplatemovement.org. Accessed April 1, 2009.
- 38.Handy SL, Boarnet MG, Ewing R, Killingsworth RE. How the built environment affects physical activity: views from urban planning. Am J Prev Med. 2002;23(2 Suppl):64–73. doi: 10.1016/s0749-3797(02)00475-0. [DOI] [PubMed] [Google Scholar]
- 39.Li F, Harmer PA, Cardinal BJ, Bosworth M, Acock A, Johnson-Shelton D, Moore JM. Built environment, adiposity, and physical activity in adults aged 50–75. Am J Prev Med. 2008;35(1):38–46. doi: 10.1016/j.amepre.2008.03.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Blackwell AG. Active Living Research and the movement for healthy communities. Am J Prev Med. 2009;36(2 Suppl):S50–S52. doi: 10.1016/j.amepre.2008.10.004. [DOI] [PubMed] [Google Scholar]
- 41.National Center for Education Statistics. 2003 National Assessment of Adult Literacy (NAAL) http://nces.ed.gov/naal/kf_demographics.asp. Accessed March 30, 2009.
- 42.Rothman RL, Housam R, Weiss H, Davis D, Gregory R, Gebretsadik T, Shintani A, Elasy TA. Patient understanding of food labels: the role of literacy and numeracy. Am J Prev Med. 2006;31(5):391–398. doi: 10.1016/j.amepre.2006.07.025. [DOI] [PubMed] [Google Scholar]
- 43.Weiss BD, Mays MZ, Martz W, Castro KM, DeWalt DA, Pignone MP, Mockbee J, Hale FA. Quick assessment of literacy in primary care: the newest vital sign. Ann Fam Med. 2005. 3(6):514–522. doi: 10.1370/afm.405. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Willis SW, Dolan MM, Bertrand RM. Problem solving on health-related tasks of daily living. Ch 12. In: Park DC, Morrell RW, Shifren K, editors. Processing of Medical Information in Aging Patients: Cognitive and Human Factors Perspectives. Mahwah: Lawrence Erlbaum Associates; 1999. [Google Scholar]
- 45.Hill-Briggs F, Smith AS. Evaluation of diabetes and cardiovascular disease print patient education materials for use with low-health literate populations. Diabetes Care. 2008;31(4):667–671. doi: 10.2337/dc07-1365. [DOI] [PubMed] [Google Scholar]
- 46.Gans KM, Risica PM, Strolla LO, Fournier L, Kirtania U, Upegui D, Zhao J, George T, Acharyya S. Effectiveness of different methods for delivering tailored nutrition education to low income, ethnically diverse adults. Int J Behav Nutr Phys Act. 2009;6:24. doi: 10.1186/1479-5868-6-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Simons-Morton DG, Cutler JA. Cardiovascular disease prevention research at the National Heart, Lung, and Blood Institute. Am J Prev Med. 1998;14(4):317–330. doi: 10.1016/s0749-3797(97)00057-3. [DOI] [PubMed] [Google Scholar]
- 48.Woolf SH. The meaning of translational research and why it matters. JAMA. 2008;299(2):211–213. doi: 10.1001/jama.2007.26. [DOI] [PubMed] [Google Scholar]
- 49.Clark NM, Becker MH. Theoretical models and strategies for improving adherence and disease management. Ch 1. In: Shumaker SA, Schron EB, Ockene JK, McBee WL, editors. The Handbook of Health Behavior Change. 2nd ed. New York: Springer Publishing Company; 1998. [Google Scholar]
- 50.US Department of Health and Human Services. Understanding and promoting physical activity. Ch 6. U.S. Surgeon General's Report on Physical Activity and Health. Washington DC: Centers for Disease Control. 1996 [Google Scholar]
- 51.Economos CD, Irish-Hauser S. Community interventions: a brief overview and their application to the obesity epidemic. J Law Med Ethics. 2007;35(1):131–137. doi: 10.1111/j.1748-720X.2007.00117.x. [DOI] [PubMed] [Google Scholar]
- 52.Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 years. N Engl J Med. 2007;357(4):370–379. doi: 10.1056/NEJMsa066082. [DOI] [PubMed] [Google Scholar]
- 53.Trogdon JG, Nonnemaker J, Pais J. Peer effects in adolescent overweight. J Health Econ. 2008;27(5):1388–1399. doi: 10.1016/j.jhealeco.2008.05.003. [DOI] [PubMed] [Google Scholar]
- 54.Cohen-Cole E, Fletcher JM. Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic. J Health Econ. 2008;27(5):1382–1387. doi: 10.1016/j.jhealeco.2008.04.005. [DOI] [PubMed] [Google Scholar]
- 55.Fowler JH, Christakis NA. Estimating peer effects on health in social networks: a response to Cohen-Cole and Fletcher; and Trogdon, Nonnemaker, and Pais. J Health Econ. 2008;27(5):1400–1405. doi: 10.1016/j.jhealeco.2008.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.US Department of Health and Human Services. Physical Activity Guidelines Advisory Committee Report 2008. http://www.health.gov/paguidelines/committeereport.aspx. Accessed April 1, 2009. [DOI] [PubMed]
- 57.Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM. Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403. doi: 10.1056/NEJMoa012512. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Appel LJ, Champagne CM, Harsha DW, Cooper LS, Obarzanek E, Elmer PJ, Stevens VJ, Vollmer WM, Lin PH, Svetkey LP, Stedman SW, Young DR Writing Group of the PREMIER Collaborative Research Group. Effects of comprehensive lifestyle modification on blood pressure control: main results of the PREMIER clinical trial. JAMA. 2003;289(16):2083–2093. doi: 10.1001/jama.289.16.2083. [DOI] [PubMed] [Google Scholar]
- 59.Jackson L. Translating the Diabetes Prevention Program into practice: a review of community interventions. Diabetes Educ. 2009;35(2):309–320. doi: 10.1177/0145721708330153. [DOI] [PubMed] [Google Scholar]
- 60.Huang TT, Goran MI. Prevention of type 2 diabetes in young people: a theoretical perspective. Pediatr Diabetes. 2003;4(1):38–56. doi: 10.1034/j.1399-5448.2003.00022.x. [DOI] [PubMed] [Google Scholar]
- 61.Davis JN, Kelly LA, Lane CJ, Ventura EE, Byrd-Williams CE, Alexandar KA, Azen SP, Chou CP, Spruijt-Metz D, Weigensberg MJ, Berhane K, Goran MI. Randomized control trial to improve adiposity and insulin resistance in overweight Latino adolescents. Obesity (Silver Spring) 2009 doi: 10.1038/oby.2009.19. (Epub ahead of print.) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Bluford DA, Sherry B, Scanlon KS. Interventions to prevent or treat obesity in preschool children: a review of evaluated programs. Obesity (Silver Spring) 2007;15(6):1356–1372. doi: 10.1038/oby.2007.163. [DOI] [PubMed] [Google Scholar]
- 63.Doak CM, Visscher TL, Renders CM, Seidell JC. The prevention of overweight and obesity in children and adolescents: a review of interventions and programmes. Obes Rev. 2006;7(1):111–136. doi: 10.1111/j.1467-789X.2006.00234.x. [DOI] [PubMed] [Google Scholar]
- 64.Foster GD, Sherman S, Borradaile KE, Grundy KM, Vander Veur SS, Nachmani J, Karpyn A, Kumanyika S, Shults J. A policy-based school intervention to prevent overweight and obesity. Pediatrics. 2008;121(4):e794–e802. doi: 10.1542/peds.2007-1365. [DOI] [PubMed] [Google Scholar]
- 65.Gortmaker SL, Peterson K, Wiecha J, Sobol AM, Dixit S, Fox MK, Laird N. Reducing obesity via a school-based interdisciplinary intervention among youth: Planet Health. Arch Pediatr Adolesc Med. 1999;153(4):409–418. doi: 10.1001/archpedi.153.4.409. [DOI] [PubMed] [Google Scholar]
- 66.US Department of Health and Human Services. We Can! (Ways to Enhance Children's Activity and Nutrition) http://www.nhlbi.nih.gov/health/public/heart/obesity/wecan/index.htm. Accessed April 1, 2009.
- 67.Armour TA, Norris SL, Jack L, Jr, Zhang X, Fisher L. The effectiveness of family interventions in people with diabetes mellitus: a systematic review. Diabet Med. 2005;22(10):1295–1305. doi: 10.1111/j.1464-5491.2005.01618.x. [DOI] [PubMed] [Google Scholar]
- 68.Norris SL, Engelgau MM, Narayan KM. Effectiveness of self-management training in type 2 diabetes: a systematic review of randomized controlled trials. Diabetes Care. 2001;24(3):561–587. doi: 10.2337/diacare.24.3.561. [DOI] [PubMed] [Google Scholar]
- 69.Norris SL, Nichols PJ, Caspersen CJ, Glasgow RE, Engelgau MM, Jack L, Snyder SR, Carande-Kulis VG, Isham G, Garfield S, Briss P, McCulloch D. Increasing diabetes self-management education in community settings: a systematic review. Am J Prev Med. 2002;22(4 Suppl):39–66. doi: 10.1016/s0749-3797(02)00424-5. [DOI] [PubMed] [Google Scholar]
- 70.Jack L, Jr, Liburd L, Spencer T, Airhihenbuwa CO. Understanding the environmental issues in diabetes self-management education research: a reexamination of 8 studies in community-based settings. Ann Intern Med. 2004;140(11):964–971. doi: 10.7326/0003-4819-140-11-200406010-00038. [DOI] [PubMed] [Google Scholar]
- 71.Cohen DA, Finch BK, Bower A, Sastry N. Collective efficacy and obesity: the potential influence of social factors on health. Soc Sci Med. 2006;62(3):769–778. doi: 10.1016/j.socscimed.2005.06.033. [DOI] [PubMed] [Google Scholar]
- 72.Sallis JF, Story M, Lou D. Study designs and analytic strategies for environmental and policy research on obesity, physical activity, and diet: recommendations from a meeting of experts. Am J Prev Med. 2009;36(2 Suppl):S72–S77. doi: 10.1016/j.amepre.2008.10.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Ozdemir V, Motulsky AG, Kolker E, Godard B. Genome-environment interactions and prospective technology assessment: evolution from pharmacogenomics to nutrigenomics and ecogenomics. OMICS. 2009;13(1):1–6. doi: 10.1089/omi.2009.0013. [DOI] [PubMed] [Google Scholar]