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1 INFLUENCE OF THE HOM E ENVIRONMENT ON DIETARY INTAKE AND WEIGHT LOSS AMONG OBESE WOMEN FROM RURAL COMMUNITIES By STACEY NICOLE MAURER A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2012
2 2012 Stacey Nicole Maurer
3 To my D ad the biggest Gator fan I have ever known
4 ACKNOWLEDGEMENTS I thank my mentor, Dr. Michael Perri, for his guidance and support throughout this process I also thank the members of my supervisory committee, Dr. Stephen Boggs, Dr. Catherine Price, and Dr. Christina McCrae, for their time and assistance. I would like to thank my family and friends for their c ontinuous love, support and encouragement.
5 TABLE OF CONTENTS page ACKNOWLEDGEMENTS ................................ ................................ .................... 4 LIST OF TABLES ................................ ................................ ................................ 6 ABSTRACT ................................ ................................ ................................ .......... 7 CHAPTER 1 INTRODUCTION ................................ ................................ ........................... 9 Overview ................................ ................................ ................................ ........ 9 Prevalence and Risks of Obesity ................................ ................................ ... 9 Influence of the Environment on Obesity Risk ................................ ............. 10 Influence of the Home Environment ................................ ............................. 14 Lifestyle Interventions Related to the Environment ................................ ...... 16 Specific Aims and Hypotheses ................................ ................................ .... 19 Specific A im 1 ................................ ................................ ................................ ... 19 Specific Ai m 2 ................................ ................................ ................................ ... 20 Specific A im 3 ................................ ................................ ................................ ... 21 Specific A im 4 ................................ ................................ ................................ ... 21 2 MATERIALS AND METHODS ................................ ................................ ..... 23 TOURS Study ................................ ................................ .............................. 23 Participants ................................ ................................ ................................ .. 24 Procedures ................................ ................................ ................................ .. 25 Measures ................................ ................................ ................................ ..... 26 Statistical Analyses ................................ ................................ ...................... 27 3 RESULTS ................................ ................................ ................................ .... 30 4 DISCUSSION ................................ ................................ .............................. 35 LIST OF REFERENCES ................................ ................................ .................... 42 BIOGRAPHICAL SKETCH ................................ ................................ ................. 48
6 LIST OF TABLES Table page 3 1 Baseline Characteristics ................................ ................................ ..................... 33 3 2 Associations Between Dietary Intake and the Home Food Environment at Baseline ................................ ................................ ................................ .............. 33 3 3 Mean Intake of Various Nutrient Groups at Baseline and Month 6 ..................... 33 3 4 Mean Number of Healthy and Unhealthy Foods in the Home at Baseline and Month 6 ................................ ................................ ................................ .............. 34
7 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science INFLUENCE OF THE HOM E ENVIRONMENT ON DIETARY INTAKE AND WEIGHT LOSS AMONG OBESE WOMEN FROM RURAL COMMUNITIES By Stacey Nicole Maurer May 2012 Chair: Michael G. Perri Major: Psychology Rural communities in the U.S. have higher rates of obesity compared to the general population, and several studies have suggested that rural households have limited access to healthy foods. However, little research attention has been given to the contribut ion of the home food environment to dietary intake and weight in rural areas. The current study examined the influence of foods in the home on dietary intake and body weight in a sample of obese adults enrolled in a lifestyle intervention The participants were 166 obese women from rural counties (MSD, age = 59.76.2 years, BMI = 36.54.8 kg/m 2 ). During the intervention, participants were encouraged to make changes to their dietary intake. At baseline, height was determined using a stadiometer. At baseline and Month 6, weight was measured with a balance beam scale, and the home food environment and dietary composition were assessed using the Family Eating and Activity Habits Questionnaire and the Block Food Frequency Questionnaire, respectively. At baseline the home food environment was not associated with BMI ( p = .808). However, at Month 6, the home food environment was associated with BMI ( r = .16, p = .039 ) The mediating role of saturated fat, carbohydrates, protein and fiber was
8 examined. Results sho wed that consumption of saturated fat significantly mediated the relationship between the food environment and BMI (95% CI [ .24, .01], p < .05). These findings suggest that modifying the home environment by decreasing the presence of high fat foods may s upport weight loss in obese individuals from rural communities.
9 CHAPTER 1 INTRODUCTION Overview The increasing prevalence of obesity in the United States over the last 40 years has highligh ted the influential role that the home environment plays in affecting obesity risk. The current study examined what aspects of the home food environment may change during the course of a behavioral lifestyle intervention, as well as how these changes may be related to dietary intake. In addition, this study attempt ed to identify specific factors within the home that may directly, or indirectly, affect body weight This was done with the objective of identifying potential targets for change in future weight management interventions. Prevalence and Risks of Obesity Fl egal Carroll, Kit and Ogden (2012 ) estimate d that approximately one third of the population of the Un ited States is obese, meaning that they have a Body Mass Index (BMI) greater than or equal to 30 kg/m 2 Further, it is estimated that another one third of the population is 25 kg/m 2 ), meaning that a greater number of individuals currently meet criteria for overweight and obese categories than for the normal weight category (Centers for Dise ase Control and Prevention, 2010 ). The dangers ass ociated with obesity are numerous; including an increased risk for developing cardiovascular disease (CVD) diabetes mellitus, hypertension, pulmonary disease, stroke and some types of cancers (National Heart, Lung and Blood Institu t e, 2010 ). In fact, obesity is associated with increased all cause mortality compared to normal weight (BMI = 18 25 kg/m 2 ) when controlling for age, race, gender, smoking status and alcohol consumption ( Flegal, Graubard, Williamson, & Gail 2005). Although
10 actual est imates of annual obesity related deaths vary from 115,000 to greater than 300,000 it is clear that obesity is associated with an increased mortality rate particularly from cardiovascular disease, diabetes mellitus, kidney disease and o besity related cancers (Allison, Fontaine, Manson, Stevens, & VanItallie, 1999, Flegal, Graubard, Williamson, & Gail 2007 Morkdad, Marks, Stroup, & Geberding, 2005 ). Influence of the Environment on Obesity Risk Certain ethnic, socioeconomic and geograph ic groups are more likely to be obese than others, indicating a variety of cultural, environmental and biological factors that are contributing to obesity prevalence. For example, r ates of obesity are higher in rural areas of the United States; in fact, it is estimated that adults in rural areas are 28% more likely to be obese than those living in urban and suburban areas (Eberhardt & Pamuk 2004). In addition, there is a higher prevalence of cardiovascular disease in rural areas c ompared to urban areas (Bennett, Olatosi, & Probst, 2008). Multiple factors may contribute to this disparity focuse d on the various contributors to cardiovascular disease and obesity risk One hypothesis is based in the fact that a strong relationship has been observed between lower socioeconomic status and higher prevalence of cardiovascular disease, and higher rates of poverty have been found in rural communities. Although CVD risk factors decreased for all income groups between 1971 and 2002, there have been minimal improvements in income g roup disparities on this issue ( Kanjilal et al., 2006, United States Department of Agriculture Economic Research Service, 2004). Additionally, while 20% of Americans live in rural areas; only 9% of the ans practice there This disparity in access to medical services coupled with the fact that rural residents typically must travel extended distances to obtain these services may affect all aspects of health care, but particularly obtaining preventive
11 health services (van Dis, 2002). In rural areas, the limited health care services that are available are typically directed towards more acute care concerns with less focus on general health promot ion and prevention (Flora, C., Flora, J., Fey 2004). Anot her conceptualization of this disparity is focused on a major contributor to obesity risk; physical inactivity Individuals living in rural areas are more likely than ever to be physically inactive, potentially because the increased mechanization of jobs t hat previously required physical labor is leading to a more sedentary lifestyle and a decrease in caloric expenditure ( Flora et al., 2004, Pearson & Lewis, 1998) This modern trend is evident when comparing physical in activity during leisure time between r ural and suburban areas, where rural residents are 50% more likely to be physically inactive during leisure time (Eberhardt & Pamuk 2004). As one might expect, there tends to be fewer physical activity and recreational facilities in areas that are less popula ted. However, Gordon Larsen, Nelson, Page, and Popkin ( 2006) found that the odds of engaging in at least five bouts of moderate to vigorous p hysical per week increased by 3% with the addition of just one physical activity facility. These odds improve d with each additional physical activity facility. Moreover rural residents are least likely to meet the recommended physical activity guidelines, and there are typically fewer sidewalks and safe places to walk in rural communities; factors that are also associated with a more sedentary lifestyle ( Parks Housemann, & Brownson 2003). However, more research has focused on the environment of rural communities as it relates to food consumption both within the home and at the neighborhood level. For the purp and can include such factors as number of stores within
12 walking distance of the home as well as types of restaurants within the county limits (Larson, Sto ry, & Nelson, 2009). where it is often more difficult to purchase high qual ity, nutritional foods (Wrigley, Warm, Margetts, & Whelan, 2002). This is important because the greatest variety of these healthy foods is likely to be found in supermarkets, where the y are also typically offered at the lowest cost. In contrast, convenience stores tend to sell high calorie foods such (i.e., high calorie, nutrient poor foods) and li ttle fresh produce (Larson et al., 2009). Ford and Dzewaltowski (2008) found that less populated areas tend to have fewer food stores, as well as a decreased availability and higher pricing of healthy foods within those stores In fact, rural and farm area s have been shown to have 14% fewer chain supermarkets compared to urb an areas (Powell, Slater, Mirtcheva, Bao, & Chaloupka, 2007). Another study found that almost 75% of rural food stores were convenience stores (Liese Weis, Pluto, Smith, & Lawson, 2007) This differential access to chain supermarkets may be more influential than previously thought ; Morland Diez Roux, and Wing (2006) observed that having a supermarket Census tracts with access to supermarkets only, and no access to convenience stores or grocery stores displayed the lowest levels of obesity (20.8%), whereas census tracts with access to only grocery stores or both grocery stores and convenience stores di splayed the highest levels of obesity (35.7% and 40.3%, respectively). A nother study f ound that a reduced access to supermarket s was associated with an increase in obesity risk, even after controlling for individual level characteristics (i.e., age,
13 educat ion, income, smoking status, sex, black race, and Hispanic ethnicity) (Lopez 2007). Further complicating this problem are regional differences in food preference and meal selection, such that traditionally s outhern foods are typically higher in fat and sa lt content Traditionally, the rural southern United States has been made up of relatively poor communities that developed specific eating patterns out of necessity One of these eating patterns is the consumption of relatively large amounts of pork, given the availability and low cost of raising hogs. As a result, foods such as bacon, sausage, and ham are consumed in many rural southern diets. In addition, using salted pork fat or lard to flavor other fo ods is a common practice (Smith, Quandt, Arcury, Wetm ore, Bell, & Vitolins, 2006 ). This disparity is further evident from observing differences in the home food environment s of individuals living in a southern state compared to individuals living in a northern state, such that the homes of individuals in the South contain more high f at foods, on average (Krukowski, Harvey Berino, & West, 2010). Furthermore, in a study of middle school aged children in rural Mississippi, results showed that intake of saturated fat and sodium exceeded the recommended daily levels while intake of calcium, fruits and vegetables were inadequate (Davy Harrell, Stewart, & King, 2004). This is of partic ular relevance because rural counties in the South tend to have some of the highest rates of obesity ( CDC 2010). Overall, the interaction of all of these factors (i.e., poverty, reduced access to preventive services, cultural factors related to diet and physical activity) has led rural areas to be slower in adopting changes in lifestyle behaviors that may alter obesity and CVD risk, such as decreasing intake of saturated fat and increasing physical activity
14 ( Jones, Parker, Ahearn, Mishra, & Variyam, 2009, Patterson Moore, Probst, & Shinogle, 2004) Influence of the Home Environment There has been an increased focus on the availability of healthy foods in the community and how this relates to obesity risk. Accordingly, additional research has found a similar relationship between the presence of healthy foods in the community and both dietary intake and household availability of these foods The majority of this research; however, has been focused primarily on the consumption of fruits and vegetables. For example, one study showed a direct relationship between living in a census tract with at least one supermarket and meeting the U.S. Department of Agriculture and the U.S. Department of Health and Human Services 2000 Dietary Guidelines for fruit and ve getable intake, although the strength of this relationship varied by race. For black individuals, each additional supermarket within their census tract was associated with a 32% increase in meeting the fruit and vegetable recommendations, whereas for white individuals, each additional supermarket was assoc iated with only an 11% increase (Morland, Wing, & Diez Roux, 2002). Despite racial differences in this sample, the effect of a greater variety of foods available within the community on dietary intake is d iscernible. Additionally, Rose and Richards ( 2004) found that households where the majority of food was purchased from a supermarket consumed more fruit per day than those where food was purchased from other stores. Importantly, t he driving factors behind this relationship may be greater selection and greater variety of t ypes of fruits and vegetables. This was illustrated in a random sample of 102 participants in New Orleans where a p ositive relationship was found between vegetable shelf space and vegetabl e consumption, such that each additional linear meter of vegetable shelf
15 space was associated with a n increase in vegetable intake of 0.35 daily servings. A similar relationship was found in this sample between more varieties of fresh vegetables and vegetable consumption, although no relationships were found for fruit (Bodor Rose, Farley, Swalm, & Scott, 2008). Although this association has been demonstrat ed in various populations, fur ther research into whether similar relationships exist for unhealthy foods as well as fruits and vegetables is relatively limited. A limited number of studies have investigated the relationship between the availability of vari ous foods in the community and the availability of foods in the home. Further, almost no research has examined the relationship between the availability of foods in the home and consumption of those foods in adults (e.g., at the micro level) Although ther e is some evidence to sugges t that the presence of healthy foods in local supermarkets and grocery stores is associated wit h a greater presence in the home as well as an increased intake of these foods (Fisher & Strogatz, 1999, Auchincloss Diez Roux, Brow n, Erdmann, & Bertoni, 2008), these relationships have only been demonstrated with a few specific foods (i.e., fruits, vegetables, low fat milk) and have not been e xamined within the context of a weight man agement intervention. Nationally, t he trend over the last 30 years has been away from eating meals in the home and more towards eating meals at re staurants. However, Nielson, Siega Riz, and Popkin (2002) estimated that up to 65% of calories are still being consumed in the home, sug gesting that this envir onment remains highly influential on eating patterns. At the base of this influence may be a combination of operant and Pavlovian conditioning (Bouton, 2010). Bouton theorized that because eating occurs in many different contexts, individuals tend to devel op associations between food and the environment over time.
16 In addition, the presence of certain food s may trigger a desire to eat even if the individual is satiated. Taken together, it is apparent how the home environment is an important influence on eati ng patterns. These micro level (i.e., home) factors related to obesity and dietary intake have been examined with some depth in children and adolescents. Results from these studies are relatively consistent and suggest that the presence of healthy foods i n the home was associated with greater intake of fruits and vegetables (Bl anchette & Brug, 2005, Hearn et Specifically, one study showed that 1.3 additional servings of fruits and/or vegetables were consumed by girls where fruits a nd vegetables were always available versus so metimes/never available (Hanson, Neumark Sztainer, Eisenberg, Story, & Wall, 2004). Another study showed that the st eating behaviors were home food behaviors (i.e., eating meals as a family, eating breakfast) an Anderson, Fox, & Lenardson, 2011). However, a consistent theme noted in the literature related ffect on children and adolescents is that children are subject to the desires and preferences of the adults in the home. It is rarely taken into account that other individuals in the home (including children, spouses, etc.) can have an adverse effect on the adult purchasing food for the household. For example, a child in the home may insist that ice cream is available, which in turn can influence an individual attempting to make healthy lifestyle changes and the home environment in general Lifestyle Interventions Related to the Environment Few studies have examined ch anges in the home food environment during the course of a weight management intervention and to our knowledge; no studies have
17 examined the simultaneous effects of the home food environment and dietary intake on body weight within this context. Currently, the standard lifestyle in tervention for obesity is comprised of three main components which are aimed at achieving the following; moderate reduction in caloric intake, increase in physical activity, and acquisition of cognitive behavioral strategies that will aid in accomplishing the first two goals. T his behavioral approach to treatment for obe sity is typically delivered weekly in a group based format for 16 to 26 weeks with the goal of producing weight losses of 0.4 to 0.5 kg per week ( Butryn Webb, & Wadden, 2011 ). After the initial intervention phase of group behavioral treatment, individuals typically lose 8 10 kg which is equivalent to 8 10% of initial weight (Wadden, Butryn, & Wilson, 2007) This has been demonstrated in multiple trials over the la st twenty years which is consistent with recommendations that even a 3 5 % decrease in body weight is associated with health benefits such as reducing blood lipid levels and blood pressure, and decreasing the risk of cardiovascular morbidity and mortality ( American Journal of Clinical Nutrition, 1998) Additionally, approximately 80% of individuals who begin a lifestyle intervention complete treatment indicating that this may be an effective way of administering treatment to obese individuals (Wadden et al. 2007 ). Unfortunately, participants on average regain one third of their lost weight within one year of treatment ending, and nearly one half of participants return to their original weight with five years (Curioni & Loureno, 2005 Perri & Corsica, 2002, Wadden et al., 2007, Wing, 2002) This striking pattern of weight regain conveys that further research into means of supporting weight maintenance is greatly needed.
18 The cornerstone of any behavioral treatment for obesity is self monitoring. This is typically done through use of food records to record types/amounts of foods and caloric intake, pedometers or other tools to measure physical activity and scales to monitor weight. Goals for dietary intake typically involve reducing daily calories consumed by 500 to 1000 kcal in a balanced fashion, meaning that there is no one nutrient group that is targeted for greater reductions than the others. However, there is some support for limiting fat intake to no more than 30% of total calories, and specifically decreasing saturated fat intake so that it makes up no more t han 10% of daily total calories. Current research supports decreasing fat intake as well as total calories because some studies have shown improved weight loss when both aspects are targeted (Pas cale Wing, Butler, Mullen, & Bononi, 1995). This indicates that modifying the home environment to decrease the availability of high fat foods may be particularly important in supporting weight management efforts over the long term. One cognitive behaviora l strategy often discussed during an intervention for obesity is the implementation of stimulus control techniques As previously outlined, and over time the association between cues and eating can strengthen. Stimulus control techniques as they relate to behavioral treatment for obesity can help individuals manage cues that may lead to overeating or making unhealthy food choices These cues (i.e., high risk situations) can include certain restaurants or aisles of the grocery Within the home, employing strategies such as storing unhealthy foods out of sight, cleaning plates immediately af ter eating, or keeping large serving dishes off the ta ble during a meal can all decrease inappropri ate
19 eating (Brownell 2000). Conversely, positive cues can be used to increase desirable behaviors such as placing fruits and vegetables in visible locations to increase consumption and placing walking shoes at the front door to increase physical activity. The body of research focused on stimulus control techniques in the context of a behavioral lifestyle intervention is limited (Wadden, Crerand, & Brock, 200 5). Some research studies that have examined the influence of an intervention on the home environment have b een focused on the spouse of the research participant or within the context of a home grocery delivery program (Gorin et al., 2008, Gorin, Raynor, N iemeier, & Wing, 2007). However, Krukowski et al (2010) attempted to quantify the influence of a behavioral lifestyle intervention on the home food environment and how that may differ for individuals living in a northern versus a southern state. Results from this study showed a significant decrease in presence of high fat foods in the home following an intervention for individuals living in both the northern and southern state but there was no significant change in availability of low fat foods in the h ome. An interaction effect was also detected, such that individuals living in the southern state displayed greater reductions in the number of high fat foods available in their homes following an intervention com pared to individuals living in the northern state. This may have been detected in part because the homes in the southern state had, on average, more high fat foods available in their homes at baseline. However these decreases in high fat food availability were not associated with weight change duri ng treatment. Specific Aims and Hypotheses Specific Aim 1 Examine the relationship between the home food environment and dietary intake in a sample of obese adults prior to undergoing a weight management
20 intervention. We hypothesized that the home food env ironment would be significantly associated with eating patterns such that (a) a greater availability of healthy foods in the home would be associated with greater intake of fiber and (b) a greater availability of unhealthy foods in the home would be as sociated with greater intake of saturated fat. For the purpose of this study, healthy foods were defined as fruits and vegetables; which are significant sources of fiber. On the other hand, the category of unhealthy foods w as defined as snack foods, ice cream, and baked goods; all of which are traditionally high in saturated fat. Specific Aim 2 Determine the impact of a behavioral lifestyle intervention on dietary intake. There are three macronutrients that compose dietary intake; protei n, carbohydrates and fat. However, for this study we also chose to include fiber as a major component of dietary intake although it is not considered a macronutrient because of its relationship with fruit and vegetable intake We also chose to include sa turated f at instead of total dietary fat. Total dietary fat is comprised of saturated fat ty acids polyunsaturated fat ty acids monounsaturated fat ty acids and trans fat ty acids Saturated fat was included over total fat because of the body of research tha t states that saturated fat, more than total fat, is associated with CVD risk (Krauss et al., 1996 ). A preliminary analysis also indicated that total fat and saturated fat were hi ghly correlated ( r = .951, p < .01). In summary, fiber was included because o We hypothesized that from baseline to Month 6, intake of saturated fat would significantly decrease and intake of fiber would significantly increase again recognizing that we generally expect healthy food consumption to increase and unhealthy food consumption
21 to decrease During the course of the intervention, consumption of saturated fat (rather than total fat) is targeted beca use of the health risks that are associated with an excessive intake of saturated fat specifically. Conversely, total fat was not exclusively fats). We also expected decreases in both protein and carbohydrates because these macronutrient groups typically make up the largest percentage of daily caloric intake. Since the intervention is directed towards decreasing total caloric intake, individuals will likely decrease th eir intake of these macronutrients as they decrease portion sizes Specific Aim 3 D etermine the impact of a behavioral lifestyle intervention on the home food environment. We expected that from pre to post treatment (i.e., baseline to Month 6 ) the mean n umber of healthy foods in the home would increase, and the mean number of unhealthy foods in the home would decrease. We also planned to examine whether the home food environment was associated with change in BMI at baseline and Month 6 However, previous studies have failed to uncover a relationship between weight change and change s in home food environment (Krukowski et al., 2010). Specific Aim 4 D etermine if consumption of fiber, protein, carbohydrates, and saturated fat mediated the relationship between the home food environment and BMI at Month 6. This was examined as a follow up to Aim 3. Since the home environment and weight change have not bee n directly related in other studies, we examined potential indirect effects and multiple potential mediators of the relationship. In a model for multiple mediation, multicollinearity among potential mediators can compromise the significance of particular i ndirect effects leading the researcher to assume that there is no indirect
22 effect when in fact there is, and vice versa (Preacher & Hayes, 2008). Since total fat and saturated fat were highly correlated, we chose to include saturated fat over total fat in this model; again referencing the unique influence that saturated fat may have on CVD risk Fiber was also included as a mediator, as it was indicated to be a component of dietary intake in the first aim We hypothesized that intake of protein, carbohydrat es, saturated fat and fiber would partially explain the relationship between the home food environment and BMI. We expect this because while the home environment may not affect weight directly, we might expect to see that the home environment is related to dietary intake, which in turn can affect BMI.
23 CHAPTER 2 MATERIALS AND METHODS TOURS Study The current study was a secondary data analysis that utilized data from the Treatment of Obesity in Underserved Rural Settings (TOURS) study, a randomized control led trial (Perri et al., 2008). The TOURS study was conducted in six rural counties in north Florida and was aimed at determining the effectiveness of three models of extended care in rural populations. The study was conducted in two phases. During the first six months of the study (i.e., intervention phase), all women attended weekly group sessions at their local county cooperative extension office. Weekly sessions were focused on helping participants achieve a moderate reduction in energy in take (i.e., 500 to 1000 Kcal per day), increasing physical activity and implementing cognitive behavioral strategies to support behavior change. In addition to an overall reduction in calories, participants were encouraged to improve their dietary intake b y decreasing intake of saturated fat, choosing lean sources of protein, and increasing consumption of fiber through intake of fruits and vegetables. Participants were provided with food and physical activity records to aid in self monitoring and goal setti ng. Sessions were focused on aiding individuals in achieving weight losses of approximately 0.4 kg per week. Individuals were also encouraged to set goals for physical activity and to consistently achieve an average of 30 minutes per day of walking at leas t six days per week. The physical activity aspect of the intervention also their daily steps by a minimum of 3,000 steps from their baseline (pre treatment) level
24 The second phase (i.e., follow up) consisted of random assignment to one of three extended care conditions: an Office based Maintenance Program Telephone based Maintenance Program or an Education Control Condition During this phase, all individuals re ceived contact twice per month although the method of communication varied The Office based Maintenance Program received contact in the for m of a Face to Face session, the Telephone based Maintenance Program participated in an individual Telephone sessio n, or the Education Control Condition received a direct Mailing/newsletter The extended care phase of the study lasted for 12 months (Month 6 to Month 18) and in each of the three conditions individuals were encouraged to continue keeping self monitoring records of their diet and physical activity. Participants Participants were women b etween the ages of 50 75 whose weight placed them in the obese category (BMI 30 50 kg/m 2 ). However, their weight had to be less than 159 kg (350 lbs). Women also had to be living in one of six rural counties in north Florida. Women were excluded if they had any uncontrolled medical conditions, any conditions that were likely to influence treatment outcomes, or any conditions for which eating and physica l activity changes would be unsafe. These exclusionary criteria included any serious or uncontrolled medical conditions, such as uncontrolled hypertension or diabetes, recent myocardial infarction or stroke, history of solid organ transplantation, serious infectious disease, abnormal lab values, or any other physical conditions likely eating and physical activity changes. If individuals reported use of antipsychot ic medications, monoamine oxidase inhibitors, systemic corticosteroids, human immunodeficiency virus or tuberculosis antibiotics, chemotherapeutics medications or
25 weight loss medications, they were excluded from participation. In addition, individuals were excluded if they reported a psychiatric disorder or excessive use of alcohol, were unable or unwilling to provide informed consent, were un able to read English at a fifth grade level, were currently participating in another research study, or were unwilli ng to be randomly assigned. Of the 559 women who responded to the study recruitment announcements, 261 were excluded. Among the 261 women who were not eligible for participation, 82 were excluded due to elevated blood pressure, 76 had abnormal lab values, 29 had contraindications based on medical history, 27 had some other abnormal result during screening, 17 had a BMI that was out of range, and 30 declined participation. In addition, only cohorts 2 and 3 were asked to fill out the Family Eating and Activit y Habits Questionnaire at both baseline and Month 6. The data from this questionnaire was a main outcome of the current study, and therefore another 132 women were excluded for missing data on this measure for at least one time point Procedures Women in the current study were recruited through a variety of means, including mailings, newspaper study announcements, and in person recruitment conducted at churches, community centers and community events. Interested women first completed a brief screening over the telephone and if they were deemed eligible, were scheduled for an in person screening visit. At the screening visit, women received a more detailed description of the study and were given the opportunity to provide informed consent. At this time, wome n were also asked to complete detailed questionnaires about their demographics, diet, physical activity, medical history, medication use, quality of life, mental health status and home environment. In addition, a blood sample was collected
26 and height, weig ht, abdominal measurements, resting heart rate, and blood pressure were assessed. Women also completed a 6 minute walk test to assess their current level of physical fitness and mobility. Women who were eligible after the first screening visit were asked t o return for a second screening visit within two weeks prior to their first group session. At the second screening visit, women repeated the 6 minute walk test and a blood sample was collected to ensure that there had been no significant changes in their m etabolic profile. Weight was also reassessed and women were excluded if they had gained greater than 4.5 kg since their first screening visit. Measures Body w eight : At baseline, height was measured using a stadiometer and weight was taken using a balance beam scale. At Month 6, weight was measured again. At both time points, weight was measured by a study nurse masked to the treatment condition of the participants and rou nded to the nearest tenth of a kilogram. Change in weight was calcula ted by taking the difference between weight at baseline and weight at Month 6. Home e nvironment: The home environment was evaluated using the Stimulus Exposure subscale of the Family Eating and Activity Habits Questionnaire (Golan & Weizman, 1998) This s ubscale is comprised of a food inventory for healthy foods (i.e., various fruits and vegetables) and unhealthy foods (i.e., snack foods, desserts) where individuals are asked to indicate whether or not these foods were present in the home within the past m onth (Golan & Weizman, 1998). From this, an estimate is obtained of the presence (i.e., number) of these various foods in the home. This questionnaire has been shown to have good test re test reliability ( r = .85).
27 Dietary i ntake : Dietary intake was assess ed using the Block 95 Food Frequency Questionnaire (Block et al., 1986) This is a food inventory where individuals are asked about foods they have eaten within the past year, as well as how much of these foods they have eaten and how often they have eaten them. From this, an estimate of daily intake of various nutrients is obtained. For the purpose of this study, only daily estimates of saturated fat, carbohydrates, protein and fiber were used. All estimates are reported in mean gram s of the given nutrient per day during the time period assessed (i.e., one year). The Block 95 FFQ was shown to be correlated with 4 day diet records ( r = 0.5 0.6), suggesting a moderate to good ability to assess dietary intake (Block, Woods, Potosky, & Clifford, 1990). Statisti cal Analyses The statistical software package PASW SPSS 18.0 for Windows (SPSS, Inc., IL) was used to conduct the statistical analyses for this research study. For the first aim we conducted two sets of four correlations each (eight total correlations), a ll using variables from the baseline assessment The first group of correlations was between availability of healthy foods in the home and dietary intake and the second group of correlations was between availability of unhealthy foods in the home and diet ary intake In the current study, dietary intake was defined as estimated daily intake of saturated fat, carbohydrates, protein and fiber all measured in grams Availability of healthy and unhealthy foods in the home was taken from the Family Eating and Activity Habits Questionnaire and dietary intake estimates were taken from the Block Food Frequency Questionnaire. The second aim was evaluated using dependent samples t tests using intake of saturated fat, carbohydrates, protein and fiber as the variables. These variables were
28 assessed at baseline and Month 6, and participant responses were used from these two time points to determine if the change in these variables following the intervention was significant. The third a im was evaluated in the same way, using dependent samples t tests with number of healthy foods in the home and number of unhealthy foods in the home as the two variables. Again, the values at baseline and Month 6 were used to assess change. In addition, as a follow up to the moment correlation was also used to determine if these changes in healthy and unhealthy foods in the home were associated with change in BMI. For this additional aim, a composite score of the home environm ent was created from the Stimulus Exposure subscale of the Family Eating and Activity Habits Questionnaire The items in this subscale were divided into presence of healthy foods in the home and presence of unhealthy foods in the home. Half of the items (i .e., items measuring the unhealthy foods in the home) were reverse scored and combined with the remaining items. The result is an overall score that represents the home environment, where a higher score indicates a hy foods in the home). As a preliminary analysis to the fourth aim, the relationship between the home environment and BMI (both at Month 6) was assessed in order to test for a direct relationship between the two variables. If this relationship was determin ed to be significant, t he Preacher and Hayes model for multiple mediation was used for the final aim to determine if estimates of dietary intake mediated the relationship between t he home food environment and BMI at Month 6 The composite score of the home food environment at Month 6 was entered into the model as the independent variable and
29 BMI at Month 6 was entered as a dependent variable. Baseline BMI was included in the model as a covariate to account for weight change du ring the intervention. Both time points were used in the model instead of change scores because changes scores have not been shown to have consistent validity (Cronbach & Furby, 1970) Intake of saturated fat, carbohydrates, protein and fiber were entered into the model as mediators. The Preacher and Hayes model for mediation was chosen over th e Baron and Kenny model to reduce the familywise error rate. The Preacher and Hayes model also allows for the meditational analys i s in the context of a non significan t original relationship. The composite score of the home environment was used in this analysis as well.
30 CHAPTER 3 RESULTS Baseline Characteristics: The study sample consisted of 166 obese women from rura l communities in north Florida, with a mean age of 59.7 years and a mean BMI of 36.5 kg/m 2 The sample was predominantly white (78.9%), married (72.7%), with an Baseline characteristics of the sample are summarized in Table 3 1. Relation ship between th e home food environment and dietary intake at baseline : Pre treatment examination of the association between the home food environm ent and dietary intake showed a significant association between number of unhealthy foods in the home and intake of fiber, as well as number of unhealthy foods in the ho me and intake of carbohydrates These results suggest that a greater availability of unhealthy foods in the home is associated with a greater intake of fiber and carbohydrates. The relationship s between number of unhealthy foods in the home and intake of protein and between number of unhealthy foods in the home and saturated fat were non significant. Results also show ed a trend towards significance for the relationship between number of unhealthy foods in the home and intake of saturated fat In addition, t here were no significant associations between healthy foods in the home and intake protein fiber, carbohydrates or saturated fat Results are shown in Table 3 2. Impact of a behavioral lifestyle intervention on dietary intake : For the second aim, we assessed whether dietary intake significantly changed from baseline to Month 6, defining dietary intake as mean daily consumption of saturated fat, carbohydrates, protein and fiber. Results from this analysis show ed that individuals decreased their consumption of protein by a mean of 10.6g (SD = 36.9g ). This change from baseline to
31 Month 6 reflect ed a significant decrease. Individuals also decreased their saturated f at intake by a mean of 9.3g (SD = 12.3 g ) which represented a significant difference. Mean intake of carbo hydrates decreased by 27.9g (SD = 12.3 g ). This decrease in carboh ydrate consumption also reflected a significant change. Although consumption of fiber increased by a mean of .91g (SD = 9.3 g), this was not a significant change. Results from this analysis are shown in Table 3 3. Impact of a behavioral lifestyle intervention on the home food environment : From baseline to Month 6, the mean number of unhealthy foods in the home decreased by 1.05 (SD = 3.73). This represent ed a significant change In addition, the mean number of healthy foods in the home also significantly changed from baseline to Month 6, such that the mean number of healthy foods increased by 1.04 (S D = 3.65). These results are shown in Table 3 4. At baseline, there was no significant association between the home food environment and BMI ( p = .808). However, an assessment of the relationship between the home environment and BMI revealed a significant association at Month 6 ( r = .16, p < .05) These results suggest that a greater availabilit y of healthy foods in the home wa s related to a lower BMI. Estimates of dietary intake as mediator s of the relationship between the home food environment and BMI : As noted, t he relationship between the home environment and BMI at post treatment was significant and the Preacher and Hayes model f or mediation was utilize d to determine if intake of saturated fat, carbohydrates, protein and fiber mediated the relation ship between the home food environment and BMI at Month 6. One thousand bootstrapped samples were used. When these four
32 mediators were entered into the model results show ed that the overall set of mediators wa s significant (95% CI [ .24, .01], p < .05). Because the strength of the relationship between the predictor and the dependent variable is reduced when the mediators are included, a partial mediation is indicated. These results suggest ed that having more h ealthy foods in the home wa s associated with a lower intake of saturated fat, carbohydrates and protein and a greater intake of fiber, which is consequently associated with a lower BMI. However, although four mediators were hypothesized, f urther examination showed that only intake of saturated fat wa s a significant mediator of this re lationship (95% CI [ .32, .01], p < .05) meaning that intake of carbohydrates (95% CI [ .03, .05]) protein (95% CI [ .03, .13]) and fiber (95% CI [ .13, .04]) did not contribute any additional significance above and bey ond the contribution of saturated fat Taken together, the results for the fourth aim show that individuals made changes to their home environment and dietary intake in the suggested directions and these changes were associated with a lower BMI following an intervention.
33 Table 3 1 Baseline Characteristics Variable n = 166 Age, in years, M ( SD ) BMI, in kg/m 2 M ( SD ) Weight, in kg, M ( SD ) Race /ethnicity White, Non hispanic n (%) African American n (%) 59.7 (6.2) 36.5 (4.8) 95.7 (14.7) 131 ( 78.9 ) 27 ( 16.3 ) Hispanic/Latina n (%) 2 ( 1.2 ) Other n (%) Education Less than 12 years of education n (%) Trade or vocational school n (%) 6 ( 3.6 ) 58 (35.2) 69 (41.8) n (%) Post baccalaureate education/training n (%) Marital Status Never married n (%) Divorced/separated n (%) Widowed n (%) Presently married n (%) Living in marriage like relationship n (%) 21 (12.7) 17 (10.3) 3 (1.8) 20 (12.1) 1 6 (9.7) 120 (72.7) 6 (3.6) Table 3 2 Associations Between Dietary Intake and the Home Food Environment at Baseline Protein Fiber Carbohydrates Saturated fat Unhealthy foods .111 .194* .175* .152 Healthy foods .032 .049 .050 .069 Note : p < .05 Table 3 3 Mean Intake of Various Nutrient Groups at Baseline and Month 6 Baseline M SD Month 6 M SD t (16 5 ) Protein 66.06 40.48 55.41 23.91 3.71 Fiber 16.22 8.71 17.13 7.18 1.26 Carbohydrates 200.14 103.47 172.22 65.94 3.74 Saturated fat 22.75 13.63 13.41 6.95 9.74 Note: All variables are measured in mean grams per day of each nutrient. ** p < .01
34 Table 3 4 Mean Number of Healthy and Unhealthy Foods in the Home at Baseline and Month 6 Baseline M SD Month 6 M SD t (165) Healthy foods 12.47 3.95 13.51 3.77 3.68 Unhealthy foods 9.33 4.55 8.27 3.89 3.65 Note: p < .01
35 CHAPTER 4 D ISCUSSION The objective of the current study was to determine the effects of a behavioral lifestyle intervention on the home food environment and dietary intake. This study also examined how these changes directly affected body weight during t he course of an intervention In addition the indirect effect of the home food environment on body weight wa s examined using intake of protein, carbohydrates, fiber and saturated fat as potential mediator s for this relationship. The major findings from the current study are as follows. First, a significant associati on was found between number of unhealthy foods in the home and daily intake of fiber as well as between number of unhealthy foods in the home and intake of carbohydrates. There was also a trend towards significance for the relationship between number of un healthy foods in the home and saturated fat. There were no significant associations found between healthy foods in the home and dietary intake The current study also found that individuals significantly change d their dietary intake following a behavioral lifestyle intervention. Results show ed that individuals significantly decrease d their daily protein, saturated fat and carbohydrate i ntake. Although consumption of fiber increased following the intervention, this did not represent a significant change. In addition, results from the current study show ed that individuals significantly change d their home food environment following an intervention. These changes occur red in the expect ed directions given the focus on behavior change and nutrition education duri ng the intervention. This means that on average the number of healthy
36 foods in the home significantly increased following an intervention and the number of unhealthy foods in the home significantly decrease d following an intervention. Finally, a significa nt direct effect between the home food environment and body weight was found at Month 6. Therefore, this relationship was examined further to determine if intake of saturated fat, fiber, protein and carbohydrates mediated the relationship between the home food environment and body weight. As a set of mediators, this group was determined to indirectly affect the relationship, but further examination revealed saturated fat as the only significant mediator. Generally, these results suggest ed dietary intake wa s related to the types of foods that were available in their home; this relationship is stronger for a greater presence of unhealthy foods but may not necessarily generalize to presence of healthy foods in the home. Results from the first aim also show ed that the relationsh ip between presence of unhealthy foods in the home and intake of saturated fat was trending towards significance, which supports previous research that suggests the presence of unhealthy foods may be more i nfluential on dietary intake than the presence of healthy foods. A significant positive association was also found between intake of fiber and unhealthy foods in the home, which was not hypothesized. A potential explanation for this finding may be found wh en examining the types of foods Activity Habits Questionnaire. For example, popcorn can be found on the unhealthy that may be found in their home in free response form. Popcorn, along with some
37 fiber. This particular questionnaire does not differen tiate between these high fiber snacks and snacks such as donuts or potato chips that may provide little to no nutritional value. The results also show ed that individuals made changes to their eating patterns as encouraged by the lifestyle intervention. Given these results, these changes may have include d eating smaller portions of high fat protein sources and decreasing consumption of foods that are high in saturated fat, whic h could include red meat, snack foods, dessert foods and high fat dairy. In addition, these results show ed decreases in consumption of carbohydrates. Considering that carbohydrates may make up the largest portion of daily caloric intake, this may indicate that individuals wer e decreasing their calories overall. The results also show ed that intake of fiber did not significantly change during the course of the intervention, which is consistent with previous research that individuals tend to decrease the amoun t of unhealthy foods in their diet without increasing their intake of healthy foods such as fruits and vegetables (Krukowski et al., 2010). In terms of the home food environment, the results show ed that individuals both increased the availability of healthy f oods in the home and decreased the number of unhealthy foods in the home simultaneously during the course of a lifestyle intervention. These results differ ed from those in a previous study that suggested that individuals tend to eliminate unhealthy foods from th e home (and their diet) without actually adding healthy foods (Krukowski et al ., 2010). The changes detected in the home food environment were relatively small; however, this inability to detect larger changes may repres ent the lack of standardization in home food environment questionnaires or a reporting bias. Further, when it is considered that even minimal changes in food
38 consumption can result in a rather significant caloric reduction, it can be argued that these seem ingly minor modifications represent clinically significant findings. Finally, the resu lts from the current study indicate d that there wa s a direct effect of the home food environment on BMI following a behavioral lifestyle intervention such that a greater availability of health y foods in the home wa s associated with a lower BMI at Month 6 Our findings are unique when compared with a previous study that found that no association between changes in the home food environment and body weight (Krukowski et al. 2010) While the notion that the food environment affects the consumption of certain foods has been demonstrated in c hildren and adolescents and frequently considered in adult populations this has never been examined within the context of a lifestyle int ervention for obesity. The results from this study show that the beneficial changes made during the course of an intervention do, in fact, affect dietary intake and consequently body weight. There are some important limitations to this study that should be addressed. First of all, only self report measures were used. Given that the individuals who participated in the study wer e awar e that the goals of the study wer e to improve their eating pat terns and environment in order to promote weight loss, they could have potentially responded to questionnaires in a way that is more socially desirable. Another limitation of the study is the sensitivity of the Family Eating and Activity Habits Questionnai re. This questionnaire only asked about a limited number of foods and may therefore not have been able to detect subtle differences In addition, the questionnaire can only give accurate data on the specific foods tha t the individuals are queried about rather than a complete picture of the home food
39 environment. Finally there is a question of whether these results could generalize to other populations. Given the characteristics of the current sample, it is unlikely th at the results shown here c ould generalize to men, adults under the age of 50, and individuals living in non rural areas. Taken together, the results from the current study indicate that the home food environment may be an important target for future weigh t management interventions. There have been a limited number of studies that have managed to find environmental differences between the homes of weight loss maintainers and treatment seeking obese individuals. One such study found individuals that have suc cessfully lost weight and maintained this weight loss tended to have more physical activity equipment available in the home, fewer TVs, a nd fewer high fat foods (Phelan et al., 2009). Results from another study showed that the home food environment could b e manipulated in a beneficial way by use of a home grocery delivery service (Gorin et al., 2007). Modifying the home food environment in these ways can help provide support for weight management efforts in the long term. Considering the weight regain pattern typically seen in many long term behavioral lifestyle interventions, such that on average parti cipants regain one third of lost weight within one year of treatment ending, further research into methods of supporting weight maintenance is necessary (Curioni & Loureno, 2005, Perri & Corsica, 2002, Wadden et al., 2007, Wing, 2002). Finally, if individ uals typically eat the types of foods that are available in their home, then increasing the number of healthy foods available in the home as well as decreasing the number of unhealthy foods in the home can help to improve the health of all family members l iving within a household.
40 There is considerable potential for future research related to the topic of the home food environment as it relates to dietary intake and body weight. For example, a direct observation of the home food environment could be impleme nted instead of a self report questionnaire. This would allow for greater accuracy in determining the types and amounts of foods available in an home food environment. Another method of improving accuracy in reporting would be to u tilize a 24 hour food recall in addition to the Block 95 Food Frequency Questionnaire. Considering that this dietary recall questionnaire asks the individual about types of foods eaten as well as amounts over the past year, it is likely that an individual may be more accurate in estimating their dietary intake during a more recent time period. Additionally, future research could examine whether the home food environment, beyond being associated with body weight, is also associated with biomarkers of health (e.g., trigl ycerides, serum cholesterol levels ) The results from the current study answer a number of key questions. The outcomes outlined here show that the availability of various types of foods in the home is associated with an increased consumption of those food s indicating that individuals tend to eat the types of the foods that are readily available Additionally, it is evident that a behavioral lifestyle intervention for obesity produces beneficial changes in dietary intake such that consumption of protein, saturated fat and carbohydrates all decrease following an intervention. Positive changes were also apparent in the home food environment during the course of the intervention as the results show ed that the number of healthy foods in the home increase d and the number of unhealthy foods decrease d Finally, a direct effect was detected between the home food environment and BMI such
41 that having more healthy foods in the home wa s associated with a lower BMI. An indirect effect was also indicated in the current s tudy when daily intake of protein, carbohydrates, saturated fat and fiber were considered as mediators Further analysis showed that only saturated fat was a mediator of this relationship which suggest ed that individuals improve their home environment (i.e ., increased number of healthy foods and decreased number of unhealthy foods), which improves their dietary intake and in turn is associated with a lower BMI. Overall, the current study highlights the importance of targeting the home food environment as pa rt of a lifestyle intervention.
42 LIST OF REFERENCES Allison, D.B., Fontaine, K.R., Manson, J.E., Stevens, J., & VanItallie, T.B. (1999). Annual deaths attributable to obesity in the United States. Journal of the American Medical Association, 282 (16), 1530 1538. doi: 10.1001/jama.282.16.1530 Auchincloss, A. Diez Roux, A., Brown, D., Erdmann, C., & Bertoni, A. (2008). Neighborhood resources for physical activity and healthy foods and their association with insulin resistance. Epidemiology, 19 146 157. Bennett, K.J., Olatosi, B., & Probst, J.C. (2008). Health disparities: A rural urban chartbook. South Carolina Rural Health Research Center. Blanchette, L., & Brug, J. (2005). Determinants of fruit and vegetable consumption among 6 12 year old childre n and effective interventions to increase consumption. Journal of Human Nutrition and Dietetics, 18 431 443. Block, G., Hartman, A.M., Dresser, C.M., Carroll, M.D., Gannon, J., & Gardner, L. (1986). A data based approach to diet questionnaire design and t esting. American Journal of Epidemiology, 124 (3), 453 469. Block, G., Woods, M., Potosky, A., & Clifford, C. (1990). Validation of a self administered diet history questionnaire using multiple diet records. Journal of Clinical Epidemiology, 43 (12), 1327 13 35. Bodor, J., Rose, D., Farley, T., Swalm, C., & Scott, S. (2008). Neighborhood fruit and vegetable availability and consumption: The role of small food stores in an urban environment. Public Health Nutrition, 11 413 420. Bouton, M.E. (2011). Learning an d the persistence of appetite: Extinction and motivation to eat and overeat. Physiology and Behavior, 103 51 58. doi:10.1016/j.physbeh.2010.11.025 Brownell, K.D. (2000). The LEARN program for weight management Dallas, TX: American Health Publishing Company. Butryn, M.L., Webb, V. & Wadden, T.A. (2011). Behavioral treatment of obesity. Psychiatric Clinics of North America, 34 841 859. doi:10.1016/j.psc.2011.08.006. Centers for Disease Control and Prevention. (2008). Prevalence of overweight, obesity, and extreme obesity among adults: United States, trends 1960 62 through 2005 6. Retrieved from h ttp://www.cdc.gov/nchs/data/hestat/overweight/overweight_adult.htm Clinical g uidelines on the identification, evaluation, and treatment of overweight and obesity in adults. (1998). American Journal of Clinical Nutrition, 68 899 917.
43 or should we? Psychological Bu lletin, 74 (1), 68 80. Curioni, C.C., & Loureno, P.M. (2005). Long term weight loss after diet and exercise: A systematic review. International Journal of Obesity, 29 1168 1174. Davy, B.M., Harrell, K., Stewart, J., & King, D.S. (2004). Body weight status, dietary habits, and physical activity levels of middle school aged children in rural Mississippi. Southern Medical Journal, 97 6, 571 577. Eberhardt, M.S., & Pamuk, E.R. (2004). The importance of place of residence: Examining health in rural and n onrural areas. American Journal of Public Health, 94 (10), 1682 1686. Elfhag, K., & Rssner, S. (2005). Who succeeds in maintaining weight loss? A conceptual review of factors associated with weight loss maintenance and weight regain. The International Asso ciation for the Study of Obesity. Obesity Reviews, 6 67 85. Fisher, B.D., & Strogatz, D.S. (1999). Community measures of low fat milk consumption: Comparing store shelves with households. American Journal of Public Health, 89 235 237. Flegal K.M., Carroll, M.D., Kit, B.K., & Ogden, C.L. (2012 ). Prevalence and trends in the distribution of body mass index among US adults: 1999 2010 Journal of the A merican Medical Association, 307 (5), E1 E7 doi: 10.1001/jama.2012.39 Flegal, K.M., Graubard, B.I., Williamson, D.F., & Gail, M.H. (2005). Excess deaths associated with underweight, overweight and obesity. Journal of the A merican Medical Association, 293 (15), 1861 1867 doi: 10.1001/jama.293.15.1861 Flegal, K.M., Graubard, B.I., Williamson, D.F., & Gail, M.H. (2007). Cause specific deaths associated with underweight, overweight and obesity. Journal of the American Medical Association, 298 (17), 2028 2037. doi: 10.1001/jama.298.17.2028 Flora, C.B., Flora, J.L., & Fey, S. (2004). Rural communities: Legacy and change. Boulder, CO: Westview Press. Ford, P.B., & Dzewaltowski, D.A. (2008). Disparities in obesity prevalence due to variation in the retail food environment: Three testable hypotheses. Nutrition Reviews, 66 (4), 216 228. doi:10.1111/j.1753 488 7.2008.00026.x French, S. A., Story, M., & Jeffrey, R.W. (2001). Environmental influences on eating and physical activity. Annual Review of Public Health, 22 309 335. doi: 0163 7525/01/0510 0309
44 Golan, M., & Weizman A. (1998). Reliability and validity of the family eating and activity habits questionnaire. European Journal of Clinical Nutrition, 52 771 777. Retrieved from http://www.stockton press.co.uk/ejcn G ordon Larse n, P., Nelson, M.C., Page, P. & Pop kin, P.M. (2006). Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics, 117 (2), 417 424. doi:10.1542/peds.2005 0058. Gorin, A.A., Raynor, H.A., Niemeier, H.M., & Wing, R.R. (2007). Home grocery d elivery improves the household food environments of behavioral weight loss participants: Results of an 8 week pilot study. International Journal of Behavioral Nutrition and Physical Activity, 4 58 63. Gorin, A.A., Wing, R.R., Fava, J.L., Jakicic, J.M., Je ffery R., West, D.S., Brelje, K., & DiLillo, V.G. (2008). Weight loss treatment influences untreated spouses and the home environment: evidence of a ripple effect. International Journal of Obesity, 32 1678 1684. Hanson, N., Neumark Sztainer D., Eisenberg, M., Story, M., & Wall, M. (2004). Associations between parental report of the home food environment and adolescent intakes of fruits, vegetables, and dairy foods. Public Health Nutrition, 8 (1), 77 85. doi:10.1079/PHN2004661 Hartley, D ., An derson, N., Fox, K. & Lenardson, J. (2011). How does the rural food environment affect rural childhood obesity? Childhood Obesity, 7 (6), 450 461. doi:10.1089/chi.2011.0086. Hearn, M.D., Baranowski, T., Baranowski, J., et al. (1998). Environmental influenc es on dietary behavior among children: Availability and accessibility of fruits and vegetables enable consumption. Journal of Health Education, 29 26 32. Jeffrey, R.W., Drewnowski, A., Epstein, L.H., Stunkard, A.J., Terance Wilson, G., Wing, R.R. & Robin Hill, D. (2000). Long term maintenance of weight loss: Current status. Health Psychology, 19 (1) [Suppl.] 5 16. doi:10.1037//0278 6133.19.1(Suppl.).5 Jones, C.A., Parker, T.S., Ahearn, M., Mishra, A.K., & Variyam, J.N. (2009). Health status and healthcare access of farm and rural populations (Report No. EIB 57). Retrieved from the United States Department of Agriculture Economic Research Service website: www.ers.usda.gov/publications/eib57/ Kanjilal, S., Gregg, E.W., Cheng, Y.J., Zhang, P., Nelson, D.E., Mensah, G. et al. (2006). Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular diseas e among US adults, 1971 2002. Archives of Internal Medicine, 166 (21), 2348 2355.
45 Krauss, R.M., Deckelbaum, R.J., Ernst, N., Fisher, E., Howard, B.V., Knopp R.H., et al. (1996). Dietary guidelines for healthy American adults: A statement for health professionals from the Nutrition Committee. American Heart Association, 94 1795 1800. Krukowski, R., Harvey Berino, J., & West, D. (2010). Differences in home fo od availability of high and low fat foods after a behavioral weight control program are regional not racial. International Journal of Behavioral Nutrition and Physical Activity, 7 (69). Retrieved from http ://www.ijbnpa.org/content/7/1/69 Larson, N.I., Story, M.T., & Nelson, M.C. (2009). Neighborhood environments: Disparities in access to healthy foods in the U.S. American Journal of Preventive Medicine, 36 (1), 74 81. doi:10.1016/j.amepre.2008.09.025 Liese, A.D. Weis, K.E., Pluto, D., Smith, E., & Lawson, A. (2007). Food store types, availability, and cost of foods in a rural environment. Journal of the American Dietetic Association, 107 (11), 1916 1923. Lopez, R.P. (2007). Neighborhood risk factors for obesit y. Obesity, 15 (8), 2111 2119. Morkdad, A.H., Marks, J.S., Stroup, D.F., & Geberding, J.L. (2005). Correction: Actual causes of death in the United States, 2000. Journal of the American Medical Association, 293 293 294. Morland, K., Diez Roux, A.V., & Wing S. (2006). Supermarkets, other food stores, and obesity: the atherosclerosis risk in communities study. American Journal of Preventive Medicine, 30 333 339. Morland, K., Wing, S., & Diez Roux, A. (2002). The contextual effect of the local food American Journal of Public Health, 92 1761 1767. Na tio nal Heart, Lung and Blood Institute (2010 ). Clinical Guideline s on the identification, evaluation, and treatment of overweight and obesity in adults The evidence report. Retrieved from http://www.nhlbi.nih.gov/guidelines/obesity/ob_home.htm Nie lson S.J., Siega Riz, A.M., & Popkin, B.M. (2002). Trends in energy intake in U.S. between 1977 and 1996: Similar shifts seen across age groups. Obesity Research, 10 370 378. s to healthful eating and physical activity among children and adolescents. Journal of the American Dietetic Association, 103 (4), 497 500.
46 Ogden, C.L., Lamb, M.M., Carroll, M.D., & Flegal, K.M. (2010). Obesity and socioeconomic status in a dults: United S tates, 1988 1994 and 2005 2008 NCHS data brief no 50. Hyattsville, MD: National Center for Health Statistics Retrieved from U.S. Department of Health and Human Services. Papas, M.A., Alberg, A.J., Ewing, R., Helzlsouer, K.J., Gary, T.L., & Klassen, A.C. ( 2007). The built environment and obesity. Epidemiologic Reviews, 29 (1), 129 143. doi: 10.1093/epirev/mxm009 Parks, S.E., Housemann, R.A., & Brownson, R.P. (2003). Differential correlates of physical activity in urban and rural adults of various socioeconomic backgrounds in the United States. Journal of Epidemiology and Community Health, 57 29 35. Retrieved from www.jech.com Pascale, R.W., Wing, R.R., Butler, B.A., Mullen, M., & Bononi, P. (1995). Effects of behavioral weight loss program stressing calorie restriction versus calorie plus fat restriction in obe se individuals with NIDDM or a family history of diabetes. Diabetes Care, 18 1241 1248. Patterson, P.D., Moore, C.G., Probst, J.C., & Shinogle, J.A. (2004). Obesity and physical inactivity in rural America. The Journal of Rural Health, 20 (2), 151 159. Pearson, T.A., & Lewis, C. (1998). Rural epidemiology: Insights from a rural population laboratory. American Journal of Epidemiology, 148 (10), 949 957. Perri, M.G., & Corsica, J.G. (2002). Improving the maintenance of weight lost in behavioral treatment of obesity. In Wadden T.A., Stunkard A.J. (Eds.), Handbook of obesity treatment (pp.357 379). New York, NY: Guilford Press. Perri, M. G., Limacher, M. C., Durning, P. E., Janicke, D. M., Lutes, L. D., Bobroff, L. B., Dale, M. S., Daniels, M. J., Radcliff, T. A., & Martin, A. D. (2008). Extended care programs for weight management in rural communities: The treatment of obesity in underserved rural settings (TOURS) randomized trial. Archives of Internal Medicine, 168 2347 2354. Phelan, S., Liu, T., Gorin A., Lowe, M., Hogan, J., Fava, J., & Wing, R.R. (2009). What distiniguishes weight loss maintainers from the treatment seeking obese? Analysis of environmental, behavioral, and psychosocial variables in diverse populations. Annals of Behavioral Medicine, 38 94 104. doi: 10.1007/s12160 009 9135 2 Powell, L., Slater, S., Mirtcheva, D., Bao, Y., & Chaloupka, F. (2007). Food store availability and neighborhood characteristics in the United States. Preventive Medicine, 44 189 195. Preacher, K.J., & Hayes, A.F (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40 (3), 879 891.
47 Rose, D., & Richards, R. (2004). Food store access and household fruit and vegetable use amo ng participants in the U.S. food stamp program. Public Health Nutrition, 7 1081 1088. Rosenkranz, R.R., & Dzewaltowski, D.A. Model of the home food environment pertaining to childhood obesity. Nutrition Reviews, 66 (3), 123 140. doi:10.1111/j.1753 4887.200 8.00017.x Smith, S.L., Quandt, S.A., Arcury, T.A., Wetmore, L.K., Bell, R.A., & Vitolins, M.Z. (2006). Aging and eating in the rural southern United States: Beliefs about salt and its effect on health. Social Science and Medicine, 62, 189 198. United States Department of Ag riculture Economic Research Service. (2004). Rural poverty at a glance Report No. 100. van Dis, J. (2002). Where we live: Health care in rural vs. urban America. Journal of the American Medical Association, 287 (1). Retrieved from www. jama.ama assn.org Wadden, T.A., & Butryn, M.L. (2003). Behavioral treatment of obesity. Endocrinology and Metabolism Clinics of North Ame rica, 32 (4), 981 1003. doi: 10.1016/S0889 8529(03)00072 0 Wadden, T.A., Butryn, M.L., & Wilson, C. (2007). Lifestyle modification for the management of obesity. Gastroenterology, 132 2226 2238. Wadden, T.A., Crerand, C.E., & Brock, J. (2005). Behavioral tr eatment of obesity. Psychiatric Clinics of North America, 151 170. doi:10.1016/j.psc.2004.09.008 Wing, R.R. (2002). Behavioral Weight Control. In: Wadden T.A., Stunkard A.J. (Eds.), Handbook of obesity treatment (pp. 301 316) New York: The Guilford Press. Wing, R.R. & Phelan, S. (2005). Long term weight loss maintenance. American Journal of Clinical Nutrition, 82 (1), 222S 225S. Retrieved from www.ajcn.org Wrigle y, N., Warm, D., Margetts, B., & Whelan A. (2002). Assessing the impact of Urban Studies, 39 2061 2082.
48 BIOGRAPHICAL SKETCH S tacey Nicole Maurer was born in Jacksonville, Florida. She graduated from Stanton College Preparatory School in 2005. Stacey attended the University of Florida and gr aduated in 2009 with a b achelors degree in p sychology and a minor in f amily, youth and community s ciences. Following graduation, Stacey worked as a research assistant in the UF Weight Management Lab at the University of Florida from 2009 2010 In this capacity, Stacey co led treatment groups promoting weight loss and increased nutrition for adults living in rural counties Rural LITE trial a behavioral lifestyle intervention for obesity. She also assisted in screening potential participants for entrance into the study as part of the assessment team. I n fall of 2010, Stacey entered the Clinical and Health Psychology doctoral program at the University of Florida in Gainesville, Florida.