A COMMUNITY INTERVENTION IMPROVES LIFESTYLE HABITS TO SUPPORT DIABETES AND HEART DISEASE PREVENTION AND MANAGEMENT AMONG OLDER ADULTS by MELINDA (MINDY) BELL (Under the Direction of Mary Ann Johnson) ABSTRACT This study evaluated a community-based education intervention to improve nutrition and lifestyle habits for diabetes and cardiovascular disease prevention and management in Georgia senior centers. Participants were a convenience sample that completed pre- and post-test questionnaires and the intervention (N = 693, mean age = 75, 84% female, 45% black). The 4month intervention had 16 sessions, with eight focused on improving knowledge and behaviors that support diabetes and cardiovascular disease prevention and management, such as meeting national recommendations for physical activity and fruit and vegetable intake, and learning the symptoms of heart attack and stroke. Following the intervention, several measures improved: knowledge of the major symptoms of heart attack and stroke; daily fruit and vegetable intake; days physically active; and physical function score. These results provide an evidence base for the effectiveness of this intervention in improving diabetes and heart disease-related knowledge and behaviors among these older adults. INDEX WORDS:

older adults, senior centers, diabetes, heart health, intervention, physical activity, nutrition

A COMMUNITY INTERVENTION IMPROVES LIFESTYLE HABITS TO SUPPORT DIABETES AND HEART DISEASE PREVENTION AND MANAGEMENT AMONG OLDER ADULTS

by

MELINDA BELL B.S.F.C.S., The University of Georgia, 2006

A Thesis Submitted to the Graduate Faculty of The University of Georgia in Partial Fulfillment of the Requirements for the Degree

MASTER OF SCIENCE

ATHENS, GEORGIA 2008

© 2008 Melinda Bell All Rights Reserved

A COMMUNITY INTERVENTION IMPROVES LIFESTYLE HABITS TO SUPPORT DIABETES AND HEART DISEASE PREVENTION AND MANAGEMENT AMONG OLDER ADULTS

by

MELINDA BELL

Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia August 2008

Major Professor:

Mary Ann Johnson

Committee:

Joan Fischer Jung Sun Lee

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DEDICATION This work is dedicated to my loving mom, dad, and sisters for their endless support and encouragement. I love you and thank you for getting me through the stress and trials of school and long days.

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ACKNOWLEDGEMENTS I would like to thank my outstanding advisory committee, Drs. Johnson, Fischer, and Lee, for their guidance and advisement throughout graduate school. Their high standards and excellence give me the motivation to always put forth my best. I also extend a special thank you to the Johnson, Fischer, and Lee lab group for all of their hard work. I think all of the students that have had the pleasure of working with older people have learned many valuable lessons that we can take with us through our careers. I feel very blessed to have been able to work with such exemplary professors and a cohesive team of students. I would also like to thank the Wellness Coordinators in Georgia’s Area Agencies on Aging: Mary Byrd, Marcia Berlin, Jennifer Crosby, Suzanne M. Elbon, Lisa D. Hale, Jami Harper, Monique Hillman, Lisa Howard, Noaleen Ingalsbe, Loreatha Jenkins, Brenda Kirkland, Ilona Preattle, and Lisa Whitley. I thank them for their dedication to serving and improving the lives of Georgia’s older adults, and for their contribution to the design of the study, recruitment of participants, data collection, delivery of the intervention, and interpretation of the results. To all the older people who have participated in Live Healthy Georgia, I thank them for their willingness to learn new things, to strive for better health, and for their patience through long questionnaires. I thank my loving family, for supporting me and for encouraging me, literally since day one. Thanks Mom, for always listening and offering good advice, even on days when I didn’t seem to make much sense.

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TABLE OF CONTENTS Page ACKNOWLEDGEMENTS.............................................................................................................v LIST OF TABLES....................................................................................................................... viii LIST OF FIGURES ........................................................................................................................ x CHAPTER 1

INTRODUCTION .........................................................................................................1

2

LITERATURE REVIEW ..............................................................................................4 The Older Adult Population ......................................................................................4 Older Americans Act Nutrition Program ..................................................................5 Cardiovascular Disease .............................................................................................6 Type 2 Diabetes.......................................................................................................17 Healthy Lifestyle Habits: Physical Activity............................................................22 Healthy Lifestyle Habits: Diet.................................................................................26 Health Belief Model ................................................................................................32 Previous Successful Interventions...........................................................................32 Rationale, Specific Aims, Hypotheses ....................................................................34

3

A COMMUNITY INTERVENTION IMPROVES LIFESTYLE HABITS TO SUPPORT DIABETES AND HEART DISEASE PREVENTION AND MANAGEMENT AMONG OLDER ADULTS .....................................................36 Abstract ...................................................................................................................37

vii Introduction .............................................................................................................38 Methods ...................................................................................................................41 Results .....................................................................................................................49 Discussion ...............................................................................................................54 Acknowledgements .................................................................................................72 4

CONCLUSIONS..........................................................................................................97

REFERENCES ............................................................................................................................100 APPENDICES .............................................................................................................................112 A

Data Tables ................................................................................................................113

B

Power Analysis ..........................................................................................................122

C

Physician’s Clearance ................................................................................................124

D

Intervention Post-Test................................................................................................126

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LIST OF TABLES Page Table 3.1: Characteristics of Participants ......................................................................................73 Table 3.2: Changes in Knowledge of Heart Attack Symptoms Following the Intervention in Georgia Senior Centers, 2006-2007................................................................................75 Table 3.3: Changes in Knowledge of Stroke Symptoms Following the Intervention in Georgia Senior Centers, 2006-2007..............................................................................................76 Table 3.4: Regression Model Exploring Predictors of Heart Attack and Stroke Knowledge at the Pre-Test in Georgia Senior Centers, 2006-2007 .............................................................77 Table 3.5: Regression Model Exploring Predictors of Change in Heart Attack and Stroke Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007 ............78 Table 3.6: Changes in Dietary Knowledge and Behaviors Following the Intervention in Georgia Senior Centers, 2006-2007..............................................................................................79 Table 3.7: Changes in Physical Activity Following the Intervention in Georgia Senior Centers, 2006-2007 .......................................................................................................................81 Table 3.8: Changes in Physical Function Following the Intervention in Georgia Senior Centers, 2006-2007 .......................................................................................................................82 Table 3.9: Changes in A1c in the Diabetes Subgroup Following the Intervention in Georgia Senior Centers, 2006-2007..............................................................................................83 Table 3.10: Changes in Diabetes Self-Management Practices in the Diabetes Subgroup Following the Intervention in Georgia Senior Centers, 2006-2007................................84

ix Table 3.11: Comparison of Changes in Diet and Physical Activity Behaviors for Diabetes SelfManagement and General Health in all Participants Following the Intervention in Georgia Senior Centers, 2006-2007................................................................................86

x

LIST OF FIGURES Page Figure 3.1: Changes in Heart Attack Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007 .......................................................................................................87 Figure 3.2: Changes in Stroke Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007......................................................................................................................88 Figure 3.3: Comparison of Heart Attack Knowledge by Race at the Pre-Test in Georgia Senior Centers, 2006-2007 .......................................................................................................89 Figure 3.4: Comparison of Stroke Knowledge by Race at the Pre-Test in Georgia Senior Centers, 2006-2007......................................................................................................................90 Figure 3.5: Comparison of Pre-Test Heart Attack Knowledge in Georgia Senior Centers, 20062007, and 2001 BRFSS Data from 17 US States ..........................................................91 Figure 3.6: Comparison of Pre-Test Stroke Knowledge in Georgia Senior Centers, 2006-2007, and 2001 BRFSS Data from 17 US States ....................................................................92 Figure 3.7: Comparison of Heart Attack Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007, and 2001 BRFSS Data from 17 US States .......................93 Figure 3.8: Comparison of Stroke Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007, and 2001 BRFSS Data from 17 US States...................................94 Figure 3.9: Comparison of Mean Changes in Diabetes Self-Management (DSM) Behaviors Following the Intervention in Georgia Senior Centers, 2006-2007, with Previous DSM Intervention, 2005-2006 ................................................................................................95

xi Figure 3.10: Comparison of Changes in Weekly Frequency (> 5 days) of Diabetes SelfManagement (DSM) Behaviors Following the Intervention in Georgia Senior Centers, 2006-2007, with Previous DSM Intervention, 2005-2006 ............................................96

1

CHAPTER 1 INTRODUCTION The number of older adults over age 65 in the US is increasing and is expected to reach over 70 million (20%) by the year 2030, with a large percentage of growth occurring among minorities (Administration on Aging, 2007). As Americans age, chronic diseases are more common, and most will be affected by chronic conditions. It is estimated that at least 80% of older Americans have at least one chronic disease, and nearly 50% have at least two. Due to the high prevalence of chronic diseases in older adulthood, health care costs are expected to increase by 25% by 2030 (Centers for Disease Control and Prevention, 2007). In Georgia, older adults represent a particularly vulnerable population in need of services to maintain health, as a large proportion of residents is characterized by poverty, low education, rural, and minority status, which are contributors to health disparities (Glass and Bachtel, 2007). These factors can impact access to health care, non-modifiable risk for chronic diseases, health knowledge, and self-care. Among the leading causes of death in the US and in Georgia are cardiovascular disease and diabetes (CDC and Merck Co., 2007; GA-DHR, 2006). These conditions can result in a number of complications and disability. For example, national stroke data indicate that 700,000 Americans suffer from a stroke each year, of which about 25% die and 15-30% are left permanently disabled (GA-DHR, 2007a). Poorly controlled diabetes has deleterious effects on multiple organs, with major complications including heart disease, amputations, neuropathy, kidney disease, and retinopathy (National Institute of Diabetes and Digestive and Kidney Diseases, 2008). However, cardiovascular disease and diabetes have many modifiable risk

2 factors related to lifestyle habits, such as diet, physical activity, and smoking (CDC and Merck Co., 2007). Chronic conditions are costly, and it is estimated that treatment costs for heart disease and diabetes in the US could be cut by $76 billion and $17 billion, respectively, by the year 2023 if Americans improved health-related behaviors and screenings (DeVol et al., 2007). Community interventions tailored to the needs of the target population provide a cost effective means of reaching large numbers of individuals in need of information and resources to take charge of their health. Therefore, the purpose of this study was to ascertain the effects of a diabetes and heart health community intervention delivered in Georgia senior centers on lifestyle habits among older adults to support diabetes and heart disease prevention and management. It is estimated that this intervention, in combination with a fall and fracture prevention intervention, reached 3,500 older Georgians. A sample of 849 participants completed a pre-test questionnaire as part of a statewide evaluation of the diabetes and heart health intervention. Of those, 693 completed both pre- and post-test questionnaires (mean age = 75). Outcomes included knowledge and behaviors related to nutrition, physical activity, physical function, and heart attack and stroke warning signs and symptoms, along with diabetes self-management practices in a subgroup with diabetes (n = 244). Chapter 2 is a review of the literature related to the target population of this intervention and their characteristics, diabetes and heart health prevention and management among older adults, important lifestyle habits that help modify risk of having these chronic conditions, the theoretical model which served as the basis used to develop the intervention (Health Belief Model), and previous interventions targeting older adults.

3 Chapter 3 is a manuscript to be submitted to the journal Preventing Chronic Disease: Public Health Research, Practice, and Policy. The manuscript includes the methods, results, and discussion of the diabetes and heart health intervention. All data tables and figures are included in Chapter 3. Chapter 4 is a discussion of the findings from the diabetes and heart health intervention and synthesis of current literature. All references are provided after Chapter 4, followed by a series of appendices containing materials used to develop the diabetes and heart health intervention. Appendix A contains additional data tables, including regression models, a comparison of people with and without diabetes at the pre-test in the sample, as well as a comparison of changes in selected variables between people with and without diabetes. Appendix B contains the power analysis used for the current study. Appendix C includes the physician’s clearance form required to participate in the physical activity portion of the intervention. Appendix D contains a sample post-test used in the intervention for participants with diabetes.

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CHAPTER 2 LITERATURE REVIEW The Older Adult Population In 2004, there were over 36 million people (12.4%) aged 65 and older in the US, with over 847,000 (9.6%) aged 65 and older in Georgia (US Census Bureau, 2005). The number of older adults over age 65 in the US continues to burgeon and is expected to reach over 70 million (20%) by the year 2030, with a large percentage of growth occurring among minorities. People are living longer, with an average life expectancy of 18.7 additional years after age 65. Another contributor to the impending explosion of the older adult population is the aging of baby boomers (AoA, 2007). The oldest old (age 85 and older) is the most rapidly increasing age group in the US and in Georgia, as there is a projected increase of 261.2% from 1990 to 2010 in Georgia (Greene, 2005). With the marked increase in the older adult population and the uncertain supply of caregivers to help provide the needs of these elders, there is an increased need for community-based services to prevent costly institutionalization, chronic diseases, and disability (Glass, 2005). Chronic diseases represent the leading causes of death in the US and in Georgia, and the risk increases with advancing age (CDC and Merck Co., 2007; GA-DHR, 2006). The estimated average annual cost of a private nursing home room is $75,190 nationally and $61,320 in Georgia (MetLife, 2006). Thus, support for home and community-based services is needed to help the increasing number of older Americans stay healthy and active, and to reduce costs associated with dependency and decreased functionality.

5 Older Americans Act Nutrition Program The Administration on Aging’s Older Americans Act Nutrition Program (OAANP) was established in 1972 to support the independence and well-being of community-dwelling older adults. It is administered under Title III of the Older Americans Act and provides grants to support nutritionally balanced meals and nutrition and wellness education and services to those aged 60 and older, with the purpose of helping older people maintain health and independence (AoA, 2004; Weddle et al., 2007). Although all older Americans have the opportunity to participate in this program, it is targeted to those with greatest social and economic need. The services offered in this program are centered upon prevention, to help older adults live longer, healthier lives, remain in the community, and avoid costly institutionalization and disability. Along with the congregate and home-delivered meals offered through the OAANP, the program reaches much further by providing a variety of additional services, such as transportation to grocery stores, nutrition screening and counseling, physical activity programs, socialization opportunities, and other services to promote the health and well-being of older people (Millen et al., 2002). The OAANP has been successful in targeting older people in great need of preventive and supportive services. The majority who participate is poor or near poor, with a large proportion of participants being in a racial or ethnic minority group, such as African American and Hispanic. Many more (57%) live alone and may be socially isolated compared to the general elderly population (25%). Participants in this program are also at nutritional risk, having a significant rate of food insecurity (Ponza et al., 1996, Millen et al., 2002). Chronic diseases, including cardiovascular disease (hypertension, stroke, heart disease) and diabetes, are common among these individuals, who have an average of 2.4 diagnosed chronic health conditions. Many

6 are overweight and obese as well, with about 42% of congregate meal recipients having a body mass index greater than 27 (Ponza et al., 1996). The OAANP has been effective in meeting the needs of the targeted older adult population (Millen et al., 2002). Meals must follow the dietary guidelines and each must provide at least 1/3 of the Recommended Dietary Allowances or Adequate Intakes. A national evaluation of the OAANP showed that nutrient intakes for program participants were generally adequate. In addition, females who participated in the meal programs tended to have somewhat higher intakes of important nutrients, such as protein and vitamin A, and lower intakes of sodium and cholesterol, compared to the general female elderly population (Ponza et al., 1996). In Georgia, congregate meals provided under the Older Americans Act contribute substantially to the food and nutrient intake of older adults. In state fiscal year 2005, 1,427,162 meals were served to 13,762 older Georgians at senior centers (Greene, 2005). Cardiovascular Disease Cardiovascular disease (CVD) is an umbrella term used to identify several heart and blood vessel conditions, including coronary heart disease, hypertension, stroke, heart attack, and heart failure (Rosamond et al., 2007). In 2004, heart disease was the leading cause of death in the US standard population and for the age 65 and older age category alone (CDC, 2006a). The risk of CVD increases with advancing age, affecting 83% and 92% of American males and females over age 80, respectively. More than 80 million American adults (1 in 3) have some form of CVD, of which 38 million are aged 60 and older (Rosamond et al., 2008). Eighty-two percent and 87% of CHD and stroke deaths occur among people age 65 and older. Older Americans also tend to have a higher prevalence of risk factors for cardiovascular disease, with over half over age 65 with obesity and high blood pressure (American Heart Association, 2008).

7 CVD is estimated to cost the US health care system $448.5 billion for 2008 and is ranked the most costly condition (Rosamond et al., 2008). The death rate for CVD is greatest in the southeastern sector of the US, including Georgia, which is estimated to have a 13% higher CVD death rate than the national rate. Additionally, the stroke death rate in Georgia is 21% higher than the national rate and Georgia is part of the geographic region of the US defined by high rates of stroke mortality referred to as “the stroke belt” (Gregory et al., 2007). CVD deaths are most common in people over age 65, with older people accounting for every 3 in 4 CVD deaths in 2005. CVD is costly to Georgia, as there were approximately 142,400 hospitalizations attributable to CVD in 2005, with an estimated charge of $28,700 per hospitalization. The direct and indirect costs of CVD in Georgia are estimated to total $9.8 billion (GA-DHR, 2007a). Georgia’s population is quite diverse, with a large proportion of African American and Hispanic elders who are at greater risk for cardiovascular disease and diabetes, and hence are more likely to suffer from heart attack and stroke (Glass, 2005). Significant disparities in stroke deaths exist, as the age-adjusted death rate for stroke was 1.4 times higher in Africans Americans compared to whites in 2004 in Georgia (Gregory et al., 2007). National stroke data indicate that 700,000 Americans suffer from a stroke each year, of which about 25% die and 15-30% are left permanently disabled. Stroke disproportionately affects older people, with 81% of stroke deaths in Georgia occurring in those aged 65 and older. Though Georgia is a fairly young state, older Georgians tend to have a high need for health and social services. Large percentages of older adults are clustered in Georgia’s rural counties, where stroke death rates tend to be highest (Gregory et al., 2007), and these elderly people have many other characteristics that may impact their risk for chronic diseases, such as being African

8 American, low education attainment, and low socioeconomic status (Glass & Bachtel, 2007). African Americans are more likely to have risk factors, such as hypertension and diabetes, and age-adjusted stroke deaths are 80% higher among African Americans compared to white people (USDHHS, 2000). Strokes are costly to Georgia, with total costs of stroke hospitalizations for 2004 totaling $533 million, or about $22,700 per stroke (Gregory et al, 2007). Knowledge of the signs and symptoms of stroke is essential, as getting immediate care can help to prevent disability and death (National Institute of Neurological Disorders and Stroke, 2007). BRFSS data (2005) indicated that 42% of older Georgians were able to correctly identify all of the major warning signs of a stroke when prompted (Gregory et al., 2007). Efforts should therefore continue to ensure older people are aware of all major signs and can take appropriate action to get help immediately. Given the vast data on the impact of cardiovascular disease on the lives of older Americans, national, state, and local efforts have focused on reducing mortality and decreasing risk for CVD by targeting risk factors and lifestyle behaviors, raising awareness of warning signs and symptoms of heart attack and stroke, promotion of appropriate health screenings, and elimination of health disparities (GA-DHR, 2008; USDHHS, 2000). Nationally, Healthy People 2010 contains goals to improve cardiovascular health and quality of life through the prevention, detection, and treatment of risk factors; early identification and treatment of heart attacks and strokes; and prevention of recurrent cardiovascular events (USDHHS, 2000). In 2006, the American Heart Association released an updated set of scientifically-based nutrition and lifestyle guidelines encouraging all Americans to adopt healthful lifestyle habits to promote heart health, including regular physical activity, maintenance of a healthy body weight, and consumption of a diet rich in fruits and vegetables and low in saturated fat (Lichtenstein et al., 2006). At the state

9 level, the Live Healthy Georgia campaign was launched by the Georgia Department of Human Resources and the Governor’s office to help all Georgians reduce their risk of chronic diseases through cost effective, healthy lifestyle behaviors, including healthy eating, physical activity, avoidance of tobacco, getting recommended health screenings, and maintaining a positive attitude (GA-DHR, 2008). While some non-modifiable factors, such as age and heredity, place elders at greater risk for CVD, it is highly preventable. Modifiable risk factors include hypertension, elevated blood cholesterol, current smoking, diabetes, obesity, as well as lifestyle factors, such as physical inactivity and poor dietary habits. Large, prospective cohort studies and intervention trials have shown that healthy lifestyle habits and control of major risk factors can significantly lower the risk of heart disease (Akesson et al., 2007; Kromhout et al., 2002; National Heart Lung and Blood Institute, 2007; Sacks et al., 2001; Stamler et al., 1999; Stampfer et al., 2000). The beneficial effects of healthful habits and risk reduction extend beyond primary prevention to persons with existing heart disease as well (Williams et al., 2002). Elements of successful CVD prevention programs include promotion of a healthful diet, regular physical activity, weight management, smoking cessation, and control of hypertension, elevated serum cholesterol, and diabetes (Kromhout et al., 2002). Maintenance of heart health is a lifelong process beginning in childhood and healthful habits are important throughout the lifespan to support a healthy cardiovascular system (AHA, 2006). Given the strong evidence base that lifestyle modification is effective in the prevention and control of cardiovascular disease, many interventions aimed at improving lifestyle behaviors for CVD risk reduction have been implemented at several levels. The Minnesota Heart Health Program, The Stanford Five-City Project, and the Pawtucket Heart Health Program are major

10 community-based heart health programs implemented in the US, which were funded by grants from the National Heart Lung and Blood Institute (Carleton et al., 1995; Farquhar et al., 1985; Luepker et al., 1994). After conducting needs assessment, these programs targeted communities to improve cardiovascular risk profiles and behaviors, and used innovative approaches to mobilize communities toward heart health promotion, as well as provided the framework for developing community-based heart disease interventions. Though these programs only showed small intervention effects when combined with strong positive secular trends in cardiovascular disease knowledge and risk factor reduction, they provide many valuable lessons concerning outreach and educational components for development and implementation of community-based interventions. For example, the theory-based Pawtucket Heart Health Program used community volunteers and organizations, as well as advertising to the public through local newspapers, to promote risk factor change programs available in the Pawtucket community. Community activities included weight loss and smoking cessation programs, health screenings, and other activities to improve modifiable CVD risk factors. Community involvement was high in this intervention and was successful in recruiting and involving individuals and organizations in heart health programs and efforts (Elder et al., 1986). The Stanford Five-City Project involved a sixyear educational intervention in two cities that used mass media campaigns activities and print materials tailored for people with low literacy skills, and interpersonal education programs (Winkleby et al., 1994). One of the overall goals of the Minnesota Heart Health Program (MHHP) was to produce strategies to promote community awareness, participation in health programs, and impact behavior change that would positively influence cardiovascular health (Mittelmark et al., 1986).

11 Regular physical activity, healthful eating patterns, and getting regular check-ups for blood pressure were lifestyle behaviors targeted through the MHHP. This theory-based program that included regular, repeated evaluation reached study communities using several effective educational strategies. Leadership from community organizations and individuals, mass media information dissemination, risk factor screenings, educational classes and campaigns for community residents and health professionals, and efforts to improve the health environment by gaining support from community sites such as grocery stores, were key components used to promote awareness and risk factor reduction of cardiovascular disease. After two years, exposure to CVD promotion activities was significantly higher in the intervention communities than in the comparison communities. Community involvement and support was high among health professionals, leaders, and residents. Contributors to the successful development of these community-based programs included initial and follow-up needs assessment, a theory base, active mobilization of communities and identification and involvement of motivated leaders from diverse backgrounds, evaluation of the program, and creation of both a health supportive environment and promotion of healthful choices for individuals. Strategies to encourage behavior change are also key components, such as educational incentives and positive reinforcement of behaviors, as they help to translate knowledge and scientific messages into achievable behaviors for community members (Mittelmark et al., 1993). Though many of these large, model programs operated on large budgets and multiple resources, smaller scale programs can use similar strategies while tailoring the intervention to meet the needs of the participants and to fit budget and resource constraints. For example, recruiting volunteers is one way of involving community members in an important project, while helping to control costs (Mittelmark et al., 1993).

12 In addition, the Bootheel Heart Health Project was a smaller, three-year communitybased program that targeted a six-county medically underserved, rural, low education and high poverty area with a relatively large proportion of African Americans (Brownson et al., 1996). Two survey evaluations of this project (N = 1006 in 1990, N = 1510 in 1994) showed that approximately 40% were over the age of 55 as well. Baseline analysis indicated that risk factors varied by various demographic characteristics. For example, physical inactivity was higher among African Americans, older people, and those with lower education levels, demonstrating that the intervention was well-targeted based on the high needs and risk level of the community participants. Innovative education activities included exercise clubs and classes, cooking demonstrations, and blood pressure and cholesterol screenings, as well as involvement of community organizations, such as churches. Community leadership and environmental change to support heart health and corresponding behaviors were also components of this intervention. Though smaller than other heart health community interventions, such as the Minnesota Heart Health Program, this project showed that communities can use available resources to effectively assess the needs and target intervention activities to high risk subgroups. Hence, programs with limited resources should target specific subgroups to provide services to those in greatest need to help stretch dollars. Other large heart health trials have shown that adults can make considerable behavior changes to improve cardiovascular risk. The Mediterranean Lifestyle Program was implemented to reduce the risk for heart disease in a high-risk sample of postmenopausal women with type 2 diabetes (N = 279), and examined the effect of a 6-month education intervention on risk behaviors related to CVD, including diet, physical activity, and stress management (Toobert et al., 2005). Using a variety of techniques such as goal setting, self-monitoring, educational

13 incentives, Mediterranean potlucks, and social support strategies, participants randomized to the intervention group showed significantly greater adherence to a healthful dietary pattern rich in fruits and vegetables and low in saturated fat, which was measured using food frequency questionnaires and self-rating of diet, as well as participation in moderate physical activity, compared to controls. This trial provides evidence for the effectiveness of multiple lifestyle behavior change interventions for older women. Knowledge of the signs and symptoms of heart attack and stroke is one of the first steps to getting emergency care immediately. The signs and symptoms of a heart attack include: pain or discomfort in the upper body, such as the jaw, neck, stomach, one or both arms, or back, feeling weak, lightheaded, or faint, chest pain or discomfort, and shortness of breath (NHLBI, 2008a). The signs and symptoms of a stroke include: sudden weakness of the face, arm or leg, especially on one side of the body, sudden confusion or trouble speaking or understanding speech, sudden trouble seeing in one or both eyes, sudden trouble walking, dizziness, or loss of balance or coordination, and sudden severe headache with no known cause (NINDS, 2008). The Behavioral Risk Factor Surveillance System (BRFSS) is used to track public knowledge of the signs and symptoms of heart attack and stroke and the data collected is used to measure progress toward the Healthy People 2010 objectives of increasing the proportion of adults who are aware of the early warning symptoms and signs of a heart attack and stroke, and the importance of accessing rapid emergency care by calling 9-1-1 (USDHHS, 2000). Thrombolytic agents are available to dissolve clots contributing to heart attack and stroke, but must be administered within the first one to three hours following heart attack and stroke symptom onset, respectively, for greatest effectiveness. Angioplasty and other procedures are also used to help reduce disability and mortality in people experiencing a heart attack (NINDS,

14 2008; NHLBI, 2008a). This information underscores the importance of symptom awareness and immediate treatment, particularly in older people who are at higher risk of heart attack and stroke. In response to the evidence that immediate care for heart attack and stroke can greatly improve health outcomes, public health efforts are underway to improve early recognition and treatment of heart attack and to improve the quality of care for these patients. Several national organizations including the American Red Cross, the American Heart Association, and the National Heart Lung and Blood Institute have partnered to launch campaigns, such as “Act in Time to Heart Attack Signs,” “Operation Heartbeat”, and “Operation Stroke,” to increase public knowledge of heart attack and stroke signs and symptoms, as well as encourage health care providers to talk with their patients about accessing rapid treatment and overcoming barriers to action (Faxon and Lenfant, 2001). In addition, the National Heart Attack Alert Program was established in 1991 by the National Heart Lung and Blood Institute to help reach the Healthy People 2010 objectives of increasing early recognition of heart attack warning signs and immediately seeking emergency care by launching a public education campaign, “Act in Time to Heart Attack Signs,” and helping medical facilities keep abreast of the latest technologies to provide evidence-based care, among other priority areas (NHLBI, 2008b). The National Institute of Neurological Disorders and Stroke (NINDS) also launched a public education campaign, “Know Stroke,” that provides several printed materials and a community education toolkit to promote stroke awareness (NINDS, 2008). Knowledge is particularly important in older people who often have many risk factors for CVD or have preexisting CVD and hence are at greater risk of a heart attack or stroke. It is recognized, however, that knowledge of heart attack signs and symptoms is but one step in the path to help-seeking behaviors, as many barriers may deter older

15 people from translating knowledge into behaviors that improve heart attack and stroke outcomes (Ryan and Zerwic, 2003). An analysis of 2001 Behavioral Risk Factor Surveillance System (BRFSS) data from 17 states and the US Virgin Islands (N = 61,018) for assessment of public recognition of heart attack warning signs indicated that 92% of participants recognized at least three of the signs, with chest pain or discomfort (95%), pain in the arm or shoulder (89%), and shortness of breath (87%) being the most widely recognized (Greenlund et al., 2004). Pain in the jaw, neck, or back (51%) and feeling weak, lightheaded, or faint (65%) were not as commonly recognized and only 11% correctly identified all of the warning signs and knew to call 9-1-1 as the first action if it was thought a heart attack was occurring. In this study, warning signs were presented to participants in a close-ended format, which may have inflated estimates of heart attack knowledge because format of the questions has been shown to impact results obtained (Goff et al., 1998; Rowe et al., 1999). Age (being younger or older compared to middle-aged), being in an ethnic minority group, and low education were all predictors of lower knowledge of heart attack signs and symptoms in regression analyses. It is interesting to note that there were not appreciable differences in knowledge according to presence of CVD risk factors, such as having high serum cholesterol, hypertension, obesity, or diabetes. The Rapid Early Action for Coronary Treatment (REACT) trial was a randomized, multicenter, community education program with the overall goal of reducing prehospital delay in response to heart attack signs and symptoms. A survey with two open-ended questions related to heart attack was used to assess knowledge in 20 US communities (Goff et al., 1998). At baseline, the median number of correct symptoms known was only three of 11 acceptable answers. Similar to BRFSS data, while most participants reported chest pain as an important symptom

16 (90%), far fewer knew other important symptoms, such as shortness of breath (51%). Level of knowledge was greater among non-Hispanic whites, those who were middle-aged compared to older and younger groups, in those with higher income, and in those with previous heart attack experience. Following the 18-month education intervention to improve knowledge of heart attack symptoms and reduce prehospital delay, the ten intervention communities showed a net increase of 13% in participants who knew at least three symptoms. This trial also showed that ethnic minorities and people with low incomes showed the greatest response to the intervention compared to other groups, thus indicating the effectiveness of targeting these groups with lower baseline knowledge (Goff et al., 2004). Estimates of heart attack knowledge from the REACT trial tended to be less than those in the BRFSS analysis, which is likely attributable to the presentation of the questions in a recall versus recognition format, respectively. Similar to heart attack, an analysis of 2001 BRFSS data from 17 states and the US Virgin Islands on stroke knowledge (N = 61,019) revealed that public recognition of signs and symptoms was low (Greenlund et al., 2003). Overall, only 20% correctly identified all six presented symptoms, with sudden confusion or trouble speaking (88%), numbness or weakness of the face, arm, or leg (94%), and sudden trouble walking, dizziness, or loss of balance (86%) being the most commonly recognized. As with heart attack, knowledge was greater among white people, middle-aged people, and those with higher education levels. Unfortunately, being very old and having a history of hypertension or stroke did not positively impact stroke knowledge, despite the greater risk of stroke imparted by these factors. Hence, in addition to increasing knowledge among the general public, targeting is needed among high-risk subgroups. Other surveys support this recommendation, as significantly lower knowledge has been documented among racial/ethnic minorities and disparities in heart disease and stroke mortality exist among

17 racial/ethnic groups and by socioeconomic status and education level as well (Cooper et al., 2000; Ferris et al., 2005). Type 2 Diabetes In Georgia, 23% of adults over age 65 have diabetes, and in 2005, it was the seventh leading cause of death (CDC, 2006a; GA-DHR, 2006). Prevalence estimates are likely an underestimate because a large number of people with diabetes are undiagnosed (USDHHS, 2000). A higher proportion of older adults in Georgia have diabetes compared to the national figure of 18% (CDC, 2006b). Diabetes is most common among older people and the incidence is likely to increase across age groups with the current obesity epidemic and longer life expectancy (USDHHS, 2000). Concurrently, diabetes is a significant problem among older Georgians in the OAANP. In a random sample of OAANP congregate meal recipients in northeast Georgia, 30% self-reported having diabetes (Stephens et al., 2005). Diabetes has many deleterious effects on health and quality of life, and complications include amputations, nephropathy, retinopathy, neuropathy, depression, and cardiovascular disease. In fact, people with diabetes are two to four times more likely to have heart disease or have a stroke compared to people without diabetes (Abbate et al., 2002). Diabetes is a costly condition as well, costing Georgia over $4 billion each year due to lost productivity, medical care, and premature death, and the annual health care costs of a person with diabetes are estimated to be $10,000 compared to $2,700 for people without diabetes (Diabetes Association of Atlanta, 2005). There are many modifiable and non-modifiable risk factors that contribute to type 2 diabetes, including heredity, ethnicity, history of gestational diabetes, overweight/obesity, and physical inactivity (National Institute of Diabetes and Digestive and Kidney Diseases, 2006). Diabetes can be prevented and managed, however, with healthy lifestyle habits and other

18 intervention, such as medication and insulin use. Due to the large role diet and physical activity play in the prevention and management of diabetes, the American Diabetes Association has issued a nutrition recommendations and interventions statement based on a review of the most recent evidence to guide health professionals and people with diabetes on effective dietary interventions to improve diabetes care (Bantle et al., 2008). One of the primary focuses of diabetes care is self-management education, as there are many self-care activities that individuals with diabetes must do on a daily basis to manage their condition and avoid complications. The Summary of Diabetes Self-Care Activities is a validated self-report tool used to assess the level of self-care in people with diabetes (Toobert et al., 2000). Important selfmanagement practices include following a healthful eating plan, participating in regular moderate physical activity, testing blood sugar, checking feet and shoes, avoidance of smoking, and taking medications as recommended by a health care provider (Mensing et al., 2007). In addition, given the high costs of diabetes and the increasing public health burden, Healthy People 2010 has set objectives for the prevention and management of diabetes in the US. The overall goal is through prevention programs, reduce the disease and economic burden of diabetes, and improve the quality of life for all persons who have or are at risk for diabetes. Diabetes education is one of the objectives outlined to reach this goal, thus highlighting its importance in prevention and management (USDHHS, 2000). Intervention trials have provided strong evidence that healthy lifestyle behaviors are effective for the primary prevention of diabetes in those at high risk (Knowler et al., 2002). The Diabetes Prevention Program (DPP) involved 3,234 adults with prediabetes, a condition that puts people at high risk for diabetes. This randomized, controlled trial showed a strong relationship between lifestyle modification, including modest weight loss and improved nutrition and

19 physical activity habits, and the prevention of type 2 diabetes. In fact, the lifestyle intervention group showed a 58% reduction in the incidence of diabetes compared to placebo, and lifestyle modification was more effective than metformin in decreasing diabetes incidence. This trial also showed that lifestyle modification was particularly effective in older adults, who represented 20% of participants in the trial (Knowler et al., 2002). Another landmark randomized controlled trial, the Diabetes Control and Complications Trial (DCCT), provided evidence that microvascular and neurologic complications of diabetes, including nephropathy, retinopathy, and neuropathy, can be delayed and the progression slowed by intensive blood glucose control in people with type 1 diabetes (Diabetes Control and Complications Trial Research Group, 1993). Similarly, the United Kingdom Prospective Diabetes Study (UKPDS) provided evidence of the benefits of glycemic control on complications in people with type 2 diabetes. This large, multicenter prospective study showed an overall microvascular complication rate reduction of 25%. In addition, there was a graded relationship between glycemic control and complications such that every percentage point decrease in hemoglobin A1c, even when absolute levels were suboptimal, resulted in a 35% reduction in risk of microvascular complications (Genuth et al., 2002). Given the burden of diabetes and the strong evidence that prevention is effective in reducing incidence and that diabetes complications can be significantly delayed and progression slowed through prudent lifestyle habits, several programs have attempted to deliver important information concerning prevention and management of this condition to people with diabetes and those at risk. Diabetes is most common in older adults and smaller interventions targeted to this subpopulation focusing on lifestyle habits are important for diabetes self care. Diabetes is a

20 chronic disease in which the affected individual is the primary caregiver. As such, selfmanagement education is a routine component of diabetes care. National standards for diabetes self-management education were developed to guide health professionals in provision of high quality care for people with diabetes (Mensing et al., 2007). Education is essential to diabetes control given the complex nature of the condition and the many associated complications. Standards for self-management education state that programs should be targeted to the specific population being addressed and regularly evaluated to ensure that the needs of recipients are being met. In addition, diabetes education should be delivered by a multidisciplinary team to address the many aspects of diabetes management, including but not limited to nutrition, medication management, eye care, and foot care. The diabetes education curriculum covers a range of topics, with nutrition, physical activity, medications where appropriate, blood glucose monitoring, and goal setting at the forefront, delivered in a manner that is understandable and facilitates behavior change in the target population. Hence, to improve outcomes and control diabetes to prevent complications, education is one of the first and most important steps to diabetes management (Mensing et al., 2007). Community-based lifestyle behavioral interventions are a cost effective means of communicating important information to people with diabetes or who are at risk, and there is sufficient evidence on the basis of glycemic control as the outcome to recommend diabetes selfmanagement education in community settings in adults with type 2 diabetes as an adjunct to clinical care (Task Force on Community Preventive Services, 2002). A review by Satterfield et al. (2003) explored activities and outcomes of community interventions for the prevention of type 2 diabetes through risk factor reduction. Most studies focused on multiple behaviors, such

21 as increasing physical activity, improvement of eating habits, weight management, and risk factor awareness among high-risk ethnic groups, and were culturally relevant to meet the needs of the targeted community. Most interventions attempted to actively engage the community in activities such as walking clubs and exercise classes, education sessions, and cooking demonstrations. Many of the studies reviewed had design flaws, however, and were not able to demonstrate significant improvements in lifestyle behaviors. However, the cultural sensitivity, community mobilization, and targeting of these studies were positive aspects, and should be used in well-designed studies that are able to capture the valuable benefits of such programs (Satterfield et al., 2003). The importance of tailoring interventions to the target population was also highlighted in a systematic review of interventions to improve diabetes care among socially disadvantaged populations, in an attempt to characterize successful interventions (Glazier et al., 2006). Reaching disadvantaged populations is a challenging area of diabetes care in need of attention, as significant health disparities exist by race and ethnicity. The diabetes death rate is 2.5 times higher in African Americans compared to whites (USDHHS, 2000), and compared to white people, African Americans and Latinos tend to have worse glycemic control and medication adherence (Heisler et al., 2007). This review identified features such as cultural adaptation, community leader and lay person involvement in intervention delivery, and use of behaviororiented tasks as common features of successful interventions that could benefit these especially at-risk population groups (Glazier et al., 2006). A randomized, controlled, community-based pilot study (N = 75) of nutrition and physical activity intervention in type 2 diabetes patients in rural Costa Rica showed several positive results reflected by clinical outcomes related to glycemic control and cardiovascular risk

22 factors (Goldhaber-Fiebert et al., 2003). The mean age of participants was 59 years and mean hemoglobin A1c for the sample was 8.6%, indicative of poor glycemic control overall. Though this study was short in duration (3 months), significant improvements were seen in the intervention group compared to controls. Hemoglobin A1c decreased by 1.8 percentage points and fasting glucose by 19 mg/dL, and there was a decrease in weight compared to an increase in the control group. The authors concluded that sustainability of the intervention results would likely depend on peer leadership and community support. This intervention, which was culturally sensitive, used a participatory approach, and included goal setting, provides evidence of the effectiveness of multiple behavior education programs in helping adults with limited resources to manage diabetes. Healthy Lifestyle Habits: Physical Activity Chronic diseases are among the leading causes of death in the US and in Georgia (CDC, 2006a, GA-DHR, 2006) and include diseases of the heart, cancer, cerebrovascular disease, diabetes, and others. All of these chronic diseases have modifiable risk factors that can be improved through modest changes in lifestyle habits, such as regular physical activity, nutrition, regular health screenings, and avoidance of smoking and excessive alcohol intake. Healthy lifestyle habits are the targets of many public health efforts to reduce the burden of chronic diseases and help Americans take charge of their health (USDHHS, 2000, USDA & USDHHS, 2005, Nelson et al., 2007). It is well-established that physical activity is an important factor in the prevention and management of chronic diseases and for maintenance of multiple body systems. Several authoritative organizations have made recommendations to encourage Americans to be physically active. The 2005 Dietary Guidelines for Americans recommends that adults of all

23 ages accumulate at least 30 minutes of moderate physical activity on most days of the week to reduce the risk of chronic disease in adulthood. For older adults, regular physical activity can also help to reduce functional declines associated with aging and prevent falls (USDA & USDHHS, 2005) and is associated with reduced risk for disability and impairments in ADLs and IADLs (Berk et al., 2006; Boyle et al., 2007). The American Heart Association and American College of Sports Medicine have also jointly developed guidelines for physical activity, recommending that older people get at least 30 minutes of moderate intensity aerobic activity five days per week or vigorous intensity activity for 20 minutes three days per week. To accommodate a variety of lifestyles and ability levels, and to compete with other activities that occupy individuals’ time, aerobic activity may be accumulated throughout the day in 10 minute bouts and provides similar benefit to health as 30 minutes of continuous activity. AHA/ACSM also recommend muscle-strengthening, flexibility, and balance exercises as part of a physical activity program to support functional independence and reduce the risk of falls in older people (Nelson et al., 2007). Despite the many health benefits of regular physical activity, a large proportion of older Americans fall short of the goals outlined by Healthy People 2010. For example, Kruger et al. (2007) estimated that as of 2001, 46% of older adults engaged in no leisure time physical activity, and only 26% engaged in at least 30 minutes of light to moderate aerobic activity at least five days per week or vigorous activity for at least 20 minutes three days per week. Further, only 8% met recommendations for both aerobic and strengthening physical activities (Kruger et al., 2007). These figures fall well below the 2010 objectives of decreasing the prevalence of no leisure time physical activity to 20% (CDC and Merck Co., 2007). In general, physical activity levels decline with age and are lowest among adults age 75 and older (Federal Interagency

24 Forum on Health Related Statistics, 2006). Physical inactivity is also more common in people with lower education (< high school), people living in rural areas, and in African Americans, particularly women (Cooper et al., 2000). Sedentary behavior can contribute to a multitude of problems, such as sarcopenia and accelerated loss of bone, declines in functional capacity, weight and fat gain, and several chronic diseases in older people (Singh, 2002). In Georgia, 36% of older adults are physically inactive, as defined by no participation in any leisure time physical activities or exercise in the past 30 days (Bryan et al., 2005). Only 33% meet the Healthy People 2010 recommendation of at least 30 minutes of moderate activity five or more days per week or 20 minutes of vigorous activity three or more days per week (CDC, 2005b). In 2004, Georgia had one of the lowest rankings (49th) for older people engaging in no leisure time physical activity (CDC and Merck Co., 2007). The contribution of regular physical activity to health is tremendous and is beneficial throughout the lifespan. People can reap the benefits of regular activity across all age groups, particularly older people who often experience functional declines and dependency (Singh, 2002). In a vulnerable population of older Georgians, such as those in the OAANP, where the prevalence of chronic diseases and the demand for inexpensive modalities to treat these conditions is high, regular physical activity could make an enormous contribution to health and well-being. Most chronic diseases are responsive to and influenced by physical activity, with benefits including reduced risk for and better control of heart disease, diabetes, colon cancer, osteoporosis, high blood pressure, as well as improved psychological well-being and body composition (USDHHS, 2000). Physical activity could save Americans billions in health care costs, as physical inactivity plays a role in the prevention and management of most chronic conditions, such as obesity, diabetes, cardiovascular disease, and osteoporosis. Based on 2005 estimates, regular physical activity

25 could save Georgia $569 million in hospital charges (GA-DHR, 2007b). Whereas the cost of invasive treatments and medications for diseases can be extremely high, physical activity is an inexpensive lifestyle behavior that improves health. Walking is the preferred exercise modality for many older people and requires little more than a good pair of shoes and safe place to walk, and is a low-risk, reasonable way to accumulate recommended daily physical activity (Simpson et al., 2003). Many outlets are available for older people, including local senior centers, community walking tracks, public parks, YMCAs, and walking clubs. Hence, physical activity is within reach of many older Georgians and because most are not getting the recommended amounts, they could realize significant health benefits from increasing their physical activity. Interventions targeting community-dwelling older adults have been implemented to increase activity levels. To effectively promote physical activity in older adults, interventions should go beyond educational approaches alone by including social and behavioral components, such as self-monitoring and goal setting. Plans for maintenance of activity following the intervention period should also be addressed, as adherence to physical activity is often difficult to maintain (Marcus et al., 2006). Reviews of group-based physical activity intervention studies for older adults have concluded that this population group can increase their physical activity levels in the short term (King et al., 1998), however, strategies for long-term maintenance are still being developed (Marcus et al., 2006). Older adults often face unique barriers to engagement in physical activity as well (Prohaska et al., 2006). For example, having a health condition, feeling that the required amount is too much, and perceiving that it is unsafe, are some of the barriers identified in Georgia OAANP participants (Johnson et al., 2006). However, many of these barriers may be overcome using effective physical activity interventions that focus on behavior change and address barriers, and extend beyond information dissemination. As such,

26 strategies to promote physical activity in older people and improve participation and maintenance have been identified. Social support, tailoring the physical activity program to the needs, interests, and ability levels of participants, goal setting and self-monitoring, assurances of safety, and offering positive reinforcement can all be valuable means to help older people adopt and maintain regular physical activity (Cress et el., 2004). A systematic review of physical activity interventions aimed at behavior change also demonstrated that community-based interventions using several techniques, including individually-adapted health behavior change, have helped adults increase their physical activity levels and improve aerobic fitness (Kahn et al., 2002). Physical activity interventions targeted to older adults who are at risk for poor mobility and functioning can demonstrate substantial benefits to participants, even at low intensity levels. For example, a one-year pilot study of congregate meal participants (N = 110) in rural southeastern communities with a high percentage of African American women (> 50%) showed that a low intensity physical activity program was helpful in maintaining and improving functional abilities, as assessed by several performance-based measures, as well as participants’ perceptions of their progress toward reaching physical activity goals. Important features of this program included initial needs assessment to determine how best to serve the target population, as well as peer leadership to support maintenance of the physical activity program after the intervention period (Sharpe et al., 1997). Healthy Lifestyle Habits: Diet A healthy diet is widely promoted for the prevention and management of chronic diseases for all Americans. The 2005 Dietary Guidelines provides the foundation for general healthy eating recommendations, with particular emphasis on plant foods, such as fruits, vegetables,

27 legumes, and whole grains. The Dietary Approaches to Stop Hypertension (DASH) trial is best known for helping adults lower blood pressure using a diet rich in fruits, vegetables, whole grains, low-fat milk products, and lean protein sources, and limited in fat, sodium, and added sugars (Sacks et al., 2001). Many other trials have used a similar eating pattern rich in plant foods to successfully prevent and manage cardiovascular disease and diabetes (Hu, 2003; Hu & Willett, 2002; Knowler et al., 2002). Older adults have unique nutritional requirements characterized by decreased energy needs and increased or unchanged need for an array of nutrients. Therefore, older adults should choose nutrient dense foods, such as fruits, vegetables, and whole grains, to supply adequate nutrients within calorie needs (USDHHS & USDA, 2005; Lichtenstein et al., 2008). Although a nutritious diet is an important part of health maintenance, many older adults fall short of the recommendations for healthy foods. Given the many benefits of diets rich in fruits and vegetables, the most recent recommendation from the 2005 Dietary Guidelines for Americans has been increased to seven to ten servings of a variety of fruits and vegetables per day for the energy requirement of 1,600 to 2,200 calories daily, an energy range that is adequate for many older people. Therefore, 1.5 cups of fruit and 2 cups of vegetables per day for a 1,600 calorie diet and 2 cups of fruit and 3 cups of vegetables for a 2,200 calorie diet are recommended. According to NHANES 1999-2002 data, while most older adults do report consuming some fruits and vegetables daily, the amount consumed is below recommendations for sedentary older men and women. This inadequacy is most pronounced in terms of the variety of vegetables consumed, as only a small proportion (20%) of intake comes from the deeply pigmented green and orange vegetables and the fiber-rich legume subgroups (Juan and Lino, 2007). In addition, according to the Behavioral Risk Factor Surveillance System (BRFSS) data,

28 nearly 69% of older adults nationwide report inadequate fruit and vegetable intake, as defined by median percent of respondents not consuming at least five servings daily (CDC, 2005b). Therefore, older people could benefit substantially from interventions to increase intake to recommended levels, as well as to increase variety and subsequent exposure to a wider array of nutrients and beneficial chemicals in fruits and vegetables. While slightly less than younger age groups in Georgia, 71% of adults over age 65 report consumption of less than five servings of fruits and vegetables daily (Bryan et al., 2005), and low intake is also reflected in Georgia OAANP participants. In a report by Johnson et al. (2006), only 28% of older adults sampled reported consumption of five or more servings of fruits and vegetables daily. The most common barriers to higher intakes identified were cost, chewing or dental problems, and difficulties with digestion. In a sample of older adults in the OAANP in north Georgia, only 14% consumed the recommended three servings daily of whole grain foods (Ellis et al., 2005). The nutritional well-being of older adults is influenced by multiple factors at several levels. Factors related to health conditions, social and economic situation, cultural and environmental factors, and individual choices all can impact the nutritional health of older people, creating challenges to achieving a healthy diet (Weddle et al., 2007). Several potential barriers to greater fruit and vegetable intake among older adults have been identified previously. In a cross section of 4,622 generally healthy older adults who provided dietary data for NHANES III, lower fruit and vegetable intake was associated with low self-reported health, poor dentition, obesity, African American ethnicity, and social isolation (Sahyoun et al., 2005). For rural older adults, meeting dietary guidelines recommendations can present an even greater challenge than for other subpopulations of older people. In a cross section of older people (N =

29 122) living in a rural, southern area, and who had low income and low education attainment, most were not getting the minimum number of daily servings of fruit, vegetables, grains, and milk products. The deficit was particularly pronounced for whole grain servings and deeply colored vegetables (Vitolins et al., 2007). These data suggest that older adults, including older Georgians, may be at risk for inadequate intake of important foods and could benefit from interventions to help improve dietary intake for chronic disease prevention and management. Emphasis on inexpensive ways to include fruits and vegetables in the daily diet and preparation methods that improve ease of chewing could help these older people overcome barriers to greater intake. To promote healthful eating patterns in older adults, specific characteristics of successful programs have been identified, which may be used to guide the development of nutrition interventions (Sahyoun et al., 2004). Interventions targeted to this population group should consider their unique needs, and tailor interventions to match the interests and ability levels of older people (Higgins and Clarke Barkley, 2003). While older adults have demonstrated increases in nutrition knowledge, changes in behavior can be more difficult to impact (Sahyoun et al., 2004; Rosenbloom et al., 2004). To maximize the effectiveness of nutrition education to improve health habits, it is therefore suggested that programs targeting older adults have a few main messages that are simple, practical, reinforced, and are tailored to the specific needs of the recipients to encourage sustained behavior change. Programs that are theory-based and encourage participants to set personal goals toward improved health, accompanied by selfmonitoring, are important for successful behavior change as well (Sahyoun et al., 2004; Mitchell et al., 2006). Hands-on activities, interactive relationships with health care professionals, and educational incentives are also useful strategies for improving nutrition-related behaviors among

30 older adults (Sahyoun et al., 2004; Higgins and Clarke Barkley, 2003). The proposed framework for nutrition interventions among older adults therefore includes use of several strategies identified from previously successful interventions, along with implementation at multiple levels, including the social and physical environment in which individuals interact, to effectively reach older adults and promote behavior change (Sahyoun et al., 2004). Multiple behavior intervention studies have been conducted to increase both good nutrition habits and physical activity habits, as these lifestyle behaviors work in concert to promote health, and the effects of such interventions may produce greater results compared to those targeting one behavior individually (Wellman et al., 2007). For example, as part of the YouCan! Steps to Healthier Aging Campaign, the older adult arm of the Steps to a Healthier US initiative, the community-based program Eat Better and Move More (EBMM) was developed to help older adults served by the Older Americans Act improve nutrition and physical activity habits (Wellman et al., 2007). This nationwide program (N = 620, mean age = 75) included educational activities that emphasized important topics, such as eating plenty of fruits, vegetables, and fiber, and sensible portion sizes. Physical activity was also a large component, and walking was promoted in educational sessions and through use of pedometers to track daily steps. A recent evaluation of this program that used pre- and post-intervention questionnaires and physical function measures showed that participants significantly improved fruit intake (31%) and vegetable intake (37%) by one or more servings daily, as well as fiber (P < 0.001). Participants also significantly increased number of days they walked and number of steps accumulated daily, and improvement was also reflected in the mean Timed Up and Go score, a measure of mobility. This program provides evidence of the effectiveness of combined nutrition

31 and physical activity community interventions for improving health behaviors in OAANP participants. Another large, randomized, multiple-behavior trial based upon the Coronary Health Improvement Project (CHIP), promoted healthy lifestyle behaviors among Rockford, Illinois community volunteers (N = 337; mean age = 51) (Aldana et al., 2006). The program was theorydriven and used activities to promote behavior change. Participants received a series of heart health and other chronic disease related lectures and educational materials over a 1-month period and were encouraged to follow a plant-based diet low in fat, sugar, and salt, and regular physical activity was emphasized. Pedometers were provided to help participants track their daily walking. Participants were encouraged to join a CHIP alumni association to help maintain their healthy habits at the end of the program. At six-month follow-up, participants in the intervention group significantly increased nutrition and physical activity behaviors, including a 2.3 serving per day increase in fruit and vegetable intake, markedly reduced saturated fat and sodium intake, and an increase in physical activity by 30%. Decreases in CVD risk factors were also demonstrated, including reductions in BMI, body fat, and blood pressure. While the results of this trial provide evidence of the importance of community-based programs in promoting important lifestyle changes for chronic disease prevention, programs should be tailored to the needs of the specific target population. Participants in this trial were primarily white, married, highly educated, and had relatively high income levels. Hence, the findings may not be generalizable to population groups with fewer resources, lower education levels, and containing high proportions of racial/ethnic groups. Results of the randomized, controlled PREMIER trial were also consistent with the positive results seen in fruit and vegetable intake and lowered intake of fat and sodium following a multiple-component nutrition intervention (Lin et al., 2007).

32 Health Belief Model Many successful interventions aimed at behavior change are based on the Health Belief Model (HBM). This theory has several components which explain why people take action to prevent ill-health conditions. It is based on the premise that individuals will make positive health-related behavior changes if they: perceive themselves as susceptible to an illness; perceive that the illness is severe enough to cause unpleasant consequences if ignored; perceive that the benefits of making behavior change are worthwhile and will produce positive outcomes; believe that the benefits outweigh the costs or barriers to change; and perceive that they are able to successfully engage in recommended health-related behaviors (Strecher and Rosenstock, 1997). The HBM has been the basis of several nutrition and education interventions in the Georgia OAANP (Speer et al., 2007; Hendrix et al., 2007; Fitzpatrick et al., 2007; McCamey et al., 2003). Previous Successful Interventions Previous community interventions targeting Georgia’s OAANP participants have been successful in improving health-related knowledge and behaviors, and due to the ongoing nature of these interventions, have reached thousands of older adults over the years. Through a partnership among the Georgia Division of Aging Services, Division of Public Health, The University of Georgia, and the aging services network, nutrition and physical activity knowledge and behaviors, along with diabetes self-management skills, have improved substantially in Georgia’s elders using community-based interventions (Speer et al., 2007; Hendrix et al., 2007; Fitzpatrick et al., 2007; McCamey et al., 2003). The most recent community intervention implemented in 2005, which promoted diabetes self-management, fruit and vegetable intake, and physical activity, showed that congregate meal recipients with poorly controlled diabetes were

33 able to achieve a clinically significant decrease in A1c of 1.15% following the multiple-behavior diabetes self-management intervention. Increased physical activity was consistently associated with this decrease in A1c. In addition, improvements in multiple self-management practices were realized following the diabetes educational intervention (Speer et al., 2007). The fruit and vegetable intervention that was implemented in conjunction with the diabetes self-management intervention in Georgia senior centers showed that elders improved their knowledge and behaviors related to fruit and vegetable intake and decreased barriers to intake (Hendrix et al., 2007). Analysis of the physical activity component of these interventions showed that self-reported changes in behavior, such as getting the recommended amounts of physical activity, increased significantly following the educator-led physical activity program, which promoted walking and used chair exercises to improve strength, balance, and flexibility. Using the Short Physical Performance Battery to assess physical function, significant improvements were seen in the total function score, and physical activity was an independent predictor of improved physical function (Fitzpatrick et al., 2007). These results have important implications for the effectiveness and need of physical activity interventions to help elders remain independent and prevent or delay nursing home placement (Guralnik et al., 1994). Participants have also demonstrated improvements in their knowledge and behaviors to decrease heart disease risk (McCamey et al., 2003). Studies of the target population have also provided valuable information to characterize health habits of these elders and have guided the development of subsequent interventions. Intake levels of important foods, health knowledge, and physical activity habits among OAANP participants have been reported. In general, intake of whole grains and fruits and vegetables is below national recommendations, and knowledge of the recommended servings of fruits and

34 vegetables per day for older people is low (Ellis et al., 2003; Hendrix et al., 2007). In addition, many fall short of minimum requirements for physical activity to reduce the risk of chronic diseases and to promote overall good health (Fitzpatrick et al., 2007). These characteristics, combined with high rates of chronic diseases, such as hypertension, heart disease, and diabetes underscore the need for community-based interventions to help older Georgians improve their nutrition and physical activity habits (Johnson et al., 2006; Stephens et al., 2005). Rationale, Specific Aims, & Hypotheses The present study expands on previous interventions conducted among Georgia’s OAANP participants that have shown positive effects on lifestyle habits to support chronic disease prevention and management, as well as health knowledge (Speer et al., 2007; Hendrix et al., 2007; Fitzpatrick et al., 2007). Given the complexity of diabetes self-care, as well as the multiple factors influencing the nutritional health of older Georgians, this study reinforces and builds on the important health messages presented in previous interventions to promote continued improvement. These previous interventions also helped to characterize participants and guided the development of the present intervention to address the specific needs of the target population. The hypotheses to be tested are that a diabetes and heart health education program increases knowledge and behaviors that support cardiovascular disease prevention and management from low pre-test levels, and 2) for people with diabetes, the program increases the frequency of self-management practices and lowers A1c. The specific aims are: 1) to determine pre-test knowledge and behaviors related to diabetes and heart disease prevention and management among OAANP participants at senior centers, and 2) ascertain the effects of a diabetes and heart health education program on knowledge and behaviors that support

35 cardiovascular disease and diabetes prevention and management, such as fruit and vegetable intake, physical activity, and diabetes self-management practices (among those with diabetes).

36

CHAPTER 3 A COMMUNITY INTERVENTION IMPROVES LIFESTYLE HABITS TO SUPPORT DIABETES AND HEART DISEASE PREVENTION AND MANAGEMENT1

1

Bell, M., Lommel, T.S., Fischer, J.G., Lee, J.S., Reddy, S., Johnson, M.A. To be submitted to Preventing Chronic Disease: Public Health Research, Practice, and Policy.

37 Abstract State-wide community-based nutrition and wellness programs have been implemented to improve the nutrition and health habits of older adults at high risk of chronic diseases in Georgia. Cardiovascular diseases, including stroke and congestive heart failure, cost $3.34 billion annually in Georgia – and at least $2.5 billion of these costs are incurred by older adults (GADHR, 2005). Therefore, the purpose of the present study was to evaluate a community-based education intervention to improve nutrition and lifestyle habits related to diabetes and cardiovascular disease prevention and management in 40 senior centers across the state of Georgia. Participants were a convenience sample that completed a pre-test questionnaire, the intervention, and a post-test questionnaire (N = 693, 84% female, mean age = 75, 55% white, 45% African American, 1% other). A subset of these participants had diabetes (n = 244). Incorporating the components of the Health Belief Model, the 4-month intervention consisted of 16 sessions (about one hour each), eight of which focused on improving knowledge and behaviors that support diabetes and heart disease prevention and management, such as meeting national recommendations for regular physical activity and fruit and vegetable intake, and learning the warning signs of heart attack and stroke. All 16 sessions included physical activity, such as chair exercises. Following the intervention, several indices of participants’ knowledge and behaviors improved. Knowledge of at least 5 of the 6 major signs of heart attack and at least 5 of the 6 major signs of stroke increased by 20- and 27-percentage points, respectively (P < 0.001). Participants reporting at least five servings of fruit and vegetables per day increased from 56% to 73% (P < 0.001), and those reporting at least 30 minutes of moderate physical activity at least 5 days in the last week increased by 10-percentage points (P = 0.001). These positive results provide evidence that this intervention improved knowledge and health-related

38 behaviors in elders, which may help management of diabetes and heart disease. The intervention materials are available online for use at http://livewellagewell.info/study/materials.htm. KEYWORDS: older adults, senior centers, diabetes, heart health, intervention, physical activity Introduction In the US, the older adult population is expected to double by the year 2030, with over 70 million (20%) over age 65 and large growth percentages occurring among minorities. People are living longer, with an average life expectancy of 18.7 additional years after age 65. Consequently, more adults are living with chronic diseases with increasing age, including cardiovascular disease and diabetes (Administration on Aging, 2007). It is estimated that at least 80% of older Americans have at least one chronic disease, and nearly 50% have at least two. Given the high costs of treating multiple chronic health conditions in older adults, health care spending in the US is projected to increase by 25% by 2030 (CDC and Merck Co., 2007). Consistent with national data, a large proportion of mortalities in Georgia are attributable to chronic conditions, which represent the top six causes of death in older people (GA-DHR, 2007). In addition, Georgia’s older adults represent a particularly vulnerable population in need of services to maintain health. Contributors to health disparities, such as low socioeconomic status, low education, living in a rural area, and being of a racial/ethnic group are characteristic of many of Georgia’s elders (Glass and Bachtel, 2007), which is particularly evident in the Older Americans Act Nutrition Program (OAANP) participants. These factors can impact access to health care, non-modifiable risk for chronic diseases, health knowledge, and self-care. Among the leading causes of death in Georgia are cardiovascular disease and diabetes (Gregory et al., 2005). However, these health conditions have many modifiable risk factors related to lifestyle habits, such as diet, physical activity, and smoking (CDC and Merck Co.,

39 2007). Nearly 75% of adults in Georgia have at least two modifiable risk factors for CVD (Gregory et al., 2005). Chronic conditions are costly, and it is estimated that treatment costs for heart disease and diabetes in the US could be cut by $76 billion and $17 billion, respectively, by the year 2023 if Americans improved health-related behaviors and screenings (DeVol et al., 2007). Community interventions can provide a cost effective means of reaching large numbers of individuals to improve their health. In fact, The American Heart Association has produced a Community Guide to help individuals and organizations improve cardiovascular health at the community level (Pearson et al., 2003). Similarly, the American Diabetes Association has developed national standards for diabetes self-management education and recognizes culturally relevant community interventions as a valuable means of helping people lower their risk for and manage type 2 diabetes (Satterfield et al., 2003; Task Force on Community Preventive Services, 2002). The OAANP is a community-based program that provides grants to support nutritionally balanced meals and nutrition and wellness education and services to those aged 60 and older. This program specifically targets those with greatest social and economic need. The majority of participants in this program is poor or near poor, with a large proportion being in a racial or ethnic minority group, such as African American and Hispanic. Many more (57%) live alone and may be socially isolated compared to the general elderly population (25%) (Millen et al., 2002; Ponza et al., 1996). Chronic diseases, including cardiovascular disease (hypertension, stroke, heart disease) and diabetes, are common among these individuals who have an average of 2.4 diagnosed chronic health conditions. Many are overweight and obese as well, with about 42% of congregate meal recipients having a body mass index greater than 27 (Ponza et al., 1996), and obesity is recognized as a major risk factor for both diabetes and cardiovascular

40 disease (Lichtenstein et al., 2006; National Institute for Diabetes and Digestive and Kidney Diseases, 2006). Nutrition and wellness services provided through the OAANP have proven effective in helping older Georgians gain access to important health information and improve their knowledge and behaviors for chronic disease prevention and management in the short term. Diabetes self-management, fruit and vegetable intake, whole grain intake, and physical activity have all been emphasized through Georgia’s wellness programs, and evaluations of these education programs have provided the evidence needed for their continued support and expansion (Ellis et al., 2005; Fitzpatrick et al., 2007; Hendrix et al., 2007; Speer et al., 2007). Therefore, the purpose of the present study was to ascertain the effectiveness of a diabetes and heart health education intervention to improve lifestyle habits for chronic disease prevention and management among community-dwelling older adults in Georgia senior centers. Building upon the components of previous successful interventions, this theory-based intervention focused on controlling risk factors and improving behaviors and knowledge for cardiovascular disease and diabetes prevention and management. The intervention was evaluated using pre- and post-tests administered to a convenience sample of older people from the 12 Georgia Area Agencies on Aging (AAA). The hypothesis is that the program increases knowledge and behaviors that support cardiovascular disease prevention and management from low pre-test levels, and for people with diabetes, the program increases the frequency of self-management practices and lowers A1c. The specific aims were to 1) assess pre-test knowledge and behaviors among OAANP participants, 2) determine the effectiveness of the intervention in improving pre-test levels using outcomes such as heart attack and stroke symptom knowledge, increased physical activity and

41 fruit and vegetable intake, and in the diabetes subgroup, increased frequency of diabetes selfmanagement practices, and 3) qualitatively compare the benefits of this intervention to our 20052006 interventions (Fitzpatrick et al., 2007; Hendrix et al., Speer et al., 2007). Methods Planning began in 2005 with a series of meetings with the Georgia Division of Aging Services, the Georgia Division of Public Health, the Diabetes Association of Atlanta, Diabetes Technologies, Inc., Directors and Wellness Coordinators affiliated with each Area Agency on Aging (AAA), senior center staff, and The University of Georgia. The overall design of the evaluation was statewide training (October, 2006), pre-test (November and December, 2006), intervention (January through April, 2007), and post-test (May and June, 2007). Pre- and posttest questionnaires and consent forms are available from the authors (MAJ) and the curriculum is available online at http://www.livewellagewell.info/study/materials.htm#2007. Sample Questionnaires and procedures were approved by the Institutional Review Board on Human Subjects of the Georgia Department of Human Resources and the University of Georgia. Wellness Coordinators for the 12 Area Agencies on Aging (AAAs) identified two to five senior centers in their area to participate in the intervention. Senior centers were selected based on the support of the senior center director, interest of the participants, and a relatively high prevalence of diabetes (as determined by the senior center). Approximately 3,500 individuals participated in at least one aspect of the intervention: this diabetes and heart health intervention, the physical activity intervention, and/or the bone health intervention (Teems, 2008). A convenience sample was enrolled in the evaluation component and recruitment goals were 70 participants in each AAA, including at least 20 people self-reporting a diagnosis of diabetes. Recruitment was

42 accomplished by Wellness Coordinators, senior center directors, and their staff. Procedures were explained and the consent form was read to participants; written informed consent was obtained from participants. Most participants were recipients of congregate meals. Homebound elders were excluded. Other exclusion criteria, as determined by interviewer assessment, were the inability of participants to understand the informed consent, answer pre- and post-test questions, or participate in the educational intervention. Physicians’ clearance for participation in the physical activity portion of the intervention was initiated before informed consent, because clearance is recommended whether or not individuals enrolled in the evaluation. These recruitment procedures resulted in a convenience sample of 849 older adults (mean age = 74 years, 83% female, 55% white, 44% black, 1% other race/ethnicity) from 40 sites (100% senior centers). Of the 849 participants enrolled in the study, 693 completed the post-test questionnaires. Pre-tests Experts in nutrition, physical activity, and diabetes (including two faculty in the Department of Foods and Nutrition, University of Georgia, and the Georgia Division of Aging Services) reviewed and edited the consent forms and pre- and post-test questionnaires to ensure content validity and cultural appropriateness based on their collective experience working with the target population. Input from Division of Aging Services staff and Wellness Coordinators was also incorporated into the questionnaires. About one hour was required to explain the study, obtain informed consent, and complete the pre-tests for each participant. Wellness Coordinators and their staff read the questions to participants and recorded their responses. Assessments included demographic information, general health including current chronic conditions (self-reported diabetes, hypertension, heart

43 disease, and arthritis), medication management practices, and height and weight (measured or self-reported). Interviewers reported the method of measurement used to obtain body weight (with scale and without shoes, with scale and with shoes, or self-report) and height (tape measure or self-report). Body mass index was calculated (BMI = (weight (pounds)/height (inches)2 ) x 703). The questionnaires focused on knowledge and behaviors related to diet, physical activity, regular health screenings, and knowledge of heart attack and stroke warning signs and symptoms. Diet-related questions focused on consumption of fruits and vegetables (three questions), as well as knowledge about recommended intakes (USDHHS & USDA, 2005; Toobert et al., 2000). A sample question is, “How many days of the last week (seven days) did you eat five or more servings of fruits and vegetables?” Total average daily fruit and vegetable intake was calculated by summing daily reported fruit intake and daily reported vegetable intake. Physical activity questions (four questions) focused on days per week of moderate physical activity and minutes per day of any light or moderate physical activity (CDC, 2005a; Toobert et al., 2000). A series of questions was used to assess frequency of recommended check-ups for heart health indicators (blood cholesterol, blood pressure), and for the eyes, ears, and feet. Two questions also asked whether participants were trying to reduce their intake of sodium and saturated fat to lower the risk of or manage blood pressure and heart disease, respectively (CDC, 2005a). A questionnaire was also used to assess knowledge of the signs and symptoms of heart attack and stroke (six questions for each condition), and actions to take should these events occur (calling 9-1-1), using questions from the Behavioral Risk Factor Surveillance System questionnaire (CDC, 2005a). A sample question is, “Do you think pain or discomfort in the jaw,

44 neck, or back are symptoms of a heart attack?” (Yes/no/don’t know). Each set of questions on heart attack and stroke signs/symptoms contained one incorrect, or “decoy” sign. Summary scores of total signs and symptoms correctly identified were computed as mean number of questions answered correctly. For both the heart attack and stroke questionnaires, responses of “don’t know” were collapsed as incorrect responses and summary scores were computed for participants who provided complete data for all signs and symptoms. For those with self-reported diabetes, diet and health practices were evaluated using nine questions contained in the Summary of Diabetes Self-Care Activities (SDSCA), a validated selfreport tool used to assess the level of self-care in areas considered essential for diabetes care (Toobert et al., 2000). Five questions were used that directly related to self-management practices for people with diabetes only, such as frequency of foot checks, spacing carbohydrates evenly, and home blood glucose monitoring, while four questions were related to diet and physical activity that were administered to all participants. Participants’ physical function was evaluated using the Short Physical Performance Battery test (SPPB) (Guralnik et al., 2004). Poor performance on this test predicts future nursing home placement, disability, and impending death (Guralnik et al., 1994). The SPPB test assesses older adults’ mobility by measuring three categories of function, which are balance, strength, and gait speed as an individual performs a standing balance test, chair stands, and an 8-foot walk, respectively, with performance in each category scored on a scale of zero to four. A summary performance score is calculated by summing each of the three category scores to give a final score ranging zero to twelve, where higher scores indicate higher performance: poor function (zero to five), moderate function (six to nine), and good function (10 to 12). Briefly, the standing balance is a semi-tandem stand, followed by either a timed tandem (completers of semi-

45 tandem) or side-by-side (non-completers of semi-tandem) stand. The 8-foot walk is a timed walk that can be done with or without an assistive device. Chair stands are five timed chair stands from the seated position. The Diabetes Association of Atlanta, Inc. and Diabetes Technologies, Inc. led the efforts to obtain A1c from those with diabetes (Accubase A1c test kit, Diabetes Technologies, Inc., Thomasville, GA). A1c is a measure of blood glucose control and diabetes management (low level indicates good management, < 7% is recommended, and > 8% is considered very poor management) (American Diabetes Association, 2008). Participants had A1c measured by a representative from the Diabetes Association of Atlanta, Inc. or Diabetes Technologies, Inc., and some sites ordered the A1c kits and had their own nurse or phlebotomist collect the blood samples. Diabetes Technologies, Inc. sent the results to UGA. Intervention After participants completed the pre-test questionnaires, the interventions were initiated at all sites and lasted four months. Each session was given one time and lasted 45 to 60 minutes. Physical activity was incorporated into every session. Nutrition, physical activity, and diabetes experts from The University of Georgia (two faculty, including one registered dietitian) reviewed the curriculum entitled “Seniors Taking Charge of Diabetes and Heart Health” and “Seniors Fight Falls and Fractures.” Based on years of related experience, these experts ensured that the curriculum was accurate, culturally appropriate, and safe for the participants, and could be delivered by individuals who were well educated, but not necessarily a registered dietitian. The curriculum was developed based on the previously successful educational interventions developed by The University of Georgia for older adults to increase knowledge and behaviors related to fruit and vegetable intake, physical activity, and diabetes self-management (Fitzpatrick

46 et al., 2007; Hendrix et al., 2007; McCamey et al., 2003; Redmond, 2004; Speer et al., 2007). The updated curriculum contained recent changes in diet and physical activity recommendations, and diabetes management (USDHHS & USDA, 2005; American Diabetes Association, 2006). In addition to attending up to eight sessions on diabetes and heart health, many participants also attended up to an additional eight sessions on bone health. The two interventions were given on alternate weeks and all 16 sessions included promotion of physical activity. Briefly, the bone health sessions each had a lesson plan with handouts, recipes, and menus, and focused on consuming adequate calcium and vitamin D from foods and supplements, being physically active, and following the instructions of a health care provider to maintain and/or prevent a decline in bone health (Teems, 2008). The physical activity intervention focused on 16 educator-led chair exercises, with many adapted from the National Institute on Aging (2001), promotion of walking, using a pedometer, and recording daily steps. For the diabetes and heart health intervention, the first lesson, “My Eight Ways to Feel Great,” defined diabetes and cardiovascular disease (CVD) and their prevalence, and introduced eight important lifestyle practices to prevent and manage these conditions that were discussed in subsequent lessons. The second lesson, “Be Physically Active Everyday,” emphasized the health benefits of regular physical activity for people with diabetes and CVD, and ways to incorporate physical activity into a healthy lifestyle. The third lesson, “Healthy Eating – Up with Fruits, Vegetables, and Whole Grains, Down with Fat and Sodium,” discussed ways to include a variety of healthy plant foods in daily meals and snacks, and how to limit intake of nutrients that can increase the risk of CVD, such as saturated fat and sodium. The fourth lesson, “Healthy Eating – Control Portions and Choose a Variety of Foods,” used the plate method to plan balanced meals using sensible portions of a variety of nutritious foods, and explained how to read food labels to

47 choose foods with less saturated fat, and added salt and sugar. The fifth lesson, “Prevent and Manage Heart Disease, Stroke, and Diabetes,” discussed ways to prevent and manage CVD and diabetes by controlling major risk factors, and how to recognize the warning signs of heart attack, stroke, and diabetes. The sixth lesson, “Get Checked for Diabetes and Heart Disease Risk Factors,” stressed the importance of recommended check-ups to control risk factors and complications related to CVD and diabetes. The seventh lesson, “Managing My Medications,” helped participants learn to manage their medicines by talking to their doctor and pharmacist, and to increase safety and organization for taking medications as recommended. The eighth lesson, “Know the Warning Signs of Heart Attack, Stroke, and Diabetes,” detailed the warning signs of heart attack, stroke, and diabetes, and emergency procedures to follow should these events occur. The conceptual framework for this intervention was based on the Health Belief Model (Strecher and Rosenstock, 1997). The intervention incorporated key components of this framework, including: perceived susceptibility and severity (e.g., emphasizing the health conditions and related complications that frequently impact older adults and are associated with lifestyle habits, such as CVD and diabetes); perceived benefits (e.g., defining how to take action by increasing physical activity and intake of a variety of healthy foods); perceived barriers (e.g., providing information and correcting misinformation about healthy foods, physical activity, and chronic diseases); cues to action (e.g., providing “how to” information on self-management of diabetes); and self-efficacy (e.g., by demonstrating ways to include a variety of healthy foods in daily meals, to be more physically active, and to control risk factors for diabetes and CVD).

48 Post-Tests The post-test was administered within one to two months following the last session of the intervention to allow participants time to make behavior changes. The post-test was very similar to the pre-test, except that additional questions were added for participants to further describe changes in their behaviors related to fruit and vegetable intake, physical activity, diabetes selfmanagement, as well as their satisfaction with the program. Statistical Analyses Pre- and post-test questionnaires were sent by the Wellness Coordinators to The University of Georgia for analyses (SAS, Version 9.1, SAS Institute, Cary, NC). Data were coded and entered according to senior center and AAA. Descriptive statistics, including frequencies, means, and standard deviations were calculated. Data from the pre-test and post-test were compared using paired t-tests. Chi-square analyses were used to compare categorical data from the pre-test and post-test. Regression analyses were used to explore the predictors of pretest knowledge and changes in knowledge for heart attack and stroke warning signs and symptoms. Variables included in these models were pre-test demographics, self-reported health, and self-reported cardiovascular disease risk factors (body mass index, diabetes, tobacco use, heart disease, high blood pressure, and high cholesterol), and the summary score for heart attack and stroke knowledge at the pre-test for the models exploring changes in knowledge following the intervention. For the diabetes subgroup, participants were excluded from analyses if it was unclear whether or not they had diabetes at pre- or post-test (n = 16). Only participants who completed pre- and post-test questionnaires and who provided pre- and post-test A1c measures were used in

49 A1c analyses (n = 116). For all statistical analyses, P < 0.05 was considered statistically significant. Exclusion criteria were applied to analyses of several variables. For the heart attack and stroke knowledge summary scores, participants who were missing responses for any of the six individual signs/symptoms were not assigned a summary score (n = 42 and n = 54, respectively). For daily minutes of physical activity, reported values > 120 minutes of daily physical activity on days participants reported being physically active were set equal to 120 minutes (n = 28). For physical function, participants who were unable to complete the 8-foot walk or chair stands were assigned a score of zero in the corresponding domain. Participants with walk times < 1.5 seconds were excluded from the analysis of the 8-foot walk domain score and the total domain score. One-hundred seventy-four people were unable to complete the chair stands exercise at pre-test and were therefore assigned a zero for the domain score. Participants with chair stand times < 5 seconds were excluded from the analysis. Participants for which a score for all three individual domains could not be assigned were excluded from calculations of total domain scores. Results Of the 849 participants who completed the pre-test questionnaires, 82% (n = 693) completed the post-test questionnaires, while 156 participants did not complete the post-test for these reasons: cognitive impairment (n = 2), homebound (n = 7), deceased (n = 8), refused (n = 19), hospitalized/sick (n = 26), no longer attended the senior center (n = 57), or no reason given (n = 37). Thus, the final sample size for statistical analyses of the pre- and post-test changes was 693. Eight non-completers provided post-test A1c measurements, however, only completers of post-test questionnaires were included in A1c analyses. Some of the analyses have less than 693

50 participants because of incomplete responses on other variables. There were no significant differences between participants who did not complete the post-test (non-completers, n = 156) and completers (n = 693) in respect to their gender, race, education, BMI, self-reported high blood pressure, heart disease, diabetes, tobacco use, high cholesterol, arthritis, A1c, and overall health rating. However, completers were more likely to be somewhat older than non-completers (mean age = 75 vs 73, respectively), and tended to have a lower waist circumference (mean over clothes = 39 inches, mean under clothes = 36 inches) versus (mean over clothes = 41 inches, mean under clothes = 41). The majority of participants were female (84%) and a large proportion was African American (45%), with a mean age of 75 (SD = 7.8; Table 3.1). Mean waist circumference and body mass index were both above recommended levels, which are BMI < 25 kg/m2; waist circumference for women < 35 inches and for men < 40 inches (National Heart Lung and Blood Institute, 1998). Approximately 75% of participants were considered overweight or obese, and the average waist circumference measurements for men and women were 40.5 and 38.8 inches, respectively. Prevalence of self-reported health conditions at pre-test for this sample of older people was high, with about one third reporting diabetes, and 31% to 73% reporting heart disease, high blood cholesterol, or hypertension. People with diabetes were oversampled, with a goal of 20 people with diabetes of 70 from each AAA (29%). About one third of participants with diabetes used insulin. Heart Attack and Stroke Symptom Knowledge Pre-test percentages of those who correctly identified the six signs/symptoms of a heart attack and six signs/symptoms of a stroke when prompted are shown in Table 3.2 and Table 3.3, respectively, as well as the change in knowledge following the intervention. At pre-test, for

51 three of the six signs/symptoms of heart attack, less than 52% identified them correctly, with few able to discern the false heart attack sign as incorrect. For the remaining three signs/symptoms, greater than 75% identified them correctly. Participants most commonly identified chest pain or discomfort as a sign of a heart attack (Figure 3.1). There was a statistically significant increase in the percentage that correctly identified five of the six individual heart attack signs/symptoms from pre- to post-test. Summary scores and the percentage of participants that correctly classified at least five of the six signs/symptoms, including the decoy question, are reported. Similar to heart attack, there was a range of pre-test values for the correct identification of the individual signs/symptoms of stroke, ranging from 18% to 87% (Figure 3.2). Sudden confusion or trouble speaking and sudden numbness or weakness of the face, arm, or leg were the symptoms most commonly identified correctly. There was a statistically significant increase in the percentage that correctly identified all of the individual stroke signs/symptoms, including the false “decoy” symptom of chest pain, from pre- to post-test. Summary scores and the percentage of participants that knew at least five of the six signs/symptoms, including the decoy question, are reported. Linear regression models were developed to explore demographic and health-related predictors of the six-item knowledge scores for heart attack symptoms and for stroke symptoms at the pre-test (Table 3.4) and for changes from the pre-test to the post-test (Table 3.5). Only participants who had complete data for all of the selected potential predictors were used in these analyses (n = 542). Lower heart attack knowledge at the pre-test was significantly associated with being older and with being black (vs. white), while there were trends for knowledge to be lower in those who had diabetes, did not have high cholesterol, and who did not have heart

52 disease (P = 0.07 to 0.10, Table 3.4). Figure 3.3 illustrates the regression results, in that black people tended to have lower knowledge across all symptoms at the pre-test. Lower stroke symptom knowledge at the pre-test was significantly associated with being older, with being black (vs. white), and with lower education, while there was a trend for knowledge to be higher among those who had high blood pressure (P = 0.08, Table 3.4). Lower knowledge among black people is shown in Figure 3.4. Regression models exploring changes in heart attack or stroke symptom knowledge were controlled for pre-test levels of knowledge (Table 3.5). Improvements in heart attack knowledge were significantly associated with being younger, having higher education, and having high blood pressure. Improvements in stroke knowledge were significantly associated with being younger, being white (vs. black), and having a higher education level. Improvements in Fruit and Vegetable Intake, Physical Activity, and Physical Function Participants showed significant improvements in fruit and vegetable knowledge and intake following the intervention (Table 3.6). Pre-test knowledge of the recommended seven to ten servings of fruits and vegetables daily for most older people was low. Following the intervention, participants who knew the correct number of servings increased by 34 percentage points (P < 0.001). Participants also significantly increased their fruit intake and vegetable intake. Total fruit and vegetable intake approached an increase of one serving/day following the intervention. No significant changes were observed in participants’ perception of cutting down on sodium or saturated fat intake to control blood pressure or blood cholesterol, respectively. Table 3.7 shows changes in physical activity following the intervention. All measures were significantly improved except for an increase in the number of minutes of physical activity accumulated on the days participants reported being physically active. Changes in physical

53 function are depicted in Table 3.8, as measured using the SPPB. All measures of physical function were improved except for the 8-foot walk domain score. Table 3.9 shows changes in A1c following the intervention in those with diabetes who completed pre- and post-test questionnaires and both A1c measures. All participants who provided pre- and post-test A1c readings had it measured at the senior center on both occasions. Mean A1c at the pre-test was 6.7%, which is within the limits of < 7% recommended by the American Diabetes Association (2008) for people with diabetes. Seventy-five percent of participants had an A1c of less than 7% at the pre-test. No change in mean A1c was detected following the intervention. Improvements in Diabetes Self Management There were significant improvements in all diabetes self-management practices evaluated (Table 3.10), and for some practices, a comparison of people without diabetes was included because these behaviors are important for everyone, regardless of diabetes status (Table 3.11). Linear regression analyses were used to determine diabetes’ contribution to changes in knowledge and behavior when controlled for pre-test demographics, BMI, overall health rating, three health conditions other than diabetes, physical function, and pre-test value for the outcome variable. People with diabetes were less likely to make changes in exercise participation than people without diabetes (P = 0.05), but no other significant associations were found with respect to diabetes status. Participant Satisfaction with Education and Physical Activity Programs Overall satisfaction with the education and physical activity programs was high among post-test completers, with 32% rating the education program as excellent, 40% very good, 24% good, 4% fair, and < 1% poor. The physical activity program was rated as excellent by 30%,

54 very good by 38%, good by 30%, fair by 3%, and poor by < 1%. Participants also reported that this intervention helped them to follow several important health behaviors: 94% to follow a healthier diet, 89% to learn the signs/symptoms of a heart attack and signs/symptoms of a stroke, and 78% to increase their physical activity level. Participants with self-reported diabetes who completed the post-test also reported that the intervention helped them with several aspects of self-management: 96% to control portion sizes of foods, 92% to maintain blood sugar levels, 81% to take better care of their feet, and 79% to space carbohydrates over the course of a day. Discussion This 4-month diabetes and heart health intervention was well-targeted to a vulnerable population of older adults, many of whom were African American, had relatively low education, and who had a high prevalence of self-reported health conditions. High proportions of Georgia’s elders are clustered in rural areas as well. These characteristics can all contribute to significant health disparities among older Georgians (Glass and Bachtel, 2007). Cardiovascular disease (CVD) and diabetes are among the leading causes of death in the US and in Georgia, and CVD deaths are most common in people over age 65, with older people accounting for every 3 in 4 CVD deaths in 2005. CVD is costly to Georgia, as there were approximately 142,400 hospitalizations attributable to CVD in 2005, with an estimated charge of $28,700 per hospitalization. Heart attacks and strokes are disabling as well, in that 66% of Americans who’ve experienced a heart attack never fully recover, and 15-30% of stroke victims are left permanently disabled (GA-DHR, 2007; USDHHS & CDC, 2006). Diabetes, which is a risk factor for CVD, is also a growing problem, affecting 23% of older Georgians. This value is likely an underestimate because many diabetes cases are undiagnosed (CDC, 2006b; GA-DHR,

55 2007c). These facts underscore the need for and value of community-based services to help Georgia’s elders in the prevention and management of diabetes and cardiovascular disease. This diabetes and heart health community intervention was well-received by participants and was associated with significant improvements in several outcomes for knowledge and behaviors related to fruit and vegetable intake, physical activity, physical function, and warning signs of heart attack and stroke. Following the intervention, people with diabetes also showed improvements in important self-management practices outlined by the Summary for Diabetes Self Care Activities (Toobert et al., 2000). Therefore, this theory-based, multiple behavior intervention was successful in helping these older adults reach goals for healthy lifestyle habits to prevent and manage diabetes and heart disease. The study activities were consistent with the Healthy People 2010 objective of increasing the proportion of adults who are aware of the early warning signs and symptoms of a heart attack and stroke and the importance of accessing rapid emergency care by calling 9-1-1 (USDHHS, 2000). Study activities, subject matter, and materials were also consistent with the updated 2005 Dietary Guidelines for Americans recommendations for fruit and vegetable intake and physical activity for health maintenance (USDHHS & USDA, 2005). Knowledge of Heart Attack and Stroke Symptoms Knowledge of heart attack and stroke signs and symptoms was assessed using questions from the Behavioral Risk Factor Surveillance System (BRFSS). Overall, pre-test knowledge of heart attack evaluated in Georgia senior centers was comparable to the patterns of 2001 BRFSS data compiled from 17 states across the US and recently compiled data from 14 US states from 2005 (Greenlund et al., 2004; Fang et al., 2008). Knowledge varied among individual symptoms and only a very small percentage correctly classified all signs and symptoms of a heart attack,

56 including the incorrect symptom, and knew to call 9-1-1 as the first action if it was thought someone was having a heart attack (Figure 3.5). Previous analyses have shown that being of an ethnic minority, being older or younger compared to middle aged, being male versus female, and lower education level are associated with decreased knowledge of heart attack symptoms, however, having major risk factors for heart attack has not appreciably affected knowledge (Greenlund et al., 2004; Fang et al., 2008). With the exception of gender, data from regression analyses were similar to that of previous studies indicating that being older and being black versus white were negatively associated with heart attack knowledge, and major risk factors for heart disease were not predictive of knowledge at the pre-test. We were not able to show a relationship between gender and knowledge, perhaps due to the low percentage of men in the sample. The findings are of concern, as those with risk factors for cardiovascular disease, which are often the majority of older people, and minority groups, are the most vulnerable to heart attack and stroke. In addition, racial minority status and low education are interrelated and both can contribute to health disparities, and tend to characterize many older adults in Georgia (Glass and Bachtel, 2007). The oldest old (aged 80 and older), compared to those aged 65-79, and blacks versus whites tended to have lower knowledge of heart attack symptoms, which is consistent with BRFSS data patterns (Figure 3.5). Similar to heart attack, pre-test stroke knowledge was comparable to patterns seen in the 2001 BRFSS analysis, in that a range of knowledge across individual symptoms was demonstrated (Greenlund et al., 2003) (Figure 3.6). In our sample, people who tended to have the greatest risk of stroke had lower awareness (older, black, lower education). Stroke disproportionately affects older people, with 81% of stroke deaths in Georgia occurring in those

57 aged 65 and older. Large percentages of older adults are clustered in Georgia’s rural counties as well, where stroke death rates tend to be highest (Gregory et al., 2007). Unfortunately, older women tend to be poorly informed about stroke warning signs and risk factors in the US, especially African American women (Ferris et al., 2005; Pancioli et al., 2003). African Americans are more likely to have risk factors, such as hypertension and diabetes, and ageadjusted stroke deaths are 80% higher among African Americans compared to white people (USDHHS, 2000). The high risk level of Georgia senior center participants imparted by age, ethnicity, overweight, diabetes, hypertension, and heart disease underscores the value of this intervention to promote awareness of signs and symptoms of heart attack and stroke. At the pre-test, knowledge was low for both heart attack and stroke, and given that the questions were closeended, these data may represent an overestimation of knowledge (Rowe et al., 2001). While some symptoms, such as chest pain for heart attack and sudden confusion or trouble speaking for stroke, were recognized by most participants, other important symptoms that can also occur with heart attack and stroke were not as well known. In fact, the specific symptoms experienced from one heart attack may not be consistent with another, even within the same individual (National Heart Lung and Blood Institute, 2008). Currently, public health initiatives are underway to improve knowledge of heart attack and stroke warning signs, such as the National Heart Lung and Blood Institute’s (NHLBI) “Act in Time to Heart Attack Signs” and the National Institute of Neurological Disorders and Stroke’s (NINDS) “Know Stroke: Know Signs, Act in Time” campaign. Georgia has received funding as part of CDC’s Heart Disease and Stroke Prevention Program since 1998, and since 2000 to implement a heart disease and stroke prevention program, with activities focused on

58 improvement of public awareness of heart attack and stroke warning signs, identification of approaches to promote heart disease and stroke prevention among racial/ethnic populations, and development of a comprehensive state prevention program that emphasizes elimination of health disparities. CDC also funds Georgia as part of the tri-state stroke network to support awareness and prevention activities, and Georgia is one of four states to receive funding to implement the Paul Coverdell National Acute Strokes Registry to monitor quality of care in hospitals (USDHHS & CDC, 2006). In addition, Georgia is funded by CDC as part of a national effort to implement the Cardiovascular Health Initiative to improve cardiovascular outcomes in communities and other sites through prevention and awareness activities and education, such as risk factor reduction and knowledge of the signs and symptoms of heart attack and stroke (GADHR, 2007a). Our study supports the need for these public health activities and indicates that targeting those at greatest risk for heart attack and stroke would be beneficial because these people also seemed to have lower knowledge compared to those in the sample who were relatively younger, were white versus black, and had higher education attainment. In a survey of 20 US communities as part of the Rapid Early Action for Coronary Treatment (REACT) trial, participants were interviewed over the phone to assess their ability to recall the signs and symptoms of a heart attack using open-ended questions. Compared to data from this trial, our sample showed a higher level of knowledge at the pre-test, however, the manner in which the questions were asked in the REACT trial could have underestimated knowledge compared to BRFSS data (Goff et al., 1998). While the close-ended BRFSS questions require participants to recognize heart attack signs read from a list, questions from the REACT trial require participants to recall signs of heart attack without memory cues. The REACT trial survey found a strong positive association between socioeconomic status and

59 knowledge, which was an even more powerful predictor than education, while risk factors for heart disease were not predictive of knowledge. Further, the REACT randomized trial, which focused on heart attack symptom recognition and seeking emergency medical care, demonstrated the ability of an 18-month community intervention to modestly and positively impact changes in knowledge of heart attack signs and symptoms. The intervention effect was greatest among participants with lower incomes and ethnic minorities, thus emphasizing the value of the intervention among high risk subgroups (Goff et al., 2004). Changes in knowledge, however, were not impacted by having cardiovascular disease risk factors. Our sample included a large proportion of African Americans (45%), however, regression analyses did not show a relationship between race and changes in knowledge, although lower education, which may be closely related to socioeconomic status, was significantly associated with smaller changes in heart attack knowledge. With the exception of hypertension, the lack of relationships between risk factor status and change in knowledge is consistent with the REACT trial and is of concern because these participants have a greater likelihood of experiencing a heart attack or stroke and are a group that could benefit most from greater awareness. Health professionals should regularly engage in educating high-risk patients regarding the signs and symptoms of heart attack and stroke and how to overcome barriers to prehospital delay, to help reinforce and personalize public health messages (Faxon and Lenfant, 2001). Following the intervention, participants’ knowledge met or exceeded that shown in 2001 BRFSS data in similar age groups of the sample. However, continued reinforcement is needed, as only 14% correctly classified all heart attack symptoms at the post-test and only 22% correctly classified all symptoms of a stroke (Figures 3.7, 3.8). A strength of our study was the additional analysis of changes in knowledge in Georgia’s senior center participants following an

60 intervention that included educational sessions that presented the major signs and symptoms of heart attack and stroke and reviewed risk factors for cardiovascular disease and diabetes. Prehospital delay and help-seeking behavior if someone was having a heart attack were outcomes beyond the scope of our study, however, further research could help to determine if OAANP participants’ increased knowledge following the intervention translates to action and reduces time to get emergency medical attention to ultimately impact disability and mortality resulting from a heart attack or stroke. The window of time to administer thrombolytic therapy to improve outcomes for heart attack and stroke is only one and three hours, respectively, although a person must arrive at the hospital much sooner for evaluation (NINDS, 2004). Others have acknowledged that recognition of the signs of heart attack and stroke is but one step towards accessing immediate medical care, and that there are other factors that impact prehospital delay (Greenlund et al., 2004). Barriers such as denial that treatment is needed, attributing symptoms to normal aging or to health conditions other than a heart attack or stroke, slow onset of symptoms, self treatment, and failure to recognize atypical symptoms such as fatigue, have been identified as possible contributors to prehospital delay (Dracup et al., 1995; Ryan and Zerwic, 2003). Sociodemographic characteristics, such as being older, low socioeconomic status, history of diabetes or angina, and being female have been related to increased delay time as well (Dracup et al., 1995). Therefore, in addition to targeting knowledge, addressing barriers that may hinder the application of such knowledge is also needed. In future investigations, it may also be interesting to assess participants’ sources of heart attack and stroke information, including warning signs/symptoms and risk factors, to better understand the reach of public health education efforts among older adults in Georgia, and to determine which communication channels are particularly valuable. A random-digit telephone

61 survey conducted in Ohio of people matched to the demographic characteristics of those with ischemic stroke found that mass media, such as television and magazines, and physicians, were important sources of stroke information used by the public (Schneider et al., 2003). Improvements in Fruits, Vegetables, Physical Activity, and Physical Function In addition to knowledge, behaviors related to fruit and vegetable intake and physical activity significantly improved following this intervention. The benefits of similar combined nutrition and physical activity interventions delivered in senior centers and tailored to the needs of older adults have been documented (Fitzpatrick et al., 2007; Hendrix et al., 2007; Speer et al., 2007; Wellman et al., 2007). Following the intervention, participants increased their daily fruit and vegetable intake by nearly one serving on average. A little over half of participants reported consuming at least five servings of fruits and vegetables daily, perhaps because at least three servings are provided by the congregate meals received at the senior center. Studies that have used diets rich in fruits and vegetables, such as the DASH trials, have provided evidence for the inclusion of these plant foods as the cornerstones of a healthy eating pattern to help prevent and manage heart disease and risk factors (Akesson et al., 2007; Hu, 2003; Hu and Willett, 2002; Sacks et al., 2001). The value of increased fruit and vegetable consumption is highlighted in the 2005 Dietary Guidelines for Americans, as higher amounts (seven to ten servings) of fruits and vegetables are now recommended for the 1,600 to 2,200 calorie levels (USDHHS & USDA, 2005). Consistent with the Dietary Guidelines, the National Fruit and Vegetable Program (2007), formerly the “5-a-day” program, recently launched the “Fruits and Veggies – More Matters” campaign to emphasize the value of increasing fruit and vegetable intake, and promote practical ways to incorporate more fruits and vegetables into the daily diet for health among Americans. By increasing fruit and vegetable consumption, older people can approach or achieve the goal

62 intake of seven to ten servings per day recommended by the 2005 Dietary Guidelines to help prevent chronic diseases (USDHHS & USDA, 2005). Following the intervention, 55% increased their fruit and vegetable intake by at least one serving daily on average, suggesting that participants at least took small steps toward increased intake. It was interesting to note that about a third of participants thought that “5 a day” or “5 or more” servings were recommended daily at the pre-test, while only 13% were aware that the recommendation was increased (seven to ten servings/day), suggesting that the “5-a-day” public health message may persist and these older people may not be aware of the most current guidelines for fruit and vegetable intake. Future analyses may explore changes in intake of specific subgroups of vegetables and fruits, such as deeply pigmented vegetables and legumes, as variety in intake of these specific foods is lacking in older Americans (Juan and Lino, 2007). In an evaluation of a previous intervention in senior centers in northeast Georgia (N = 54) that used ten educational sessions focused on consumption of specific subgroups of fruits and vegetables over a six month period, fruit and vegetable variety was lacking. At the pre-test, at least half of participants consumed less than one serving per week of many phytochemical-rich fruits and vegetables, such as berries, spinach, cruciferous vegetables, carrots and sweet potatoes, tomato products, and beans (Wade, 2003). Exploring the ability of food assistance programs, such as the Georgia Senior Farmer’s Market Nutrition Program, to positively impact fruit and vegetable intake variety could also provide valuable information about the benefits of these programs. The results of the current evaluation support evidence of the effectiveness of a community intervention conducted in Georgia senior centers in 2005-2006, which showed that participants significantly improved their fruit and vegetable intake and were able to overcome some barriers related to low intake (Hendrix et al., 2007). However, compared to our previous

63 intervention, “Serving Up Fruits, Vegetables, and Physical Activity Everyday!”, fruit and vegetable intake did not increase to the same magnitude. In the previous intervention, daily intake improved by 1.7 servings, and knowledge of recommendations for fruits and vegetables increased by 50 percentage points, compared to the more modest increase of 0.9 servings and 34 percentage points, respectively, in the current intervention. The more modest results realized in the current study could be attributable to methodological differences in determining fruit and vegetable intake, in that an 8-item by meal screener was used in the 2005-2006 intervention, which could have inflated reported intakes because of the greater number of questions asked. Additionally, the previous intervention’s eight educational sessions focused on fruit and vegetable intake only, so there was greater emphasis on increased intake and knowledge compared to the current intervention (Hendrix et al., 2007). However, the increase of 0.9 servings is within the range of change of 0.1 to 1.4 servings per day reported from other interventions that had a control group, had at least three months between follow up, and were delivered in a variety of settings (Pommerleau et al., 2005). Promotion of fruit and vegetable intake in the current intervention was in the context of a whole diet approach used to incorporate healthy dietary messages into the education program at each session. The 2005 Dietary Guidelines for Americans recommends at least 30 minutes of moderate physical activity on most days of the week to reduce the risk for chronic diseases (USDHHS & USDA, 2005), however, nearly 2/3 of older Georgians do not meet this recommendation (CDC, 2005b). Regular physical activity is a key lifestyle component to maintaining independence, as it is recommended to help reduce functional declines and falls and is associated with higher levels of functioning in older adults (USDHHS & USDA, 2005; Berk et al., 2006). Physical activity levels tend to decline with age and are particularly low among African American females

64 (Interagency Forum on Health Related Statistics, 2006; Cooper et al., 2000). Physical activity interventions, including those of low intensity, are effective in helping older people improve and maintain physical functioning (Sharpe et al., 1997). Our physical activity intervention was implemented among a predominately female population of older adults that included a high percentage of African Americans. Hence, the intervention was well-targeted to those who tend to have low activity levels. Following this physical activity intervention, which included promotion of walking and used 24 chair exercises divided into four modules to improve strength and flexibility of the upper and lower body, participants reported increasing their days per week of physical activity and there was also a significant increase in the number of days reported getting the recommended 30 minutes of moderate physical activity. This intervention, which incorporated behavior change principles such as goal setting, tailoring the intervention to the needs and ability levels of participants, overcoming barriers, and delivery of the physical activity intervention in a supportive environment, helped these elders move closer to the recommendation for moderate activity (Cress et al., 2004). The magnitude of the changes in physical activity is very similar to our previous intervention conducted in 2005-2006, which included promotion of walking, 16 chair exercises to increase strength and flexibility, and was based on principles of the Administration on Aging’s You Can! Program (Fitzpatrick et al., 2007). In a qualitative comparison to this previous intervention, days per week physically active increased to the same degree, although physical activity levels started out somewhat higher in the current evaluation (approximately 4.3 to 4.9 days in 2005-2006 vs. 4.5 to 5.1 days in 2006-2007). In addition, average days participated in at least five days of moderate activity in the past week increased equally between the interventions (approximately 4.3 to 5.0 days in 2005-2006 vs. 4.0 to 4.6 days in 2006-2007). Lessons learned

65 from the 2006 physical activity intervention helped to guide its expansion for the current 2007 intervention, and more exercises were added, many of which used inflated or foam balls as tools to increase interest and variety. There are many opportunities available at senior centers to increase physical activity. Some sites offer exercise classes, have walking clubs or walking trails nearby, and have dances and other activities to promote physical activity and socialization. Hence, the environment surrounding the intervention appeared to be conducive to helping participants increase their physical activity, and also could have increased pre-test levels of physical activity. Though we only examined short-term changes in physical activity, it would be valuable to determine participants’ ability to sustain these changes. Maintenance of behavior change is often more difficult to achieve and following physical activity intervention activities, improvements in physical activity tend to regress. Therefore, future studies should also focus on strategies for impacting and assessing long-term adoption of behavior changes (Marcus et al., 2006). Physical function was assessed using the Short Physical Performance Battery (SPPB) (Guralnik et al., 1994). This tool measures functional ability in community-dwelling older adults, where higher performance scores are associated with higher levels of functioning and lower reports of disability and need for help with activities of daily living (ADLs). A previous evaluation of a similar intervention in 2005-2006 among older Georgians in senior centers showed significant improvements in the total function score following a physical activity intervention that promoted walking using step counters, and used 16 exercises for the upper and lower body in a series of education sessions (Fitzpatrick et al., 2007). Building upon the positive results of the previous intervention, a significant increase in participants’ total performance score was demonstrated. Compared to the 2005-2006 intervention, the change in the total function

66 score increased almost equally, with higher pre-test values in the current study. An interesting result was the greater improvement in the chair sit and reach distance, a measure of flexibility, in the current study. To better understand the mechanisms of improvements in the various categories of physical function, it may be helpful to classify the chair exercises into domains according to the category of physical fitness they are designed to improve, such as flexibility for the lower body, strength for the upper body, and so on. For example, the CS-PFP test, a validated instrument to measure physical function, classifies specific tasks into domains of physical fitness, which together translate into measures of daily functional ability (Cress et al., 1996). In addition, determining the number of sessions participants performed the intervention exercises in the absence of the educator could also provide additional information to help track the frequency of participation. There is a graded relationship between scores on the SPPB and the likelihood of disability, nursing home admission, and death (Guralnik et al., 1994). Therefore, improvements in performance scores could translate to improved daily functioning and reduced health care costs for these older people. The estimated average annual cost of a private nursing home room is $75,190 nationally and $61,320 in Georgia (MetLife, 2006). Thus, support for home and community-based services is needed to help the increasing number of older Americans stay healthy and active, and to reduce costs associated with dependency and decreased functionality. Prospective studies have shown that physical activity levels among older people are associated with disability and that older adults can improve their physical functioning by participation in regular systematic exercise (Berk et al., 2006; Cress et al., 1999). This finding was supported in our previous intervention, in that an increase in physical activity was identified as an independent predictor of improvements in physical function (Fitzpatrick et al., 2007).

67 Improvements in Diabetes Self Management The diabetes subgroup showed significant improvements in self-management practices that are core components of diabetes care, such as checking feet, checking blood sugar, and spacing carbohydrates (Toobert et al., 2000). The intervention was implemented using approaches that were consistent with principles outlined in the Standards for Diabetes SelfManagement Education, including tailoring the education to the needs of the target population and use of a structured curriculum to drive program activities using a participatory approach (Mensing et al., 2007). Although there was no change in A1c levels following the intervention, self-management practices remained robust measures of improvement compared to our 20052006 diabetes self-management intervention, “Seniors Taking Charge of Diabetes!” (Speer et al., 2007). However, the magnitude of some of these changes was lower than in the 2005-2006 intervention (Figures 3.9, 3.10). Hence, the more concentrated focus on diabetes selfmanagement used in the previous intervention may be the most beneficial when selfmanagement practices are the primary outcomes targeted. Following our previous diabetes self-management intervention, there was an overall decrease in A1c of 0.25% (n = 144), and a decrease of 1.15% (n = 24) with those considered to be in poor control of their diabetes at the pre-test (A1c > 8%) (Speer et al., 2007). The lack of change in A1c in the current evaluation may be attributable to the majority (75%) of participants that provided pre- and post-test measures having initial A1c within limits recommended by the American Diabetes Association (A1c < 7%) and the relatively smaller number of people with pre- and post-test A1c measures (n = 116). In addition, the 2005-2006 diabetes self-management intervention sessions tended to have a greater focus on and reinforcement of self-management practices. While the current intervention included important messages for diabetes self-

68 management, these practices shared emphasis with important heart health messages for everyone, regardless of diabetes status, such as recognition of the signs and symptoms of heart attack and stroke. It is important to note, however, that heart disease is a major complication of diabetes, and maintaining a healthy cardiovascular system is an important aspect of diabetes care, so combining aspects of both diseases has advantages (Abbate et al., 2002). A randomized controlled pilot study included participants who began a combined nutrition education and physical activity program with an average A1c of 8.6% at baseline in the intervention group (indicative of poor control). Following the program, there was a statistically significant decrease in A1c for the intervention group of 1.8%, which is closer to that demonstrated in our 2005-2006 intervention among participants with high initial A1c (Goldhaber-Fiebert et al., 2003). A one percentage point decrease in A1c can greatly lower the risk of microvascular complications in people with diabetes, even when levels are suboptimal (Genuth et al., 2002). Hence, participants with high initial A1c levels should be encouraged to attend group-based educational sessions to help improve their control of diabetes and lower their A1c level (Norris et al., 2002). Strengths and Limitations A major strength of this study was the ability to show improvements in multiple behaviors and aspects of knowledge using a culturally sensitive program delivered to participants within a familiar environment. Similar evaluations have documented the benefits of combined nutrition and physical activity programs among OAANP participants (Fitzpatrick et al., 2007; Hendrix et al., 2007; Wellman et al., 2007), and successful community group education programs for diabetes have been developed as well (Goldhaber-Fiebert, 2003; Norris et al., 2002; Speer et al., 2007). Our intervention approach encouraged behavior change by including

69 components such as goal setting and self-monitoring, reinforcement and repetition of key nutrition and physical activity messages at each session, educational incentives, addressing barriers to change, games and activities that provided opportunities to apply knowledge and actively engage participants, and cultural sensitivity based on previous experience with the target population (Sahyoun et al., 2004). This intervention differed from our previous 2005-2006 diabetes self-management (Speer at al., 2007), fruit and vegetable (Hendrix et al., 2007), and physical activity (Fitzpatrick et al., 2007) interventions by incorporating specific heart health messages, including risk factors, warning signs, and lifestyle strategies for prevention and management. A large proportion of participants self-reported having risk factors for heart disease, such as hypertension, high cholesterol, and diabetes, as well as existing heart disease. In addition, heart disease is a major complication of diabetes, such that having diabetes imparts the same risk of having a heart attack as someone without diabetes who has had a previous heart attack (Abbate et al., 2002). As such, there was an urgent need to disseminate important heart health messages among the target population. There are some limitations to this study. Due to the nature of the educational sessions delivered in senior centers, it was not possible to include a control group for comparison to the intervention group. In addition, because our study involved a convenience sample, results are not readily generalizable to all older adults, nor to all of those receiving services from Georgia’s senior centers. Differences in backgrounds (e.g., dietitians, nurses, recreational therapists), styles of delivery, and adherence to protocol among educators across the state could have lead to variation in how the intervention was delivered to and received by participants as well. Also a strength, however, educators were encouraged to be sensitive to the learning abilities and needs

70 of their audience, and to emphasize the educational materials that best met the needs of their participants. Quality control measures were in place, including organization of a statewide training session to explain the implementation process and intervention activities, by sending a UGA representative to assist each site with testing and implementation, and by providing technical assistance via telephone or email. Lesson plans were scripted to guide educators through the lessons, and were written in an easily understandable and culturally relevant manner based on previous experience with the target population. This study relied mostly on self report measures provided via pre- and post-test questionnaires. Anthropometric measures such as height, weight, and waist circumference were obtained. While some participants had their height and weight measured by the interviewer using a bathroom scale and tape measure, most self-reported their height, and some self-reported weight (23%). Hence, body mass index and waist circumference were not consistently assessed in this study. Alternatively, some measures were obtained objectively. For example, physical function was assessed using the SPPB, a validated tool for the assessment of physical function in older people that is predictive of nursing home placement and impending death (Guralnik et al., 1994). A1c measurement is also an objective assessment of glycemic control over the last three months. Hence, the data collected was based on a combination of self-report using questions from validated questionnaires, and objective measurements. Many participants were lost to follow-up, however, a large enough sample was recruited so that dropout would not limit the statistical power. Difficulties with scheduling A1c collection led to significant missing data, however, the target sample size was planned accordingly to account for this problem based on a similar evaluation conducted the previous year.

71 Based on the results of this study, we have an improved understanding of the needs and characteristics of the target population to guide the development of future interventions for older adults in Georgia senior centers. Future efforts may include review and reinforcement of heart health messages and complex aspects of diabetes care, and plans for helping participants make long-term changes in healthy lifestyle habits using strategies such as peer leadership. Provision of high quality educational programs to improve the health and well-being of older Georgians is a high priority and an ongoing effort among state and local leaders. Hence, building on the successes of the current intervention, the educational materials, and the core messages, which are readily accessible online, the reach of this community intervention and future programs will continue to help serve the high needs of a growing number of older Georgians.

72 Acknowledgments Supported by the Georgia Division of Aging Services in contract with The University of Georgia (#4279307070449-99) and the 12 Georgia Area Agencies on Aging, and the Department of Foods and Nutrition and the Georgia Agricultural Experiment Station (HATCH #GEO 00575 and #GEO 00576), The University of Georgia.

73 Table 3.1. Characteristics of Participants

Pre-Test (All Participants)

Pre-Test and Post-Test Completed

n

Mean (SD) or %a

n

Mean (SD) or %a

Age (years) ≤ 69 70-79 ≥ 80

849

74 (8.0) 28 45 27

693

75 (7.8) 27 46 27

Gender Male Female

849

Race/ethnicity White Black Other

849

Education (years) Body mass index (kg/m2) < 25 25 to < 30 > 30 Waist Circumference (inches) Males Females Tobacco use Self-reported health conditions

843 826

Diabetes High blood pressure Heart disease Ever been told by a health professional that blood cholesterol is high Arthritis Self-reported health

693 17 83

16 84 693

55 44 1 10.5 (3.2) 29.7 (6.6) 24 35 41

824

54 45 <1 692 678

10.5 (3.2) 29.6 (6.5) 25 35 41

675

836

40.9 (5.0) 39.0 (5.8) 9

681

40.5 (5.2) 38.8 (5.7) 9

844 840 841

37 72 32

688 685 685

36 73 31

848 845

56 73

692 690

55 73

846

691

74 Excellent Very good Good Fair Poor A1c (%) <7 7 to < 8 ≥8 Diabetes medications None Insulin only Pills only Insulin and pills a

184

4 14 44 33

4 15 43 32

6 6.7 (1.2) 74 15

6 6.7 (1.2) 75 15

149

11 302

9 239

12

11

13 60 15

15 57 16

Percentages may not add up to 100% because of rounding.

75 Table 3.2. Changes in Knowledge of Heart Attack Symptoms Following the Intervention in Georgia Senior Centers, 2006-2007 Which of the following do you think is a symptom of a heart attack? Pain or discomfort in the jaw, neck, or back d Yes No Don’t know Feeling weak, lightheaded, or faint d Yes No Don't know Chest pain or discomfort d Yes No Don’t know Sudden trouble seeing in one or both eyes Yes d No Don’t know Pain or discomfort in the arms or shoulder d Yes No Don’t know Shortness of breath d Yes No Don’t know Summary score (mean correct answers)e Frequencies of correct answers (%) <5 >5 All 6 correct Know to call 9-1-1 if someone was having a heart attack or stroke a

n

Pre-test Mean (SD) or %a

846

Pre-test Mean (SD) or %b

Post-test Mean (SD) or %

Change Mean (SD) or %c

49 18 33

66 14 20

17 -4 -13

< 0.0001

51 20 30

65 16 18

14 -4 -12

< 0.0001

87 3 9

92 3 5

5 0 -4

0.02

24 30 46

36 35 29

12 5 -17

< 0.0001

76 8 16

86 6 8

10 -2 -8

< 0.0001

78 6 16

86 6 9

8 0 -7

0.0003

3.7 (1.5)

4.3 (1.4)

0.6 (1.6)

< 0.0001

42 58 14

-20 20 7

< 0.0001

651

62 38 7

680

84

92

8

< 0.0001

n 682

48 19 33 841

677 50 20 31

844

675 87 4 9

841

676 25 29 46

842

677 76 8 16

843

681 76 7 16

825

3.7 (1.5)

825

651 651

64 36

840

P-value

84

< 0.0001

Percentages may not add up to 100% because of rounding. Completed both the pre-test and post-test. c Differences may not equal post-test minus pre-test because of rounding. d Correct response to question. e Response of “don’t know” collapsed with incorrect responses. Maximum score is 6. Participants who were missing one or more responses were not used to calculate the summary score. b

76 Table 3.3. Changes in Knowledge of Stroke Symptoms Following the Intervention in Georgia Senior Centers, 2006-2007 Which of the following do you think is a symptom of a stroke? Sudden confusion or trouble speaking d Yes No Don’t know Sudden numbness or weakness of face, arm, or leg, especially on one side d Yes No Don’t know Sudden trouble seeing in one or both eyes d Yes No Don’t know Sudden chest pain or discomfort Yes d No Don’t know Sudden trouble walking, dizziness, or loss of balance d Yes No Don’t know Sudden severe headache with no known cause d Yes No Don’t know Summary score (mean correct answers)e Frequencies of correct answers (%) <5 >5 All 6 correct Know to call 9-1-1 if someone was having a heart attack or stroke a

n

Pre-test Mean (SD) or %a

842

Pre-test Mean (SD) or %b

Post-test Mean (SD) or %

Change Mean (SD) or %c

86 3 11

92 3 5

6 0 -6

0.002

87 2 11

94 2 5

7 0 -6

0.0002

58 9 33

75 7 17

17 -2 -16

< 0.0001

46 18 36

43 34 23

-3 16 -13

< 0.0001

74 6 20

85 5 10

11 -1 -10

< 0.0001

61 11 28

79 6 14

18 -5 -14

< 0.0001

3.9 (1.6)

4.6 (1.4)

0.8 (1.7)

< 0.0001

27 73 22

-27 27 14

< 0.0001

639

54 46 8

680

84

92

8

< 0.0001

n 679

85 3 12

842

678 87 2 10

837

669 58 9 33

830

661 45 19 36

844

681 73 6 21

845

681 60 11 29

810

3.8 (1.6)

810

639 639

54 46

840

P-value

84

< 0.0001

Percentages may not add up to 100% because of rounding. Completed both the pre-test and post-test. c Differences may not equal post-test minus pre-test because of rounding. d Correct response to question. e Response of “don’t know” collapsed with incorrect responses. Maximum score is 6. Participants who were missing one or more responses were not used to calculate the summary score. b

77 Table 3.4. Regression Model Exploring Predictors of Heart Attack and Stroke Knowledge at the Pre-Test in Georgia Senior Centers, 2006-2007

Pre-test heart attack knowledge (n = 542) Intercept Age (years) Gender (0 = male, 1 = female) Race (1 = white, 2 = black) Education (years) Self-reported health (0 = poor, 1 = poor, 2 = good, 3 = very good, 4 = excellent) Body Mass Index (kg/m2) Diabetes (0 = no, 1 = yes) Tobacco use (0 = no, 1 = yes) Heart disease (0 = no, 1 = yes) High blood pressure (0 = no, 1 = yes) High cholesterol (1 = yes, 2 = no)a Pre-test stroke knowledge (n = 542) Intercept Age (years) Gender (0 = male, 1 = female) Race (1 = white, 2 = black) Education (years) Self-reported health (0 = poor, 1 = poor, 2 = good, 3 = very good, 4 = excellent) Body Mass Index (kg/m2) Diabetes (0 = no, 1 = yes) Tobacco use (0 = no, 1 = yes) Heart disease (0 = no, 1 = yes) High blood pressure (0 = no, 1 = yes) High cholesterol (1 = yes, 2 = no)a a

Parameter Estimates (SEM)

P-value

5.91 (0.9) -0.02 (0.0) 0.08 (0.2) -0.48 (0.1) 0.03 (0.0) 0.00 (0.1)

< 0.0001 0.04 0.66 0.0003 0.21 0.95

-0.00 (0.0) -0.24 (0.1) -0.30 (0.2) 0.23 (0.1) -0.03 (0.1) -0.22 (0.1)

0.92 0.07 0.16 0.10 0.82 0.09

5.12 (0.9) -0.02 (0.0) 0.04 (0.2) -0.51 (0.1) 0.07 (0.0) 0.09 (0.1)

< 0.0001 0.006 0.80 0.0001 0.0004 0.21

0.01 (0.0) -0.16 (0.1) 0.03 (0.2) 0.04 (0.1) 0.26 (0.1) -0.10 (0.1)

0.24 0.25 0.88 0.79 0.08 0.44

Participants who answered “don’t know/not sure” were excluded from the analysis.

78 Table 3.5. Regression Model Exploring Predictors of Change in Heart Attack and Stroke Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007

Changes in heart attack knowledge (n = 542) Intercept Age (years) Gender (0 = male, 1 = female) Race (1 = white, 2 = black) Education (years) Self-reported health (0 = poor, 1 = poor, 2 = good, 3 = very good, 4 = excellent) Body Mass Index (kg/m2) Diabetes (0 = no, 1 = yes) Tobacco use (0 = no, 1 = yes) Heart disease (0 = no, 1 = yes) High blood pressure (0 = no, 1 = yes) High cholesterol (1 = yes, 2 = no)a Heart attack knowledge at pre-test (0 to 6 correct responses) Changes in stroke knowledge (n = 542) Intercept Age (years) Gender (0 = male, 1 = female) Race (1 = white, 2 = black) Education (years) Self-reported health (0 = poor, 1 = poor, 2 = good, 3 = very good, 4 = excellent) Body Mass Index (kg/m2) Diabetes (0 = no, 1 = yes) Tobacco use (0 = no, 1 = yes) Heart disease (0 = no, 1 = yes) High blood pressure (0 = no, 1 = yes) High cholesterol (1 = yes 2 = no)a Stroke knowledge at pre-test (0 to 6 correct responses) a

Parameter Estimates (SEM)

P-value

5.59 (0.8) -0.03 (0.0) 0.04 (0.1) -0.13 (0.1) 0.05 (0.0) -0.07 (0.1)

< 0.0001 0.0003 0.77 0.27 0.01 0.28

-0.01 (0.0) -0.06 (0.1) -0.23 (0.2) 0.02 (0.1) 0.27 (0.1) -0.12 (0.1) -0.72 (0.0)

0.15 0.61 0.21 0.89 0.04 0.27 < 0.0001

6.10 (0.8) -0.03 (0.0) -0.08 (0.2) -0.30 (0.1) 0.06 (0.0) -0.01 (0.1)

< 0.0001 < 0.0001 0.60 0.01 0.0006 0.89

0.00 (0.0) -0.13 (0.1) 0.02 (0.2) 0.11 (0.1) 0.05 (0.1) -0.09 (0.1) -0.83 (0.0)

0.60 0.27 0.92 0.39 0.73 0.42 < 0.0001

Participants who answered “don’t know/not sure” were excluded from the analysis.

79 Table 3.6. Changes in Dietary Knowledge and Behaviors Following the Intervention in Georgia Senior Centers, 2006-2007

n Fruit and Vegetable Knowledge How many fruits and vegetables should older people eat each day? Correct answer (7, 8, 9, 10, or 7 to 10 daily) Incorrect answer or “don’t know” Dietary Behaviors How many servings of fruits and 100% fruit juices do you usually have each day? < 1 serving > 2 servings Increased fruit intake by > 1 serving daily How many servings of vegetables do you usually eat each day? < 2 servings > 3 servings Increased vegetable intake by > 1 serving daily Total servings of fruits and vegetables eaten per dayd < 5 servings > 5 servings > 7 servings Increased total fruit and vegetable intake by > 1 serving daily How many days of the last week did you eat five or more servings of fruits and vegetables? < 5 days > 5 days Increased number of days by > 1 day per week How many days of the last week have you followed a healthful eating plan? < 5 days > 5 days Are you cutting down on saturated fat in your diet (to

Pre-test Mean (SD) or %a

Pre-test Mean (SD) or %b

Post-test Mean (SD) or %

Change Mean (SD) or %c

P-value

13

13

47

34

< 0.0001

87

87

53

-34

2.1 (1.4) 36 64

2.7 (1.4) 19 81

0.6 (1.6) -17 17

838

840

n

659

2.2 (1.4) 35 65

679

< 0.0001 < 0.0001

53

842

2.8 (1.1) 43 57

682

2.8 (1.1) 43 57

3.1 (1.2) 32 68

0.3 (1.3) -11 11

< 0.0001 < 0.0001

41 835

835

5.0 (2.1) 44 56 19

671

671

4.9 (2.0) 44 56 18

5.8 (2.1) 27 73 34

0.9 (2.3) -17 17 16

< 0.0001 < 0.0001 < 0.0001

55

825

3.7 (2.6) 56 44

657

3.7 (2.6) 56 44

4.6 (2.3) 41 59

0.9 (3.0) -15 15

< 0.0001 < 0.0001

51 836

4.6 (2.4) 679 40 60

845

686

4.7 (2.4) 39 61

5.4 (2.0) 25 75

0.8 (2.7) -14 14

< 0.0001 < 0.0001

80 help manage or lower your risks of developing heart disease)? Yes No Don’t know Are you cutting down on sodium or salt (to help lower or control your blood pressure)? Yes Don’t use salt No Don’t know a

88 10 2

848

88 10 2

91 7 1

3 -3 -1

0.20

73 15 11 <1

78 14 8 1

5 -1 -3 0

0.16

690 74 15 11 <1

Percentages may not add up to 100% because of rounding. Completed both the pre-test and post-test. c Differences may not equal post-test minus pre-test because of rounding. d Value calculated by summing daily fruit intake and daily vegetable intake responses. b

81 Table 3.7. Changes in Physical Activity Following the Intervention in Georgia Senior Centers, 2006-2007

n

Pre-test Mean (SD) or %a

n

How many days of the week do you participate in any physical activity (light or moderate)? (days)d

814

4.5 (2.5)

647

4.5 (2.4)

About how many minutes of physical activity do you do on the days you are physically active? (minutes)d

814

35.6 (28.2)

647

Daily physical activity (= days X minutes/7) (minutes)

814

26.6 (27.1)

843

4.0 (2.6) 51

On how many days of the last seven days did you participate in at least 30 minutes of moderate physical activity? < 5 days > 5 days On how many of the last seven days did you participate in a specific exercise session other than what you do around the house as part of your daily activities? a

Change c

P-value

5.1 (2.1)

0.6 (2.8)

< 0.0001

36.9 (28.7)

39.0 (27.1)

2.1 (33.0)

0.12

647

27.6 (27.8)

31.3 (27.4)

3.7 (32.4)

0.004

680

4.0 (2.6) 51

4.6 (2.3) 41

0.7 (3.0) -10

< 0.0001 0.0005

49

59

10

2.0 (2.0)

2.7 (2.0)

0.6 (2.5)

49

838

2.1 (2.0)

Pre-test Post-test Mean Mean (SD) (SD) or %b or %

680

< 0.0001

Percentages may not add up to 100% because of rounding. Completed both the pre-test and post-test. c Differences may not equal post-test minus pre-test because of rounding. d Maximum value allowed was 120 minutes. Values greater than 120 minutes were set equal to 120 minutes. b

82 Table 3.8. Changes in Physical Function Following the Intervention in Georgia Senior Centers, 2006-2007

n

Pre-test Mean (SD) or %

na

Short Physical Performance Battery (SPPB)c Total scored 802 7.7 (2.7) 627 Poor (0-5) 20 Moderate (6-9) 51 Good (10-12) 28 Standing balance scoree 825 2.9 (1.1) 658 Poor (0-2) 36 Good (3-4) 64 8-foot walk (seconds)f 798 3.7 (2.0) 622 8-foot walk score 808 3.1 (1.1) 635 Unable (0) 1 Poor (1-2) 26 Good (3-4) 73 Chair stands 16.2 (seconds)g 647 (6.5) 489 Chair stands score 821 1.7 (1.3) 653 Unable (0) 21 Poor (1-2) 48 Good (3-4) 31

Pre-test Mean (SD) or %

Post-test Mean (SD) or %

Changeb

P-value

7.8 (2.6) 19 53 28

8.2 (2.9) 19 42 38

0.4 (2.2) 0 -11 10

< 0.001 0.0002

3.0 (1.1) 35 65

3.1 (1.2) 29 71

0.1 (1.2) -6 6

0.002 0.03

3.6 (2.0) 3.1 (1.1) 1 25 74

3.7 (2.6) 3.1 (1.1) 1 25 74

0.0 (2.7) 0.0 (1.0) 0 0 0

0.84 0.25 0.56

15.7 (5.4) 1.8 (1.3) 20 48 32

14.2 (5.3) 2.0 (1.5) 20 38 42

-1.5 (4.7) 0.3 (1.2) 0 -10 10

< 0.001 < 0.001 0.0004

-2.4 (4.7)

-0.9 (3.9)

1.6 (4.0)

< 0.001

Chair Sit and Reach Distance (inches) a

809

-2.5 (4.8)

625

Completed both the pre-test and post-test. Differences may not equal post-test minus pre-test because of rounding. c The SPPB total score ranges from 0 to 12 and is calculated from the combined scores of the standing balance (0 to 4), 8-foot walk (1 to 4), and five chair stands (1 to 4) (Guralnik et al., 1994). d Participants who did not complete all three individual domain measures at pre- and post-test were excluded from calculation of total domain score. e Standing balance is a timed semi-tandem stand, followed by either a timed tandem (completers of semitandem) or side-by-side (non-completers of semi-tandem) stand. f 8-foot walk is a timed walk that can be done with or without an assistive device. Ten people who were unable complete this exercise at pre-test were therefore assigned a 0 for the domain score. Participants with walk times < 1.5 seconds were excluded from the analysis. g Chair stands are five timed chair stands from the seated position. One hundred seventy-four people were unable to complete this exercise at pre-test and were therefore assigned a 0 for the domain score. Participants with chair stand times < 5 seconds were excluded from the analysis. b

83 Table 3.9. Changes in A1c in the Diabetes Subgroup Following the Intervention in Georgia Senior Centers, 2006-2007

Mean changes in A1c Total sample (n = 116)b Pre-test A1c > 8% (n = 12) A1c (%) categories < 7% > 7 - < 8% > 8% a

Pre-test Mean (SD) or %

Post-test Mean (SD) or %

Change Mean (SD) or %a

P-value

6.7 (1.1) 9.3 (1.1)

6.7 (1.3) 9.4 (1.6)

0.01 (0.7) 0.16 (1.3)

0.93 0.68

75 15 10

72 17 11

-3 2 1

0.83

Change may not equal post-test minus pre-test because of rounding. Only participants who self-reported diabetes at pre-test and post-test and who provided pre- and post-test A1c measures were included in analyses.

b

84 Table 3.10. Changes in Diabetes Self-Management Practices in the Diabetes Subgroup Following the Intervention in Georgia Senior Centers, 2006-2007a Pre-test Mean (SD) or %b,c

Post-test Mean (SD) or %c

Change Mean (SD) or %d

36 43 21

27 45 27

-9 2 6

0.10

191

3.1 (2.9) 34

4.4 (2.6) 52

1.3 (3.5) 18 47

< 0.0001 0.0003

225

5.0 (2.6) 63

5.5 (2.3) 71

0.4 (2.2) 8 28

0.004 0.09

10 56 15 17

8 60 15 16

-2 4 0 -1

0.90

223

6.3 (2.1) 89

6.5 (1.8) 92

0.2 (1.4) 3 5

0.05 0.19

232

5.0 (2.9) 68

5.8 (2.3) 79

0.8 (3.0) 11 28

< 0.0001 0.005

230

3.5 (3.3) 47

4.7 (3.0) 63

1.3 (3.7) 16 36

< 0.0001 0.0003

238

4.6 (2.5) 60

5.5 (1.9) 76

0.9 (2.7) 16 43

< 0.0001 0.0001

232

3.8 (2.7) 47

4.6 (2.4) 57

0.8 (3.0) 10 50

0.0002 0.04

n What kind of effect does diabetes have on your daily activities? No effect Little effect Large effect Thinking about your diet, on how many days of the last week (seven days) did you space carbohydrates evenly? > 5 days/week Added > 1 day (%) On how many days of the last week (seven days) did you test your blood sugar? > 5 days/week Added > 1 day (%) What medications do you take for your diabetes? None Pills only Insulin only Pills and insulin On how many days of the last week (seven days) did you take your diabetes medication as prescribed by your doctor? > 5 days/week Added > 1 day (%) On how many days of the last week (seven days) did you check your feet? > 5 days/week Added > 1 day (%) On how many days of the last week (seven days) did you inspect the inside of your shoes? > 5 days/week Added > 1 day (%) On how many days of the last seven days have you followed a healthy eating plan? > 5 days/week Added > 1 day (%) On how many days of the last seven days did you eat five or more servings of fruits and vegetables? > 5 days/week Added > 1 day

P-value

233

224

85 How many days of the last seven days did you participate in at least 30 minutes of moderate physical activity? > 5 days/week Added > 1 day (%) How many days of the last seven days did you participate in a specific exercise session other than what you do around the house or as part of your work? > 5 days/week Added > 1 day (%) a

238

3.7 (2.6) 44

4.3 (2.3) 51

0.6 (2.9) 7 46

0.002 0.10

238

2.0 (2.0) 18

2.4 (2.0) 18

0.5 (2.5) 0 43

0.004 0.81

Questions taken from The Summary of Diabetes Self-Care Activities Measure (Toobert et al., 2000). b Participants with diabetes who completed both the pre-test and post-test questionnaires. c Percentages may not add up to 100% because of rounding. d Differences may not equal post-test minus pre-test because of rounding.

86 Table 3.11. Comparison of Changes in Diet and Physical Activity Behaviors for Diabetes SelfManagement and General Health in all Participants Following the Intervention in Georgia Senior Centers, 2006-2007a

Diet On how many days of the last seven days have you followed a healthy eating plan?c No diabetes Diabetes On how many days of the last seven days did you eat five or more servings of fruits and vegetables?c No diabetes Diabetes Total servings of fruits and vegetables eaten per dayc No diabetes Diabetes Physical Activity How many days of the last seven days did you participate in at least 30 minutes of moderate physical activity?c No diabetes Diabetes How many days of the last seven days did you participate in a specific exercise session other than what you do around the house or as part of your work?d No diabetes Diabetes Physical function total domain scorec No diabetes Diabetes a

n

Pre-test Mean (SD) or %

Post-test Mean (SD) or %

Change (SD)b

P-value

426 238

4.7 (2.4) 4.6 (2.5)

5.4 (2.0) 5.5 (1.9)

0.7 (2.7) 0.9 (2.7)

< 0.0001 < 0.0001

410 232

3.5 (2.6) 3.8 (2.7)

4.6 (2.3) 4.6 (2.4)

1.1 (3.0) 0.8 (3.0)

< 0.0001 0.0002

419 236

4.8 (1.9) 5.1 (2.1)

5.8 (2.1) 5.9 (2.2)

1.0 (2.2) 0.7 (2.4)

< 0.0001 < 0.0001

426 238

4.1 (2.6) 3.7 (2.6)

4.8 (2.3) 4.3 (2.3)

0.7 (3.0) 0.6 (2.9)

< 0.0001 0.002

426 238

2.1 (2.0) 2.0 (2.0)

2.8 (2.0) 2.4 (2.0)

0.7 (2.5) 0.5 (2.5)

< 0.0001 0.004

390 222

8.1 (2.6) 7.3 (2.7)

8.5 (2.9) 7.7 (2.9)

0.4 (2.2) 0.5 (2.2)

0.0005 0.002

Questions taken from The Summary of Diabetes Self-Care Activities Measure, except for total physical function score (Toobert et al., 2000). b Differences may not equal post-test minus pre-test because of rounding. c Diabetes was not a significant predictor of change in linear regression model after controlling for pre-test demographics, body mass index, self-reported health, high blood pressure, high cholesterol, heart disease, tobacco use, total physical function score, and pre-test response. d Diabetes was a significant negative predictor of change in linear regression model after controlling for pre-test demographics, body mass index, self-reported health, high blood pressure, high cholesterol, heart disease, tobacco use, total physical function score, and pre-test response.

87

Change in Heart Attack Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007 100

92 87

90

86

80

86 78

76

70

Pre-test, all ages

66

65

% Correct

60 50

Post-test, all ages

51

49

40

35 30

30 20

14 7

10

al l6

h K

no w

at of br e

or tn es s Sh

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ai nt

tp

d, f he s

C

ea k W

Pa

in

in

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,n

ec k

,l ig ht he ad e

,o rb ac k

0

Figure 3.1. Changes in Heart Attack Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007 *Sudden trouble seeing in one or both eyes is not a symptom of a heart attack. Due to the inclusion of “don’t know” as a response option for each sign/symptom presented, there was also an increase in the number of participants who incorrectly classified this symptom. Refer to Table 3.2.

88

Change in Stroke Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007 100

94

92 87

86

90

85

80

79

75

74

% Correct

70

Pre-test, all ages

61

58

60

Post-test, all ages

50 40

34

30

22

18

20

8

10

al l6 w no

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re

K

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on

w

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ea

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ro u

fa

bl

e

ce

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sp

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rm

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0

Figure 3.2. Changes in Stroke Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007 *Sudden chest pain or discomfort is not a symptom of a stroke. This symptom was included in the questionnaires as the “decoy” question. There was a significant increase in the percentage of participants who correctly classified this symptom. Refer to Table 3.3.

89

Comparison of Heart Attack Knowledge by Race (Pre-test) 100 90

90

85

84 80

80

76

70

66 58

% Correct

60

GA senior center data (pre-test, white)

53

50

46 39

40

GA senior center data (pre-test, black)

36

30 21

20 8

10

2

11 ll 9

ea

ca

br

to

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de

rt om

,f ed ad he

ht lig k, ea

W

fo

nt ai

ck ba or k, ec ,n w ja in in Pa

ng

0

Figure 3.3. Comparison of Heart Attack Knowledge by Race at the Pre-Test in Georgia Senior Centers, 2006-2007

90

Comparison of Stroke Knowledge by Race (Pre-test) 100

93

90

90

82

81

80

80

% Correct

70

65

65

65

GA senior center data (pre-test, white)

57

60 50

50 GA senior center data (pre-test, black)

40 30

22

20

12

10

10

4

1 91 ll to

an

d

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ow

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Su

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w

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w

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ea

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e,

sp

ar

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m

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0

Figure 3.4. Comparison of Stroke Knowledge by Race at the Pre-Test in Georgia Senior Centers, 2006-2007 *Sudden chest pain or discomfort is not a symptom of a stroke. This symptom was included in the questionnaires as the “decoy” question. There was a significant increase in the percentage of participants who correctly classified this symptom. Refer to Table 3.3.

91

Comparison of Heart Attack Knowledge (Pre-test) 100 90 80 National 2001 BRFSS data (age 65-79)

% Correct

70 60

GA senior center data (pre-test, age 65-79)

50 40

National 2001 BRFSS data (age 80+)

30

GA senior center data (pre-test, age 80+)

20 10

91

1

th

ll ca

br

tn

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ho ,s

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co is /d pa in

st he C

se

m

fa d, he ad e

ht lig k, ea W

fo rt

in t

k ac rb ,o ec k ,n w ja in in Pa

ng

0

Figure 3.5. Comparison of Pre-Test Heart Attack Knowledge in Georgia Senior Centers, 20062007, and 2001 BRFSS Data from 17 US Statesa a

Data from Greenlund et al., 2004. *Sudden trouble seeing in one or both eyes is not a symptom of a heart attack. Due to the inclusion of “don’t know” as a response option for each sign/symptom presented, there was also an increase in the number of participants who incorrectly classified this symptom. Refer to Table 3.2.

92

Comparison of Stroke Knowledge (Pre-test) 100 90 80

National 2001 BRFSS data (age 65-79)

% Correct

70 60

GA senior center data (pre-test, age 65-79)

50 40

National 2001 BRFSS data (age 80+)

30 20

GA senior center data (pre-test, age 80+)

10

91 ll

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nf

us

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n,

ea

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tro

ub

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le

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sp

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0

Figure 3.6. Comparison of Pre-Test Stroke Knowledge in Georgia Senior Centers, 2006-2007, and 2001 BRFSS Data from 17 US Statesa a

Data From Greenlund et al., 2003. *Sudden chest pain or discomfort is not a symptom of a stroke. This symptom was included in the questionnaires as the “decoy” question. There was a significant increase in the percentage of participants who correctly classified this symptom. Refer to Table 3.3.

93

Comparison of Heart Attack Knowledge (Post-test) 100 90 80

National 2001 BRFSS data (age 65-79)

70

GA senior center data (post-test, age 65-79)

% Correct

60 50

National 2001 BRFSS data (age 80+)

40

GA senior center data (post-test, age 80+)

30 20 10

1 91 ca ll

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0

Figure 3.7. Comparison of Heart Attack Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007, with 2001 BRFSS Data from 17 US Statesa a

Data From Greenlund et al., 2004. *Sudden trouble seeing in one or both eyes is not a symptom of a heart attack. Due to the inclusion of “don’t know” as a response option for each sign/symptom presented, there was also an increase in the number of participants who incorrectly classified this symptom. Refer to Table 3.2.

94

Comparison of Stroke Knowledge (Post-test) 100 90 80

National 2001 BRFSS data (age 65-79)

% Correct

70 60

GA senior center data (post-test, age 65-79)

50

National 2001 BRFSS data (age 80+)

40 30

GA senior center data (post-test, age 80+)

20 10

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97

CHAPTER 4 CONCLUSION The overall goal of this study was to determine the impact of a diabetes and heart health intervention conducted in Georgia’s senior centers on knowledge and behaviors that support prevention and management of diabetes and cardiovascular disease. The hypotheses were that the program would increase knowledge and behaviors that support cardiovascular disease prevention and management from low pre-test levels, and for people with diabetes, the program would increase the frequency of self-management practices and lower A1c. The specific aims were to 1) assess pre-test knowledge and behaviors among OAANP participants, 2) determine the effectiveness of the intervention in improving pre-test levels using outcomes such as heart attack and stroke symptom knowledge, increased physical activity, and fruit and vegetable intake, and in the diabetes subgroup, increased frequency of diabetes self-management practices, and 3) qualitatively compare the benefits of this intervention to our 2005-2006 interventions (Fitzpatrick et al., 2007; Hendrix et al., 2007; Speer et al., 2007). The major findings of this study were: 1) knowledge varied among the individual heart attack and stroke signs and symptoms, and knowledge improved for most signs and symptoms following the intervention, 2) 56% reported consuming at least five servings of fruits and vegetables regularly at the pre-test, however, fruit and vegetable intake increased by nearly one serving per day and 55% of participants increased their total intake by at least one serving following the intervention, and 3) many measures of physical activity and physical function improved following the intervention. In the diabetes subgroup, the mean A1c level at the pre-

98 test was 6.7%, which is within the American Diabetes Association (2008) recommended level of < 7%. There were no significant changes in A1c following the intervention, which could be attributable to generally favorable A1c levels at the pre-test. However, all self-management practices improved significantly, and these improvements can help with maintenance of blood glucose goals. Comparison of the fruit and vegetable and diabetes self-management outcomes to the 2005-2006 interventions suggest that focusing exclusively on behaviors related to these specific outcomes can result in greater changes. This diabetes and heart health intervention, which expanded on the previously successful fruit and vegetable, physical activity, and diabetes self-management 2005-2006 interventions, adds to the evidence base that these health and wellness programs are effective for helping older adults in Georgia senior centers improve their health knowledge and behaviors in the short term. This study differed from the 2005-2006 interventions in that heart disease risk factors and warning signs/symptoms were emphasized, and heart attack and stroke knowledge was assessed. Regression analyses revealed that knowledge of heart attack and stroke signs and symptoms tended to be lower among black people and those who were relatively older, and changes in knowledge were negatively associated with education as well. With the exception of having high blood pressure, risk factors for heart disease were not significantly associated with changes in knowledge following the intervention. These findings, which are similar to the associations found in analyses of 2001 and 2005 BRFSS data, indicate the need for further reinforcement of heart health messages in vulnerable populations, including the signs and symptoms of heart attack and stroke, as those at greatest risk tended to have lower knowledge (Fang et al., 2008; Greenlund et al., 2003; Greenlund et al., 2004).

99 This study provides the framework for further expansion and opportunities to build upon and reinforce the important health messages contained in this diabetes and heart health intervention. Future interventions may focus on reviewing important heart health concepts, such as risk factors and their management, and warnings signs, as well as complex aspects of diabetes self-management. While knowledge of heart attack and stroke signs and symptoms improved, correctly classifying all symptoms for heart attack and stroke remained low at 14% and 22%, respectively. The physical activity programs could also be expanded to include other tools in addition to balls, such as resistance bands, to increase intensity and variety. Additional monitoring of the frequency participants perform the exercises, such as in the absence of the educator, could also be used to better understand the impact of the physical activity intervention. Based on the results of this study, we have an improved understanding of the needs and characteristics of the target population to guide the development of future interventions for older adults in Georgia senior centers. Provision of high quality educational programs to improve the health and well-being of older Georgians is a high priority and an ongoing effort among state and local leaders. Hence, building on the successes of the current intervention, the educational materials, and the core messages, which are readily accessible online, the reach of this community intervention and future programs will continue to help meet the high needs of a growing number of older Georgians.

100

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112

APPENDICES

113

APPENDIX A DATA TABLES

114 Additional regression analyses were completed to determine the impact of ruralness on changes in heart attack and stroke knowledge, as well as changes in fruit and vegetable intake. In the previous 2005-2006 intervention, ruralness was an independent and negative predictor of changes in fruit and vegetable intake compared to more urbanized areas. This observation was attributable perhaps to barriers, such as lack of transportation to food stores and low access to supermarkets (Hendrix et al., 2007). Counties from which evaluation data were obtained were categorized according to the Four Georgias model, which is based on the U.S. Census Bureau metropolitan and nonmetropolitan characteristics and population growth from 1980-1990. Urban (metropolitan) counties are characterized as having cities with over 50,000 in a county with a population of at least 100,000, and are the center for social, cultural, and economic activity. Suburban areas are characterized as having more than a third of residents commuting to the core city to work, many of whom are white, affluent, and well-educated. Rural growth areas tend to attract tourism, and may be located near a military base or other center that sustains economic growth. Rural decline areas are characterized by long-term population loss, scarcity of employment opportunities, and low education and skill levels among residents. These areas are of the most concern, as working adults often have migrated out of these areas, and the health of residents is often poorer compared to other areas because of limited access to health care (Bachtel, 2008). In regression analyses at the pre-test, ruralness was not associated with heart attack or stroke knowledge when controlled for pre-test demographics and self-reported health conditions. However, ruralness was a positive, independent predictor of change in heart attack knowledge following the intervention (P = 0.008), and there was a trend for greater change in stroke knowledge (P = 0.07).

115 In the regression model exploring predictors of change in daily fruit and vegetable intake, higher education, reporting a diagnosis of heart disease, and knowledge of the recommended daily servings of fruits and vegetables for older people at the post-test were significant predictors of greater change from pre- to post-test, while ruralness was not a significant predictor. To explore the potential collective effect of exposure to the previous year’s intervention, senior centers were classified according to whether or not the 2005-2006 intervention was implemented and evaluated at the senior center previously. About 68% of senior centers did not participate in the 2005-2006 intervention, while about a third were repeat centers. This classification system provides a crude estimate of participants’ previous exposure to intervention activities and the important health messages conveyed therein. Adding this variable to the regression model did not appreciably alter the results (data not shown).

116 Table A.1. Regression Model Exploring Predictors of Heart Attack and Stroke Knowledge at the Pre-Test in Georgia Senior Centers, 2006-2007

Pre-test heart attack knowledge (n = 542) Intercept Age (years) Gender (0 = male, 1 = female) Race (1 = white, 2 = black) Education (years) Self-reported health (0 = poor, 1 = poor, 2 = good, 3 = very good, 4 = excellent) Body Mass Index (kg/m2) Diabetes (0 = no, 1 = yes) Tobacco use (0 = no, 1 = yes) Heart disease (0 = no, 1 = yes) High blood pressure (0 = no, 1 = yes) High cholesterol (1 = yes, 2 = no)a Ruralness (1 = urban, 2 = suburban, 3 = rural growth, 4 = rural decline) Pre-test stroke knowledge (n = 542) Intercept Age (years) Gender (0 = male, 1 = female) Race (1 = white, 2 = black) Education (years) Self-reported health (0 = poor, 1 = poor, 2 = good, 3 = very good, 4 = excellent) Body Mass Index (kg/m2) Diabetes (0 = no, 1 = yes) Tobacco use (0 = no, 1 = yes) Heart disease (0 = no, 1 = yes) High blood pressure (0 = no, 1 = yes) High cholesterol (1 = yes, 2 = no)a Ruralness (1 = urban, 2 = suburban, 3 = rural growth, 4 = rural decline) a

Parameter Estimates (SEM)

P-value

5.71 (0.9) -0.02 (0.0) 0.08 (0.2) -0.46 (0.1) 0.03 (0.0) 0.01 (0.1)

< 0.0001 0.03 0.63 0.0005 0.16 0.90

-0.00 (0.0) -0.24 (0.1) -0.30 (0.2) 0.23 (0.1) -0.03 (0.1) -0.21 (0.1) 0.08 (0.1)

0.82 0.07 0.16 0.10 0.83 0.10 0.21

4.89 (1.0) -0.03 (0.0) 0.05 (0.2) -0.49 (0.1) 0.08 (0.0) 0.10 (0.1)

< 0.0001 0.004 0.76 0.0003 0.0002 0.18

0.01 (0.0) -0.15 (0.1) 0.03 (0.2) 0.04 (0.1) 0.27 (0.1) -0.09 (0.1) 0.09 (0.1)

0.30 0.25 0.89 0.79 0.08 0.49 0.16

Participants who answered “don’t know/not sure” were excluded from the analysis.

117 Table A.2. Regression Model Exploring Predictors of Change in Heart Attack and Stroke Knowledge Following the Intervention in Georgia Senior Centers, 2006-2007

Changes in heart attack knowledge (n = 542) Intercept Age (years) Gender (0 = male, 1 = female) Race (1 = white, 2 = black) Education (years) Self-reported health (0 = poor, 1 = poor, 2 = good, 3 = very good, 4 = excellent) Body Mass Index (kg/m2) Diabetes (0 = no, 1 = yes) Tobacco use (0 = no, 1 = yes) Heart disease (0 = no, 1 = yes) High blood pressure (0 = no, 1 = yes) High cholesterol (1 = yes, 2 = no)a Heart attack knowledge at pre-test (0 to 6 correct responses) Ruralness (1 = urban, 2 = suburban, 3 = rural growth, 4 = rural decline) Changes in stroke knowledge (n = 542) Intercept Age (years) Gender (0 = male, 1 = female) Race (1 = white, 2 = black) Education (years) Self-reported health (0 = poor, 1 = poor, 2 = good, 3 = very good, 4 = excellent) Body Mass Index (kg/m2) Diabetes (0 = no, 1 = yes) Tobacco use (0 = no, 1 = yes) Heart disease (0 = no, 1 = yes) High blood pressure (0 = no, 1 = yes) High cholesterol (1 = yes 2 = no)a Stroke knowledge at pre-test (0 to 6 correct responses) Ruralness (1 = urban, 2 = suburban, 3 = rural growth, 4 = rural decline) a

Parameter Estimates (SEM)

P-value

5.27 (0.8) -0.03 (0.0) 0.06 (0.1) -0.10 (0.1) 0.05 (0.0) -0.06 (0.1)

< 0.0001 0.0001 0.70 0.40 0.004 0.34

-0.02 (0.0) -0.06 (0.1) -0.24 (0.2) 0.02 (0.1) 0.27 (0.1) -0.11 (0.1) -0.72 (0.0) 0.15 (0.1)

0.09 0.62 0.19 0.89 0.03 0.34 < 0.0001 0.008

5.87 (0.9) -0.03 (0.0) -0.07 (0.2) -0.28 (0.1) 0.07 (0.0) -0.00 (0.1)

< 0.0001 < 0.0001 0.65 0.02 0.0003 0.97

0.00 (0.0) -0.13 (0.1) 0.01 (0.2) 0.11 (0.1) 0.05 (0.1) -0.08 (0.1) -0.84 (0.0) 0.11 (0.1)

0.72 0.28 0.94 0.39 0.70 0.49 < 0.0001 0.07

Participants who answered “don’t know/not sure” were excluded from the analysis.

118 Table A.3. Regression Model Exploring Predictors of Changes in Total Daily Fruit and Vegetable Intake Following the Intervention in Georgia Senior Centers, 2006-2007a

Changes in fruit and vegetable intake (n = 523) Intercept Age (years) Gender (0 = male, 1 = female) Race (1 = white, 2 = black) Education (years) Knowledge of recommended intake at post-testb Ruralness (1 = urban, 2 = suburban, 3 = rural growth, 4 = rural decline) Food security (0 = no, 1 = yes) Self-reported health (0 = poor, 1 = poor, 2 = good, 3 = very good, 4 = excellent) Body Mass Index (kg/m2) Tobacco use (0 = no, 1 = yes) High cholesterol (0 = no, 1 = yes) High blood pressure (0 = no, 1 = yes) Heart disease (0 = no, 1 = yes) Diabetes (0 = no, 1 = yes) Arthritis (0 = no, 1= yes) Total fruit and vegetable intake at pre-test Physical activity at pre-test (daily minutes) Change in physical activity pre- to post-test (daily minutes) Total physical function score at pre-test (range 0 to 12) Change in physical function score pre- to post-test a

Parameter Estimates (SEM)

P-value

1.87 (1.4) -0.00 (0.0) 0.23 (0.2) -0.05 (0.2) 0.06 (0.0) 1.14 (0.2) 0.04 (0.1)

0.19 0.80 0.31 0.78 0.04 < 0.0001 0.65

0.11 (0.2) -0.01 (0.1)

0.65 0.94

0.01 (0.0) -0.01 (0.3) -0.20 (0.2) 0.09 (0.2) 0.45 (0.2) -0.05 (0.2) 0.25 (0.2) -0.60 (0.0) 0.00 (0.0) 0.00 (0.0) 0.01 (0.0) 0.06 (0.0)

0.44 0.96 0.25 0.64 0.02 0.80 0.19 < 0.0001 0.65 0.30 0.82 0.19

Total daily fruit and vegetable intake is the sum of responses to the questions, “How many servings of fruit do you usually eat each day?” and “How many servings of vegetables do you usually eat each day?” c How many fruits and vegetables should older people eat each day? 1 = correct answer (7, 8 , 9, 10, or 7 to 10 daily), 0 = incorrect answer or “don’t know”

119 Table A.4. Comparison of Baseline Characteristics of Participants who Completed the Post-test According to Self-Reported Diabetes Status in Georgia Senior Centers, 2006-2007

n

Pre-test Mean (SD) or %

433 244

Difference (SE)a

P-valueb

75.8 (8.0) 72.8 (7.1)

3.0 (7.7)

< 0.0001

433 244

85 84

1

0.75

431 241

41 55

14

0.0005

433 244

10.5 (3.2) 10.5 (3.2)

0.1 (3.2)

0.81

432 244

16 22

6

0.09

433 244

67 53

14

0.0003

422 240

28.6 (6.0) 31.5 (7.0)

3.0 (6.3)

< 0.0001

433 243

73 73

0

0.93

427 244

68 84

16

< 0.0001

428 243

30 34

4

0.27

433 243

50 63

13

0.002

432 244

91 94

3

0.15

430 243

87 91

4

0.09

420 234

4.0 (2.8) 6.4 (3.3)

2.4 (3.0)

< 0.0001

Age No diabetes Diabetes Gender (% female) No diabetes Diabetes Race (% African American) No diabetes Diabetes Education (years) No diabetes Diabetes Do you always have enough money to buy the food you need? (% no) No diabetes Diabetes Self-reported health (% good, very good, or excellent) No diabetes Diabetes Body Mass Index (kg/m2) No diabetes Diabetes Do you have arthritis? (% yes) No diabetes Diabetes Do you have high blood pressure? (% yes) No diabetes Diabetes Do you have heart disease? (% yes) No diabetes Diabetes Have you ever been told that your cholesterol is high? (% yes) No diabetes Diabetes How long has it been since you last had your cholesterol checked? (% reporting at least every 5 years) No diabetes Diabetes Are you cutting down on saturated fat? (% yes versus response of “no” or “don’t know” combined) No diabetes Diabetes How many prescription medications, including insulin, do you take? No diabetes Diabetes

120 Knowledge of heart attack signs summary scorec No diabetes Diabetes Knowledge of stroke signs summary scorec No diabetes Diabetes How many fruits and vegetables should older people eat each day? (% participants answered correctly)c,d No diabetes Diabetes Total servings of fruits and vegetables eaten per dayc,e No diabetes Diabetes On how many days of the last seven days have you followed a healthy eating plan?c No diabetes Diabetes On how many days of the last seven days did you eat five or more servings of fruits and vegetables?c No diabetes Diabetes On how many days of the last seven days did you participate in at least 30 minutes of moderate physical activity?c No diabetes Diabetes On how many days of the last seven days did you participate in a specific exercise session other than what you do around the house or as part of your work?c No diabetes Diabetes Physical function total domain scorec No diabetes Diabetes a

400 235

3.8 (1.4) 3.6 (1.6)

0.2 (0.12)

0.17

393 230

3.8 (1.6) 3.9 (1.6)

0.0 (0.13)

0.87

411 232

11 15

4.4

0.10

419 236

4.8 (1.9) 5.1 (2.1)

0.3 (0.16)

0.06

426 238

4.7 (2.4) 4.6 (2.5)

0.1 (2.4)

0.70

410 232

3.5 (2.6) 3.8 (2.7)

0.3 (2.6)

0.15

426 238

4.1 (2.6) 3.7 (2.6)

0.4 (0.21)

0.04

426 238

2.1 (2.0) 2.0 (2.0)

0.1 (2.0)

0.46

390 222

8.1 (2.6) 7.3 (2.7)

0.8 (2.6)

0.0002

Differences may not equal difference between people with and without diabetes because of rounding. P-value is for difference between baseline characteristics of people with versus without diabetes. c People who completed both the pre-test and post-test for specified variable. d Correct answers accepted are 7, 8, 9, 10, or 7 to 10 daily. e Value calculated by summing daily fruit intake response and daily vegetable intake response. b

121 Table A.5. Changes in Body Mass Index and Waist Circumference Following the Intervention in

Georgia Senior Centers, 2006-2007

Body mass index (kg/m2) (n = 617)b < 25 25 to < 30 > 30 Waist circumference (inches) Over clothes (n = 498) Under clothes (n = 33) Males Over clothes (n = 87) Under clothes (n = 2) Females Over clothes (n = 411) Under clothes (n = 31) a

Pre-test Mean (SD) or %

Post-test Mean (SD) or %

Change Mean (SD) or %a

29.5 (6.5) 25 35 40

29.3 (6.3) 26 36 38

-0.22 (1.5) 1 1 -2

0.0003

39.4 (5.7) 34.7 (4.3)

38.5 (5.4) 34.2 (4.6)

-0.91 (2.7) -0.53 (1.7)

< 0.001 0.08

40.6 (5.4) 38.0 (4.2)

39.9 (4.9) 37.3 (1.8)

-0.71 (2.5) -0.75 (2.5)

0.009 0.74

39.2 (5.7) 34.5 (4.3)

38.2 (5.5) 34.0 (4.7)

-0.96 (2.8) -0.52 (1.7)

< 0.001 0.10

P-value

Change may not equal post-test minus pre-test because of rounding. Participants with differences in height > 2 or < -2 inches or weight changes > 25% or < -25% from pre- to post-test were excluded from analyses of changes in BMI. c Only participants who self-reported diabetes at pre-test and post-test and who provided pre- and post-test A1c measures were included in analyses.

b

122

APPENDIX B POWER ANALYSIS

123 The target number of enrolled participants was 3,000 (about 250 per AAA) and of those, the target number for evaluation was 840 to undergo the pre-tests (about 70 per AAA). Recruitment goals were guided by expected dropout rates estimated from the 2005-2006 intervention (Fitzpatrick et al., 2007; Hendrix et al., 2007; Speer et al., 2007). Based on the 2005-2006 intervention, it was assumed that at least 70% of the participants would complete the post-test questionnaires. The target number of enrolled participants in the diabetes intervention was 240 (about 20 per AAA). Assuming a loss of 40% due to dropout and/or lack of complete information provided from participants among the subgroup with diabetes, the anticipated number of total participants completing the diabetes intervention, completing the post-test questionnaire, and providing blood A1c was 144 (Speer et al., 2007). The proposed sample sizes had adequate power (DSS Research Inc., 2006). Only 280 participants were needed at post-test to show that a ten percentage point change in following a recommended behavior was statistically significant (e.g., from 30% at pre-test to 40% at posttest, power = 0.80, ά = 0.05), while 128 were needed to show a 15 percentage point change. In the 2005-2006 diabetes self management intervention, it was found that out of 144 people who gave blood, 24 had an A1c > 8.0% (poor control); the diabetes intervention decreased A1c by 1.15 percentage points (SD = +1.09) in those with poor control (P < 0.0001). Assuming a similar benefit to A1c in the current intervention, 20 people were be needed to show a 1.0 percentage point change (considered a clinically significant decrease).

124

APPENDIX C PHYSICIAN CLEARANCE

125 Physician's Clearance to Participate in Physical Activity and Walking Your patient, __________________, has indicated an interest in participating in a nutrition, physical activity, and walking program offered at their local senior center. The program is designed to help older adults eat better and walk more, and was developed by the Georgia Division of Aging Services and the University of Georgia. Participants will wear step counters to monitor the number of steps they take each day. About every two weeks each participant will be given a daily step goal based on the average daily steps from the previous week. The new step goal will be about a 10% increase. Also, about every one or two weeks, there will be lessons on nutrition, physical activity, and walking at the senior centers. Along with the lessons, about five to thirty minutes of group physical activity, including chair exercises for improving flexibility, balance, and strength will be offered. When and where possible, a group walking activity will also be included. RELEASE TO REQUEST PERMISSION FROM PHYSICIAN I give permission to ______________________________to ask my physician if I may participate in the physical activity and walking program at my senior center. I give my physician my approval to sign the form. Participant signature: _________________________

Date: __________________

Participant printed name: ______________________ PHYSICIAN SIGNATURE My patient,_____________________ has medical approval to participate in the physical activity and walking program at their senior center. ___ The patient has no known contraindications to moderate physical activity. ___ The patient has conditions in which moderate physical activity is contraindicated. Physician Signature: _________________________Date: __________________ Physician printed name: ______________________ Physician address: ___________________________ Physician phone: ____________________________ Physician FAX: ____________________________ Form adapted from: Eat Better & Move Better, A Guide Book for Community Programs, National Resource Center on Nutrition, Physical Activity and Aging, Florida International University, funded by grants from the Administration on Aging, US Department of Health and Human Services.

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APPENDIX D INTERVENTION POST-TEST (WITH DIABETES)

127

To be completed May and June 2007

WITH DIABETES POST-TEST

Questionnaires should be administered by a trained interviewer.

128

LIVE HEALTHY GEORGIA Name of Interviewer: ID of Participant: Phone number to use to clarify information and get step counts: 1. County/Senior Center 2. Date (M/D/Y): ___/___/___ 3. Age of Participant: ___ ___ ___ 4. Gender: Male (0) Female (1) 5. Ethnicity: White (1) Black (2) Hispanic/Latino (3) Asian (4) Other (5) 6. How many years did you complete in school: ____ years 7. How would you rate your overall health? Circle one: Poor (0) Fair (1) Good (2) Very good (3) Excellent (4) No (0) Yes (1) 8. Do you use any tobacco products such as cigarettes, cigars, pipe, or chewing tobacco? No (0) Yes (1) 9. Do you have diabetes? No (0) Yes (1) 10. Do you have high blood pressure? No (0) Yes (1) 11. Do you have heart disease such as angina, congestive heart failure, heart attack or other heart problems? No (0) Yes (1) 12. Do you have arthritis? 13. During the past 30 days, have you had symptoms of pain, aching, or No (0) Yes (1) stiffness in or around a joint? No (0) Yes (1) 14. Do you always have enough money to buy the food you need? 15. How many prescription medications, including insulin, do you take? 16. How many over the counter medications do you take? (such as a daily multivitamin, supplements, Aspirin®, etc.) No (0) Yes (1) 17. Do you go to one pharmacy for all of your medications? No (0) Yes (1) 18. Do you have a written list of all of your prescription medications, non-prescription medications, and dietary supplements? No (0) Yes (1) 19. Do you carry this written list with you in your purse or wallet? No (0) Yes (1) 20. Have you had a physician, pharmacist, or other health professional look at all of your medications in the past 6 months? No (0) Yes (1) 21. Do you always throw out your medications when they are expired (past their “use by” date)? No (0) Yes (1) 22. Do you use a pillbox or other system to help you take your medications? No (0) Yes (1) 23. Do you know the name of each of your medications?

Line 1 1-6

10-12 13-18 19-21 22 23 24-25

26 27 28 29 30 31 32 33 34-35 36-37 38 39 40 41 42 43 44

24. Do you know what each of your medications is for? 25. Do you know the possible side effects of each of your medications?

No (0) No (0)

Yes (1) Yes (1)

45 46

129

Read Questions to Participants and Circle their Answers Line 1

DIET AND PHYSICAL ACTIVITY 26. How many fruits and vegetables should older people eat each day? (Circle the participant’s response) 0 1 2 3 4 5 6 7 8 9 10 “5 a day” “5 or more a day” “7 to 10 a day” DK Missing 27. How many servings of fruits and 100% fruit juices do you usually have each day? 28. How many servings of vegetables do you usually eat each day? 29. On how many DAYS of the last WEEK (seven days) did you eat five or more servings of fruits and vegetables? 30. How many DAYS of the last WEEK (seven days) have you followed a healthful eating plan? 31. How many DAYS of the last WEEK (seven days) did you participate in at least 30 minutes of moderate physical activity? Examples of moderate activities are regular walking, housework, yard work, lawn mowing, painting, repairing, light carpentry, ballroom dancing, light sports, golf, or bicycling on level. 32. How many days of the week do you participate in any physical activity (light or moderate)? 33. About how many minutes of physical activity do you do on the days you are physically active? 34. How many DAYS of the last WEEK (seven days) did you participate in a specific exercise session other than what you do around the house or as a part of your daily activities (e.g., chair exercises, yoga, aerobics, organized walking programs, using workout machines, etc.)? FALL AND FRACTURE PREVENTION: HOME SAFETY, FOOD AND FITNESS “What kinds of things have you done at home to prevent falls? Do you or have you . . . “ 35. Removed things you might trip over? 36. Removed small throw rugs OR use double-sided tape to keep the rugs from slipping? 37. Keep items you use often in cabinets you can reach easily without using a step stool? 38. Put grab bars next to your toilet and in the tub or shower? 39. Used non-slip mats in the bathtub and on shower floors? 40. Improved the lighting in your home? 41. Had handrails and lights put in on all staircases?

42. Wear shoes both inside and outside the house?

47-48

0 1 2 3 4 5 6 7 49

0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7

50 51

0 1 2 3 4 5 6 7 52

0 1 2 3 4 5 6 7

53

0 1 2 3 4 5 6 7 54

____ minutes 0 1 2 3 4 5 6 7

55-57

58

No (0) Yes (1) No (0) Yes (1)

59 60

No (0) Yes (1) 61

No (0) Yes (1) No (0) Yes (1) No (0) Yes (1) No (0) Yes (1) Don’t have stairs (8) No (0) Yes (1)

62 63 64

65 66

130

Read Questions to Participants and Circle their Answers Falls and fractures 43. Have you had a fracture or broken bone after age 50? 44. Have you fallen in the past year? 45. Do you feel limited in your daily life by a fear of falling? 46. Have you ever been told by a doctor or other health professional that you have osteoporosis?

No (0) No (0) No (0) No (0)

Yes (1) Yes (1) Yes (1) Yes (1)

67 68 69 70

131

Calcium- and vitamin D-rich foods and supplements 47. Do you get a stomachache, gas, or diarrhea after drinking milk? No (0) Yes (1) 48. How many servings of milk products should most older people 0 1 2 3 4 DK eat daily? How often do you eat or drink or take these items? 49. Milk as a beverage (including soy milk)? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK 50. Milk on cereal (including soy milk)? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK 51. Yogurt? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK 52. Cheese, such as a slice or in a sandwich? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK 53. Calcium-fortified orange juice? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK 54. Calcium-fortified cereal? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK 55. Broccoli, leafy greens, such as mustard, turnip or collard greens? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK 56. Salmon? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK 57. Calcium supplement? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK 58. Calcium supplement with vitamin D? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK 59. Multivitamin with vitamin D? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK 60. Vitamin D-only supplement? <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day* DK For the data coder: <1/wk 1/wk 2/wk 3/wk 4/wk 5/wk 6/wk 1/day 1-2/day 2/day 2-3/day 3/day*

Line 2 10 11

12-13

14-15

16-17

18-19

20-21

22-23

24-25

26-27

28-29

30-31

32-33

34-35

132 DK/Miss 00 01 02 03 04 *includes 3 or more per day

05

06

07

10

14

17

21

99

133 Get Checked Questions (Adapted from BRFSS, http://www.cdc.gov/brfss/questionnaires/pdf-ques/2005brfss.pdf) Question Write or Circle Code Answer Line 2

61. About how long has it been since you last had a bone mineral density test?

62. About how long has it been since you last had your blood cholesterol checked?

63. Have you ever been told by a doctor, nurse, or other health professional that your blood cholesterol is high? 64. Are you cutting down on saturated fat in your diet (to help manage or lower your risks of developing heart disease)? 65. About how long has it been since you last had your blood pressure checked?

66. Are you cutting down on sodium or salt (to help lower or control your blood pressure)? 67. When was the last time you visited ANY eye care professional? (To have your eyes and vision checked?)

68. When was the last time you visited ANY ear care professional? (To have your hearing or hearing aids checked?) 69. When was the last time you had your feet checked by a health care professional, such as a doctor or nurse?

1) Within the past year 2) Within the past 2 yr 3) Within the past 5 yr 4) 5 or more yrs ago 5) Never 1) Within the past year 2) Within the past 2 yr 3) Within the past 5 yr 4) 5 or more yrs ago 5) Never 1) Yes 2) No

7 Don’t know/not sure 9 Refused 36

7 Don’t know/not sure 9 Refused 37

7 Don’t know/not sure 9 Refused 38

1) Yes 2) No

7 Don’t know/not sure 8 Refused 39

1) Within past month 2) Within past year 3) Within past 2 yrs 4) 2 or more years ago 5) Never 1) Yes 2) No 3)Do not use salt 1) Within past month 2) Within past year 3) Within past 2 yrs 4) 2 or more years ago 5) Never 1) Within past month 2) Within past year 3) Within past 2 yrs 4) 2 or more years ago 5) Never 1) Within past month 2) Within past year 3) Within past 2 yrs 4) 2 or more years ago 5) Never

7 Don’t know/not sure 9 Refused

40

7 Don’t know/not sure 9 Refused 41

7 Don’t know/not sure 9 Refused 42

7 Don’t know/not sure 9 Refused 43

7 Don’t know/not sure 9 Refused 44

134

Symptoms and Signs of Heart Attacks and Strokes From: http://www.cdc.gov/brfss/questionnaires/pdf-ques/2005brfss.pdf Which of the following do you think is a symptom of a heart attack? For each, tell me “yes,” “no,” or you’re “not sure.” 70. (Do you think) pain or discomfort in the jaw, neck, or back (are symptoms of a heart attack?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused 71. (Do you think) feeling weak, lightheaded, or faint (are symptoms of a heart attack?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused 72. (Do you think) chest pain or discomfort (are symptoms of a heart attack?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused 73. (Do you think) sudden trouble seeing in one or both eyes (is a symptom of a heart attack?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused 74. (Do you think) pain or discomfort in the arms or shoulder (are symptoms of a heart attack?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused 75. (Do you think) shortness of breath (is a symptom of a heart attack?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused Which of the following do you think is a symptom of a stroke? For each, tell me “yes,” “no,” or you’re “not sure.” 76. (Do you think) sudden confusion or trouble speaking (are symptoms of a stroke?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused 77. (Do you think) sudden numbness or weakness of face, arm, or leg, especially on one side (are symptoms of a stroke?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused 78. (Do you think) sudden trouble seeing in one or both eyes (is a symptom of a stroke?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused 79. (Do you think) sudden chest pain or discomfort (are symptoms of a stroke?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused 80. (Do you think) sudden trouble walking, dizziness, or loss of balance (are symptoms of a stroke?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused 81. (Do you think) severe headache with no known cause (is a symptom of a stroke?) 1-Yes 2-No 7-Don’t know/not sure 9-Refused 82. If you thought someone was having a heart attack or a stroke, what is the first thing you would do? Read list to participant and circle their answer.

Line 2

45 46 47

48

49 50

51

52 53 54

55 56

57

135 1-Take them to the hospital 2-Tell them to call their doctor 3-Call 911 4-Call their spouse or a family member 5-Do something else 7-Don’t know/Not sure 9-Missing

136

Line 3

FOR THOSE WITH DIABETES 1. What kind of effect does diabetes have on your daily activities? No effect (1) Little effect (2) Large effect (3) 2. Thinking about your diet, on how many DAYS of the last WEEK (seven days) did you space carbohydrates evenly? 3. On how many DAYS of the last WEEK (seven days) did you test your blood sugar? 4. What medications do you take for your diabetes? 0-None 1-pills only 2-insulin only 3-pills and insulin 5. On how many DAYS of the last WEEK (seven days), did you take your diabetes medication as prescribed by your doctor? 6. On how many DAYS of the last WEEK (seven days) did you check your feet? 7. On how many DAYS of the last WEEK (seven days) did you inspect the inside of your shoes? 8. What should your hemoglobin A1c level be? ___%

1

2 3 10

0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7

11 12 13

0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7

14 15 16 17 9=DK

137

After attending the health, nutrition, and physical activity education programs at your center these past few months, have you done any of the following? Read the list and circle the answers. 1. Increased your physical activity?

Line 4

No (0) Yes (1) 10

2. Tried to follow a healthier diet?

No (0) Yes (1) 11

3. Increased your intake of fruit?

No (0) Yes (1) 12

4. Increased your intake of vegetables?

No (0) Yes (1) 13

5. Started taking a supplement with calcium and vitamin D?

No (0) Yes (1) 14

6. Eaten more calcium-rich foods?

No (0) Yes (1) 15

7. Learned the warning signs of a heart attack?

No (0) Yes (1) 16

8. Learned the warnings signs of a stroke?

No (0) Yes (1) 17

9. Taken better care of your feet?

No (0) Yes (1) 18

10. Talked with your doctor about bone health and osteoporosis?

No (0) Yes (1) 19

11. Had your medications reviewed?

No (0) Yes (1) 20

12. Taken your medications as recommended by your doctor?

No (0) Yes (1) 21

13. Made your home a safer place to prevent falls?

No (0) Yes (1) 22

14. Made a recipe from one of the lessons?

No (0) Yes (1) 23

15. If you have diabetes, did these programs help you space carbohydrates over the day?

No (0) Yes (1) No diabetes (8) 24

16. If you have diabetes, did these programs help you maintain your blood sugar levels?

No (0) Yes (1) No diabetes (8) 25

17. If you have diabetes, did these programs help you control portion sizes of foods?

No (0) Yes (1) No diabetes (8) 26

18. What was your overall level of satisfaction with these health and nutrition education programs? Circle one: Poor (0) Fair (1) Good (2) Very good (3) Excellent (4) 19. What was your overall level of satisfaction with this physical activity program? Circle one: Poor (0) Fair (1) Good (2) Very good (3) Excellent (4) 20. How many sessions of the health, nutrition, and physical activity

0 1 2 3 4 27

0 1 2 3 4 28

138 education programs did the participant attend? Staff should document with attendance records. Maximum is 16 sessions.

Please ask the participant for any additional comments about the education programs, physical activity programs, menus, recipes, games, etc.:

29-30

139

WAIST CIRCUMFERENCE:

Instructions for Measuring Waist Circumference The measurement should be made under the clothes. To measure waist circumference, locate the upper hipbone and the top of the right iliac crest. Place a measuring tape in a horizontal plane around the abdomen at the level of the iliac crest. Before reading the tape measure, ensure that the tape is snug, but does not compress the skin, and is parallel to the floor. The measurement is made at the end of a normal expiration. A high waist circumference is associated with an increased risk for type 2 diabetes, dyslipidemia, hypertension, and CVD in patients with a BMI between 25 and 34.9 kg/m2. High-Risk Waist Circumference Men: > 40 in (> 102 cm) Women: > 35 in (> 88 cm) http://www.nhlbi.nih.gov/guidelines/obesity/prctgd_c.pdf Line 5 1013

59. Waist Circumference = __________ INCHES

60. How was measurement made?

(1) Under clothes OR (2) Over clothes

61. What is your current height without shoes? _______ feet and ____ inches

1 2

14

1517

62. What is your current weight without clothes? _______ pounds 1820

63. How was weight measurement made? PREFERRED: With a scale and without shoes (1) With a scale and with shoes (2)

21

140 Self-report (3) 64. Chair Sit-and-Reach: sit in stable chair, knees straight, bend over, reach with arms straight to toes, then measure with a ruler: Number of inches person is short of reaching the toes: ___ ___ . ___ (-)

2225

or Number of inches person reaches beyond toes: ___ ___ . ___ (+) Measure to the nearest ½ inch

2629

141

ID: __________ DATE (M/D/Year): _______ STAFF NAME: ___________ PHYSICAL PERFORMANCE

Physical Performance Test-Task Descriptions Equipment: Stopwatch, 8-Ft Tape Measure, Ruler, Folding Chair ASB

STANDING BALANCE: Time each item until >10.0 sec. OR until participant moves feet or reaches for support. 1a) SEMI-TANDEM (heel of one foot placed at midposition of the other) *If can hold for 10 seconds, move to 1b) *If can NOT hold for 10 seconds, move to 1c)

RECORD TIME IN SECONDS

Line 6 UGA Staff can score with open coding

Time to the nearest 10th second: 10-13 a) ___ ___ . ___ > 10.0 sec. Go to b) < 10.0 sec. Go to

1b) TANDEM (heel to toe, one foot directly in front of the other)

c)

14-17

1c) SIDE-BY-SIDE (toes lined up evenly and feet touching)

b) ___ ___ . ___

18-21

c) ___ ___ . ___ ASB D

AFW

DOMAIN SCORE: If A= <10 & C= 0-9, score= 0 score= 1 A= ≥10 & B= 0-2, score= 2 score= 3 A= ≥10 & B= ≥10, score= 4 8 FOOT WALK:

A= <10 & C= 10, A= ≥10 & B= 3-9,

Participant begins at standing position and will walk a straight distance of 8-feet, measured with tape on the floor. Instruct the participant to walk at normal gait using any assistive devices. If possible, have them begin walking a few feet before starting mark, and continue walking a few feet past the 8-foot mark. Tester will start and stop watch at the distance marks. Complete the walk twice.

AFW D ACS

DOMAIN SCORE: 1= ≥5.7 2= 4.1-5.6 3= 3.2-4.0 4= ≤3.1 CHAIR STANDS: Participant is asked to stand one time from a seated position in an armless, straight-backed chair (such as a folding metal chair) with their arms folded across their

22 SCORE: _______

Time to the nearest 10th second:

23-26

1) ___ ___ . ___ 2) ___ ___ . ___ Use best (lowest) time Assistive device used? NO (0) YES (1) Describe __________ SCORE: _______ Time to the nearest 10th second: 1) ___ ___ . ___

27

28

142 29-32

chest. If able, participant is asked to stand-up and sit-down 5 times as quickly as possible while being timed. If not able to perform, then the test is complete. ACSD DOMAIN SCORE: 1= ≥16.7 2= 13.7-16.6 3= 11.2-13.6 4= ≤11.1 TDS TOTAL SCORE: Add all 3 domain scores (1-12)

SCORE: _______ TOTAL SCORE:__ __

Coding: 8 = physically unable, 9=refused, 7=not applicable. Good function (score of 10 to 12); moderate function (score of 6 to 9); poor function (score of 0 to 5). THE END

33 34-35